Robotic path planning and navigation algorithms enable autonomous mobile robots to navigate complex environments. These algorithms allow robots to be deployed in search and rescue missions, where they can locate victims in hazardous areas inaccessible to humans. Additionally, autonomous driving algorithms hold the potential to revolutionize transportation by enabling self-driving cars to navigate roads safely and efficiently. This paper focuses on developing an algorithm for autonomous navigation of the mobile robot (Tortoise bot) using robot operating system (ROS), based on publish-subscribe communication facilitating seamless interaction between various nodes. Tortoise Bot is a differential wheeled robot whose movement is based on two separately driven wheels placed on either side of the robot body. The Raspberry Pi acts as the controller. A 360-degree LiDAR and RGB-D camera are used for creating a 3D map of the environment. Various state-of-the art software tools, such as Gazebo, Ubuntu, MATLAB and RPI imager are used. Initially, a simulation based on simple obstacle avoidance is done using gazebo and MATLAB. Another simulation based on trajectory tracking is implemented using MATLAB to study the kinematic model. Online navigation and path planning are executed with the help of teleoperation and Dynamic Window Approach algorithm. A novel Wander algorithm is created that avoids obstacles on its path. Object recognition is also implemented during autonomous navigation using YOLO.

This article presents a combined route and path planning strategy to guide Skid-Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity, and ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrated the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

ACL instability poses a significant challenge in traumatology and orthopedic medicine, often requiring accurate diagnosis for appropriate treatment. While the pivot-shift test offers a crucial means of assessment, its reliance on subjective interpretation underscores the need for supplementary imaging studies. This study aims to address this limitation by developing a Bayesian classification algorithm tailored for integration into a mobile application. Using the built-in inertial sensors of smartphones, this new approach aims to dynamically evaluate rotational stability during knee examinations. Orthopedic specialists conducted knee evaluations on 52 subjects, with subsequent analysis revealing interesting insights. Intraobserver and interobserver analyses, as measured by ICC, demonstrated strong agreement both in terms of timing between maneuvers (ICC = 0.94) and signal amplitude (ICC = 0.71-0.66). Notably, the Bayesian algorithm successfully classified 95% of joint hypermobility cases, with an additional 7 cases of hyperlaxity identified by the Pivot-Shift Meter (PSM). These findings highlight the practicality and effectiveness of implementing a Bayesian classification algorithm within a mobile application for assessing and categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

As split learning (SL) becomes popular for addressing data privacy and resource limitation issues in edge model training process, it is crucial to pre-validate whether the extensive use of wireless and computing resources in SL can achieve the expected training latency and convergence. While digital twin network (DTN) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for SL tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreport resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 13.44% and convergence estimation accuracy by 116.74% compared to traditional equation-based methods.

In this work we present some driving strategies for a type of omnidirectional mobile robot, the offset-differential robot. This system presents omnidirectionality while using any type of standard wheels, allowing for applications in all terrain. The work is based on the full kinematic and dynamic analysis of the robot, including the passive elements, to explain some unexpected behaviors that appear during its motion. The stable and unstable behaviors are characterized and some driving strategies are stated in order to achieve the desired performance regarding precise positioning and speed.

The Dubai Digital Government launched its recent guidelines, which call for artificial intelligence systems to be subject to legal accountability. This study discusses the extent to which autonomous robots can be granted legal personality in UAE law and the consistency of this approach with the provisions of Islamic jurisprudence. This research paper answered two main questions: First, the extent to which these guidelines are considered the beginning of work on granting legal personality to AI systems in the UAE. Second, what form of legal personality can be given to autonomous robots in UAE law to be consistent with the provisions of Islamic jurisprudence as a primary source of legislation in the country? The research concluded the impossibility of considering autonomous robots as a "thing" and classifying them within the concept of "persons." It also concluded that it is possible to give them legal personality according to two legislative solutions: granting them partial or incomplete performance eligibility like minors.

This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

Use of MS Excel as a platform for teaching simple iterative solution of partial differential equations runs into issues of reliability of the computation because of the large number of calculations required. This article documents the problem and an empirical solution. The cause of this unreliability was found by detailed examination of the numerical results from three simultaneous iterations. Computers of several types from different manufacturers were found to fail occasionally in updating variables in the numerical iteration. A simple work-around is suggested and was tested on several platforms.

Obstacle-avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies fuzzy logic to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on multi-layer perceptron and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive obstacle-avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “ experience ” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

Beginning with navigation system design, this paper presents a comprehensive strategy for enhancing the search and rescue capabilities of agile mobile robots. Towards this, the autonomous ground vehicle (AGV) utilizes surface classification to determine and prioritize the terrain it is traversing. Our developed system design incorporates real-time terrain data with task objectives at a high level, ensuring that the robot can effectively navigate complex and ever-changing environments. This design, in conjunction with the introduction of a novel lightweight surface classification model, forms the basis of our adaptive terrain perception and decision-making systems, enabling robots such as Jackal to adapt rapidly and make the decisions necessary to complete the task. Subsequently, we exhaustively validated these systems through a series of extensive experiments in a variety of terrains, including normal and mixed terrains, demonstrating their robustness and efficacy in real-life situations.

As a result of extensive research in the field of mobile robots (rovers) and swarms, a number of algorithms exist to assist them for executing a mission in the three levels of software architecture: strategic (interaction loop level), tactic (planning) and operational (sensing, control and actuation). They allow them to achieve their goals while adapting to their environment through a multitude of methods designed for each situation. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction applied to help humans in hazardous environment such as militarized zone. In this paper, we will present some interesting algorithms for interactive and autonomous mobile robots acting in swarms in an open and crowded environment. A discussion will focus on comparing different algorithms and their advantages and disadvantages.

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

The Giant Babax, an endemic bird in China and a nationally protected wildlife species of Class Ⅱ, is primarily distributed in southern and southeastern Tibet. To investigate the factors influencing nesting site selection, this study conducted research on nest characteristics and nesting site preferences at four sample sites around Lhasa from March to October 2023y, using systematic search methods, tracking methods, and sampling methods. The research findings are as follows: (1) The average nest depth was (7.70±1.47)cm, nest height was (19.58±2.97)cm, inner diameter was (10.64±1.73)cm, outer diameter was (24.65±4.8)cm, average weight was (162.3±13.5)g, and average nest spacing within the plot was 396.93m. (2) The nests of Giant Babax were predominantly found on vegetation such as Salix longistamina (38%), Hippophae gyantsensis (20%), Rosa sericea Lindl. (14%), and Populus szechuanica Schneid(11%). They showed a preference for artificial secondary forests as nesting sites. (3) Giant Babax exhibited a preference for habitats with robust trees that had high canopies while being distant from houses and roads, but close to water sources; moreover, the canopy of the nesting tree played a crucial role in their choice of nesting sites. (4) The reproductive success rate reached 76%, with key factors affecting it being the canopy coverage of the nesting tree and the number of helpers. These research results provide fundamental data support for population conservation efforts and ecological resource management strategies concerning the Giant Babax.

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Preterm birth accounts for about 10% of births worldwide. Studying risk factors for perinatal brain damage is essential, as findings suggest that almost 20% of disabilities are linked to risks in the early stages of development. This research aimed to study longitudinal changes in intelligence from 6 to 8 years of age in a sample of 39 preterm children with a history of risk of brain damage and a control group of 35 children born at term. The Wechsler Intelligence Scale (WISC-IV) was used for cognitive measurement. The results showed that the preterm group obtained significantly lower scores than the control group. The working memory indicator significantly affected the interaction between age and prematurity. We consider that it is crucial to expand the knowledge we have about the neurocognitive development of premature infants both in specific cognitive domains and in age ranges so that the information obtained helps to predict the probability of presenting cognitive alterations from early stages and, therefore, to implement intervention strategies and programs based on scientific evidence and complemented in their design by clinical experience, empirical and theoretical knowledge of the different professionals involved in infant cognitive intervention.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

Diacylglycerol kinase (DGK) is a lipid kinase that phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Based on converting one important signaling molecule (DAG) to another (PA), DGK plays an important role in plant responses to abiotic stress including waterlogging stress. However, no studies have been reported on the characterization of the DGK gene family in the waterlogging-tolerant kiwifruit germplasm, Actinidia valvata Dunn. Here, we identified 18 AvDGK genes in the A. valvata genome. The phylogenetic analysis showed that AvDGKs can be classified into three clusters, and members within the same cluster have similar domain distribution, exon-intron structures and conserved motif compositions. Chromosome localization analysis revealed that all the AvDGK genes are located across 18 different chromosomes. There were 29 duplicated gene pairs in kiwifruit and all had undergone purifying selection during evolution. Promoter cis-element analysis revealed that the cis-elements within AvDGK genes are associated with multiple functions, including phytohormone signal transduction, stress response, and plant growth and development. The expression pattern analyses indicated that AvDGK play an important role in the fruit development and plant response to waterlogging stress. AvDGKs gene family in the tetraploid A. valvata genome might promote PA synthesis and subsequent signal transduction both under short-term and long-term waterlogging stress. These results provide information regarding the structural characteristics and potential function of DGK genes within kiwifruit and lay a fundamental basis for further research into breeding for enhancing the kiwifruit’s tolerance to waterlogging stress.

