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The main objective of the study was to determine the effect of transition from a conventional milking system (CMS) to an automatic milking system (AMS) on survival of 6361 Polish Holstein-Friesian cows to second (SL2), third (SL3) and fourth (SL4) lactation as well culling reasons. The cows were born between 2002 and 2015 and calved between 2004 and 2018. All data for the survival analysis and culling reasons of cows in 17 herds during operation of CMS and AMS were extracted from the SYMLEK official milk recording system. Cow survival (SL2, SL3 and SL4) was analysed with multiple logistic regression using the following effects in the model: milking system (MS), first calving season (CS), age at first calving (AFC), ease of first calving (CE), birth of a dead calf at first calving (DC), milk yield (MY) for full first lactation (MY – this effect was ignored in SL2 analysis), herd (H), and MS × H interaction. In the next stage of the study, χ2 test was used to analyse culling reasons of cows (udder diseases, low fertility – infertility and reproductive disorders, locomotor diseases, low milk yield, other diseases – metabolic, digestive and respiratory diseases, accidents and chance events) in the first, second and third lactation and collectively in the first three lactations. Logistic regression analysis indicated a significant effect of MS, AFC, DC on SL2 and SL3, and of MY on SL3 and SL4. Moreover, H and MS × H interaction had a highly significant effect on SL2, SL3, and SL4. Cows used in AMS barns were characterized by significantly worse SL2 and SL3 compared to CMS (odds ratio), by 27.8% and 31.0%, respectively. It was also observed that the effect of switching from CMS to AMS on cow survival was determined by herd membership – in most herds this effect was unfavourable. A distinctly positive effect of milking automation on cow survival (SL2, SL3, SL4) was noted in only one barn (herd) – it was a new barn with a considerably expanded number of milked cows, where the lying area was covered with straw. When analysing the reasons for culling in the first three lactations collectively, it was found that after the AMS system was introduced into the herds, there were increases in the rate of culling for locomotor diseases (by 0.85 percentage points (p.p.)), low milk yield (1.36 p.p.) and other diseases (3.01 p.p.). It was also observed that the automation of milking reduced culling due to udder diseases by 0.37 p.p., low fertility by 3.24 p.p., and accidents and chance events by 1.60 p.p.

The aim of the present study was to evaluate the changes in selected production and functional traits of Polish Holstein-Friesian cows after switching from a conventional (CMS) to an automatic milking system (AMS). The study consisted of 3398 Polish Holstein- Friesian dairy cows, from 16 herds in which CMS was changed to AMS. Cows were in their 1st (L1) or 2nd lactation (L2). The data consisted of milk yield [MY, kg], fat content [FC, %], protein content [PC, %], dry matter [DM, %], lactose content [LC, %], urea content [MU, mg/l], somatic cell count [SCC, thous./ml] and score [SCS, log]. The milking system had a significant impact on milk yield, fat, lactose, dry matter and urea contents. Regardless of lactation number, milk derived from CMS was characterised by higher values for FC, PC, DM SCC and SCS, while milk from AMS had higher MY, LC and MU. Multifactor analysis of variance also confirmed significant effect of herd, season, herd × milking system interaction on SCS in milk of cows in L1. In the studied herds change from CMS to AMS was evaluated separately for cows in L1 and L2. The transitioning from CMS to AMS resulted in the decrease of fat content in 6 L1 and 7 L2 herds, dry matter in 8 L1 and 5 L2 herds. SCS in milk also decreased in 4 L1 and 5 L2 herds. The change caused the increase of MY in 11 L1 and 9 L2 herds, lactose content in 6 L1 and 4 L2 herds and urea content in 9 L1 and 10 L2 herds. AMS may positively affect milk yield and health status, however, the change of milking system should be also accompanied by the change in herd management.

