Browse Articles
Discover research articles across all indexed journals
Children and adults differ in how primary and secondary incentives modulate valuation, effort, and cognitive control
Rewards have a profound impact on human motivation, cognition, affect, and behaviour. The study of reward processing and incentive effects therefore occupies a central place in psychology and cognitive neuroscience. One common assumption when comparing groups or individuals is that different reward types are valued similarly. Here we examined this assumption in a sample of 51 adults and 39 children (7–12 years) using both primary and secondary rewards. Across three tasks – subjective valuation, willingness to exert cognitive effort, and reward-related modulation of cognitive control – adults showed stronger effects of secondary relative to primary reinforcers, whereas children showed comparatively similar responses across reward types. While we interpret our findings as consistent with age-group differences in the value assigned to secondary reinforcers, larger longitudinal studies using more closely matched incentives will be required to determine how such differences emerge across development. More broadly, our work highlights the importance of carefully considering incentive value when comparing different groups on reward-related processes.
Outside Back Cover: Fused‐Ring Acceptor–π–Acceptor Architecture Enables Near‐Infrared‐Absorbing Mesoporous Covalent Organic Frameworks for Enhanced H <sub>2</sub> O <sub>2</sub> Photosynthesis (Angew. Chem. Int. Ed. 25/2026)
Distance-based Device-To-Device outage reduction for 5G wireless systems
Device-to-Device (D2D) communication constitutes that closely spaced nodes communicate with each other without the use of a centralized base station that carries the communication data. This helps in increasing the data rate, decreasing latency, and expanding the bandwidth and capacity which promotes it to play an important part in next-generation wireless technology. Regardless, the efficient utilization of the available resources is a crucial challenge in D2D systems especially in limited areas; considering that the spectrum is shared across the cell. This paper investigates the spectrum sharing strategies for D2D communication in the context of a single cell by presenting a simulation study of D2D communication performance under different resource allocation scenarios. The first scenario divides the coverage area into inner and outer regions, and the spectrum as well; one for each region, the second scenario assigns resources to nearby users within a range of 10 meters and reuses the resources by other distant users if the separation is more than 50 meters. The variations in the simulation parameters helps to analyze the impact of user location, density, and resource allocation in outdoor urban environments. The simulation results are evaluated in terms of spectrum utilization, and outage probability. These results are analyzed to understand the impact of resource allocation on the performance of the system and thereby show that the second scenario improves the system performance in terms of the outage probability providing useful insights for designing and optimizing D2D communication systems in an outdoor urban environment especially in next-generation-networks.
Chronic absenteeism in Canadian kindergarten classes, pre- and post-COVID-19, and its association with concurrent developmental vulnerability
Students who frequently miss school are at greater risk for academic difficulty. High levels of absenteeism as early as kindergarten have been associated with long-term consequences, such as low reading proficiency in Grade 3 and low academic achievement in Grade 5, both of which have been associated with lower rates of high school graduation and enrollment in post-secondary education. The prevalence of school absenteeism has increased significantly since the COVID-19 pandemic and there have been sustained shifts in student attendance rates from kindergarten to Grade 12 since 2020. The goals of this population-level, repeated cross-sectional cohort study were to compare rates of chronic absenteeism, defined as being absent from school at least 10% of the time, in kindergarten in Canada before and after the onset of the COVID-19 pandemic, and examine the association between children’s chronic absenteeism and their concurrent developmental vulnerability. A total of 513,159 kindergarten children participated in the study, with 284,712 (55.5%) being in the pre-COVID-19 cohort (2017−2020) and 228,447 (44.5%) in the post-COVID-19 cohort (2020−2023). Across Canada, rates of chronic absenteeism increased from pre- to post-COVID-19, from 17.7% to 41.3%, with differences by jurisdiction. The greatest increase was seen in Ontario, while the smallest increase was seen in British Columbia. Children attending kindergarten in the post-COVID-19 cohort were three times more likely to be chronically absent compared to their peers attending kindergarten before the onset of the pandemic. Despite this, chronic absenteeism in the post-COVID-19 period was associated with reduced odds of overall developmental vulnerability, a pattern that is likely attributable to shifts in the composition of chronically absent children. In the post-COVID-19 cohort, a greater percentage of children who were chronically absent resided in higher SES neighbourhoods compared to their chronically absent peers attending school before the onset of the pandemic. While increasing rates of school absenteeism should not be ignored, our results suggest that chronic absenteeism following COVID-19 might be more nuanced than before. The jurisdictional differences in rates of chronic absenteeism observed in this study could be due to the various public health measures put in place by the various provincial and territorial governments. It is also possible that the children from higher SES neighbourhoods missed more school after the onset of the COVID-19 pandemic because their parents had the capability to work from home, making it easier to keep their child(ren) home from school. The decreased association between chronic absenteeism and developmental vulnerability post-COVID-19 may be partially related to changes in access to online learning resources, which could have enabled some students to remain academically engaged while at home. However, further research is needed to better understand the underlying reasons for school absence and how the relation between absenteeism and academic achievement may vary across developmental stages to effectively support learning trajectories.
