IoT devices have a wide spectrum of applications in the real-life environment. While these applications range based on the area covered, having the best scenario related to the devices covering the optimal area is a challenge. In this work, we consider the improvement of the industrial laboratory by transferring it to the smart lab using the IoT devices. We analyzed the tradeoffs between different scenarios of the smart lab with the focus on the security and congestion of the network and its effects on the overall performance. For the smart lab case study we can conclude that security-enabled feature will not significantly affect the performance of the smart lab compared to the benefits of the IoT-integrated devices on the overall improvement of the lab experience given that the traditional lab had significant time delay.

Combating eutrophication requires changes in land and water management in agricultural catchments and implementation of mitigation measures to reduce phosphorus (P), nitrogen (N) and suspended sediment (SS) losses. To date, such mitigation measures have been built in many agricultural catchments, but there is a lack of studies evaluating their effectiveness. Here we evaluated the effectiveness of mitigation measures in a clay soil-dominated headwater catchment by combining the evaluation of long-term and high-frequency data with punctual measurements upstream and downstream of three mitigation measures: lime-filter drains, a two-stage ditch, and a sedimentation pond. Long-term hydrochemical data at the catchment outlet showed a significant decrease in P (-15%) and SS (-28%) and an increase in nitrate nitrogen (NO-N, +13%) concentrations. High-frequency (hourly) measurements with a wet-chemistry analyzer (total and reactive P) and optical sensor (NO-N and SS) showed that the catchment is an abundant source of nutrients and sediments and that their transport is exacerbated by prolonged drought and resuspension of stream sediments during storm events. Lime-filter drains showed a decrease in SS by 76% and total P by 80% and an increase in NO-N by 45% compared with traditional drains, potentially indicating pollution swapping. The effectiveness of two-stage ditch and sedimentation pond was less evident and depended on the prevalent hydrometeorological conditions that drove the resuspension of bed sediments and associated sediment-bound P transport. These results suggest that increased frequency of prolonged drought due to changing weather patterns and resuspension of SS and sediment-bound P during storm events can override the generally positive effect of mitigation measures.

The evolution of phosphorus (P) management decision support tools (DSTs) and systems (DSS), in support of food and environmental security has been most strongly affected in developed regions by national strategies (i) to optimize levels of plant available P in agricultural soils, and (ii) to mitigate P runoff to water bodies. In the United States, Western Europe, and New Zealand, combinations of regulatory and voluntary strategies, sometimes backed by economic incentives, have often been driven by reactive legislation to protect water bodies. Farmer-specific DSSs, either based on modeling of P transfer source and transport mechanisms, or when coupled with farm-specific information or local knowledge, have typically guided best practices, education, and implementation, yet applying DSSs in data poor catchments and/or where user adoption is poor hampers the effectiveness of these systems. Recent developments focused on integrated digital mapping of hydrologically sensitive areas and critical source areas, sometimes using real-time data and weather forecasting, have rapidly advanced runoff modeling and education. Advances in technology related to monitoring, imaging, sensors, remote sensing, and analytical instrumentation will facilitate the development of DSSs that can predict heterogeneity over wider geographical areas. However, significant challenges remain in developing DSSs that incorporate "big data" in a format that is acceptable to users, and that adequately accounts for catchment variability, farming systems, and farmer behavior. Future efforts will undoubtedly focus on improving efficiency and conserving phosphate rock reserves in the face of future scarcity or prohibitive cost. Most importantly, the principles reviewed here are critical for sustainable agriculture.

In process automation installations, the I/O system connect the field devices to the process controller over a fieldbus, a reliable, real-time capable communication link with signal values cyclical being exchanged with a 10–100 millisecond rate. If a deviation from intended behaviour occurs, analyzing the potentially vast data recordings from the field can be a time consuming and cumbersome task for an engineer. For the engineer to be able to get a full understanding of the problem, knowledge of the used I/O configuration is required. In the problem report, the configuration description is sometimes missing. In such cases it is difficult to use the recorded data for analysis of the problem.In this paper we present our ongoing work towards using neural network models as assistance in the interpretation of an industrial fieldbus communication recording. To show the potential of such an approach we present an example using an industrial setup where fieldbus data is collected and classified. In this context we present an evaluation of the suitability of different neural net configurations and sizes for the problem at hand.

There is a growing interest in using the Blockchain for resolving IoT security and trustworthiness issues existing in today’s complex systems. Blockchain concerns trust in peer to peer networks by providing a distributed tamper-resistant ledger. However, the combination of these two emerging technologies might create new problems and vulnerabilities that attackers might abuse.In this paper, we aim to investigate the trust mechanism of Lightweight Scalable BlockChain (LSB), that is a Blockchain specifically designed for Internet of Things networks, to show that a malicious participant in a Blockchain architecture have possibility to pursue an On-Off attack and downgrade the integrity of the distributed ledger. We choose a remote software update process as an instance to represent this violation. Finally, using the actor-based language Rebeca, we provide a model of a system under attack and verify the described attack scenario.

