Drones have been included in more and more activities in various domains, such as military, commercial and personal use. The existing legislative framework insufficiently addresses the responsibility and preventive measures angles in case of vulnerability exploitation and negligence in drone usage. Such aspects can be addressed by the industry in technological processes and standardization. These are especially important aspects given the high impact that misuse of drones can have on individuals, property and buildings within the flight zone when the drone is misused. The aim of this research paper is to investigate how these elements are viewed in existing legislation and by individuals, while taking into account the technical specifics and the stakeholder ecosystem of drone usage. In this respect, we use a complex questionnaire which was sent to a final number of 233 respondents pertaining to firms specialized in IT, legal and cybersecurity. The responses have been analyzed from a qualitative and quantitative perspective. Our results highlight the areas of improvement in the existing standardization and find the followings: (1) stakeholders across the drone ecosystem are viewed as having a shared liability in certain use cases, (2) preventive measure implementation should be dispersed across the stakeholders of drone usage and (3) automation of prevention measures is considered more useful in case of malfunctioning or misuse of drones rather than user manual intervention. In addition, we make proposals to accommodate new policy requirements for the above use cases. The results of this research paper assist policy makers in improving existing standardization framework and technological processes concerning drone usage, but also stakeholders of the drone ecosystem in generating increased trust of the drone users. Further, this research paper can also assist drone software and hardware producers in calibrating their products to ensure trust of the users. In addition, trust in the use of drones for commercial and personal purposes is increased through standardization and proper approaches for situations that may cause damages to drones and to third parties.

Drones have been used in recent years more and more in various economic sectors (e.g., military, agriculture, retail, transport), but also for personal use and entertainment. The current legislative framework and cyber security standards do not fully address the identification of liable stakeholders in the drone ecosystem for cyber-incidents and the requirement to implement preventive cyber-security measures. The aim of this paper is to investigate how the usage of drones fits in the context of the digital economy. For this purpose, we use a complex questionnaire which was sent to a total of 233 respondents from May to July 2021. The responses are analyzed from a qualitative and quantitative perspective. Our results highlight the areas of improvement in the existing legislation and find the following: (1) respondents are willing to pay additional direct and indirect costs related to cyber security to benefit from more secure drones, (2) the entire ecosystem involved in drone production, distribution, and usage is responsible for ensuring the prevention of security breaches, and (3) respondents perceive a shared liability of stakeholders for certain types of cyber-attacks depending on the role of the stakeholders in the drone ecosystem and the type of vulnerability exploited by the cyber-attack. The details on the specific cyber-attack use cases detail each of the above for each type of cyber-attack. Finally, we make proposals to accommodate the new types of use cases brought by the use of drones in various economic contexts. The results of this research paper assist policy makers in terms of improvement to existing legislation in terms of the drone ecosystem. In addition, they increase visibility for stakeholders in the drone ecosystem in terms of aspects to focus on in order to increase the trust of clients in drone usage.

Today’s Application Performance Monitoring evolution is in a direct relationship with fast growing e-commerce demands and associated technology prerequisites. To fulfill the high-demanding range of market expectations, tools need to be more efficient and robust, considering newly developed algorithms that can perform optimal calculations in a correct and fast way. The technological upgrade that the e-business world is facing is very often translated into new possibilities of supervising the resources for planning accordingly and setting new thresholds in various e-commerce sectors. This means that Application Performance Monitoring must be more concise, more results oriented and more determined in managing big sets of data along with high transaction ranges. This optic is not only related to administrative purposes, but it also means a paradigm change in the world of resource allocation and digitization, in the context of a fast-changing information society. The newly proposed Application Performance Monitoring approach includes tools that can handle multiple integrations, Application Programming Interface calls with external parties and a wide range of transaction mechanisms that can be implemented in a fast and efficient way. Hence, this article proposes different models for monitoring mechanisms/tools that can integrate functions, procedures and even applications with the purpose of highlighting efficient resource allocation structures and their potential beneficial role in e-commerce optimization sector. Various integration schemas are proposed to be taken into consideration when developing an Application Performance Monitoring tool for solving complex monitoring requests, along with some web performance analysis because of associated mechanisms implementation. As a result, this article proposes different Application Performance Monitoring integration mechanisms for handling e-commerce and in general e-business solutions high demanding and complex requests.

