Search Results (62,744)

A geodesic or geodetic line on a sphere is called the orthodrome. Research has shown that the orthodrome can be defined in a large number of ways. This article provides an overview of various definitions of the orthodrome. We recall the definitions of the orthodrome according to the greats of geodesy, such as Bessel and Helmert. We derive the equation of the orthodrome in the geographic coordinate system and in the Cartesian spatial coordinate system. A geodesic on a surface is a curve for which the geodetic curvature is zero at every point. Equivalent expressions of this statement are that at every point of this curve, the principal normal vector is collinear with the normal to the surface, i.e., it is a curve whose binormal at every point is perpendicular to the normal to the surface, and that it is a curve whose osculation plane contains the normal to the surface at every point. In this case, the well-known Clairaut equation of the geodesic in geodesy appears naturally. It is found that this equation can be written in several different forms. Although differential equations for geodesics can be found in the literature, they are solved in this article, first, by taking the sphere as a special case of any surface, and then as a special case of a surface of rotation. At the end of this article, we apply calculus of variations to determine the equation of the orthodrome on the sphere, first in the Bessel way, and then by applying the Euler–Lagrange equation. Overall, this paper elaborates a dozen different approaches to orthodrome definitions. Full article

Reactive oxygen species (ROS) are often seen solely as harmful byproducts of oxidative metabolism, yet evidence reveals their paradoxical roles in both promoting and inhibiting cancer progression. Despite advances, precise context-dependent mechanisms by which ROS modulate oncogenic signaling, therapeutic response, and tumor microenvironment dynamics remain unclear. Specifically, the spatial and temporal aspects of ROS regulation (i.e., the distinct effects of mitochondrial versus cytosolic ROS on the PI3K/Akt and NF-κB pathways, and the differential cellular outcomes driven by acute versus chronic ROS exposure) have been underexplored. Additionally, the specific contributions of ROS-generating enzymes, like NOX isoforms and xanthine oxidase, to tumor microenvironment remodeling and immune modulation remain poorly understood. This review synthesizes current findings with a focus on these critical gaps, offering novel mechanistic insights into the dualistic nature of ROS in cancer biology. By systematically integrating data on ROS source-specific functions and redox-sensitive signaling pathways, the complex interplay between ROS concentration, localization, and persistence is elucidated, revealing how these factors dictate the paradoxical support of tumor progression or induction of cancer cell death. Particular attention is given to antioxidant mechanisms, including NRF2-mediated responses, that may undermine the efficacy of ROS-targeted therapies. Recent breakthroughs in redox biosensors (i.e., redox-sensitive fluorescent proteins, HyPer variants, and peroxiredoxin–FRET constructs) enable precise, real-time ROS imaging across subcellular compartments. Translational advances, including redox-modulating drugs and synthetic lethality strategies targeting glutathione or NADPH dependencies, further highlight actionable vulnerabilities. This refined understanding advances the field by highlighting context-specific vulnerabilities in tumor redox biology and guiding more precise therapeutic strategies. Continued research on redox-regulated signaling and its interplay with inflammation and therapy resistance is essential to unravel ROS dynamics in tumors and develop targeted, context-specific interventions harnessing their dual roles. Full article

Almost one in three firefighters develop mental health disorders at some point during their careers, a rate double that in the general population. Frequent exposures to potentially traumatic situations can contribute to symptoms of these disorders, two of the most common being depression and post-traumatic stress disorder (PTSD). While various psychological interventions have been implemented among this group, reports of their effectiveness include mixed results. To this end, the current study endeavored to test the effectiveness of a 12-week intervention combining occupationally-tailored high-intensity functional training (HIFT) and psychological resilience training (RES) in reducing depressive and post-traumatic stress symptoms (PTSSs), as well as increasing psychological resilience and mental wellbeing, in career firefighters. Thirty career firefighters completed four mental health measurements over 17 weeks while anthropometrics and physical performance (i.e., number of stations completed in 20 min during an eight-station simulated job-task circuit workout [T-CAC]) were measured pre- and post-intervention. Pre to post comparisons were made via repeated-measures t-tests. Significant mean differences were observed for T-CAC stations completed, PTSSs, and psychological resilience between pre- and post-intervention. In future interventions, researchers should actively engage firefighters, maximize integration with daily operations, and employ culturally-relevant practices to explore the links between physical and mental health. Full article

