Search Results (6,414)

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

Background: The long-term success of total knee arthroplasty (TKA) is often compromised by stress shielding, which can lead to bone resorption and even implant loosening. This study employs finite element analysis (FEA) to compare the stress-shielding effects of a knee prosthesis made from polyether ether ketone (PEEK) with a traditional titanium Ti6Al4V implant on an osteoporotic tibial bone model. Methods: Stress distribution and the stress-shielding factor (SSF) were evaluated at seven critical points in the proximal tibia under physiological loading conditions. Results: Results indicate that the PEEK prosthesis yields a more uniform stress transmission, with von Mises stress levels within the optimal 2–3 MPa range for bone maintenance and consistently negative or near-zero SSF values, implying minimal stress shielding. Conversely, titanium implants exhibited significant stress shielding with high positive SSF values across all points. Additionally, stress concentrations on the polyethylene liner were lower and more evenly distributed in the PEEK model, suggesting reduced wear potential. Conclusions: These findings highlight the biomechanical advantages of PEEK in reducing stress shielding and preserving bone integrity, supporting its potential use to improve implant longevity in TKA. Further experimental and clinical validation are warranted. Full article

Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl₂ extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R² ≥ 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy. Full article

Shandong Province is rich in geothermal resources, mainly stored in sandstone reservoirs. The setting of reasonable well spacing in the early stage of large-scale recharge has not attracted enough attention. The problem of small well spacing in geothermal engineering is particularly prominent in the sandstone thermal reservoir production area represented by Dezhou. Based on the measured data of temperature, flow, and water level, this paper constructs a typical engineering numerical model by using TOUGH2 software. It is found that when the distance between production and recharge wells is 180 m, the amount of production and recharge is 60 m³/h, and the temperature of reinjection is 30 °C, the temperature of the production well will decrease rapidly after 10 years of production and recharge. In order to solve the problem of thermal breakthrough, three optimization schemes are assumed: reducing the reinjection temperature to reduce the amount of re-injection when the amount of heat is the same, reducing the amount of production and injection when the temperature of production and injection is constant, and stopping production after the temperature of the production well decreases. However, the results show that the three schemes cannot solve the problem of thermal breakthrough or meet production demand. Therefore, it is necessary to set reasonable well spacing. Therefore, based on the strata near the Hydrological Homeland in Decheng District, the reasonable spacing of production and recharge wells is achieved by numerical simulation. Under a volumetric flux scenario ranging from 60 to 80 m³/h, the well spacing should exceed 400 m. For a volumetric flux between 80 and 140 m³/h, it is recommended that the well spacing be greater than 600 m. Full article

The safety of lithium-ion batteries during transportation is critically important. However, current standards exhibit limitations, as their environmental testing parameter thresholds fail to fully encompass actual transportation conditions. To enhance both safety and standard applicability, in this study, we focused on four representative environmental conditions: temperature, vibration, shock, and low atmospheric pressure. Field measurements were conducted across road, rail, and air transport modes using a self-developed data acquisition system based on the NearLink communication technology. The measured data were then compared with the threshold values defined in current international and national standards. The results reveal that certain measured values exceeded the upper limits prescribed by existing standards, indicating limitations in their applicability under extreme transport conditions. Based on these findings, we propose revised testing parameters that better reflect actual transport risks, including a temperature cycling range of 72 ± 2 °C (high) and −40 ± 2 °C (low), a shock acceleration limit of 50 gn, adjusted peak frequencies in the vibration PSD profile, and a minimum pressure threshold of 11.6 kPa. These results provide a scientific basis for optimising safety standards and improving the safety of lithium-ion battery transportation. Full article

