Browse Articles

Role of Site-Specific Iron in Fe-Doped Nickel Hydroxide Toward Water Oxidation Revealed by Spatially Resolved Imaging at the Single-Particle Level

Depth prediction of urban waterlogging based on BiTCN-GRU modeling

With China’s rapid urbanization and the increasing frequency of extreme weather events, heavy rainfall-induced urban waterlogging has become a persistent and pressing challenge. Accurately predicting waterlogging depth is essential for disaster prevention and loss mitigation. However, existing hydrological models often require extensive data and have complex structures, resulting in low prediction accuracy and limited generalization capabilities. To address these challenges, this paper proposes a hybrid deep learning-based approach, the BiTCN-GRU model, for predicting waterlogging depth in urban flood-prone areas. This model integrates Bidirectional Temporal Convolutional Networks (BiTCN) and Gated Recurrent Units (GRU) to enhance prediction performance. Specifically, the gated recurrent units (GRU) is employed for this prediction task. Bidirectional temporal convolutional network (BiTCN) can effectively capture the information features during rainfall and waterlogging depth by forward and backward convolution and use them as inputs to GRU. Experimental results demonstrate the great performance of the proposed model, achieving MAE, RMSE, and R2 values of 1.56, 3.62, and 88.31% for Minshan Road, and 3.44, 8.08, and 92.64% for Huaihe Road datasets, respectively. Compared to models such as GBDT, LSTM, and TCN-LSTM, the BiTCN-GRU model exhibits higher accuracy in predicting waterlogging depth. This hybrid model provides a robust solution for short-term waterlogging prediction, offering valuable scientific insights and theoretical support for urban waterlogging disaster prevention and mitigation.

Anthropogenic activities accelerate LULC conversion and only a sustainable development scenario is optimal for agro-pastoral ecotone development

User-centric innovation strategies for cultural creative products in China’s rural tourism

Cultural creative products play an important role in the revitalization of tourism-oriented rural areas and the preservation of cultural heritage. However, existing products fail to fully meet the diverse needs of consumers and lack a systematic design framework. This study proposes a user-centered framework aimed at guiding the innovation of cultural creative products in China’s rural tourism. During the exploration of user needs, grounded theory was used to identify three core user needs: emotional identification, personalization and uniqueness, and enhancement of quality of life. Using the KANO model, these needs were further categorized into basic needs, performance needs, and excitement needs, clarifying their priorities. In the stage of translating user needs into actionable design elements, the Analytic Hierarchy Process (AHP) was employed to assign weights to each need, and Quality Function Deployment (QFD) theory was used to construct the House of Quality, refining 14 user needs into 9 key design elements. The findings indicate a positive correlation between multifunctional design and ergonomic design, that usability and convenience are closely related to human-machine ergonomics, and that the integration of cultural symbols with emotional design and innovative forms significantly enhances the cultural value and user experience of the products. This study provides a systematic theoretical framework and practical guidance for the innovation of rural cultural creative products, with significant implications for future research and practice.

Computation of fractal dimension and power draw of regrinding depending on particle size distribution

Abstract This article uses particle size distribution to examine the fractal dimension and power draw of grinding. The tailings from dry magnetic separators, which are fed into the regrinding mill of the Gol Gohar iron ore mine, were used as the sample under study. The size of the balls, the filling particles, and the fractional ball filling have been studied as input variables, with the response variables being the fractal dimension and power draw. The results demonstrate that increasing energy usage does not increase the fractal dimension. Conversely, a direct relationship was shown between the fractal dimension and the quantity of fine particles. The self-similarity principle of fractal geometry can be used to predict the repeated process that results in variations in power draw over time. This might be the start of numerous studies that foresee the rising power draw peaks and reduce their duration by adjusting the effective parameters.