In general, the silkworm, Bombyx mori, has a diapause trait in its eggs. Therefore, transgenic silkworm can be produced by embryonic microinjection using eggs laid by a non-diapause strain in B. mori. In this study, we performed microinjection using eggs of diapause strains which have good characteristics for industrial use, such as a big cocoon, thin and smooth silk, and tolerance against disease due to the growing industrial use of transgenic silkworms. For the conversion of egg diapause traits from diapause to non-diapause types, we used anti-serum against the diapause hormone of B. mori (BmDH), which was injected into maternal pupae, producing non-diapause eggs at a high rate. Finally, we attempted microinjection using three diapause strains with different voltinism (i.e., number of generations of an organism in a year) and were able to successfully produce transgenic silkworms in all three of them, demonstrating that our method is applicable to a wide range of silkworm strains with a diapause trait. Full article

Background/Objectives: Family caregivers of individuals with schizophrenia often face considerable psychological and physical strain due to the complexity of caregiving. Although psychoeducation has demonstrated benefits in alleviating this burden, its provision via telenursing remains underexplored in Saudi Arabia. This study evaluated the effect of a psychoeducational program delivered via telenursing on reducing caregiver burden. Methods: A quasi-experimental design was used with 60 caregivers from a tertiary mental health hospital in northern Saudi Arabia, who were divided equally into intervention and control groups. The intervention group participated in a structured four-week psychoeducational program via Zoom, while the control group received routine care. Caregiver burden was assessed using the Family Burden Interview Schedule (FBIS), a validated tool designed to measure the objective and subjective burden experienced by family members caring for individuals with mental illness. The FBIS was administered before and three months after the intervention. The statistical analysis included independent and paired t-tests and ANOVA. Results: The pre-intervention scores showed no significant differences, confirming baseline equivalence. The post-intervention scores showed a significant reduction in burden among the intervention group (p < 0.001), while no meaningful change occurred in the control group. Additionally, a lower burden was associated with higher education, sufficient income (i.e., the caregiver’s perception of being able to meet essential household expenses without financial strain), strong family support, and absence of caregiver illness. Conclusions: These findings suggest that psychoeducation through telenursing is an effective strategy for reducing caregiver burden and improving support accessibility, particularly for those in remote areas. Full article

This work presents Quasar, an open-source tool developed to boost the characterization of how variability effects impact radiation sensitivity in digital circuits. Quasar receives a SPICE netlist as input and automatically determines robustness metrics, such as the critical Linear Energy Transfer, for every configuration in which a Single Event Transient fault can propagate an error. The tool can handle ranges from small basic cells to median multi-gate circuits in a few seconds, speeding up the traditional fault injection mechanism based on a large number of electrical simulations. The tool’s workflow explores logical masking to reduce the design space exploration, i.e., reducing the necessary number of electrical simulations, as well as regression methods to speed up variability evaluations. Quasar already has shown the potential to provide useful results, and a prototype has also been published. This work presents a more polished and complete version of the tool, one that optimizes the tool’s search process and allows not only for a fast evaluation of the radiation robustness of a circuit, but also for an analysis of how fabrication process metrics impact this robustness, such as Work Function Fluctuation. Full article

Most research on domestic dog (Canis familiaris) behavior has focused on pets with restricted movement. However, free-ranging dogs exist in diverse cultural contexts globally, and their interactions with humans are less understood. Tourists can facilitate unrestricted dog movement into wilderness areas, where they may negatively impact wildlife. This study investigated which stimuli—namely, voice, touch, or food—along with inherent factors (age, sex, sociability) motivate free-ranging dogs to follow a human stranger. We measured the distance (up to 600 m) of 129 free-ranging owned and stray dogs from three villages in southern Chile as they followed an experimenter who presented them one of the above stimuli or none (control). To evaluate the effect of dog sociability (i.e., positive versus stress-related or passive behaviors), we performed a 30 s socialization test (standing near the dog without interacting) before presenting a 10 s stimulus twice. We also tracked whether the dog was in the company of other dogs. Each focus dog was video-recorded and tested up to three times over five days. Generalized linear mixed-effects models revealed that the food stimulus significantly influenced dogs’ motivation to follow a stranger, as well as a high proportion of sociable behaviors directed towards humans and the company of other dogs present during the experiment. Juveniles tended to follow a stranger more than adults or seniors, but no effects were found for the dog’s sex, whether an owner was present, the repetition of trials, the location where the study was performed, or for individuals as a random variable. This research highlights that sociability as an inherent factor shapes dog–stranger interactions in free-ranging dogs when food is given. In the context of wildlife conservation, we recommend that managers promote awareness among local communities and tourists to avoid feeding dogs, especially in the context of outdoor activities close to wilderness. Full article

