Browse Articles

Rubber planting and deforestation

State-to-state dynamics of H + LiHe+ (<i>v</i> = 0, <i>j</i> = 0) → LiH+ + He reaction

This work reports the new and up-to-date dynamical investigation of an exoergic reaction, H + LiHe+ (v = 0, j = 0) → LiH+ + He, carried out using the Coriolis coupled time-dependent quantum mechanical and quasi-classical trajectory methods up to the collision energy of 1.0 eV. The potential energy surface used in this work was recently developed in our group [Rawat et al., J. Chem. Phys. 161, 124308 (2024)]. Various reaction observables, such as reaction probability, integral cross sections (ICSs), rate constants, and energy disposal mechanism, are investigated at both state-selected and state-to-state levels. Attempts are made to analyze the reaction mechanism by combining the observed results. Total reaction probability for different J (three-body total angular momentum) collisions and ICSs at state-selected and state-to-state levels show rich resonances, a consequence of the well present in the underlying potential energy surface. The low-energy (∼0.074 96 eV) collision-induced dissociation channel affects the reaction, showing visible effects on reaction observables. These results are significantly different from the previously reported results, which likely improves the understanding of lithium chemistry in the early universe.

Multi-sensor remote sensing captures geometry and slow-to-fast sliding transition of the 2017 Mud Creek landslide

Abstract Landslides pose a significant hazard worldwide. Despite advances in landslide monitoring, predicting their size, timing, and location remains a major challenge. We revisit the 2017 Mud Creek landslide in California using radar interferometry, pixel tracking, and elevation change measurements from satellite and airborne radar, lidar, and optical data. Our analysis shows that pixel tracking of optical imagery captured the transition from slow motion to runaway acceleration starting ~ 1 month before catastrophic failure—an acceleration undetected by satellite InSAR alone. Strain rate maps revealed a new slip surface formed within the landslide body during acceleration, likely a key weakening mechanism. Failure forecast analysis indicates the acceleration followed a hyperbolic trend, suggesting failure time could have been predicted at least 6 days in advance. We also inverted for the landslide thickness during the slow-moving phase and found variations from &lt; 1 to 36 m. While thickness inversions provide important first-order information on landslide size, more work is needed to better understand how landslide subsurface properties and deforming volumes may evolve during the transition from slow-to-fast motion. Our findings underscore the need for integrated remote sensing techniques to improve landslide monitoring and forecasting. Future advancements in operational monitoring systems and big data analysis will be critical for tracking slope instability and improving regional-scale failure predictions.

Seed quality drives grain yield in Ethiopian and Senegalese sorghum: Insights from machine learning

Accurately predicting grain yield remains a major challenge in sorghum breeding, particularly across genetically and geographically diverse germplasm. To address this, we applied a phenotype-informed machine learning (PIML) framework to analyze nine phenotypic traits in 179 Ethiopian and Senegalese accessions. Using hierarchical clustering and oversampling with ADASYN, we achieved high classification accuracy (0.99) for phenotypic group assignment. Grain yield prediction was most effective with a Neural Boosted model (NTanH(3)NBoost(8)), achieving a mean R2  of 0.36 and RASE (equivalent to RMSE) of 4.87. Feature importance analysis consistently identified seed weight and germination rate as the strongest predictors of grain yield, while disease resistance traits showed limited predictive value. These findings suggest that early selection based on seed quality traits may provide a practical strategy for improving sorghum yield under field conditions, especially in resource-limited environments.

Exact quantum dynamics of methanol: Full-dimensional <i>ab initio</i> potential energy surface of spectroscopic quality and variational vibrational states

The methanol molecule is a sensitive probe of astrochemistry, astrophysics, and fundamental physics. The first-principles elucidation and prediction of its rotational–torsional–vibrational motions are enabled in this work by the computation of a full-dimensional, ab initio potential energy surface (PES) and numerically exact quantum dynamics. An active-learning approach is used to sample explicitly correlated coupled-cluster electronic energies, and the datapoints are fitted with permutationally invariant polynomials to obtain a spectroscopic-quality PES representation. Variational vibrational energies and corresponding tunneling splittings are computed up to the first overtone of the C–O stretching mode by direct numerical solution of the vibrational Schrödinger equation with optimal internal coordinates and efficient basis and grid truncation techniques. As a result, the computed vibrational band origins finally agree with experiment within 5 cm−1, allowing for the exploration of the large-amplitude quantum mechanical motion and tunneling splittings coupled with the small-amplitude vibrational dynamics. These developments open the route toward simulating rovibrational spectra used to probe methanol in outer space and in precision science laboratories, as well as for probing interactions with external magnetic fields.