To achieve efficient and flexible production at affordable prices, industrial automation is pushed towards a digital transformation. Such a transformation assumes an enhancement of current Industrial Automated Control Systems with a large amount of IoT-devices, forming an Industrial Internet of Things (IIoT). The aim is to enable a shift from automatic towards autonomous control in such systems. This paper discusses some of the main challenges IIoT systems are facing with respect to cybersecurity. We discuss our findings in an example of a flow-control loop, where we apply a simple threat model based on the STRIDE method to deduce cybersecurity requirements in an IIoT context. Moreover, the identified requirements are assessed in the light of current state of the art solutions, and a number of challenges are discussed with respect to a large-scale IIoT system, together with some suggestions for future work.

Abstract The majority of past studies assessed the impact of oil price on stock returns using aggregate stock price index from different countries and assuming the effects to be symmetric. In this paper, we investigate asymmetric causality not only from oil price to stock returns but also from stock returns to oil price. To reduce aggregation bias, we use data from nine different sectors of the U.S. economy. We found that an increase in oil price causes returns of three sectors, while a decrease in oil price causes returns of four sectors, all in the short run. On the other hand, we found that an increase in returns in three sectors causes oil price to rise, while a decrease in returns in six sectors causes oil price to decline. We do not discover significant long-run causal relationship in either direction.

Condition monitoring enables companies from all industrial branches to maintain high availability of their automation systems. Thus, condition monitoring is a fundamental technology to maximize operational productivity by predicting potential fault occurrences that lead to unavailability of the automation systems. Today, performing condition monitoring is successfully achieved using custom-developed software solutions. However, the development of these software solutions is a challenging task as it requires extensive knowledge from multiple engineering disciplines. In this context, there is a high need for a systematic way to formalize this knowledge. Ontologies have had significant success in fulfilling this need by providing a formal model of domain concepts and their relations.In this paper, we present our condition monitoring ontology for automation systems and contribute with a conceptual model. This ontology incorporates ISO standards for condition monitoring and key performance indicators (KPIs). KPIs aggregate numerous sensor values in a few understandable numbers and, therefore, provide more efficient insight in condition of automation systems. To evaluate our work, we developed a condition monitoring knowledge-based system for a centrifugal separator based on the proposed ontology in an industrial project and report about lessons learned.

The process of beer brewing is very complex as it has to fulfill strict demands on the product quality as well as on the availability and the performance of the plant. As a consequence, a condition monitoring of the beer brewing process and its visualization plays an important role such that all relevant deviations are detected as early as possible by the production manager. While a general process for condition monitoring already exists, there currently exists no approach to realize this process for the domain of beer brewing.Therefore, this paper presents a condition monitoring system for the beer brewing process developed in an industrial project. This condition monitoring system is based on Key Performance Indicators (KPIs) that support the production manager in evaluating the actual state of the production processes. A set of brewery-specific KPIs are determined and discussed in the paper. In addition, software architecture and visualization of the KPIs in a brewery-specific dashboard are presented. We evaluate our concept at various beer breweries and report about lessons that we have learned.

Abstract Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare. To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance. The results of this study demonstrate that clinical engineering and health technology management benefit from application of machine learning in terms of cost optimization and medical device management. Automated systems, based on machine learning algorithms, can predict defibrillator performance with high accuracy. Systems based on Random Forest classifier with Genetic Algorithm feature selection yielded highest accuracy among other machine learning systems. Adoption of such systems will help in overcoming challenges of adapting maintenance and medical device supervision mechanism protocols to rapid technological development of these devices. Due to increased complexity of healthcare institution environment and increased technological complexity of medical devices, performing maintenance strategies in traditional manner is causing a lot of difficulties.

Introduction: Occupational stress is inevitable, but prolonged and intense can lead to serious health problems. Neglecting this significant aspect of work and inadequate treatment of the first indicators results in the reduced working capacity of healthcare professionals. Material and methods: Assessing working conditions of 100 healthcare professionals regarding stress impact on empathy and workability was the primary goal of this study. A survey was conducted to identify differences between work in intensive care units and other clinical departments all related to empathy, workability, and stress perceiving to determine what is a better predictor of workability. Results: In the research group “intensive care units”, significantly lower empathy quotients, poorer workability, and different stressors were registered compared to research groups named “other departments”. Conclusion: The main conclusion of the study states different dynamics in the working environment of intensive care units compared to other departments that could potentially harm the personal capacity of healthcare professionals. 

