Automatic recognition of behavioral patterns

Naloga obravnava izgradnjo napovednih modelov na osnovi realnih podatkov. Cilj opisanih postopkov je modeliranje in napovedovanje obnašanja igralcev v igralniški industriji. Osnova za izdelavo napovednega modela je priprava podatkov za algoritme strojnega učenja. Uspešnost obdelave realnih podatkov z nečistočami in napakami pomembno vpliva na možnost izgradnje smiselnega napovednega modela. Prvi cilj modeliranja vedenja je izgradnja modela, ki napoveduje, ali bo igralec naredil drugi depozit. Natančnost razvitega modela zadostuje potrebam domene in je primerna za implementacijo v praksi. Drugi cilj modeliranja je izgradnja širšega napovednega modela, ki opisuje razvoj posameznikovih igralnih navad. Predstavljena je smiselnost drugega pristopa in njegova skladnost z zahtevami domene. Preizkus napovedne moči širšega napovednega modela presega okvire te naloge saj je dolgotrajna in zahteva veliko specifičnega domenskega znanja.This thesis explores development of predictive models on real life datasets. The goal of approaches, described in this thesis, is modeling and prediction of players\u27 behavior in casino industry. The basis for creation of predictive model is preparation of real life datasets for machine learning algorithms. Sensible curation of real life datasets that include missing values, inaccuracies and other noise determines the possibility for development of accurate predictive models. First goal of predictive behavioral modeling is creation of automated prediction model that predicts if the player will make a second deposit. Accuracy of developed model is sufficient for implementation in real life casino operation. Second goal is to develop broader predictive model that describes and predicts development of player’s behavior. Sensibility of proposed approach and its compliance with domain demands is presented. Real predictive strength of proposed model is out of scope of this work as it requires a lot of additional domain knowledge

Effects of stage of lactation and time of year on plasmin-derived proteolytic activity in bovine milk in New Zealand

The objective of this study was to determine the effects of stage of lactation (SOL) and time of year on plasmin-derived proteolytic activity in the milk of pasture-fed dairy cows in New Zealand. Four herds of 20 Friesian cows were used, one herd calving in each of January, April, July and October. Cows grazed ryegrass/white clover pasture only, except during June (winter) when all cows received supplementary pasture silage. Milk samples were collected on four occasions during the year (spring, summer, autumn and winter) from each cow in milk, to give a total of three samples per cow (early, mid and late lactation; c. 30, 120 and 220 days after calving, respectively). Milk samples were analysed for plasmin-derived proteolytic activity. There was no effect of either SOL or time of year on plasmin activity and therefore yields of plasmin followed patterns in milk yield (highest in early lactation and in summer). There were effects of both SOL and time of year on plasminogen-derived and total plasmin plus plasminogen-derived activity, both of which were highest in late lactation and in spring. Changes in plasminogen-derived activity and total plasmin plus plasminogen-derived activity due to SOL were not only due to the decrease in milk yield associated with advancing lactation, because enzyme yields were also increased with advancing lactation. Similarly, effects of time of year on plasminogen-derived activity and total plasmin plus plasminogen-derived activity could not be attributed solely to concomitant changes in milk yield, and may be influenced by the variation in the quality and quantity of feed during the year inherent in a pasture-based dairy system. Effects of SOL on proteolytic activity were greater than, and independent of, effects of time of year

Milk whey protein concentration and mRNA associated with β-lactoglobulin phenotype

Two common genetic variants of β-lactoglobulin (β-lg), A and B, exist as co- dominant alleles in dairy cattle (Aschaffenburg, 1968). Numerous studies have shown that cows homozygous for β-lg A have more β-lg and less α-lactalbumin (α-la) and casein in their milk than cows expressing only the B variant of β-lg (Ng-Kwai-Hang et al. 1987; Graml et al. 1989; Hill, 1993; Hill et al. 1995, 1997). These differences have a significant impact on the processing characteristics of the milk. For instance, the moisture-adjusted yield of Cheddar cheese is up to 10% higher using milk from cows of the β-lg BB phenotype compared with milk from cows expressing only the A variant (Hill et al. 1997). All these studies, however, describe compositional differences associated with β-lg phenotype in established lactation only. No information is available on the first few weeks of lactation, when there are marked changes in the concentrations of β-lg and α-la (Pérez et al. 1990)