The development of virtual models has recently been generalized in the field of complex mechatronic product design. Virtual models have various advantages such as: the systemic design approach, most often parametric, the implementation of extreme, expensive, and perhaps risky scenarios for the operation of the designed product, the testing of a large number of variants, etc. The main contribution of the paper consists of the development of an interface family called DeSeRoI (Dedicated Serial Robot Interface). Each member of the interface family integrates Simulink libraries and is created by the programmer in accordance with user requests. It follows that the interfaces composing the DeSeRoI family can have different levels of complexity depending on user needs and constraints. We are implementing a MATLAB-based application with user-provided configuration for serial robots in order to automatically visualize the kinematics data and simulation. Within MATLAB, two scripts are created responsible for generating Simulink models that compute the kinematics problem. One of the scripts (Main) saves information about the configuration of the robot and the other one (Generates_Simulink_Models) computes the kinematics problem. Different types of functions (software module) have been developed. Using these functions, the Simulink models result as modular entities. The automated approach eases user’s interaction and understanding of serial robots’ kinematics by performing the simulation procedure in a shorter amount of time and with higher efficiency, assuring a precise outcome despite the chosen parameters.

High-rise building fires pose a serious threat to the lives and property safety of the people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescue. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of autonomous optimal deployment of base stations in high-rise building fire environment based on UAV group. A novel problem formulation is proposed that solves the non line of sight (NLOS) positioning problem in complex unknown environment. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environment. The NLOS positioning problem in the fire field environment is turned into the line of sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations deployment goal and to avoid collision. Finally, positioning accuracy of our method and that of common method were compared. The simulation result showed that the positioning accuracy of simulated firefighter in the fire field environment was improved greatly.

Nanomagnetism and spintronics are currently active areas of research, with one of the main goals being the creation of low-energy-consuming magnetic memories based on nanomagnet switching. These types of devices could also be implemented in neuromorphic computing by crafting artificial neurons (ANs) that emulate the characteristics of biological neurons, through the implementation of neuron models such as the widely used leaky integrate-and-fire (LIF) with a refractory period. In this study, we have carried out numerical simulations of a 120 nm diameter, 250 nm length ferromagnetic nanowire (NW) with the aim of exploring the design of an artificial neuron based on the creation and destruction of a Bloch-point domain wall. To replicate signal integration, we applied pulsed trains of spin-currents to the opposite faces of the ferromagnetic NW. These pulsed currents (previously studied only in the continuous form) are responsible for inducing transitions between the stable single vortex (SV) state to the metastable Bloch point domain wall (BP-DW) state. To ensure the system exhibits the leak and refractory properties, the NW was placed in an homogeneous magnetic field of the order of mT in the axial direction. The suggested configuration fulfills the requirements and characteristics of a biological neuron, potentially leading to the future creation of Artificial Neural Networks (ANNs) based on reversible changes in the topology of magnetic NWs.

This paper presents experimental results of static and dynamic obstacle avoidance methods for a two-wheel mobile robot with independent control using a deep Q-learning (DQL) reinforcement learning algorithm. This is an alternative method that combines the Q-learning (QL) algorithm with a neural network. Neural networks in the DQL algorithm act as Q matrix table approximators for each pair (State - Action). The effectiveness of the proposed solution was verified through simulations, programming, and experimentation. This DQL algorithm is compared to the QL algorithm. First, the mobile robot communicated with the control script using the robot operating system (ROS). The mobile robot is code programmed using a Python in the ROS operating system combined with the DQL controller in Gazebo software. The mobile robot then performed experiments in a workshop with many different experimental scenarios. This DQL controller is improved in term of the computation time, convergence time, planning trajectory accuracy, and obstacles avoidance. Therefore, the DQL controller solved the path optimization problems for mobile robots better than the Q-learning controller.

We analyze the transformation of a very broad class of metrics that can be put in terms of static coordinates. Starting from a general ansatz, we obtain a relation for the parameters in which one can impose further symmetries, or restrictions. One of the simplest restrictions leads to FLRW cases, while transforming from the initial static to other static-type coordinates can lead to near horizon coordinates, Wheeler-Regge, isotropic coordinates, among others. As less restrictive cases, we show an indirect route for obtaining Kruskal-Szekeres within this approach, as well as Lema\^{\i}tre coordinates. We use Schwarzschild spacetime as prototype for testing the procedure in individual cases. However, application to other spacetimes, such as de-Sitter, Reissner-Nordstr\"{o}m or Schwarzschild de Sitter, can be readily generalized.

In this paper, a method is developed for presenting a novel virtual torque sensor based on precise model and position measurements avoids the need of traditional strain gauges and amplifiers. More specifically, the harmonic drive compliance model and the Gaussian process regression (GPR) technique are used together to achieve virtual torque sensor measurement. While the harmonic drive compliance model provides the analytic part, the Gaussian process regression method is used to reconstruct the unmolded part based on motor-side and link-side joint angles as well as motor current. After an automatic offline calibration, the method allows for a lean online implementation. The virtual torque sensor measurement is compared with measurements of a commercial torque sensor, and the results have attested the effectiveness of the proposed method.

The demand for improved service in mobile networks is always increasing. That means signal transmission with higher capacity is required. To achieve that goal a combined optical and wireless link using double polarization multiplexing has been developed. A high quality signal transmission is achieved by applying high polarization extinction ratio. The new concept is validated by experiments varying the bit rate and fiber length. A signal with 12 Gbit/s bit rate was transmitted over a 25 km long combined link with about 1.10-8 bit error rate. That result is much better than the already published data which were measured on links transmitting information with 2.5 Gbit/s bit rate. The polarization multiplexing technique is very practical for capacity enhancement in a specific section of an existing optical fiber by inserting a new link into it without deploying a new fiber.

The rationale of this study is to explore underlying sustainability assessment constructs in the context of WCSM implementation as a key focus by manufacturing industries in the journey of achieving world-class status in their manufacturing operations. This study primarily intends to collect primary data from 143 plant managers and supervisors through the structured questionnaires at select manufacturing companies in south India on the basis of area-cum-purposive sampling technique. The survey questionnaire is designed exclusively to reflect the sustainability assessment dimensions in terms of social, economic and environmental issues to examine the impetus of WCSM implementation on the competitive advantage of manufacturing companies. The empirical findings of this study clearly revealed that sustainable manufacturing practices, sustainability programs, creation of sustainable products, safety for employee, community and product, operational efficiency and integration of sustainability goals in day-to-day manufacturing activities were considered to be the key indicators by the manufacturing firms in their never-ending journey towards becoming the world class. Moreover, the WCSM practices & techniques adopted by sample manufacturers have a substantial positive influence on the achievement of competitive advantage in their manufacturing and business operations.

Retinal vein cannulation involves puncturing an occluded vessel on the micron scale. Even single millinewton force can cause permanent damage. An ophthalmic robot with a piezo-driven injector is precious enough to perform this delicate procedure, but the uncertain viscoelastic characteristics of the vessel makes it difficult to achieve the desired contact force without harming the retina. The paper utilizes a viscoelastic contact model to explain the mechanical characteristics of retinal blood vessels in order to tackle this issue. The uncertainty in the viscoelastic properties is considered an internal disturbance of the contact model, and an active disturbance rejection controller is then proposed to precisely control the contact force. The experimental results show that this method can precisely adjust the contact force at the millinewton level even when the viscoelastic parameters vary significantly (up to 403.8%). The root mean square (RMS) and maximum value of steady-state error are 0.32 mN and 0.41 mN. The response time below 2.51 s with no obvious overshoot.

