Correction: Gligoric et al. IOTA-Based Distributed Ledger in the Mining Industry: Efficiency, Sustainability and Transparency. Sensors 2024, 24, 923.

In the original publication [...].

IOTA-Based Distributed Ledger in the Mining Industry: Efficiency, Sustainability and Transparency.

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance.

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing.

Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

Prospective Cohort Study of the Kinetics of Specific Antibodies to SARS-CoV-2 Infection and to Four SARS-CoV-2 Vaccines Available in Serbia, and Vaccine Effectiveness: A 3-Month Interim Report.

Real-life data on the performance of vaccines against SARS-CoV-2 are still limited. We here present the rates of detection and levels of antibodies specific for the SARS-CoV-2 spike protein RBD (receptor binding domain) elicited by four vaccines available in Serbia, including BNT-162b2 (BioNTech/Pfizer), BBIBP-CorV (Sinopharm), Gam-COVID-Vac (Gamaleya Research Institute) and ChAdOx1-S (AstraZeneca), compared with those after documented COVID-19, at 6 weeks and 3 months post first vaccine dose or post-infection. Six weeks post first vaccine dose, specific IgG antibodies were detected in 100% of individuals fully vaccinated with BNT-162b2 (n = 100) and Gam-COVID-Vac (n = 12) and in 81.7% of BBIBP-CorV recipients (n = 148), while one dose of ChAdOx1-S (n = 24) induced specific antibodies in 75%. Antibody levels elicited by BNT-162b2 were higher, while those elicited by BBIBP-CorV were lower, than after SARS-CoV-2 infection. By 3 months post-vaccination, antibody levels decreased but remained ≥20-fold above the cut-off in BNT-162b2 but not in BBIBP-CorV recipients, when an additional 30% were seronegative. For all vaccines, antibody levels were higher in individuals with past COVID-19 than in naïve individuals. A total of twelve new infections occurred within the first 3 months post-vaccination, eight after the first dose of BNT-162b2 and ChAdOx1-S (one each) and BBIBP-CorV (six), and four after full vaccination with BBIBP-CorV, but none required hospitalization.

Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning.

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93-0.97, 0.82-0.94 and 0.73-0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

SmartTags: IoT Product Passport for Circular Economy Based on Printed Sensors and Unique Item-Level Identifiers.

In this paper, we present a method that facilitates Internet of Things (IoT) for building a product passport and data exchange enabling the next stage of the circular economy. SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard enable unique identification of objects on a per item-level (including Fast-Moving Consumer Goods-FMCG), collecting, sensing, and reading of parameters from environment as well as tracking a products' lifecycle. The developed ontology is the first effort to define a semantic model for dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability of identifiers (QR codes) showed good performance for detection of sensor state printed over and outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible. The evaluation of the decoding performance of the QR code data matrix printed with sensors was also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.