Search Results (3,711)

Rapid urbanization poses a serious threat to China’s ecological security. However, the current single-spatio-temporal-scenario ecological network constructed based on the current situation of natural resources and land use ignores the dynamic changes in network and urban development, and its stability and connectivity lack verification feedback. On the basis of constructing the existing ecological network, the paper forecasts the expansion of future urban land use and optimizes the overall ecological network of the study area from three aspects: ecological source areas, corridors, and ecological nodes. The results showed the following: (1) From 1990 to 2020, a large amount of forest land in the Guanzhong region was converted into cultivated land, while a large amount of cultivated land was converted into urban land, and the same applies to 2020–2050. From 1990 to 2050, the overall habitat quality in the Guanzhong region shows a downward trend, exhibiting a gradual decrease from the center towards the edges. (2) The ecological source area in the Guanzhong region decreases by 775.38 km², and the number of potential ecological corridors increases, but their length decreases by 1.75 km. The overall connectivity of the ecological network during the two periods is not high, and the degree of network closure is relatively low. (3) Comparative analysis before and after optimization shows that the optimized ecological network has greater ecological stability and landscape connectivity. In other words, the optimized ecological network can supplement and improve the original ecological network, thereby making it more sustainable and in line with the regional characteristics of the study area. Full article

Background and Objectives: Biofilm formation on cochlear implants (CIs) poses a major problem for surgeons, leading to a high incidence of explantation and revision surgery. Therefore, developing appropriate and cost-effective biofilm detection and prevention techniques is of the essence. In this systematic review, we sought to investigate the development of biofilm formation on CIs. We also elaborated on experimental preventative biofilm measures. Materials and Methods: We conducted a systematic search of both in vitro and in vivo literature published in PubMed, Scopus, and ScienceDirect, until 15 June 2024, for published studies evaluating the biofilm formation and strategies for inhibiting biofilm formation on CIs. Depending on the type of the included study, we assessed quality with the modified Consolidated Standards of Reporting Trials tool, the Joanna Briggs Institute Case Reports Critical Appraisal Tool, a modified Delphi technique, and the ROBINS-I tool. We synthesized the available information on biofilm formation on CIs and the infection prevention capacity of the included antibiofilm agents. Results: A total of 26 studies were included in this systematic review. Biofilms in CIs are usually localized in their recesses such as their removable magnet pocket as opposed to their smooth surfaces. S. aureus and P. aeruginosa are the most commonly isolated microorganisms, and they tend to be strong biofilm producers. The optimal treatment strategy for a biofilm-infected CI is explantation. Most of the examined biofilm prevention methods in CIs present sufficient antibiofilm activity. Conclusions: Biofilm formation in CIs is considered one of the most dreadful complications. There have been no specific guidelines for the treatment of those cases, with removal and/or replacement of the CI being the treatment of choice. Various experimental prevention methods provide promising antibiofilm activity both in vivo and in vitro. Full article

Mycoplasma infections pose significant challenges in the poultry industry, necessitating effective therapeutic interventions. Tiamulin, a veterinary antibiotic, has demonstrated efficacy against Mycoplasma species. However, the emergence of resistant Mycoplasma species could dramatically reduce the therapeutic potential, contributing to economic losses. Optimizing the tiamulin’s pharmacokinetic profile via nanocarrier incorporation could enhance its therapeutic potential and reduce the administration frequency, ultimately reducing the resistant strain emergence. Niosomes, a type of self-assembled non-ionic surfactant-based nanocarrier, have emerged as a promising drug delivery system, offering improved drug stability, sustained release, and enhanced bioavailability. In this study, niosomal nanocarriers encapsulating tiamulin were prepared, characterized and assessed in Mycoplasma-inoculated broilers following oral administration. Differential scanning colorimetry (DSC) confirmed the alterations in the crystalline state following components integration into the self-assembled structures formed during the formulation procedure. Transmission electron microscopy (TEM) showed the spherical nanostructure of the formed niosomes. The formulated nanocarriers exhibited a zeta potential and average hydrodynamic diameter of −10.65 ± 1.37 mV and 339.67 ± 30.88 nm, respectively. Assessment of the pharmacokinetic parameters following oral administration to Mycoplasma gallisepticum-infected broilers revealed the ability of the niosomal nanocarriers to increase the tiamulin’s bioavailability and systemic exposure, marked by significantly higher area under the curve (AUC) (p < 0.01) and prolonged elimination half-life (T_1/2) (p < 0.05). Enhanced bioavailability and prolonged residence time are crucial factors in maintaining therapeutic concentrations at reduced doses and administration frequencies. This approach provides a viable strategy to decrease the risk of subtherapeutic levels, thereby mitigating the development of antibiotic resistance. The findings presented herein offer a sustainable approach for the efficient use of antibiotics in veterinary medicine. Full article

