Adaptive Mechanism Research Articles

Longitudinal Associations among Identity, Internalization of Appearance Ideals, Body Image, and Eating Disorder Symptoms in Community Adolescents and Emerging Adults: Adaptive and Maladaptive Pathways.

Previous research examined the role of identity confusion and body dissatisfaction in eating disorder symptoms, but an integrative perspective including identity synthesis and positive body image is lacking. The current study used three-wave longitudinal data (T1: N = 403; 52.1% female; Mage = 14.85, SD = 0.89) spanning two years to examine the directionality of effects among identity, internalization of appearance ideals, body image, and eating disorder symptoms, with attention to adaptive mechanisms. Results revealed a maladaptive pathway in which internalizing appearance ideals posed a risk for body dissatisfaction and eating disorder symptoms. Conversely, an adaptive pathway highlighted the potential protective role of positive body image regarding identity formation and internalization of appearance ideals. This study provides further insight into the critical role of appearance ideal internalization in the development of eating disorders and the potential value of positive body image. Future research and prevention/intervention efforts should focus on promoting resilience to appearance ideal internalization and enhancing positive body image in the context of identity struggles and eating disorder symptoms.

Phospholipid biosynthesis modulates nucleotide metabolism and reductive capacity.

Phospholipid and nucleotide syntheses are fundamental metabolic processes in eukaryotic organisms, with their dysregulation implicated in various disease states. Despite their importance, the interplay between these pathways remains poorly understood. Using genetic and metabolic analyses in Saccharomyces cerevisiae, we elucidate how cytidine triphosphate usage in the Kennedy pathway for phospholipid synthesis influences nucleotide metabolism and redox balance. We find that deficiencies in the Kennedy pathway limit nucleotide salvage, prompting compensatory activation of de novo nucleotide synthesis and the pentose phosphate pathway. This metabolic shift enhances the production of antioxidants such as NADPH and glutathione. Moreover, we observe that the Kennedy pathway for phospholipid synthesis is inhibited during replicative aging, indicating its role in antioxidative defense as an adaptive mechanism in aged cells. Our findings highlight the critical role of phospholipid synthesis pathway choice in the integrative regulation of nucleotide metabolism, redox balance and membrane properties for cellular defense.

Adaptive genetic mechanisms in mammalian Parp1 locus.

PARP1 is a highly conserved nuclear protein in multicellular organisms that by modulating chromatin opening facilitates gene expression during development. All reported Parp1 null knockout mice strains are viable with no developmental anomalies. It was believed that functional redundancy with other PARP family members, mainly PARP2, explain such a controversy. However, while PARP2 has similar catalytic domain to PARP1, it lacks other domains, making the absence of developmental problems in Parp1 mice knockouts unlikely. Contrary to prior assumptions, in our analysis of the best-investigated Parp1 knockout mice strain we identified persistent mRNA expression, albeit at reduced levels. Transcript analysis revealed an alternatively spliced Parp1 variant lacking exon two. Subsequent protein analysis confirmed the existence of a truncated PARP1 protein in knockout mice. The decreased level of pADPr was detected in Parp1 knockout ES cells with Western Blotting analysis, but immunofluorescence staining did not detect any difference in distribution or level of pADPr in nuclei of knockout ES cells. pADPr level in double Parp1 Parg mutant ES cells greatly exceeded its amount in normal and even in hypomorph Parg mutant ES cells, suggesting the presence of functionally active PARP1. Therefore, our findings challenge the conventional understanding of PARP1 depletion effects.

