Browse Articles
Discover research articles across all indexed journals
Kinematic demands of FIFA’s EPTS validation circuit compared to a sport-specific alternative
Abstract This study compared the kinematic demands of FIFA’s Electronic Performance and Tracking Systems (EPTS) validation circuit with an alternative sport-specific circuit (SSC). The aim was to determine which circuit presents high-intensity demands known to challenge EPTS accuracy. Four amateur soccer players (age: 29.4 ± 5.9 years) performed both circuits using global positioning system (GPS). Speed, acceleration and change of direction (COD) was divided into zones and compared between the circuits. Results showed that the SSC resulted in greater distance, time and entrances in speed zones 3 and 4, as well as in moderate and high acceleration/deceleration zones. SSC also exhibited more time and entries in high-speed COD zones. FIFA’s circuit demonstrated higher peak speed and more time in speed zone 5. Differences in COD between circuits reflect the intensity and type of exercises performed, with the SSC including more sprints and rapid direction changes. In conclusion, the SSC presents more kinematic demands in high-intensity situations critical for EPTS validation, whereas the FIFA circuit demonstrates less challenging movement patterns. FIFA could improve the validation protocol by updating the circuit to include more demanding sport-specific movements thus increasing the evaluated devices validity in high-intensity situations which are of very high interest for sports.
Prediction of crack repair percentage in self-healing concrete using machine learning
Influence of altitudinal zones on soil nutrient dynamics and fertility constraints in agricultural terrains of Yunnan, China
Enhancing reservoir characterization in the Temsah gas field through high-resolution seismic analysis and three-dimensional modeling
Abstract The offshore Temsah Gas Field, located about 65 km NNW of Port Said in the northeastern Nile Delta Basin, is structurally complex, with NE–SW and NW–SE normal faults that control reservoir compartmentalization and hydrocarbon entrapment. The Sidi Salem Formation, the primary reservoir, comprises interbedded sandstone and shale facies with significant hydrocarbon potential. This study integrates high-resolution post-stack time-migrated 2D seismic data and well log analysis from four wells and twenty-nine 2D seismic lines to delineate reservoir structures, evaluates the hydrocarbon potential of the Sidi Salem Formation, and builds a 3D geological model to enhance field productivity, while also clarifying fault geometries, quantifying key petrophysical parameters, and pinpointing new exploration prospects. Seismic interpretation reveals a prominent horst block with a three-way dip closure and several fault-bounded traps. Petrophysical analysis indicates net reservoir thickness of 22–120 m, effective porosity of 19–34%, shale content of 8–27%, and hydrocarbon saturation of 70–79%. Integration of seismic and petrophysical data delineates sandstone-rich zones with enhanced reservoir quality, mainly within the upthrown fault blocks. The resulting 3D model supports volumetric estimation, identifying a new fault-bounded prospect with an estimated GIIP of ~5.33 TCF. This integrated workflow reduces structural uncertainty, refines reservoir characterization, and offers a reproducible approach for exploration in fault-controlled deltaic reservoirs.
An XGBoost-SHAP analysis of the driving factors of carbon emissions in China’s first-tier cities
Multi-omics profiling identifies ESM1 as a key mediator of immunoevasion through the SPP1 pathway in bladder cancer
A deep neural network-based green space design optimization framework for smart cities
A complex structure of escaping helium spanning more than half the orbit of the ultra-hot Jupiter WASP-121 b
Abstract Atmospheric escape of close-in exoplanets, driven by stellar irradiation, influences their evolution, composition, and atmospheric dynamics. The near-infrared metastable helium triplet (10833 Å) has become a key probe of this process, enabling mass loss rate measurements for dozens of exoplanets. Only a few studies, however, have detected absorption beyond transit, supporting the presence of hydrodynamic outflows. None have yet precisely identified the physical extent of the out-of-transit signal, either due to non-continuous or short-duration observations. This strongly limits our ability to measure accurate mass-loss rates and to understand how the stellar environment shapes outflows. Here we present the continuous, full-orbit helium phase-curve observation of an exoplanet: the ultra-hot Jupiter WASP-121 b, obtained with the James Webb Space Telescope (JWST) and the Near Infrared Imager and Slitless Spectrograph (NIRISS). We detect significant helium absorption at > 3 σ over nearly 60% of the orbit, revealing a persistent and large-scale outflow. The signal separates into a dense leading tail moving toward the star and a trailing tail pushed away by stellar irradiation. Both appear to remain collisional far from the planet, implying strong hydrodynamic escape. While qualitatively consistent with theoretical expectations, current models cannot reproduce the full spatial and kinematic structure, limiting precise mass-loss estimates. These results demonstrate JWST’s ability to map exoplanet outflows in detail and highlight its synergy with ground-based spectroscopy.
