Intersecting two conics is a classical problem that is frequently encountered in many different areas of science, engineering, and art. For example, under perspective projection (e.g., in camera images), any degree-two curve (a conic) or surface (a quadric) projects to a conic. This is important since polynomials of degree two are commonly used to approximate the contour or surface of many real-world objects. This manuscript describes a simple solution to the conic intersection problem using a change of projective coordinates. Exploiting the properties of self-polar triangles, we show how to reduce the task to simply solving an eigenvalue problem of degree three and a quadratic equation in one variable. The self-polar triangle method provides an attractive alternative to more common methods, especially in settings that require explicit algorithmic solutions.

Neuropeptides regulate diverse physiological processes in insects, including feeding, reproduction, and development, and have therefore emerged as promising species-specific targets for next-generation pest control strategies. In this study, we focused on 13 feeding-related neuropeptides previously identified from our whole-body RNA-seq dataset of Eurygaster integriceps. Using a combination of bioinformatics and expression analyses, we characterized precursor structures, predicted mature peptides, and quantified expression across developmental stages, between sexes, and in key feeding-associated organs of head and gut. Several neuropeptides, including Ast-A, Ast-B, Ast-C, AKH, SIF, ITP, Burs, sNPF, NPF, and Crz showed higher expression in males than in females, suggesting sexually dimorphic regulation of feeding and metabolism. Moreover, most neuropeptides were expressed at higher levels in the head compared to the gut, which is consistent with central neuro-regulatory functions. Furthermore the expression pattern of neuropeptides of life cycle developmental stages revealed peak expression in early developmental stages, along with stage-specific variation indicating coordinated regulation of growth, feeding behavior, and digestive physiology. Taken together, these findings provide the first detailed molecular and expression atlas of feeding-related neuropeptides in E. integriceps and offer foundational insights for developing future neuropeptide-based, species-specific, and environmentally safe biocontrol strategies.

Malnutrition is a major clinical challenge among individuals receiving maintenance hemodialysis (HD), contributing to increased complications and reduced survival. The burden is particularly pronounced in resource-limited settings, where gaps in nutritional assessment and management persist. This cross-sectional study evaluated the prevalence and determinants of malnutrition among 181 HD patients from four districts in Bangladesh. Based on the International Society of Renal Nutrition and Metabolism (ISRNM) criteria, 42.5% of patients were malnourished, including 10.5% with severe malnutrition, while the Malnutrition Inflammation Score (MIS) identified malnutrition in 72.9% of participants. Patients who reused dialyzers more than twice had significantly higher odds of malnutrition [AOR = 5.8; 95% CI: 1.2-28.7]. Age, body mass index (BMI), and diabetes mellitus were also independently associated with poor nutritional status. These findings underscore the need for strengthened nutritional assessment and early identification of at-risk patients in HD care. Implementing validated diagnostic tools, such as those used in this study, may help reduce disease burden and improve survival outcomes in resource-constrained dialysis settings.

The proliferation of misinformation on short video platforms poses a significant challenge to digital governance and public trust. While platforms are central actors, their strategic role in rumor governance remains underexplored. This study introduces a tripartite evolutionary game model that incorporates the platform as a bounded rational strategic actor alongside rumor spreaders and information clarifiers, and combines this model with the extended SICR (Susceptible-Rumor spreader-Clarifier-Recovered) information diffusion framework, where game strategies and payoffs are defined based on user roles. This combined framework allows us to analyze the strategic interactions among these three agents. Numerical simulations identify key evolutionary stable states and reveal that increasing the probability and perceived benefits of platform intervention can effectively steer the system towards an ideal equilibrium where platforms strictly supervise, and clarifiers actively debunk rumors. A case study of the "Hengshan Bus" incident supports the model's applicability, demonstrating that proactive platform intervention accelerates the dissemination of corrective information. Our findings illustrate the important and strategically complex role of platforms in self-regulation, and provide insights for co-governance mechanism design on Chinese short-video platforms.

