Search Results (2,199)

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

We study the dynamics of von Neumann entropy driven by the Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) equation, focusing on its camel-like behavior—a hump-like entropy evolution reflecting the system’s adaptation to its environment. Within this framework, we analyze quantum correlations under decoherence and environmental interaction for three sets of quantum states. Our results show that the sign of the entanglement entropy’s derivative serves as an indicator of the system’s drift toward either classical or quantum information exchange—an insight relevant to quantum error correction and dissipation in quantum thermal machines. We parameterize quantum states using both single-parameter and Bloch-sphere representations, where the angle $\theta$ on the Bloch sphere corresponds to the state’s position. On this sphere, we construct gradient and basin maps that partition the dynamics of quantum states into stable and unstable regions under decoherence. Notably, we identify a Braiding ring of decoherence-unstable states located at $\theta ={\displaystyle \frac{3\pi }{4}}$; these states act as attractors under a constructed Lyapunov function, illustrating the topological and dynamical complexity of quantum evolution. Finally, we propose a testable experimental setup based on camel-like entropy and discuss its connection to the theoretical framework of this entropy behavior. Full article

Drilling fluid loss is a common and complex downhole problem that occurs during drilling in deep fractured formations, which has a significant negative impact on the exploration and development of oil and gas resources. Establishing a drilling fluid loss model for the quantitative analysis of drilling fluid loss is the most effective method for the diagnosis of drilling fluid loss, which provides a favorable basis for the formulation of drilling fluid loss control measures, including the information on thief zone location, loss type, and the size of loss channels. The previous loss model assumes that the drilling fluid is driven by constant flow or pressure at the fracture inlet. However, drilling fluid loss is a complex physical process in the coupled wellbore circulation system. The lost drilling fluid is driven by dynamic bottomhole pressure (BHP) during the drilling process. The use of a single-phase model to describe drilling fluids ignores the influence of solid-phase particles in the drilling fluid system on its rheological properties. This paper aims to model drilling fluid loss in the coupled wellbore–-fracture system based on the two-phase flow model. It focuses on the effects of well depth, drilling pumping rate, drilling fluid density, viscosity, fracture geometric parameters, and their morphology on loss during the drilling fluid circulation process. Numerical discrete equations are derived using the finite volume method and the “upwind” scheme. The correctness of the model is verified by published literature data and experimental data. The results show that the loss model without considering the circulation of drilling fluid underestimates the extent of drilling fluid loss. The presence of annular pressure loss in the circulation of drilling fluid will lead to an increase in BHP, resulting in more serious loss. Full article

The accuracy of phase unwrapping is a decisive factor in achieving high-precision dimensional measurement using the projected fringe profilometry. However, discontinuities at truncation points inevitably lead to phase jumps, especially when measuring objects with complex hollow features, resulting in significantly increased errors. To address this issue, this paper proposes an adaptive phase correction algorithm based on row and column constraints. First, the algorithm identifies the main normal phase distribution region in each column and interpolates abnormal values deviating from this region, ensuring smooth phase distribution in the column direction. Then, it detects each continuous non-zero segment in every row, locates phase jump positions, and performs local corrections. This approach enhances the overall continuity of the phase map and effectively compensates for phase jump errors. Experimental results demonstrate that the proposed method can effectively suppress phase jumps caused by object edges and hollow regions, achieving an absolute error of less than 0.05 mm in measured step height differences in standard blocks. This provides a reliable phase preprocessing solution for the optical measurement of complex-shaped objects. Full article

