Search Results (11,993)

In collaborative projects, traditional resource allocation methods often rely on company-assigned contribution rates, which can be subjective and lead to unfair outcomes. To address this, we propose a quadratic programming model that integrates participants’ self-reported rankings of their contributions across projects with company evaluations. The model aims to minimize deviations from company-assigned rates while ensuring consistency with participants’ perceived contribution rankings. Extensive simulations demonstrate that the proposed method reduces allocation errors by an average of 50.8% compared to the traditional approach and 21.4% against the method considering only individual estimation tendencies. Additionally, the average loss reduction in individual resource allocation ranges from 40% to 70% compared to the traditional method and 10% to 50% against the estimation-based method, with our approach outperforming both. Sensitivity analyses further reveal the model’s robustness and its particular value in flawed systems; the error is reduced by approximately 75% in scenarios where company evaluations are highly inaccurate. While its effectiveness is affected by factors such as team size variability and self-assessment errors, the approach consistently provides more equitable allocation of resources that better reflects actual individual contributions, offering valuable insights for improving fairness in team projects. Full article

The gas reservoir of the Sinian Dengying Formation (Member 4) in Sichuan Basin exhibits extensive development of inter-clast dissolution pores and vugs within its carbonate reservoirs, characterized by low porosity (average 3.21%) and low permeability (average 2.19 mD). With the progressive development of the Moxi (MX)structure, the existing stimulation techniques require further optimization based on the specific geological characteristics of these reservoirs. Through large-scale true tri-axial physical simulation experiments, this study systematically evaluated the performance of three principal acid systems in reservoir stimulation: (1) Self-generating acid systems, which enhance etching through the thermal decomposition of ester precursors to provide sustained reactive capabilities. (2) Gelled acid systems, characterized by high viscosity and effectiveness in reducing breakdown pressure (18%~35% lower than conventional systems), are ideal for generating complex fracture networks. (3) Diverting acid systems, designed to improve fracture branching density by managing fluid flow heterogeneity. This study emphasizes hybrid acid combinations, particularly self-generating acid prepad coupled with gelled acid systems, to leverage their synergistic advantages. Field trials implementing these optimized systems revealed that conventional guar-based fracturing fluids demonstrated 40% higher breakdown pressures compared to acid systems, rendering hydraulic fracturing unsuitable for MX reservoirs. Comparative analysis confirmed gelled acid’s superiority over diverting acid in tensile strength reduction and fracture network complexity. Field implementations using reservoir-quality-adaptive strategies—gelled acid fracturing for main reservoir sections and integrated self-generating acid prepad + gelled acid systems for marginal zones—demonstrated the technical superiority of the hybrid system under MX reservoir conditions. This optimized protocol enhanced fracture length by 28% and stimulated reservoir volume by 36%, achieving a 36% single-well production increase. The technical framework provides an engineered solution for productivity enhancement in deep carbonate gas reservoirs within the G-M structural domain, with particular efficacy for reservoirs featuring dual low-porosity and low-permeability characteristics. Full article

Performance validity tests (PVTs) and symptom validity tests (SVTs) are central to evaluating neuropsychological test results in clinical adult ADHD assessments. Although their relationships have been widely examined, the constructs these measures assess remain poorly understood in applied contexts. This study investigates the conceptual similarities and distinctions of performance validity measures, i.e., the Groningen Effort Test (GET), the Medical Symptom Validity Test (MSVT), and the Amsterdam Short-Term Memory (ASTM) test, within a comprehensive diagnostic battery for adult ADHD. The diagnostic battery included symptom self-reports and a continuous performance test (CPT). Network and factor analyses investigated these relationships. A three-factor structure was hypothesized, consisting of (1) performance validity measures, (2) continuous performance measures, and (3) symptom reports (including embedded SVTs). Data from a large clinical referral sample (N = 461) of adults with suspected ADHD were analyzed to explore these constructs. Network analysis revealed that the PVTs did not form a cohesive network with CPT measures. Symptom reports, including embedded SVTs, formed their own cluster, separate from performance-based attention measures. Factor analysis rejected a unified construct of performance validity measures. Regression analysis showed that cognitive deficits, education level, and impulsivity predicted ASTM test performance, whilst the MSVT and GET did not. These findings suggest that PVTs should be interpreted in the context of ADHD assessment, particularly in high-stakes forensic evaluations, where the accuracy of performance evaluation is critical. Future research should explore multidimensional models of performance validity, addressing domain-specific underperformance and individual variability in ADHD evaluations. Full article