Low back pain (LBP) is one of the most common musculoskeletal conditions, resulting in continuous pain and disability. However, reliable evidence exists for only a few existing therapies for LBP. This study aimed to determine the efficacy of a newly designed cutting-edge massage device that uses a mechanical sequential ascending method. Fifteen individuals with LBP underwent mechanical sequential ascending massage method with thermal therapy (AM-TH), thermal therapy (TH), rotating roller massage method with thermal therapy (RM-TH), and clinical assessments. We analysed the participants’ biomechanical muscle properties, pain, satisfaction, and disability measured immediately before and after treatment. There was a significant decline in tone and stiffness in the upper and lower regions of the paraspinal muscles when AM-TH was administered on three or more occasions (p < 0.05). AM-TH was more effective than RM-TH in reducing pain, with a significant reduction in pain over time (p < 0.05). Participants were most satisfied with RM-TH; over time, RM-TH was effective in improving participants’ disabilities (p < 0.05). AM-TH has been demonstrated to be effective in individuals with LBP, improving muscle tone and stiffness and reducing pain. Therefore, it may be a useful treatment for LBP.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

This scientific paper explores the increasingly widespread use of collaborative robots in the industrial environment, highlighting them as a solution to challenges related to the high costs of human labour and the associated management difficulties. The authors focus on the process of defining requirements for the implementation of collaborative robots, providing strategies based on mathematical management models. This approach translates into significant benefits for companies, such as cost reduction, quality improvement, and increased operational efficiency. Additionally, the paper proposes an innovative mathematical model for assessing total costs, budgeting, profitability, and electric power consumption associated with the operation of collaborative robots. This model allows for a comparison between the costs involved in using human labour and those associated with collaborative robots, providing critical information for technological investment decisions. The efficiency of this mathematical model is demonstrated through a practical application, where collaborative robots were integrated into a production environment, and costs and efficiency were evaluated compared to the use of human labour. This scientific paper provides a systematic and efficient approach to implementing collaborative robots in industrial processes, benefiting from strategies based on mathematical management models and an original mathematical model for evaluating performance and associated costs.

As coastal and marine ecosystems, even the inland waters can be deeply affected by human-driven impacts. Consequently, the internal waters belonging to closed or semi-closed basins due to their hydrographic and morphological conditions can be subject to even more dramatic consequences in terms of conservation and overall ecosystem functioning maintaining. Despite the Albano Lake is listed among the Natura 2000 network sites (IT6030038), several elements of its landscape complexity have already experimented clearly visible alterations: from certain landscape features alterations to the loss of some iconic vegetal and animal species characterizing the lake environment in the past. Therefore, a virtuous commitment awaits us and we have the moral duty of acting in order to restore such degraded habitats. Each environmental restoration intervention and/or clean up session can be considered relevant in order to give back the right dignity to one of the most naturalistically relevant environments of Central Italy. For each field session a plethora of data can be acquired both onshore and underwater by diving operators. Therefore, the data management of a huge temporal and spatial variability can be remarkable. It can be made easier relying on artificial intelligence (AI) supporting geospatial databases for GIS analysis to be run for their role as decision-supporting tools. On this perspective AIs can be used to model environmental systems, greatly helping to reduce use conflicts and human-driven impacts, whilst understanding the ecological characteristics and related ecosystem functioning.

Placenta accreta spectrum (PAS) disorders are characterized by abnormal trophoblastic invasion into the myometrium, leading to significant maternal health risks. PAS includes placenta accreta (invasion <50% of the myometrium), increta (invasion >50%), and percreta (invasion through the entire myometrium). The condition is most associated with previous cesarean deliveries, and its incidence increases with the number of cesareans. Other risk factors include advanced maternal age, multiparity, and various uterine surgeries. The increasing global cesarean rates heighten the importance of early PAS diagnosis and management. The study reviewed molecular mechanisms driving PAS, focusing on signaling pathways, genetic expression, biomarkers, and non-coding RNAs involved in trophoblastic invasion. It explored genetic expression and key regulatory processes such as apoptosis, cell proliferation, invasion, and inflammation. The manuscript was compiled from recent scientific literature (2014 – 2024) from the Scopus, PubMed, Google Scholar, and Web of Science databases. Placenta Acreta Spectrum; acreta; increta; percreta; trophoblast invasion; miRNAs, lncRNAs; genetic expression; biomarkers; signaling pathways were the employed keywords to compile relevant literature. The PAS is an important obstetric health problem that challenges maternal health risks associated with abnormal trophoblastic invasion that relies on abnormal placentation, uterine scarring, and impaired decidualization. Identifying new biomarkers like AFP, sFlt-1, β-hCG, PlGF, and PAPP-A aids early detection and management. Understanding genetic expression and non-coding RNAs is crucial for unraveling PAS complexities. Besides, aberrant signaling pathways like Notch, PI3K/Akt, STAT3, and TGF-β offer potential therapeutic targets to modulate trophoblastic invasion. This review underscores the need for interdisciplinary care, early diagnosis, and ongoing research into PAS biomarkers and molecular mechanisms to improve prognosis and quality of life for affected women.

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment but has also posed significant challenges to network security, especially the physical layer security challenges,which is crucial for the reliable transmission of messages interconnected devices and sensors. Focus on the enhancing the physical layer security in the IIoT communication, this paper proposed a mechanism of Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate device is known, while eavesdropper on the random distribution of device locations, forming the SSOP model related to device locations from the perspective of insecure regions (ISR) and secure regions (SR), and deduce the closed-form expression for its upper bound. Subsequently under the constraint of SSOP conditions, establish an optimization model with the objective of system throughput maximization based on Spatial Secrecy Outage Probability(SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in the MATLAB, we verified the accuracy of the definition and derived upper bound of SSOP. The experimental results also show that SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. These performance are crucial for understanding and optimizing the security performance of IIoT communication systems.

(1) Background: LUS has emerged as a simple, rapid and non-invasive method for the dynamic assessment of pulmonary congestion, a major prognostic factor and a therapeutic target in AHF. (2) Methods: In the single-center prospective observational study, 42 patients hospitalized for AHF in the post-emergency polyvalent medicine department of CHU Grenoble were successively included between May 2021 and July 2022. Clinical examination, LUS and electrolyte panel results were collected daily. Vital status was assessed 30 days after the last LUS. The primary endpoint was the evolution of the number of B-lines according to the dose of diuretic administered ; (3) Results: We performed 188 LUS. Our patients were elderly (85.8 [SD 8.1]) and comorbid. The median number of B-lines decreased from 17 at admission to 7 in the middle of the hospital stay, then stagnated until discharge. Simultaneously, the median dose of diuretics, initially stable at a maximum of 40 mg/d IV, decreased to 20mg/d IV by the end of hospital stay. Patients with CKD had a higher mean number of B-lines at entry (24.2 [SD 11.6]) compared to those without CKD (8.2 [SD 8.8]). However, the evolution of B-lines number was independent of creatinine levels. 30-day all-causes mortality was significantly related to the number of B-lines at discharge (15.2 [SD 10.1] in deceased patients versus 3.9 [SD 5.4] in living patients, p

To characterize the fecal microbiota of lactating Bactrian camels, this study employed a random selection of camels, assigning them to three groups: the first parity group, the third parity group, and the fifth parity group. The fecal microbial community of lactating camels was assessed using 16S rRNA amplicon sequencing, and the resulting library was sequenced on an Illumina NovaSeq platform. The first parity group was characterized by Verrucomicrobiaceae as a distinctive bi-omarker, whereas Clostridiaceae exhibited a similar role to the third parity group. In terms of functional analysis, the relative abundance of the Steroid biosynthesis pathway was highest in the first parity group, whereas the fifth parity group demonstrated a higher relative abundance of the Tuberculosis pathway compared to the other groups. The first parity group exhibited the greatest presence of seven types of enzymes, whereas the fifth parity group displayed the highest levels of two specific enzymes. Additionally, a positive association was observed between the relative abundance of the Steroid biosynthesis pathway and Verrucomicrobiaceae. Furthermore, the relative abundance of the Tuberculosis pathway displayed positive correlations with 14 enzymes. In conclusion, this study revealed that different parity was associated with distinct fecal microbial ecologies in lactating Bactrian camels.

