Metabolic reprogramming within the tumor microenvironment (TME) is a pivotal driver of CD8+ T cell dysfunction in cancer. Tumor cells outcompete T cells for essential nutrients, including glucose and amino acids, while accumulating immunosuppressive metabolites such as lactate and 2-hydroxyglutarate. Beyond direct functional impairment, emerging research reveals that these metabolic alterations orchestrate CD8+ T cell transcriptional programs by remodeling their epigenome-via histone modifications, DNA methylation, and non-coding RNA networks-thereby dictating their differentiation, cytotoxic potential, and memory formation. A deeper understanding of how TME-derived metabolic signals shape the epigenetic landscape of CD8+ T cells is crucial for improving current cancer immunotherapeutic strategies. This review systematically delineates how key TME metabolic features, including nutrient deprivation and oncometabolite accumulation, regulate CD8+ T cell fate through epigenetic pathways. Furthermore, we discuss promising therapeutic strategies that target the metabolism-epigenetics axis to reinvigorate CD8+ T cell anti-tumor immunity, offering novel perspectives for enhancing adoptive cell therapy and immune checkpoint blockade. In cancer, tumor cells create a harsh environment that weakens the body’s key fighter cells, known as CD8+ T cells. These T cells are essential for attacking and destroying cancer, but tumors outcompete them for vital nutrients and fill the area with suppressive chemicals.Recent research shows that these chemical changes do more than just starve or poison the T cells. They actually rewire the T cells’ internal “control switches,” changing which genes are turned on and off. This rewiring dictates whether a T cell becomes a powerful attacker, gets exhausted, or forms a long-lasting memory.This article explains how the lack of nutrients and buildup of harmful chemicals in the tumor environment manipulate these control switches to impair T cell function. We also discuss promising new treatment strategies that target this link between metabolism and control switches to reinvigorate T cells, with the goal of making current immunotherapies more effective.
State space exploration has been a core challenge in reinforcement learning, due to the difficulty in designing an intrinsic reward function that can guide agents to precisely find high-novelty states in the state space. Most existing methods design the reward function without fully utilizing any knowledge concerning environments, which often produces inaccurate reward signals. Instead, this paper assumes the hindsight knowledge extracted from the agent's previous exploration experience can help intrinsic reward designs. Therefore, the counterfactual intrinsic reward assignment method is presented, which uses the counterfactual reasoning mechanism from the causal learning field to mine hindsight knowledge from previously explored states, and utilizes this knowledge for better intrinsic reward assignment to the agent. The core idea is to "backtrack" the agent's obtained exploration result and "reflect on" whether a more novel state could have been discovered, if the agent had selected another action at the time. Concretely, the method first samples a batch of "counterfactual" actions that differ from the current action from a policy, then uses a structural causal model to predict their corresponding next states. Subsequently, the method will give a large reward if the state actually explored is more novel than that in counterfactual reasoning, otherwise a low reward, thereby "rewarding" agents to effectively identify and find novel states in the state space. Simulations show the effectiveness in improving state space exploration, with the performance enhanced over 10 OpenAI Gym environments.
Heat is now the deadliest weather hazard in the United States. California faces this hazard in two forms, acute heat waves affecting millions and chronic exposure, which affects its large agricultural workforce. The state directs climate resilience resources using tools such as composite vulnerability indices and competitive grant programs. Whether these instruments reach the communities actually experiencing heat-related illness has not been systematically tested against health outcomes. We map heat-related Medicaid claims across California ZIP codes from 2011 to 2019 to evaluate who bears the health burden of heat, how well existing vulnerability indices capture it, and whether state grant funding reaches the highest-burden communities. We show that ZIP codes with the highest claim rates have lower median incomes, more farmworkers, and more mobile homes than the state average. Heat-related claim rates rise 24.4% per 1 °C in majority-cropland ZIP codes, compared with 20.6% per 1 °C in majority built-up areas. Of three vulnerability indices tested, only the CDC heat and health index, which itself incorporates emergency room data, correlates strongly with observed claim rates. Our analysis suggests that State Extreme Heat and Community Resilience Program funding broadly tracks county-level claim counts, but several high-burden counties, including Kern, Fresno, and Imperial, are substantially underfunded. We conclude that using medical claims data in conjunction with indices could lead to a more effective allocation of funding to communities experiencing heat risk in California than considering indices alone.
