The crowding-out effect of cause-marketing on private donations

This paper studies consumption-portfolio optimization problems with habit formation in a regime-switching market. The habit level, which reflects the endogenous impact of past consumption, also involves regime switching and jump diffusion. Because of the presence of general utility functions and path-dependent random parameters, we use the market completion method and introduce some additional jump assets to address the problems. After reducing the problems to solving a stochastic Hamilton-Jacobi-Bellman equation, we derive the optimal control by a joint adoption of envelope theorem and backward stochastic partial differential equation. In general, the optimal portfolio strategy includes the demand of jump assets for hedging against the regime-switching and jump-diffusion risk. In particular, for power/logarithmic utility, we obtain a closed-form solution in the enlarged complete market. For comparison, we also study the power/logarithmic utility case with many specific conditions in the primal incomplete market by restricting the positions of the jump assets to zero. Funding: This research was supported by the National Natural Science Foundation of China [Grants 12401611 and 12571520], Major Program of the Key Research Institute on Humanities and Social Science of China Ministry of Education [Grant 22JJD790091], the 111 Project [Grant B17050], PolyU [Grant 1-CE28], and CTBU [Grant 2355010].

ABSTRACTTumor dynamic models are vital for evaluating oncology treatments and guiding clinical drug development decisions. However, few studies rigorously assess their predictive capabilities, especially when forecasting tumor trajectories from clinical trials with short or inconsistent follow‐up across treatment arms. Poor predictive performance or biases related to follow‐up time could potentially limit the general utility of tumor growth inhibition (TGI) models. This study quantitatively evaluates prediction bias across several established tumor dynamic models, comparing five classical pharmacometric TGI models with the deep learning‐based Tumor Dynamic Neural‐ODE (TDNODE) framework. Using time‐truncated clinical trial data from 3106 patients with non‐small cell lung cancer (NSCLC) across four completed atezolizumab phase III studies, we consistently observed moderate‐to‐high positive bias in the predictions from pharmacometric models, particularly with more limited follow‐up. By examining the structures of these models and comparing them to observed data, we highlight how the assumed kinetic patterns potentially lead to biased parameter estimation and systemic overestimation of tumor size when applied to immature datasets. In contrast, the TDNODE framework, using deep learning, demonstrated promising early results, exhibiting improved predictive performance in the same evaluations. These findings underscore the critical need to address prediction bias in tumor dynamic modeling with immature data and to consider alternative approaches to established paradigms for certain drug development applications. This study also generally demonstrates the potential of novel methods, such as deep learning, to potentially enhance the reliability of tumor dynamics modeling, especially in challenging early‐phase clinical decision‐making scenarios.

The availability of gene function prediction datasets helps researchers to consider possible functions for uncharacterized genes for hypothesis generation, candidate gene prioritization, and many other applications. Many such datasets are based on the Gene Ontology (GO) function graph. For plants this can be problematic because the most specific GO terms available are often derived from the biology of non-plant taxa (e.g., functions specific to nerve function would not seem likely to map to plant biological processes given that plants lack nerves). To balance the need for functional specificity while limiting to functions relevant to plant biology, researchers often limit to the GO Slim plant subset, but, by design, that subset consists of very general terms and limits real utility for, e.g., specific hypothesis generation. Worse yet, sometimes researchers choose to simply throw out terms if they are not relevant to plant biology (rather than traversing the GO graph to select the most specific term in that hierarchy that is compatible with plant biology). We created GO Big, a Gene Ontology subset type, to improve the biological relevance of gene function predictions for taxon-specific biology applications. GO Big plant subsets retain maximal functional specificity for hypothesis generation while limiting to terms applicable to the biology of plants. In brief, we used a curatorial approach to generate two GO Big subsets, a general subset derived from terms with experimentally validated functions across Viridiplantae species, and a species-specific subset for maize (Zea mays ssp. mays). Annotating genes with assignments that better reflect the biology of a taxon can pave the way for more biologically accurate and testable hypotheses for genes of interest. The subsets produced here can help plant biologists limit genome-wide gene function prediction sets to functions possible for plant genes, and the process to generate GO Big subsets is described in detail to enable others to create GO Big subsets for additional taxon sets, including ones for protists, fungi, and other phylogenetic categories.

