How Narcissism Relates to Social Rank Dynamics in Teams

Team performance can be impaired when two team members both believe they outrank one another in status (upward-status disagreement; USD; Kilduff et al., 2016). Drawing from the narcissistic admiration and rivalry concept (Back et al., 2013), the current study examined how two forms of narcissism distinctively relate to USDs across a team’s lifecycle. Gathering data at four time points, I studied over 126 small task teams from inception to dissolution. The results indicate that narcissistic admiration did not predict one’s status perception tendency or absolute status. However, narcissistic admiration predicted the number of USDs one experiences during team formation and before team dissolution. Narcissistic rivalry predicted other-status derogation across all time points and a decrease in absolute status over time. Moreover, narcissistic rivalry predicted the number of USDs one experiences during team formation. My thesis highlights how both forms of narcissism may lead to undesirable social outcomes

An Analysis of the Cause of Privacy Paradox among SNS Users: take Chinese College Students as an Example

It has been proved that the privacy paradox does exist, yet the cause of the phenomenon remains vague. This article tries to analyze the [Inserted: s]cause of privacy paradox phenomenon on SNS (WeChat) among Chinese college students based on Privacy Calculus Theory and the TPB model and introduces two new factors: the credibility of SNS and the cost of protecting privacy. Through a questionnaire and interview survey,[Inserted: a ] our result shows that there is no significant correlation between users’ privacy concerns and the intention of privacy disclosure. While the more users trust the SNS platform, the more possibility they tend to disclose their private information[Inserted: te], and the cost of privacy protection can somehow weaken the relationship between the intention and the actual behavior. Therefore, [Inserted: ship]by increasing SNS\u27s credibility, users tend to disclose more personal information to SNS providers, which may improve the competitiveness of SNSs and contribute to their sustainable development

DASICS: Enhancing Memory Protection with Dynamic Compartmentalization

In the existing software development ecosystem, security issues introduced by third-party code cannot be overlooked. Among these security concerns, memory access vulnerabilities stand out prominently, leading to risks such as the theft or tampering of sensitive data. To address this issue, software-based defense mechanisms have been established at the programming language, compiler, and operating system levels. However, as a trade-off, these mechanisms significantly reduce software execution efficiency. Hardware-software co-design approaches have sought to either construct entirely isolated trusted execution environments or attempt to partition security domains within the same address space. While such approaches enhance efficiency compared to pure software methods, they also encounter challenges related to granularity of protection, performance overhead, and portability. In response to these challenges, we present the DASICS (Dynamic in-Address-Space Isolation by Code Segments) secure processor design, which offers dynamic and flexible security protection across multiple privilege levels, addressing data flow protection, control flow protection, and secure system calls. We have implemented hardware FPGA prototypes and software QEMU simulator prototypes based on DASICS, along with necessary modifications to system software for adaptability. We illustrate the protective mechanisms and effectiveness of DASICS with two practical examples and provide potential real-world use cases where DASICS could be applied.Comment: 16 pages, 6 figure

The construction of an ecological security pattern based on the comprehensive evaluation of the importance of ecosystem service and ecological sensitivity: a case of Yangxin County, Hubei Province

An ecological safety pattern is the basic guarantee for pollution control, ecological environmental protection, economic construction, and sustainable social development, and is an important means of integrated and coordinated development, protection, and governance. This study proposes a research method to identify ecological source sites based on the evaluation of the importance of ecosystem services and ecological sensitivity, and to identify ecological corridors using the minimum cumulative resistance model. The empirical analysis was carried out in Yangxin County, Hubei Province, as an example. The results show that the study area has 16 key corridors and 29 potential corridors, a total of 45 ecological corridors, with a total length of 452 km. In addition, these ecological corridors present a network-like intertwined distribution, connecting various ecological source sites and ensuring the connectivity between ecological source sites, while the well-developed water source sites in the study area provide excellent ecosystem service functions such as water containment for the area

WebArena: A Realistic Web Environment for Building Autonomous Agents

With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for language-guided agents that is highly realistic and reproducible. Specifically, we focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains: e-commerce, social forum discussions, collaborative software development, and content management. Our environment is enriched with tools (e.g., a map) and external knowledge bases (e.g., user manuals) to encourage human-like task-solving. Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions. The tasks in our benchmark are diverse, long-horizon, and designed to emulate tasks that humans routinely perform on the internet. We experiment with several baseline agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 14.41%, significantly lower than the human performance of 78.24%. These results highlight the need for further development of robust agents, that current state-of-the-art large language models are far from perfect performance in these real-life tasks, and that WebArena can be used to measure such progress.Comment: Our code, data, environment reproduction resources, and video demonstrations are publicly available at https://webarena.dev

