Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a competitiveness model for South African National Parks

The tourism industry is an extremely competitive industry which is mainly caused by the ever changing needs of tourists. However, despite the constant change in tourist behaviour, violent terrorist attacks on countries across the globe and the weakening of the world economies, the tourism industry showed significant growth over the last few decades. The fast growth rate of tourism has also lead to the tourist being more aware of ecotourism destinations and the key role these destinations play in conserving the natural and cultural environments in the midst of globalisation and urbanisation. The pressure on ecotourism destinations such as national parks is increasing as more and more natural land are being taken up for urban development. In the case of South African National Parks (SANParks), the decrease in government funding pressurizes the resources and national parks have to identify ways to generate their own income through tourism activities. SANParks manages 22 national parks across South Africa with the aim to conserve South Africa’s biodiversity, provide recreational activities for tourists and build long-term relationships with the local communities to enhance the communities’ quality of life. Hence, in order to remain competitive it is important to determine the parks' competitive advantages. Therefore the aim of this study was to develop a competitiveness model for SANParks by using the Kruger National Park as case study. The Kruger National Park is not only one of the largest and most recognised national parks in the world, but also boasts with a large variety of fauna and flora, variety of accommodation facilities such as bush lodges, chalets and camping facilities as well as various tourist activities. The literature review indicated that destinations differ from each other and therefore ecotourism destinations such as national parks would have a distinct set of competitive advantage factors. However, these factors can only be determined if the needs of tourists are anticipated and park specific products and services are developed to satisfy the needs of visitors to the park and ultimately lead to tourist loyalty. Once a competitive position has been established, a national park can obtain benefits such as an increase in tourist numbers and revenue delivery of high quality products and services at the park as well as trigger a higher quality of life for the local community.
Environmental Decision-making utilizing a Web GIS to Monitor Hazardous Industrial Emissions in the Valencian community of Spain

Air pollution is a critical issue in contemporary times. For this reason, officials and environmental managers are in need of suitable tools for visualization, manipulation and analysis of environmental data. Environmental concerns in Europe have encouraged the European Environmental Agency (EEA) to create the European Pollutant Release and Transfer Register (E-PRTR). The E-PRTR is vital and valuable because society will benefit if the data are used to improve monitoring and consequently advance environmental management. However, the data are not accessible in an interoperable way, which complicates their use and does not allow for a contribution to environmental monitoring. This paper describes a Web GIS system developed for the monitoring of industrial emissions using environmental data released by the EEA. Four research objectives are addressed: (1) design and create an interoperable spatial database to store environmental data, (2) develop a Web GIS to manipulate the spatial database, facilitate air pollution monitoring and enhance risk assessment, (3) implement OGC standards to provide data interoperability and integration into a Web GIS, (4) create a model to simulate distribution of air pollutants and assess a population’s exposure to industrial emissions. The proposed approach towards interoperability is an adoption of servicebased architecture for implementation of a three-tier Web GIS application. This system’s prototype is developed using open source tools for the Valencian Community of Spain.
Freeze-Thawing Condition to Obtain the Chitosan-Calcium Phosphate Composites with Controllable Degradation Degree

Synthesized hybrid material for potential use as nerve conductors (conduits) in the form of a chitosan matrix with uniformly distributed brushite microparticles. The applied multistage synthesis includes a 5-fold process of freezing at a temperature of −18°C and thawing (at room temperature and under the influence of microwave irradiation (MW)) of the calcium (Ca)/chitosan (CS) colloidal suspension. Further gradient thawing in sodium hydrogen phosphate solution promotes in situ formation of nanocrystalline amorphous calcium phosphate (ACP) and brushite microparticles. SEM micrograph, XRD, porosity, degradation and swelling capacity were analyzed. The technique allows to the creation of mechanically strengthened material with a low degree of swelling (115-138%) and a biodegradation period of 75–200 days depending on the freeze-thaw method, which corresponds to the healing time of the damaged nerve. The application of MW reduces the degree of porosity and increases the content of brushite, which acts as a reinforcing bioactive admixture and a source of Ca2+ ions, affecting the direct regeneration of neurons.
A Visualization-aware Loss for Point Cloud Comparison