Treating autosomal dominant polycystic kidney disease (ADPKD) has always been a challenge because the disease is too complex for single-target drugs, which are often held back by side effects. This narrative review explores a different strategy: using plant-derived polyphenols to target multiple disease pathways at the same time. Looking at research from 2005 to 2026, we break down how key compounds like resveratrol, curcumin, naringenin, quercetin, and epigallocatechin-3-gallate (EGCG) actually work. Preclinical studies show these molecules can slow down cyst growth by tackling inflammation, rapid cell division, and tissue scarring all at once, while also resetting the skewed energy metabolism of cystic cells. Some mechanisms are strikingly specific, such as naringenin's direct interaction with polycystin-2 and quercetin's ability to clear senescent cells. Yet, the real-world hurdle is poor absorption; a recent clinical trial with standard curcumin fell short simply because the compound could not reach the kidneys in high enough concentrations. Moving forward, the field needs to focus on testing these compounds in realistic animal models, designing smart nanoformulations to improve bioavailability, and exploring combinations that could safely complement current therapies like tolvaptan.
Incidence of chronic obstructive pulmonary disease and asthma diagnosis were lower during and after the Coronavirus disease 2019 pandemic in Alberta, Canada. However, it is unknown whether incidences were actually lower or if the pandemic created circumstances where patients did not seek care. As such, the objective of the current study was to explore the impact of COVID-19 on patient and clinician experiences of healthcare access and delivery. The study was conducted between October 2023 and July 2024. We used interpretive description, a qualitative approach with the end-goal of informing clinical decisions. Analysis was informed by Braun and Clarke's six phases of reflexive thematic analysis. We completed thirteen interviews. Two key themes were generated: (1) The pandemic impacted care-seeking behaviours; and (2) A time and place for virtual and in-person care. Clinicians discussed how access to entry points to the health system were impacted by the pandemic and highlighted how strategies to manage health and stressors impacted symptoms and subsequent care-seeking behaviours. Participants highlighted the positives of virtual and in-person care with the consensus that both are valuable. Future use of virtual care modalities should include a visual element at minimum and prioritize the therapeutic relationship.
The case for early vasopressor initiation in septic shock has been argued in detail in physiologic reviews and randomized trials. The evidence base is no longer the limiting factor. What remains limiting is delivery. Across most U.S. emergency departments and many international settings, patients with septic shock still do not reliably receive norepinephrine within the first hour of recognition. This review reframes the early-vasopressor question from a physiologic argument into an implementation problem and identifies three structural barriers that operate independently of any individual clinician's understanding of the underlying evidence. The first is regulatory: the SEP-1 quality measure, despite a documented physician exception for the fluid requirement, continues to incentivize a fluids-first sequence as the institutional default. The second is cultural: the gap between policies that permit peripheral norepinephrine administration and the workflows, scope-of-practice arrangements, and standing orders required to actually start it at the bedside. The third is upstream: time-to-vasopressor is partly a downstream surrogate for time-to-recognition, and interventions that target only the pressor decision miss the larger source of delay. We propose a parallel resuscitation framework with explicit protocolized triggers and stratify implementation considerations across U.S. academic centers, U.S. community emergency departments, and resource-limited international settings. Closing the gap means stopping the physiology argument and rebuilding the operational architecture.