This paper proposes a day-ahead scheduling framework to analyze and optimize the impact of the coordinated active and reactive power management of wind turbines (WTs) and battery energy storage systems (BESSs) on the energy losses and CO2 emissions of AC microgrids (MGs). Within this framework, the BESS plays a central role by absorbing surplus renewable generation, mitigating curtailment, supporting voltage regulation, and ensuring a stable and reliable dispatch over a 24-hour horizon. A population-based genetic algorithm (PGA) is proposed as the main solution methodology, while particle swarm optimization (PSO) and the multiverse optimizer (MVO) are employed as benchmark methods for comparison. To ensure a fair assessment, all optimization techniques are implemented under the same parallel processing scheme, using the same decision-variable encoding, feasibility correction procedure, and hourly sequential AC power-flow method. The objective is to minimize network energy losses and CO2 emissions under both grid-connected and islanded operating modes. The proposed methodology is validated on 33-node and 69-node MGs, both evaluated under variable demand and wind-generation scenarios to capture the uncertainty and temporal variability associated with renewable production and load behavior. In addition, the BESS model includes charging/discharging efficiency, self-discharge effects, and battery lifetime assessment under the proposed operating scenarios, allowing a more realistic representation of storage performance. The optimization methods are evaluated over 100 independent runs using the best solution, average solution, standard deviation, and computational time as performance indicators. The results show that the proposed PGA provides the most robust and repeatable performance, while also highlighting the operational contribution of the BESS, reducing renewable curtailment, and guaranteeing compliance with all technical constraints, under deterministic baseline operation and under uncertain time-varying operating conditions in both test systems.

Anabolic agents such as parathyroid hormone (PTH) analogs and sclerostin antibody (Scl-Ab) enhance fracture healing through distinct mechanisms, but whether the order of administration affects healing remains unclear. In a murine tibial osteotomy model, we compared PTH monotherapy (40 μg/kg), Scl-Ab monotherapy (25 mg/kg), and two sequential regimens with matched treatment duration and cumulative exposure between sequential groups: PTH followed by Scl-Ab (PTH → Scl-Ab) and Scl-Ab followed by PTH (Scl-Ab → PTH). Treatment was started on postoperative day 1, and the sequential groups changed agents after 1 week. PTH monotherapy significantly increased mineralized callus volume, whereas Scl-Ab monotherapy produced higher callus volumetric bone mineral density (vBMD) and greater mechanical strength. Among the sequential regimens, Scl-Ab → PTH resulted in significantly higher callus vBMD and ultimate load to failure than PTH → Scl-Ab, despite comparable callus volumes. Histological analysis showed a higher proportion of cartilaginous tissue in the PTH monotherapy and PTH → Scl-Ab groups, suggesting persistence of an earlier phase of endochondral ossification. These findings indicate that early Scl-Ab administration followed by PTH promotes earlier callus mineralization and mechanical recovery than the reverse sequence in this murine model. Although the study was limited primarily to early healing time points, the results suggest that the temporal sequence of anabolic signaling, rather than agent selection alone, can influence early fracture repair.

A two-pore domain K+ (K2P) channel, THIK-1, plays important roles in microglia and macrophage. THIK-1 is known to be activated by arachidonic acid and G protein-coupled receptors and inhibited by anesthetics. Steroids, such as cholesterol, estradiol and progesterone, are known to modulate several K+ channels and they might be potential modulators of THIK-1. We examined the effects of steroids on THIK-1 and found that estradiol inhibits mouse THIK-1 by approximately 40% (IC50 = 4.9 ± 1.5 μM). Docking simulations of THIK-1 with estradiol indicated possible docking sites, which were further assessed by introducing an alanine mutation into a residue at or near these locations. The F142A, V269A, and Y273A mutations reduced the inhibitory effect of estradiol. These residues are situated within the upper cavity above the Y gate in THIK-1 (pond), suggesting that the pond conformation is essential for estradiol-mediated inhibition. Conversely, the F145A and F276A mutations, located outside this region, were inhibited by 10 nM estradiol and enhanced inhibition by estradiol, estrone, estriol, and progesterone, likely due to conformational changes that facilitate steroid inhibition. The mouse T237S mutation, which corresponds to the reported human THIK-1 variant, produced effects similar to those seen with the F145A and F276A mutations, but to a lesser degree. In summary, estradiol-mediated THIK-1 inhibition depends on residues located in the pond, which may have physiological or pathological significance for THIK-1 variants which are inhibited by low concentration of estradiol.

Front-of-pack nutrition labeling (FoPNL) and restrictions on food advertising are regulatory strategies to support informed dietary choices and reduce exposure to unhealthy food. These measures are often integrated through a policy package, that employs a Nutrient Profile Model (NPM) for the classification of food. In Brazil, however, these policies remain fragmented, and current food advertising regulations do not fully cover all channels, particularly digital media. This study applied the NPM adopted in Brazil's FoPNL (RDC No. 429/2020 in conjunction with IN No. 75/2020, hereafter RDC 429) and those proposed by the Pan American Health Organization (PAHO) for identifying foods subject to advertising restrictions on social media posts directed at children and adolescents. The sample included posts from ultra-processed food brands/products on Instagram (n = 623), TikTok (n = 257), and YouTube (n = 114). Both NPM were applied to assess non-compliance at the food-item level and at the advertisement level (considering posts containing at least one non-compliant food). At the food-item level, 61.28% of products exceeded at least one item of RDC 429, with the highest prevalence on TikTok (72.63%), while 93.86% were non-compliant according to PAHO, reaching 100% on TikTok and 98.5% on YouTube. At the advertisement level, 49.53% of posts contained at least one non-compliant food under RDC 429, and 74.13% under PAHO, with YouTube showing the greatest potential for restriction (62.16% RDC; 99.10% PAHO). Both models are feasible for regulating digital food advertising: the PAHO provides a robust framework, while RDC 429 offers a strategic starting point given its current implementation in Brazil.

