Coherent Group Research Articles

Thymic Mimetic Cells: Ontogeny as Immunology.

Medullary thymic epithelial cells (mTECs) generate immunological self-tolerance by ectopically expressing peripheral-tissue antigens (PTAs) within the thymus to preview the peripheral self to maturing Tcells. Recent work, drawing inspiration from old histological observations, has shown that subtypes of mTECs, collectively termed mimetic cells, co-opt developmental programs from throughout the organism to express biologically coherent groups of PTAs. Here, we review key aspects of mimetic cells, especially as they relate to the larger contexts of molecular, cellular, developmental, and evolutionary biology. We highlight lineage-defining transcription factors as key regulators of mimetic cells and speculate as to what other factors, including Aire and the chromatin potential of mTECs, permit mimetic cell differentiation and function. Last, we consider what mimetic cells can teach us about not only the thymus but also other tissues.

An archaeological and archaeometric study of Late Punic–Roman Pottery from the Tas-Silġ Sanctuary and the Żejtun Villa, Malta

Abstract This interdisciplinary study contributes to the understanding of the use of raw materials and pottery production techniques in Late Punic–Late Republican Malta, focusing on the Tas-Silġ sanctuary and the Żejtun Villa. Plates, bowls and cooking vessels were described typologically, and their fabrics were characterised using polarised light microscopy and Energy Dispersive X-Ray Fluorescence. The aims were to classify these vessels into integrated and coherent fabric groups based on all analyses, to better understand the local production of vessels and to assess a possible local provenance. Four integrated fabric groups were identified and represent local productions using distinct raw materials or production techniques. These groups can be distinguished typologically, macroscopically, petrographically and chemically. Multivariate techniques, including the chemical analysis of Maltese clays, were produced to enhance the fabric classification and discuss their raw materials. The raw materials identified are consistent with what is known in Maltese geology. One group is distinctive, and the results suggest the possible use of a previously unidentified raw material, Terra Rossa, found over the Upper Coralline Limestone. This new classification provides the basis for further studies of Late Punic–Roman sites in the Maltese islands and the future identification of imports and exports from the Maltese islands.

Coniferous Biomass for Energy Valorization: A Thermo-Chemical Properties Analysis

Forest biomass energy, when utilized responsibly, presents a carbon-neutral and viable alternative to fossil fuels for energy storage. This research investigates the energy potential of various coniferous species, focusing on their complex chemical compositions and suitability for energy production. Key characteristics such as moisture content, volatile matter, ash content, and fixed carbon were analyzed, along with elemental composition (including nitrogen, carbon, oxygen, and hydrogen) and both gross and net heating values across different species. The proximate analysis revealed significant interspecies variations. For example, Pseudotsuga menziesii and Chamaecyparis lawsoniana exhibited the lowest moisture contents. Elemental analyses showed a broad range of values, with Larix decidua having the lowest nitrogen content and Sequoiadendron giganteum the highest carbon content. Gross and net heating values also varied considerably, with Podocarpus macrophyllus showing the lowest values and Pinus strobus the highest. Principal component analysis (PCA) was employed to identify underlying patterns, revealing correlations between the analyzed variables and the energy potential of the species. Additionally, PCA combined with cluster analysis allowed for the identification of coherent groups of species with similar characteristics. Overall, these findings highlight the diverse energy valorization potential inherent in coniferous species, underscoring the importance of considering specific chemical compositions for efficient energy production. The insights provided here are valuable for selecting coniferous species for energy valorization, emphasizing the need to consider both chemical composition and calorific potential.

SceneDirector: Interactive Scene Synthesis by Simultaneously Editing Multiple Objects in Real-Time.

Intelligent tools for creating synthetic scenes have been developed significantly in recent years. Existing techniques on interactive scene synthesis only incorporate a single object at every interaction, i.e., crafting a scene through a sequence of single-object insertions with user preferences. These techniques suggest objects by considering existent objects in the scene instead of fully picturing the eventual result, which is inherently problematic since the sets of objects to be inserted are seldom fixed during interactive processes. In this article, we introduce SceneDirector, a novel interactive scene synthesis tool to help users quickly picture various potential synthesis results by simultaneously editing groups of objects. Specifically, groups of objects are rearranged in real-time with respect to a position of an object specified by a mouse cursor or gesture, i.e., a movement of a single object would trigger the rearrangement of the existing object group, the insertions of potentially appropriate objects, and the removal of redundant objects. To achieve this, we first propose an idea of coherent group set which expresses various concepts of layout strategies. Subsequently, we present layout attributes, where users can adjust how objects are arranged by tuning the weights of the attributes. Thus, our method gives users intuitive control of both how to arrange groups of objects and where to place them. Through extensive experiments and two applications, we demonstrate the potentiality of our framework and how it enables concurrently effective and efficient interactions of editing groups of objects.