Royal jelly enhances migration of human dermal fibroblasts and alters the levels of cholesterol and sphinganine in an in vitro wound healing model

Oral administration of royal jelly (RJ) promotes wound healing in diabetic mice. Concerns have arisen regarding the efficacy of RJ on the wound healing process of normal skin cells. In this study, a wound was created by scratching normal human dermal fibroblasts, one of the major cells involved in the wound healing process. The area was promptly treated with RJ at varying concentrations of 0.1, 1.0, or 5 mg/ml for up to 48 hrs and migration was analyzed by evaluating closure of the wound margins. Furthermore, altered levels of lipids, which were recently reported to participate in the wound healing process, were analyzed by HPTLC and HPLC. Migration of fibroblasts peaked at 24 hrs after wounding. RJ treatment significantly accelerated the migration of fibroblasts in a dose-dependent manner at 8 hrs. Although RJ also accelerated the migration of fibroblasts at both 20 hrs and 24 hrs after wounding, the efficacy was less potent than at 8 hrs. Among various lipid classes within fibroblasts, the level of cholesterol was significantly decreased at 8 hrs following administration of both 0.1 ug/ml and 5 mg/ml RJ. Despite a dose-dependent increase in sphinganines, the levels of sphingosines, ceramides, and glucosylceramides were not altered with any concentration of RJ. We demonstrated that RJ enhances the migration of fibroblasts and alters the levels of various lipids involved in the wound healing process

Circum-Mediterranean cultural heritage and medicial plant uses in traditional animal healthcare: a field survey in eight selected areas within the RUBIA project

During the years 2003¿2005, a comparative ethnobotanical field survey was conducted on remedies used in traditional animal healthcare in eight Mediterranean areas. The study sites were selected within the EU-funded RUBIA project, and were as follows: the upper Kelmend Province of Albania; the Capannori area in Eastern Tuscany and the Bagnocavallo area of Romagna, Italy; Cercle de Ouezanne, Morocco; Sierra de Aracena y Picos de Aroche Natural Park in the province of Huelva, Spain; the St. Catherine area of the Sinai Peninsula, Egypt; Eastern and Western Crete, Greece; the Paphos and Larnaca areas of Cyprus; and the Mitidja area of Algeria. One hundred and thirty-six veterinary preparations and 110 plant taxa were recorded in the survey, with Asteraceae and Lamiaceae being the most quoted botanical families. For certain plant species the survey uncovered veterinary phytotherapeutical indications that were very uncommon, and to our knowledge never recorded before. These include Anabasis articulata (Chenopodiaceae), Cardopatium corymbosum (Asteraceae), Lilium martagon (Liliaceae), Dorycnium rectum (Fabaceae), Oenanthe pimpinelloides (Apiaceae), Origanum floribundum (Lamiaceae), Tuberaria lignosa (Cistaceae), and Dittrichia graveolens (Asteraceae). These phytotherapeutical indications are briefly discussed in this report, taking into account modern phytopharmacology and phytochemistry. The percentage of overall botanical veterinary taxa recorded in all the study areas was extremely low (8%), however when all taxa belonging to the same botanical genus are considered, this portion increases to 17%. Nevertheless, very few plant uses were found to be part of a presumed "Mediterranean" cultural heritage in veterinary practices, which raises critical questions about the concept of Mediterraneanism in ethnobotany and suggests that further discussion is required. Nearly the half of the recorded veterinary plant uses for mammals uncovered in this survey have also been recorded in the same areas in human folk medicine, suggesting a strong link between human and veterinary medical practices, and perhaps also suggesting the adaptive origins of a few medical practices. Since most of the recorded data concern remedies for treating cattle, sheep, goats, and camels, it would be interesting to test a few of the recorded phytotherapeuticals in the future, to see if they are indeed able to improve animal healthcare in breeding environments, or to raise the quality of dairy and meat products in the absence of classical, industrial, veterinary pharmaceuticals