Advanced Persistent Threats (APTs) pose significant cybersecurity challenges due to their multi-stage complexity. Knowledge graphs (KGs) effectively model APT attack processes through node-link architectures; however, the scarcity of high-quality, annotated datasets limits research progress. The primary challenge lies in balancing annotation cost and quality, particularly due to the lack of quality assessment methods for graph annotation data. This study addresses these issues by extending existing APT ontology definitions and developing a dynamic, trustworthy annotation framework for APT knowledge graphs. The framework introduces a self-verification mechanism utilizing large language model (LLM) annotation consistency and establishes a comprehensive graph data metric system for problem localization in annotated data. This metric system, based on structural properties, logical consistency, and APT attack chain characteristics, comprehensively evaluates annotation quality across representation, syntax semantics, and topological structure. Experimental results show that this framework significantly reduces annotation costs while maintaining quality. Using this framework, we constructed LAPTKG, a reliable dataset containing over 10,000 entities and relations. Baseline evaluations show substantial improvements in entity and relation extraction performance after metric correction, validating the framework’s effectiveness in reliable APT knowledge graph dataset construction. Full article

Supporting dementia carers is a global priority. As a Randomised Controlled Trial (RCT) (n = 352) of the Word Health Organization recommended, an internationally disseminated ‘iSupport’ e-health intervention was conducted, revealing no measurable benefits to the wellbeing of adult dementia carers. This process evaluation contributes original insights of the trial outcomes. Its aims were to ascertain the usability and acceptability of iSupport, participant engagement and adherence to iSupport, and contextual factors influencing its implementation and potential impact. The process evaluation followed a mixed-method design. The following data were collected from all participants randomised to iSupport (n = 175): (1) post-intervention evaluation questionnaire (n = 93) containing the 10-item System Usability Scale and bespoke items exploring acceptability, engagement, and perceived impact; (2) qualitative interviews (n = 52) with a sub-sample of participants who were purposively sampled according to age, scores on the outcome measures, and gender, as these interviews aimed to generate contextual detail and explanatory accounts; and (3) ‘Access’ data from the iSupport platform (n = 175). Descriptive statistics was used to report on the frequency of survey responses whilst a thematic analysis approach was followed to identify themes from the qualitative interview data. Data sets were analysed independently and then used with respect to one another in order to generate explanatory pathways related to the usability, acceptability, and the impact of iSupport. Despite good trial retention, 8.3% of participants (n = 32) did not spend any time on iSupport, and 54% (n = 94) spent between 30 min and 1.5 h. Factors driving this were the following: time constrains, method of delivery, and content characteristics. Positive impacts of iSupport were also described. Participants, including those with extensive caring experience, reported how iSupport had made them feel reassured, valued, and more able to ask for help. They also reported having an improved outlook on their caring role and on the needs and feelings of the person living with dementia. Research and practice should focus on exploring blended delivery, including self-directed and interactive components, such as regular contact with a health professional. These insights are critical for supporting the global implementation and adaptation of iSupport and offer valuable directions for future research. Full article

Canopy coverage is a key indicator for judging maize growth and production prediction during the seedling stage. Researchers usually use deep learning methods to estimate canopy coverage from maize images, but fully supervised models usually need pixel-level annotations, which requires lots of manual labor. To overcome this problem, we propose ASLNet (Automatic Scribble Labeling-based Semantic Segmentation Network), a weakly supervised model for image semantic segmentation. We designed a module which could self-generate scribble labels for maize plants in an image. Accordingly, ASLNet was constructed using a collaborative mechanism composed of scribble label generation, pseudo-label guided training, and double-loss joint optimization. The cross-scale contrastive regularization can realize semantic segmentation without manual labels. We evaluated the model for label quality and segmentation accuracy. The results showed that ASLNet generated high-quality scribble labels with stable segmentation performance across different scribble densities. Compared to Scribble4All, ASLNet improved mIoU by 3.15% and outperformed fully and weakly supervised models by 6.6% and 15.28% in segmentation accuracy, respectively. Our works proved that ASLNet could be trained by pseudo-labels and offered a cost-effective approach for canopy coverage estimation at maize’s seedling stage. This research enables the early acquisition of corn growth conditions and the prediction of corn yield. Full article