Sustainable water treatment using thermally stable natural clay: dual adsorption–thermolysis approach for organic pollutants and nitrate removal

Enhancement of antiphotoaging properties of Cannabis sativa stem water extracts by fermentation with Lacticaseibacillus casei

Skin photoaging, driven primarily by UVB radiation, leads to collagen degradation and oxidative stress, contributing to the visible signs of aging, such as wrinkles and loss of skin elasticity. This process is mediated by the upregulation of matrix metalloproteinase-1 (MMP-1), which is triggered by reactive oxygen species, and the activation of photoaging-related signaling pathways, including ERK, JNK, and p65. In the present study, we evaluated the antiphotoaging potential of fermented and non-fermented Cannabis sativa stem water extracts, focusing on their ability to suppress MMP-1 expression and reduce oxidative stress in UVB-irradiated human dermal fibroblasts. Unlike previous studies that have primarily focused on leaves or flowers, our study highlights the stems of C. sativa as a novel and underutilized source of bioactive compounds for skin protection. Using Lacticaseibacillus casei for fermentation, we observed enhanced bioactivity in the fermented extracts, particularly in terms of a 6.6% greater inhibition of MMP-1 expression and 68.3% increased flavonoid content, compared to the non-fermented extracts. Fermented water extract demonstrated the most potent suppression of UVB-induced signaling pathways and collagen breakdown. Our findings suggest that fermentation enhances the antiphotoaging properties of C. sativa stems, offering a promising potential for natural, plant-based skin care solutions aimed at preventing UVB-induced skin aging.

AIASSE—<i>Ab</i> <i>initio</i> augmented structure solving engine

We have developed a hybrid methodology that self-consistently integrates empirical potential-based refinement of total scattering data with first-principles density functional theory-based molecular dynamics calculations. The aim is to provide a holistic and coherent description of disordered materials, spanning from the bulk, macroscopic scale probed in diffraction experiments down to the atomic and electronic levels. In this study, we present this new methodology as implemented within a software package, revisiting previous measurements on dense fluid krypton, SiO2 glass, low-density amorphous ice, and water–methanol liquid mixtures.

Ameliorative effects of coriander extract supplementation in mitigating nickel toxicity in grass carp (Ctenopharyngodon idella)

Persistence with daily growth hormone among children and adolescents with growth hormone deficiency in Japan

Background Pediatric growth hormone deficiency (pGHD) is treated with daily somatropin (recombinant human growth hormone) injections. High rates of discontinuation and poor adherence to treatment, which are associated with worse growth outcomes, have been documented previously, for example in the US and Europe. Discontinuation of somatropin has not yet been evaluated using real-world data in Japan. Objectives To describe discontinuation of, and persistence to, daily somatropin treatment among children with pGHD in Japan. Methods This was a retrospective cohort study of children (≥3 and &lt;16 years old) who were prescribed somatropin, using 2 Japan-based databases, Japan Medical Data Center (JMDC) and Medical Data Vision (MDV). Children were required to have ≥1 prescription for somatropin (first prescription = index date) within each study period (1 January 2002–30 September 2021 for JMDC and 1 January 2009–31 October 2021 for MDV) and ≥1 GHD diagnosis code without a somatropin prescription during the 6-months pre-index period. Children were required to be continuously enrolled in the database ≥6 months preceding and ≥3 months following index date. Children were followed for up to 48 months post-index. Early persistence was defined as the proportion of children with ≥1 refill of somatropin subsequent to the initial prescription. Discontinuation was defined as the first observation of a gap in therapy (using &gt;60, &gt; 90, and 120-day gap thresholds) between successive somatropin prescription fill dates. Persistence was defined as continuous refills of somatropin with no gaps in therapy. Time to discontinuation/non-persistence was evaluated using Kaplan-Meier methods, and Cox proportional hazards models identified predictors of time to discontinuation. This analysis utilized de-identified patient data from 2 large, Japanese-based retrospective databases; as such this study does not meet the requirements for institutional review board (IRB) review. Results Among the children included in this study (JMDC N = 452, MDV N = 573), most were male (JMDC 64.8%, MDV 60.0%). Mean age (standard deviation) was 8.8 (3.6) years in JMDC and 7.5 (3.6) years in MDV. Early persistence was high across both cohorts (JMDC 91.2%, MDV 83.4%). Using the 90-day gap definition for discontinuation, a sizable proportion of children discontinued over the follow-up period: JMDC 19% at 12 months, 35% at 48 months; and MDV 33% at 12 months, 54% at 48 months. Fewer discontinuations were observed with the 120-day gap definition (~16% at 48 months in JMDC, ~ 28% at 48 months in MDV) and more were observed with the 60-day gap definition (~67% at 48 months in JMDC, ~ 83% at 48 months in MDV). No meaningful predictors of discontinuation were identified. Conclusions Despite high early persistence with somatropin, many children with pGHD in Japan were increasingly non-persistent over time: at 48 months post-index, at least 16% of children discontinued therapy, using the JDMC database and the most conservative measure of gap allowance. These results suggest a need for new strategies to support somatropin medication use over time among children with pGHD in Japan.

Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation

Abstract Identifying syndesmotic instability in ankle fractures using conventional radiographs is still a major challenge. In this study we trained a convolutional neural network (CNN) to classify the fracture utilizing the AO-classification (AO-44 A/B/C) and to simultaneously detect syndesmosis instability in the conventional radiograph by leveraging the intraoperative stress testing as the gold standard. In this retrospective exploratory study we identified 700 patients with rotational ankle fractures at a university hospital from 2019 to 2024, from whom 1588 digital radiographs were extracted to train, validate, and test a CNN. Radiographs were classified based on the therapy-decisive gold standard of the intraoperative hook-test and the preoperatively determined AO-classification from the surgical report. To perform internal validation and quality control, the algorithm results were visualized using Guided Score Class activation maps (GSCAM).The AO44-classification sensitivity over all subclasses was 91%. Furthermore, the syndesmosis instability could be identified with a sensitivity of 0.84 (95% confidence interval (CI) 0.78, 0.92) and specificity 0.8 (95% CI 0.67, 0.9). Consistent visualization results were obtained from the GSCAMs. The integration of an explainable deep-learning algorithm, trained on an intraoperative gold standard showed a 0.84 sensitivity for syndesmotic stability testing. Thus, providing clinically interpretable outputs, suggesting potential for enhanced preoperative decision-making in complex ankle trauma.

A new cow identification method using near-infrared spectral measurements and main components of raw milk features

Recent advances in cow identification have been instrumental in enhancing understanding of disease progression, optimizing vaccination strategies, improving production management, ensuring animal traceability, and facilitating ownership assignment. Cow identification and tracking involve the precise recognition of individual cows and their products through unique identifiers or markers. Traditional methods like computer vision, ear tags, branding, tattooing, microchips, and other electrical methods have been widely employed for cow identification and tracking over an extended period of time. However, these methods are prone to reliability issues caused by external factors such as physical damage, tag loss, weather-induced fading or damage, and the need for a software-based management system with RFID, which may not always be satisfactory for identifying cows. Merging near-infrared spectroscopy and routinely collected main components of raw milk (fat, protein, lactose, urea, and somatic cell count) with artificial intelligence offers a non-invasive, data-driven approach for cow identification, potentially increasing applicability in farm environments where such milk data are already part of routine monitoring. In this study, we presented an alternative approach to cow identification utilizing near-infrared spectral measurements alongside laboratory reference values for the main components of raw milk. In order to test our proposed method, we used a publicly available and newly released dataset of 1224 different measurements collected from 41 cows over a period of 8 weeks. Depending on the considered measurements and number of cows, the Naïve Bayes, Decision Tree, and Support Vector Machines classifiers achieved classification accuracy rates of between 69.23%−98.63%, 61.87%−100%, and 58.53%−97.26%, respectively. We believe that the proposed method has great potential to be an alternative way for cow identification applications.

Injured plants use gaseous cues to initiate repair of their outer layers

DGS-Yolov7-Tiny: a lightweight pest and disease target detection model suitable for edge computing environments

Radiographic criteria in developmental dysplasia of the hip in late infancy, inter and intrareader agreement

Introduction Developmental Dysplasia of the hip (DDH) is a common pediatric disorder screened for by antero-posterior (AP) pelvic radiographs in infants aged between 4–9 months. We chose from the radiographic indicators commonly used in the diagnosis the acetabular index, the Shenton line and the ossification and symmetry of the femoral head to assess for their reliability and variability among readers. In addition, this study aimed to obtain the mean age of appearance of the ossification center of the femoral head in the Lebanese population. Methods 149 pelvic AP radiographs of children between 4 and 9 months of age were collected. The criteria were assessed by three experienced readers: one orthopedic surgery fellow resident, one first-year and one second-year orthopedic surgery residents twice separated by a three-month interval. Results The bivariate analyses found a difference in the right Acetabular angle and left Acetabular angle significantly in the first- and second-year resident with a p &lt; 0.01. No significant difference was found when comparing the readings of each reader independently for the other variables or with the fellow. We found a significant difference p = 0.047 when comparing the readings of the first-year resident and the fellow of the right AI. Whereas the left AI readings revealed significant differences between the fellow and the second-year resident (p = 0.008) and between the first and second-year residents (p &lt; 0.001). Inter and intrareader consistency was high for the Shenton line rupture and the appearance of the ossification center on the femoral head but none of the parameters proved sufficient to significantly be associated with an acetabular angle&gt; 30°. The average age of ossification center appearance in the Lebanese population was determined to be 5.57 months, aligning with global averages. Conclusion These findings call for a diagnostic approach that integrates multiple parameters and focuses on the importance of standardized training to enhance the consistency of radiographic assessments. Further investigations should aim to establish more precise protocols and evaluate the diagnostic strength of individual parameters.