Copper mining drives economic growth, with the global demand expected to reach 120 million metric tons annually by 2050. However, mining produces tailings containing heavy metals (HMs), which poses environmental risks. This study investigated the efficacy of phytoremediation (Phy) combined with electrokinetic treatment (EKT) to increase metal uptake in Carpobrotus aequilaterus grown in tailings from the Metropolitan Region of Chile. The plants were exposed to varying voltages and treatment durations. In the control (no EKT), the root metal contents were Fe (1008.41 mg/kg) > Cu (176.38 mg/kg) > Mn (103.73 mg/kg) > Zn (30.26 mg/kg), whereas in the shoots, the order was Mn (48.69 mg/kg) > Cu (21.14 mg/kg) > Zn (17.67 mg/kg) > Fe (27.32 mg/kg). The optimal EKT (15 V for 8 h) significantly increased metal uptake, with roots accumulating Fe (5997.24 mg kg⁻¹) > Mn (672 mg kg⁻¹) > Cu (547.68 mg kg⁻¹) > Zn (90.99 mg kg⁻¹), whereas shoots contained Fe (1717.95 mg kg⁻¹) > Mn (930 mg kg⁻¹) > Cu (219.47 mg kg⁻¹) > Zn (58.48 mg kg⁻¹). Although EKT enhanced plant growth and biomass, higher voltages stressed the plants. Longer treatments were more effective, suggesting that EK–Phy is a promising method for remediating metal-contaminated tailings. Full article

Mobile robot navigation is a critical aspect of robotics, with applications spanning from service robots to industrial automation. However, navigating in complex and dynamic environments poses many challenges, such as avoiding obstacles, making decisions in real-time, and adapting to new situations. Reinforcement Learning (RL) has emerged as a promising approach to enable robots to learn navigation policies from their interactions with the environment. However, application of RL methods to real-world tasks such as mobile robot navigation, and evaluating their performance under various training–testing settings has not been sufficiently researched. In this paper, we have designed an evaluation framework that investigates the RL algorithm’s generalization capability in regard to unseen scenarios in terms of learning convergence and success rates by transferring learned policies in simulation to physical environments. To achieve this, we designed a simulated environment in Gazebo for training the robot over a high number of episodes. The training environment closely mimics the typical indoor scenarios that a mobile robot can encounter, replicating real-world challenges. For evaluation, we designed physical environments with and without unforeseen indoor scenarios. This evaluation framework outputs statistical metrics, which we then use to conduct an extensive study on a deep RL method, namely the proximal policy optimization (PPO). The results provide valuable insights into the strengths and limitations of the method for mobile robot navigation. Our experiments demonstrate that the trained model from simulations can be deployed to the previously unseen physical world with a success rate of over $88\%$. The insights gained from our study can assist practitioners and researchers in selecting suitable RL approaches and training–testing settings for their specific robotic navigation tasks. Full article

Background: Psoriatic arthritis (PsA), a chronic inflammatory disease, mainly affects the joints, with approximately 30% of psoriasis patients eventually developing PsA. Characterized by both innate and adaptive immune responses, PsA poses significant challenges for effective treatment. Recent advances in drug delivery systems have sparked interest in developing novel formulations to improve therapeutic outcomes. The current research focuses on the development and evaluation of a nanosponge-loaded, cyclo-oxygenase-2 (COX-2) inhibitor-based topical gel for the treatment of PsA. Methods: Nanosponges (NSs) were prepared by using beta-cyclodextrin as a polymer and dimethyl carbonate (DMC) as a crosslinker by melting, and gels were prepared by employing carbopol and badam gum as polymers. Results: Solubility studies confirmed that the prepared nanosponges were highly soluble. FT-IR studies confirmed the formation of hydrogen bonds between lumiracoxib and beta-cyclodextrin. SEM confirmed that the prepared formulations were roughly spherical and porous in nature. The average particle size was 190.5 ± 0.02 nm, with a zeta potential of −18.9 mv. XRD studies showed that the crystallinity of lumiracoxib decreased after encapsulation, which helped to increase its solubility. The optimized nanosponges (NS2) were incorporated in an optimized gel (FG10) to formulate a nanosponge-loaded topical gel. The optimized gel formulation exhibited a homogeneous consistency, with a pH of 6.8 and a viscosity of 1.15 PaS, indicating its suitability for topical application and stability. The in vitro diffusion studies for the topical gel showed drug release of 82.32% in 24 h. The optimized formulation demonstrated significant antipsoriatic activity, as confirmed through cytotoxicity studies conducted on HaCaT cells. Conclusions: On the basis of the findings, it can be concluded that the prepared nanosponge-loaded topical gel formulation presents a promising solution for the effective management of PsA, offering enhanced drug solubility, sustained release, and improved therapeutic potential. Full article