The rapid expansion of urbanization and inadequate planning have triggered a water balance crisis in many cities, manifesting as both the need for artificial lake supplementation and frequent urban flooding. Using the Xuanwu Lake watershed in Nanjing as a case study, this research aims to optimize the Blue–Green Infrastructure (BGI) network to maximize rainfall utilization within the watershed. The ultimate goal is to restore natural water balance processes and reduce reliance on artificial supplementation while mitigating urban flood risks. First, the Soil Conservation Service Curve Number (SCS–CN) model is employed to estimate the maximum potential of natural convergent flow within the watershed. Second, drawing on landscape connectivity theory, a multi-level BGI network optimization model is developed by integrating the Minimum Cumulative Resistance (MCR) model and the gravity model, incorporating both hydrological connectivity and flood safety considerations. Third, a water balance model based on the Storm Water Management Model (SWMM) framework and empirical formulas is constructed and coupled with the network optimization model to simulate and evaluate water budget performance under optimized scenarios. The results indicate that the optimized scheme can reduce artificial supplementation to Xuanwu Lake by 62.2% in June, while also ensuring effective supplementation throughout the year. Annual runoff entering the lake reaches 13.25 million cubic meters, meeting approximately 13% of the current annual supplementation demand. Moreover, under a 100-year return period flood scenario, the optimized network reduces total watershed flood volume by 35% compared to pre-optimization conditions, with flood-prone units experiencing reductions exceeding 50%. These findings underscore the optimized BGI network scheme’s capacity to reallocate rainwater resources efficiently, promoting a transition in urban water governance from an “engineering-dominated” approach to an “ecology-oriented and self-regulating” paradigm. Full article

Introduction: Chronic kidney disease (CKD) affects roughly 10% of the global population and significantly increases cardiovascular risk. While renin–angiotensin system inhibitors (RASi) remain a therapeutic mainstay, recent evidence supports the renoprotective value of sodium–glucose cotransporter-2 inhibitors (SGLT2i) and finerenone. This study evaluated the real-world use of guideline-directed medical therapy (GDMT) among patients with cardiorenal disease in Greece and explored factors influencing prescribing patterns. Methods: The Hellenic Cardiorenal Morbidity Snapshots (HECMOS 1 and 2) enrolled all cardiology inpatients across Greece on 3 March, 2022, and 5 June, 2024. Comorbidities and medication data were based on self-report and chart review. CKD patients eligible for SGLT2i and finerenone were identified per guideline criteria. Multivariable logistic regression was used to identify predictors of SGLT2i use. Results: From a total of 923 and 1222 patients enrolled in HECMOS 1 and 2, CKD was present in 26% and 27%, respectively. SGLT2i use prior to hospitalization rose from 15% in HECMOS 1 to 30.4% in HECMOS 2. In HECMOS 1, diabetes mellitus was the strongest predictor of SGLT2i use (OR 12.01, 95% CI 3.31–45.56, p < 0.001), while heart failure predicted use in HECMOS 2 (OR 4.10, 95% CI 1.70–9.88, p = 0.002). Finerenone was prescribed in only 1.7% of eligible patients in HECMOS 2. RASi usage among CKD patients remained stable across both cohorts (42.1% vs. 41.7%), with renal dysfunction showing no impact on prescribing patterns. Conclusions: SGLT2i use in patients with CKD and cardiovascular disease doubled over 2 years, indicating progress in implementing GDMT. However, overall use of disease-modifying therapies remains suboptimal, underscoring the need for further improvement in real-world care. Full article

The instability of the energy supply, growing demand and the need to reduce carbon emissions are priority challenges in developing countries such as Ecuador, where power outages affect productivity and generate economic losses. Therefore, solar energy is positioned as a sustainable alternative. The objective of this study is to evaluate a pilot photovoltaic (PV) system for residential housing in coastal areas in the Santa Elena province, Ecuador. The methodology included the following: (i) criteria for the selection of three representative residential housings; (ii) design of a distributed generation system using PVsyst software; and (iii) proposal of strategic guidelines for the design of PV systems. This proposed system proved to be environmentally friendly, achieving reductions of between 16.4 and 32 tonnes of CO₂ in the first 10 years. A return on investment (ROI) of 16 years was achieved for the low-demand (L) scenario, with 4 years for the medium-demand (M) scenario and 2 years for the high-demand (H) scenario. The sensitivity analysis showed that the Levelized Cost of Energy (LCOE) is more variable in the L scenario, requiring more efficient designs. It is proposed to diversify the Ecuadorian energy matrix through self-supply PV systems, which would reduce electricity costs by 6% of consumption (L scenario), 30% (M scenario), and 100% (H scenario). Although generation is concentrated during the day, the net metering scheme enables compensation for nighttime consumption without the need for batteries, thereby improving the system’s profitability. The high solar potential and high tariffs make the adoption of sustainable energy solutions a justifiable choice. Full article