Currently, agro-industrial residues have been used to obtain bioactive compounds. The fermentation process is an alternative for the use of these residues and to be able to accumulate metabolites that may be of interest to different industrial sectors. The aim of this study was to evaluate solid-state fermentation conditions using pineapple waste as substrate with Aspergillus niger spp., to release polyphenolic compounds using a Plackett-Burman exploratory design. Temperature, humidity, inoculum, NaNO3, MgSO4, KCl and KH2PO4 conditions in the fermentation process were evaluated. The results revealed that the Aspergillus niger HT3 strain reached a hydrolyzable tannins accumulation of 10.00 mg/g, while Aspergillus niger Aa20 reached a condensed tannins release of 82.59 mg/g. The KH2PO4 affect the release of condensed tannins with A. niger Aa20 and MgSO4 affect the release of hydrolyzable tannins with A. niger HT3. In addition, a positive antioxidant activity was demonstrated for the DPPH, ABTS and FRAP technique. The HPLC analysis was able to identify compounds in the fermented pineapple peel such as 3-feruloylquinic acid, caffeic acid, lariciresinol and 3-hydroxyphloretin 2'-O-xylosyl-glucoside, among others. The solid-state fermentation process is a biotechnological alternative for the release of bioactive compounds.

This paper systematically discusses the application of reinforcement learning in automatic driving system. Reinforcement learning frameworks show significant advantages in optimizing decision making, predictive perception, path planning, and controller design, exceeding the limitations of traditional supervised learning methods. The paper highlights the critical role of components such as scene understanding, positioning, and map making in autonomous driving systems, which provide reliable environmental awareness through deep learning and sensor fusion technologies to support intelligent decision-making in complex urban environments. In addition, the paper discusses innovative approaches to safety reinforcement learning to reduce risk in autonomous driving and ensure that systems adhere strictly to safety constraints while maximizing expected rewards. These findings provide an important theoretical and practical basis for further improving algorithm robustness, managing multi-agent interactions, and integrating ethical considerations in the future.

Elderly individuals frequently perceive companion robots devoid of warmth, resulting in a reluctance to accept these robots' services. This perception has become a significant barrier to the introduction of companion robots into households. To address this issue, this study employs KE methods to translate the elderly's need for warmth perception from companion robots into elements of modelling design. Initially, 20 pairs of affective words and 22 sample images of companion robots were chosen. The "rational-emotional" and "dim-bright" dimensions were identified as positively influencing the elderly's warmth perception of companion robots through SD method analysis. Subsequently, a hierarchical analysis table was established, with warmth perception as the root layer, modelling style and color matching as the basic layer, "rational-emotional" and "dim-bright" as the expansion layer, and the eight characteristic elements of companion robots as the morphological layer. The link between affective imagery and design elements was then established from the sample robots with higher warmth perception scores. Based on the correlation between the two, various design schemes were formed through random combinations. Finally, the design schemes were evaluated using the warmth perception scale, and the warmth perception scores of each sample model were obtained. This research provides design references for designing companion robots for the elderly with a "sense of warmth".

Introduction: Most industrialized and developed countries now working to implement more advanced transportation systems. These include autonomous vehicle(AV) systems of various forms, such as delivery vehicles, which eliminate the crashes, injuries, and fatalities associated with human-operated machinery. Self-driving vehicles have the potential to make roads safer, but some safety concerns need to be addressed. Before AV systems become commonplace in developing countries, society needs to determine how safe they truly are. Objectives: This paper provides brief descriptions and examines the realities for AV in developing countries using results from infrastructure-based assessment of countries worldwide and from a questionnaire-based survey. Methods: Relevant research papers, articles, reports, and departmental websites were used as the main data sources. Ethiopia was chosen as a representative case. Different aspects of AV infrastructure, its current status in developing countries, and perspectives on AV systems are explored through interviews with approximately 1500 randomly selected individuals. The main variables in the assessment were physical infrastructure, digital infrastructure, and societal perceptions on AV systems. Results: Surprisingly, the results indicated that 55.82%, 78.58%, 87.78%, and 86.09% of Ethiopian interviewees agreed with statements regarding high ease of use, usefulness, positive attitude, and benefits, respectively, of AV. However, it should be noted that some interviewees did not provide a response to certain questions.

The expansion of food production is getting more important due to a rising world population, which is relying on food security on a regional and local scale. Intensive food production systems create a negative impact on the regional ecosystem because of agrochemical pollution and nutrient rich water discharges into nearby rivers. Furthermore, these systems are highly depending on regional water resources causing water scarcity and soil erosion due to the overexploitation of natural resources in general. The objective of this article is to review the water usage in the two most water intensive food production systems, agriculture and aquaculture showing lacking areas, like system management and climate change, which must be considered in the implementation of sustainable water footprint. In addition, the review includes an analysis if the combination of both production system into aquaponic food production and the possibilities of water saving. There are a variety of water footprint analyses for crop and aquatic animal production, but there is also a lack of information about the system management including irrigation systems, system cleaning processes, water substitution, pond removal, evaporation due to climate change and especially in aquaculture, the water footprint of industrial elaborated fish feed.

Industrial robots are slowly but surely entering manufacturing companies. This paper explores the effects that robots have on productivity, exports, quality, sustainability and labor in European manufacturing companies. There is lack of research on industrial robot usage and its effects in developed countries. Most research is done on Chinese companies and often the data is outdated. The data in this paper is from the European Manufacturing Survey project conducted in 2022 and includes 476 manufacturing companies. Results of the effects of Industrial robots on Quality, Labor Productivity, Exports and Environmental saving technologies are done by use of T-test between industrial robots’ adopters and non-adopters. However, the effect of higher investment into environmental technology by industrial robots’ adopters was researched by 2-step OLS regression analysis with control variables representing the contextual characteristics of companies. The results show positive effects on all variables. Results show that larger usage of robots are in low to medium-technology-intensity industries, that robots contribute to labor productivity, exports and that companies employing robots usually also employ environmentally saving technologies.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, Dynamic Programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. Through case studies, we demonstrate the effectiveness of the proposed approach in navigating complex scenarios, contributing to the future of autonomous maritime navigation. Through case studies, we show the efficacy of our approach in navigating complex scenarios.

Chronic conditions like type 2 diabetes mellitus (T2DM), arterial hypertension (HTN), and obesity are major global health issues, causing significant morbidity and mortality. In Latin America, these conditions contribute to millions of premature deaths annually, posing challenges exacerbated by limited healthcare access and poor primary care quality, particularly in Mexico. Complementary medicine presents a potential solution, offering minimally invasive techniques to improve physical, mental, and spiritual well-being. Effective treatment adherence, crucial for positive outcomes, is influenced by self-efficacy, the ability to manage one's behavior in response to medical conditions. However, treatment adherence rates remain low, indicating a pressing public health concern. This study aimed to explore how perception of complementary medicine and treatment adherence predict self-efficacy among individuals with chronic conditions in Mexico. Analyzing data from 113 participants revealed significant correlations between variables, with treatment adherence positively associated with self-efficacy and perception of holistic medicine negatively correlated with self-efficacy. Regression analysis showed that incorporating perception of complementary medicine and treatment adherence significantly explained self-efficacy levels, suggesting the potential of complementary therapies in holistic healthcare approaches. However, collinearity between variables highlights the need for further research to better understand these relationships and their implications for improving healthcare outcomes in Mexico. Integrating complementary therapies with conventional treatments could enhance overall well-being and treatment adherence, offering promising avenues for addressing chronic conditions in the region. These findings underscore the importance of comprehensive healthcare approaches that integrate both conventional and complementary modalities to optimize health outcomes and quality of life for individuals with chronic conditions in Mexico and beyond.

Present paper is dealing with the adaptive static balancing of robot or other mechatronic arms that are moving in vertical plane and whose static loads are variable, by using counterweights and springs. Some simple passive and approximate solutions are proposed and an example is shown. The active and exact solutions by using adaptive real time control in the case of unknown variation of static loads are simulated on VIPRO platform developed at Institute of Solid Mechanics of Romanian Academy.

Mining wastes generate a high environmental impact, and population exposure to metals and metalloids. Phytoremediation is a technology that uses plants to remediate polluted sites, one of its limitations is seed germination in soil with high content of metals and metalloids. B. dactyloides is a native specie from semiarid regions, while C. dactylon is an introduced specie, both are tolerant to drought and low temperatures. Owing, the objective of this research was to evaluate the germination of both species, exposed to a multi-polluted soil with As, Cd, Pb and Zn of a mining site, pondering different pH conditions (from 5.0 to 9.0). The study considered 4 repetitions by type of seed and soil pH. B. dactyloides showed higher germination percentage (83%) with pH 7.8; while the greater germination of C. dactylon was 34 % at pH 6.0. In the soil at pH 5, the germination in both species was not reached, owing that metals are more bioavailable in an acid environment. C. dactylon is less tolerant to As, Cd, Pb and Zn, so it is considered not effective for phytoremediation process. B. dactyloides has a high phytoremediation potential for multi-polluted sites, but further experiments are needed.

