Search Results (43)

Virtual Archaeology is defined as ‘the scientific discipline that seeks to research and develop ways of using computer-based visualizations for the comprehensive management of archaeological heritage’. In essence, it involves the creation of virtual models of various types whose purpose is to represent elements of the past based on historical data obtained from research. It is a discipline that has experienced a boom in recent years, thanks to the democratization of both technology and training, and has become one of the most fruitful branches of what is known as Digital Humanities. However, despite its name, it has applications beyond the field of archaeology, notably in Art History. In this sense, it allows recovering the original likeness of lost or altered works, the formulation of research hypotheses, or the generation of resources with great didactic and dissemination potential. This study aims to offer an overview of the fundamentals of the discipline and explore the possibilities it offers to Medieval Art History. Furthermore, this study serves as a starting point for new projects. Full article

Wildfires occur frequently in various regions of the world, causing serious damage to natural and human resources. Traditional wildfire prevention and management methods are often hampered by monitoring challenges and low efficiency. Digital twin technology, as a highly integrated virtual simulation model, shows great potential in wildfire management and prevention. At the same time, the virtual–reality combination of digital twin technology can provide new solutions for wildfire management. This paper summarizes the key technologies required to establish a wildfire digital twin system, focusing on the technical requirements and research progress in fire detection, simulation, and prediction. This paper also proposes the wildfire digital twin (WFDT) model, which integrates real-time data and computational simulations to replicate and predict wildfire behavior. The synthesis of these techniques within the framework of a digital twin offers a comprehensive approach to wildfire management, providing critical insights for decision-makers to mitigate risks and improve emergency response strategies. Full article

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods. Full article

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” and “depression”. Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for overreliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings. Full article

Forest landscape space is the basic unit of forest landscape resources. Healthy forest landscape resources can not only improve the sustainable cycle of forest ecological service function, but also have a positive impact on human health and well-being. Evidence supports the view that the forest environment is beneficial to people’s health, and further discussion of the dose response between environmental attributes and physical and mental recovery has been widely carried out by scholars. As an important component of environmental attributes, it is necessary to clarify the relationship between vegetation distribution and users’ health in order to better plan, design, and utilize forest environmental resources. This study mainly used the virtual immersive forest environment video in VR, and used the difference test in SPSS 23.0 to clarify whether the distribution of vegetation in the forest environment will affect the physiological and psychological recovery effect of users. The main results are as follows: (1) Experiencing the forest environment can promote users’ physiological and psychological health, and its recovery effect is significantly better than the indoor environment (p < 0.05). (2) The distribution of vegetation in the forest environment will affect users’ physiological and psychological recovery effect. Among them, in the cluster and randomly distributed forest environments, the relaxation and concentration of users can be improved mainly by alleviating their negative emotions. In the evenly distributed forest environment, users mainly achieve the purpose of relaxation by improving their vitality and positive emotions. These results show that the distribution of vegetation is one of the factors for the restoration of forest environment. In the future design and management of the forest environment, the health and well-being of users can be effectively enhanced by getting involved with the vegetation distribution in the site, aiming to provide a scientific basis for the promotion of the rehabilitation function of forest landscape space and its sustainable utilization, thus promoting the sustainable development of forest resources and improving people’s quality of life. Full article

Purpose: To develop and validate machine learning models for predicting the length of stay (LOS) in the Pediatric Intensive Care Unit (PICU) using data from the Virtual Pediatric Systems (VPS) database. Methods: A retrospective study was conducted utilizing machine learning (ML) algorithms to analyze and predict PICU LOS based on historical patient data from the VPS database. The study included data from over 100 North American PICUs spanning the years 2015–2020. After excluding entries with missing variables and those indicating recovery from cardiac surgery, the dataset comprised 123,354 patient encounters. Various ML models, including Support Vector Machine, Stochastic Gradient Descent Classifier, K-Nearest Neighbors, Decision Tree, Gradient Boosting, CatBoost, and Recurrent Neural Networks (RNNs), were evaluated for their accuracy in predicting PICU LOS at thresholds of 24 h, 36 h, 48 h, 72 h, 5 days, and 7 days. Results: Gradient Boosting, CatBoost, and RNN models demonstrated the highest accuracy, particularly at the 36 h and 48 h thresholds, with accuracy rates between 70 and 73%. These results far outperform traditional statistical and existing prediction methods that report accuracy of only around 50%, which is effectively unusable in the practical setting. These models also exhibited balanced performance between sensitivity (up to 74%) and specificity (up to 82%) at these thresholds. Conclusions: ML models, particularly Gradient Boosting, CatBoost, and RNNs, show moderate effectiveness in predicting PICU LOS with accuracy slightly over 70%, outperforming previously reported human predictions. This suggests potential utility in enhancing resource and staffing management in PICUs. However, further improvements through training on specialized databases can potentially achieve better accuracy and clinical applicability. Full article