Background/Objectives: Fentanyl test strips (FTS) are a harm reduction tool used to detect fentanyl in illicit substances. However, little is known regarding Americans' beliefs regarding FTS. Therefore, the purpose of this study was to assess the U.S. general public's FTS knowledge and perceptions. Methods: This study utilized a cross-sectional design. Adults ≥18 residing in the U.S. were recruited to participate in an anonymous online survey via Amazon Mechanical Turk (MTurk). Participants received $5 upon survey completion. The survey instrument was informed by the Health Belief Model, and primary outcome measures included: (1) FTS knowledge (13-items); (2) perceived susceptibility to fentanyl exposure (8-items); (3) perceived severity of fentanyl exposure (10-items); (4) perceived FTS benefits (9-items); (5) perceived barriers to FTS access (13-items); (6) comfort using and accessing FTS (15-items); (7) confidence using and accessing FTS (11-items); and (8) FTS utilization intentions (6-items). Outcomes were measured via 5-point Likert-type scales (1 = strongly disagree, 5 = strongly agree). Data were analyzed using descriptive statistics and Mann-Whitney U tests to compare differences in scale scores across participant sociodemographics. Predictors of FTS utilization intentions were assessed via multiple linear regression, controlling for participant age, race, sex, geographic setting (rural/urban), and recreational drug use history (yes/no) (α = 0.05). Results: Of n = 206 respondents, the majority were male (55.8%) and White (83.0%) with a mean age of 46.4. Approximately 81% resided in urban areas and 58.5% reported a history of recreational drug use. Participants who identified as Black, Asian, Indigenous, Pacific Islander, or Multiracial reported significantly higher mean (SD) perceived susceptibility compared to White participants (2.06 [0.54] vs. 1.91 [0.58]; p = 0.034). Participants residing in urban areas reported significantly higher comfort using and accessing FTS (3.61 [0.86]) than those in rural areas (3.29 [0.92]; p = 0.048), and younger individuals (≤44.5 years) were more confident in their ability to access FTS (3.75 [0.73]) compared to their older counterparts (3.60 [0.64]; p = 0.048). Perceived susceptibility (β = 0.442; p < 0.001), benefits (β = 0.250; p = 0.020), and comfort (β = 0.453; p < 0.001) were positive predictors of FTS utilization intention (R2 = 0.417). Conclusions: Perceptions regarding FTS varied across race, geographic setting, and age. Perceived susceptibility, perceived benefits, and comfort positively predicted the U.S. general public's FTS utilization intentions. Future interventions may leverage these influential factors to enhance FTS uptake.

With aging populations, a double burden of disease, and post-COVID-19 economic strain, managing non-communicable diseases (NCDs) has become a major challenge in low- and middle-income countries (LMICs). While most studies examine inequities in general healthcare utilization, few focus specifically on NCDs services. This review addresses that gap by synthesizing evidence on associated factors of equitable NCDs service utilization and assessing interventions to improve utilization. A systematic review and meta-analysis were conducted following PRISMA-Equity guidelines, including studies published between 2014 and 2024. Eligible studies examined socioeconomic and demographic associated factors of NCDs service utilization or evaluated interventions to reduce inequities. In the meta-analysis, pooled estimates for NCDs service utilization were performed using a random effects model. Heterogeneity among studies was assessed using I² statistics. Twenty-three studies were included. Overall, NCDs service utilization showed a clear pro-rich pattern, with wealthier groups consistently utilizing more services. The pooled outpatient NCD service utilization was 52.84% (95% CI: 41.04–64.64). Compared with the poorest wealth quintile, higher wealth status was significantly associated with greater NCDs service utilization (AOR = 1.44; 95% CI: 1.18–1.74). Socioeconomic status was the strongest associated factor, while gender, rural residence, and insurance status showed no consistent effects. Interventions such as patient-centered care, provider training, system-level reforms, and digital health integration showed promising outcomes. This review highlights that inequities in NCDs service utilization are driven primarily by poverty and structural barriers, not demographic factors alone. By focusing specifically on NCDs, it adds new evidence to equity literature that has previously concentrated on general healthcare use. Targeted pro-poor strategies and innovative interventions are essential to reduce disparities and improve NCD outcomes in LMICs.

