Wireless data traffic has experienced an unprecedented boost in past years, and according to data traffic forecasts, within a decade, it is expected to compete sufficiently with wired broadband infrastructure. Therefore, the use of even higher carrier frequency bands in the THz range, via adoption of new technologies to equip future THz band wireless communication systems at the nanoscale is required, in order to accommodate a variety of applications, that would satisfy the ever increasing user demands of higher data rates. Certain wireless applications such as 5G and beyond communications, network on chip system architectures, and nanosensor networks, will no longer satisfy speed and latency demands with existing technologies and system architectures. Apart from conventional CMOS technology, and the already tested, still promising though, photonic technology, other technologies and materials such as plasmonics with graphene respectively, may offer a viable infrastructure solution on existing THz technology challenges. This survey paper is a thorough investigation on the current and beyond state of the art plasmonic system implementation for THz communications, by providing in-depth reference material, highlighting the fundamental aspects of plasmonic technology roles in future THz band wireless communication and THz wireless applications, that will define future demands coping with users’ needs.

Strict adherence to data integrity and quality standards is crucial for the pharmaceutical industry to minimize undesired effects and ensure that medicines are of the required quality and safe for patients. A common data quality standard in the pharmaceutical industry is ALCOA+, which is a set of guiding principles for ensuring data integrity. Failure to comply with ALCOA+ guidelines, usually detected after audit inspections, may result in serious consequences for pharmaceutical manufacturers, such as the incurrence of fines, increase in costs, and production delays. It is, therefore, imperative to devise methods able to monitor ALCOA+ compliance and detect decreasing trends in data quality automatically. In this paper we present ALCOAi, a deep learning model based on the transformer architecture, which is able to process large quantities of non-homogeneous data and compute current and future ALCOA+ compliance. The proposed model can estimate trends concerning most ALCOA+ principles. The model was tested on a real dataset comprising raw sensor data, machine-provided values, and human-entered free-text data from two pharmaceutical manufacturing lines. The performed tests led to promising results in forecasting ALCOA+ compliance.

Nowadays, data integrity has become a critical issue in the pharmaceutical regulatory landscape, one that requires data to be compliant to ALCOA principles (i.e., data must be Attributable, Legible, Contemporaneous, Original, and Accurate). In this paper, we propose a method which exploits semantic web technologies to represent pharma manufacturing data in a unified manner and evaluate in a systematic manner their ALCOA compliance. To this purpose, in the context of a pharma manufacturing environment, a data integrity ontology (DIOnt) is proposed to be utilized as the basis for the semantic representation of pharma production data and the associated regulatory compliance management processes. We further show that semantic annotations can be used to represent the required ALCOA compliance information, and that semantic reasoning combined with SQWRL queries can be used to evaluate ALCOA compliance. The proposed approach has been implemented in a proof-of-concept prototype and validated with real world pharma manufacturing data, supporting the combined execution of SWRL rules and SQWRL queries with the aim to support the ALCOA compliance assessment and calculate non-compliance percentages per each ALCOA principle.

Illegal logging has always been considered as a major environmental and social global concern, as it is directly associated with deforestation and climate change. Nowadays, EU Regulation No 995/2010 has been successfully enforced to impede the placement of illegally produced timber within the EU market and therefore to efficiently enhance sustainable forest management and restore ecosystem balance. However, EU 995 regulatory compliance and enforcement itself is quite complex, since it requires long-term conformity, on a common basis for various heterogeneous groups and communities of stakeholders, in a global, even beyond EU, rule regulation framework. To make things worse, such a framework must be applied to the entire supply distribution chain and a wide variety of wood products, ranging from paper pulp to solid wood and flooring. Hence, in such complex and multivariate information environments, an ontological approach can more efficiently support regulatory compliance and knowledge management, due to its openness and richness of semantics for representing, analyzing, interpreting and managing such kind of information. In this paper, a rule-based regulatory compliance ontology is proposed, which fully captures EU Regulation No 995/2010 concepts and compliance rules and guidelines, as well as Greek legislations governing wood trade. The proposed ontology can be the basis for a computerized system providing automated support for illegal wood trade and monitoring EU regulation information provision and audit information storage and analysis.