Browse Articles

High-capacity reversible hydrogen storage in Na-decorated Al–N–O nanocluster: a DFT-D3 study

Zoo housed meerkats do not recognise human emotions

Abstract Domestic mammals can categorically process and respond functionally to human emotional cues. However, to date, it is unclear whether this ability is underlaid by shared mammal emotional processing, associative learning through exposure to humans, or has arisen through the domestication process. To disambiguate these hypotheses, we investigated whether zoo-housed meerkats ( Suricata suricatta ), who have not undergone the process of domestication but are regularly exposed to human contact, recognise human emotions, and whether this is modulated by human interaction levels. 35 meerkats were exposed to human emotional expressions (happy, sad, fearful and neutral) to assess their behavioural responses, and a cross-modal paradigm was used to examine expectancy violation responses when presented with emotionally incongruent signal pairs. While isolated findings suggest human interaction level modulated some behavioural responses to the test paradigms, no patterns of functional responses, lateralised processing, or cross-modal integration consistent with those documented in domestic mammals emerged, either for the whole study population or for those meerkats with a history of close interaction with humans. Thus, zoo-housed meerkats do not show strong evidence of human emotion recognition. Our findings support the hypothesis that the capacity for human emotion recognition in domestic species has arisen through artificial selection for attention to human communicative cues.

Pioneer vegetation shapes soil microbial community assembly and ecosystem functions in riparian sand mining sites

Abstract Pioneer vegetation initiates the recovery of degraded ecosystem; However, its influence on soil microbial communities in riparian sand mining sites remains poorly understood. Four naturally established pioneer plant species ( Artemisia scoparia , Imperata cylindrica , Saccharum arundinaceum , and solitary trees) were selected in a representative sand mining site along the Huai River, and systematically investigated soil microbial community structure, assembly processes, co-occurrence network stability, and functional potential to elucidate the regulatory mechanisms of pioneer vegetation on soil microbial communities and ecosystem functions. The results showed that pioneer vegetation types shaped distinct soil microbial communities, with bacterial communities more responsive than fungi. Total carbon (TC), total phosphorus (TP), and nitrate nitrogen (NO 3 -N) collectively explaining 76.8% of the variation in bacterial community composition. Microbial community composition exhibited strong vegetation-specific patterns. A. scoparia plots significantly enriched Actinobacteria , whereas solitary tree plots significantly enriched Basidiomycota . Network stability varied markedly with vegetation type; Functional prediction revealed that bacterial nitrogen cycling potential was significantly modulated by vegetation type, with nitrification potential being significantly lowest in I. cylindrica . Structural equation modeling demonstrated that vegetation type indirectly influenced soil multifunctionality via regulating soil physical properties, with contrasting pathways in bacterial and fungal models. Our findings suggest potential ecological mechanisms by which pioneer plants influence early ecosystem recovery in degraded riparian zones through the “plant-soil-microbe” continuum, thus providing a scientific basis for functional trait-based ecological restoration.

Distinct genome-wide salivary DNA methylation changes following superset and repeated sprint training in youth male basketball players

Impact of crop residue burning on regional air quality over the Indo-Gangetic Plain: a comparative study during different cultivation patterns

Physics-informed multi-task learning for permeability prediction and probabilistic HFU modeling: a case study from the Lower Bahariya Reservoir, Shahd SE field Egypt