Abstract Sequential filtering and spatial inverse problems assimilate data points distributed either temporally (in the case of filtering) or spatially (in the case of spatial inverse problems). Sometimes it is possible to choose the position of these data points (which we call sensors here) in advance, with the goal of maximising the expected information gain (or a different metric of performance) from future data, and this leads to an Optimal Experimental Design (OED) problem. Here we revisit an interpretation of optimising sensor placement as an integration with respect to a general probability measure $$\xi $$ ξ . This generalises the problem of discrete-time sensor placement (which corresponds to the special case where the probability measure is a mixture of Diracs) to an infinite-dimensional, but mathematically more well-behaved setting. We focus on the continuous-time stochastic filtering setting, whose solution is governed by the Zakai equation. We derive an expression for the Fréchet derivative of a general OED utility functional, the key to which is an adjoint (backwards in time) differential equation. This paves the way for utilising new gradient-based methods for solving the corresponding optimisation problem, as a potentially more efficient alternative to (semi-)discrete optimisation methods, e.g. based on greedy insertion and deletion of sensor placements.

This study investigates the elastic stiffness constants, elastic moduli and eigen values of the ‎stiffness matrix of nematic liquid crystalline compounds 4,4'-di-3-alkyl azoxybenzene and ‎‎4,4'-di-4-alkyl azoxybenzene using two different computational processes, viz.,General ‎Utility Lattice Program (GULP) and the ELATE Program from reported experimental data ‎of cell parameters. The study also attempts to predict the liquid crystal structure in terms of ‎elastic constants. Elastic constats determine the thermal stability, stiffness and degree of ‎molecular order, which determines the range of temperature of the liquid crystalline phase. ‎This study included the comparison of different physical parameters of the above ‎compounds and also made an attempt to understand the reason why 4,4'-di-3-alkyl ‎azoxybenzene exhibits the mesophase for a wider range of temperature(42°C)than smaller ‎mesophase range of temperature(9°C)in 4,4'-di-4-alkyl azoxybenzene. The two ‎dimensional representation of the variation of elastic moduli of the above compounds, ‎which explore the stability of crystals are also discussed. The study revealed that the elastic ‎stiffness constants, elastic moduli, anisotropy of elastic moduli and eigen values of the ‎stiffness matrix of liquid crystalline compound 4,4'-di-3-alkyl azoxybenzene are more than ‎those of 4,4'-di-4-alkyl azoxybenzene. The two dimensional variation of Young’s modulus, ‎shear modulus and Poisson’s ratio in xy-plane, xz-plane and yz-plane is more in 4,4'-di-3-‎alkyl azoxybenzene compared tothat in 4,4'-di-4-alkyl azoxybenzene. Interestingly, the ‎linear compressibility of compound 4,4'-di-4-alkyl azoxybenzene in xy-plane ,xz-plane and ‎yz-plane is more compared to linear compressibility of compound 4,4'-di-3-alkyl ‎azoxybenzene. The higher value of elastic moduli and eigenvalues of the stiffness matrix in ‎‎4,4'-di-3-alkyl azoxybenzene indicates that the intermolecular force is stronger and resists ‎reorientation more, which may stabilize the nematic phase for a wider range of ‎temperature(temperature range of 42°C) compared to that in 4,4'-di-4-alkyl azoxybenzene ‎‎(temperature range of 9°C). Poisson’s ratio of 4,4'-di-3-alkyl azoxybenzene is negative, ‎which specifically for display devices, can contribute to improved flexibility, ‎responsiveness and better display.

Background: The US military medical evacuation mission is responsible for expediently evacuating the battlefield ill and injured. Medical evacuation planning involves constructing a robust network of medical platforms and facilities capable of moving and treating large numbers of casualties. We introduce the first known medium to simulate these networks in an educational setting and evaluate both offline planning and online decision-making performance. The Medical Evacuation Wargaming Initiative (MEWI) is a custom-built, high-fidelity multiplayer simulation that models tactical-level medical evacuation operations. Methods: This study demonstrates the impact of simulation-based training on medical evacuation decision-making. We visualize performance data collected from two MEWI iterations executed in the US Army’s Medical Evacuation Doctrine Course. We consider post-simulation Likert-type survey data from participants and external observer notes to identify key planning decision points, document medical evacuation lessons learned, and quantify general utility. Results: The results highlight that participation in simulation-based medical evacuation scenarios substantially improves uptake of medical evacuation lessons learned and enhances cooperative decision-making. Discussion and Conclusion: MEWI is a substantial step forward in the field of high-fidelity training tools for military medical evacuation education. Our study findings offer critical insights into improving medical evacuation across the joint force.