Lane detection is a foundational technology for autonomous driving systems. It involves identifying the boundaries of lanes on the road and ensuring the vehicle stays within these boundaries. Accurate lane detection is crucial for safety, navigation and traffic management. This paper presents an artificial intelligent based autonomous road lane detection and navigation system for vehicles. The system can detect and analyze road lanes accurately and efficiently in real-time, with frame rates of up to 17 FPS. By utilizing image processing techniques, the system can identify the location and boundaries of road lanes and obstacles on the road and provide accurate and reliable navigation guidance to the driver. The system is integrated with sensors and actuators to provide comprehensive autonomous navigation. The efficiency of the proposed system is demonstrated using accuracy of lane detection on straight and curve lanes covering frames per second and motor of the speed as parameters for assessment. Results show that the proposed system detects lane as entire lane detection, partial lane detection and no detection status on different settings.

Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI protocols to these imbalances is crucial. This paper explores unbalanced PSI scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities. It introduces three innovative solutions for unbalanced PSI: unbalanced PSI protocol based on Cuckoo filters, unbalanced PSI protocol based on single cloud assistance, and unbalanced PSI protocol based on dual cloud assistance, each addressing the shortcomings of the previous solution. These protocols primarily optimize database computational load, storage capacity, protocol execution time, and security. Depending on performance and security needs, different protocols can be employed for applications such as private contact discovery.

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

Larrea tridentata is an endemic plant from the Mexican highlands used in traditional medicine to treat hypertension and kidney stones, among many ailments. This study evaluated a vasorelaxant compound: 5- Hydroxy-2-(4-hydroxy-3-methoxyphenyl)-6,7,8-trimethoxy-4H-1-benzopyran-4-one (also known as 8-Methoxycirsilineol: 8-M) which we purified from an extract of this plant. Mesenteric artery rings were isolated from male guinea pigs for isometric force measurements. Freshly isolated smooth muscle cells from mesenteric artery were used for ionic current measurement for calcium and potassium. 10 μM 8-M completely relaxed vascular rings contracted with phenylephrine in a dose-dependent fashion with an EC50 = 4.26 ± 1.16 10-6 M and 4.56 ± 1.64 10-6 M for endothelium intact and endothelium-free tissue, respectively. The relaxant effect of 8-M was not affected by maximal doses of nifedipine, verapamil or iberiotoxin. Nevertheless, the relaxant effect was partially abolished by addition of 10 mM Tetraethylammonium (TEA). In accordance to this, calcium currents evoked by depolarization were not affected by 8-M, whereas TEA-sensitive potassium currents, were increased upon its addition. Vasorelaxation due to 8-M is partly due to activation of KV currents in an endothelium independent mode.

Autonomous navigation in dynamic environments poses a significant challenge in the field of ro-botics with the primary goals of ensuring both smooth and safe movement. This study introduces control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance au-tonomous robot navigation in dynamic environments, emphasizing on collision-free path plan-ning. This strategy employs, a path planning technique to develop a trajectory that allows the ro-bot to navigate smoothly while avoiding collisions with both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experi-mental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, partic-ularly in terms of computational efficiency and navigational smoothness.

This paper deals with the analytical modeling and control of rectilinear snake robots. During recent times snake robots have created much interest among researchers. The rectilinear pattern gait is one of the four biological snake locomotion modes. Rectilinear snakes have been widely used in rescue operations especially in rough terrains especially in narrow spaces where human intervention is not easy. Computational analysis of rectilinear motion is done using MATLAB.

Following technological advances, autonomous and remote-controlled ships are performing trials in some sea areas; a mandatory goal-based MASS code, expected to enter into force on 1 January 2032, is under discussion in the International Maritime Organization (IMO). However, there are no unified standards regarding verification methods to define operating limitations for cutting-edge technologies, which are expected to be elements of MASS, so it is difficult to assess the risk factors for the management and operation of MASS. This paper outlines the regulatory framework for MASS, followed by a discussion of its application, considering the differences between MASS and automated driving, and how to develop safety management and operation using standards for automated driving. A requirements management framework developed as a system and software engineering standards to build safety management and operation of MASS is proposed. It is also suggested that the requirements management framework could be used not only for MASS development but also for regulatory review from a requirements management view.

Larus michahellis increased its population throughout the 20th century in its worldwide distribution area. The increase in the Salinas de San Pedro del Pinatar has produced changes in the habitats due to the contribution of guano, predation on birds, incidents with workers, and the production of salt. The objective is to analyze the impacts of L. michahellis on the landscape, habitats, waterfowl, salt production, and workers, as well as to evaluate the effectiveness of control activities. Censuses of wintering L. michahellis have been carried out between 1990-2021, of nesting aquatic birds between 1994-2021, and nests and eggs of L. michahellis have been eliminated between 2000-2021. The result has been a decrease in pairs of L. michahellis, recovery of waterfowl populations, colonization of others, absence of incidents with workers, and reduction of damage to salt production. However, to reach a definitive solution, measures should be adopted to prevent L. michahellis from accessing the main sources of human food: urban solid waste dumps, aquaculture farms, and fish discards.

The rapid growth of the sharing economy has propelled shared mobility to the forefront of attention. The continuous advancement of autonomous driving technology also brings new opportunities and challenges to the shared mobility market. This study comprehensively analyzes the market potential concerning autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis, PESTLE analysis, and Porter’s Five Forces. The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction and the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted with a low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics.

This study investigates the permeability and California Bearing Ratio (CBR) of base course materials for flexible pavements, focusing on materials from local quarries in Querétaro and Puebla, Mexico. Recognizing the fundamental role of the base layer in load distribution and hydraulic conductivity, the research evaluates these properties at different grain sizes and moisture contents. The objective is to define an optimal range that maximizes both drainage and mechanical resistance, crucial to extending the useful life of the pavement. Four different quarries were selected for material sampling and extensive testing was performed to determine permeability and CBR values under different conditions. The findings reveal the need for broader experimentation and methodological improvements, particularly in the measurement of hydraulic conductivity, a parameter not yet regulated for these materials in Mexico. The results demonstrated permeability values ranging between 1.2810^-3 and 1.9010^-3 cm/s, highlighting the significant impact of the percentage of fine material and its plasticity on the hydraulic and mechanical performance. Despite achieving permeability rates lower than the optimal value suggested in American manuals (0.35 cm/s), even with a fine material content of less than 5%, the study highlights the potential of local materials to meet the international standards with proper processing and testing. This research contributes to the global body of knowledge in pavement engineering by linking local geomaterial properties to broader applicable engineering practices. It suggests that even locally sourced materials, when appropriately evaluated and used, can significantly influence overall pavement solutions, promoting sustainability and cost-effectiveness in pavement construction.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

During the most recent years, a lot of research has been done in creating robots with more self-governance so that they can overcome the challenges that real world environments present. The robot's limited versatility in real world applications can be overcome by the development of Legged robots. Also, as they permit movement in unavailable territory to robots with wheels, Legged Robots are more advantageous. But the potency of the legged robots explicitly its energy usage among alternate points of view really fall behind robots that use wheels. So, the present status of development, there are as yet a few perspectives that need to be analysed, optimized and enhanced. This paper presents review of literature of various biologically inspired legged robots, various techniques adopted for their analysis and optimization and the analysis and optimization of the ones that are not biologically inspired

Environmental monitoring programs rely on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. Nevertheless, the employment of unmanned surface vehicles (USV) equipped with integrated sensors represents a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The USVs have been deployed by Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyllα, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for thein situ determination of copper and lead was also evaluated herewith. The results obtained demonstrate that both the timely detection of potential pollution from both anthropogenic activities and natural processes as well as the monitoring of inland waters are made feasible through the employment of USV.

Three autonomous communities have been the ones that, in the 2022-2023 season, anticipated the document approved by the Public Health Commission, recommending influenza vaccination for all children aged 6 to 59 months. The main objective of this work is to evaluate the attitude of healthcare professionals towards the first universal vaccination campaign in our region, as well as the acceptability of the vaccines used and the attitude towards the piloting of school vaccination. Cross-sectional, survey-based, descriptive study. All healthcare professionals involved in the campaign were invited to participate. The 91.9% of surveyed professionals thought that flu vaccination from 6 to 59 months was important or very important. 89.8% had previous experience regarding the intramuscular vaccine. The professionals valued the intranasal vaccine significantly more positively; however, there were no differences when asking for each of the vaccines without comparison. The inhaled vaccine was preferred by 97.5% for the following campaign. Piloted school vaccination showed 75% acceptance. The inhaled vaccine was preferred by most professionals and the piloted school vaccination was highly accepted, and independently associated to the importance of vaccination as considered by physicians, being a medical doctor, and participation in the pilot program.

This paper introduces a three-layer system, proposing a comprehensive model of granular mixture transport over a mobile sloped bed in a steady flow. This system, consisting of the bottom, contact and upper zones, provides complete, continuous sediment velocity and concentration vertical profiles. The model considers gravity’s effect on sediment transport in the bottom (dense) layer, a crucial factor often overlooked in previous studies. The induced movement in the mobile sublayer of the dense layer causes an increase in velocity at the upper boundary of this layer, significantly affecting the vertical distributions of velocity and concentration above this layer. We described the interactions between fractions and an intensive sediment mixing and sorting process in the contact layer. The proposed shear variation, with an increase in its maximum value at the bed, adds to the novelty of our approach. The data sets used for the model’s validation cover various conditions, including slopes, grain diameters, densities and grain mobility conditions, from incipient motion to a fully mobilised bed. This extensive validation process instils confidence in the theoretical description and its applicability to real-world scenarios.

Most crops are fertilized with high amounts of nitrogen, and have an alarmingly low utiliza-tion efficiency. For this reason, the coordination between the fertilizer contribution and the ni-trogen requirements of the crop is very important. Therefore, the objective of this study was to determine the nitrogen use efficiency, and create new NUE indices that can determine and calculate the final destination of the assimilated nitrogen, for the formation and development of green bean plant organs, fertilized with molybdenum nanofertilizer applied foliarly and combined with soil fertilization of ammonium nitrate. The plants were grown in a greenhouse covered with anti-aphid mesh and irrigated with nutrient solution. Three sources of foliar Molybdenum (Nano fertilizer, Molybdenum Chelate and Sodium Molybdate) were applied in four doses of 0, 5, 10 and 20 ppm Mo, complemented with edaphic fertilization of NH4NO3 (0, 3, 6 and 12 mM of N). As results, the NUE indices showed that with the application of the nanofertilizer, the total biomass production increased 41.65% more than with the application of the chelate, and 36.84% more than with the application of molybdate. Finally, it is con-cluded that the use of NUE indices is an important approach that evaluates the fate of nitro-gen and accurately estimates plant yield.

The algorithm described in this paper inherits the concepts of layered fusion, insertion, and set from four convex hull-based heuristic algorithms. It summarizes relevant definitions and theorems, proposes valuable research hypotheses, combines grouping and insertion, and innovatively introduces the practice of "grouped insertion."

Neural or mental simulation of actions is a powerful tool for allowing cognitive agents to develop Prospection Capabilities that are crucial for learning and memorizing key aspects for challenging skills. In previous studies we developed an approach based on the animation of the redundant human Body Schema based on the Passive Motion Paradigm (PMP). In this paper we show that this approach can be easily extended to hyper-redundant serpentine robots as well as to hybrid configurations where the serpentine robot is functionally integrated with a traditional skeletal infrastructure. A simulation model is analyzed in detail showing that it incorporates spatio-temporal features discovered in the biomechanical studies of biological hydrostats like the elephant trunk or the octopus tentacles. It is proposed that such generative internal model can be the basis for a cognitive architecture appropriate for serpentine robots, independent of the underlying design and control technologies. Although robotic hydrostats have received a lot of attention in the last decades, the great majority of research activities have been focused on the actuation/sensorial/material technologies that can support the design of hyperredundant soft robots as well as the related control methodologies. The cognitive level of analysis has been limited to motion planning, without addressing synergy formation and metal time travel. This is where the contribution of the paper is focused on.

The application of space robotic manipulators and heightened autonomy for ios represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on drl to solve for the space manipulator path-planning during the motion synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-dof robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The ppo drl algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joints rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an ios mission.

To encourage proper waste management from electrical and electronic devices (e-waste), it is necessary to invest heavily in the development of recycling technologies. One way to improve the process is to automate separating the shredded parts of e-waste using a robot. This paper's literature review, utilizing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, showcases potential robotic technologies for e-waste separation. However, the intricate design of these devices can pose significant challenges in their implementation. Various legal, organizational, and sociological obstacles have left Serbia's e-waste management practice underdeveloped, resulting in an unsatisfactory recycling rate. In this paper we examined the possibility of using robots on the precise example of recycling refrigerators in a recycling center in Eastern Serbia, concluding that such a solution would have multiple positive effects, both on the employees and the working environment, on the operations of the recycling center itself, and on increasing the e-waste recycling rate in the country.

Linear Hashing is an important algorithm for many key-value stores. Spiral Storage was invented to overcome the poor fringe behavior of Linear Hashing, but after an influential study by Larson, seems to have been discarded. Since almost 50 years have passed, we repeat Larson’s comparison with in-memory implementation of both to see whether his verdict still stands. Our study shows that Spiral Storage has slightly better look-up performance, but slightly poorer insert performance.

Keywords: Echocardiographic alterations; left ventricle; dog; Trypanosoma cruzi; flow patterns; dilated cardiomyopathy; Chagas disease

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, developing architectures that let them establish empathetic relationships with users. Several models have been proposed in recent years to solve this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioural models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 1007 articles found (at the end 30 articles were selected). For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analysing the aspects that can be improved so that an enjoyable interaction between robots and users can be established.

Since it was proposed, the Robot Operating System (ROS) has fostered solutions for various problems in robotics in the form of ROS packages. One of these problems is Simultaneous Localization and Mapping (SLAM), a problem solved by computing the robot pose and a map of its environment of operation at the same time. The increasingly availability of robot kits ready to be programmed and also of RGB-D sensors often pose the question of which SLAM package should be used given the application requirements. When the SLAM subsystem must deliver estimates for robot navigation, as is the case of applications involving autonomous navigation, this question is even more relevant. This work introduces an experimental analysis of GMapping and RTAB-Map, two ROS compatible SLAM packages, regarding their SLAM accuracy, quality of produced maps and use of produced maps in navigation tasks. Our analysis aims ground robots equipped with RGB-D sensors for indoor environments and is supported by experiments conducted on datasets from simulation, benchmarks and from our own robot.

Aiming at the APF algorithm in the collision-free path planning and LQR algorithm tracking process, there are problems such as unreachable target points, local optimization, low safety coefficient of autonomous lane change collision avoidance tracking control and poor stability, this paper proposes an optimized APF method, which solves the defects of the traditional artificial potential field method by adding the distance adjustment factor, the safety distance protection model and the sub-target virtual potential field, and at the same time generates the autonomous driving car collision-free path. The path tracking control system is designed based on feed-forward control and DLQR controller, in which the vehicle dynamics state space model is used as the basis, the model is linearized and discretized, and the corresponding constraint variables are set to minimize the control command objective cost function under the constraints, and the objective of the DLQR controller.The objective is to accurately determine the steering angle in order to achieve path tracking and eliminate the lateral error between the reference path and the actual path caused by external interference.Finally, the simulink model and CarSim parameter configuration joint simulation platform are built, and different obstacle scenarios and different speeds are selected for collision-free path planning and tracking motion simulation research and analysis, which verifies the accuracy and practicability of the algorithm, and shows more obvious advantages in obstacle avoidance ability, path smoothing degree, and collision avoidance safety stability. The ideas proposed in this paper are expected to be further extended and popularized to actual path planning problems, which has important practical significance and engineering value.