Abstract Accurate permeability prediction is essential for reliable reservoir characterization and simulation, yet remains challenging due to complex nonlinear relationships and subsurface heterogeneity. Conventional hydraulic flow unit (HFU) methods rely on discrete rock typing and fixed porosity–permeability relationships, limiting their ability to capture continuous variations. Physics-informed neural networks (PINNs) offer a data-driven alternative with embedded physical constraints, but their effectiveness is often limited by weak enforcement of physics during inference. In this study, a physics-guided multi-task neural network (MT-PINN) is proposed to simultaneously predict permeability and hydraulic flow units within a unified framework. The model integrates data-driven learning with physics-based relationships and probabilistic rock typing, enabling permeability to be estimated as a weighted combination of multiple flow units and allowing smoother transitions between facies. The proposed approach was evaluated using core and well log data and compared against conventional HFU and standard PINN methods. Within the studied dataset, the MT-PINN demonstrated improved predictive performance, with a higher correlation coefficient ( $$\:R\:=\:0.936$$ ) compared to HFU ( $$\:R\:=\:0.89$$ ) and PINN ( $$\:R\:=\:0.90$$ ), along with a reduction in log-scale error. The model also provides more continuous and stable permeability predictions along depth. In addition to permeability estimation, the MT-PINN outputs both discrete HFU classifications and associated class probabilities, which can be incorporated into 3D stochastic reservoir modeling workflows for uncertainty-aware multi-realization analysis. The proposed framework demonstrates the potential to integrate traditional petrophysical methods with modern machine learning techniques, providing a promising workflow for permeability prediction and reservoir characterization within the studied reservoir.

Fractional modeling of CD38–mediated multiple myeloma dynamics with immune interaction and therapy effects dynamics

Shape-preserving minimum trace (SP-MinT): a regularized forecast reconciliation method for hierarchical time series

Abstract Forecast reconciliation has become the standard for ensuring coherence in hierarchical time series. However, state-of-the-art methods like Minimum Trace (MinT) prioritize the minimization of error variance, often at the expense of distorting the temporal morphology of the forecast. This paper reframes forecast reconciliation as a multi-objective problem, showing that variance-optimal coherence is insufficient for operational decision-making, and proposing a shape-aware reconciler that explicitly encodes temporal structure. We introduce Shape-Preserving Minimum Trace (SP-MinT), a novel framework that regularizes the optimization process with domain-informed priors constructed from historical day-of-week profiles. We validate the method using a rigorous rolling cross-validation on real-world electricity demand data from Victoria, Australia. The results demonstrate that SP-MinT outperforms the standard MinT-WLS benchmark by reducing the Root Mean Squared Error (RMSE) by 31.94% and the Shape Error (Dynamic Time Warping) by 43.16%. By bridging the gap between statistical optimality and morphological fidelity, SP-MinT offers grid operators hierarchically coherent forecasts that respect physical ramping constraints.

Non-toxic harpagophytum procumbens (Burch.) DC. ex Meisn. dry extract exhibits repellent and antioxidant activities in nauphoeta cinerea

dual functional microwave sensor with high-gain array antenna using loaded U-resonator and asymmetric T-junction

Battery-aware approximate wireless telemetry framework for resilient wearable ECG monitoring under extreme power constraints

Abstract Wearable electrocardiogram (ECG) patches utilizing Bluetooth Low Energy (BLE) face a critical, yet under- characterized, failure mode: as battery state of charge (SoC) depletes, firmware-mandated reductions in RF transmission power elevate the bit error rate (BER) in Rayleigh-fading channels, causing conventional QRS detection to fail during the most clinically important periods of continuous cardiac monitoring. This paper presents the Battery-Aware Approximate Wireless Telemetry (BAWT) framework, a co-designed solution that jointly optimizes a Peukert-corrected Li-ion discharge model, a six-state adaptive RF power controller, and a Rayleigh-fading indoor channel model. At the receiver, the proposed Bayesian Adaptive Feature Estimator (BAFE) fuses a Wiener-optimal morphological bandpass prior with a channel-SNR-derived MMSE-Wiener weight, enabling reliable QRS extraction from severely corrupted bit streams without forward error correction hardware. Validated on the MIT-BIH Arrhythmia and PTB-XL clinical databases against the ANSI/AAMI EC57 standard, BAWT demonstrates substantial performance gains: at the clinically critical 20% SoC operating point, BAFE raises mean QRS sensitivity from 35.1% to 91.9% on MIT-BIH and from 32.9% to 82.8% on PTB-XL, with the majority of individual records satisfying the Se ≥ 95% clinical threshold. The adaptive power controller extends the critical operating window by 355% over fixed full-power operation, and a fully characterized Pareto-optimal frontier enables system designers to navigate the trade-off between battery lifetime extension (up to 144.5%) and clinical QRS detection accuracy across the full SoC range. These results establish a rigorous co-design framework for robust, power-aware wearable cardiac monitoring compliant with ANSI/AAMI EC57. .

