Search Results (12)

Rural landscape character assessment (LCA) is significant for identifying and understanding rural landscapes and maintaining the cultural connotations of the rural vernacular. Taking the rural area of Songzi City as an example, this study identifies the landscape character (LC) and analyzes the coupling between the current state of its LC and a construction project based on the depth of rural landscape planning in the county and combining the ecology, arable land, and water body protection boundary as constraints. Thus, we obtain the “point, line, and surface” site selection suggestions for the construction activities of leisure agriculture, power grid, and energy facilities, and the zoning classification and layout control strategies for LC are subsequently proposed. The results show the following: (1) The county LC factor is a combination of natural and human factors used to obtain 165 LC areas in Songzi City. (2) The current state of rural LC is used to determine LCs from shallow to deep and to provide the basis for index selection and judgment for evaluation. (3) The coupling relationship between rural LC and construction projects varies and must be judged using subjective and objective methods, desktop research combined with field analyses, and multi-stakeholder participation. Based on the perspective of coupling and coordinating human and landscape, this study applies local-scale LCA to practice, strengthens the interface with rural construction planning, and provides research ideas and methodological references for the sustainable control of rural LC. Full article

The growing adoption of smart grid systems presents significant advancements in the efficiency of energy distribution, along with enhanced monitoring and control capabilities. However, the interconnected and distributed nature of these systems also introduces critical security vulnerabilities that must be addressed. This study proposes a secure communication protocol specifically designed for smart grid environments, focusing on authentication, secret key establishment, symmetric encryption, and hash-based message authentication to provide confidentiality and integrity for communication in smart grid environments. The proposed protocol employs the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, Elliptic Curve Diffie–Hellman (ECDH) for secure key exchange, and Advanced Encryption Standard 256 (AES-256) encryption to protect data transmissions. The protocol follows a structured sequence: (1) authentication—verifying smart grid devices using digital signatures; (2) key establishment—generating and securely exchanging cryptographic keys; and (3) secure communication—encrypting and transmitting/receiving data. An experimental framework has been established to evaluate the protocol’s performance under realistic operational conditions, assessing metrics such as time, throughput, power, and failure recovery. The experimental results show that the protocol completes one server–client request in 3.469 ms for a desktop client and 41.14 ms for a microcontroller client and achieves a throughput of 288.27 requests/s and 24.30 requests/s, respectively. Furthermore, the average power consumed by the protocol is 37.77 watts. The results also show that the proposed protocol is able to recover from transient network disruptions and sustain secure communication. Full article

In the context of managing museums, historical, artistic, and archaeological heritage, an advanced decision support system (DSS) can serve as the engine for a business game platform, optimizing decision paths and management strategies. In complex, multi-parameter scenarios, the final decision is often only part of the process; it is equally essential to follow the decision-making path, that is, the sequence of actions necessary to reach the objective. The DSS presented here simplifies the problem by transforming the initial n-dimensional space, defined by the critical success factors (CSFs) selected by experts, into a two-dimensional space. Indeed, thanks to this approach, the computational complexity is reduced to the point that the technological solution can be used even on standard desktop computers and not only on high-performance computing systems. Moreover, the user does not necessarily need to be an IT expert but rather a specialist in the cultural domain. Through grid-based motion algorithms and a hierarchy of CSF priorities, the system quickly identifies optimal solutions in the 2D plane and then maps them back to the n-dimensional space to maintain consistency with the original context. Since the correspondence between n-dimensional micro-states and two-dimensional macro-states is not one-to-one, the DSS returns the specific micro-state of interest from the optimal macro-state, selecting the most effective path. This research aims to develop algorithms that by minimizing entropy and optimizing the system’s dynamics, build optimal paths in the 2D plane, with algorithms capable of restoring the solution in the initial space. Several use cases in the form of business games have been conducted, demonstrating the value of the proposed solution. The result of this work is a simulation environment useful for museum experts to analyze the impact of their management strategies. Thanks to the ability to assign weights to each of the critical success factors (CSFs), the system can display both qualitative and quantitative simulations of museum dynamics as the weights associated with different CSFs vary. Given the system’s generality, it is applicable to various fields where complex business games are required, such as cultural heritage management, logistics, transportation, healthcare systems, and, more broadly, any context where strategic business analysis is needed for the economic enhancement of resources and their optimization. Full article

Greece is expanding its energy grid system with submarine power and fiber optic cables between the mainland and the Aegean Sea islands. Additionally, pipelines have been installed to support natural gas facilities, and sites are being demarcated for the development of offshore wind parks. The above developments have necessitated extensive geotechnical surveying of the seabed; however, the survey data cannot be accessed for academic inspection or for desktop studies of future developments. This is further hindered by the limited geotechnical information in the Aegean and Ionian Seas. This review examines the existing information concerning the geotechnical behavior of the surficial sedimentary layers, including certain challenges associated with geotechnical sampling and CPTu interpretation. Certain prospects are discussed regarding marine geotechnical research in Greece, with examples from other European countries. The marine geotechnical data in Greece include geotechnical analyses of sediments cores and slope stability estimations, which are commonly associated with the seismic profiling of unstable slope areas. Underlying mechanisms of slope failure have mainly been attributed to the interbedded presence of weak layers (e.g., sapropels, tephra and underconsolidated sediments), the presence of gas and the cyclic loading from earthquake activity. Due to the limited geotechnical information, geological studies have contributed considerably to describing the distributions of gravity-induced events and lithostratigraphy. Within this context, a geological/geotechnical database is suggested where data can be collated and utilized for future studies. Full article