Workload management is a cornerstone of contemporary human resource management with widespread applications in private and public sectors. The challenges in human resource management are particularly pronounced within the public sector: particularly in task allocation. The absence of a standardized workload distribution method presents a significant challenge and results in unnecessary costs in terms of man-hours and financial resources expended on surplus human resource utilization. In the current research, we analyze how to deal with the “race condition” above and propose a dynamic workload management model based on the response time required to implement each task. Our model is trained and tested using comprehensive employee data comprising 450 records for training, 100 records for testing, and 88 records for validation. Approximately 11% of the initial data are deemed either inaccurate or invalid. The deployment of the ANFIS algorithm provides a quantified capability for each employee to handle tasks in the public sector. The proposed idea is deployed in a virtualized platform where each employee is implemented as an independent node with specific capabilities. An upper limit of work acceptance is proposed based on a documented study and laws that suggest work time frames in each public body, ensuring that no employee reaches the saturation level of exhaustion. In addition, a variant of the “slow start” model is incorporated as a hybrid congestion control mechanism with exceptional outcomes, offering a gradual execution window for each node under test and providing a smooth and controlled start-up phase for new connections. The ultimate goal is to identify and outline the entire structure of the Greek public sector along with the capabilities of its employees, thereby determining the organization’s executive capacity. Full article

Historical sites are incomplete relics left by human activities and are also valuable resources for human society, with most of them buried deep underground. Because the protection of original historical sites is difficult, very few sites choose this method. Many funerary objects that have been in historical sites are scattered in museums, losing their original context and complicating their utilization. Thus, using digital technology to protect and utilize historical sites and to explore their outstanding value from historical, aesthetic, and anthropological perspectives is a foremost concern. Therefore, this study aims to develop a comprehensive method for the protection and utilization of historical sites, that is, digital protection and utilization based on a digital twin. We constructed a historical site digital twin model using qualitative and vertical methods, including a physical entity, virtual entity, twin data center, digital twin service, and connection. We also established a technical framework of data acquisition and processing, digital protection, and digital utilization, forming a layered management and application of digital resources. In digital protection, information in the real world and the virtual world are connected to monitor risks, collect data, create simulations, and propose protection strategies, quickly and accurately. In digital utilization, the knowledge graph is constructed to associate seemingly unrelated information, explore potential knowledge, and improve information sharing. In addition, the method is validated by means of case studies of historical sites in China. In this paper, the historical sites of the Northern Qi Dynasty in Taiyuan, Shanxi, especially the Xuxianxiu Tomb and Lourui Tomb, are discussed in detail. The results indicate that this method is effective for the protection and utilization of historical sites. Full article

In response to the challenges posed by climate change, including extreme weather events, such as heavy rainfall and droughts, the agricultural sector is increasingly seeking solutions for the efficient use of resources, particularly water. Pivotal aspects of smart agriculture include the establishment of weather-independent systems and the implementation of precise monitoring and control of plant growth and environmental conditions. Hydroponic cultivation techniques have emerged as transformative solutions with the potential to reduce water consumption for cultivation and offer a sheltered environment for crops, protecting them from the unpredictable impacts of climate change. However, a significant challenge lies in the frequent need for human intervention to ensure the efficiency and effectiveness of these systems. This paper introduces a novel system with a modular architecture, offering the ability to incorporate new functionalities without necessitating a complete system redesign. The autonomous hydroponic greenhouse, designed and implemented in this study, maintains stable environmental parameters to create an ideal environment for cultivating tomato plants. Actuators, receiving commands from a cloud application situated at the network’s edge, automatically regulate environmental conditions. Decision-making within this application is facilitated by a PID control algorithm, ensuring precision in control commands transmitted through the MQTT protocol and the NGSI-LD message format. The system transitioned from a single virtual machine in the public cloud to edge computing, specifically on a Raspberry Pi 3, to address latency concerns. In this study, we analyzed various delay aspects and network latency to better understand their significance in delays. This transition resulted in a significant reduction in communication latency and a reduction in total service delay, enhancing the system’s real-time responsiveness. The utilization of LoRa communication technology connects IoT devices to a gateway, typically located at the main farm building, addressing the challenge of limited Internet connectivity in remote greenhouse locations. Monitoring data are made accessible to end-users through a smartphone app, offering real-time insights into the greenhouse environment. Furthermore, end-users have the capability to modify system parameters manually and remotely when necessary. This approach not only provides a robust solution to climate-induced challenges but also enhances the efficiency and intelligence of agricultural practices. The transition to digitization poses a significant challenge for farmers. Our proposed system not only represents a step forward toward sustainable and precise agriculture but also serves as a practical demonstrator, providing farmers with a key tool during this crucial digital transition. The demonstrator enables farmers to optimize crop growth and resource management, concretely showcasing the benefits of smart and precise agriculture. Full article

Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI’s role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI’s transformative potential in healthcare, highlighting ChatGPT’s versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike. Full article

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management. Full article

Groundwater is an essential water source for drinking, domestic, irrigation and industrial production in Luxi Plain, Shandong Province, China. Understanding the spatial–temporal changes in groundwater quality and its influencing factors in the region were required for better utilization of groundwater resources and efficient design of groundwater management strategies. In this study, the hydrochemical characteristics of groundwater in the study area were analyzed, and significant evolution was found from 2018 to 2020 due to silicate and carbonate weathering, evaporation and human activities. Moreover, the entropy water quality index (EWQI) was used to assess groundwater quality from 2018 to 2020. The EWQI values in 2018–2020 were 129.5, 90.5 and 94.0, respectively, and 31.7% of the groundwater in 2019 and 20.0% in 2020 can be used directly for drinking in the study area; others can be used for domestic water or irrigation. The potable groundwater, with an EWQI value of <50 (ranked as class Ⅰ or Ⅱ water quality), was mainly distributed in the west and southeast of the study area. The potential health risk due to oral intake and dermal intake was further assessed based on the human health risk assessment (HHRA) model. The results showed that, 37.3%, 6.7% and 3.3% of the groundwater samples for adults exceeded the acceptable limit for non-carcinogenic risk of 1.0 in 2018–2020, while for children, they were 88.2%, 30.0% and 56.7%, respectively. The high non-carcinogenic risks virtually all occurred in the counties or districts with higher agricultural or economic values. This work may provide useful information for local groundwater conservation and management and help to ensure a sustainable and healthy water supply for drinking, domestic and agricultural needs. Full article

With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability. Full article

Freshwater is fundamental for all aspects of human well-being and sustainable development. The supply of freshwater resource largely depends on the natural water cycle, leading to extremely unequal distribution over the world. This uneven distribution and increasing freshwater demand results in spatial and temporal physical freshwater shortage. By discussing the limitations of desalination techniques and the shortcomings of existing pathways for freshwater transfer including water transfer projects, bottled water market, and virtual water trade, we suggest that international freshwater trade as an additional pathway is necessary. The analysis of the cost structure of freshwater production and transportation and the hypothetical examples between potential exporting and importing countries show the feasibility of international freshwater trade. The establishment of a global freshwater market is confronted with six challenges, namely, natural sustainability, ecological safety, opinions of stakeholders, market access mechanism, pricing mechanism, and infrastructure system. We conclude that a global freshwater market is expected to make contributions to achieving SDG 6 by mitigating spatial and temporal freshwater scarcity and by resolving transboundary freshwater conflicts and managing local freshwater consumptions. Full article

There is a long and interesting history between honeybees and humans. From the beginning, honey has been utilized not only as a sweetener, but also as an ointment and a drug to treat several diseases. Until the discovery of antibiotics, honey was a very popular product used to protect and preserve skin and promote wound healing, to counteract gastrointestinal pains and disorders of the oral cavity, and for other diseases. After the development of antibiotic resistance, honey again gained interest for its use in wound management. Subsequently, more recently, in vitro and in vivo studies have displayed antimicrobial, antioxidant, and other effects of honey and honeybee products, as well as protection of cardiovascular, respiratory, nervous, and gastrointestinal systems. Moreover, recent studies have demonstrated that beehive products are also able to influence the phenotype of skin cells, such as keratinocytes, fibroblasts, and endothelial cells, involved in correct wound healing. This review will characterize the great potential of honeybee products in the field of health and skin care, considering that honey is a virtually inexhaustible natural resource which people, as bees have been domesticated over the centuries, can freely access. Full article