Pulmonary infections in elderly patients cause high morbidity and mortality. Conventional culture has low sensitivity and slow turnaround, delaying targeted therapy. Metagenomic next-generation sequencing (mNGS) is an emerging technology, but its diagnostic performance and cost-effectiveness are unclear. This study therefore aims to evaluate its diagnostic performance compared to conventional culture in older adults with pulmonary infections and to assess its cost-effectiveness. From March 2020 to March 2023, 522 patients (aged 55-69 years) diagnosed with pulmonary infections were enrolled at Peking University Shenzhen Hospital. Of these, 168 patients underwent simultaneous mNGS and conventional culture testing using bronchoalveolar lavage fluid (BALF) samples, while the remaining 354 patients received culture testing alone. Pathogen detection results were compared to assess the diagnostic performance of mNGS versus traditional culture methods. Additionally, cost-effectiveness analyses of the two diagnostic strategies-as well as the impact of mNGS testing timing post-admission-were conducted in the overall cohort and across stratified subgroups. Among the 168 patients who underwent both tests, mNGS identified a greater diversity and abundance of microorganisms than culture (overall detection: 89.88% vs. 26.79%; pathogen detection: 67.86% vs. 18.45%, p < 0.001). mNGS testing yielded a net economic benefit of 1202.70 CNY per patient overall and 3831.15 CNY among pathogen-positive cases. Delaying mNGS testing tended to be associated with increased hospitalization length of stay (LOS) and costs, with the most pronounced difference observed around 6 days after admission (p < 0.001). Early mNGS testing (within 6 days of admission) provided a net benefit of 6346.00 CNY. BALF-based mNGS showed higher positivity rates and a broader pathogen detection spectrum compared to conventional culture methods in this study. Early implementation of mNGS shows strong potential to guide the treatment of pulmonary infections and reduce healthcare costs for elderly and aging patients. Diagnosing pulmonary infections in elderly patients is challenging because traditional “culture” methods often fail to find the specific germ. In this study, we analyzed bronchoalveolar lavage fluid samples from 522 patients aged 55–69, we compared a newer DNA-based testing technology called metagenomic next-generation sequencing (mNGS), a method that can simultaneously detect all genetic material in samples and identify virtually any pathogen capable of causing infection, with traditional culture methods. We then compared the results of the two methods and calculated total hospital costs—including testing, length of stay, and other related expenses. Our findings highlight three key advantages. First, mNGS detected a much wider variety of pathogens than culture methods. Second, despite the higher upfront cost of the sequencing test, using mNGS was actually cheaper overall than relying on culture, saving an average of 1202.70 CNY per patient by guiding better treatment. Third, timing is critical: performing mNGS early (within 6 days of admission) saved significantly more money compared to delayed testing. These results suggest that using mNGS early is not only clinically superior but also reduces the financial burden on patients and hospitals.
This study investigated context-associated variation in vocalizations in Malinois dogs through acoustic parameter analysis. Vocalizations from thirty adult Malinois dogs (15 males, 15 females) aged 2 to 3 years were recorded across 11 behaviourally defined contexts. Using Praat software, key acoustic parameters-fundamental frequency (F0), harmonic-to-noise ratio (HNR), and formant frequencies-were extracted and analyzed. Results indicated that different vocalization types (barking, whimpering, growling, snarling, howling) exhibited distinct acoustic profiles. Whimpering and howling showed significantly higher F0 values than barking (p < 0.05), with whimpering uniquely displaying both low and high F0 components. Dogs in contexts expected to be positively valenced (e.g., food anticipation) showed lower HNR than those in contexts expected to be negatively valenced (e.g., separation) (p < 0.05). However, the actual internal states were not independently verified. Formant analysis revealed that snarling and howling had lower Formant 1 (F1) values (p < 0.05), while formant dispersion varied with emotional state. These findings suggest that acoustic analysis of dog vocalizations can provide objective insights into dogs' motivational and arousal changes, thereby improving our understanding of canine vocal communication, social behavior, and the human-dog bond. This approach has potential applications for working-line Malinois breeding programs and for enhancing human-working dog interactions.
Adhesive joints typically require high safety factors, as their mechanical performance is highly sensitive to environmental and manufacturing variations. Health monitoring can reduce these safety factors by continuously assessing the condition of the joint. While intrinsic and extrinsic sensing approaches exist, they are often based on periodic inspection or manual sensor integration, which limits their suitability for continuous in-service monitoring. This study investigates a novel sensor placement using additively manufactured strain sensors deposited by jet dispensing across the adhesive gap. Tensile lap-shear specimens were fabricated using CFRP (carbon-fiber-reinforced plastic) laminate, an epoxy adhesive, and silver-ink strain sensors placed internally within the joint and externally across the adhesive gap. Mechanical testing revealed that externally printed sensors produced an average resistance change of 65.3% near the failure stress of the adhesive joint, an order of magnitude higher than sensors embedded within the adhesive layer with 6.6% average resistance change. However, the average coefficient of variation increased as well, from 7.6% for internal to 32.6% for external. This sensor response exceeds reported environmentally induced variations in printed sensors and thus represents a promising candidate for condition monitoring. Further work is required to demonstrate actual damage detection capabilities and assess long-term stability under environmental and cyclic loading conditions.