Unique osteological evidence for human-animal gladiatorial combat in Roman Britain

The spectacle of Roman gladiatorial combat captures the public imagination and elicits significant scholarly interest. Skeletal evidence associated with gladiatorial combat is rare, with most evidence deriving from written or visual sources. A single skeleton from a Roman cemetery outside of York where gladiators arguably were buried presented with unusual lesions. Investigation, including comparative work from modern zoological institutions, has demonstrated that these marks originate from large cat scavenging. Thus, we present the first physical evidence for human-animal gladiatorial combat from the Roman period seen anywhere in Europe.

ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites

Abstract This study investigates the effectiveness of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional erosion-based method, for machining carbon fiber-reinforced polymer (CFRP) composites. The focus was on analyzing key process parameters—abrasive size, feed rate, and standoff distance (SOD)—under submerged cutting conditions and their impact on material removal rate (MRR), kerf width, and surface roughness. Experimental trials were conducted, and advanced computational techniques, including Response Surface Methodology (RSM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were used for parameter optimization and predictive analysis. The results showed that submerged cutting significantly improved machining quality by reducing surface roughness and ensuring uniform kerf widths. Increasing the jet diameter in underwater conditions stabilized the nozzle, leading to smoother and more precise cuts. Among the predictive models, XGBoost demonstrated the highest accuracy and efficiency in forecasting MRR, while Random Forest and ANN provided competitive performance. The integration of RSM and machine learning (ML) techniques enabled effective optimization of machining parameters, showcasing the potential for cost-effective and high-precision CFRP machining. These findings are particularly relevant for industries like aerospace and automotive, where machining efficiency and precision are crucial.

Rocks and clay: Potters’ technological choices within the cultural dynamics of Bronze Age Kazakhstan

Through regular interactions with their neighbors, diverse groups inhabiting areas along the Inner Asian Mountain Corridor during the Bronze Age formed dynamic interregional networks that saw the proliferation and persistence of shared material cultures over vast geographic areas. In this paper we advocate for ceramics analyses that combine both macro- and micro-scale technological studies alongside those of style, in order not to lose sight of the actual people who drove defining transformations in the Bronze Age. We present a petrographic study of pottery from the Zhetysu region, southeastern Kazakhstan, to examine diachronic technological traditions with a special focus on routines of selection and raw material processing. Our results demonstrate site-specific potting technologies as well as traits that transcend both time and space across episodes of high genetic turnover in the human population.

Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination

Abstract This study aims to compare and evaluate the performance of GPT-3.5, GPT-4, and GPT-4o in the 2020 and 2021 Chinese National Medical Licensing Examination (NMLE), exploring their potential value in medical education and clinical applications. Six hundred original test questions from the 2020 and 2021 NMLE (covering five types of questions) were selected and input into GPT-3.5, GPT-4, and GPT-4o for response. The accuracy of the models across different question types and units was recorded and analyzed. Statistical methods were employed to compare the performance differences among the three models. GPT-4o demonstrated significantly higher overall accuracy than GPT-4 and GPT-3.5 (P < 0.001). In the 2020 and 2021 exams, GPT-4o achieved accuracy rates of 84.2% and 88.2%, respectively, with the highest accuracy observed in questions related to the digestive system (Unit 3), reaching 94.75%. GPT-4 showed moderate performance, while GPT − 3.5 had the lowest accuracy. Additionally, GPT-4o exhibited a clear advantage in complex question formats, such as case analysis questions (A3/A4 type) and standard matching questions (B1 type). GPT-4o outperformed its predecessors in the NMLE, demonstrating exceptional comprehension and problem-solving abilities in non-English medical examinations. This study provides important insights into the application and promotion of generative AI in medical education and clinical practice.