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models. Full article

Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results. Full article

Non-contact, automated health monitoring is a cornerstone of modern precision livestock farming, crucial for enhancing animal welfare and productivity. Infrared thermography (IRT) offers a powerful, non-invasive means to assess physiological status. However, its practical use on farms is limited by a key challenge: accurately locating regions of interest (ROIs), like the eyes and face, in the blurry, low-resolution thermal images common in farm settings. To solve this, we developed a new framework called ID-APM, which is designed for robust ROI registration in agriculture. Our method uses a trinocular system and our RAP-CPD algorithm to robustly match features and accurately calculate the target’s 3D position. This 3D information then enables the precise projection of the ROI’s location onto the ambiguous thermal image through inverse disparity estimation, effectively overcoming errors caused by image blur and spectral inconsistencies. Validated on a self-built dataset of farmed sika deer, the ID-APM framework demonstrated exceptional performance. It achieved a remarkable overall accuracy of 96.95% and a Correct Matching Ratio (CMR) of 99.93%. This research provides a robust and automated solution that effectively bypasses the limitations of low-resolution thermal sensors, offering a promising and practical tool for precision health monitoring, early disease detection, and enhanced management of semi-wild farmed animals like sika deer. Full article

The first-order false belief (FB) test is frequently employed to assess theory of mind (ToM); however, it faces substantial criticism regarding its inadequacies. Critics argue that the responses remain binary and are influenced by the presence and location of the object. This study aims to address these criticisms by manipulating an object’s location through three alternative FB tasks, thereby enhancing the understanding of children’s reasoning strategies (reality, belief, or perceptual access reasoning) and offering a language skill-independent measure of ToM. This study involved 150 children aged 3–6 years who were administered standard and three alternative FB tasks along with a receptive vocabulary acquisition test. The findings revealed that children predominantly utilized reality reasoning, identifying the object’s location as the correct response. However, in a condition where the object was physically removed, the percentage of correct responses increased significantly, and the use of belief reasoning increased. While age and language skills were found to be directly correlated with FB performance, the object’s interference with belief reasoning in younger children was reduced. In light of these findings, the three alternative tasks are posited to offer a promising, more accurate measure of FB understanding, independent of the object’s presence and language skill. Full article

Background/Objectives: Conductive hearing loss (CHL) recurrence or persistence after stapes surgery is often due to prosthesis displacement or malfunction, with malleus fixation being a less common cause. While persistent CHL linked to malleus fixation can be managed with appropriate diagnosis and surgical intervention, recurrent CHL cases remain poorly documented. This report describes a rare case of recurrent CHL due to malleus neck fixation, likely secondary to surgical trauma. Case Presentation: A 49-year-old woman underwent bilateral stapedectomy. CHL worsened after two years. CT showed right incus erosion and a left bony bridge. Revision surgery corrected the right side. Left tympanotomy revealed malleus fixation from a prior atticotomy. Removing the bony bridge restored ossicular mobility and hearing, stable at 6 and 12 months. Discussion: Malleus fixation after stapedectomy is rare and often related to congenital anomalies, chronic otitis media, tympanosclerosis, or surgical trauma. Bone dust or fragments from surgery may promote new bone formation, causing delayed fixation. Ossicular fixation can develop postoperatively and may be missed during primary surgery. High-resolution CT aids in diagnosis, especially in revision cases, while intraoperative palpation is key to detecting subtle abnormalities. Treatment options include ossicular mobilization, prosthesis revision, or chain reconstruction, tailored to the fixation’s location and severity. Conclusions: Surgical trauma should be considered a potential cause of recurrent CHL post-stapedectomy. Thorough removal of bone debris through aspiration and irrigation during surgery is essential to minimize this risk and optimize long-term hearing outcomes. Full article