Long-term biological surveillance of SARS-CoV-2 in critical points for municipal sewage catchment in light of wastewater-based epidemiology, public health and environmental hygiene
Predictive models of melanoma metastasis based on dermatoscopy in an international retrospective human reader study
Development of a laser-based pre-damage for metal-polymer caps for sealing medical ampoules
Abstract The closure of injection vials is realized by multi-material crimp caps consisting of aluminum and polymer. To improve the handling, previous investigations have determined which force for opening is perceived as comfortable and can be achieved with one hand. The potential of both material properties is utilized to develop a hybrid joining process which facilitates the opening of the vials. Undercut microstructures are introduced into the metal by laser radiation, which are filled by injection molding and ensure a microform-fit connection. This method leads to the elimination of adhesives and bonding agents, which require special regulations in medical technology. The aim of this research is to develop laser-based structuring for the aluminum sheet that ensures a reliable metal-polymer joint and breaks under a defined force. The challenge is to identify laser parameters that do not create a cutout or damage the thin aluminum sheet. A mechanical analysis is done by using a tensile testing machine in four different push-through arrangements to validate the opening forces. As a result, the targeted forces for single-handed opening of the injection vial are achieved. The metal-polymer joint remains connected while the flared cap is split open at an introduced pre-damage.
Evidence of a cascading positive tipping point towards electric vehicles
Abstract Electric vehicles have recently seen rapid innovation, decline in cost and a rise in popularity. Past a tipping point where uptake becomes self-propelling, electric vehicles could irreversibly replace internal combustion engine vehicles, as industry discontinues conventional production chains. Here we provide evidence that this tipping point has occurred or lies within the next few years in lead markets of the European Union and China, and potentially the United States, which could spill out into peripheral vehicle markets across the rest of the world. The historical evidence shows a sudden decline in conventional vehicle sales starting around 2019 concurrent to a rapid rise in sales of electric vehicles. Critically, we observe a loss of resilience of the incumbent technology consistent with the approach to a tipping point. We use simulations of technology evolution to identify timescales for cost-parity and policy frameworks that could accelerate the transition to largely eliminate combustion vehicles before 2050.
DFT and QTAIM insights into C20 fullerene derivatives as advanced sensors for phencyclidine drug detection in clinical settings
Abstract The unauthorized use of phencyclidine (PCP) has serious public health consequences, which prompts the need for new sensing approaches that are fast, sensitive and accessible. This study used Density Functional Theory (DFT) and the Quantum Theory of Atoms in Molecules (QTAIM) to examine pristine fullerene C 20 and two doped ones (AlC 19 and ZnC 19 ) as new sensors for PCP. Geometry optimization and analyses of the molecular electrostatic potential (MEP), electronic properties (HOMO-LUMO gap; chemical potential, electrophilicity-based charge transfer), and sensing performance (adsorption energy, recovery time and electrical conductivity) were performed. Results illustrate that doping significantly changes the electronic and structural properties of the C 20 framework. Although pristine C 20 and ZnC 19 have limited potential, AlC 19 is promising as a multifunctional material. AlC 19 has the strongest interaction with PCP, with an adsorption energy (Eads) of -49.44 kcal.mol -1 , demonstrating excellent potential to remove PCP in adsorbed form. As an electrochemical sensor, AlC 19 showed a large increase in electrical conductivity (from 2.71 × 10 9 in the pristine AlC 19 to 2.77 × 10 9 in the AlC 19 @PCP complex) and a long recovery time after PCP binding, making it ideal for disposable sensor application (strong and irreversible binding). Furthermore, AlC₁₉ showed exceptional performance as a colorimetric sensor, exhibiting a significant shift in the UV-Vis absorption maximum (from 470 nm to 524 nm) after complexation with PCP. Both NBO and QTAIM analysis revealed that AlC 19 @PCP exhibits a very strong donor-acceptor interaction and a moderate hydrogen-bond-like character, which contributed to its strong performance. All of the above establishes that AlC 19 is an effective disposable electrochemical sensor, a colorimetric sensor, and an effective adsorbent of PCP that can be further utilized to develop a multi-functional sensing system that could allow for detection of the drug and remediation of the environment.