Metamaterial absorbers in terahertz (THz) based bands have garnered significant attention for their potential applications in military stealth, terahertz imaging, and other fields. Nevertheless, the limited bandwidth, low absorption rate, and heavy weight greatly reduce the further development and wide application of terahertz absorbers. To solve these problems, we propose a polystyrene (PS)-based ultra-broadband metamaterial absorber integrated with a polyethylene terephthalate (PET) double-sided adhesive layer and a patterned indium tin oxide (ITO) film through the simulation method, which operates in the THz band. The electromagnetic wave absorption properties and underlying physical absorption mechanisms of the proposed metamaterial absorbers are comprehensively modeled and rigorously numerically simulated. The research demonstrates the metamaterial absorber can achieve absorption performance of over 90% for fully polarized incident waves in the ultra-wideband range of 1.2–10 THz, especially achieving perfect absorption characteristics of over 99.9% near 1.8–1.9 THz and 5.8–6.2 THz. The proposed absorber has a lightweight physical property of 0.7 kg/m² and polarization-insensitive characteristic, and it achieves a broad-angle that allows a range of incidence angles up to 60°. The simulation research results of this article provide theoretical support for the design of terahertz absorbers with ultra-wideband absorption characteristics. Full article

A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu²⁺ and Ag⁺ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the solvent environment: It selectively detects Cu²⁺ in acetonitrile and Ag⁺ in an ethanol–water mixture. Upon binding with either ion, SQ-68 undergoes significant absorption changes in the NIR region, accompanied by visible color changes, enabling naked-eye detection. Spectroscopic studies confirm a 1:1 binding stoichiometry with both Cu²⁺ and Ag⁺, accompanied by hypochromism. The detection limits are 0.09 μM for Cu²⁺ and 0.38 μM for Ag⁺, supporting highly sensitive quantification. The sensor’s practical applicability was validated in real water samples (sea, lake, and tap water), with recovery rates ranging from 73–95% for Cu²⁺ to 59–99% for Ag⁺. These results establish SQ-68 as a reliable and efficient chemosensor for environmental monitoring and water quality assessment. Its dual-analyte capability, solvent-tunable selectivity, and visual detection features make it a promising tool for rapid and accurate detection of heavy metal ions in diverse aqueous environments. Full article

Blasting operations in surface mining pose a risk of flyrock, which is a critical safety concern for both personnel and infrastructure. This study presents the use of unmanned aerial vehicles (UAVs) and photogrammetric techniques to improve the accuracy of blast design, particularly in relation to controlling burden values and reducing flyrock. The research was conducted in a basalt quarry in Lower Silesia, where high rock fracturing complicated conventional blast planning. A DJI Mavic 3 Enterprise UAV was used to capture high-resolution aerial imagery, and 3D models were created using Strayos software. These models enabled precise analysis of bench face geometry and burden distribution with centimeter-level accuracy. The results showed a significant improvement in identifying zones with improper burden values and allowed for real-time corrections in blasthole design. Despite a ten-fold reduction in the number of images used, no loss in model quality was observed. UAV-based surveys followed software-recommended flight paths, and the application of this methodology reduced the flyrock range by an average of 42% near sensitive areas. This approach demonstrates the operational benefits and enhanced safety potential of integrating UAV-based photogrammetry into blasting design workflows. Full article

In this work, we demonstrate high-efficiency 385 nm AlGaN-based near-ultraviolet micro light emitting diode (NUV-Micro LED) arrays. The epi structure is prepared using a novel AlN-inserted superlattice electrical blocking layer which enhances hole spreading in the p-type region significantly. The NUV-Micro LED arrays in this work comprise 228 chips in parallel with wavelengths at 385 nm, and each single chip size is 15 × 30 μm². Compared with conventional bulk AlGaN-based EBL structures, the NUV-Micro LED arrays that implemented the new hole spreading enhanced superlattice electrical blocking layer (HSESL-EBL) structure proposed in this work had a remarkable increase in light output power (LOP) at current density, increasing the range down from 0.02 A/cm² to as high as 97 A/cm². The array’s light output power is increased up to 1540% at the lowest current density 0.02 A/cm², and up to 58% at the highest current density 97 A/cm², measured under room temperature (RT); consequently, the WPE is increased from 13.4% to a maximum of 30.18%. This AlN-inserted HESEL-EBL design significantly enhances both the lateral expansion efficiency and the hole injection efficiency into the multi quantum well (MQW) in the arrays, improving the concentration distribution of the holes in MQW while maintaining good suppression of electron leakage. The array’s efficiency droop has also been greatly reduced. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