To assess responsiveness of the spondyloarthritis (SpA)-specific universal health utility from the ASAS Health Index (U-ASAS-HI) compared with generic health utilities (EQ-5D-5L and SF-6D). Data were used from patients with SpA participating in the ASAS-HI international validation study and starting TNF inhibitor (TNFi), conventional synthetic DMARD (csDMARD) or NSAID. A priori hypotheses on correlation of change in utility and change in external health anchors between baseline and follow-up were tested. Standardized response mean (SRM) and Cohen's effect size (ES) were calculated in each treatment group. The ability of changes in utilities to discriminate between BASDAI-50% (non)-responders was assessed by standardized estimate of change and receiver operating characteristic (ROC) analyses. 219 patients were included (110 TNFi, 37 csDMARD, and 72 NSAID). Mean (s.d.) age was 37 (13) years and 63% were male. Hypotheses on correlations of change scores were confirmed for 75% of comparisons for U-ASAS-HI and EQ-5D-5L, but not for SF-6D. As expected, SRM and ES for the U-ASAS-HI were large in the TNFi-treated group, moderate in the csDMARD group and small to moderate in the NSAID group. The hypothesized larger SRM and ES for U-ASAS-HI compared with EQ-5D and SF-6D could not be consistently confirmed across the three treatment groups. Ability to discriminate between BASDAI-50% responders and non-responders did not differ among utility instruments in ROC comparison. In a context where change is expected, the SpA-specific U-ASAS-HI correlates as expected with changes in other SpA-specific outcomes and shows good responsiveness, which is similar to but not better than for generic utilities.

This paper studies optimal investment and reinsurance strategies for insurers facing parameter uncertainty, addressing three objectives: maximising expected terminal utility, minimising ultimate bankruptcy probability and maximising expected terminal utility under constraints. When no constraints are imposed and the utility is exponential, we derive the approximate analytical solution and the associated optimal strategy. For general utility functions and the bankruptcy minimisation problem, explicit solutions are unavailable, so we propose a policy improvement algorithm that approximates the value function. The algorithm exploits the identity between the entropy-regularised reinforcement learning value function and the viscosity solution of the exploratory Hamilton-Jacobi-Bellman equation, expressing the optimal feedback strategy through the derivative of the value function to obtain the optimal distributional control. Finally, the effectiveness of the proposed numerical methods is validated through numerical examples.

While use of the emergent adult stage of aquatic insects as stream bioindicators has lagged well behind that of the benthic stage, adult insects of large and diverse taxa such as Trichoptera have great potential for indicating stream conditions. To explore their associations with stream variables, nearly 500,000 adult caddisfly specimens were collected from 798 ultraviolet blacklight samples throughout the northcentral United States. From an original pool of 11 stream characteristic variables, a non-metric multidimensional scaling ordination determined four that were strongly associated with adult caddisfly assemblages: (1) mean summer stream temperature, (2) stream gradient, (3) base flow as a percentage of total flow, and (4) percentage of intact upstream terrestrial habitat. Association values for 209 common species, 60 common genera, and 17 common families from the region are herein presented based on weighted mean specimen abundance and coefficient of variation for each of the four determined variables. While validation of these presented association values using other datasets is necessary to confirm their general utility, this work represents a first step in using adult caddisflies to indicate both natural and anthropogenic stream conditions.