In this work we share lessons learned from our previous installations of wireless sensor networks and hope these lessons will help other research groups to install real-world applications more efficiently. First, we rediscovered the well-known KISS principle (Keep it Simple, Stupid), which holds true for real-world deployments of sensor networks. Second, we underestimated practical issues in outdoor installations, and it led to unexpected and time-consuming problems. For instance, we did not notice that the selected voltage regulator works reliably only in temperatures above zero degree Celsius, leading to packet losses and a long debugging process. Third, apart from extensive software tests before deployment, our protocols and applications include also self-healing instructions. They detect software bugs on run-time and restart motes if needed. With all these three major steps, we were able to run real-world sensor networks for several years without major problems.

Real-time mobile applications require reliable wireless communication and usually the major problem is the last mile bottleneck: from the end user to the Internet gateway. To deal with this problem, we implemented a sub-layer between the IP and TCP/UDP layers and named it Layer3.5, which bonds all communication links available to the user and improve the performance: throughput, latency, and reliability. This paper introduces the Layer3.5 architecture and also preliminary evaluation results. For example, we verified that the Layer 3.5 doubles the network throughput when using two LTE routers in parallel. Further, we included also TCP Proxies in our sublayer to deal with TCP performance problems on packet losses in wireless networks. Based on our tests, the the Layer3.5 with only a single LTE router achieves a throughput two times higher than the standard TCP in scenarios with 1% packet error rate. The Layer3.5 guarantees the continuity of services on network problems by switching the network traffic to other links and the TCP/UDP connection is not broken, even when the IP address changes. Further, end user devices and applications benefit from the Layer3.5 immediately, no adaptation are needed, provided the Internet gateway implements this new sub-layer.

Background: Pseudomonas aeruginosa is a Gram-negative bacteria responsible for infections in immunocompromised patients and is one of the most common causes of nosocomial infections particularly in intensive care and burn units. We aimed to investigate the population structure of P. aeruginosa strains isolated from patients at different hospital wards. Methods: We analysed the possible presence of P. aeruginosa epidemic or endemic strains in hospitals of the selected region. A genotyping analysis was performed for 202 P. aeruginosa isolates collected from patients of eleven hospitals in north-western Poland. Collections of P. aeruginosa were genotyped using pulsed-field gel electrophoresis (PFGE). Phenotypic screening for antibiotic susceptibility was performed for the common antimicrobial agents.Results: P. aeruginosa isolates were distributed among 116 different pulsotype groups. We identified 30 groups of clonally related strains, each containing from 2 to 17 isolates and typed the obtained 13 unique patterns, designated as A, D, E, J, K, M, N, Ó, P, T, X, AC, AD, and AH. The two largest clusters, D and E, contained 17 and 13 isolates, respectively. Strains of these groups were continuously isolated from patients at intensive care units and burn units, indicating transmission of these strains. Conclusions: In this study, we demonstrate the clonal relatedness of P. aeruginosa strains and their constant exchange in hospitals over a period of 15 months. The obtained results indicate a predominantly non-clonal structure of P. aeruginosa.

Background Pseudomonas aeruginosa is a Gram-negative bacteria responsible for infections in immunocompromised patients and is one of the most common causes of nosocomial infections particularly in intensive care and burn units. We aimed to investigate the population structure of P. aeruginosa strains isolated from patients at different hospital wards. Methods: We analysed the possible presence of P. aeruginosa epidemic or endemic strains in hospitals of the selected region. A genotyping analysis was performed for P. aeruginosa isolates (n = 202) collected from patients of eleven hospitals in north-western Poland. Collections of P. aeruginosa were genotyped using pulsed-field gel electrophoresis (PFGE). Phenotypic screening for antibiotic susceptibility was performed for the common antimicrobial agents. Results Pseudomonas aeruginosa isolates were distributed among 116 different pulsotype groups. We identified 30 groups of clonally related strains, each containing from 2 to 17 isolates and typed the obtained 13 unique patterns, designated as A, D, E, J, K, M, N, Ó, P, T, X, AC, AD, and AH. The two largest clusters, D and E, contained 17 and 13 isolates, respectively. Strains of these groups were continuously isolated from patients at intensive care units and burn units, indicating transmission of these strains. Conclusions In this study, we demonstrate the clonal relatedness of P. aeruginosa strains and their constant exchange in hospitals over a period of 15 months. The obtained results indicate a predominantly non-clonal structure of P. aeruginosa.