Unlocking precision diagnostics: A multimodal framework integrating metabolomics with advanced machine learning techniques
Integrating multiple omics modalities is a crucial strategy in cancer research, particularly in metabolomics, enabling early detection and detailed exploration of cancer biomarker signatures. This study evaluates five strategies for integrating metabolomics data from liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance. Deep Transfer Learning and Multiple Kernel Learning demonstrated superior performance, significantly improving classification accuracy, sensitivity, and robustness compared to single-modality analyses. Deep Transfer Learning employed a custom autoencoder for feature extraction followed by artificial neural network classification, while Multiple Kernel Learning optimized kernel matrices across different modalities. Feature extraction in the Deep Transfer Learning approach, combined with the selection of important features and subsequent analysis, revealed elevated levels of monounsaturated phospholipids such as phosphatidylcholine 30:1, phosphatidylethanolamine 32:1, and sphingomyelin 32:1 in HER2-positive cases. Additionally, β-alanine, gluconic acid, and N-acetylaspartic acid were increased, whereas 5’-deoxy-5’-methylthioadenosine and nicotinamide were decreased. These methods advance cancer detection, biomarker discovery, and the development of precise diagnostic and therapeutic tools while offering robust and adaptable strategies for multi-omics data integration across diverse biological datasets.
16S rRNA-based metagenomics insights into the microbial diversity and functional attributes of soils from the rhizosphere of selected C4 crops of farms in Mpumalanga and Limpopo provinces, South Africa
The rhizosphere serves as a hub for a variety of microorganisms that are highly beneficial to crop production and improvement of soil health. However, intensive farming practices including utilization of agrochemicals can cause a decline in microbial diversity that could severely compromise soil health and crop productivity. Here we investigated the taxonomic abundance and functional diversity of the microbial communities of sorghum and pearl millet rhizosphere soil samples from sixteen farms in Mpumalanga and Limpopo Provinces of South Africa. Soil samples were collected at the rhizosphere of sorghum and pearl millet crops and pooled into 34 samples. The soil samples were used for 16S rRNA amplicon sequencing analysis, soil physicochemical properties, and community-level physiological profiles. The results indicated that carbon utilization was highest in the majority of soil samples from Jane Furse, which also demonstrated greater microbial richness. The 16S rRNA amplicon sequencing analysis provides insight into the relative abundance of soil microbial communities, where at phylum level Planctomycetes, Proteobacteria, and Actinobacteria were the most predominant in all farms, but their relative abundances varied. Our results revealed that physicochemical properties could affect microbial abundance and diversity. The distance-based redundancy analysis (dbRDA) explained 46.8% of the variation in the soil bacterial community structure, with Mn, Fe, NO₃⁻-N, and Ca identified as the key soil physicochemical variables shaping community composition. Thus, this study may contribute to advancing sustainable agricultural practices by providing baseline data that may inform future bioinoculant development.
Reservoir Computing‐Based Glucose Sensing With an Enzymatic Reaction Network
ABSTRACT The survival of living systems relies on their capacity to process physicochemical information from the environment and make distinct decisions. Inspired by this concept, we report a scalable protease‐peptide‐based reservoir sensor for the detection of glucose stimuli. In this system, glucose oxidase (GOx) functions as a ‘sensor module’ that preprocesses glucose into a pH signal that can be further processed by the protease network. The effect of glucose induced pH change on the protease‐peptide network is systematically investigated. GOx mediated pH drop ultimately affect protease activities that lead to an observable response in the reservoir output. Finally, we demonstrate the protease‐based network's capacity to perform diverse information processing tasks by leveraging principles of reservoir computation. The enzymatic reservoir sensor not only functions as a linear glucose sensor but also exhibits binary switch like responses at both high and low glucose concentrations. Furthermore, the sensor can respond to dynamic glucose inputs, such as changes in the periodicity of glucose pulses or variations in both pulse amplitude and periodicity.