Autonomous driving is a worthwhile and popular field in both the industry and the academia community, with point clouds commonly utilized. Recently, numerous methods have been proposed to model point clouds for subsequent applications. However, little attention has been paid into the fundamental research of point cloud comparison. Objectively speaking, point cloud comparison plays an irreplaceable role in point cloud modeling. The main significance is that it is the general guidance and assessment for any method. Recently, modeling related to the human interaction and simulation is the prevailing research tendency, highlighting the significance of visual effects. However, current comparison metrics including Chamfer Distance and Earth Movig Distance are lacking in the consideration of the visualization effects. Therefore, we first give an intuitive visual comparison and analysis of those two distances. On this basic, we propose our visualization-aware loss with a structural matching scheme. Our loss is composed with the Max-Resample Chamfer Distance (MCD) and the Matching Consistency Loss (MCL). Specifically, MCD is a new metrics, which removes redundant point pairs to be more sensitive to the maintenance of the global visualization effect. As a training auxiliary, we originally propose a multi-scale MCL, which can apparently reduce the visible artifacts. Quantitative and qualitative experiments have demonstrated the exactness and the effectiveness of our distance metrics and auxiliary loss on several point cloud networks and datasets.
A Novel Reduced Current Amplitude Fluctuation PDM Control Strategy for Modular-Structured Series-Resonant Converters

This paper presents a novel reduced current amplitude fluctuation pulse-denisty modulation (PDM) control strategy for modular-structured series-resonant converters by combining the advantages of interleaved PDM, stepped PDM, and augmented PDM principles. The proposed strategy aims to mitigate the disadvantages of existing PDM methods. By introducing step-by-step changes in the pulse density in each channel of the converter while preserving the interleaving principle, the proposed strategy ensures more uniform power losses and lower fluctuations in the output current amplitude. Simulation results demonstrate the effectiveness of the proposed strategy in reducing current amplitude fluctuations.
Analysis of Urban Planning Changes by Biological Metaphor (Case of DG-ABC Model)

Urban has been defined as an autonomous and complex system. Despite the overwhelming differences between urban growths around the world, their growth still follows some universal mechanisms. Darwin’s theory of evolution might have enlightened an innovative view to perform the research of urban development, as a consequence many researchers have been trying to link their research to the biological metaphor of urban evolution. In the context of an analogy to biology, urban can be seen as ‘organic’ and many concepts from biology can be borrowed to explain the ‘uncertainty’ and ‘relativity’ of urban growth processes. Urban DNA is one of the innovative concepts, which has been used to describe the unique characteristics of urban and the common fundamental elements of each urban area. As Hall (2008) stated “every city has its own DNA, something that makes it unique, and that's part of what makes this city unique”. The main ideas of applying the concept of urban DNA involve identifying the key factors/ metrics which reflect urban characteristics, therefore allowing the understanding the cities’ characteristics with urban DNA. This would allow, among other things, to identify or propose optimal urban form and allowing ‘smooth’ transition of growth patterns and the characteristics of urban from a suboptimal urban form. As Silva (2004) explained, the possibility of defining this ‘key’ (DNA) for each region seems to be of great significance in the planning studies. It allows for the understanding of how the different elements that constraint the functioning of urban system progress and constrain different regions, and what function they should have in shaping future
Economía del agua y gestión de recursos hídricos Water economics and water resources management

This paper presents advances in the literature related to demands, prices and costs of water services in different sectors, and the role of economic methods and models can play in the design of efficient instruments and policies for water management in the new institutional framework defined by the Water Framework Directive. It specifically deals with the contribution of Economics to water management in agriculture and urban use, and the different approaches to assess the economic value of water in the environment. This is followed by the state-of-the-art in models that integrate water supply and demand at the basin scale coupling hydrology, management and economics (hydroeconomic models), and several applications to solving water management conflicts. We conclude with an analysis of trends and future challenges in the role of models and the economic analysis in water resources management.
Active Lane Change for Safety Enhanced Autonomous Driving

This paper proposed an active lane change algorithm for autonomous vehicles using safety-critical model predictive control method. By incorporating safety-critical constraints into the algorithm, the safety hazard caused by surrounding vehicles are addressed. The proposed algorithm can actively react to mandatory lane change requirement. This has been evaluated in random generated driving scenarios. A safety hazard caused by merging surrounding vehicle is highlighted to demonstrate the effectiveness of the control algorithm.
Special Session - Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications

This paper explores the challenges and opportunities of integrating non-volatile memories (NVMs) into embedded systems for machine learning. NVMs offer advantages such as increased memory density, lower power consumption, non-volatility, and compute-in-memory capabilities. The paper focuses on integrating NVMs into embedded systems, particularly in intermittent computing, where systems operate during periods of available energy. NVM technologies bring persistence closer to the CPU core, enabling efficient designs for energy-constrained scenarios. Next, computation in resistive NVMs is explored, highlighting its potential for accelerating machine learning algorithms. However, challenges related to reliability and device non-idealities need to be addressed. The paper also discusses memory-centric machine learning, leveraging NVMs to overcome the memory wall challenge. By optimizing memory layouts and utilizing probabilistic decision tree execution and neural network sparsity, NVM-based systems can improve cache behavior and reduce unnecessary computations. In conclusion, the paper emphasizes the need for further research and optimization for the widespread adoption of NVMs in embedded systems presenting relevant challenges, especially for machine learning applications.
Design of Terahertz Yagi-Uda Antenna Based on Compressed 3rd-Order Mode Dipole

A single-mode Yagi-Uda based on a compressed third-order mode dipole at Terahertz is designed in this paper. The proposed antenna is fed by a transition from Coplanar waveguide (CPW) to Coplanar stripline (CPS). A two-branch balun is used in the feeding structure to accomplish an equal amplitude of current on the two arms of the dipole and a phase difference of 180°. The material object of the proposed antenna is fabricated and a higher precision lithography process is used in this process, contributing to improve the performance of the proposed antenna in the high frequency. The antenna with measured |S11| < −10 dB accomplishes a relative bandwidth of 9% from 116 to 128 GHz.
VoStress – Voice-based Detection of Acute Psychosocial Stress

Current stress assessment methods include self-reports and biomarkers which are evaluated in often complex, laboratory procedures. Due to that investigating new indicators for acute stress is crucial for the development of automatic stress detection systems. A promising extension might be provided by investigating speech, which has been shown to be affected by negative emotions and threatening situations. For that reason, we extracted verbal acoustics from audio data collected during a study where N=21 participants underwent the Trier Social Stress Test (TSST), the gold standard for laboratory stress induction, and a stress-free control condition (friendly-TSST) while concurrently collecting cortisol via saliva samples to assess the biological response to stress. Our results show that acute stress leads to significant (p < 0.05) alterations of acoustic features. A stepwise backward multiple linear regression model explained 58.8 % of the variance of the maximum cortisol increase. In addition to that, we performed classification experiments that distinguished stress from non-stress situations with an accuracy of 80.0 ± 12.7 %. While further research is needed to validate our approach, we are convinced that the information extracted from speech can be a valuable indicator for automatic stress detection systems and can even predict the biological response to stress situations.
Relay-dependent Path Replanning for Disconnected Drones Using Self-organized Communication Network

Drones are increasingly used in the delivery business. It is critical to ensure reliable and low-latency control and non-payload communication (CNPC) between delivery drones and the ground control station (GCS). Currently, delivery drones communicate with the GCS through Long Term Evolution (LTE) cellular network, but it is designed for ground users. Drones flying at low altitudes may suffer from poor communication with the GCS or even disconnect with the GCS, bringing huge pressure on social security. To ensure reliable drone-to-ground communication, a relay-dependent communication protocol is proposed to cope with situations when a drone loses connection with the GCS. Then we propose a path replanning algorithm that considers the low CNPC latency constraint during the whole fight. Experimental results show that our replanning algorithm is superior in terms of completion time. Moreover, we deploy the communication protocol on the Raspberry Pi equipped with a Long Range Radio (LoRa) module, and it works well in the outdoor environment.
Burnout Prediction and Analysis in Shift Workers: Counterfactual Explanation Approach

Shift work disrupts sleep and causes chronic stress, resulting in burnout syndrome characterized by emotional exhaustion, depersonalization, and decreased personal accomplishment. Continuous biometric data collected through wearable devices contributes to mental health research. However, direct prediction of burnout risk is still limited, and interpreting machine learning (ML) models in healthcare poses challenges. In this paper, we develop machine learning models that utilize wearable and survey data, including rhythm features, to predict burnout risk among shift workers. Additionally, we employ the DiCE (Diverse Counterfactual Explanations) framework to generate interpretable explanations for the ML model, aiding in the management of burnout risks. Our experiments on the AMED dataset show that incorporating rhythm features significantly enhances the predictive performance of our models. Specifically, sleep and heart rate features have emerged as significant indicators for accurately predicting burnout risk.
A Deep Reinforcement Learning Based Hierarchical Eco-Driving Strategy for Connected and Automated HEVs