Background: Cancer-associated thrombosis (CAT) is a leading cause of morbidity and mortality in cancer patients. Although international guidelines provide comprehensive recommendations for venous thromboembolism (VTE) prevention and treatment, the degree to which clinicians adhere to these guidelines in routine practice remains unclear, particularly in countries with limited national data such as Turkey. Methods: A cross-sectional, descriptive survey was conducted among oncology specialists (medical oncologists, radiation oncologists, and surgical oncologists) and cardiologists practicing across Turkey. A structured, case-based questionnaire comprising 21 multiple-choice questions was distributed electronically via SurveyMonkey. The questionnaire assessed perioperative VTE prophylaxis approaches, VTE risk assessment practices in ambulatory patients, primary and long-term secondary thromboprophylaxis preferences, acute VTE treatment strategies, and management of special clinical scenarios. Responses were analyzed using descriptive statistics and compared between oncologist and cardiologist groups. Results: A total of 84 physicians participated (34 oncologists [40.5%], 50 cardiologists [59.5%]). Perioperative and inpatient VTE prophylaxis practices were largely concordant with guideline recommendations, with 67.9% individualizing prophylaxis decisions and 66.7% initiating prophylaxis in hospitalized immobile patients when not contraindicated. However, only 33.7% routinely performed VTE risk assessment in ambulatory patients, and 64.6% did not use any validated risk scoring system. Low-molecular-weight heparin (LMWH) was the preferred agent for acute VTE treatment (72.6%), while direct oral anticoagulants (DOACs) gained preference in long-term secondary thromboprophylaxis (42.2%). No statistically significant differences were observed between oncologists and cardiologists across all survey items (all p > 0.05). Notably, 94.1% of respondents expressed a need to update their knowledge regarding CAT management. Conclusions: While oncologists and cardiologists in Turkey demonstrate general awareness of CAT guidelines, significant gaps persist in VTE risk stratification and primary prophylaxis for ambulatory cancer patients. The near-universal self-reported need for knowledge updates highlights the urgency for structured multidisciplinary education programs, integration of validated risk scoring tools into clinical workflows, and development of nationally adapted clinical practice guidelines. These findings reflect self-reported practices and may not fully represent actual clinical behavior; future studies incorporating medical record reviews or prescription data are needed to validate these observations.
Emergency nursing involves rapid decision-making, undifferentiated patient presentations, and limited opportunity for follow-up, often leaving patient and family outcomes unknown. Although outcome ambiguity has been linked to occupational distress, its nature and impact remain poorly understood. Existing knowledge is largely inferred from broader research on burnout and secondary trauma, leaving a gap in understanding how 'not knowing' shapes the professional and personal lives of emergency nurses. This study aimed to explore the frequency, scope, and impact of ambiguity relating to patient and/or significant others' clinical, personal, and social outcomes, and to identify strategies used by emergency nurses to mitigate its effects. A 17-item online survey was analysed using descriptive and inferential statistics and reflexive thematic analysis of free-text responses. Almost all participants (99%) reported experiencing outcome ambiguity, most related to whether a patient survived or died. Negative impacts were reported on professional practice (74.8%) and personal life (84.9%). Three themes describing ambiguity salience were identified in free-text data: the impact of extreme events, the vulnerability of paediatric patients, and impacts on the clinician self. Outcome ambiguity is pervasive in emergency nursing and affects both professional practice and personal wellbeing. Rare but extreme cases carry disproportionate emotional weight, highlighting the inseparability of clinical, emotional, and ethical dimensions of emergency nursing. Addressing ambiguity is critical to supporting emergency nurses' wellbeing.