Adaptive Weighting via Federated Evaluation Mechanism for Domain Adaptation with Edge Devices

Adaptive Weighting via Federated Evaluation Mechanism for Domain Adaptation with Edge Devices

Role of hypoxia-inducible-factor-1α (HIF-1α) in ferroptosis of adipose tissue during ketosis

Postpartum cows experience lipolysis in adipose tissue due to negative energy balance (NEB), and accumulation of free fatty acids (FFA) leads to metabolic stress in adipose tissue. Ferroptosis is a type of cell death triggered by excessive buildup of iron-dependent lipid peroxides, which is involved in the occurrence and development of various metabolic diseases in nonruminants. However, it is still unclear whether ferroptosis occurs in the adipose tissue of ketotic cows and the regulatory mechanisms behind ferroptosis. Despite multiple studies demonstrating the significant involvement of hypoxia-inducible-factor-1α (HIF-1α) in regulating cellular dysfunction, its specific function in adipose tissue of ketotic dairy cows remains uncertain, particularly its regulation of oxidative stress and ferroptosis. The study aimed to explore the impact of HIF-1α on oxidative stress and ferroptosis in bovine subcutaneous adipose tissue and isolated adipocytes. The adipose tissue of clinical ketosis cows (n = 15) with a serum BHB concentration of 3.13 mM (interquartile range = 0.14) and healthy cows (n = 15) with a serum BHB concentration of and 0.58 mM (interquartile range = 0.13) was collected. The results showed that the concentrations of lipid peroxidation malondialdehyde (MDA), reactive oxygen species (ROS), Fe2+ and total iron were increased in adipose tissue of cows with ketosis, while the contents of glutathione (GSH) were reduced. Furthermore, the protein levels of HIF-1α, heme oxygenase 1 (HMOX1), catalase (CAT), superoxide dismutase 1 (SOD1), acyl-CoA synthetase 4 (ACSL4), and nuclear factor erythroid-derived 2-like 2 (NFE2L2) exhibited higher abundance in adipose tissue obtained from cows with ketosis, whereas the protein abundance of solute carrier family 7 member 11 (SLC7A11), glutamate cysteine ligase catalytic subunit (GCLC), kelch-like ECH-associated protein 1 (KEAP1), glutamate cysteine ligase regulatory subunit (GCLM) and glutathione peroxidase 4 (GPX4) were lower. To simulate the ferroptosis state of adipose tissue in ketotic cows, primary bovine adipocytes were isolated from the adipose tissue of healthy cows and cultured with erastin to construct ferroptosis model. Adipocytes were cultured with either an adenovirus overexpressing HIF-1α or small interfering RNA targeting HIF-for 48 h, followed by exposure to erastin (1 μM) for 24 h. Treatment with erastin led to higher protein abundance of CAT, SOD1, NFE2L2 and HMOX1, while it inhibited the protein expression levels of GCLC, SLC7A11, GCLM, GPX4 and KEAP1. Furthermore, erastin treatment elevated the levels of ROS, MDA, Fe2+, total iron and reduced the content of GSH. The overexpression of HIF-1α reversed the erastin-induced decreases in the protein abundance of GPX4 and SLC7A11, as well as the levels of MDA, ROS, Fe2+ and total iron, while significantly increasing protein abundance and content of CAT, SOD1, NFE2L2, HMOX1, GCLC, GCLM, GPX4, SLC7A11 and GSH. Conversely, the silencing of HIF-1α further exacerbated the erastin-induced levels of MDA, ROS, Fe2+ and total iron, while inhibiting the upregulation of SOD1, CAT, NFE2L2 and HMOX1. Collectively, these findings suggest that activation of HIF-1α may function as an adaptive mechanism to mitigate ferroptosis and alleviate oxidative stress in adipose tissue.

ISPPFL: An incentive scheme based privacy-preserving federated learning for avatar in metaverse