Semantic priming supports infants’ ability to learn names of unseen objects

Human language permits us to call to mind representations of objects, events, and ideas that we cannot witness directly, enabling us to learn about the world far beyond our immediate surroundings. When and how does this capacity emerge? To address this question, we evaluated infants at 12 and 15 months, asking whether they establish a representation of a novel noun’s meaning in the absence of any visible referents, and use this representation to identify a candidate referent when it later becomes available. During training, infants (67 12-month-olds; 67 15-month-olds) were primed with words and images of objects from a particular semantic neighborhood (e.g., fruits) and were also introduced to a novel noun (e.g., “a modi”), used to name a hidden object. During test, infants heard that noun again, this time with two unfamiliar objects present—one from the primed neighborhood (e.g., a dragon fruit) and the other from an unrelated semantic neighborhood (e.g., an ottoman). If infants can represent something about the meaning of the novel noun in the absence of a visible referent and then use such a representation when a candidate referent appears, then at test, they should prefer the object from the primed semantic neighborhood. At 15 months, infants succeeded. In contrast, 12-month-olds did not succeed on this task even after a full week of vocabulary training designed to boost the effect of priming. It is possible then that 12-month-olds’ representations of novel nouns’ meaning are not yet sufficiently rich (if any at all) to guide their choice of referent when one does appear. Together, these findings suggest that the capacity to establish a representation of a novel noun’s meaning in the absence of any visible referent and use this representation later to identify a candidate referent object emerges between 12 and 15 months.

Angiotensin converting enzyme 2 in patients with sepsis associate with comorbidities but neither with mortality nor with organ failure

Abstract High levels of circulating angiotensin converting enzyme 2 (ACE2) are associated with several chronic diseases and mortality risk. Less is known about the prognostic value of ACE2 in patients with sepsis. In the present study, we aimed to investigate the association between plasma ACE2 levels on admission to the ED and 28-day mortality, organ failure, and level of care in a prospectively recruited observational study sample. Six hundred patients with sepsis admitted to the emergency in Malmö 2013–2015 were included in the analysis. Uni- and multivariable binary logistic regression was conducted to investigate the association between ACE2 and 28-day mortality, organ failure, and level of care. Plasma ACE2 levels were increased in patients with male sex, high age, and comorbidities, including diabetes, cardiovascular disease, and cancer. Plasma ACE2 levels associated with hospitalization and intermediate care unit stay in univariate but not multivariate analysis. Plasma ACE2 did neither associate with 28-day mortality, OR 1.19 (95% CI 0.86–1.65, p = 0.29), nor with organ failure, OR 1.08 (95% CI 0.73–1.56. p = 0.72). Future studies investigating the dynamics of circulating ACE2 levels in patients with sepsis longitudinally are warranted.

Preventing the preventable: Assessing the burden of incessant caesarean deliveries in select Indian states using NFHS-5

The World Health Organization (WHO) recommends that life-saving Caesarean sections (CS) should account for 10–15% of deliveries. Southern states of India have good obstetric facilities and better public health systems than other parts of India. However, CS rates in select states are considerably higher. This study examines the prevalence and determinants of preventable CS among mothers in select Indian states, specifically the states that have high institutional deliveries (>95%) viz. Andhra Pradesh, Kerala, Karnataka, and Tamil Nadu. It further compares the complications faced by CS and vaginal delivery cases. Data has been derived from a cross-sectional survey, the National Family Health Survey-5. Bivariate and Logistic regression analyses were used. The main outcome measured is Preventable C-sections, defined as per conditions in Robson’s criteria of 10-group classification based on obstetric characteristics for a woman. Results show that the preventable CS rates in these selected states were much higher than at the national level. Key correlates were higher education and middle socioeconomic status. The study highlights the need for policy reforms, maternal education, and healthcare system improvements to reduce unnecessary CS in select states of India, where the health system is exceptionally good but , have another health burden in form of demand for unnecessary caesarean deliveries.