ABSTRACT Background: Social support is an established determinant of health behaviors, yet its role in COVID 19 booster uptake and general healthcare utilization remains unclear. This gap is especially relevant for drug use disorders (DUDs) communities, who face stigma, disrupted social networks, and barriers to preventive and routine care. Examining how different types of social support relate to preventive behavior and healthcare engagement in this population may clarify whether support operates differently across health behaviors. Objectives: This study examines how social support relate to COVID-19 booster uptake and general healthcare utilization among individuals with DUDs or their family member(s). Methods: Data were drawn from multiple survey components of the NIH All of Us program (2020–2022), a national longitudinal cohort study. The final sample of 3221 respondents, of whom 29.2% were male. We conducted Exploratory and Confirmatory Factor Analyses to derive two dimensions of social support: emotional support, defined as feelings of understanding and companionship, and physical support, defined as practical assistance with routine tasks, such as transportation or chores. These factors were used as predictors in multivariable logistic regression models assessing their associations with COVID-19 booster uptake and general healthcare utilization, adjusting for socio-demographic characteristics and self-reported health. Results: Emotional social support was positively associated with COVID-19 booster uptake (OR = 1.186, p < .05), while physical support was not. Neither type of support was linked to general healthcare utilization. Conclusion: Public health programs serving individuals affected DUDs may benefit from integrating peer and family engagement strategies to support COVID-19 booster uptake.

D-DARTS: an alternative method for NaV1.5 affinity molecules identification based on dual-drug affinity responsive target stability.

ABSTRACT - Smart city initiatives aim to enhance urban living by integrating technology with sustainable development; however, their success largely depends on public lifestyle patterns and usage behavior. This study examines the lifestyle patterns of the general public and their utilization of smart city facilities in Coimbatore city. Primary data were collected from 665 respondents through a structured interview schedule using a convenience sampling method. The study employs percentage analysis and weighted average ranking to assess preferences related to transportation, energy usage, water conservation, waste management, and environmental practices. The findings reveal that respondents strongly prefer simple and cost-effective practices such as using public transportation, walking or cycling for short distances, sharing rides, and adopting basic energy-saving measures. In contrast, technology-intensive practices like gamified conservation applications, IoT-enabled systems, and electric vehicles show lower adoption. The study highlights the influence of socio-economic factors on lifestyle choices and emphasizes the need for improved awareness, incentives, and citizen engagement to enhance effective utilization of smart city facilities. Keywords: Smart City Facilities, Lifestyle Patterns, Sustainable Urban Living, Public Participation, Weighted Average Ranking

Extractive operations are expanding in low- and middle-income countries (LMICs), resulting in a growing burden of silicosis. Given the need to promote knowledge and awareness of this disease, we are concerned by uncritical use of data reported by the Global Burden of Disease (GBD) project. We find that the outputs of the GBD for silicosis in LMICs conflict with recent available empirical data, including our research experience in mining cohorts, suggesting substantial underestimation in these countries. We attribute this, inter alia, to generic utilization of country vital statistics and misalignment of the models with industrial and occupational predictors of silicosis. Scarcity of data for model inputs and empirical silicosis estimates remains a serious barrier to accurate modelling. However, over-reliance on complex modelling tools can produce unintended consequences for public health policy and discourse. Collaborative global and country surveillance of silicosis is needed, aided by the expansion of newly available low-cost screening technology.

IntroductionOne of the inconveniences in the general utilization of primary angioplasty (PTCA) would seem to be the delay in its application. Most of present data come from clinical trials from other countries, whereas little is known about its application in the regular practice in our country. ObjectivesTo analize the periods of time needed for each stage of a PCTA in a center where the treatment of choice is used as a first step for a time-optimization program, and to determine if the delay is due to a situation related to the patient or to the healthcare system. Material and MethodsThis is a prospective observational study in patients with AMI lasting less than 12 hours. The “patient time” was counted from the onset of the symptoms to arrival at hospital, and the “medical care time” was determined from hospital arrival to balloon inflation. ResultsPCTA was performed in 224 patients admitted with diagnosis of AMI. The median values (25th to 75th percentile) were “patient time”: 60 minutes (40-150), “medical care time”: 93 minutes (72-128). “Medical care time” includes: time 1 (hospital arrival-EICT activation): 20 minutes (10-45), time 2 (EICT activation-admission to cath lab): 38 minutes (23-52), time 3 (admission to cath lab- first balloon inflation): 31 minutes (21-45). Conclusions“Patient time” represents less than half the total time consumed. “Medical care time” determines the higher delay for the procedure; therefore, more emphasis should be placed in improving this time, within which EICT activation-first balloon inflation time constitutes a key factor.