The metaverse, seen as the internet’s successor, is a digital world with its own values and economy, linked to the physical world. Accessed via digital avatars using VR equipment, it involves extensive personal data, raising concerns about data security and privacy. Federated learning is a distributed machine learning technique that ensures privacy by allowing metaverse avatars to share knowledge without revealing user data. However, recent research shows that federated learning still faces privacy threats, such as Source Inference Attacks (SIAs) in Horizontal Federated Learning (HFL) and Label Inference Attacks (LIAs) in Vertical Federated Learning (VFL). To solve these problems, in this paper, we propose the first Incentive Scheme based Privacy-Preserving Federated Learning for avatar in metaverse (ISPPFL). The framework is composed of a privacy risks auditor, perturbation generation mechanism and adaptive incentive mechanisms, which effectively defends against privacy risks. We conducted comprehensive experiments on two distinct datasets in various scenarios. The result demonstrated that, the ISPPFL can effectively defend against privacy attacks while maintain model accuracy. For SIAs under HFL, compared to the baseline, the introduction of perturbation and incentive mechanisms could minimize privacy risk indicator (PRI) to around 20%, while maintain the model performance. For LIAs under VFL, the PRI of the model with perturbation generation mechanisms could decrease by approximately 10% compared to the model trained without defenses. Simultaneously, with the introduction of adaptive incentive mechanisms, the PRI could drop from 90% to 69%. Ultimately, the paper also summarizes the completed work and proposes directions for future research.

A micro-geoarchaeological look at adaptive and glocal socio-economic aspects: the case of Middle Bronze Age Tel Dor, Israel

Adaptative mechanisms used by coastal urban Middle Bronze Age (MBA) settlements in the southern Levant are regularly studied from the point of view of maritime connectivity. Here we look into the adaptive mechanisms of MBA Tel Dor by investigating the utilization of geogenic (rock, soil, sediment) resources for construction — materials used for wall foundations, mudbrick superstructure and wall plaster — and for pottery making. The composition and fabric of archaeological materials were compared to those of local natural sediments from two boreholes. The results show that stones for wall construction relied on the local kurkar, that red-coloured mudbricks were produced from local Carmel coast sediments, while beige-coloured mudbricks were produced by mixing local red sediments with non-coastal calcareous rock fragments, and that wall plaster was made of non-coastal geogenic marl. From a small pilot sample of pottery, six petrofabrics were identified, including the local Carmel coast, along with five other, distant, production centres. These characteristics suggest that while the site’s inhabitants utilized local resources, they were firmly interconnected with regional, ‘global’, Mediterranean networks, suggesting MBA Dor was a glocalized society, and that diversification of resources served as a promotor of resilience for this coastal society, allowing it to thrive for millennia.

Genome sequencing of Elaeocarpus spp. stem blight pathogen Pseudocryphonectria elaeocarpicola reveals potential adaptations to colonize woody bark

BackgroundElaeocarpus spp. stem blight, caused by Pseudocryphonectria elaeocarpicola, is a destructive disease, which will significantly reduce the productivity and longevity of Elaeocarpus spp. plants, especially in the Guangdong Province of China. However, few information is available for P. elaeocarpicola. To unravel the potential adaptation mechanism of stem adaptation, the whole genome of P. elaeocarpicola was sequenced by using the DNBSEQ and PacBio platforms.ResultsP. elaeocarpicola harbors 44.49 Mb genome with 10,894 predicted coding genes. Genome analysis revealed that the P. elaeocarpicola genome encodes a plethora of pathogenicity-related genes. Analysis of carbohydrate-active enzymes (CAZymes) revealed a rich variety of enzymes participated in plant cell wall degradation, which could effectively degrade cellulose, hemicellulose and xyloglucans in the plant cell wall and promote the invasion of the host plant. There are 213 CAZyme families found in P. elaeocarpicola, among which glycoside hydrolase (GH) family has the largest number, far exceeding other tested fungi by 53%. Besides, P. elaeocarpicola has twice as many genes encoding chitin and cellulose degradation as Cryphonectria parasitica, which belong to the same family. The predicted typical secreted proteins of P. elaeocarpicola are numerous and functional, including many known virulence effector factors, indicating that P. elaeocarpicola has great potential to secrete virulence effectors to promote pathogenicity on host plants. AntiSMASH revealed that the genome encoded 61 secondary metabolic gene clusters including 86 secondary metabolic core genes which was much higher than C. parasitica (49). Among them, two gene cluster of P. elaeocarpicola, cluster12 and cluster52 showed 100% similarity with the mycotoxins synthesis clusters from Aspergillus steynii and Alternaria alternata, respectively. In addition, we annotated cytochrome P450 related enzymes, transporters, and transcription factors in P. elaeocarpicola, which are important virulence determinants of pathogenic fungi.ConclusionsTaken together, our study represents the first genome assembly for P. elaeocarpicola and reveals the key virulence factors in the pathogenic process of P. elaeocarpicola, which will promote our understanding of its pathogenic mechanism. The acquired knowledge lays a foundation for further exploration of molecular interactions with the host and provide target for management strategies in future research.

