Search Results (33,165)

This research focuses on enhancing cooperative localization for heterogeneous mobile robots composed of a quadcopter and an unmanned ground vehicle. The study employs sensor fusion techniques, particularly the Extended Kalman Filter, to fuse data from various sensors, including GPSs, IMUs, and cameras. By integrating these sensors and optimizing fusion strategies, the research aims to improve the precision and reliability of cooperative localization in complex and dynamic environments. The primary objective is to develop a practical framework for cooperative localization that addresses the challenges posed by the differences in mobility and sensing capabilities among heterogeneous robots. Sensor fusion is used to compensate for the limitations of individual sensors, providing more accurate and robust localization results. Moreover, a comparative analysis of different sensor combinations and fusion strategies helps to identify the optimal configuration for each robot. This research focuses on the improvement of cooperative localization, path planning, and collaborative tasks for heterogeneous robots. The findings have broad applications in fields such as autonomous transportation, agricultural operation, and disaster response, where the cooperation of diverse robotic platforms is crucial for mission success. Full article

This study presents the first successful integration of Fe₃O₄ and Li_0.5Cr_0.5Fe₂O₄ into a well-defined core/shell nanostructure through a two-step synthesis that combines co-precipitation and sol–gel auto-combustion methods. Unlike conventional composites, the core/shell design effectively suppresses the magnetic dead layer and promotes exchange coupling at the interface, leading to enhanced saturation magnetization, superior magnetic heating (specific absorption rate; SAR), and improved dielectric properties. Our research introduces a novel interfacial engineering strategy that simultaneously optimizes both magnetic and dielectric performance, offering a multifunctional platform for applications in magnetic hyperthermia, electromagnetic interference (EMI) shielding, and microwave devices. Full article

Nematodes are among the organisms found in treated water. While generally considered harmless to human health, under certain conditions, they may serve as vectors for pathogenic viruses and bacteria, posing potential risks. Conventional disinfection processes in water treatment can contribute to the inactivation or removal of nematodes, but their effectiveness varies. This study, conducted at a water treatment plant (WTP) in Mashhad, Iran, aimed to determine the optimal dose and contact time of sodium hypochlorite and ozone for enhancing nematode inactivation in the affected surface water. This research combined primary disinfection using sodium hypochlorite at the existing WTP with a pilot ozone injection system to evaluate their individual and combined effectiveness. The results show that sodium hypochlorite at a concentration of 2 mg/L achieved 68% nematode inactivation. At 2.0 mg/L, with a 20min contact time, ozone disinfection resulted in 39% inactivation. However, the combined application of sodium hypochlorite and ozone significantly improved efficiency, reaching 92% nematode inactivation when sodium hypochlorite and ozone were applied at 2 mg/L and 3 mg/L, respectively, with a 20min ozone contact time. These findings indicate that, among the disinfection methods examined, the combined use of sodium hypochlorite and ozone is the most effective approach for nematode inactivation in drinking water, offering a promising strategy for improving water quality and safety. Full article

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

Background: Papillary thyroid microcarcinoma (PTMC), a subtype of papillary thyroid carcinoma ≤ 1 cm in diameter, has shown a marked increase in incidence in recent decades, largely due to the widespread use of neck ultrasonography and fine needle aspiration cytology. Despite its generally indolent course, optimal management of PTMC remains controversial, with treatment strategies ranging from active surveillance to total thyroidectomy. Methods: This retrospective study analyzes five years of experience at a single tertiary care center, including 130 patients diagnosed with PTMC following thyroid surgery between July 2018 and December 2023. Clinical, cytological, and pathological data were collected and analyzed to identify factors influencing surgical decision-making and postoperative outcomes. Patients underwent either total thyroidectomy or hemithyroidectomy, with central and lateral lymph node dissection performed as indicated. Follow-up included clinical and biochemical surveillance for a mean duration of 3 years. Results: Total thyroidectomy was performed in 89.3% of patients, while hemithyroidectomy was limited to 10.7%. Multifocality was observed in 26.1% of cases, with bilateral involvement in 17.7%. Occult lymph node metastases were found in 14.6% (central compartment) and 3.8% (lateral neck). Postoperative radioactive iodine therapy was administered in 23.8% of patients. At final follow-up, 90.7% were disease-free. No significant predictors of recurrence or adverse outcomes were identified, though multifocality and lymph node involvement influenced surgical planning. Conclusions: Our findings support a risk-adapted surgical approach to PTMC, favoring total thyroidectomy in patients with suspicious or multifocal disease to avoid reoperation. While active surveillance and minimally invasive techniques are emerging, total thyroidectomy remains a safe and effective strategy in selected cases. Prospective, multicenter studies are needed to further refine management guidelines for this increasingly common thyroid malignancy. Full article