In this study, we present a wearable sensing system for monitoring the physiological status of damaged biological tissues based on a flexible, frequency-coded electromagnetic spiral resonator array. The physiological parameter evaluation is performed in a contactless way, avoiding the placing of electronically active elements directly upon the patient’s skin, thus ensuring safety and comfort. Firstly, we report in detail the physical principles behind the sensing strategy: a passive array is interrogated through an actively fed external single-loop probe that is inductively coupled with the double-layer spiral unit cells. The variation in the physiological parameters influences the array response, thus providing sensing information, due to the different complex dielectric permittivity values related to the tissue status. Moreover, the proposed frequency-coded approach allows for spatial information on the lesion to be retrieved, thus increasing the sensing ability. In order to prove the validity of this general methodology, we created a numerical test case, designing a practical implementation of the wearable sensing system working at a radiofrequency regime (10–100 MHz). In addition, we also fabricated prototypes, exploiting PCB technology, and realized stratified phantoms by incorporating opportune additives to control the dielectric properties. The numerical results and the experimental verification demonstrated the validity of the developed sensing strategy, showing satisfying agreement and, thus, proving the good sensibility and spatial resolution of the frequency-coded array. These results can open the path to a radically novel approach for self-care and monitoring of inflamed status and, more generally, for wearable sensing devices in biomedical applications. Full article

Background: Antigen-targeting immunotherapies hinge on the accurate identification of immunogenic epitopes that elicit robust T-cell responses. However, current computational approaches focus primarily on MHC binding affinity, leading to high false-positive rates and limiting the clinical utility of antigen selection methods. Methods: We developed the neoIM (for “neoantigen immunogenicity”) model, a first-in-class, high-precision immunogenicity prediction tool that overcomes these limitations by focusing exclusively on overall CD8 T-cell response rather than MHC binding. neoIM, a random forest classifier, was trained solely on MHC-presented non-self peptides (n = 61.829). Its performance was assessed against that of currently existing alternatives on several in vitro immunogenicity datasets. In addition, its clinical impact was investigated in two retrospective analyses of clinical trial data by assessing the effect of neoIM-based antigen selection on the positive immunogenicity rate of personal vaccine designs. Finally, the potential for neoIM as a biomarker was investigated by assessing the correlation between neoIM scores and overall survival in a melanoma patient cohort treated with checkpoint inhibitors (CPI). Results: neoIM was found to substantially outperform publicly available tools in regards to in vitro benchmarks based on ELISpot assays, with an increase in predictive power of at least 30%, reducing false positives and improving target selection efficiency. In addition, using neoIM scores during patient-specific antigen prioritization and selection was shown to yield up to 50% more clinically actionable antigens for individual patients in two recent clinical trials. Finally, we showed that neoIM could further refine response prediction to checkpoint inhibition therapy, further demonstrating the importance of evaluating neoantigen immunogenicity. Conclusions: These findings establish neoIM as the first computational tool capable of accurately predicting epitope immunogenicity beyond MHC affinity. By enabling more precise target discovery and prioritization, neoIM has the potential to accelerate the development of next-generation antigen-based immunotherapies. Full article

Background/Objectives: Central sensitization (CS) is identified as a cause of pain in various musculoskeletal diseases, and breathing pattern disorders (BPDs) are reported to be correlated with chronic pain. This study aimed to analyze the relationship between CS and BPDs through regression analysis. Methods: A cross-sectional study was designed according to the strengthening the reporting of observational studies in epidemiology (STROBE) guidelines. Forty participants with moderate to extreme CS (central sensitization inventory for Koreans; CSI-K ≥ 40) were enrolled, and their respiratory motion (manual assessment of respiratory motion; MARM), respiratory function (self-evaluation of breathing questionnaire; SEBQ), respiratory muscle strength (maximal inspiratory pressure; MIP, maximal expiratory pressure; MEP), pain intensity (numeric pain rating scale; NPRS), pain cognition (Korean version of pain catastrophizing scale; K-PCS), muscle tone and stiffness were measured. Results: Among participants with moderate to extreme CS, 82.5% showed BPDs and 42.5% reported severe pain intensity. Regression analysis revealed significant relationships between respiratory and pain variables. K-PCS demonstrated significant negative relationships with MARM area (β = −0.437, R² = 0.191) and positive relationships with SEBQ (β = 0.528, R² = 0.279). In the subgroup with BPDs, strong regression relationships were found between MARM area and NPRS usual pain (β = −0.486, R² = 0.237) and K-PCS (β = −0.605, R² = 0.366). Multiple regression analysis showed that MARM area and SEBQ together explained 41.2% of variance in pain catastrophizing. The comprehensive muscle stiffness prediction model using CSI-K, K-PCS, and muscle tone showed remarkably high explanatory power (R² = 0.978). Conclusions: In individuals with moderate to extreme CS, respiratory dysfunction was prevalent and significantly predictable through regression models with pain intensity and pain cognition. These quantitative regression relationships between breathing mechanics, pain measures, and muscle properties provide clinical prediction tools and suggest the importance of assessing breathing patterns in CS management. Full article