Rheological characteristics and modification mechanism of rock asphalt compound modified binder based on grey relational analysis
To address the limitations of conventional asphalt in resisting high-temperature rutting and oxidative aging, a Rock Asphalt Compound additive (RCA) —comprising natural rock asphalt with surface-treated Nano-TiO 2 —was investigated as a sustainable modifier. This study systematically evaluates the rheological performance and modification mechanism of RCA-modified A-90# asphalt binders with dosages ranging from 0% to 31.6%. The viscoelastic behaviors were characterized using Dynamic Shear Rheometer (DSR), Bending Beam Rheometer (BBR), and Rotational Viscosity (RV) tests, while the chemical interactions were probed via FTIR spectroscopy. Furthermore, Grey Relational Analysis (GRA) was innovatively introduced to quantify the sensitivity of performance indicators to RCA dosage. Results indicate that RCA fundamentally alters the colloidal structure of the binder, acting as a potent stiffening agent. The high-temperature rutting factor ( G */sin δ ) increases exponentially with dosage, and the Penetration Index (PI) improves from −1.392 to −0.354, indicating reduced temperature susceptibility. FTIR analysis confirms the modification is primarily a physical blending process, enhanced by the introduction of polar functional groups (S = O, C = O). Crucially, GRA results quantitatively identify that the dominant function of RCA is the enhancement of aging resistance (R = 4.3430), followed by stiffness (R = 3.7567), surpassing its effect on ductility. However, high dosages negatively impact low-temperature relaxation (m-value). Considering the trade-off between the significant gains in anti-aging/rutting performance and the limitations in thermal cracking, an optimal dosage of 21.8% is recommended. At this dosage, the binder achieves a superior rutting factor of 25.5 kPa (64°C) while maintaining an m-value of 0.311 (−12°C), satisfying Superpave specifications.
Gate-tunable WSe2/Bi2Te2Se van der Waals heterostructure photodetectors for broadband photodetection
Band offset engineering of van der Waals heterostructures is a critical strategy to broaden the response range of optoelectronic devices and realize multifunctional device applications, and the adoption of high-mobility material systems in heterostructure design is essential for optimizing the overall performance of optoelectronic devices. Herein, we design and fabricate a Type-II band alignment van der Waals heterostructure based on n-type Bi2Te2Se and p-type WSe2, with strong interlayer coupling at the heterointerface verified via systematic structural and spectroscopic characterizations. The devices exhibit a prominent photovoltaic effect with clear open-circuit voltage and short-circuit current, enabling stable self-powered photodetection at zero bias without any external power supply. This self-powered photodetection exhibits excellent performance over an ultra-broadband from 254 to 1310 nm, delivering a maximum responsivity of 0.22 A/W and a high specific detectivity of 4.3 × 109 Jones. More importantly, they present a distinct gate-tunable transition from p-type dominated ambipolar transport to n-type dominated unipolar transport, with the responsivity magnified by 3 times via rational gate voltage regulation. Spatially resolved photocurrent mapping directly visualizes the gate-enhanced photocarrier separation at the heterointerface, and the device further enables high-quality broadband photoelectric imaging and convolutional neural network-based image recognition. This work establishes a reliable platform for ternary Bi-based heterostructures, highlighting their potential for low-power, multifunctional optoelectronics and next-generation artificial vision systems.
Perceptions of physical and occupational therapists on the utility of surface electromyography data in spinal cord injury rehabilitation
Purpose Spinal cord injury (SCI) impacts physical, emotional, and social well-being, contributing to decreased quality of life and increased healthcare burden. Surface electromyography (sEMG), a non-invasive tool for measuring muscle activity, has demonstrated potential as a biomarker for recovery in SCI research, yet remains underutilized in clinical practice. Understanding how physical therapists (PTs) and occupational therapists (OTs) perceive the use of sEMG is necessary for integrating sEMG into post-SCI treatment and advancing personalized rehabilitation. Materials and methods A cross-sectional, qualitative descriptive design was employed. Ten participants (9 PTs and 1 OT) were recruited through convenience sampling. Semi-structured interviews were conducted and analyzed inductively using a thematic analysis approach. Results Two major themes were identified: 1) Perceived value of the use of electrophysiology and sEMG data in clinical practice. Participants valued sEMG as an adjunct assessment tool for providing objective feedback after incomplete SCI and setting goals during treatment. 2) Barriers and facilitators to implementing sEMG. Key barriers highlighted include the lack of training and standardized protocols. Continued training, resources, and educational support were key facilitators. Conclusion PTs and OTs perceive sEMG as a valuable tool in SCI rehabilitation, but desire education and standardized protocols to support its clinical integration.