Publisher Correction: IVC treatment between primary and second TURBT may improve the prognosis of high-risk NMIBC patients receiving BCG treatment

Megafauna mobility: Assessing the foraging range of an extinct macropodid from central eastern Queensland, Australia

Understanding the factors that influence the geographic range of extinct megafaunal species is crucial for reconstructing their ecology and extinction dynamics. For extant herbivores, it has been demonstrated that large body mass provides the potential for greater geographic range. Allometric scaling relationships are observed in placental mammals but have not been well-established for marsupials, in particular, extinct marsupial megafauna. Here, we employ a phylogenetic generalised least squares regression model using extant macropodids to estimate home ranges for individuals from the extinct genus Protemnodon. The regression model predicts a mean home range of 11.6 ±  5.8 km2 This prediction, centred on Mt Etna caves, incorporates several distinct geological features with variable, known 87Sr/86Sr isotope ratios. Fossil Protemnodon individuals recovered from cave deposits at Mt Etna returned 87Sr/86Sr values similar to that of the host limestone, in which the cave systems formed, and the broader Mount Alma Formation. This similarity suggests that individuals foraged close to where they were fossilised, indicating a smaller home range than predicted. Smaller home ranges for individuals with a large body-mass were unexpected, attributed to a unique combination of individual behaviour, diet and/or locomotion regime within stable rainforest environments. Our results suggest that, foraging ranges in marsupial megaherbivores may be more strongly associated with environmental quality rather than body mass. New in-situ uranium-thorium and single-grain TT-OSL ages refine, and are in agreement with, previous interpretations of chronology, indicating that rainforest-adapted fauna persisted at Mt Etna until at least 280 ka. We propose that small home ranges in a stable environment, such as rainforests, predisposed these megafauna macropodids to extinction after 280ka, driven by an increasingly dry and unstable climate. Our results underscore the need for regionally specific biologies of individuals, populations and species when considering extinction pathways for Pleistocene fauna.

A combined model method was used to identify the main influencing factors of soil heavy metal pollution sources in Qian river, China

Does digital literacy affect farmers’ adoption of agricultural social services? An empirical study based on China Land Economic Survey data

Agricultural social services (ASS) are crucial in alleviating resource constraints and advancing agricultural modernization. Using the data derived from the China Land Economic Survey (CLES2022), this study empirically investigates how digital literacy influences farmers’ adoption of ASS, employing both Probit and Propensity score matching (PSM) models. Additionally, it explores the mediating roles of long-term production vision and part-time employment degrees in this relationship. The findings are: (1) digital literacy exhibited a statistically significant positive effect on farmers’ adoption of ASS at the 1% significant level. Moreover, this impact varied among participants in technology training, different education levels, and varying levels of risk preference; (2) long-term production vision and part-time employment degrees act as mediators, enhancing the positive impact of digital literacy on farmers’ adoption of ASS. Based on these findings, recommendations have been developed to improve farmers’ digital literacy, promote the adoption of ASS, and enhance farmers’ long-term production vision as well as degree of part-time.

Large field-of-view event-mode camera for high-precision epithermal neutron resonance imaging

Abstract A large-area event-mode camera system coupled with a $$^{6}$$ LiF-ZnS:Ag scintillator is applied for neutron resonance imaging (NRI) on the energy-resolved neutron imaging (ERNI) flight path, also known as Flight Path 5 (FP5), at the Los Alamos Neutron Science Center (LANSCE). This novel neutron imaging system, featuring a 120 $$\times$$ 120 mm $$^2$$ field of view, efficiently captures resonance information across the entire image in a single acquisition, significantly reducing beam time requirements compared to conventional energy-resolved neutron imaging systems. High-quality neutron radiographs with enhanced spatial resolution are achieved through the reconstruction of neutron events based on observations of individual photons emitted from the scintillator. The system demonstrates reduced background through neutron/gamma discrimination capabilities while maintaining sharpness across a large fields of view. In the measurements presented here, a spatial resolution of approximately 340 $$\mu$$ m was achieved using center-of-gravity photon cluster centroiding. We demonstrate the system’s capability for quantitatively determining isotopic distributions in various thin samples, as well as automatically reconstructing complex scenes with overlapping resonances from diverse samples. These results are obtained using standard data analysis tools, despite the relatively slow $$^{6}$$ LiF-ZnS:Ag scintillator, which may not be optimal for absorption resonance detection. The capabilities demonstrated here offer a valuable, versatile, and cost-effective solution for high spatial and temporal resolution, large field-of-view energy-resolved neutron imaging, with potential applications across various scientific and industrial domains.