In this paper, we investigate the problem of optimal control of complex multistage chemical reactions, which is considered a nonlinear global constrained optimization problem. This class of problems is computationally expensive due to the inclusion of multiple parameters and requires parallel computing systems and algorithms to obtain a solution within a reasonable time. However, the efficiency of parallel algorithms can differ depending on the architecture of the computing system. One available approach to deal with this is the development of specialized optimization algorithms that consider not only problem-specific features but also peculiarities of a computing system in which the algorithms are launched. In this work, we developed a novel parallel population algorithm based on the mind evolutionary computation method. This algorithm is designed for desktop girds and works in synchronous and asynchronous modes. The algorithm and its software implementation were used to solve the problem of the catalytic reforming of gasoline and to study the parallelization efficiency. Results of the numerical experiments are presented in this paper. Full article

Obtaining a good socket fit is an iterative process dependent on the skill and experience of the prosthetist creating it and requires individualisation based on the size and shape. There is no standard measurement system used to aid prosthetic socket creation despite the severe impacts on physical health and quality of life if one is ill fitting. Pressure sensors embedded in a prosthetic socket were used to collect data at the socket–residuum interface. To choose an interpolation method, the sensor array was simplified to a 2D grid with a border for extrapolation and tested using previously collected walking test pressure data. Four multivariable interpolation methods were evaluated to create a colour map of the pressure data. Radial basis function interpolation was chosen, as it produced a clear image with a graduated interpolation between data points, and was used to create a colour map across the surface of a 3D prosthetic socket model. For the model to be accessible to clinical audiences, a desktop application was created using PyQt to view the model. The application allowed for connection to the sensors via Bluetooth, with the pressure data updating on the 3D model in real time. Clinician feedback on the application showed the potential for a clinical product; however, further development informed by feedback from rehabilitation clinicians and prosthesis users is required. Full article

AudienceMR is designed as a multi-user mixed reality space that seamlessly extends the local user space to become a large, shared classroom where some of the audience members are seen seated in a real space, and more members are seen through an extended portal. AudienceMR can provide a sense of the presence of a large-scale crowd/audience with the associated spatial context. In contrast to virtual reality (VR), however, with mixed reality (MR), a lecturer can deliver content or conduct a performance from a real, actual, comfortable, and familiar local space, while interacting directly with real nearby objects, such as a desk, podium, educational props, instruments, and office materials. Such a design will elicit a realistic user experience closer to an actual classroom, which is currently prohibitive owing to the COVID-19 pandemic. This paper validated our hypothesis by conducting a comparative experiment assessing the lecturer’s experience with two independent variables: (1) an online classroom platform type, i.e., a 2D desktop video teleconference, a 2D video screen grid in VR, 3D VR, and AudienceMR, and (2) a student depiction, i.e., a 2D upper-body video screen and a 3D full-body avatar. Our experiment validated that AudienceMR exhibits a level of anxiety and fear of public speaking closer to that of a real classroom situation, and a higher social and spatial presence than 2D video grid-based solutions and even 3D VR. Compared to 3D VR, AudienceMR offers a more natural and easily usable real object-based interaction. Most subjects preferred AudienceMR over the alternatives despite the nuisance of having to wear a video see-through headset. Such qualities will result in information conveyance and an educational efficacy comparable to those of a real classroom, and better than those achieved through popular 2D desktop teleconferencing or immersive 3D VR solutions. Full article

The traditional flow of coastal ocean model data is from High-Performance Computing (HPC) centers to the local desktop, or to a file server where just the needed data can be extracted via services such as OPeNDAP. Analysis and visualization are then conducted using local hardware and software. This requires moving large amounts of data across the internet as well as acquiring and maintaining local hardware, software, and support personnel. Further, as data sets increase in size, the traditional workflow may not be scalable. Alternatively, recent advances make it possible to move data from HPC to the Cloud and perform interactive, scalable, data-proximate analysis and visualization, with simply a web browser user interface. We use the framework advanced by the NSF-funded Pangeo project, a free, open-source Python system which provides multi-user login via JupyterHub and parallel analysis via Dask, both running in Docker containers orchestrated by Kubernetes. Data are stored in the Zarr format, a Cloud-friendly n-dimensional array format that allows performant extraction of data by anyone without relying on data services like OPeNDAP. Interactive visual exploration of data on complex, large model grids is made possible by new tools in the Python PyViz ecosystem, which can render maps at screen resolution, dynamically updating on pan and zoom operations. Two examples are given: (1) Calculating the maximum water level at each grid cell from a 53-GB, 720-time-step, 9-million-node triangular mesh ADCIRC simulation of Hurricane Ike; (2) Creating a dashboard for visualizing data from a curvilinear orthogonal COAWST/ROMS forecast model. Full article