Residency programs will need to assess residents using both Accreditation Council for Graduate Medical Education (ACGME) milestones and entrustable professional activities (EPAs) to meet ACGME and American Board of Pediatrics requirements in the coming year. Identifying ways to optimize assessment efforts using both frameworks is important. The authors applied a model developed for predicting milestone levels from EPA entrustment-supervision levels among categorical pediatrics residents to make predictions for internal medicine-pediatrics residents. During three academic years (2021-2024), the authors conducted a multi-site prospective cohort study at 8 United States internal medicine-pediatrics residency programs. They generated predictions of the 22 ACGME pediatrics milestones from the 17 general pediatrics EPAs determined by program clinical competency committees (CCCs). Predicted milestones were compared with actual ACGME-reported milestones. Overall association between predicted and reported milestones was estimated using a partial correlation coefficient (adjusting for program, resident, and competency) and fitted mixed effects regressions for differences between predicted and reported milestone levels with competency as a fixed effect and program and resident as random effects. Across 378 internal medicine-pediatrics residents, 6101 EPA entrustment-supervision levels and 7784 ACGME milestone levels were collected. Across all competencies, the marginal mean probability of an exact match between CCC reported and model predicted milestone levels was 38%; however, the likelihood of being within 0.5 level was 93% and within 1 level was over 99%. The authors present a method for predicting milestone levels from EPA levels. Predicted milestone levels should be vetted with CCCs.
Eye muscle area (EMA) and backfat thickness (BFT) are key determinants of pig carcass value. While ultrasound imaging allows non-invasive estimation of these traits in live animals, measurement accuracy is often compromised by image noise. Manual segmentation of muscle and fat boundaries is not only labor-intensive but also prone to inter-operator variability. To overcome these limitations, this study developed a deep learning-based framework for automated segmentation of pig ultrasound images to accurately estimate EMA and BFT. We constructed a large-scale dataset comprising 10,088 pig ultrasound images and evaluated six neural network architectures. Among them, ReAMS-UNet achieved the highest segmentation performance for the eye muscle region, with an Intersection over Union (IoU) of 0.9788 and a Dice coefficient of 0.9893. The model's EMA estimates showed highly consistent with manual analyses, yielding a mean absolute error (MAE) of 0.359 cm2 and a coefficient of determination (R2) of 0.9964. For BFT estimation, an integrated approach combining precise image binarization with eye muscle segmentation resulted in an MAE of 0.562 mm and an R2 of 0.9910 compared to manual measurements. Furthermore, correlation analysis with actual carcass data revealed Pearson correlation coefficients exceeding 0.9 for both traits, demonstrating that the framework performs on par with experienced technicians. These results underscore the potential of the proposed method to enhance efficiency and objectivity in the pig industry.
Construction materials like steel and concrete have been used for thousands of years; however, their industrial-scale production began relatively recently in the 19th century. These materials are still being improved as the drive to build taller buildings, longer bridges, larger dams, and similar engineering marvels keeps pushing boundaries and requirements to previously unimaginable values. Yet, testing and characterization of construction materials that make all that progress possible are overshadowed in scientific literature by more trendy materials such as graphene, composites, nanomaterials, smart materials, and biomaterials. The objective of this review was to identify, collect, and systematically analyze recent papers in which the researchers performed experimental testing on construction materials to document how state-of-the-art experimental practice extends beyond what standardized protocols prescribe. This paper covers Uniaxial Tensile Testing (UT), Compact Tension C(T), Uniaxial Compression (UC), and Single Edge Notched Bending SEN(B), as they are the most commonly used and best-suited techniques for construction material analysis. State-of-the-art papers featuring these techniques were systematically gathered using AI-assisted literature discovery tools, and their contributions beyond ISO and ASTM standards were identified and summarized. Using this review, material scientists and engineers can quickly discover the most influential and relevant papers with the actual experimental data and can apply the testing procedures described in these papers in their laboratories so they can compare their results with the previously published measurements and make an engineering decision based on appropriate comparisons.