Efficient prediction of aerodynamic loads on wind turbine blades under yawed inflow remains challenging due to the complexity of three-dimensional unsteady flow phenomena. In this work, a modified blade element momentum (BEM) method, incorporating multiple correction models, is systematically compared with high-fidelity computational fluid dynamics (CFD) simulations for the NREL Phase VI wind turbine across a range of inflow velocities (7–15 m/s) and yaw angles ($0^{°}$–${60}^{°}$). A normalized absolute error metric, referenced to experimental measurements, is employed to quantify prediction discrepancies at different yaw conditions, wind speeds, and spanwise blade locations. Results indicate that the corrected BEM method maintains good agreement with measurements under non-yawed attached flow, with errors within 2%, but its accuracy declines substantially in separated and yawed flow regimes, where errors can exceed 20% at high yaw angles (e.g., ${60}^{°}$) and low tip-speed ratios. CFD flow-field visualizations, including vorticity and Q-criterion iso-surfaces, reveal that yawed inflow strengthens vortex interactions on the leeward side and generates Coriolis-driven spanwise vortex structures, promoting stall progression from tip to root. These unsteady phenomena induce load fluctuations that are not captured by steady-state BEM formulations. Based on these insights, future studies could incorporate vortex structure and spanwise flow features extracted from CFD into unsteady correction models for BEM, enhancing prediction robustness under complex operating conditions. Full article

Monitoring environmental risks in operational landfills that contain closed cells requires non-invasive techniques capable of accurately characterizing subsurface contaminant dynamics. Electrical Resistivity Tomography (ERT) was selected because it enables continuous imaging across capped cells without intrusive drilling, with high sensitivity to the strong conductivity/resistivity contrasts that differentiate leachate (very low resistivity) from landfill gas or dry waste (high resistivity). This study employed ERT to spatially characterize contaminant distribution in closed cells within a landfill system in the Caribbean region of Colombia. Fifteen geophysical survey lines were acquired using Wenner, Dipole–Dipole, and Gradient arrays and processed through 2D, 2.5D, and 3D inversion models. The results revealed extensive low-resistivity zones (<2.1 Ω·m) in the southeastern sector, interpreted as leachate accumulations, some reaching the surface. Conversely, high-resistivity anomalies (>154 Ω·m) were identified in the southwestern area, associated with potential biogas pockets. Although these high-resistivity volumes represent <1.1% of the total modeled volume, their location and depth may pose geoenvironmental risks due to internal pressure build-up and preferential migration pathways. Existing leachate and gas collection systems showed adequate performance, though targeted corrective actions are recommended. ERT proved to be a precise, scalable, and cost-effective method for mapping subsurface contamination, offering critical insights for post-closure landfill management in tropical settings. Full article

Indoor and outdoor air quality in school environments varies significantly with respect to particulate matter (PM) concentrations, carbon dioxide (CO₂) levels, and microclimatic conditions, all of which have a direct impact on the health, well-being, and performance of both students and staff. This study reports the findings of a monitoring campaign focused on PM₁₀ and PM_2.5 concentrations in two schools located in the urban area of Belgrade, Serbia. Measurements were carried out using low-cost sensor devices positioned in classrooms and in the surrounding outdoor environment. The PM concentration data were corrected through collocation with reference-grade automatic analyzers (Grimm EDM 180) from the National Air Quality Monitoring Network (NAQMN). During the winter season, the indoor-to-outdoor (I/O) concentration ratio for classrooms ranged between 0.7 and 0.8, indicating that indoor PM levels were generally lower than outdoor levels—likely a result of limited ventilation and reduced particle infiltration from outdoor sources. Conversely, in the summer season, the average I/O ratio typically exceeded 1.0 (ranging from 1.3 to 1.5), pointing to a more pronounced influence of indoor sources, such as occupant activities, resuspension of settled dust, and insufficient air exchange. Importantly, in over 60% of the measurements conducted during the summer period, indoor PM concentrations surpassed those outdoors, underscoring the critical need to address indoor emission sources and implement effective ventilation strategies, particularly during warmer months. Full article