Psychological distress remains a significant public health concern, particularly among youth. With the growing integration of mobile and wearable technologies into daily life, digital phenotyping has emerged as a promising approach for early self-detection and intervention in psychological distress. Objectives: The study aims to determine how behavioral and device-derived data can be used to identify early signs of emotional distress and to develop and evaluate a prototype system that enables users to self-detect these early warning signs, ultimately supporting early intervention and improved mental health outcomes. Method: To achieve this, this study involved a multi-phase, mixed-method approach, combining literature review, system design, and user evaluation. It started with a scoping review to guide system design, followed by the design and development of a prototype system (ESFY) and a mixed-method evaluation to assess its feasibility and utility in detecting early signs of psychological distress through digital phenotyping. Results: The results demonstrate the potential of digital phenotyping to support early self-detection for psychological distress while highlighting practical considerations for future deployment. Conclusions: The findings highlight the value of integrating active and passive data streams, prioritizing transparency and user empowerment, and designing adaptable systems that respond to the diverse needs and concerns of end users. The recommendations outlined in this study serve as a foundation for the continued development of scalable, trustworthy, and effective digital mental health solutions. Full article

Background/Objectives: Chronic pain symptoms can disrupt cognitive processes. Such interruptions may negatively impact one’s overall functioning, causing frustration and distress when engaging in important tasks. This experience has been referred to as cognitive intrusion of pain. To date, only one adult self-report measure of cognitive intrusion of pain exists: the Experience of Cognitive Intrusion of Pain (ECIP). The purpose of the current study was to examine the psychometric properties of the ECIP in a sample of pediatric patients with chronic pain. Methods: The internal consistency reliability, factor structure, and validity of the ECIP were evaluated in a sample (N = 182) of youth ages 11 to 18 who presented to a multidisciplinary chronic pain clinic at a large Midwestern children’s hospital in the United States. Results: Results suggest excellent reliability (α = 0.94). Confirmatory factor analysis results supported a one-factor model, with excellent model fit. The ECIP demonstrated evidence of convergent validity, with moderate and positive correlations with measures of pain-related limitations in functioning, pain symptoms, anxiety, and depression. Regarding discriminant validity evidence, the ECIP was minimally and inversely related to measures of readiness to transition to self-managed care and global health. Conclusions: Overall, the ECIP demonstrated strong initial reliability and validity evidence for use in pediatric chronic pain. Further research is recommended in more diverse samples and to evaluate the clinical utility of the ECIP. Full article

Background/Objectives: De novo stress urinary incontinence (SUI) can develop postoperatively in patients without prior symptoms, and can persist beyond 6 months, posing clinical challenges. This study aimed to identify predictors of persistent de novo SUI after transvaginal mesh (TVM) surgery and to evaluate management strategies. Methods: A retrospective review of 817 women with anterior and apical pelvic organ prolapse (POP) (stage II–IV) who underwent TVM surgery from 2013 to 2021 was conducted. Fifty patients developed de novo SUI postoperatively. Assessments included urodynamic studies, validated symptom questionnaires, and POP quantification (POP-Q) staging. Logistic regression analysis was used to identify predictors of persistent symptoms. Results: Spontaneous resolution occurred in 30% (15/50) of participants within six months, while 70% (35/50) had persistent SUI. Concomitant posterior mesh repair was more frequent in the persistent group compared to the self-limiting group (29% vs. 7%), and was significantly associated with symptom persistence (OR 5.6, 95% CI, 0.65–48.4; p = 0.03, chi-square test). During conservative management with observation alone, 30% (15/50) experienced spontaneous resolution within 6 months, while 70% (35/50) had persistent symptoms. Among those with persistent symptoms, 56% required no further treatment, 10% improved with vaginal laser therapy, and 4% underwent sling surgery. Conclusions: Conservative management remains critical in the early postoperative period, given the high rate of spontaneous symptom resolution. For persistent cases, minimally invasive options such as vaginal laser therapy may be beneficial. Notably, only 4% required anti-incontinence surgery. Full article