Anapole mediated giant photothermal nonlinearity in nanostructured silicon

Featured with a plethora of electric and magnetic Mie resonances, high index dielectric nanostructures offer a versatile platform to concentrate light-matter interactions at the nanoscale. By integrating unique features of far-field scattering control and near-field concentration from radiationless anapole states, here, we demonstrate a giant photothermal nonlinearity in single subwavelength-sized silicon nanodisks. The nanoscale energy concentration and consequent near-field enhancements mediated by the anapole mode yield a reversible nonlinear scattering with a large modulation depth and a broad dynamic range, unveiling a record-high nonlinear index change up to 0.5 at mild incident light intensities on the order of MW/cm2. The observed photothermal nonlinearity showcases three orders of magnitude enhancement compared with that of unstructured bulk silicon, as well as nearly one order of magnitude higher than that through the radiative electric dipolar mode. Such nonlinear scattering can empower distinctive point spread functions in confocal reflectance imaging, offering the potential for far-field localization of nanostructured Si with an accuracy approaching 40 nm. Our findings shed new light on active silicon photonics based on optical anapoles

Fungus Pichia kudriavzevii XTY1 and heterotrophic nitrifying bacterium Enterobacter asburiae GS2 cannot efficiently transform organic nitrogen via hydroxylamine and nitrite

Heterotrophic nitrification is a process of organic nitrogen degradation completed by the participation of heterotrophic nitrifying microorganisms, which can accelerate the nitrogen transformation process. However, the current research mainly focuses on heterotrophic nitrifying bacteria and their ammonium degradation capacities. And there is little accumulation of research on fungi, the main force of heterotrophic nitrification, and their capacities to transform organic nitrogen. In this study, novel heterotrophic nitrifying fungus (XTY1) and bacterium (GS2) were screened and isolated from upland soil, and the strains were identified and registered through GenBank comparison. After 24 h single nitrogen source tests and 15N labeling tests, we compared and preliminarily determined the heterotrophic nitrification capacities and pathways of the two strains. The results showed that XTY1 and GS2 had different transformation capacities to different nitrogen substrates and could efficiently transform organic nitrogen. However, the transformation capacity of XTY1 to ammonium was much lower than that of GS2. The two strains did not pass through NH2OH and NO2− during the heterotrophic nitrification of organic nitrogen, and mainly generated intracellular nitrogen and low N2O. Other novel organic nitrogen metabolism pathways may be existed, but they remain to be further validated

Abnormal Topology of the Structural Connectome in the Limbic Cortico-Basal-Ganglia Circuit and Default-Mode Network Among Primary Insomnia Patients

Purpose: Primary insomnia (PI) is the second most common mental disorder. However, the topologic alterations in structural brain connectome in patients with PI remain largely unknown.Methods: A total of 44 PI patients and 46 age-, gender-, and education level matched healthy control (HC) participants were recruited in this study. Diffusion tensor imaging (DTI) and resting state MRI were used to construct structural connectome for each participant, and the network parameters were employed by non-parametric permutations to evaluate the significant differences between the two groups. Relationships between abnormal network metrics and clinical characteristics, including the disease duration, the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI), the Self-Rating Anxiety Scale (SAS), and the Self-Rating Depression Scale (SDS), were investigated with Spearman’s correlation analysis in PI patients.Results: PI patients demonstrated small-world architecture with lower global (P = 0.005) and local (P = 0.035) efficiencies compared with the HC group. The unique hub nodal properties in PI patients were mainly in the right limbic cortico-basal-ganglia circuit. Five disrupted subnetworks in PI patients were observed in the limbic cortico-basal-ganglia circuit and left default-mode networks (DMN) (P < 0.05, NBS corrected). Moreover, most unique hub nodal properties in the right limbic cortico-basal-ganglia circuit were significantly correlated with disease duration, and clinical characteristics (SAS, SDS, ISI scores) in PI processing.Conclusion: These findings suggested the abnormal anatomical network architecture may be closely linked to clinical characteristics in PI. The study provided novel insights into the neural substrates underlying symptoms and neurophysiologic mechanisms of PI

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed