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Design-level decisions in open-source software (OSS) projects are often made through structured mechanisms such as proposals, which require substantial community discussion and review. Despite their importance, the proposal process is resource-intensive and often leads to contributor frustration, especially when proposals are declined without clear feedback. Yet, the reasons behind proposal rejection remain poorly understood, limiting opportunities to streamline the process or guide contributors effectively. This study investigates the characteristics and outcomes of proposals in the Go programming language to understand why proposals are declined and how such outcomes might be anticipated. We conduct a mixed-method empirical study on 1,091 proposals submitted to the Go project. We quantify proposal outcomes, build a taxonomy of decline reasons, and evaluate large language models (LLMs) for predicting these outcomes. We find that proposals are more often declined than accepted, and resolution typically takes over a month. Only 14.7% of declined proposals are ever resubmitted. Through qualitative coding, we identify nine key reasons for proposal decline, such as duplication, limited

The R Coronae Borealis (RCB) variables are rare, hydrogen-deficient, carbon-rich supergiants known for large, erratic declines in brightness due to dust formation. Recently, the number of known RCB stars in the Milky Way and Magellanic Clouds has increased from $\sim$30 to 162. We use all-sky and targeted photometric surveys to create the longest possible light curves for all known RCB stars and systematically study their declines. Our study, the largest of its kind, includes measurements of decline activity levels, morphologies, and periodicities for nearly all RCB stars. We confirm previous predictions that cool RCB stars exhibit more declines than warm RCBs, supporting a relationship between dust formation and condensation temperatures. We also find evidence for two distinct dust production mechanisms. R CrB and SU Tau show decline onsets consistent with a Poisson process, suggesting their dust production is driven by stochastic processes, such as convection. In contrast, RY Sgr's declines correlate with its pulsation period, suggesting that its dust production is driven by pulsationally-induced shocks. Finally, we show that the dust properties of the related class of DY~Per var

It is well established that cosmic supermassive black hole (SMBH) growth peaks at $z\approx1.5-2$, followed by a strong decline of $\approx1-1.5\,\rm dex$ toward the present day, with the comoving number density of higher-luminosity active galactic nuclei (AGNs) peaking at higher redshift (referred to as "AGN downsizing"). We leverage the best current measurements of the SMBH accretion distribution, based upon data from nine well-characterized extragalactic fields with a "wedding-cake" design, to investigate and quantify the drivers of the drastic decline in cosmic SMBH growth. The decline in the typical Eddington ratio ($λ_\mathrm{Edd}$) of AGNs (decreasing by $\approx1.35\,\rm dex$ from $z\approx1.5-2$ to $z\approx0.2$) is the dominant driver for the broad decline in SMBH growth, rather than a shift of accretion activity to less-massive SMBHs. As $λ_\mathrm{Edd}$ decreases toward lower redshift, the primary contributor to the cosmic SMBH accretion density ($ρ_\mathrm{BHAR}$) has shifted from high-$λ_\mathrm{Edd}$ AGNs to low-$λ_\mathrm{Edd}$ AGNs, even though the latter always dominate the comoving AGN number density at $z<4$. We also find that the decline in SMBH growth towar

Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alzheimer's disease (AD) onset and progression remains unclear. This study aimed to explore whether the presence and location of lobar microbleeds are associated with amyloid-$β$ (A$β$)-PET, tau tangle formation (tau-PET), and longitudinal cognitive decline. We analyzed 1,573 ADNI participants with MR imaging data and information on the number and location of microbleeds. Associations between lobar microbleeds and pathology, cerebrospinal fluid (CSF), genetics, and cognition were examined, focusing on regional microbleeds and domain-specific cognitive decline using ordinary least-squares regression while adjusting for covariates. Cognitive decline was assessed with ADAS-Cog11 and its domain-specific sub-scores. Participants underwent neuropsychological testing at least twice, with a minimum two-year interval between assessments. Among the 1,573 participants (692 women, mean age 71.23 years), 373 participants had microbleeds. The presence of microbleeds was linked to cognitive decline, particularly in the semantic, language, and praxis doma

Population decline is projected to become widespread in Europe, with the continental population set to reverse its longstanding trajectory of growth within the next five years. This represents unfamiliar demographic territory. Despite this, literature on decline remains sparse and our understanding porous. Particular epistemological deficiencies stem from a lack of both cross-national and temporal analyses of population decline. This study seeks to address these gapsthrough the novel application of sequence and cluster analysis techniques to examine variations in population decline trajectories since 2000 in 696 sub-national areas across 33 European territories. The methodology allows for a holistic understanding of decline trajectories capturing differences in the ordering, timing, magnitude and spatial structure of population decline. We identify a typology of population decline distinguishing seven distinct pathways to depopulation and chart their geographies. Results revealed differentiated pathways of depopulation in continental sub-regions, with consistent and rapid declines in the east, persistent but moderate declines in central Europe, accelerating declines in the south an

Online knowledge communities (OKC) such as Stack Exchange, Reddit, and Zhihu have long functioned as socio technical infrastructures for collective problem solving. The rapid adoption of Generative AI (GenAI) introduces both complementarity and substitution. Large language models (LLMs) offer faster, more accessible drafts, yet divert traffic and contributions away from OKC that also provided their training data. To understand how communities adapt under this systemic shock, we report a mixed-methods study combining an online survey (N=217) and interviews with 11 current users. Findings show that while users increasingly rely on AI for convenience, they still turn to OKC for complex, ambiguous, or trust sensitive questions. Participants express polarized attitudes toward AI, reflecting divergent hopes and uncertainties about its role. Yet across perspectives, sustaining sociability, empathy, and reciprocity emerges as essential for community resilience. We argue that GenAI's impact constitutes not a terminal decline but a design challenge: to reimagine socio-technical complementarities that balance automation's efficiency with human judgment, trust, and collective stewardship in th

Decline-curve analysis (DCA) is a widely utilized method for production forecasting and estimating remaining reserves in gas reservoir. Based on the assumptions that past production trend can be mathematically characterized and used to predict future performance. It relies on historical production data and assumes that production methods remain unchanged throughout the analysis. This method is particularly valuable due to its accuracy in forecasting and its broad acceptance within the industry. Wells in the same geographical area and producing from similar geological formations often exhibit similar decline curve parameters. This study applies DCA to forecast the future production performance and estimate the ultimate recovery for the Semutang gas field's well 5 in Bangladesh. Using historical production data, decline curves were generated based on exponential, hyperbolic, and harmonic model equations. The cumulative production estimations were 11,139.34 MMSCF for the exponential model, 11,620.26 MMSCF for the hyperbolic model, and 14,021.92 MMSCF for the harmonic model. In terms of the well's productive life, the estimates were 335.13 days, 1,152 days, and 22,611 days, respectivel

Cognitive decline is a natural part of aging. However, under some circumstances, this decline is more pronounced than expected, typically due to disorders such as Alzheimer's disease. Early detection of an anomalous decline is crucial, as it can facilitate timely professional intervention. While medical data can help, it often involves invasive procedures. An alternative approach is to employ non-intrusive techniques such as speech or handwriting analysis, which do not disturb daily activities. This survey reviews the most relevant non-intrusive methodologies that use deep learning techniques to automate the cognitive decline detection task, including audio, text, and visual processing. We discuss the key features and advantages of each modality and methodology, including state-of-the-art approaches like Transformer architecture and foundation models. In addition, we present studies that integrate different modalities to develop multimodal models. We also highlight the most significant datasets and the quantitative results from studies using these resources. From this review, several conclusions emerge. In most cases, text-based approaches consistently outperform other modalities.

Early detection is crucial for timely intervention aimed at preventing and slowing the progression of neurocognitive disorder (NCD), a common and significant health problem among the aging population. Recent evidence has suggested that language-related functional magnetic resonance imaging (fMRI) may be a promising approach for detecting cognitive decline and early NCD. In this paper, we proposed a novel, naturalistic language-related fMRI task for this purpose. We examined the effectiveness of this task among 97 non-demented Chinese older adults from Hong Kong. The results showed that machine-learning classification models based on fMRI features extracted from the task and demographics (age, gender, and education year) achieved an average area under the curve of 0.86 when classifying participants' cognitive status (labeled as NORMAL vs DECLINE based on their scores on a standard neurcognitive test). Feature localization revealed that the fMRI features most frequently selected by the data-driven approach came primarily from brain regions associated with language processing, such as the superior temporal gyrus, middle temporal gyrus, and right cerebellum. The study demonstrated the

Projections from global climate models reveal a significant inter-model spread in future rainfall changes in the tropical Atlantic by the end of the 21st century, including alterations to the Intertropical Convergence Zone (ITCZ) and monsoonal regions. While existing studies have identified various sources of uncertainty, our research uncovers a prominent role played by the decline of the Atlantic Meridional Overturning Circulation (AMOC) for the inter-model spread. Firstly we examine 30 climate model simulations (using the ssp5-8.5 scenario) from the CMIP6 archive and show that models that present a more substantial AMOC decline exhibit an equatorward shift of the ascending branch of the Atlantic regional Hadley circulation, resulting in a southward displacement of the ITCZ. Conversely, models characterized by a smaller AMOC decline do not indicate any ITCZ displacement. Secondly, we use targeted experiments (using the abrupt 4xCO2 experiment) to specifically isolate the effects of a weakened AMOC from the changes in precipitation that would occur if, under continuous global warming, the AMOC did not weaken. Our results demonstrate that net precipitation anomalies in the abrupt 4x