Background and Objectives: The perioperative use of beta-blockers remains controversial due to conflicting evidence of their risks and benefits. The aim of this study was to evaluate the association between chronic beta-blocker (bb) therapy and perioperative cardiac events in non-cardiac surgeries using 24 h continuous Holter monitoring. Materials and Methods: A prospective observational study was conducted on patients undergoing elective or emergency non-cardiac surgery at a Romanian tertiary care hospital. The patients were divided into two groups: G1 (not receiving Bb) and G2 (on chronic Bb). The incidences of perioperative cardiac events, such as severe bradycardia (<40 b/min), new-onset atrial fibrillation (AF), extrasystolic arrhythmia (Ex), and sustained ventricular tachycardia (sVT) and arterial hypotension, were compared between the two groups using clinical, electrocardiography (ECG), and Holter ECG data. Beta-blocker indications, complications, and outcomes were analyzed using chi-squared tests and logistic regression. Results: A total of 100 consecutive patients (63% men, mean age of 53.7 years) were enrolled in the study. G2 included 30% (n = 30) of patients on chronic beta-blocker therapy. The indications included atrial fibrillation (46.7%, n = 14), arterial hypertension (36.7%, n = 11), extrasystolic arrhythmias (10%, n = 3), and chronic coronary syndrome (6.6%, n = 2). Beta-blocker use was significantly associated with severe bradycardia (n = 6; p < 0.001) in G2, whereas one patient in G1 had bradycardia, and 15 and 1 patients had hypotension (p < 0.001) in G1 and G2, respectively. The bradycardia and arterial hypotension cases were promptly treated and did not influence the patients’ prognoses. The 14 patients with AF in G2 had a 15-fold higher odds of requiring beta-blockers (p < 0.001, odds ratio (OR) = 15.145). No significant associations were found between beta-blocker use and the surgery duration (p = 0.155) or sustained ventricular tachycardia (p = 0.857). Ten patients developed paroxysmal postoperative atrial fibrillation (AF), which was related to longer surgery durations (165 (150–180) vs. 120 (90–150) minutes; p = 0.002) and postoperative anemia [hemoglobin (Hg): 10.4 (9.37–12.6) vs. 12.1 (11–13.2) g/dL; p = 0.041]. Conclusions: Patients under chronic beta-blocker therapy undergoing non-cardiac surgery have a higher risk of perioperative bradycardia and hypotension. Continuous Holter monitoring proved effective in detecting transient arrhythmic events, emphasizing the need for careful perioperative surveillance of these patients, especially the elderly, in order to prevent cardiovascular complications These findings emphasize the necessity of tailored perioperative beta-blocker strategies and support further large-scale investigations to optimize risk stratification and management protocols. Full article

Mangroves, as a key component of the blue-carbon ecosystem, have exceptional carbon sequestration capacity and are mainly distributed in tropical coastal regions. In the Solomon Islands, ongoing degradation of mangrove forests, primarily due to land conversion and timber exploitation, highlights an urgent need for high-resolution spatial data to inform effective conservation strategies. The present study introduces an efficient and accurate methodology for mapping mangrove habitats and prioritizing protection areas utilizing open-source satellite imagery and datasets available through the Google Earth Engine platform in conjunction with a U-Net deep learning algorithm. The model demonstrates high performance, achieving an F1-score of 0.834 and an overall accuracy of 0.96, in identifying mangrove distributions. The total mangrove area in the Solomon Islands is estimated to be approximately 71,348.27 hectares, accounting for about 2.47% of the national territory. Furthermore, based on the mapped mangrove habitats, an optimized hotspot analysis is performed to identify regions characterized by high-density mangrove distribution. By incorporating spatial variables such as distance from roads and urban centers, along with mangrove area, this study proposes priority mangrove protection areas. These results underscore the potential for using openly accessible satellite data to enhance the precision of mangrove conservation strategies in data-limited settings. This approach can effectively support coastal resource management and contribute to broader climate change mitigation strategies. Full article