Chainsaw is the main tool for work in a phase of wood felling and processing of forest harvesting in BiH. The aim of the research was to determine fuel and lubricant consumption of the chainsaws Husqvarna 365 and Dolmar PS – 7310 in the phase of felling and processing of wood assortments at the area of P.J. Forest Office „Zavidovići“, forest compartment 203. The volumetric method was applied for the measurement of fuel and lubricants consumption, with precise determination of fuel and lubricant quantity in chainsaw tanks. In total 140 trees of the sessile oak were felled (70 trees with chainsaw Husqvarna 365 and 70 trees with chainsaw Dolmar PS – 7310) during the research. The diameter at the breast height of felled trees ranged from 15 to 84 cm, while tree height ranged from 10,3 to 37,2 m. The total volume of processed assortments was 180,11 m3. The average fuel consumption per m3 processed assortments was 0,306 L/m3 for trees felled by chainsaw Husqvarna 365 which is for 0,042 L/m3 larger in comparison to chainsaw Dolmar PS – 7310 with average fuel consumption of 0,264 L/m3. The average lubricant consumption per m3 processed assortments was 0,102 L/m3 for trees felled by chainsaw Husqvarna 365 which is for 0,012 L/m3 larger in comparison to chainsaw Dolmar PS – 7310 with average lubricant consumption of 0,09 L/m3. Considering conducted research it can be assumed that there are differences in fuel and lubricant consumption between this two type of chainsaws for considered working conditions, but differences are not statistically significant.

Neonikotinoid grubu pestisitler, nikotine benzeyen davranislariyla sinirdokularini etkileyen insektisitlerdir. Bu insektisit grubunun ozellikle balarisi kolonileri uzerindeki olumcul etkileri bilinmektedir. Bunun yanindaki diger olumsuz ekolojik etkileri nedeniyle bazi turlerinin kullanimi cesitli ulkelerde yasaklanmistir. Neonikotinoid grubundan bir insektisit turu olan Clothianidin, su ve toprak gibi cevresel ortamlarda bulunabilen yasaklanmis insektisit turlerinden biridir. Clothianidin kalici ve yuksek hareketlilige sahip, hidrolize direncli, yeraltisuyuna ve yuzeysel sulara sizma potansiyeline sahip bir neonikotinoid grubu pestisit turu olarak bilinmektedir. Clothianidinin gorunur isik karsisindaki fotodegradasyon davranisini belirlemek ve aritma formulasyonlari gelistirmek bu kirleticilerle kirlenmis atiksularin yonetiminde onemlidir. Bu calismanin amaci, Clothinanidin’in ileri oksidasyon yontemleriyle giderim etkinligini arastirmaktir. Calisma kapsaminda Clothianidin’in UV-A, UV-C isinlari karsisindaki kararliliginin yaninda TiO 2 ve oksijen destegi ile su icerisindeki fotokatalitik bozunmasi da arastirilmistir. Clothianidinin sulu cozeltisi 0-120 dakika arasinda degisen sureler boyunca UV-A, UV-A+TiO 2 , UV-A+TiO 2 +O 2 ve UV-C uygulamalarina tabi tutulmus, surec esnasinda degisen Clothianidin konsantrasyonu HPLC analizleriyle izlenmistir. UV-A uygulamasina tabi tutulan cozeltideki Clothianidin duzeyinin sabit kaldigi, kirleticinin herhangi bir bozunmaya ugramadigi gozlenmistir. Ancak TiO 2 varliginda yapilan UV-A uygulamasi, herhangi bir giderim etkisi gostermeyen UV-A uygulamasina gore uygulama suresiyle artan bir Clothianidin giderim egilimiyle sonuclanmistir.  UV-A+TiO 2 uygulamasinin %42 oraninda bir giderimle sonuclandigi gorulmustur. UV-A+TiO 2 surecine oksijen ilave edilerek yapilan uygulamada ise oksijenin Clothianidin’in bozunmasina olumlu bir etkisinin oldugu, giderim veriminin artarak %42’den %69 duzeyine ulastigi gorulmustur. UV-C uygulamasi ile 5 dakika gibi kisa bir surede %99’a varan bir giderim verimi elde edilmistir. TiO 2 ve oksijen varliginda gerceklestirilen fotokatalik yontemin ve ozellikle UV-C uygulamasinin diger yontemlerden cok daha etkili oldugu ve aritma cozumlerinde tercih edilebilecegi sonucuna varilmistir.

Coronary artery disease (CAD) is a pathological process characterized by atherosclerotic plaque accumulation in the epicardial arteries, whether obstructive or non-obstructive. This process can be modified by lifestyle adjustments, pharmacological therapies, and invasive interventions designed to achieve disease stabilization or regression. The disease can have long, stable periods but can also become unstable at any time, typically due to an acute atherothrombotic event caused by plaque rupture or erosion. However, the disease is chronic, most often progressive, and hence serious, even in clinically apparently silent periods. The dynamic nature of the CAD process results in various clinical presentations, which can be conveniently categorized as either acute coronary syndromes (ACS) or chronic coronary syndromes (CCS). The Guidelines presented here refer to the management of patients with CCS. The natural history of CCS is illustrated in Figure 1.