Crop rotation diversification using legumes has been advocated as one of the solutions to improve the resilience of the crop system to various environmental stresses and the use of nitrogen resources. However, when forage legumes or grass-legumes are ploughed, there is a high risk of nitrate leaching, especially in sandy soils. The aim of the study was to determine the effect strip tillage intercropping management on soil mineral nitrogen (SMN), water-extractable organic carbon (WEOC) content and winter wheat (WW, Triticum aestivum L.) grain yield compared to forage legume and WW monocropping with conventional tillage. In intercropping was used winter wheat - black medick (Medicago lupulina L.) WW+BM, winter wheat - white clover (Trifolium repens L.) WW+WC, and winter wheat - Egyptian clover (Trifolium alexandrinum L.) WW+EC. Research was repeated in two experiments. The results showed that after strip tillage and WW sowing less or similar SMN content cud be detected, as in conventional tillage and WW + forage legumes intercropping. WW grain yield in intercrops decreased, compared to the grasses monocultures that were ploughed before WW sowing. The highest amount of WEOC was in intercropping soil growing WW+WC or in all fields (except WW+EC) after applying strip tillage. During the research period, the amounts of mobile humic substances (HSs) and mobile humic acids (HAs) changed analogously. Their content increased substantially in fields with WC and EC, regardless of whether the grasses were ploughed or grown with WW.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

The objective of this study was to carry out the extraction and characterization of chitin and chitosan from the exoskeletons of the Farfantepenaeus californiensis species of shrimp, as well as its application in the formulation of polymeric films plasticized with glycerol used in the process of food packaging. The characterization of the extracted polymer was made by Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and physicochemical analysis of chitin and chitosan, resulting in a theoretical performance of 29.33% and 16.46%, respectively. In terms of quality, it was determined that the extracted chitosan had a molecular weight of 1.10 x105 g/mol and a deacetylation degree of 80.23%, which classifies it as a low-molecular weight chitosan, suitable for food packaging. It was shown that the extracted chitosan is fit for the production of the films by using the casting method, presenting a tension resistance of 1 MPa. The Antibacterial analysis was carried out using Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), bacterial models commonly found in food. The results showed an effective inhibition capability of the polymeric materials. Overall, the results for both chitosan and the films indicate a high potential for application in the food packaging sector.

Mobile devices (MD) are used by mobile cloud computing (MCC) customers users because of their 11 portability and robust connectivity and the ability to house and operate third-party applications 12 (apps). However, the apps installed on a MD may pose data security risks to the MD owner and to 13 other MCC users, especially when the requested permissions include access to sensitive data (e.g., 14 user’s location and contacts). Calculating the risk score of an app or quantifying its potential 15 harmfulness based on user input or on data gathered while the app is actually running may not 16 provide reliable and sufficiently accurate results to avoid harmful consequences. This study 17 develops and evaluates a risk assessment framework for Android-based MDs that does not depend 18 on user input or on actual app behaviour. Rather, an app risk evaluator assigns a risk category to 19 reach resident app based on the app’s classification (benign or malicious) and the app’s risk score. 20 The app classifier (a trained machine learning model) considers the permissions and the intents 21 requested by the app. The apps risk score is calculated by a probabilistic function based on the 22 app’s use of a set of selected dangerous permissions. The results from the testing of an instance of 23 the framework on a MD with real-life resident apps indicated that the proposed security solution 24 was effective and feasible.

Federated learning (FL) has emerged as a crucial technology in today’s data-centric technological environment, enabling decentralized machine learning and safeguarding user privacy. This study introduces “Federated Operations (FedOps) Mobile,” a novel FL framework optimized for the dynamic and heterogeneous ecosystem of mobile devices. FedOps Mobile enhances traditional FL approaches for mobile devices by integrating real-time operational control and advanced on-device training capabilities using TensorFlow Lite and CoreML, addressing critical challenges in scalability, efficiency, and system heterogeneity. Our approach utilizes a wide range of devices, facilitated by intelligent client-selection mechanisms. This mechanism evaluates the capabilities and readiness of multiple devices per client to ensure fair and efficient network participation. The framework also utilizes remote device control for seamless task management and sustained engagement, enabling continuous learning without compromising the user experience. We conducted extensive experiments to validate the framework’s performance, focusing on three core aspects: operational efficiency, model personalization, and resource optimization in multi-device environments. The results demonstrate that the proposed method is effective for efficient client selection, energy consumption, and model optimization.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

MiR-204 is a small noncoding RNA with diverse regulatory roles in breast cancer (BC) development and progression. miR-204 is implicated in the instauration of fundamental traits acquired during the multistep development of BC known as hallmarks of cancer. It may act as a potent tumor suppressor by inhibiting key cellular processes like angiogenesis, vasculogenic mimicry, invasion, migration, and metastasis. It achieves this by targeting multiple master genes involved in these processes, including HIF-1α, β-catenin, VEGFA, FAK, FOXA1, among others. Additionally, miR-204 modulates signaling pathways like PI3K/AKT and interacts with HOTAIR and DSCAM-AS1 lncRNAs, further influencing tumor progression. Beyond its direct effects on tumor cells, miR-204 shapes the tumor microenvironment by regulating immune cell infiltration, suppressing pro-tumorigenic cytokine production, and potentially influencing immunotherapy response. Moreover, miR-204 plays a crucial role in metabolic reprogramming by directly suppressing metabolic genes within tumor cells, indirectly affecting metabolism through exosome signaling, and remodeling metabolic flux within the tumor microenvironment. This review aims to present an update on the current knowledge regarding the role of miR-204 in the hallmarks of BC. In conclusion, miR-204 is a potential therapeutic target and prognostic marker in BC, emphasizing the need for further research to fully elucidate its complex roles in orchestrating aggressive BC behavior.

A typical orchard's mechanical operation consists of three or four stages: lining, digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging the planting hole is the most time-consuming operation. In fruit orchards, robots' usage is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GIS), to develop an autonomous drilling machine and use the developed robot to open planting holes. There isn't a comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS software was used for designing and drawing of the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

Presently, smartphones support a large range of applications, many of which require high computing power. This presents a problem because smartphones offer limited computing power, storage, and energy. Fortunately, Cloud computing (CC) is rapidly becoming known as the state of the art technology in the computer world. CC allows users to use unlimited dynamic resources when necessary. Mobile Cloud Computing (MCC) is integration into a mobile environment of the concept of cloud computing which eliminates barriers to the performance of mobile devices. The demand for mobile cloud applications and mobile user services is strong. This gives MCC a great opportunity to do business and research. However, security problems are a major obstacle to the adaptability of mobile cloud computing. This study presents the definitions of Cloud Computing, Mobile Computing (MC) and Mobile Cloud Computing including the architecture and applications of MCC. Furthermore, it discusses the challenges and opportunities faced in MCC.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots are becoming part of human society, they gradually assume the role of partners for humans wherever necessary: as assistants, collaborators, or companions. Companion robots are sup-posed to provide social contact to those humans who could not have it otherwise. These com-panion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored ter-ritory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting acceptance of robots as intimate partners, public perception of robots in in-timate roles, and the possible influence of cross-cultural differences in these domains. We also discuss possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term “Intimate companion robots” to reduce the negative connotations of other terms used so far and improve the social perception of research in the domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.

Various designs of spherical robots using barycenter offset mechanisms were previously reported. This kind of robots offers advantages such as turning in placeor moving in any direction at any time. Yet, the approaches resulting in a balanced robot, which could roll with reduced energy costs once accelerated, are scarce. We introduce anovel robot of this type. The feasibility of the approach is verified on a cylindrical robot. This paper presents an open-source, remotely controlled, robotic cylinder. The robot uses a specifically designed mechanism to displace a mass in its center, which causes the cylinder to roll forwards or backwards. The movement characteristics of the cylinder, as well as the mathematical and physical limitations of the mechanism were studied. An intuitive user interface allows the robot to be controlled through WiFi. This robot was manufactured only with readily available and inexpensive materials, which would make it a suitable platform for education, typically in the fields of physics and engineering.