Calving-driven fjord dynamics resolved by seafloor fibre sensing

Abstract Interactions between melting ice and a warming ocean drive the present-day retreat of tidewater glaciers of Greenland1–3, with consequences for both sea level rise4 and the global climate system5. Controlling glacier frontal ablation, these ice–ocean interactions involve chains of small-scale processes that link glacier calving—the detachment of icebergs6—and submarine melt to the broader fjord dynamics7,8. However, understanding these processes remains limited, in large part due to the challenge of making targeted observations in hazardous environments near calving fronts with sufficient temporal and spatial resolution9. Here we show that iceberg calving can act as a submarine melt amplifier through excitation of transient internal waves. Our observations are based on front-proximal submarine fibre sensing of the iceberg calving process chain. In this chain, calving initiates with persistent ice fracturing that coalesces into iceberg detachment, which in turn excites local tsunamis, internal gravity waves and transient currents at the ice front before the icebergs eventually decay into fragments. Our observations show previously unknown pathways in which tidewater glaciers interact with a warming ocean and help close the ice front ablation budget, which current models struggle to do10. These insights provide new process-scale understanding pertinent to retreating tidewater glaciers around the globe.

Cortical spectral dynamics of vibrotactile frequency processing

Hypogonadism and its associated factors among adult male type 2 diabetes mellitus patients at the university of gondar comprehensive specialized hospital, Northwest Ethiopia 2024: A comparative cross-sectional study

Introduction Hypogonadism is an established complication in male patients with type 2 diabetes mellitus, potentially exacerbating metabolic dysregulation and impairing quality of life. Global studies have documented an elevated prevalence of hypogonadism among diabetic individuals. However, there remains a scarcity of data regarding the prevalence and contributing factors of hypogonadism in patients with type 2 diabetes mellitus in Ethiopia, particularly in the Gondar region. Methods and Materials This comparative cross-sectional study included 330 participants (165 with type 2 diabetes mellitus and 165 age-matched controls), selected via systematic random sampling from June 27 to August 20, 2024. Data were collected through structured questionnaires and 5 mL blood samples were obtained by laboratory technologist. Analysis using SPSS version 25 involved descriptive statistics, independent t-tests, and binary logistic regression to identify factors associated with hypogonadism, with significance set at p &lt; 0.05. Result The mean age of participants was 57 ± 8.9 years in the type 2 diabetes mellitus (T2DM) group and 55 ± 8.6 years among controls. Hypogonadism was identified in 30.3% of individuals with T2DM, compared to 7.2% in the control group. Multivariable logistic regression revealed that T2DM, elevated fasting blood glucose, advancing age, dys-regulated high-density lipoprotein levels, and obesity were independently associated with increased odds of hypogonadism. Conclusion and Recommendation The prevalence of hypogonadism was markedly higher in individuals with type 2 diabetes mellitus compared to the control group, concomitant with a significantly lower mean testosterone level. Multivariate analyses identified fasting blood glucose, high-density lipoprotein levels, advanced age, the presence of diabetes, and obesity as independent risk factors for hypogonadism. Notably, modifiable factors such as obesity, in conjunction with key metabolic indicators, underscore potential targets for intervention to alleviate the burden of hypogonadism in diabetic patients.

Electromagnetic induction heating of polymer nanocomposites: a computational study on design parameters

Wear state identification of reciprocating sliding friction Pairs with frictional vibration

Real-time monitoring of the wear state of reciprocating sliding friction pairs has long been a challenging issue. To address this problem, this paper innovatively proposes a new method of constructing feature vectors based on the fractal parameters of frictional vibration signals and employing a nonlinear support vector machine to identify different wear states. Three typical wear states, namely running-in wear, normal wear, and severe wear, were designed by adjusting the amount of lubricating oil and distinguished by variations in the friction coefficient. Unlike conventional time-frequency or statistical features, our approach uniquely employs multifractal spectrum parameters to characterize wear states. The research results demonstrate that this method achieves recognition accuracies exceeding 90% for all three wear states in 10-fold cross-validation, indicating the effectiveness of the nonlinear support vector machine in realizing the recognition of different wear states of reciprocating sliding friction pairs. This achievement not only provides a new technical approach for online monitoring of wear states but also offers a valuable reference for the application of nonlinear signal analysis in other fields.