CART-based wide-area damping controller for inter-area oscillations in bulk power system consisting of WAMS data

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions.

How Does Regional Science Address Environmental Issues? A Bibliometric Analysis of Two Decades of Publications

Using bibliometric methods, this paper is aimed at providing an overview of Regional Science publications that address environmental issues. The analysis covers a corpus of 1145 articles that refer to the environment in their title, abstract or keywords and published in 18 journals between 1999 and 2020. Although these publications account for only 6 percent of the articles published in these journals over that period, their number gradually increased. To gain a clearer picture of the environmental issues addressed, we characterise them according to whether an integrative or topical approach is adopted and by their spatial dimensions. This analytical framework is first applied by searching for specific terms in their title, keywords and abstract. The results show that both approaches are equally effective; further, these articles address intraregional or interregional environmental issues more than global environmental issues. Second, we conduct a keyword co-occurrence analysis revealing four coherent thematic article groups treating environmental issues from the perspectives of amenities and migration, governance and policy, innovation and clusters, and land use and urban sprawl. More detailed analyses of each group allowed us to refine our understanding of how environmental issues were addressed. Finally, a cross-cutting view of the four thematic groups suggests that Regional Science approaches environmental issues through spatial disparities and inequalities, spatial interactions and interdependencies, and the spatial dimension of public action. The research perspectives in each of these areas are highlighted.

Enhancing power system stability with adaptive under frequency load shedding using synchrophasor measurements and empirical mode decomposition

This paper introduces an adaptive under frequency load shedding (AUFLS) scheme based on synchrophasor measurements and Empirical Mode Decomposition (EMD) to enhance power system stability during significant disturbances. The traditional UFLS methods, which rely on fixed frequency thresholds, often lead to unnecessary disconnections and a lack of flexibility. The proposed AUFLS scheme dynamically adapts to the magnitude of disturbances, frequency response, and voltage stability index, resulting in a more efficient and responsive system. Various UFLS techniques are reviewed, highlighting the advantages of adaptive strategies. The novelty of this research lies in the innovative application of the EMD algorithm within the AUFLS framework. This method identifies the center of inertia (CoI) of the rate of change of frequency (RoCoF) from frequency signals and calculates the total active power imbalance due to disturbances. Furthermore, EMD is applied to the voltage angle at each bus to identify and form coherent bus groups. These coefficients are subsequently employed to allocate the required active power shedding within each group to maintain system stability. Voltage stability indices are calculated for each group, and load shedding is distributed among the buses based on normalized voltage stability indices, thereby enhancing the precision of load shedding decisions and the overall stability of the power system. Results demonstrate that the proposed scheme provides satisfactory frequency response while requiring less load shedding compared to traditional methods, making it effective for modern power systems with high penetration of renewable energy sources (RES). The performance of the proposed scheme is assessed using the IEEE 39-bus test system, demonstrating its effectiveness in improving system resilience to frequency disturbances. The research concludes that the AUFLS scheme offers a promising approach for enhancing the resilience of power systems to frequency disturbances.

A Comparative Analysis of Machine Learning Algorithms for Identifying Cultural and Technological Groups in Archaeological Datasets through Clustering Analysis of Homogeneous Data

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions.

CABRA: Clustering algorithm based on regular arrangement

Clustering is an unsupervised learning technique for organizing complex datasets into coherent groups. A novel clustering algorithm is presented, with a simple grouping concept depending on only one hyperparameter, which makes it suitable for further extensions to any topology and space. It is compared to state-of-the-art algorithms, overall achieving a better performance independently on the structure and complexity of the data, making the proposed algorithm a valuable tool for real applications such as market segmentation, sentiment analysis and anomaly detection.

Emotions recognition in audio signals using an extension of the latent block model

Emotion detection in human speech is a significant area of research, crucial for various applications such as affective computing and human–computer interaction. Despite advancements, accurately categorizing emotional states in speech remains challenging due to its subjective nature and the complexity of human emotions. To address this, we propose leveraging Mel frequency cepstral coefficients (MFCCS) and extend the latent block model (LBM) probabilistic clustering technique with a Gaussian multi-way latent block model (GMWLBM). Our objective is to categorize speech emotions into coherent groups based on the emotional states conveyed by speakers. We employ MFCCS from time-series audio data and utilize a variational Expectation Maximization method to estimate GMWLBM parameters. Additionally, we introduce an integrated Classification Likelihood (ICL) model selection criterion to determine the optimal number of clusters, enhancing robustness. Numerical experiments on real data from the Berlin Database of Emotional Speech (EMO-DB) demonstrate our method’s efficacy in accurately detecting and classifying emotional states in human speech, even in challenging real-world scenarios, thereby contributing significantly to affective computing and human–computer interaction applications.