Differences in excess mortality during the coronavirus disease 2019 pandemic across marital statuses in Japan were investigated using data from across the country. Mortality data from the Vital Statistics records spanning 2010-2023 were utilized. Age-standardized mortality rates were computed by sex, marital status, year, and cause of death, and expected post-pandemic rates were estimated using pre-pandemic values with a quasi-Poisson regression model. Furthermore, the age-standardized percentage of the excess number of deaths relative to the expected number of deaths (hereafter, P-score) after the pandemic started was calculated by sex, marital status, and cause of death, using the expected and observed number of deaths. A declining trend in all-cause mortality rate was observed across all groups before the pandemic, regardless of sex and marital status, while mortality rates increased across all groups after the pandemic began. The largest decrease in all-cause mortality rate was observed among never-married persons before the pandemic in both men and women, while the smallest increase after the pandemic was observed among married persons. The highest age-standardized P-score for all-cause mortality was observed among never-married men and women, while that for married persons was the lowest among men. For women, the age-standardizedP-scores were closer across groups, with confidence intervals overlapping among marital-status categories. In contrast, there was a significant difference in the age-standardized P-scores between never-married and married persons among women aged <65 years. In conclusion, the percentage of excess all-cause mortality was the highest among never-married persons during the pandemic period in Japan, particularly in men, and it is important to continue to monitor the trend in the future.

Accurate effort estimation of user stories is a key challenge in agile software development due to both subjectivity and the complexity of natural language requirements. This paper proposes a hybrid Deep Learning (DL) model for data driven effort estimation using large scale textual data and advanced semantic modeling. One of the significant contributions is the development of a dataset of 6,956 user stories which was collected from many heterogeneous sources and then meticulously cleaned and refined to 4,079 high quality instances, by using a systematic preprocessing and expert validation. Following a Design Science Research (DSR) methodology, a hybrid model integrating a pre-trained Bidirectional Encoder Representations from Transformers (BERT) encoder with a Long Short-Term Memory (LSTM) is developed to capture both contextual semantics and sequential dependencies in user story description. The DL model is evaluated against multiple Machine Learning (ML) baselines using a robust multi-metric framework. Experimental findings show the superior performance of the proposed model with a Mean Absolute Error (MAE) = 0.6481, Root Mean Square Error (RMSE) = 1.4559 and = 0.6581, which is a huge improvement over the conventional methods. To ensure practical relevance in discrete Scrum planning, the continuous model outputs were mapped to the standard Fibonacci sequence, achieving a classification accuracy of 72%. To guarantee the consistency of performance improvements, the statistical validation is done with the Wilcoxon Signed-Rank Test to show that the improvements are significant (p < 0.05). Moreover, the model is operationalized as web based decision support system to enable real time estimation in agile software development. The results reveal the efficacy of using large scale curated datasets alongside hybrid DL model to reduce estimation bias and boost predictive quality to deliver a scalable and strong solution for intelligent agile project management. Future work will focus on integrating eXplainable AI (XAI) techniques and validating the model across real world industrial datasets to further enhance transparency and generalizability.

Under the modernization of grassroots governance, veterans constitute a high-quality human resource for enhancing grassroots social governance capacity. Drawing on questionnaire survey data from 525 veterans in Guizhou Province, China, this study identifies three distinct veteran groups using latent class analysis (LCA): Utilitarian (51.3%), Mission-Oriented (23.8%), and Development-Focused (24.9%). Empirical results reveal clear heterogeneity in policy responsiveness and behavioral mechanisms across groups. First, the Utilitarian group responds most strongly to Subsidies, with participation behavior predominantly driven by economic rationality. Second, the Mission-Oriented group demonstrates honor-driven motivations, with symbolic recognition playing a central role in stimulating governance engagement. Third, the Development-Focused group prioritizes career advancement and exhibits significant responsiveness to promotion-related incentives. Based on these findings, this study constructs a mediation model of "Policy Instruments (PI)-Behavioral Expectations (BE)-Participation Behavior (PB)" and proposes an intervention pathway centered on "expectation diagnosis" and "instrument matching." By revealing the internal mechanism through which policy instruments (PI) influence participation behavior (PB) via Behavioral expectations (BE), this research provides empirical evidence and theoretical insights for shifting incentive strategies from "uniform provision" to "precision adaptation.".