Zinc‐Coordinated Lipids: Facilitators for Enhanced mRNA Delivery Efficacy
Abstract The advancement of mRNA therapeutics necessitates more efficient delivery systems. Current clinically advanced lipid nanoparticles (LNPs) primarily bind mRNA through electrostatic interactions, with limited exploration of other intermolecular forces that can benefit multiple delivery processes. Here, we design zinc‐coordinated lipids (Zn‐CL) to formulate LNPs, enabling high mRNA delivery efficacy in vivo. The zinc‐coordinated moieties in ionizable lipids show strong affinity toward phosphate groups, yielding tight yet reversible mRNA encapsulation. In addition, due to the abundance of phosphate groups in biological membranes, Zn‐CL lipids engage them to boost enhanced cellular uptake. Once inside endosomes, competitive binding of zinc‐coordinated moieties to the membranes strengthens endosomal destabilization and weakens Zn‑CL/mRNA interactions, enabling efficient endosomal escape as well as rapid cytosolic mRNA release and resolving the long‑standing paradox between stable encapsulation and efficient release. As a result, zinc coordination introduction simultaneously solves multiple mRNA delivery obstacles, ultimately leading to 29‐fold higher mRNA translation than FDA‐approved SM‐102 LNPs in vivo. Notably, this coordination strategy is extendable to other metal ions, with cobalt‐coordinated lipids facilitating spleen‐targeted mRNA delivery. These findings highlight the substantial potential of metal‐coordinated lipids as a versatile platform for enhancing mRNA therapeutic delivery.
Discovery of a β-arrestin-biased CCKBR agonist that blocks CCKBR-dependent long-term potentiation
Novel temperature-based spectral topological indices for QSPR modeling of polyacenes in predicting physicochemical properties
Nanostructures as indicator for deformation dynamics
Abstract We determine the feedback between fault dynamics and fault gouge structures by examining gouge structures that form during rupture and slip of initially intact granite under upper crustal conditions. Experiments were conducted under quasi-static (3 × 10 −5 mm/s), weakly dynamic (0.27 mm/s) and fully dynamic (≫1.5 mm/s) slip conditions, with or without fluids, and limited slip displacement (max. 4 mm). The extent in gouge amorphization positively correlates with deformation rate, and we detect evidence of melting, e.g., magnetite nanograins, associated with the highest deformation rates. Gouge nanostructure is directly correlated to power dissipation rather than total energy input. The presence of amorphous material has no detectable impact on the strength evolution during rupture. We highlight that gouge textures, generally associated with large displacements and/or elevated pressure and temperature conditions, can form during small slip events (Mw < 2) in the upper crust from initially intact materials.
Intelligent retinal disease detection using deep learning
Abstract The rising prevalence of retinal diseases is a significant concern, as certain untreated conditions can lead to severe vision impairment or even blindness. Deep learning algorithms have emerged as a powerful tool for the diagnosis and analysis of medical images. The automated detection of retinal diseases not only aids ophthalmologists in making accurate clinical decisions but also enhances efficiency by saving time. This study proposes a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images. For this research, a balanced dataset was compiled by integrating data from various sources. Artificial Neural Networks (ANN) and transfer learning techniques were utilized to differentiate between healthy eyes and those affected by diabetic retinopathy, cataracts, or glaucoma. Multiple feature extraction methods were employed in conjunction with ANN for the multi-classification of retinal diseases. The results demonstrate that the model combining Artificial Neural Networks (ANN) with MobileNetV2 and DenseNet121 architectures, along with Principal Component Analysis (PCA) for feature extraction and dimensionality reduction, as well as the Discrete Wavelet Transform (DWT) algorithm, achieves highly satisfactory performance, attaining a peak accuracy of 98.2%.
Physics-based broadband characterization of weak earthquakes
Abstract Analyses of abundant small earthquakes have the potential to map the Earth’s small-scale stress and frictional properties. However, standard seismological characterizations lack the resolution to capture the physical complexity of earthquake rupture, particularly its spatial and temporal heterogeneity. Advanced dynamic rupture models, which integrate elastodynamic simulations with frictional laws, have so far been applied to large earthquakes. Here, we develop a Bayesian dynamic inversion from station-specific (apparent) source spectra, extending up to 25 Hz, for slip-weakening friction, assuming heterogeneous parameterization along a finite-extent planar fault with resolution down to ~100-m scale. The approach is demonstrated on two Mw~4 earthquakes in Central Italy with distinct spectral behavior, one directive and one nondirective. Results show that the inversion resolves key mean source parameters, pinpoints possible pitfalls in standard estimates, and infers a power-law decay of stress and friction heterogeneity spectra down to the smallest scales. Such advanced studies promise to unravel so-far elusive small-scale characteristics of earthquake ruptures.