The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for more effective intervention and management. This study uses images acquired via ultrasound and elastography to classify liver steatosis using classical machine learning classifiers, including random forest and support vector machine, as well as deep learning architectures, such as ResNet50V2 and DenseNet-201. The neural network demonstrated the most optimal performance, achieving an F1 score of 99.5% on the ultrasound dataset, 99.2% on the elastography dataset, and 98.9% on the mixed dataset. The results from the deep learning approach are comparable to those of machine learning, despite objectively not achieving the highest results. This research offers valuable insights into the domain of medical image classification and advocates the integration of advanced machine learning and deep learning technologies in diagnosing steatosis. Full article

Agile Earth observation satellite scheduling is crucial for space-based remote-sensing services. The sharply rising demands and explosion of the solution space pose significant challenges to the optimization of observation task scheduling. To address this issue, we propose a deep reinforcement learning-based attention decision network (ADN) to determine the task scheduling sequence. We also construct a Markov decision process model in which the original and direct attributes are defined to describe the environment and used as the input of the ADN. Moreover, a start-time-shift-based local search is proposed to improve the observation plan generated by the ADN model. A comprehensive experiment was conducted, and the results proved that the attention mechanism in our ADN was beneficial for the training process to converge to better strategies. Compared with other advanced algorithms, the proposed method obtained a better total profit in the test sets. Furthermore, our methods exhibit considerable time efficiency, even for large-scale problems. Full article

The reliance on soil acidity correctives and mineral fertilizers poses a threat to food production due to the finite nature of these resources and their susceptibility to price volatility from importation. Soil remineralizers have emerged as a sustainable alternative. This study assessed silicate agrominerals as soil remineralizers to replace limestone, gypsum, and conventional fertilizers in a no-tillage system. Conducted in a tropical climate on sandy/medium-textured Ultisol, twelve treatments (combinations of liming, gypsum, mineral fertilization, and remineralizer) were tested for their effects on soybean and sorghum agronomic traits. Applying a remineralizer at 2500 kg ha⁻¹ enhanced soybean productivity by 15% and sorghum by 35% in succession, along with increases in P, S, Ca, Mg, sum of bases (SB), cation exchange capacity (CEC), base saturation (V%) in the 0–0.20 m layer and organic matter in the 0–0.40 m layer, benefiting soil microbiological parameters, with the treatment combining all four products improving soil fertility; however, for better crop productivity, split applications appear to be an alternative to avoid nutrient imbalance. Due to the finer particle size of the remineralizer, which allows faster nutrient release, further research is recommended to investigate the long-term impacts on soil microbiota dynamics, optimal doses and combinations, and economic viability across various soil types and climates. Full article

As maximum power point tracking (MPPT) algorithms have developed towards multi-task intelligent computing, processors in photovoltaic power generation control systems must be capable of achieving a higher performance. However, the challenges posed by the complex environment of photovoltaic fields with regard to processor reliability cannot be overlooked. To address these issues, we proposed a novel approach. Our approach uses error rate and performance as switching metrics and performs joint statistics to achieve efficient adaptive switching. Based on this, our work designed a redundancy-mode switchable three-core processor system to balance performance and reliability. Additionally, by analyzing the relationship between performance and reliability, we proposed optimization methods to improve reliability while ensuring a high performance was maintained. Finally, we designed an error injection method and verified the system’s reliability by analyzing the error rate probability model in different scenarios. The results of the analysis show that compared with the traditional MPPT controller, the redundancy mode switchable multi-core processor system proposed in this paper exhibits a reliability approximately 5.58 times that of a non-fault-tolerant system. Furthermore, leveraging the feature of module switching, the system’s performance has been enhanced by 26% compared to a highly reliable triple modular redundancy systems, significantly improving the system’s reliability while ensuring a good performance is maintained. Full article