This paper proposes a quantum algorithm, named Dicke state quantum search (DSQS), to set qubits in the Dicke state $|D_k^n⟩$ of D states in superposition to locate the target inputs or solutions of specific patterns among $2^n$ unstructured input instances, where n is the number of input qubits and $D=\left({\displaystyle \genfrac{}{}{0pt}{}{n}{k}}\right)=\mathrm{O}\left(n^k\right)$ for $min(k,n-k)\ll n/2$. Compared to Grover’s algorithm, a famous quantum search algorithm that calls an oracle and a diffuser O($\sqrt{2^n}$) times, DSQS requires no diffuser and calls an oracle only once. Furthermore, DSQS does not need to know the number of solutions in advance. We prove the correctness of DSQS with unitary transformations, and show that each solution can be found by DSQS with an error probability less than 1/3 through O($n^k$) repetitions, as long as $min(k,n-k)\ll n/2$. Additionally, this paper proposes a classical algorithm, named DSQS-VCP, to generate quantum circuits based on DSQS for solving the k-vertex cover problem (k-VCP), a well-known NP-complete (NPC) problem. Complexity analysis demonstrates that DSQS-VCP operates in polynomial time and that the quantum circuit generated by DSQS-VCP has a polynomial qubit count, gate count, and circuit depth as long as $min(k,n-k)\ll n/2$. We thus conclude that the k-VCP can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3 under the condition of $min(k,n-k)\ll n/2$. Since the k-VCP is NP-complete, NP and NPC problems can be polynomially reduced to the k-VCP. If the reduced k-VCP instance satisfies $min(k,n-k)\ll n/2$, then both the instance and the original NP/NPC problem instance to which it corresponds can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3. All statements of polynomial algorithm execution time in this paper apply only to VCP instances and similar instances of other problems, where $min(k,n-k)\ll n/2$. Thus, they imply neither NP ⊆ BQP nor P = NP. In this restricted regime of $min(k,n-k)\ll n/2$, the Dicke state subspace has a polynomial size of $D=\left({\displaystyle \genfrac{}{}{0pt}{}{n}{k}}\right)=\mathrm{O}\left(n^k\right)$, and our DSQS algorithm samples from it without asymptotic superiority over exhaustive enumeration. Nevertheless, DSQS may be combined with other quantum algorithms to better exploit the strengths of quantum computation in practice. Experimental results using IBM Qiskit packages show that the DSQS-VCP quantum circuit can solve the k-VCP successfully. Full article

Background: Cerebral palsy (CP) is a static, non-progressive brain pathology that affects mobility and musculoskeletal health. Objective: This review aims to describe the pediatric orthopedic management strategy at one specialty center with focus on optimal lifelong mobility function for ambulatory CP. Methods: Beginning in the 1990s, a protocol was developed to proactively monitor children with surgical or conservative interventions. After three decades, we undertook a prospective institutional review, board-approved 25–45-year-old adults callback study. Inclusion criteria were all children treated through childhood who could be located and were willing to return for a full evaluation. Results: Pediatric orthopedic interventions focused on regular surveillance with proactive treatment of progressive deformities. When function was impacted, we utilized multi-level orthopedic surgery guided by instrumented gait analysis. Childhood outcomes of this approach were evaluated through retrospective studies. Results show high correction rates were achieved for planovalgus foot deformity, knee flexion contracture, torsional malalignments, and stiff-knee gait. Our prospective adult callback study evaluated 136 adults with CP, gross motor function classification system levels I (21%), II (51%), III (22%), and IV (7%), with average ages of 16 ± 3 years (adolescent visit) compared with 29 ± 3 years (adult visit). Adults in the study had an average of 2.5 multi-level orthopedic surgery events and 10.4 surgical procedures. Compared with adults without disability, daily walking ability was lower in adults with CP. Adults with CP had limitations in physical function but no increased depression. A higher frequency of chronic pain compared with normal adults was present, but pain interference in daily life was not different. Adults demonstrated similar levels of education but higher rates of unemployment, caregiver needs, and utilization of Social Security disability insurance. Conclusions: The experience from our center suggests that consistent, proactive musculoskeletal management at regular intervals during childhood and adolescence may help maintain in gait and mobility function from adolescence to young adulthood in individuals with CP. Full article