The paper discusses a control strategy that merges position and force control into a single control structure. The structure, denoted as the universal motion controller in our previous work, can be utilized to build a smart actuating system that runs a mechanical system with $n$ degrees of freedom. A smart actuating system has an integrated controller and it can be used in plug-and-play fashion for different trajectory tracking and force control tasks, defined either in configuration space, or in the task space. The only input of the actuating system is the attraction force in configuration space. Based on the attraction force, the smart actuating system is capable of imposing input forces to the mechanical system that will ensure execution of a specified task.

Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down.

Background: Sternotomy is a classical surgical procedure for approaching the heart and mediastinum. Sternotomy wound infections can be superficial or deep. Objective: The aim of this study is to retrospectively evaluate the results of two treatments for deep sternal wound infection (DSWI), closed treatment (debridement, refixation and retrosternal irrigation) and open treatment (debridement, VAC therapy and then pectoral flap). Methods: Retrospective analysis of two methods of treatment of DSWI in the period of six years. The first group (G1): surgical debridement, sternum fixation with, if necessary, retrosternal irrigation. The second group (G2): surgical debridement, open sternum with VAC therapy and subsequent pectoral flap with sternum refixation if necessary. Sternotomy wound infection will be classified according to the depth of the affected areas and the time of infection. Risk factors, outcome, local findings, number of revisions, number of hospital treatment days, types of isolates, etiology of sternotomy, time from onset of sternal instability to first surgical treatment will be observed.: Results: The number of patients with DSWI was 16, which represents 1% of all sternotomy in the observed period. Mortality in the DSWI group was 35%. Surgical myocardial revascularization was initially performed in 73% of patients with DSWI. Two risk factors for DSWI were in 32% of patients and 25% had diabetes mellitus. The average time for DSWI development in G1 was 10 days (min 0, max 30) and in G2 was 20 days (min 12, max 30). Number of revisions in G1 (min 1, max 2), G2 (min 1, max 3). Average number of hospital days were in G1 23.50 days (SD 13.15), and in G2 38.17 days (SD 28.65). The sternum was osteomyelitic and fragmented in 20% of patients. More than one revision occurred in 40% of patients. The main initial isolate was Enterococcus faecalis in 27% of all DSWI (dominantly in G1 2/3 of all): Conclusion: We found that there is no statistically significant difference in observed treatments, and that each treatment has its own indications. We suggested that studies with a larger sample are needed for a definite opinion on this issue.

Simple Summary Recently, the interactions between microbiota and the host have been reported to induce the onset and progression of human cancer via epithelial–mesenchymal transition (EMT). In contrast, some microorganisms can protect against cancer growth, indicating an anticancer therapeutic action of such microbiota. In the review, we summarize findings from the literature, exploring the underlying mechanisms by which pathogenic microorganisms induce EMT. We also highlight the potential of exploiting these complex interactions for developing new biological therapies. Abstract Advancement in the development of molecular sequencing platforms has identified infectious bacteria or viruses that trigger the dysregulation of a set of genes inducing the epithelial–mesenchymal transition (EMT) event. EMT is essential for embryogenesis, wound repair, and organ development; meanwhile, during carcinogenesis, initiation of the EMT can promote cancer progression and metastasis. Recent studies have reported that interactions between the host and dysbiotic microbiota in different tissues and organs, such as the oral and nasal cavities, esophagus, stomach, gut, skin, and the reproductive tract, may provoke EMT. On the other hand, it is revealed that certain microorganisms display a protective role against cancer growth, indicative of possible therapeutic function. In this review, we summarize recent findings elucidating the underlying mechanisms of pathogenic microorganisms, especially the microbiota, in eliciting crucial regulator genes that induce EMT. Such an approach may help explain cancer progression and pave the way for developing novel preventive and therapeutic strategies.

Rapid population growth requires more intense production of food industry, with two major consequences: significant amount of food processing residues and more sewage sludge originating from biological wastewater treatment plant. Sludge is a big concern for the disposal for wastewater treatment plant. The European Union makes an effort regarding the reduction of organic fractions disposed at a landfill. Composting is a cost-effective and ecological-friendly alternative for managing biodegradable organic fractions. Experiments of co-composting of sewage sludge, green waste and food waste, at carbon/nitrogen ratios 8.75, 18.00 and 24.90, were performed during three months by monitoring temperature, pH, moisture, carbon and nitrogen proportion, carbon/nitrogen ratio, and germination index. The results showed that co-composting of sewage sludge, green waste, and food waste is effective and results in the production of quality compost.