Multicategory fire damage detection of post‐fire reinforced concrete structural components

AbstractThis paper introduces an enhanced you only look once (YOLO) v5s‐D network customized for detecting various categories of damage to post‐fire reinforced concrete (RC) components. These damage types encompass surface soot, cracks, concrete spalling, and rebar exposure. A dataset containing 1536 images depicting damaged RC components was compiled. By integrating ShuffleNet, adaptive attention mechanisms, and a feature enhancement module, the capability of the network for multi‐scale feature extraction in complex backgrounds was improved, alongside a reduction in model parameters. Consequently, YOLOv5s‐D achieved a detection accuracy of 93%, marking an 11% enhancement over the baseline YOLOv5s network. Comparison and ablation tests conducted on different modules, varying dataset sizes, against other state‐of‐the‐art networks, and on public datasets validate the resilience, superiority, and generalization capability of YOLOv5s‐D. Finally, an application leveraging YOLOv5s‐D was developed and integrated into a mobile device to facilitate real‐time detection of post‐fire damaged RC components. This application can integrate diverse fire scenarios and data types, expanding its scope in future. The proposed detection method compensates for the subjective limitations of manual inspections, providing a reference for damage assessment.

Local adaptation, recombination, and the fate of neopolyploids.

Polyploidy is widely recognized as an important speciation mechanism because it isolates tetraploids from their diploid progenitors. Polyploidy also provides new genetic material that may facilitate adaptive evolution. However, new mutations are more likely to arise after a neopolyploid already has successfully invaded a population. Thus, the role of adaptive forces in establishing a polyploid remains unclear. One solution to this apparent paradox may lie in the capacity of polyploids to suppress recombination among preexisting locally adapted alleles. The local adaptation mechanism requires that spatially heterogeneous selection acts on multiple loci and that gene flow introduces maladapted alleles to the population where the polyploid forms. The mechanism requires neither strong genetic drift nor any intrinsic benefit of genome doubling and can accommodate any mode of gene action. A unique prediction of the mechanism is that adaptive alleles should predate polyploidization, a pattern consistent with observations from a few well-studied polyploids. The mechanism is also consistent with the coexistence of both diploid and tetraploid cytotypes, fitness heterogeneity among independently derived polyploids, and the prevalence of outcrossing among older polyploids. The local adaptation mechanism also makes novel predictions about circumstances favoring polyploid invasions that can be tested using molecular genetic or comparative approaches.

Development of a wheeled wall‐climbing robot with an internal corner wall adaptive magnetic adhesion mechanism

AbstractThe wall‐climbing robot is a growing trend for robotized intelligent manufacturing of large and complex components in shipbuilding, petrochemical, and other industries, while several challenges remain to be solved, namely, low payload‐to‐weight ratio, poor surface adaptability, and ineffective traversal maneuverability, especially on noncontinuous surfaces with internal corners (non‐CSIC). This paper designs a high payload‐to‐weight ratio wheeled wall‐climbing robot which can travel non‐CSIC effectively with a payload capacity of up to 75 kg, and it can carry a maximum load of 141.5 kg on a vertical wall. By introducing a semi‐enclosed magnetic adhesion mechanism, the robot preserves a redundant magnetic adsorption ability despite the occurrence of significant gaps between localized body components and the wall surface. In addition, by ingeniously engineering a passive adaptive module into the robot, both the surface adaptability and crossability are enhanced without increasing the gap between the body and the wall, thereby ensuring the optimization of the adsorption force. Considering the payload capacity and diversity when climbing on vertical walls, inclined walls, ceilings, and internal corner transitions, control equations for internal corner transitions and comprehensive simulations of magnetic adsorption forces are performed using FEA tools. Finally, a functional prototype was developed for rigorous experimental testing, with the results confirming that the robot successfully meets the desired functionality and performance benchmarks.

Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO.

MedT2T: An adaptive pointer constrain generating method for a new medical text-to-table task

Medical information extraction is a crucial task in the governance of healthcare data within medical information systems in the medical internet network, aimed at extracting vital information from existing content. However, structuring this key information into a table is currently a challenge, hindering the development of AI-driven smart health. In this study, we study the medical text-to-table task based on a new generative perspective. To address the challenges of ineffective numerical embedding, flexible table formats, and dense medical terminology and numerical entities in an end-to-end manner, we present the innovative medical text-to-table model called MedT2T. This model, built on the BART backbone, operates in an end-to-end manner and comprises three essential modules: Encoder, Decoder, and Adapter. The Encoder utilizes an innovative adaptive medical numerical constraint to facilitate precise embedding and generation of medical numerical data. The generated output of the Decoder adheres to relational constraints and table formats, ensuring the desired structure and organization. Additionally, the Adapter incorporates an adaptive pointer generation mechanism, allowing for dynamic referencing of medical terminology and numerical information either from the source text or generated through the vocabulary distribution of the Decoder. Our method outperforms existing baselines in terms of exact match, character level match, and BERTScore. We also proved that MedT2T can serve as an essential table extraction tool to bring informative gains for medical downstream classifiers and predictors. This study not only achieved accurate entity generation for tables from lengthy medical texts to improve physician efficiency in accessing critical information for decision-making, but also provided large-scale structured training table data for downstream tasks such as AI-driven smart healthcare.

Editorial Expression of Concern: Generation of TRAIL-resistant cell line models reveals distinct adaptive mechanisms for acquired resistance and re-sensitization.

Editorial Expression of Concern: Generation of TRAIL-resistant cell line models reveals distinct adaptive mechanisms for acquired resistance and re-sensitization.

Leaf Trait Variations and Ecological Adaptation Mechanisms of Populus euphratica at Different Developmental Stages and Canopy Heights

The ability of plants to alter specific combinations of leaf traits during development and in response to abiotic stress is crucial for their success and survival. While there are numerous studies on the variation of leaf traits within the canopies of Populus species, the application of network analysis to understand the variation and combinations of these traits across different growth stages is rare. The leaves of Populus euphratica, a dominant species in arid regions, exhibit notable morphological variations at different developmental stages and canopy heights in response to water scarcity and climate change. In this study, 34 leaf traits (morphological, chemical, photosynthetic, and hydraulic) and their roles in drought adaptation were investigated in 60 Populus euphratica plants at five developmental stages and five canopy heights using leaf trait network (LTN) analysis. The aim was to analyze adaptive strategies to arid environments at different developmental stages and canopy heights through the interdependence of leaf traits. The results showed that the internal coordination capacity of leaf trait networks decreased and then increased with each developmental stage, while the functional modules of leaf trait networks were loosely connected and aggregated with the increase in tree diameter at breast height. With increasing canopy height, the coordination linkage’s ability between leaf traits showed an increasing then decreasing trend, and the traits of the leaves in the canopy at 6 m were more closely connected, less modular, and simpler in topology compared with those in the other layers. Leaves form functional modules by coordinating specific traits that promote growth and resist drought. Leaf photosynthesis, water transport, and nutrient traits were central to different developmental stages, whereas leaf morphology, nutrient metabolism, and drought-resistance-related traits were central to the canopy height. Leaf morphology and osmoregulatory traits play key roles in leaf trait network regulation, including leaf length and width, leaf shape index, soluble sugars, and soluble proteins, which are important “intermediary traits” in the Populus euphratica leaf network. Further analysis revealed that structural traits were important at different developmental stages and canopy heights. When resources are limited, the leaf preferentially maintains a stable connection between structural traits to enhance photosynthesis, and these traits and their combinations might confer drought resistance. During the rapid development stage, the connection between chemical traits becomes important, and the leaf grows by rapidly accumulating nutrients. In summary, this study provides new perspectives and insights into the drought adaptation strategies of P. euphratica at different developmental stages and canopy heights by analyzing leaf trait networks.