Stacking ensemble learning for PCC voltage prediction in SEIG-ELC based off-grid micro-hydro systems

Association of dietary sodium, sugar, and trans fatty acids intake with male infertility: a case–control study among Iranian male adults

Distribution statistics apodization with cross-correlation applied to medical ultrasound imaging

The hidden role of glomalin-related soil protein in mediating soil organic carbon dynamics in response to urbanization

Abstract Urbanization can alter soil organic carbon (SOC) storage, while glomalin-related soil protein (GRSP) is crucial for SOC sequestration. However, whether and how GRSP mediates the response of SOC to urbanization remains unclear. We investigated 184 soil samples by analyzing GRSP and its C-functional structures along an urban expansion gradient using redundancy analysis and structural equation modeling (SEM) in Nanchang, China. Four C-functional groups of GRSP were characterized using infrared spectroscopy, including aromatic hydrocarbons, aliphatic hydrocarbons, proteins, and polysaccharides/nucleic acids. Average GRSP and SOC contents were 2.38 and 16.76 g·kg⁻¹, respectively, and both decreased significantly during urbanization ( p  < 0.05). Crucially, the relative abundance of the fourC-functional groups of GRSP exhibited a more pronounced decline with urbanization than that of bulk soil C-functional groups, providing a mechanistic basis for its sensitivity. SEM indicated that GRSP was significantly associated with the pathway linking urbanization and SOC, suggesting that urbanization may be indirectly associated with SOC variation through changes in soil properties (e.g., enzyme activities) and GRSP. This study suggests that GRSP content and its C-functional structure may serve as potential biochemical indicators of urbanization-associated changes in SOC dynamics. It provides a biochemical tool for assessing soil carbon vulnerability in urban ecosystems.

Application of ultrasound combined with microbubbles in conversion therapy for advanced liver cancer

A cross-varietal comparative analysis of mesocarp microstructure influencing physical and sensory attributes in date palm fruits

The quality and reliability of short videos about diabetic foot on TikTok and Bilibili: a cross-sectional study

AQP1 knockdown inhibits hepatocellular carcinoma growth and invasion and weakens the oncogenic activity of exosomal GPC1 in a mouse model

Translation, cultural adaptation, and psychometric evaluation of the NASA-TLX questionnaire among various occupational groups of Persian speakers

Abstract Mental workload is a critical determinant of employee performance, safety, and well-being. The NASA Task Load Index (NASA-TLX) is one of the most widely used instruments for assessing perceived mental workload; however, a validated Persian version for occupational settings has been lacking. This study aimed to translate, culturally adapt, and evaluate the psychometric properties of the NASA-TLX in a Persian-speaking occupational population. The original NASA-TLX was translated and culturally adapted following established guidelines. Content validity was evaluated using the Content Validity Index (CVI) and modified Kappa coefficients. Structural validity was assessed using exploratory factor analysis, and known-groups validity was examined by comparing three occupational groups. Internal consistency and test–retest reliability were evaluated using Cronbach’s alpha and intraclass correlation coefficients (ICCs). Agreement between the weighted and unweighted scoring methods was assessed using Pearson’s correlation and Bland–Altman analysis. A total of 605 participants completed the Persian NASA-TLX. Content validity was excellent (I-CVI range: 0.88–1.00; S-CVI/Ave: 0.92; modified Kappa: 0.87–1.00). Exploratory factor analysis supported a unidimensional structure, explaining 73.1% of the total variance. Known-groups analysis demonstrated significant differences in perceived workload across occupational groups. Internal consistency was excellent (α = 0.922), and test–retest reliability was high (ICC = 0.841–0.913). Bland–Altman analysis demonstrated acceptable agreement while identifying a systematic difference between the two scoring methods. The Persian NASA-TLX provides a valid and reliable instrument for assessing perceived mental workload in Persian-speaking occupational settings. The findings support its use for ergonomic assessment, occupational research, and cross-cultural workload comparisons. The unweighted scoring method may be suitable for routine field applications, whereas the weighted method remains preferable when a more detailed workload profile is required.