Aim To investigate the influence of specific intrapopulation genetic structures on interpopulation relationships. Special focus was the influence of island population isolation on the substructuring of the Croatian population, and the influence of regional population groups on the substructuring of Southeast European populations. Methods Autosomal short tandem repeat (STR) loci were analyzed by using four forensic parameters: matching probability (PM), power of discrimination (PD), power of exclusion (PE), and polymorphic information content (PIC) on a sample of 2877 unrelated participants of both sexes. A sample set comprising 590 participants was analyzed for the first time, and 2287 participants were included from previous studies. The analysis was performed with PowerStats v. 1.2. Results The analysis of forensic parameters for all nine loci in the Croatian subpopulations showed the largest deviations in the populations of the islands of Korčula and Hvar. The smallest deviations were found in the mainland population. As for Southeast European populations, the largest deviations were found in the population of North Macedonia, followed by Romania, Albanians from Kosovo, and Montenegro, while the smallest deviations were found in the population of Hungary. Conclusion The comparison of forensic parameters between different subpopulations of Croatia and Southeast Europe indicates that the isolation of individual Croatian subpopulations and rare alleles in their gene pool affect the values of forensic parameters. Specific features of (sub)populations should be taken into account for appropriate sampling of the total population when creating a DNA database of STR markers.

Aim To use the method of meta-analysis to assess the influence of island population isolation on the sub-structuring of the Croatian population, as well as the influence of regional population groups on the sub-structuring of the Southeastern European population with regard to basic population genetic statistical parameters calculated by using STR locus analysis. Methods Bio-statistical analyses were performed for 2877 unrelated participants of both sexes from Southeastern Europe. Nine autosomal STR loci (D3S1358, vWA, FGA, TH01, TPOX, CSF1PO, D5S818, D13S317, and D7S82) were analyzed by using standard F-statistics and population structure analysis (Structure software). Results Genetic differentiation of Croatian subpopulations assessed with the FST method was higher at the level of the Croatian population (0.005) than at the level of Southeastern Europe (0.002). The island of Vis showed the most pronounced separation in the Croatian population, and Albanians from Kosovo in the population of Southeast Europe, followed by Croatia, Bosnia and Herzegovina, and Hungary. Conclusion The higher structure of Croatian subpopulations in relation to Southeastern Europe suggest a certain degree of genetic isolation, most likely due to the influence of endogamy within rural island populations.

Abstract The Logistics Performance Index (LPI) performed by the World Bank is an indicator of the logistics environment quality of a country in which logistics operators act. The LPI is an interactive tool designed to help countries identify challenges, innovative solutions, and opportunities they face in their work in the field of trade and logistics. The aim of this paper is to conduct a comparative analysis and ranking of the LPI of the countries in the Western Balkans (Bosnia and Herzegovina, North Macedonia, Albania, Serbia and Montenegro), calculated by the World Bank for 2018, using an integrated Criteria Importance Through Intercriteria Correlation (CRITIC)-Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) model and thus show the real picture of the logistics environment. In order to determine the performance of countries and show the overall logistics performance, six key dimensions are used: customs, infrastructure, international transport, logistics capability, tracking and tracing of goods and shipment delivery within scheduled or expected times. Using the CRITIC method, the weight values of the previously mentioned six criteria were calculated, whereby the criterion related to shipment delivery within scheduled times was singled out as the most significant criterion. Then, by applying the MARCOS method, the countries of the Western Balkans were ranked on the basis of the six defined criteria. Based on the results obtained, the best-ranked country is Serbia. The analysis of the sensitivity of the results to changes in the significance of the criteria does not show significant changes in the ranking.

Using recent advancements in high-performance computing data assimilation to combine satellite InSAR data with numerical models, the prolonged unrest of the Sierra Negra volcano in the Galápagos was tracked to provide a fortuitous, but successful, forecast 5 months in advance of the 26 June 2018 eruption. Subsequent numerical simulations reveal that the evolution of the stress state in the host rock surrounding the Sierra Negra magma system likely controlled eruption timing. While changes in magma reservoir pressure remained modest (<15 MPa), modeled widespread Mohr-Coulomb failure is coincident with the timing of the 26 June 2018 moment magnitude 5.4 earthquake and subsequent eruption. Coulomb stress transfer models suggest that the faulting event triggered the 2018 eruption by encouraging tensile failure along the northern portion of the caldera. These findings provide a critical framework for understanding Sierra Negra’s eruption cycles and evaluating the potential and timing of future eruptions.