Molecular mechanism of a coastal cyanobacterium Synechococcus sp. PCC 7002 adapting to changing phosphate concentrations

AbstractPhosphorus concentration on the surface of seawater varies greatly with different environments, especially in coastal. The molecular mechanism by which cyanobacteria adapt to fluctuating phosphorus bioavailability is still unclear. In this study, transcriptomes and gene knockouts were used to investigate the adaptive molecular mechanism of a model coastal cyanobacterium Synechococcus sp. PCC 7002 during periods of phosphorus starvation and phosphorus recovery (adding sufficient phosphorus after phosphorus starvation). The findings indicated that phosphorus deficiency affected the photosynthesis, ribosome synthesis, and bacterial motility pathways, which recommenced after phosphorus was resupplied. Even more, most of the metabolic pathways of cyanobacteria were enhanced after phosphorus recovery compared to the control which was kept in continuous phosphorus replete conditions. Based on transcriptome, 54 genes potentially related to phosphorus-deficiency adaptation were selected and knocked out individually or in combination. It was found that five mutants showed weak growth phenotype under phosphorus deficiency, indicating the importance of the genes (A0076, A0549-50, A1094, A1320, A1895) in the adaptation of phosphorus deficiency. Three mutants were found to grow better than the wild type under phosphorus deficiency, suggesting that the products of these genes (A0079, A0340, A2284–86) might influence the adaptation to phosphorus deficiency. Bioinformatics analysis revealed that cyanobacteria exposed to highly fluctuating phosphorus concentrations have more sophisticated phosphorus acquisition strategies. These results elucidated that Synechococcus sp. PCC 7002 have variable phosphorus response mechanisms to adapt to fluctuating phosphorus concentration, providing a novel perspective of how cyanobacteria may respond to the complex and dynamic environments.

Effects of Drought Stress on Leaf Functional Traits and Biomass Characteristics of Atriplex canescens.

Drought is a critical factor constraining plant growth in arid regions. However, the performance and adaptive mechanism of Atriplex canescens (A. canescens) under drought stress remain unclear. Hence, a three-year experiment with three drought gradients was performed in a common garden, and the leaf functional traits, biomass and biomass partitioning patterns of A. canescens were investigated. The results showed that drought stress had significant effects on A. canescens leaf functional traits. A. canescens maintained the content of malondialdehyde (MDA) and the activity of superoxide dismutase (SOD), but the peroxidase (POD) and catalase (CAT) activity decreased, and the content of proline (Pro) and soluble sugar (SS) increased only under heavy drought stress. Under drought stress, the leaves became smaller but denser, the specific leaf area (SLA) decreased, but the dry matter content (LDMC) maintained stability. Total biomass decreased 60% to 1758 g under heavy drought stress and the seed and leaf biomass was only 10% and 20% of non-stress group, but there had no significant difference on root biomass. More biomass was allocated to root under drought stress. The root biomass allocation ratio was doubled from 9.62% to 19.81% under heavy drought, and the root/shoot ratio (R/S) increased from 0.11 to 0.25. The MDA was significantly and negatively correlated with biomass, while the SPAD was significantly and positively correlated with total and aboveground organs biomass. The POD, CAT, Pro and SS had significant correlations with root and seed allocation ratio. The leaf morphological traits related to leaf shape and weight had significant correlations with total and aboveground biomass and biomass allocation. Our study demonstrated that under drought stress, A. canescens made tradeoffs between growth potential and drought tolerance and evolved with a conservative strategy. These findings provide more information for an in-depth understanding of the adaption strategies of A. canescens to drought stress and provide potential guidance for planting and sustainable management of A. canescens in arid and semi-arid regions.