Imaginative elaboration in agenesis of the corpus callosum: topic modeling and perplexity.

Previous studies have found deficits in imaginative elaboration and social inference to be associated with agenesis of the corpus callosum (ACC; Renteria-Vasquez et al., 2022; Turk et al., 2009). In the current study, Thematic Apperception Test (TAT) responses from a neurotypical control group and a group of individuals with ACC were used to further study the capacity for imaginative elaboration and story coherence. Topic modeling was employed utilizing Latent Diritchlet Allocation to characterize the narrative responses to the pictures used in the TAT. A measure of the difference between models (perplexity) was used to compare the topics of the responses of individual participants to the common core model derived from the responses of the control group. Story coherence was tested using sentence-to-sentence Latent Semantic Analysis. Group differences in perplexity were statistically significant overall, and for each card individually (p < .001). There were no differences between the groups in story coherence. TAT narratives from persons with ACC were normally coherent, but more conventional (i.e., more similar to the core text) compared to those of neurotypical controls. Individuals with ACC can make conventional social inferences about socially ambiguous stimuli, but are restricted in their imaginative elaborations, resulting in less topical variability (lower perplexity values) compared to neurotypical controls.

The reflective measurement model of adherence to non-pharmaceutical interventions (NPIs) in accordance with normalization process theory (NPT) in coherent and convenient social subgroups: PLS-SEM analysis.

Non-pharmaceutical interventions (NPIs) decrease COVID-19 transmission. Reliability and validity of adherence to NPIs in accordance with normalization process theory (NPT) in coherent and convenient social subgroups using reflective measurement model assessment has not been evaluated. In February 2021, a sample of medical students and people with substance use disorders in treatment as coherent (based on continuous probability distribution) vs. convenient groups (based on convenience, not equal probability) composed of travellers and COVID-19 suspected persons from Split-Dalmatia County (SDC) (n = 656) in the Mediterranean completed self-administered surveys. Partial least squares structural equation modelling (PLS-SEM) was used to measure reflective model assessment of adherence to NPIs according to NPT. PLS-SEM reflective model assessment provided two-group specific factors in inverse relationships which determined adherence to NPIs with excellent goodness-of-fit [χ2 = 1.292, df = 1; P = 0.297, CFI = 1, TLI = 0.997, RMSEA = 0.011 (90% CI 0-0.105), RMSEA P = 0.604, SRMR = 0.008, Hoelter CN (α = 0.05) = 2322.757]. Significant negative factors covariance estimate (-0.716) revealed an inverse relationship between first (adherence to NPIs and internal locus of control (LoC) (0.640)) and second factor; young adulthood age (≤25) and highest level of education (1362). As the first factor increased the second tended to decrease. LoC is expected potential mechanism by which sex (MLsex = -0.017, SE = 0.007, P < 0.016) and belonging to coherent subgroups (MLgroup = -0.008, SE = 0.003, P = 0.015) can produce indirect effect of adherence to NPIs. Coherent subgroups had a more pronounced tendency toward integration of NPIs in everyday life. Group factors that facilitate the normalization were higher educated younger adults with a tendency toward external LoC.

Cluster methods in function better selection

Cluster analysis methods, also known as taxonomic methods, are intended for grouping objects and subjects according to certain characteristics, attributes and properties. Cluster analysis looks at relevant objects and attributes, classifying them into two or more independent groups. Cluster analysis supplemented with discriminant analysis is used in confirmatory and fundamental research. In numerous statistical-methodological procedures, these methods are applied when setting up and testing various hypotheses. Grouping methods are particularly useful in the process of different selections with the aim of forming coherent groups, which may or may not necessarily be statistically different. There are several models of clustering (grouping), always with one goal, which is greater proximity (similarity) of an entity belonging to a group compared to an entity belonging to another group. Two basic grouping models are recognizable, Hierarchical and Non-Hierarchical. Both models have the same goal, which is the formation of several independent homogeneous groups from one common group of entities. The hierarchical approach does not define the number of clusters in advance (a priori), in contrast to the Non-Hierarchical Model which defines in advance number of clusters. The grouping model is chosen depending on the specific problem and the set goal of grouping. In the process, several different models are often applied, and then one is chosen as in this research. It is important to point out that the theoretical number of clusters (groups) is often not realistically applicable in practice. Using the example of this research, it was proven that the first grouping was not a good solution. Through the subsequent, second and third iteration, as well as the application of additional discriminative methods, three optimal clusters were determined in the population of girls and boys. Satisfactory optimal grouping was obtained on the basis of gender criteria and achieved results on psycho-motor tests.