The hand–eye calibration of laser profilers and industrial robots is a critical component of the laser vision system in welding applications. To improve calibration accuracy and efficiency, this study proposes a position-constrained calibration compensation algorithm aimed at optimizing the hand–eye transformation matrix. Initially, the laser profiler is mounted on the robot and used to scan a standard sphere from various poses to obtain the theoretical center coordinates of the sphere, which are then utilized to compute the hand–eye transformation matrix. Subsequently, the positional data of the standard sphere’s surface are collected at different poses using the welding gun tip mounted on the robot, allowing for the fitting of the sphere’s center coordinates as calibration values. Finally, by minimizing the error between the theoretical and calibrated sphere center coordinates, the optimal hand–eye transformation matrix is derived. Experimental results demonstrate that, following error compensation, the average distance error in hand–eye calibration decreased from 4.5731 mm to 0.7069 mm, indicating that the proposed calibration method is both reliable and effective. Full article

Current lidar-inertial SLAM algorithms mainly rely on the geometric features of the lidar for point cloud alignment. The issue of incorrect feature association arises because the matching process is susceptible to influences such as dynamic objects, occlusion, and environmental changes. To address this issue, we present a lidar-inertial SLAM system based on the LIO-SAM framework, combining semantic and geometric constraints for association optimization and keyframe selection. Specifically, we mitigate the impact of erroneous matching points on pose estimation by comparing the consistency of normal vectors in the surrounding region. Additionally, we incorporate semantic information to establish semantic constraints, further enhancing matching accuracy. Furthermore, we propose an adaptive selection strategy based on semantic differences between frames to improve the reliability of keyframe generation. Experimental results on the KITTI dataset indicate that, compared to other systems, the accuracy of the pose estimation has significantly improved. Full article

Ladle furnace slag (LFS), a by-product of steel refining, shows a promising reuse pathway as an alternative additive or substitute for Portland cement due to its high alkalinity and similar chemical composition to clinkers. However, LFS is often stored in large, open surface areas, leading to many environmental issues. To tackle waste management challenges, LFS can be recycled as supplementary cementitious material (SCM) in many cementitious composites. However, LFS contains some mineral phases that hinder its reactivity (dicalcium silicate (γ-C₂S)) and pose long-term durability issues in the cured cemented final product (free lime (f-CaO) and free magnesia (f-MgO)). Therefore, LFS needs to be adequately treated to enhance its reactivity and ensure long-term durability in the structures of the cementitious materials. This literature review assesses possible LFS treatments to enhance its suitability for valorization. Traditional reviews are often multidisciplinary and explore all types of iron and steel slags, sometimes including the recycling of LFS in the steel industry. As the reuse of industrial by-products requires a knowledge of their characteristics, this paper focuses first on LFS characterization, then on the obstacles to its use, and finally compiles an exhaustive inventory of previously investigated treatments. The main parameters for treatment evaluation are the mineralogical composition of treated LFS and the unconfined compressive strength (UCS) of the final geo-composite in the short and long term. This review indicates that the treatment of LFS using rapid air/water quenching at the end-of-refining process is most appropriate, allowing a nearly amorphous slag to be obtained, which is therefore suitable for use as a SCM. Moreover, the open-air watering treatment leads to an optimal content of treated LFS. Recycling LFS in this manner can reduce OPC consumption, solve the problem of limited availability of blast furnace slag (GGBFS) by partially replacing this material, conserve natural resources, and reduce the carbon footprint of cementitious material operations. Full article

Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies. Full article

The SM2 public key cryptographic algorithm is widely utilized for secure communication and data protection due to its strong security and compact key size. However, the intensive large integer operations it requires pose significant computational challenges, which can limit the performance of Internet of Things (IoT) terminal devices. This paper introduces an optimized implementation of the SM2 algorithm specifically designed for IoT contexts. By segmenting large integers as polynomials within a modified Montgomery modular multiplication algorithm, the proposed method enables parallel modular multiplication and reduction, thus addressing storage constraints and reducing computational redundancy. For scalar multiplication, a Co-Z Montgomery ladder algorithm is employed alongside Single Instruction Multiple Data (SIMD) instructions to enhance parallelism, significantly improving efficiency. Experimental results demonstrate that the proposed scheme reduces the computation time for the SM2 algorithm’s digital signature by approximately 20% and enhances data encryption and decryption efficiency by about 15% over existing methods, marking a substantial performance gain for IoT applications. Full article