Background and Objectives: The surgical management of three-wall orbital fractures remains a significant challenge due to complex anatomy, limited exposure, and the absence of clear landmarks. These extensive reconstructions are rare and traditionally burdened by high complication rates and inconsistent outcomes. This study presents a standardized surgical protocol for complex three-wall orbital reconstruction, highlighting the role of digital planning and a novel two-piece interlocking patient-specific implant (PSI). Materials and Methods: Between 2018 and 2024, 17 patients with unilateral three-wall orbital fractures underwent reconstruction using digitally planned, patient-specific two-piece titanium implants designed to restore the orbital floor, medial, and lateral walls. Implant positioning was assessed through qualitative evaluation of postoperative CT scans and quantitative comparison between planned and actual implant positions, as well as orbital volume analysis between reconstructed and unaffected orbits. Clinical outcomes were evaluated pre- and postoperatively. Results: Reconstruction was classified as ideal in 16 cases (94.1%) and satisfactory in one case (5.9%). Quantitative analysis demonstrated a high level of concordance between the planned and postoperative implant positions, with a mean deviation of 0.982 ± 0.107 mm (95% CI: 0.927-1.037 mm). All implants were positioned within 1.5 mm of the planned location. Postoperative orbital volumes closely approximated those of the contralateral side, with a mean volume difference of 1.371 ± 0.176 cm3 (95% CI: 1.280-1.461 cm3). Diplopia resolved in all patients, and enophthalmos was fully corrected in 15 cases (88.2%). No major complications or revision surgeries were observed. Conclusions: The proposed two-piece interlocking PSI enabled precise and reproducible reconstruction of complex three-wall orbital fractures. This approach demonstrates that even technically demanding orbital reconstructions can be performed with greater reliability, leading to favorable functional and aesthetic outcomes.
Among renewable energy sources, hydropower has been the most economical and well-established technology for decades. However, the construction of hydropower plants (HPPs) may have (unknown) cumulative ecological and socioeconomic ramifications in the short and long term. In Africa, 673 large HPPs are proposed. If implemented, they will alter all major river networks through dam construction and reservoir inundation, although the actual extent remains unknown. This study conducts an integrated assessment of the impacts of all proposed HPPs at both basin and continental scales. Projected reservoir areas were overlaid with spatially explicit datasets on megafauna abundance, protected areas, cropland, and human resettlement. We further calculated indices of river regulation and fragmentation, as well as potential sediment entrapment and evaporation associated with the projected reservoirs. By integrating these indicators, we identified 102 HPPs that fall within the top quarter of projects with the greatest potential overall impact. HPP capacity size alone proved to be an inadequate impact indicator, as underlined by the highest- and lowest-ranked HPPs, both of which exhibited comparably low capacities. A sensitivity analysis revealed that the ranking depends on both the number of HPPs considered and the selection of indicators included in the analysis. This study provides evidence-based information to support decision-making when balancing renewable electricity needs against the environmental and socioeconomic impacts of HPP development at basin and continental scales.
The rapid growth in generative models for medical imaging demands robust methodologies for preserving patient privacy and ensuring that the synthetic data remains authentic. The leaking of identifiable patterns from real clinical data into generated images presents serious difficulties in terms of regulatory compliance, ethical deployment, and clinical trust. This work, therefore, attempts to identify the actual clinical images mistakenly included in a generative adversarial network during its training and enables privacy-preserving generative modeling. A hybrid Siamese architecture consisting of ResNet-50 and vision transformer backbones is introduced to learn robust feature representations. Cross-attention is incorporated into the model for multiscale feature fusion, and an adaptive similarity metric is developed to improve discriminative comparison of paired images. We use a hybrid loss function that combines an angular term and a binary cross-entropy term during training for stable optimization. Through extensive evaluation on lung CT scan datasets, we show that our model has shown the highest average accuracy ([Formula: see text]) and an equally good F1-score ([Formula: see text]) This is a reasonable classification performance and also points out the difficulties involved in the detection of generative fingerprints. These findings support the superior discriminative power of the model for subtle generative 'fingerprints'. With a presented novel privacy-preserving framework for generative model validation, the proposed method can be a contribution to the development of secure and ethical validation for clinical AI systems.