Advanced therapeutic dressings for effective wound healing

Advanced therapeutic dressings that take active part in wound healing to achieve rapid and complete healing of chronic wounds is of current research interest. There is a desire for novel strategies to achieve expeditious wound healing due to the enormous financial burden worldwide. This paper reviews the current state of wound healing and wound management products, with emphasis on the demand for more advanced forms of wound therapy and some of the current challenges and driving forces behind this demand. The paper reviews information mainly from peer reviewed literature and other publicly available sources such as the FDA. A major focus is the treatment of chronic wounds including amputations, diabetic and leg ulcers, pressure sores, surgical and traumatic wounds (e.g. accidents and burns) where patient immunity is low and the risk of infections and complications are high. The main dressings include medicated moist dressings, tissue engineered substitutes, biomaterials based biological dressings, biological and naturally derived dressings, medicated sutures and various combinations of the above classes. Finally, the review briefly discusses possible prospects of advanced wound healing including some of the emerging approaches such as hyperbaric oxygen, negative pressure wound therapy and laser wound healing, in routine clinical care

Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

Anomaly detection systems are vital in ensuring the availability of modern High-Performance Computing (HPC) systems, where many components can fail or behave wrongly. Building a data-driven representation of the computing nodes can help with predictive maintenance and facility management. Luckily, most of the current supercomputers are endowed with monitoring frameworks that can build such representations in conjunction with Deep Learning (DL) models. In this work, we propose a novel semi-supervised DL approach based on autoencoder networks and clustering algorithms (applied to the latent representation) to build a digital twin of the computing nodes of the system. The DL model projects the node features into a lower-dimensional space. Then, clustering is applied to capture and reveal underlying, non-trivial correlations between the features.The extracted information provides valuable insights for system administrators and managers, such as anomaly detection and node classification based on their behaviour and operative conditions. We validated the approach on 240 nodes from the Marconi 100 system, a Tier-0 supercomputer located in CINECA (Italy), considering a 10-month period.ISSN:0302-9743ISSN:1611-334

Semi-supervised anomaly detection on a Tier-0 HPC system

Automated and data-driven methodologies are being introduced to assist system administrators in managing increasingly complex modern HPC systems. Anomaly detection (AD) is an integral part of improving the overall availability as it eases the system administrators' burden and reduces the time between an anomaly and its resolution. This work improves upon the current state-of-the-art (SoA) AD model by considering temporal dependencies in the data and including long-short term memory cells in the architecture of the AD model. The proposed model is evaluated on a complete ten-month history of a Tier-0 system (Marconi100 from CINECA consisting of 985 nodes). The proposed model achieves an area under the curve (AUC) of 0.758, improving upon the state-of-the-art approach that achieves an AUC of 0.747

RUAD: Unsupervised anomaly detection in HPC systems

The increasing complexity of modern high-performance computing (HPC) systems necessitates the introduction of automated and data-driven methodologies to support system administrators’ effort towards increasing the system's availability. Anomaly detection is an integral part of improving the availability as it eases the system administrator's burden and reduces the time between an anomaly and its resolution. However, current state-of-the-art (SOTA) approaches to anomaly detection are supervised and semi-supervised, so they require a human-labelled dataset with anomalies — this is often impractical to collect in production HPC systems. Unsupervised anomaly detection approaches based on clustering, aimed at alleviating the need for accurate anomaly data, have so far shown poor performance. In this work, we overcome these limitations by proposing RUAD, a novel Recurrent Unsupervised Anomaly Detection model. RUAD achieves better results than the current semi-supervised and unsupervised SOTA approaches. This is achieved by considering temporal dependencies in the data and including long-short term memory cells in the model architecture. The proposed approach is assessed on a complete ten-month history of a Tier-0 system (Marconi100 from CINECA with 980 nodes). RUAD achieves an area under the curve (AUC) of 0.763 in semi-supervised training and an AUC of 0.767 in unsupervised training, which improves upon the SOTA approach that achieves an AUC of 0.747 in semi-supervised training and an AUC of 0.734 in unsupervised training. It also vastly outperforms the current SOTA unsupervised anomaly detection approach based on clustering, achieving the AUC of 0.548.ISSN:0167-739XISSN:1872-711