Over the last decade, polymers have attracted increasing attention for the fabrication of electronic devices due to the innovative results that can be achieved using additive manufacturing (AM) processes. Intrinsically conductive polymers are commonly used to obtain flexible and stretchable devices. They also enable the customisation of electronic devices when processed through AM. However, their main limitation is the reduction in electrical conductivity under mechanical deformation, such as bending. Extrinsically conductive nanocomposites, incorporating conductive fillers into polymer matrices, demonstrate the ability to retain electrical conductivity even following repeated bending, presenting a promising solution to the limitations of intrinsically conductive polymers. However, a gap remains in optimising their processing conditions for diverse 3D printing technologies. Moreover, fillers should be carefully selected according to the application’s specific needs. Dielectric polymers are also very promising for various electronic applications, but they are less investigated and have lower visibility than their conductive counterparts. This review presents three classes of polymer materials, i.e., intrinsically and extrinsically conductive polymers and insulators, discussing their advantages, drawbacks, and applications for 3D printing in electronics. This overview concludes with assessing future investigation areas needed to unlock the possibilities of 3D-printed polymers in electronics. Full article

Most public seismic recordings, sampled at hundreds of Hz, tend to be unlabelled, i.e., not catalogued, mainly because of the sheer volume of samples and the amount of time needed by experts to confidently label detected events. This is especially challenging for very low signal-to-noise ratio microseismic events that characterise landslides during rock and soil mass displacement. Whilst numerous supervised machine learning models have been proposed to classify landslide events, they rely on a large amount of labelled datasets. Therefore, there is an urgent need to develop tools to effectively automate the data-labelling process from a small set of labelled samples. In this paper, we propose a semi-supervised method for labelling of signals recorded by seismometers that can reduce the time and expertise needed to create fully annotated datasets. The proposed Siamese network approach learns best class-exemplar anchors, leveraging learned similarity between these anchor embeddings and unlabelled signals. Classification is performed via soft-labelling and thresholding instead of hard class boundaries. Furthermore, network output explainability is used to explain misclassifications and we demonstrate the effect of anchors on performance, via ablation studies. The proposed approach classifies four landslide classes, namely earthquakes, micro-quakes, rockfall and anthropogenic noise, demonstrating good agreement with manually detected events while requiring few training data to be effective, hence reducing the time needed for labelling and updating models. Full article

Quantum state discrimination (QSD) is a fundamental task in quantum information processing, improving the computation efficiency and communication security. Non-Hermitian (NH) PT-symmetric systems were found to be able to discriminate two quantum states better than the Hermitian strategy. In this work, we propose a QSD approach based on P-pseudo-Hermitian systems with real spectra. We theoretically prove the feasibility of realizing QSD in the real-spectrum phase of a P-pseudo-Hermitian system, i.e., two arbitrary non-orthogonal quantum states can be discriminated by a suitable P-pseudo-Hermitian Hamiltonian. In detail, we decide the minimal angular separation between two non-orthogonal quantum states for a fixed P-pseudo-Hermitian Hamiltonian, and we find the orthogonal evolution time is able to approach zero under suitable conditions, while both the trace distance and the quantum relative entropy are employed to judge their orthogonality. We give a criterion to choose the parameters of a P-pseudo-Hermitian Hamiltonian that evolves the two initial orthogonal states faster than a fixed arbitrary PT-symmetric one with an identical energy difference. Our work expands the NH family for QSD, and can be used to explore real quantum systems in the future. Full article

Background/Objectives: The quality of clinical studies is largely determined by the bioanalytical methods used for testing study samples. Rigorous assay validation following defined criteria, for example, the European Medicines Agency guideline for bioanalytical method validation, is a prerequisite for such assays. Alpha1-antitrypsin (AAT) measurement, i.e., the specific measurement of AAT protein and its associated elastase-inhibitory activity, is an integral part of assay panels for clinical studies addressing AAT deficiency. Specifically, AAT must be measured in the matrix of citrated human plasma as well as in diluted solutions with high salt concentrations obtained through bronchoalveolar lavage (BAL). Sensitive and selective measurement methods are required, as BAL has a low level of AAT. Methods: We present the validation data obtained for three AAT measurement methods. Two of them, nephelometry and the enzyme-linked immunosorbent assay, which clearly differ in their sensitivity, provide AAT protein concentrations. The third is the highly sensitive, newly developed elastase complex formation immunosorbent assay that specifically measures the inhibitory activity of AAT against its pivotal target, protease neutrophil elastase. Using samples with relevant AAT concentrations, we addressed the assays’ characteristics: accuracy, precision, linearity, selectivity, specificity, limit of quantification and short-term analyte stability Results: Overall, the three methods demonstrated low total errors, a combined measure reflecting accuracy and precision, even at low analyte concentrations of less than 0.5 µg/mL; adequate linearity over the required assay range; and acceptable selectivity and specificity. Furthermore, the short-time stability of the analyte was also demonstrated. Conclusions: All three AAT measurement methods met the acceptance criteria defined by the guidelines on bioanalytical assay validation, qualifying these methods for clinical sample analysis. Full article