With the rapid development of heterogeneous satellite networks integrating geostationary earth orbit (GEO) and low earth orbit (LEO) satellite systems, along with the significant growth in the number of satellite users, it is essential to consider frequency compatibility and coexistence between GEO and LEO systems, as well as to design effective system resource allocation strategies to achieve efficient utilization of system resources. However, existing beam-hopping (BH) resource allocation algorithms in LEO systems primarily focus on beam scheduling within a single time slot, lacking unified beam management across the entire BH cycle, resulting in low beam-resource utilization. Moreover, existing algorithms often employ iterative optimization across multiple resource dimensions, leading to high computational complexity and imposing stringent requirements on satellite on-board processing capabilities. In this paper, we propose a BH-based beam scheduling and resource allocation framework. The proposed framework first employs geographic isolation to protect the GEO system from the interference of the LEO system and subsequently optimizes beam partitioning over the entire BH cycle, time-slot beam scheduling, and frequency and power resource allocation for users within the LEO system. The proposed scheme achieves frequency coexistence between the GEO and LEO satellite systems and performs joint optimization of system resources across four dimensions—time, space, frequency, and power—with reduced complexity and a progressive optimization framework. Simulation results demonstrate that the proposed framework achieves effective suppression of both intra-system and inter-system interference via geographic isolation, while enabling globally efficient and dynamic beam scheduling across the entire BH cycle. Furthermore, by integrating the user-level frequency and power allocation algorithm, the scheme significantly enhances the total system throughput. The proposed progressive optimization framework offers a promising direction for achieving globally optimal and computationally tractable resource management in future satellite networks. Full article

This study joins a 20 mm thick 5000-series aluminum alloy using hot-wire insertion combined with narrow-gap laser welding to evaluate the feasibility and welding characteristics of this technique. The findings indicate that weld formation is primarily influenced by the laser energy density and material deposition rate. A strategy for improving weld beads is introduced incorporating a reoriented laser spot during the final pass on narrow-gap joints. This approach improves penetration and produces defect-free joints. The optimal processing conditions result in complete joint formation with four welding passes. Microstructural analysis reveals that the aluminum matrix morphology evolves according to the local thermal history during welding. Measurements show that the weld region is slightly harder than the base metal, whereas slightly lower hardness is observed at the fusion line and inter-pass boundaries, which correlates with the microstructure result. Full article

Background/Objectives: The role of body composition in sports performance has been widely studied, particularly in soccer. Understanding how anthropometric characteristics impact movement efficiency and neuromuscular performance is crucial for optimizing player performance. This study examined the relationship between body composition and locomotor performance in elite soccer players. Methods: Thirty-six male soccer players from the Mexican National Team participated in the study, where body composition was assessed using surface anthropometry. Players underwent tests to measure countermovement jump (CMJ) performance, sprinting speed, maximum acceleration, and distance covered during two games of the CONCACAF Nations League quarterfinals. Correlation matrices were created to identify the most significant associations, followed by generalized linear models (GLMs) to associate body composition variables with performance metrics. Results: Anthropometric profile tables were created by playing position. Higher body fat percentage (%BF) was associated with lower performance. Specifically, higher %BF was associated with slower sprint speed (B = −0.74 m/s, p < 0.001) and shorter distance covered (B = −4.86 m/min, p < 0.001). Conversely, greater muscularity, reflected by corrected girth values for the thigh and calf, was associated with improved CMJ performance. Thigh corrected girth was positively associated with concentric mean force (B = 48.85 N, p < 0.001), and calf corrected girth was positively associated with peak power (B = 240.50 W, p < 0.001). These findings underscore the importance of low body fat and high lean mass for efficient movement. Conclusions: The results highlight the critical role of body composition in enhancing soccer performance, particularly for explosive movements like jumps, sprints, and accelerations. This study suggests that monitoring and optimizing body composition should be a central focus of nutrition, training, and conditioning strategies, adapted to the specific positional demands of professional soccer. Full article