Evaluation of intake of aged garlic extract and organosulfur compounds on progressive hearing loss in DBA/2J mice

Garlic is rich in organosulfur compounds with high antioxidant capacity and is known to have various health benefits. Aged garlic is a particularly effective source of these active compounds because it contains fewer toxic components. This study evaluated the effects of S-allyl cysteine (SAC), a major functional organosulfur compound in garlic, and aged garlic extract (AGE) on the suppression of progressive hearing loss. SAC and AGE were dissolved in drinking water at 1% (w/w) and administered to DBA/2J mice, a model of early progressive hearing loss, for 12 weeks, starting at 4 weeks of age. While the results revealed a trend toward weight loss in the SAC group, the weight of the AGE group was comparable to that of the control group, and no adverse events were observed in either group. The hearing ability of the mice was measured using auditory brainstem responses at 4, 8, 12, and 16 weeks of age. Hearing loss at 8 and 16 kHz progressed over the 12-week period, with neither sample inhibiting hearing loss. In contrast, another organosulfur compound, N-acetylcysteine (NAC), administered in 1% w/w drinking water and evaluated for hearing loss over time, significantly suppressed hearing loss progression in DBA/2J mice. These results indicate that the NAC and SAC differ in their ability to prevent hearing loss and demonstrate that the inhibitory effects of functional food components on hearing loss can be evaluated over a short period in DBA/2J mice.

PoulTrans: a transformer-based model for accurate poultry condition assessment

Abstract Recent advances in deep learning have significantly enhanced the accuracy of poultry image recognition, particularly in assessing poultry conditions. However, developing intuitive decision support tools remain a significant challenge. To address this, we present PoulTrans, an innovative image captioning framework that leverages a Convolutional Neural Network (CNN) integrated with a CSA_Encoder-Transformer architecture to generate detailed poultry status reports. This model incorporates visual features extracted by CNNs into the Channel Spatial Attention Segmentation Encoder (CSA_Encoder), which produces segmented channel and spatial attention outputs. To optimize multi-level attention and improve the semantic precision of the status descriptions, we introduced a Channel Spatial Memory-Guided Transformer (CSMT) and a novel PS-Loss function. The performance of PoulTrans was tested on the PSC-Captions dataset, achieving top scores of 0.501, 0.803, 4.927, 0.608, and 1.882 for the BLEU-4, ROUGE-L, CIDEr, SPICE, and Sm metrics, respectively. Comprehensive analyses and experiments have validated the effectiveness and reliability of our model, providing advanced tools for automated poultry status generation and enhancing the digital experience for poultry farmers. Our code is available at: https://github.com/kong1107800/PoulTrans.

From vulnerability to duplicity: Examining the connection between childhood adversity and deception

Deception research has traditionally evaluated how individual differences like personality traits and demographics correlate with lying. However, the establishment of adverse childhood experiences (ACEs) as an individual difference that also links to deception remains underexplored. To this end, the present study (N = 784 students) investigated the relationship between ACEs and deception in adulthood. Results indicated that individuals with more (versus less) adverse childhood experiences, particularly those involving maltreatment and victimization, reported more daily white and big lies, independent of aversive personality traits like narcissism and Machiavellianism. Consistent with other studies on individual differences and deception, the effect sizes were small, but systematic. Together, these findings support the dispositional honesty hypothesis, indicating that foundational childhood experiences and events can shape or signal deceptive behavior. Generally, the study contributes to our underexamined knowledge base of the developmental antecedents of lying, emphasizing the role that adversity plays during childhood to influence deceptive behavior beyond commonly studied personality traits.