LSGDDN-LCD: An appearance-based loop closure detection using local superpixel grid descriptors and incremental dynamic nodes

Loop Closure Detection (LCD) is an essential component of visual Simultaneous Localization and Mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint changes, and dynamic objects in scenes. This paper introduced an online appearance based LCD using Local Superpixel Grids Descriptor (LSGD) and Dynamic Nodes (DN), i.e., LSGDDN-LCD, to find similarities between scenes via handcrafted features extracted from the LSGD. Additionally, we proposed the adaptive mechanism to group similar scenes called DynamicNodes, which incrementally adjusted the database in an online manner, allowing for efficient and online retrieval of previously viewed images without need of the pre-training. Experimental results confirmed that the LSGDDN-LCD significantly improved LCD precision–recall and efficiency, and outperformed several state-of-the-art (SOTA) approaches on public and our own datasets, indicating its great potential as a generic LCD framework. Our implementation of the LSGDDN-LCD approach and the datasets were open-sourced on GitHub (https://github.com/BaoshengZhang0/LSGDDN-LCD.git).

A longitudinal examination of appraisal, coping, stress, and mental health in students: A cross-lagged panel network analysis.

In their seminal Transactional Theory of Stress, Lazarus and Folkman described a complex adaptation mechanism that involves appraisal and coping processes that lead to the subjective experience of stress. In this study, we examined the temporal order of all components of this theory using a cross-lagged panel analysis. A sample of 354 students (62.10% female, mean age=20.63y.o.) participated in a 5-week study and completed weekly self-reported measures of prospective appraisal, coping, core burnout, and mental health (i.e. depression, anxiety, and stress). Using cross-lagged panel network analyses, we analyzed how each measured variable is predicted by all variables from the previous measurement moment. Convergent with the transactional stress theory, we found that threat appraisals and negative self-perception predicted future anxiety and depression. Contrary to expectations, core burnout and somatization emerged as significant predictors of poor mental health outcomes, suggesting a self-perpetuating spiral. Furthermore, our study highlighted the importance of recognizing the bidirectional connections between appraisals, coping strategies, and mental well-being.

Optimal adaptive coordination of overcurrent relays in power systems protection using a new hybrid metaheuristic algorithm

AbstractAdvent of distributed generation and progression towards an intelligent grid infrastructure within the domain of contemporary electrical power systems have created dynamic load profiles. Accompanying these developments, protective relays are faced with an evolving electrical load landscape and variable fault current conditions, resulting in disparate operational timings throughout the diurnal cycle. In light of these challenges, this paper delineates the formulation and simulation of a novel adaptive protection strategy for overcurrent relays, meticulously tailored to accommodate the fluctuations in electrical load. To construct a robust framework for this adaptive mechanism, a series of hypothetical fault current scenarios are meticulously crafted to activate the relays within the briefest time interval feasible. Further innovating within this sphere, this paper introduces a new hybrid algorithm, deftly amalgamating the strengths of three preeminent metaheuristic models: Improved Harmony Search, Particle Swarm Optimization, and Differential Evolution. Simulations and analyses substantiate the efficacy of the algorithm in optimizing the coordination among overcurrent relays aiming to uphold the overarching protective imperatives of the grid. For the IEEE 6‐bus system, the mean value of the objective function during 24 h in Monte Carlo is 292.6607 and very close to 272.0758 in the simulation of eight stochastic scenarios, which contributes to the validity of the approach in practical settings. Also, in the IEEE 30‐bus system, the results of the mean relay operation time set for the hours with the lowest and highest consumption load are 17.1297 and 14.8049 s, which reveals the increase in the operation speed of the relays.