Volatile organic compounds (VOCs) are presently posing a rather considerable threat to both human health and environmental sustainability. Among these, n-butanol is commonly identified as bringing potential hazards to environmental integrity and individual health. This study presents the creation of a highly sensitive n-butanol gas sensor utilizing cobalt-doped zinc oxide (ZnO) derived from a metal–organic framework (MOF). A series of x-Co/MOF-ZnO (x = 1, 3, 5, 7 wt%) nanomaterials with varying Co ratios were generated using the homogeneous co-precipitation method and assessed for their gas-sensing performances under a low operating temperature (191 °C) and UV excitation (220 mW/cm²). These findings demonstrated that the 5-Co/MOF-ZnO sensor presented the highest oxygen vacancy (O_v) concentration and the largest specific surface area (SSA), representing the optimal reactivity, selectivity, and durability for n-butanol detection. Regarding the sensor’s response to 100 ppm n-butanol under UV excitation, it achieved a value of 1259.06, 9.80 times greater than that of pure MOF-ZnO (128.56) and 2.07 times higher than that in darkness (608.38). Additionally, under UV illumination, the sensor achieved a rapid response time (11 s) and recovery rate (23 s). As a strategy to transform the functionality of ZnO-based sensors for n-butanol gas detection, this study also investigated potential possible redox reactions occurring during the detection process. Full article

Background: Heart failure (HF) is frequently associated with skeletal muscle wasting, reduced functional capacity, and malnutrition. High-protein diets offer a promising nutritional intervention to improve these outcomes in individuals with HF. Objective: This systematic review evaluated randomized controlled trials of high-protein dietary interventions in HF populations, with emphasis on intervention characteristics, quantitative benefits, and risk of bias. Methods: We conducted a comprehensive search in PubMed, MEDLINE, Embase, and Cochrane CENTRAL from inception to June 2025. Eligible studies enrolled adults (≥18 years) with HF, implemented high-protein regimens (≥1.1 g/kg/day or ~25–30% of energy), and reported on functional capacity, body composition, muscle strength, clinical outcomes, or biochemical markers. Two reviewers independently screened, extracted data, and assessed bias (Cochrane RoB 2). Heterogeneity in dosing, duration, and outcomes precluded meta-analysis; we therefore provide a narrative synthesis. Results: Ten trials (nine randomized controlled trials, one pilot) involving 1080 patients (median n = 38; range 21–652) were included. High-protein interventions yielded mean improvements in six-minute walk distance of +32 ± 14 m, lean body mass gain of +1.6 ± 0.9 kg, and 9 ± 4% enhancement in quality-of-life scores; muscle strength effects varied from −2% to +11%. Two studies reported an 18% reduction in HF readmissions (p < 0.05). The risk-of-bias assessment identified two low-risk, three moderate-risk, and one high-risk study. Key limitations include small sample sizes, varied protein dosing (1.1–1.5 g/kg/day), short follow-up (2–6 months), and outcome heterogeneity. Conclusions: High-protein dietary strategies appear to confer modest, clinically relevant gains in functional capacity, nutritional status, and HF readmission risk. Larger, well-powered trials with standardized dosing and longer follow-up are necessary to establish optimal protein targets, long-term efficacy, and safety. Full article