This study delves into the complex interplay among attachment styles, hypersexuality, and sexual and couple satisfaction in emerging adults within the Mexican population. Despite established correlations between anxious and avoidant attachment styles and emotion dysregulation and hypersexuality, the roles of sexual and couple satisfaction in this context have remained relatively unexplored. This study aims to fill this gap by examining how these variables interact and contribute to psychological distress and sexual risk behaviors. Data analysis on 214 emerging adults, utilizing the Experiences in Close Relationships scale, Couple Satisfaction Index, Sexual Satisfaction Scale, SCL-90, and SAST-R, revealed a significant association between risky sexual behaviors—such as a higher number of sexual partners, drug and alcohol use during sexual activity, and lack of sexual protection—and heightened levels of hypersexuality. Notably, individuals with hypersexual tendencies reported diminished levels of sexual satisfaction but elevated levels of couple satisfaction. Avoidant attachment emerged as the attachment style most strongly correlated with early sexual experiences. Moreover, the abbreviated versions of the Couple Satisfaction Index (CSI-4) and the Experiences in Close Relationships scale (ECR-8) exhibited commendable psychometric properties, enhancing their applicability for research and clinical purposes. This research furnishes valuable insights into the complex dynamics among attachment styles, hypersexuality, and couple and sexual satisfaction, emphasizing the need for targeted interventions to foster healthier sexual and relationship behaviors among emerging adults.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

Abstract: The conundrum of Latin American countries in the green transition unveils new opportunities and old structural problems. There is a clear advantage in the energy grid for Argentina, Brazil, and Mexico, coming mainly from hydropower, solar, and wind energy. In 2022, Brazil had the largest solar photovoltaic energy generation capacity in Latin America and the Caribbean, with over 24 gigawatts. Mexico ranked second, with some 9 gigawatts, and Chile ranked third, with a capacity of around 6 gigawatts. The window of opportunity for the industrialized Latin American countries resides chiefly in greening the natural resources industries and agriculture and husbandry, mainly for Brazil and Argentina. Latin America faces the dual challenge of promoting renewable energy and upgrading its value chains while managing the socioeconomic dynamics inherent in its developmental stages. It poses an enormous challenge for Latin American countries because of the phenomenon of "time compression" (Whittaker et al., 2020). Developing country's innovation and technology-intensive segments coexist with informal labor and low-skilled workers. Far from disappearing with economic growth, such coexistence becomes reinforced through an increasingly wide array of temporary and insecure jobs. Time compression condenses different stages of industrial and economic development within the same space and time, directly affecting the indicators related to the reduction of environmental impacts. Instead of locking in one path for this transition, Latin American countries had better not cling to panaceas but consider the threats, the risks, and the need to experiment with multiple paths and solutions simultaneously. However, this change implies the formation of a genuine developmental and environmental coalition guided by rigorous strategic considerations not captured by immediate interests.

Autonomous vehicle technology, employing image sensors for road marking recognition, is largely dependent on the Lane Departure Warning System (LDWS) to prevent vehicular deviations from driving lanes. The effectiveness of image-based lane recognition, however, is susceptible to environmental influences like weather conditions and the surrounding road environment. This study investigates the impact of road marking retro-reflectivity on LDWS under varied environmental conditions. Conducted at the Yeoncheon SOC Demonstration Research Center, the experiments encompassed a spectrum of weather scenarios, incorporating rainfall and shifts between day and night lighting. Controlled wear was applied to white, yellow, and blue road markings, followed by measuring their retro-reflectivity at different stages of degradation. The LDWS's performance was evaluated by assessing the recognition rates of these markings under these diverse environmental settings. The study reveals that enhanced retro-reflectivity significantly boosts LDWS detection, especially in challenging weather conditions. Furthermore, the research led to the development of a simulation framework to analyze the cost-effectiveness of road marking maintenance, aligning it with the safety needs of autonomous vehicles. This framework suggests the necessity for updated road marking guidelines that cater to the advanced needs of driver assistance systems and autonomous vehicle technology.

In recent decades, many attempts have been made to automate the entire welding process, how-ever, there remain many non-automated welding operations that present a constant hazard to workers. This article presents an automated welding solution with collaborative robots, with this contribution, we intend to help companies in this sector increase productivity, improve quality, effectively reduce costs, and improve working conditions.

An easier method for the calibration of differential drive robots is presented. Most of the calibration is done on-board and it is not necessary to expend too much time taking note of the robot's position. The calibration method does not need a big free space to perform the tests. The bigger space is just in a straight line, which is easy to find. Results with the proposed method are compared with those from UMB as a reference, and they show very little deviation while the proposed calibration is much simpler.

This study compares the Melbourne Decision-Making Questionnaire (MDMQ) and the General Decision-Making Style (GDMS), two of the most widely used decision-making questionnaires in the literature, in a large age- and sex-weighted general population sample of 714 men (45.7%) and 848 women (54.3%) between 18 and 90 years old. The objective was to evaluate the convergent and construct validity between several aspects of decision-making styles questionnaires. The results indicate that the two questionnaires replicate the factorial structure of four and five factors reported in the original studies respectively, through exploratory and confirmatory procedures in our cross-cultural context. The domains of both questionnaires that represent a strong or large correlation are Vigilance with Rational (.50), Hypervigilance, Buck-passing, and Procrastination with Avoidant (.45, .52 and .60). A Structural Equations Model (SEM) between both questionnaires indicates that both latent factors formed by the domains of the MDMQ and the GDMS obtain a correlation of .96. It is concluded that the two questionnaires measure similar aspects of the decision-making construct.

The economic, social and environmental benefits generated by the use of urban public transportation infrastructure constitute a complex dynamic urban public transportation infrastructure utilization benefit system. This paper evaluates the coupling coordination among these three benefits taking four Chinese autonomous municipalities as an example. These four cities have large-scale urban public transportation infrastructures but their utilization has many serious problems. The basic function of urban public transportation infrastructure has not been fully played in these cities. Whether the different benefits of urban public transportation infrastructure have been developed in harmony or not is unclear. We analyzed the coordinated development among three benefits by constructing coupling coordination degree model and used Gini coefficient to study the difference of coordinated development among three benefits of four cities. The result shows that the levels of coordinated development among three benefits of urban public transportation infrastructure were lower in these four cities and have positive correlation with it of urban public transportation infrastructure utilization benefit. Raising the level of urban public transportation infrastructure utilization benefit is the most crucial solution of promoting the coordinated development among three benefits.

Several studies with kaempferol (KP) and linearolactone (LL) have shown antiparasitic activities. However, the toxicity of treat-ments with both active principles is unknown. Therefore, this study aimed to evaluate the toxicological effects of treatments with KP and LL on the amoebic liver abscess (ALA) model in M. auratus administered intraperitoneally (i.p.). For this, ALA in-duction was carried out in male hamsters with 1.5 x105 E. histolytica trophozoites inoculated in the left hepatic lobe. The lesion evolved for four days and the i.p. of the treatments with KP (5 mg/kg body weight/day) and LL (10 mg/kg body weight/day) was carried out for four consecutive days. For histopathological evaluation, NMR studies, paraclinical analysis, and necropsy were performed. The results revealed that the inhibition of ALA by KP and LL were similar to that metronidazole (MTZ). Biochemical parameters revealed hepatic and renal alterations in all treatment groups, mainly for KP (AST and BUN). The lesions found in the organs were those that are directly linked to the pathology. In conclusion, KP and LL decrease ALA development but have few toxicological effects. Both compounds combined with MTZ could be therapeutic alternatives for amoebiasis caused by E. histolytica.

Background: Vaccine hesitancy was one of India's problems after the COVID-19 vaccine roll-out. Earlier studies carried out to understand people's perception of the vaccine were mainly based on online or offline community-based surveys. These studies do not help to understand the actions taken by people towards vaccination. Hence, this study has been designed to understand the exact behavior of people toward the vaccine. Methods: Age-wise, the population was sub-grouped into three groups, above 18 years, 12-14 years, and 15-18 years. The data analysis has been done using cumulative coverage of vaccines in the said age groups. Results: The study shows a substantial population has missed second and booster doses of vaccine at the state, regional, and national levels in all three age groups. Even for the states that have shown the smallest number of people who missed their dose, their number is in the thousands. Conclusion: Further research is needed to know, in total population, how many people have not even taken a single dose of vaccine. Policy-level efforts are needed to cover the entire population of the country for at least a single dose and vulnerable populations, for additional booster doses after primary doses.

Autonomous Driving (AD) has become a prominent research area in the field of Artificial Intelligence (AI) and Machine Learning (ML) in recent years. This opens the door wider for self-driving cars to surpass conventional vehicles in the current market share. Despite its apparent simplicity, AD is composed of complex and heterogeneous systems, which are in need of a high level of coordination and alignment to ensure both full automation and safety. Therefore, numerous research studies have been conducted over the last few years to facilitate such coordination and accelerate the capacity of these types of vehicles to be self-managed in complex situations. The paper summarises these approaches that led to building what is known nowadays as the autonomous driving pipeline. Moreover, although skepticism exists regarding the practicality of AD as a viable alternative to traditional vehicles, extensive research suggests the multiple benefits of relying on them for mobility. While challenges remain in implementing AD in the real world, including regulation and technical issues, substantial progress in recent years indicates a growing acceptance of AD in the near future. This paper further explores the advantages and opportunities of the conducted such systems in facilitating the practicality of Autonomous Driving.