Prospective memory (PM) - remembering to perform future intentions (Rummel et al., 2023, Prospective memories in the wild: Predicting memory for intentions in natural environments. Memory & Cognition, 51(5), 1061-1075. https://doi.org/10.3758/s13421-022-01379-y) - is essential for maintaining daily functioning and fulfilling important obligations (e.g., taking medication). Yet, various factors can lead to PM failures. Stress - the body's physiological response to a difficult situation/event (Stewart & McFarland, 2020, An investigation of the relations between stress and prospective memory. Journal of Cognitive Psychology, 32(2), 131-145. https://doi.org/10.1080/20445911.2020.1724116) - may be one such factor. Extant research into the stress-PM relationship is limited and yields ambiguous findings, primarily due to poor PM measurement validity. Therefore, our study aimed to examine whether acute and/or chronic stress (measured via stress response and stressor exposure) impair PM performance in everyday environments, using a predominately university-based sample. We developed a novel, ecologically valid paradigm - based on Actual Week (Rendell & Craik, 2000, Virtual week and actual week: Age-related differences in prospective memory. Applied Cognitive Psychology, 14(7), S43-S62. https://doi.org/10.1002/acp.770) - where participants performed a variety of researcher-assigned PM tasks over a four-day period. Overall, acute stress (i.e., our short-term stress indices, aggregated over four-evenings) did not correlate with PM performance, but chronic stressor exposure negatively correlated with PM performance; albeit with a small effect. Additionally, all stress measures were associated with impaired self-reported PM, suggesting that actual PM performance and perceived PM ratings show modest overlap, and likely tap into partially distinct aspects of PM. Our findings highlight the need for more accurate approaches of evaluating stress and PM in everyday life.
This study proposes a condition-based maintenance monitoring method based on Geometry-based Optical Focus Metrology (GOFM) to detect wafer table edge deterioration early and enable proactive interventions before actual Critical Dimension (CD) bridge defects occur. In advanced Deep Ultraviolet (DUV) immersion photolithography, prolonged equipment operation mechanically wears the wafer table, inducing Edge-Roll-Off (ERO). Because conventional optical metrology struggles to separate this localized defocus from process noise, this work utilizes the existing GOFM technique to isolate the pure focus residual within the 140-147 mm radius region. To quantify this hardware-specific degradation, a mathematical dual-indicator system was constructed. This framework integrates a statistical threshold, the Range Percentile 97%, to reject baseline measurement noise, and a geometric variable, Slope × 3, to capture the topographical drop in the outermost 3 mm. Analysis of long-term time-series data from multiple High-Volume Manufacturing (HVM) scanners confirmed a strong correlation (R2=0.93) between these indicators. Furthermore, we proved that the drift trajectory of Slope × 3 deterministically predicts mechanical failure prior to defect occurrence on production wafers. Based on these findings, an automated condition-based maintenance architecture was designed using an OR-logic decision gate. By triggering a preemptive table replacement at a quality-based critical warning threshold, this system converts routine time-based scheduling into a data-driven paradigm, maximizing both edge yield and equipment uptime. Furthermore, this proposed framework establishes a solid foundation for future extensions toward machine learning-based predictive maintenance.
Lithium-ion batteries with high energy density and long cycle life have been widely used as secondary batteries in electric vehicles and energy storage systems. With the growing demand for high energy density in lithium-ion batteries, silicon-based materials, which possess a high theoretical specific capacity (4200 mAh g-1), are regarded as core candidates for anode materials. However, Si-based materials undergo severe volume expansion (up to 300%), which leads to the collapse of the electrode structure, inducing pulverization of the active material and capacity loss, thereby hindering the commercial application of silicon-based materials. To address these issues, scholars from various countries have developed many silicon-based materials with different compositions and three-dimensional structures, and have made some research progress. This review first elaborates on the lithium storage mechanisms and advantages of diverse silicon-based anode materials by taking Si, SiOx, SiNx, and SiPx as representative examples with distinct characteristics. Subsequently, from the two aspects of dimensional design (0D, 1D, 2D and 3D) and architecture design (core-shell, sandwich-like and network structure), the design strategies for various silicon-based anode structures and their enhancement on electrochemical performance are analyzed. Finally, this review elucidated the challenges faced by silicon-based anodes from the perspectives of mechanism elucidation, structural customization, industrialization, and full-cell applications. It also proposed future development directions for silicon anodes by combining actual challenges and focusing on aspects such as structure optimization, machine learning, advanced characterization techniques, and mechanistic analysis.