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

Static Priority-based Multiple Access (SPMA) is an emerging and promising wireless MAC protocol which is widely used in Unmanned Aerial Vehicle (UAV) networks. UAV (Unmanned Aerial Vehicle) networks, also known as drone networks, refer to a system of interconnected UAVs that communicate and collaborate to perform tasks autonomously or semi-autonomously. These networks leverage wireless communication technologies to share data, coordinate movements, and optimize mission execution. In SPMA, traffic arriving at the UAV network node can be divided into multiple priorities according to the information timeliness, and the packets of each priority are stored in the corresponding queues with different thresholds to transmit packet, thus guaranteeing the high success rate and low latency for the highest-priority traffic. Unfortunately, the multi-priority queue scheduling of SPMA deprives the packet transmitting opportunity of low-priority traffic, which results in unfair conditions among different-priority traffic. To address this problem, in this paper we propose the method of Adaptive Credit-Based Shaper with Reinforcement Learning (abbreviated as ACBS-RL) to balance the performance of all-priority traffic. In ACBS-RL, the Credit-Based Shaper (CBS) is introduced to SPMA to provide relatively fair packet transmission opportunity among multiple traffic queues by limiting the transmission rate. Due to the dynamic situations of the wireless environment, the Q-learning-based reinforcement learning method is leveraged to adaptively adjust the parameters of CBS (i.e., idleslope and sendslope) to achieve better performance among all priority queues. The extensive simulation results show that compared with traditional SPMA protocol, the proposed ACBS-RL can increase UAV network throughput while guaranteeing Quality of Service (QoS) requirements of all priority traffic. Full article

Mesenchymal stem cells (MSCs), i.e., adult stem cells with immunomodulatory and secretory properties, contribute to tissue growth and regeneration, including healing processes. Some metal nanoparticles (NPs) are known to exhibit antimicrobial activity and may further potentiate tissue healing. We studied the effect of Ag, CuO, and ZnO NPs after in vitro exposure of mouse MSCs at the transcriptional level in order to reveal the potential toxicity as well as modulation of other processes that may modify the activity of MSCs. mRNA–miRNA interactions were further investigated to explore the epigenetic regulation of gene expression. All the tested NPs mediated immunomodulatory effects on MSCs, generation of extracellular vesicles, inhibition of osteogenesis, and enhancement of adipogenesis. Ag NPs exhibited the most pronounced response; they impacted the expression of the highest number of mRNAs, including those encoding interferon-γ-stimulated genes and genes involved in drug metabolism/cytochrome P450 activity, suggesting a response to the potential toxicity of Ag NPs (oxidative stress). Highly interacting MiR-126 was upregulated by all NPs, while downregulation of MiR-92a was observed after the ZnO NP treatment only, and both effects might be associated with the improvement of MSCs’ healing potency. Overall, our results demonstrate positive effects of NPs on MSCs, although increased oxidative stress caused by Ag NPs may limit the therapeutical potential of the combined MSC+NP treatment. Full article

The groundwater inverse problem has several challenges such as instability, non-uniqueness, and complexity, especially for heterogeneous aquifers. Solving the inverse problem is the traditional way to calibrate models, but it is both time-consuming and sensitive to errors in the measurements. This study explores the use of machine learning (ML) surrogate models, namely Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to solve the inverse problem for the groundwater model calibration. Datasets for 20 hydraulic conductivity fields were created randomly based on statistics of hydraulic conductivity from the available data of the Northern Aquifer of Qatar, which was used as a case study. The corresponding hydraulic head values were obtained using MODFLOW simulations, and the data were used to train and validate the ML models. The trained surrogate models were used to estimate the hydraulic conductivity based on field observations. The results show that both RF and XGBoost have considerable predictive skill, with RF having better R² and RMSE values (R² = 0.99 for training, 0.93 for testing) than XGBoost (R² = 0.86 for training, 0.85 for testing). The ML-based method lowered the computational effort greatly compared to the classical solution of the inverse problem (i.e., using PEST) and still produced strong and reliable spatial patterns of hydraulic conductivity. This demonstrates the potential of machine learning models for calibrating complex groundwater systems. Full article