Scanning electron microscopy has been developed for topographic analysis at the nanometer scale. Herein, we present a silicon p-n diode with multi-annular configuration to detect backscattering electrons (BSE) in a homemade desktop scanning electron microscope (SEM). The multi-annular configuration enables the enhancement of the topography contrast of 82.11 nA/μm as compared with the commercial multi-fan-shaped BSE detector of 40.08 nA/μm. Additionally, we integrated it with lateral p-n junction processing and aluminum grid structure to increase the sensitivity and efficiency of the multi-annular BSE detector that gives higher sensitivity of atomic number contrast and better surface topography contrast of BSE images for low-energy detection. The responsivity data also shows that MA-AL and MA p-n detectors have higher gain value than the MA detector does. The standard deviation of measurements is no higher than 1%. These results verify that MA p-n and MA-AL detectors are stable and can function well in SEM for low-energy applications. It is demonstrated that the multi-annular (MA) detectors are well suited for imaging in SEM systems. Full article

In arid, coastal cities, water demand is often met through large-scale desalination systems. However, the energy required to run desalination plants remains a drawback. Further, numerous low-density population areas lack not only fresh water availability, but in most of the cases electrical grid connection or any other energy source as well. The challenge, consequently, is to ensure adequate fresh water supplies at the lowest possible cost. The main objective of this work is to assess the freshwater production from a reverse osmosis desalination system powered by a wave energy converter, the Overtopping Breakwater for Wave Energy Conversion (OBREC). The desktop analysis is illustrated through a case study on the Fenoarivo Atsinanana coast, along north-eastern Madagascar. The novel aspect of the analysis method is the application of a specific numerical code calibrated using preliminary results from a two-year monitoring campaign of the first OBREC prototype in operation in Naples Harbour (Italy). Instead of dissipating the incoming wave energy, the system collects the overtopping water above the sea level and the potential energy is converted into electricity through low head turbines. Then, the flow will be driven towards the desalination system. This configuration seems like a promising opportunity for developing countries to meet their water supply needs while at the same time developing their renewable energy potential. Full article

Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no exception. Via a learning framework, very good classification accuracy can be obtained, but several parameters need to be optimized. This article describes a practical framework for efficient distributed parameter optimization. The proposed solutions are applicable for many research groups with heterogeneous computing infrastructures and for various machine learning algorithms. These infrastructures can easily be connected via distributed computation frameworks. We use the Hadoop framework to run and distribute both grid and random search strategies for hyperparameter optimization and cross-validations on a cluster of 21 nodes composed of desktop computers and servers. We show that significant speedups of up to 364× compared to a serial execution can be achieved using our in-house Hadoop cluster by distributing the computation and automatically pruning the search space while still identifying the best-performing parameter combinations. To the best of our knowledge, this is the first article presenting practical results in detail for complex data analysis tasks on such a heterogeneous infrastructure together with a linked simulation framework that allows for computing resource planning. The results are directly applicable in many scenarios and allow implementing an efficient and effective strategy for medical (image) data analysis and related learning approaches. Full article

Roads play an indispensable role as part of the infrastructure of society. In recent years, society has witnessed the rapid development of laser mobile mapping systems (LMMS) which, at high measurement rates, acquire dense and accurate point cloud data. This paper presents a way to automatically estimate the required excavation volume when widening a road from point cloud data acquired by an LMMS. Firstly, the input point cloud is down-sampled to a uniform grid and outliers are removed. For each of the resulting grid points, both on and off the road, the local surface normal and 2D slope are estimated. Normals and slopes are consecutively used to separate road from off-road points which enables the estimation of the road centerline and road boundaries. In the final step, the left and right side of the road points are sliced in 1-m slices up to a distance of 4 m, perpendicular to the roadside. Determining and summing each sliced volume enables the estimation of the required excavation for a widening of the road on the left or on the right side. The procedure, including a quality analysis, is demonstrated on a stretch of a mountain road that is approximately 132 m long as sampled by a Lynx LMMS. The results in this particular case show that the required excavation volume on the left side is 8% more than that on the right side. In addition, the error in the results is assessed in two ways. First, by adding up estimated local errors, and second, by comparing results from two different datasets sampling the same piece of road both acquired by the Lynx LMMS. Results of both approaches indicate that the error in the estimated volume is below 4%. The proposed method is relatively easy to implement and runs smoothly on a desktop PC. The whole workflow of the LMMS data acquisition and subsequent volume computation can be completed in one or two days and provides road engineers with much more detail than traditional single-point surveying methods such as Total Station or GPS profiling. A drawback is that an LMMS system can only sample what is within the view of the system from the road. Full article