Cooperation in the Gig Economy: Insights from Upwork Freelancers

Existing literature on online labor markets predominantly focuses on how freelancers individually complete tasks and projects. Our study examines freelancers' willingness to work collaboratively. We report results from a survey of 122 freelancers on a leading online labor market platform (Upwork) that examine freelancers' preferences for collaborative work arrangements, and that explore several antecedents of cooperative behaviors. We then test if actual cooperative behavior matches with freelancers' stated preferences through an incentivized social dilemma experiment. We find that respondents cooperate at a higher rate (85%) than reported in previous comparable studies (between 50-75%). This high rate of cooperation may be explained by an ingroup bias. Using a sequential mediation model, we demonstrate the importance of a sense of shared expectations and accountability for cooperation. We contribute to a better understanding of the potential for collaborative work on online labor market platforms by assessing if and what social factors and collective culture exist among freelancers. We discuss the implications of our results for platform designers by highlighting the importance of platform features that promote shared expectations and improve accountability. Overall, contrary to existing literature and predictions, our results suggest that freelancers in our sample display traits that are more consistent with belonging to a coherent group with a shared collective culture, rather than being anonymous actors in a transaction-based market.

Supervised contrastive learning for graph representation enhancement

Graph Neural Networks (GNNs) have exhibited significant success in various applications, but they face challenges when labeled nodes are limited. A novel self-supervised learning paradigm has emerged, enabling GNN training without labeled nodes and even surpassing GNNs with limited labeled data. However, self-supervised methods lack class-discriminative node representations due to the absence of labeled information during training. In this paper, we exploit a supervised graph contrastive learning approach (SGCL) framework to tackle the issue of limited labeled nodes, ensuring coherent grouping of nodes within the same class. We propose augmentation techniques based on a novel centrality function to highlight important topological structures. Additionally, we introduce a supervised contrastive learning method that removes the necessity for negative samples and simplifies complex elements effortlessly. Our approach combines supervised contrastive loss and node similarity regularization while achieving consistent grouping of unlabeled nodes with labeled ones. Furthermore, we utilize the pseudo-labeling technique to propagate label information to distant nodes and address the underfitting problem, especially with low-degree nodes. Experimental results on real-world graphs demonstrate that SGCL outperforms both semi-supervised and self-supervised methods in node classification.

N-cadherin directs the collective Schwann cell migration required for nerve regeneration through Slit2/3 mediated contact inhibition of locomotion.

Collective cell migration is fundamental for the development of organisms and in the adult, for tissue regeneration and in pathological conditions such as cancer. Migration as a coherent group requires the maintenance of cell-cell interactions, while contact inhibition of locomotion (CIL), a local repulsive force, can propel the group forward. Here we show that the cell-cell interaction molecule, N-cadherin, regulates both adhesion and repulsion processes during rat Schwann cell (SC) collective migration, which is required for peripheral nerve regeneration. However, distinct from its role in cell-cell adhesion, the repulsion process is independent of N-cadherin trans-homodimerisation and the associated adherens junction complex. Rather, the extracellular domain of N-cadherin is required to present the repulsive Slit2/Slit3 signal at the cell-surface. Inhibiting Slit2/Slit3 signalling inhibits CIL and subsequently collective Schwann cell migration, resulting in adherent, nonmigratory cell clusters. Moreover, analysis of ex vivo explants from mice following sciatic nerve injury showed that inhibition of Slit2 decreased Schwann cell collective migration and increased clustering of Schwann cells within the nerve bridge. These findings provide insight into how opposing signals can mediate collective cell migration and how CIL pathways are promising targets for inhibiting pathological cell migration.