The advancement in vehicle connectivity and autonomy has fostered the development of eco-driving technology, aimed at optimizing driving behaviors to reduce vehicle energy consumption. This study proposed a real-time deep reinforcement learning based hierarchical eco-driving control strategy to optimally control a connected and automated hybrid electric vehicle in a traffic scenario with multiple constraints. The speed optimization layer of the proposed strategy employs a twin-delayed deep deterministic policy gradient (TD3) based reference speed planning strategy to compute the optimized speed, based on a pre-trained optimal policy and observed environmental states. Specifically, to learn the optimal policy, a multi-objective reward function is designed that integrates fuel consumption reward and shaping reward involving car-following and road speed limit. Additionally, a rule-based competition-decision model is embedded within the speed optimization layer to ensure compliance with traffic light rules. In the vehicle controller layer, a real-time controller is implemented to specify appropriate actuator variables for the hybrid powertrain to track the reference speed and conduct energy management control. Simulation results show that the proposed TD3 based eco-driving strategy achieves remarkable energy saving performance by optimizing the speed. Besides, the proposed eco-driving strategy is capable of satisfying the constraint of diverse traffic scenarios, including car-following and traffic light, while also being computationally lightweight.
Automated Pricing-based Provisioning of SDN/NFV Services in Distributed Multi-access Edge Computing using Cooperative Multi-Agent Deep Reinforcement Learning

The disruption caused by Software Defined Network and Network Function Virtualization (SDN/NFV) technologies will have many impacts on the telecom network. Specifically, the network architecture based on ETSI MANO comprising Virtual Infrastructure Manager (VIM), Virtual Network Function Manager (VNFM), and NFV Orchestrator (NFVO) will significantly change how we operate and manage the telecom network. These impacts on the network architecture will be made gradually. Thus, the migration from non-virtualized networks to all virtualized networks will happen step by step. By introducing new actors into the telecom ecosystem, NFV-MANO will bring in new business models. It is envisaged that these new actors/models will promote competition hence the demand for more flexible charging models with real-time charging. In this research, we will address the architectural realization of the SDN/NFV charging model under the business model of MANO (MANagement and Orchestration) when the service provider (SP) uses the Network Function Virtualization Infrastructure (NFVI) from the NFVIaaS Provider in a distributed multi-access edge computing (MEC) environment. To optimize the Quality of Service (QoS) of MEC resource allocation for incoming services and maximize the overall operating profit of the service provider adopting our charging model, we also propose a new cooperative multi-agent actor-critic based deep reinforcement learning (MADRL) method trained with proximal policy optimization algorithm, namely Coop MAPPO. The results of our experiments showcase the superiority of the Coop-MAPPO multi-agent system over alternative decision-making approaches, with its potential for enhancing operational efficiency and profitability while minimizing failure rates.
Development of a Framework Dealing with Partial Data Unavailability and Unstructuredness to Support Post-Market Surveillance

Under the European Union Medical Device (MD) Regulation 2017/745, expert panel’s decision on providing a scientific opinion on the Clinical Evaluation Assessment Report for high-risk MD is required, as part of the conformity assessment procedure. To this aim, the perceived risk of similar MDs already on the market, based on the European Medical Device Nomenclature (EMDN), could help. To generate such information, we propose a generalized framework to automatically collect and display in an aggregated way the publicly available safety notices (SNs), even when characterized by partial unstructuredness and incompleteness. This novel approach was tested on the Dutch data, consisting of 3618 SNs from 2015 to 2022, retrieved from the official government website by Web scraping. After the identification of named entities, the best match MD was searched within the Italian and Portuguese datasets of devices using Natural Language Processing techniques. Algorithm performance was tested on potentially equal SNs (472) published by both the Dutch and Italian authorities: assignment of the same EMDN code at level 1 was present in 454 out of 472 (96.19%) SNs, at level 2 in 447 (94.70%) SNs, at level 3 in 433 (91.74%) SNs. The proposed approach was able to cope with public data unavailability and incompleteness, thus providing structured data with appropriate EMDN usable for aggregation and safety signal detection.
TDOA Localization Algorithm for Multiply Disjoint Sources without Extra Variables

This paper addresses the passive localization problem for multiple disjoint sources with sensor position errors. Although the previous closed-form solutions can achieve optimal performance at low noise levels, they may suffer from performance loss at high noise levels since they assume the unknown source position is independent of the extra parameters. To overcome this drawback, we first eliminate the nuisances by using a projection matrix and give the coarse position estimate. Then, the coarse estimate is improved by using the weighted least squares (WLS) technique. The proposed method can attain the Cramer-Rao lower bound (CRLB) in theory and simulations under mild measurement noise levels. Furthermore, numerical simulations indicate that the proposed method outperforms the existing methods.