High-sensitivity C-reactive protein (hsCRP) is a marker of inflammation and predicts cardiovascular (CV) risk in individuals without known atherosclerotic CV disease (ASCVD). More information about its clinical relevance will help evaluate the general utility of hsCRP as a routine clinical biomarker to identify patients at residual risk. In this population-based study, hsCRP was measured in 448 653 UK Biobank participants without known ASCVD. The association of hsCRP with major adverse cardiovascular events (MACE), CV death and all-cause death was assessed using Cox proportional hazards models. The cohort had a median age of 57 years, 55.4% were female, and median hsCRP levels were 1.32 mg/L. A repeat hsCRP measurement in 15 967 participants after 4.4 years showed long-term stability. In covariate-adjusted models individuals with hsCRP levels >3 mg/L had a 34% higher risk of MACE, a 61% and 54% increased risk of CV death and all-cause death compared to those with hsCRP <1 mg/L. Subjects with hsCRP levels ≥2 mg/L vs <2 mg/L had a 22% increased risk of MACE, and a 37% and 34% higher risk of CV death and all-cause death. The association of hsCRP with all endpoints was consistent across subgroups. Predictive performance of hsCRP ranked above conventional risk factors. Integration of hsCRP improved SCORE2 and provided a total net reclassification improvement of 14.1% for prediction of MACE. These data confirm hsCRP as a clinically relevant predictor of CV events in individuals without known ASCVD and support its assessment in primary prevention.

Reproducibility is a core principle of science, and access to a study's data is essential to reproduce its findings. However, data sharing is uncommon in the discipline of communication sciences and disorders (CSD), often due to concerns related to privacy and disclosure risks. Synthetic data offer a potential solution to this barrier by generating artificial data sets that do not represent real individuals yet retain statistical properties and relationships from the original data. This study aimed to explore the feasibility and preliminary utility of synthetic data to promote transparency and reproducibility in the discipline of CSD. Ten open data sets were obtained from previously published research within the American Speech-Language-Hearing Association "Big Nine" domains (articulation, cognition, communication, fluency, hearing, language, social communication, voice and resonance, and swallowing) across a range of study outcomes and designs. Synthetic data sets were generated with the synthpop R package. General utility was assessed visually and with the standardized ratio of the propensity mean squared error (S_pMSE). Specific utility assessed whether inferential relationships from the original data were preserved in the synthetic data set by comparing model fit indices, coefficients, and p values. All synthetic data sets showed strong general utility, maintaining univariate and bivariate distributions. Six of nine synthetic data sets that used inferential statistics showed strong specific utility, maintaining inferential relationships from the original analysis. Specific utility was low in three data sets with hierarchical structures. Findings suggest that synthetic data can effectively maintain statistical properties and relationships across a wide range of nonhierarchical data commonly seen in the discipline of CSD. Other approaches for hierarchical data need to be explored in future work. Researchers who use synthetic data should assess its utility in preserving their results for their own data and use-case. https://doi.org/10.23641/asha.30569957.

The problem of appropriately sizing routers’ buffers has been a research issue which has elicited considerable interests for more than 30 years now. The question that has been so much debated is: How large should these buffers be? Three main buffers’ sizing rules exist in the literature, which are: Bandwidth-Delay Product (BDP), small-buffers’ and the tiny-buffers’ rules. But researchers are largely agreed that, the BDP formula specifies unrealistically large buffers; while the generic utility of the small and tiny buffers’ formulas have been questioned by most researchers. Some researchers have even opined that, deriving a single universal formula for dimensioning the buffers may not be possible: But, the congestion problem of data networks has largely been linked to inappropriately sized buffers. The main objective of this paper is to report the application, in the context of Internetworking Protocol (IP) networks, of a novel formula that was derived in a previously published paper; which can be used to appropriately specify these buffers. Additionally, we argue that, the formula is indeed a unique solution of the buffers’ sizing problem. The justification for this position is premised on the fact that, the formula may specify what we refer to as very-tiny buffers’, in addition to specifying literature’s tiny buffers’ capacities - a clear validation of the widely-held view that, the BDP formula specifies unrealistically large buffers. The reported formula however, has a huge advantage over literature’s tiny and small buffers’ formulas; as, it is ‘application-generic’, unlike literature’s tiny and small buffers’ formulas.