Proanthocyanidins are a subclass of flavonoids formed through a poorly understood polymerization process that forms chains of 3–30 catechins and epi-catechins. Proanthocyanidins serve as UV protectants and antifeedants that accumulate in diverse plant species, including the lotus. To identify candidate genes underlying proanthocyanidin synthesis and polymerization, we generated and functionally annotated transcriptomes from seedpods and seed epicarps of two lotus cultivars, “Guoqing Hong” and “Space Lotus”, which accumulate markedly divergent proanthocyanidin levels across the immature, near-mature, and mature developmental stages. Our transcriptome analysis was based on a total of 262.29 GB of raw data. We aligned the transcriptome data with the lotus genome and obtained an alignment efficiency that ranged from 91.74% to 96.44%. Based on the alignment results, we discovered 4774 new genes and functionally annotated 3232 genes. A total of 14,994 differentially expressed genes (DEGs) were identified from two-by-two comparisons of transcript libraries. We found 61 DEGs in the same developmental stage in the same tissue of different species. Comparative transcriptome analysis of seedpods and seed epicarps from two cultivars identified 14,994 differentially expressed genes (DEGs), of which 10 were functionally associated with proanthocyanidin synthesis and 9 were possibly implicated in the polymerization reactions. We independently quantified the expression of the candidate genes using qRT-PCR. Significant differences in the expression of candidate genes in different tissues and periods of lotus species are consistent with particular genes contributing to the polymerization of catechins and epi-catechins into proanthocyanidins in lotus seedpods and seed epicarps. Full article

The evidence for photobiomodulation in reducing postoperative pain after endodontic instrumentation is classified as low or very low certainty, indicating a need for further research. Longitudinal pain assessments over 24 h are crucial, and studies should explore these pain periods. Background/Objectives: This double-blind, randomized controlled clinical trial evaluated the effect of PBM on pain following single-visit endodontic treatment of maxillary molars at 4, 8, 12, and 24 h. Primary outcomes included pain at 24 h; secondary outcomes included pain at 4, 8, and 12 h, pain during palpation/percussion, OHIP-14 analysis, and frequencies of pain. Methods: Approved by the Research Ethics Committee (5.598.290) and registered in Clinical Trials (NCT06253767), the study recruited adults (21–70 years) requiring endodontic treatment in maxillary molars. Fifty-eight molars were randomly assigned to two groups: the PBM Group (n = 29), receiving conventional endodontic treatment with PBM (100 mW, 333 mW/cm², 9 J distributed at 3 points near root apices), and the control group (n = 29), receiving conventional treatment with PBM simulation. Pain was assessed using the Visual Analog Scale. Results: Statistical analyses used chi-square and Mann–Whitney tests, with explained variance (η²). Ten participants were excluded, leaving 48 patients for analysis. No significant differences were observed in postoperative pain at 24, 4, 8, or 12 h, or in palpation/percussion or OHIP-14 scores. Pain frequencies ranged from 12.5% to 25%. Conclusions: PBM does not influence post-treatment pain in maxillary molars under these conditions. These results emphasize the importance of relying on well-designed clinical trials to guide treatment decisions, and future research should focus on personalized dosimetry adapted to the anatomical characteristics of the treated dental region to enhance the accuracy and efficacy of therapeutic protocols. Full article

Background: The knee joint performs a vital function in human movement, supporting significant loads and ensuring stability during daily activities. Methods: The objective of this study was to develop and validate a subject-specific framework to model knee flexion–extension by integrating 3D gait data with individualized musculoskeletal (MS) and finite element (FE) models. In this proof of concept, gait data were collected from a 52-year-old woman using Xsens inertial sensors. The MS model was based on the same subject to define realistic loading, while the 3D knee FE model, built from another individual’s MRI, included all major anatomical structures, as subject-specific morphing was not possible due to unavailable scans. Results: The FE simulation showed principal stresses from –28.67 to +44.95 MPa, with compressive stresses between 2 and 8 MPa predominating in the tibial plateaus, consistent with normal gait. In the ACL, peak stress of 1.45 MPa occurred near the femoral insertion, decreasing non-uniformly with a compressive dip around –3.0 MPa. Displacement reached 0.99 mm in the distal tibia and decreased proximally. ACL displacement ranged from 0.45 to 0.80 mm, following a non-linear pattern likely due to ligament geometry and local constraints. Conclusions: These results support the model’s ability to replicate realistic, patient-specific joint mechanics. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article