The Social Intellect and its relationship with some Technical Skills in Football Young Players

The relationship between the mental side and skillful performance is one of the important concept in team sport games such as football. Social intellect is considered important and one of the technical requirements in the players' personality because it is connected with the player's ability to deal with others and build good social relations among the team so that they will be a coherent group for doing the sport achievement. Therefore, the importance of conducting this research lies in knowing the relationship between the social intellect and the performance of some technical skills by football young players. It must be taken into consideration that the psychological and social sides are not paid sufficient attention by the coaches and specialists in Iraqi football affairs. Also, there is a rarity of studies conducted on the players' social intellect in Iraqi sport teams. This research aims at knowing the nature of the relationship between social intellect and the performance of some technical skills by the football young players. The researcher used the descriptive approach in surveying manner. The sample consisted of some league youth clubs of premier level. Also, he used social intellect measurement for players and consequently he concluded that there is significant correlation between the players' social intellect and the performance of some technical in the young players.

A survey for variable young stars with small telescopes – VIII. Properties of 1687 Gaia selected members in 21 nearby clusters

ABSTRACT The Hunting Outbursting Young Stars (HOYS) project performs long-term, optical, multifilter, high cadence monitoring of 25 nearby young clusters and star-forming regions. Utilizing Gaia DR3 data, we have identified about 17 000 potential young stellar members in 45 coherent astrometric groups in these fields. Twenty one of them are clear young groups or clusters of stars within 1 kpc and they contain 9143 Gaia selected potential members. The cluster distances, proper motions, and membership numbers are determined. We analyse long-term (≈ 7 yr) V-, R-, and I-band light curves from HOYS for 1687 of the potential cluster members. One quarter of the stars are variable in all three optical filters, and two-thirds of these have light curves that are symmetric around the mean. Light curves affected by obscuration from circumstellar materials are more common than those affected by accretion bursts, by a factor of 2–4. The variability fraction in the clusters ranges from 10 per cent to almost 100 per cent, and correlates positively with the fraction of stars with detectable inner discs, indicating that a lot of variability is driven by the disc. About one in six variables shows detectable periodicity, mostly caused by magnetic spots. Two-thirds of the periodic variables with disc excess emission are slow rotators, and amongst the stars without disc excess two-thirds are fast rotators – in agreement with rotation being slowed down by the presence of a disc.

Novel primers to identify a wider diversity of butyrate-producing bacteria.

Butyrate-producing bacteria are a functionally important part of the intestinal tract flora, and the resulting butyric acid is essential for maintaining host intestinal health, regulating the immune system, and influencing energy metabolism. However, butyrate-producing bacteria have not been defined as a coherent phylogenetic group. They are primarily identified using primers for key genes in the butyrate-producing pathway, and their use has been limited to the Bacillota and Bacteroidetes phyla. To overcome this limitation, we developed functional gene primers able to identify butyrate-producing bacteria through the butyrate kinase gene, which encodes the enzyme involved in the final step of the butyrate-producing pathway. Genomes extracted from human and rat feces were used to amplify the target genes through PCR. The obtained sequences were analyzed using BLASTX to construct a developmental tree using the MEGA software. The newly designed butyrate kinase gene primers allowed to recognize a wider diversity of butyrate-producing bacteria than that recognized using currently available primers. Specifically, butyrate-producing bacteria from the Synergistota and Spirochaetota phyla were identified for the first time using these primers. Thus, the developed primers provide a more accurate method for researchers and doctors to identify potential butyrate-producing bacteria and deepen our understanding of butyrate-producing bacterial species.

A fully distributed algorithm for dynamic economic dispatch of cross regional power systems based on alternating direction multiplier method

n this study, a novel approach to dynamic economic dispatch for multi-region power systems is introduced, leveraging the alternating direction multiplier method (ADMM) for computational efficiency. The method begins by dividing an interconnected power system into coherent groups, assigning local processors to each area to estimate black-box transfer function models using Lagrange multipliers. These processors then collaborate to form a global transfer function model through a consensus algorithm, leading to the derivation of a transfer function residue for the optimal wide-area control loop. A wide-area damping controller is designed from this residue, and its effectiveness is validated on two area and IEEE-39 bus test systems using RTDS/RSCAD and MATLAB co-simulation platforms. Simultaneously, the study proposes an economic scheduling model that aims to minimize operational costs while meeting various operational constraints. By severing inter-regional connections, the ADMM enables distributed resolution of the optimization problem, breaking it down into regional sub-problems that are iteratively solved to reach the global optimum. This process eliminates the need for a centralized data repository for multiplier updates, supporting a fully distributed scheduling approach. Additionally, a multi-period optimization technique is integrated to address the power system’s temporal dependencies. The approach's validity is demonstrated through empirical analysis of a tri-regional interconnected system based on the IEEE standard test system, confirming the strategy's effectiveness in economic dispatch.