Towards ubiquitous requirements engineering through recommendations based on context histories

The growing technological advance is causing constant business changes. The continual uncertainties in project management make requirements engineering essential to ensure the success of projects. The usual exponential increase of stakeholders throughout the project suggests the application of intelligent tools to assist requirements engineers. Therefore, this article proposes Nhatos, a computational model for ubiquitous requirements management that analyses context histories of projects to recommend reusable requirements. The scientific contribution of this study is the use of the similarity analysis of projects through their context histories to generate the requirement recommendations. The implementation of a prototype allowed to evaluate the proposal through a case study based on real scenarios from the industry. One hundred fifty-three software projects from a large bank institution generated context histories used in the recommendations. The experiment demonstrated that the model achieved more than 70% stakeholder acceptance of the recommendations.

Smart Cutting Tools Used in the Processing of Aluminum Alloys

The processing of aluminum alloys in optimal conditions is a problem that has not yet been fully resolved. The research carried out so far has proposed various intelligent tools, but which cannot be used in the presence of cooling-lubricating fluids. The objective of the research carried out in the paper was to design intelligent tools that would allow a control of the vibrations of the tool tip and to determine a better roughness of the processed surfaces. The designed intelligent tools can be used successfully in the processing of aluminum alloys, not being sensitive to coolants-lubricants. In the research, the processing by longitudinal turning of a semi-finished product with a diameter Ø = 55 mm of aluminum alloy A2024-T3510 was considered. Two constructive variants of smart tools were designed, realized, and used, and the obtained results were compared with those registered for the tools in the classic constructive variant. The analysis of vibrations that occur during the cutting process was performed using the following methods: Fast Fourier Transform (FFT); Short-Time Fourier-Transformation (STFT); the analysis of signal of vibrations. A vibration analysis was also performed by modeling using the Finite Element Method (FEM). In the last part of the research, an analysis of the roughness of the processed surfaces, was carried out and a series of diagrams were drawn regarding curved profiles; filtered profiles; Abbott–Firestone curve. Research has shown that the use of smart tools in the proposed construction variants is a solution that can be used in very good conditions for processing aluminum alloys, in the presence of cooling-lubrication fluids.

Using artificial intelligence methods for shear travel time prediction: A case study of Facha member, Sirte basin, Libya

Shear wave travel time logs are major acoustic logs used for direct estimation of the mechanical properties of rocks. They are also important for prediction of critical drawdown pressure of the reservoir. However, core samples are sometimes not available for direct laboratory measurements, and the time-consuming dipole shear imager tool is generally not used. Hence, there is a need for simple indirect techniques that can be used reliably. In this study, cross-plots between the available measured shear travel time and compressional travel time from three oil wells were used, and three artificial intelligence tools (fuzzy logic, multiple linear regression and neural networks) were applied to predict the shear travel time of Facha member (Gir Formation, Lower Eocene) in Sirte Basin, Libya. The predicted times were compared to those obtained by the equation of Brocher. The basic wireline data (gamma ray, neutron porosity, bulk density and compression travel time) of five oil wells were used. Based on principle component analysis, two wireline data sets were chosen to build intelligent models for the prediction of shear travel time. Limestone, dolomite, dolomitic limestone and anhydrite are the main lithofacies in the Facha member, with an average thickness of about 66 m. The simple equation gave 87% goodness of fit, which is considered comparable to the measured shear travel time logs. The Brocher equation yielded adequate results, of which the most accurate was for the Facha member in the eastern part of the Sirte basin. On the other hand, the three intelligent tools’ predictions of shear travel time conformed with the measured log, except in the eastern area of the basin.

Open Cultural Data and MediaWiki Software for a Museum: The Use Case of Musée Saint-Raymond (Toulouse, France)

Freely accessible online databases developed by cultural and artistic institutions (e.g., museums, libraries, universities, studios, etc.) enable the transnational dissemination of catalogues of cultural and creative works, exploiting the advantages of modern technologies. Intelligent tools, which use advanced algorithms to classify and contextualize data, can foster knowledge mainly in two ways: (1) providing a stable and accessible basis for large amounts of data; (2) promoting cultural heritage. A case of skillful use of such tools is the Saint-Raymond Museum, the archaeological museum of Toulouse. For several years it has been working on the open data front and on putting its catalogue online on Wikimedia platforms, in various forms.

CIB W040: International Surveys Considering Moisture Safety in Buildings During Design and Construction

We conducted comparative surveys of design consultants in three countries to determine current knowledge and experienced moisture problems. The study is part of the CIB W040 research roadmap needs analyses for realigning research efforts with stakeholder requirements for moisture safety. Survey results show that a third of construction projects in the last five years were affected by moisture problems, even though practitioners applied multiple preventative measures at least some of the time. Water installations caused approximately 20 % of the moisture damage. In each country, preventing moisture damage was necessary; the means to address problems varied, with no one dominating solution. Design and construction guidelines were more helpful than the building code requirements. Information is available, but designers need dedicated time and budget for implementing better moisture safety. A quantitative goal is to increase the frequency of moisture safety measures while increasing the availability of tools. The usefulness of selected measures and instruments is strongly case-specific. Subtopic analysis such as causes of moisture damage due to leaky water installations needs more detailed investigation. Further research is needed building upon the online survey results to develop intelligent tools preventing moisture damage in the design, construction, and building occupancy phases.

Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations

In recent years, law enforcement authorities have increasingly used mathematical tools to support criminal investigations, such as those related to terrorism. In this work, two relevant questions are discussed: “How can the different roles of members of a terrorist organization be recognized?” and “are there early signs of impending terrorist acts?” These questions are addressed using the tools of entropy and network theory, more specifically centralities (degree, betweenness, clustering) and their entropies. These tools were applied to data (physical contacts) of four real terrorist networks from different countries. The different roles of the members are clearly recognized from the values of the selected centralities. An early sign of impending terrorist acts is the evolutionary pattern of the values of the entropies of the selected centralities. These results have been confirmed in all four terrorist networks. The conclusion is expected to be useful to law enforcement authorities to identify the roles of the members of terrorist organizations as the members with high centrality and to anticipate when a terrorist attack is imminent, by observing the evolution of the entropies of the centralities.

Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental “causal” role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a “historical” process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process — and not a static-stable condition — treatments should be tailored according to the “timing-frame” of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes — from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.

Human-Centric Design Requirements and Challenges for Enabling Human-AI Interaction in Engineering Design: An Interview Study

AI technologies are enabling the development of not only active tools that provide decision-support, but also interactive tools that seek human input and feedback. As interactive tools facilitate human-AI interaction, their design needs to be informed by human-centric requirements, that is, the needs of the users of such tools. In the context of engineering design, there is a gap in our understanding of designing intelligent tools that facilitate human-AI interaction. To fill this gap, the research question of this study is, What are the human-centric design requirements for the design of AI agents to enable human-AI interaction in engineering design contexts? To answer this question, we conducted an interview study with faculty members in engineering design. The faculty predominantly discussed engineers, designers, and engineering design students as the potential stakeholders who would directly benefit from human-AI interaction. For such stakeholders, we identify several human-centric design requirements and challenges in designing AI tools that facilitate human-AI interaction in engineering design. We find that the requirements focused on the need to understand the stakeholders’ cognition and the engineering design contexts. The results of our study point to the need for the theory of mind in AI agents to enable them to infer stakeholder preferences while engaging in engineering design activities.

Psychiatry in the Digital Age: A Blessing or a Curse?

Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor–patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.