Superluminal spacetime crystals induced by anomalous velocity modulation
Time-modulated media offer powerful opportunities for controlling light, yet extending such concepts to optical frequencies has remained challenging. Here, we propose a different route to photonic spacetime crystals based on modulation of the anomalous velocity in low-symmetry conductors, particularly Weyl semimetals. We show that when driven by a strong optical pump, the anomalous velocity of Bloch electrons induces an ultrafast spacetime modulation that propagates with a superluminal phase velocity relative to the dielectric background. This self-induced modulation enables unidirectional light transport below the optical gap and, near the epsilon-near-zero point, gives rise to collective parametric resonance and stimulated emission of volume plasmons. These findings identify Weyl semimetals as a promising platform for realizing optical spacetime crystals and open a pathway toward active and nonreciprocal photonic systems governed by quantum geometric effects.
Expression of concern: Protein solubility, digestibility and fractionation after germination of sorghum varieties
Origin of threshold voltage instability in vertical GaN trench MOSFETs characterized by charge pumping
We systematically investigate threshold voltage (Vth) instability in vertical GaN trench MOSFETs on sapphire using a charge pumping (CP) method combined with pulsed gate measurements. A negative Vth shift and reduced hysteresis are observed when switching the gate bias from DC to pulsed mode with a negative quiescent gate bias; this behavior is attributed directly to oxide trapping in the gate stack. Using the CP technique, we quantitatively extract the trap density, energy levels, and depth profiles of both border oxide traps and interface states in the Al2O3/p-GaN sidewall gate stack. An integrated border trap density of 7.28 × 1012 cm−2 is deduced, and amorphous Ga–O–Al bonds formed during atomic layer deposition are identified as the potential physical origin of the Vth shift and hysteresis (ΔVth). Our analysis demonstrates that the CP approach goes beyond offering valuable insights into interface properties; it provides a powerful, quantitative diagnostic tool to directly guide the optimization of gate dielectrics in next-generation reliable vertical GaN trench MOSFETs for power applications.
Explainable machine learning reveals diverse yield-determining factors among Thai rice farmer cohorts: Implications for targeted agricultural support
Rice yield prediction and optimization remain crucial challenges in Thailand’s agricultural sector. This study presents an explainable machine learning framework for predicting farm-level rice yields and identifying key factors affecting productivity. We collected comprehensive data from 1,722 smallholder farmers in central Thailand, encompassing 58 agronomic and economic variables. Four automated machine learning (AutoML) frameworks – AutoGluon, auto-sklearn, h2o, and mljar – were evaluated using 5-fold cross-validation, with AutoGluon achieving the best performance (root mean square error: 0.532 tonnes/hectare, mean absolute error: 0.372 tonnes/hectare, R²: 0.538). Using global SHapley Additive exPlanations (SHAP) analysis, we identified farmers’ social networks, rental costs during harvest, and total harvesting expenses as the most influential predictors of rice yields. Notably, stronger social network connectivity was associated with higher yields, suggesting that information sharing and collective knowledge within farming communities play a key role in improving productivity. Clustering analysis based on individual SHAP values revealed six distinct farmer cohorts, each characterized by unique patterns of feature importance. These cohort-specific insights demonstrate the potential of combining AutoML with explainability techniques to move beyond uniform agricultural recommendations towards precision support tailored to the specific needs of different farmer cohorts.
Enhancing nonreciprocal filtering through surface modification of ferrimagnetic insulators
To address the significant reduction in magnetostatic surface waves (MSSW) nonreciprocity as ferrimagnetic insulators are scaled to the nanometer level, this study demonstrates an effective interface engineering methodology through targeted surface oxidation of 100 nm yttrium iron garnet (YIG) thin films. By implementing a sequential chemical treatment using FeCl3 and piranha solutions, we introduce a 30-nm-deep oxygen gradient near the YIG surface and increase the surface Fe3+/Fe2+ ratio from 2.7 to 7.3. This surface modification effectively restored the asymmetric dynamic dipolar stray fields associated with MSSW precession, which are essential for nonreciprocity, while simultaneously reducing the Gilbert damping coefficient from 8.2 × 10−4 to 4.8 × 10−4. Experimental characterization of the resulting filter prototype at 6.9 GHz reveals that surface treatment enhances the nonreciprocal isolation from 1.1 to 16.2 dB, narrows the signal passband from 200 to 70 MHz, and achieves optimized microwave transmission with an insertion loss of 4.2 dB. Together with semi-analytical modeling, these results show that surface oxidation converts a chemical depth gradient into direction-dependent effective magnetic parameters, thereby enhancing MSSW nonreciprocity through direction-dependent frequency response and dynamic-loss asymmetry. This work establishes a physically grounded interface engineering route for high-isolation, low-loss nonreciprocal components in integrated radio frequency and microwave front-end systems.