Neuropathic pain is a significant global clinical issue that poses substantial challenges to both public health and the economy due to its complex underlying mechanisms. It has emerged as a serious health concern worldwide. Recent studies involving dorsal root ganglion (DRG) stimulation have provided strong evidence supporting its effectiveness in alleviating chronic pain and its potential for sustaining long-term pain relief. In addition to that, there has been ongoing research with clinical evidence relating to the role of small non-coding ribonucleic acids known as microRNAs in regulating gene expressions affecting pain signals. The signal pathway involves alterations in neuronal excitation, synaptic transmission, dysregulated signaling, and subsequent pro-inflammatory response activation and pain development. When microRNAs are dysregulated in the dorsal root ganglia neurons, they polarize macrophages from anti-inflammatory M2 to inflammatory M1 macrophages causing pain signal generation. By reversing this polarization, a therapeutic activity can be induced. However, the direct delivery of these nucleotides has been challenging due to limitations such as rapid clearance, degradation, and reduction in half-life. Therefore, safe and efficient carrier vehicles are fundamental for microRNA delivery. Here, we present a comprehensive analysis of miRNA-based nano-systems for chronic neuropathic pain, focusing on their impact in dorsal root ganglia. This review provides a critical evaluation of various delivery platforms, including viral, polymeric, lipid-based, and inorganic nanocarriers, emphasizing their therapeutic potential as well as their limitations in the treatment of chronic neuropathic pain. Innovative strategies such as hybrid nanocarriers and stimulus-responsive systems are also proposed to enhance the prospects for clinical translation. Serving as a roadmap for future research, this review aims to guide the development and optimization of miRNA-based therapies for effective and sustained neuropathic pain management. Full article

With the exponential growth of engineering monitoring data, data-driven neural networks have gained widespread application in predicting retaining structure deformation in foundation pit engineering. However, existing models often overlook the spatial deflection correlations among monitoring points. Therefore, this study proposes a novel deep learning framework, CGCA (Convolutional Gated Recurrent Unit with Cross-Attention), which integrates ConvGRU and cross-attention mechanisms. The model achieves spatio-temporal feature extraction and deformation prediction via an encoder–decoder architecture. Specifically, the convolutional structure captures spatial dependencies between monitoring points, while the recurrent unit extracts time-series characteristics of deformation. A cross-attention mechanism is introduced to dynamically weight the interactions between spatial and temporal data. Additionally, the model incorporates multi-dimensional inputs, including full-depth inclinometer measurements, construction parameters, and tube burial depths. The optimization strategy combines AdamW and Lookahead to enhance training stability and generalization capability in geotechnical engineering scenarios. Case studies of foundation pit engineering demonstrate that the CGCA model exhibits superior performance and robust generalization capabilities. When validated against standard section (CX1) and complex working condition (CX2) datasets involving adjacent bridge structures, the model achieves determination coefficients (R²) of 0.996 and 0.994, respectively. The root mean square error (RMSE) remains below 0.44 mm, while the mean absolute error (MAE) is less than 0.36 mm. Comparative experiments confirm the effectiveness of the proposed model architecture and the optimization strategy. This framework offers an efficient and reliable technical solution for deformation early warning and dynamic decision-making in foundation pit engineering. Full article

Urban Air Mobility (UAM) is envisioned to revolutionize urban transportation by improving traffic efficiency and mitigating surface-level congestion. One of the fundamental challenges in implementing UAM systems lies in the optimal siting of vertiports, which requires a delicate balance among infrastructure construction costs, passenger access costs to their assigned vertiports, and the operational connectivity of the resulting vertiport network. This study develops an integrated mathematical model for vertiport location decision, aiming to minimize total system cost while ensuring UAM network connectivity among the selected vertiport locations. To efficiently solve the problem and improve solution quality, a hybrid genetic algorithm is developed by incorporating a Minimum Spanning Tree (MST)-based connectivity enforcement mechanism, a fundamental concept in graph theory that connects all nodes in a given network with minimal total link cost, enhanced by a greedy initialization strategy. The effectiveness of the proposed algorithm is demonstrated through numerical experiments conducted on both synthetic datasets and the real-world transportation network of New York City. The results show that the proposed hybrid methodology not only yields high-quality solutions but also significantly reduces computational time, enabling faster convergence. Overall, this study provides practical insights for UAM infrastructure planning by emphasizing demand-oriented vertiport siting and inter-vertiport connectivity, thereby contributing to both theoretical development and large-scale implementation in complex urban environments. Full article