Drivers on food delivery platforms often run a loss on low-paying orders. In response, workers on DoorDash started a campaign, #DeclineNow, to purposefully decline orders below a certain pay threshold. For each declined order, the platform returns the request to other available drivers with slightly increased pay. While contributing to overall pay increase the implementation of the strategy comes with the risk of missing out on orders for each individual driver. In this work, we propose a first combinatorial model to study the strategic interaction between workers and the platform. Within our model, we formalize key quantities such as the average worker benefit of the strategy, the benefit of freeriding, as well as the benefit of participation. We extend our theoretical results with simulations. Our key insights show that the average worker gain of the strategy is always positive, while the benefit of participation is positive only for small degrees of labor oversupply. Beyond this point, the utility of participants decreases faster with increasing degree of oversupply, compared to the utility of non-participants. Our work highlights the significance of labor supply levels for the

Decline of immunity is a phenomenon characterized by immunocompromised host and plays a crucial role in the epidemiology of emerging infectious diseases (EIDs) such as COVID-19. In this paper, we propose an age-structured model with vaccination and reinfection of immune individuals. We prove that the disease-free equilibrium of the model undergoes backward and forward transcritical bifurcations at the critical value of the basic reproduction number for different values of parameters. We illustrate the results by numerical computations, and also find that the endemic equilibrium exhibits a saddle-node bifurcation on the extended branch of the forward transcritical bifurcation. These results allow us to understand the interplay between the decline of immunity and EIDs, and are able to provide strategies for mitigating the impact of EIDs on global health.

Multimodal neuroimages, such as diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI), offer complementary perspectives on brain activities by capturing structural or functional interactions among brain regions. While existing studies suggest that fusing these multimodal data helps detect abnormal brain activity caused by neurocognitive decline, they are generally implemented in Euclidean space and can't effectively capture intrinsic hierarchical organization of structural/functional brain networks. This paper presents a hyperbolic kernel graph fusion (HKGF) framework for neurocognitive decline analysis with multimodal neuroimages. It consists of a multimodal graph construction module, a graph representation learning module that encodes brain graphs in hyperbolic space through a family of hyperbolic kernel graph neural networks (HKGNNs), a cross-modality coupling module that enables effective multimodal data fusion, and a hyperbolic neural network for downstream predictions. Notably, HKGNNs represent graphs in hyperbolic space to capture both local and global dependencies among brain regions while preserving the hierarchical structure of brain networks. Extensive e

Based on the Arps equation, we propose two stochastic models for curve decline useful in oil engineering context. Theoretical properties and simulations of these models are provided. The first passage time distribution of these stochastic models to a constant level is then studied. In conclusion, we discuss about statistical inference of the parameters from the observations of the oil production cumulative rate.

The COVID-19 pandemic accelerated the use of preprints, aiding rapid research dissemination but also facilitating the spread of misinformation. This study analyzes media coverage of preprints from 2014 to 2023, revealing a significant post-pandemic decline. Our findings suggest that heightened awareness of the risks associated with preprints has led to more cautious media practices. While the decline in preprint coverage may mitigate concerns about premature media exposure, it also raises questions about the future role of preprints in science communication, especially during emergencies. Balanced policies based on up-to-date evidence are needed to address this shift.

A set of high-resolution optical spectra of RCrB acquired before, during, and after its 1995-1996 decline is discussed. All of the components reported from earlier declines are seen. This novel dataset provides new information on these components including several aspects not previously seen in declines of RCrB and other RCBs. In the latter category is the discovery that the decline's onset is marked by distortions of absorption lines of high-excitation lines, and quickly followed by emission in these and in low excitation lines. This 'photospheric trigger' implies that dust causing the decline is formed close to the star. These emission lines fade quickly. After 1995 November 2, low excitation narrow (FWHM ~12 km s-1) emission lines remain. These appear to be a permanent feature, slightly blue-shifted from the systemic velocity, and unaffected by the decline except for a late and slight decrease of flux at minimum light. The location of the warm, dense gas providing these lines is uncertain. Absorption lines unaffected by overlying sharp emission are greatly broadened, weakened, and red-shifted at the faintest magnitudes when scattered light from the star is a greater contributor

A key issue to Alzheimer's disease clinical trial failures is poor participant selection. Participants have heterogeneous cognitive trajectories and many do not decline during trials, which reduces a study's power to detect treatment effects. Trials need enrichment strategies to enroll individuals who will decline. We developed machine learning models to predict cognitive trajectories in participants with early Alzheimer's disease (n=1342) and presymptomatic individuals (n=756) over 24 and 48 months respectively. Baseline magnetic resonance imaging, cognitive tests, demographics, and APOE genotype were used to classify decliners, measured by an increase in CDR-Sum of Boxes, and non-decliners with up to 79% area under the curve (cross-validated and out-of-sample). Using these prognostic models to recruit enriched cohorts of decliners can reduce required sample sizes by as much as 51%, while maintaining the same detection power, and thus may improve trial quality, derisk endpoint failures, and accelerate therapeutic development in Alzheimer's disease.