Editorial Note: Regulation of Vapor Pressure Deficit by Greenhouse Micro-Fog Systems Improved Growth and Productivity of Tomato via Enhancing Photosynthesis during Summer Season
Oxygen vacancies-assisted carrier transport toward improved Au/HfO2−x/Si Schottky photodetectors
Au/Si Schottky junctions are important building blocks for fabricating inexpensive and efficient photodetectors (PDs), whereas Si surface defects induce severe interfacial non-radiative recombination that markedly degrades the performance of the resulting devices. Herein, oxygen vacancy (VO)-rich HfO2−x thin films are employed to passivate Si surface defects to fabricate improved Au/HfO2−x/n-Si Schottky PDs. It shows that HfO2 thin films effectively passivate n-Si surface defects, while the presence of VO defects compromises the passivation efficiency. This results in improved Au/HfO2/n-Si Schottky PDs, but the improvements are limited by inefficient interfacial hole transport caused by the insulating HfO2. In contrast, the interfacial hole transport is markedly enhanced in HfO2−x thin films owing to VO defect-assisted hole tunneling, thereby leading to highly improved Au/HfO2−x/n-Si Schottky PDs. These results demonstrate that efficient interfacial carrier transport is vital for realizing improved dielectric-passivated PDs and provide a promising pathway for fabricating high-performance Si-based heterojunction PDs.
Effects of local heat on metabolic health, frailty risk, and exercise adaptations in pre-diabetic older adults: Protocol for the Heat and Exercise in Aging as Therapy (HEAT) clinical trial
Introduction Glycemic dysregulation is a hallmark of type 2 diabetes (T2D) and contributes to skeletal muscle (SKM) loss and frailty risk, especially in older adults. Glycemic control and physical function are supported by SKM capillarization and mitochondrial function, and their impairment contributes to T2D development. While high-intensity interval training (HIIT) is a promising intervention, adherence and effectiveness remain concerns for prescribing HIIT among older adults at risk for T2D. Local heat therapy (LHT) may be a more practical initial strategy to improve SKM architectural factors and precondition SKM, enhancing physiological adaptations to exercise in this population. Methods and analysis Heat and Exercise in Aging as Therapy (HEAT) is a two-phase, randomized, sham-controlled clinical trial investigating the efficacy of LHT to improve glycemic control and decrease frailty risk via improved SKM architecture among older adults with prediabetes. LHT is tested as a standalone intervention and as a means to precondition SKM for subsequent HIIT, improving exercise adaptations. In Phase 1, LHT and sham (CON) groups apply heat pads for 90 minutes/day, 6 days/week, for 12 weeks. A separate HIIT group completes 4x4-minute cycling intervals at 90–95% VO₂peak, 3 days/week. In Phase 2, LHT and CON groups begin HIIT. Participants (≥50 years) have impaired fasting glucose (100–125 mg/dL) and/or HbA1c (5.7–6.4%). Biospecimen collection and clinical assessments occur at baseline (T1), after Phase 1 (T2), and Phase 2 (T3). To our knowledge, this is the first study to determine the use of local heat pad on pre-diabetic older population. If successful, LHT may be a practical, scalable, non-invasive intervention to improve glycemic control and reduce frailty risk in older adults with prediabetes, preventing progression to T2D.
Self-powered broadband UV–NIR photodetector based on NbOCl2/WSe2 van der Waals heterostructure
Achieving self-powered operation together with broadband photodetection in a single device remains a key challenge for next-generation optoelectronic systems, primarily due to limited spectral response and high power consumption of conventional photodetectors (PDs). Although two-dimensional van der Waals (vdW) heterostructures provide a highly viable framework for performance enhancement, efficient self-powered broadband detection is still hindered by insufficient built-in electric fields and incomplete carrier separation. Here, we report an NbOCl2/WSe2 vdW heterojunction PD that enables efficient self-powered broadband photodetection. By leveraging the intrinsic polarization of NbOCl2 and favorable type-II band alignment at the heterointerface, a robust internal electric field is induced, thereby accelerating the separation and transport of photogenerated carriers. Under 477 nm illumination, the PD delivers an open-circuit voltage of 100 mV and a short-circuit current of 0.264 nA, achieving a responsivity of 39.17 mA/W in the self-powered mode. The PD exhibits broadband spectral sensitivity spanning from 255 to 1010 nm, an on/off current ratio of approximately 102, and fast response times of 7.14/7.37 ms. Moreover, a signal identification system is demonstrated to demonstrate the broadband imaging capability, including wavelength-dependent image reconstruction and ASCII-coded optical signal transmission. These results highlight the strong potential of NbOCl2/WSe2 vdW heterostructures for self-powered broadband optoelectronic applications.