The teaching and learning of academic essays faced difficulties in in-person classes. Challenges in academic essay instruction were exacerbated in online learning environments during COVID-19. Yet some institutions arranged sophisticated platforms to share instructional materials for imparting English language education online. The focal university was one such progressive institution. Like other courses, English language education was delivered through 'Arwa', an online platform developed by the university to support teaching and learning during the pandemic. The phenomenological study aimed to understand how instructional materials designed for an English foundation course contributed to first-year undergraduates' experiences of learning persuasive essay writing. Data were collected primarily through semi-structured interviews and supplemented by an open-ended questionnaire administered to the same eight participants. Phenomenological thematic analysis was applied to interpret the data. The findings reveal how participants experienced the instructional design and materials as supporting their engagement with outlining, drafting, editing, and revising persuasive essays. Participants also described collaborative writing and rubric-guided peer feedback as meaningful learning experiences that supported their engagement with persuasive essay writing. From a phenomenological perspective, the study contributes to understanding how learners make sense of persuasive essay writing through structured online instruction, collaborative writing, and rubric-guided peer feedback. Pedagogically, the findings highlight the value of sequenced instructional materials, collaborative writing, and guided peer interaction in supporting students' engagement with persuasive writing in online and blended learning environments.

Accurate sleep stage classification serves as a crucial foundation for sleep health assessment and disease diagnosis. However, existing approaches still encounter several challenges, including limited feature representation, redundancy in extracted information, and difficulties in effectively integrating cross-modal physiological signals. To address these issues, we propose a novel framework entitled Multi-Branch Multi-Scale Fusion with Adaptive Sparse Self-Attention and Cross-Modal Integration (MFFNet). Specifically, considering the prominent time-frequency characteristics of sleep signals, multiple branches are designed to capture multi-scale information, including a time-granular feature extraction branch and a time-frequency extraction branch. Furthermore, a multi-head adaptive sparse self-attention mechanism is introduced to suppress redundant information while emphasizing discriminative features. In addition, we employ an adaptive cross-modal fusion strategy that dynamically integrates information from EEG and EOG, and further visualize the contribution of each modality to sleep stage classification. Experiments conducted on the Sleep-EDF-39 and Sleep-EDF-153 datasets demonstrate the effectiveness of the proposed approach. Using the Fpz-Cz EEG channel and EOG signals, MFFNet achieves accuracy rates of 84.26% and 81.86%, with F1 scores of 75.91% and 73.38%, respectively, highlighting its competitive performance in sleep stage assessment.

Fiber-reinforced polymer composites have attracted considerable attention in engineering applications owing to their high strength-to-weight ratio, corrosion resistance, and tailorable mechanical properties. Hybridization provides an effective approach for combining the advantageous characteristics of different reinforcement materials to achieve improved overall laminate performance. In the present study, three six-ply hybrid composite laminates, namely J2/C2/J2, J2/G2/J2, and C2/G2/C2, with a [0°/90°] stacking sequence and a relatively high fiber content obtained through controlled hand lay-up fabrication, were fabricated using the hand lay-up technique. Test specimens were prepared according to the relevant ASTM standards and subjected to tensile, compressive, and flexural testing. In addition, finite element simulations were performed using ANSYS Mechanical APDL 2020 R1 to validate the experimental observations. The experimental results demonstrated that natural-synthetic fiber hybridization significantly enhanced the mechanical performance of the investigated laminates. The incorporation of carbon fiber into jute-based laminates resulted in an average strength improvement of approximately 88%, whereas glass-fiber incorporation produced an improvement of approximately 55%. Among the investigated configurations, the C2/G2/C2 laminate exhibited the highest tensile strength (approximately 140 MPa), compressive strength (approximately 39 MPa), and flexural strength (approximately 109 MPa). The numerical predictions showed good agreement with the experimental measurements, with maximum deviations below 1.2%. The results confirm the effectiveness of hybridization in enhancing the mechanical response of composite laminates and identify the C2/G2/C2 configuration as the most promising among the investigated laminate systems. These findings demonstrate the influence of natural-synthetic fiber hybridization on laminate mechanical performance and provide useful information for the design of lightweight composite structural components. Further studies involving pressure-vessel fabrication and internal-pressure testing are required before pressure-vessel applications can be established.