With the increase of the world’s population, the constant growth in food demand generates the need to find new ways of optimizing agricultural workflows. Today, a wide variety of software is dedicated to precision agriculture that helps the farmers gather data otherwise hardly available and extract information to minimize crop losses or prevent diseases. However, these solutions hardly allow a complete workflow from gathering the images, processing the datasets, and mapping and detecting the crop diseases. The solutions can be precisely applied where needed, without the need of manually exchanging information between applications. In this paper, we are proposing an architecture as well as a possible implementation for a web platform that can manage such workflows. The platform was implemented using ASP.NET Core 3.1 with C# as the main programming language. Following the best practices in terms of maintainability, the integration with third-party software was developed using proxy components that implement each components’ SDK or API, making these external solutions easily interchangeable. The first module presented in the paper covers the integration of third-party UAV controlling platforms. We integrated the UgCS commercial solution using the provided .NET SDK for our scenario. The data gathered by the UAVs controlled by this module, consisting of RGB, thermal and multispectral images, were stored using Azure Blob Storage cloud service. The location of each image data was acquired by extracting the XMP metadata and further stored using a PostgreSQL database with the PostGIS extension. Next, we provided a way to automatically generate the orthophoto imagery from the acquired data by integrating the Python API available in Agisoft Metashape. Lastly, using the products obtained from the previous step, we calculated different vegetation indices of the analyzed fields using C# and analyzed the outcomes using deep-learning models to identify and map vegetation health states. The platform has been implemented and tested for several case studies located across Romania, reaching satisfactory results.

The educational environment has been fundamentally changed with the increasingly rapid development of generative artificial intelligence (GenAI), thus raising expectations that this technology will be integrated into the academic environment, being useful for both teachers and students. This study analyzes students’ perceptions of artificial intelligence tools, which can have both negative and positive aspects on the learning process. This survey was structured into 4 dimensions: perceived usefulness, ease of use, perceived risks and behavioral intention to adopt and integrate them within universities. The research was conducted on a sample of 119 students from both the master’s and bachelor’s cycles, most of the questions being based on the Likert scale. Comparative analysis was made between the 2 study cycles and statistical indicators were described. The results demonstrate that students have a positive perception towards generative artificial intelligence tools. Both the ease of use, as well as the usefulness that students perceive using these tools, had scores well above the neutral level of the Likert scale, with scores of 4.07 and 3.61, respectively. These values indicate that students are perceptive to alternative learning methods. However, there are also high scores on the risk that comes with integrating these tools into academic activities. The ethical implications raise questions among students, as it is very difficult to distinguish plagiarism from original texts. Despite acknowledging certain associated risks, students express a clear intention to integrate these tools into the academic environment. The study contributes to existing literature by providing an empirical perspective on how GenAI is perceived and used in the contemporary academic environment.

Understanding labor market dynamics requires a systematic analysis of job posting data, but manual processing of large volumes of unstructured text remains impractical. This paper presents an automated workflow for collecting, filtering, and semantic enrichment of IT labor market data from Romanian recruitment platforms. We collected 56.062 publicly available job postings from BestJobs.ro over 244 days, using automated web data extraction techniques. The raw data went through a multi-stage processing workflow, using locally implemented large language models (Llama3.3:70b, Qwen2.5:14b and Qwen2.5:7b-instruct) through the Ollama platform, for category filtering, occupational classification according to the ESCO taxonomy, skills extraction, and semantic skills mapping. The flow reduced the initial data set to 2,152 validated job postings, classified into 75 ESCO occupations, with 2,797 unique skills extracted and mapped to 881 ESCO skill concepts. The methodology demonstrates that locally deployed open-weight LLMs can effectively replace cloud-based APIs for large-scale information mining tasks, while maintaining data confidentiality and eliminating per-query costs. Our approach provides a reproducible framework for processing labor market data, which can be adapted to other domains and languages.