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Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines.

Developing smart city applications typically faces challenges such as meeting several complex requirements, integrating heterogeneous data sources, and considering geographical information representing the real-world urban space. Smart city platforms have a key role in realizing these applications as they provide high-level services that can be (re)used by developers. Despite the existence of many platforms to support the development of smart city applications, most of them do not associate the offered services with geographical information, do not support performing semantic queries on the available data, and have limitations that may burden development tasks. This paper presents Smart Geo Layers (SGeoL), a platform for developing smart city applications. Besides integrating urban data with geographical information, SGeoL relies on an underlying middleware infrastructure and leverages it by including high-level abstractions for (i) context data management, (ii) integration of heterogeneous data, (iii) semantic support, (iv) data analysis and visualization, and (v) data security and privacy support. This paper also reports experiences on the real use of SGeoL and empirical results on the evaluation of its performance and scalability.

Context Data on the presence/quantification of the neurotrophic adipokines retinol-binding protein-4 (RBP4), clusterin, and pigment epithelium-derived factor (PEDF) in human cerebrospinal fluid (CSF) are scarce and migration of these adipokines across of the blood-brain barrier (BBB) is uncertain. Objective This work aimed to quantify RBP4, PEDF, and clusterin in paired serum and CSF samples of patients undergoing neurological evaluation. Methods A total of 268 patients (109 male, 159 female) were included. Adipokine serum and CSF concentrations were measured by enzyme-linked immunosorbent assay in duplicate. Results RBP4 was abundant in serum (mean, 31.9 ± 24.2 μg/mL). The serum concentrations were approximately 145 times higher than in CSF (CSF to serum RBP4 ratio, 8.2 ± 4.3 × 10–3). PEDF was detectable in serum (mean, 30.2 ± 11.7 μg/mL) and concentrations were approximately 25 times higher than in CSF (CSF to serum PEDF ratio, 42.3 ± 15.6 × 10–3). Clusterin serum concentrations were abundant with mean levels of 346.0 ± 114.6 μg/mL, which were approximately 40 times higher than CSF levels (CSF to serum clusterin ratio, 29.6 ± 23.4 × 10–3). RBP4 and PEDF serum levels correlated positively with CSF levels, which were increased in overweight/obese patients and in type 2 diabetic patients. The CSF concentrations of all 3 adipokines increased with BBB dysfunction. RBP4 in CSF correlated positively with inflammatory parameters. In detail, only RBP4 showed the kinetics and associations that are mandatory for a putative mediator of the fat-brain axis. Conclusion RBP4, PEDF, and clusterin are permeable to the BBB and increase with the measure of BBB dysfunction. RBP4 represents an inflammatory neurotrophic adipokine and is a promising mediator of the fat-brain axis.

We propose two online-learning algorithms for modeling the personal preferences of users of interactive systems. The proposed algorithms leverage user feedback to estimate user behavior and provide personalized adaptive recommendation for supporting context-dependent decision-making. We formulate preference modeling as online prediction algorithms over a set of learned policies, i.e., policies generated via supervised learning with interaction and context data collected from previous users. The algorithms then adapt to a target user by learning the policy that best predicts that user’s behavior and preferences. We also generalize the proposed algorithms for a more challenging learning case in which they are restricted to a limited number of trained policies at each timestep, i.e., for mobile settings with limited resources. While the proposed algorithms are kept general for use in a variety of domains, we developed an image-filter-selection application. We used this application to demonstrate how the proposed algorithms can quickly learn to match the current user’s selections. Based on these evaluations, we show that (1) the proposed algorithms exhibit better prediction accuracy compared to traditional supervised learning and bandit algorithms, (2) our algorithms are robust under challenging limited prediction settings in which a smaller number of expert policies is assumed. Finally, we conducted a user study to demonstrate how presenting users with the prediction results of our algorithms significantly improves the efficiency of the overall interaction experience.

Objective To develop a collection of concept-relationship-concept tuples to formally represent patients’ care context data to inform electronic health record (EHR) development. Materials and Methods We reviewed semantic relationships reported in the literature and developed a manual annotation schema. We used the initial schema to annotate sentences extracted from narrative note sections of cardiology, urology, and ear, nose, and throat (ENT) notes. We audio recorded ENT visits and annotated their parsed transcripts. We combined the results of each annotation into a consolidated set of concept-relationship-concept tuples. We then compared the tuples used within and across the multiple data sources. Results We annotated a total of 626 sentences. Starting with 8 relationships from the literature, we annotated 182 sentences from 8 inpatient consult notes (initial set of tuples = 43). Next, we annotated 232 sentences from 10 outpatient visit notes (enhanced set of tuples = 75). Then, we annotated 212 sentences from transcripts of 5 outpatient visits (final set of tuples = 82). The tuples from the visit transcripts covered 103 (74%) concepts documented in the notes of their respective visits. There were 20 (24%) tuples used across all data sources, 10 (12%) used only in inpatient notes, 15 (18%) used only in visit notes, and 7 (9%) used only in the visit transcripts. Conclusions We produced a robust set of 82 tuples useful to represent patients’ care context data. We propose several applications of our tuples to improve EHR navigation, data entry, learning health systems, and decision support.

This article sets in context Data Warehouses (DWs) and Online Analytical Processing (OLAP) against the backdrop of databases and Big Data and shows how data warehouses and OLAP were incorporated in...

Proactive adaptation of business processes can prevent and mitigate future problems during the execution of business processes. For the proactive adaptation of business processes, adaptation decisions should be made based on whether a business process instance is heading to a business goal. Recently, much work struggles to improve predictive business process monitoring efficiency, i.e., accuracy and prediction earliness. As such, process designers can find more necessary adaptations and have more remaining time for the adaptations. Almost all of these previous efforts concentrate on business-related data generated by business processes. However, besides business-related data, context data has a huge impact on how a business process instance is unfolding to its completion. Context data should be noticed in the area of predictive business process monitoring. To remedy this shortage, in this paper, we propose a predictive business process monitoring framework based on a joint of business-related data and context data to predict incompliance with business goals. We evaluated the framework concerning the measures of accuracy, prediction earliness, and cost time based on a real-life online banana purchase process.

The subject of this study is a ceramic bird-feeder has been discovered in Olympos excavation in the 2017. The bird-feeder has been found in the southeast corner of the stylobate, which runs from the front of the pronaos to the east during the excavation works aimed to reveal the cella and pronaos of the temple in 2017. The findspot of the bird feeder is in the east of the viridarium of the Episcopeion, in an Early Byzantine period dump between the southeast corner of the stylobate and the north wall of the Room 20 (M20). The clay of the ceramic bird-feeder resembles to the Late Roman D Ware of red slip ceramics which are mostly considered to be from Cypriot origin. According to the general chronology of the area and the context data, the bird-feeder from Olympos is belonging to the 6th century AD.

As the data or information gets increased in various applications, it is very much essential to make the retrieval and modeling easier and simple. Number of modeling aspects already exists for this crisis. Yet, context awareness modeling plays a significant role in this. However, there requires some advancement in modeling system with the incorporation of advanced technologies. Hence, this survey intends to formulate a review on the context-aware modeling in two aspects: context data retrieval and context data modeling. Here, the literature analyses on diverse techniques associated with context awareness modeling. It reviews 60 research papers and states the significant analysis. Initially, the analysis depicts various applications that are contributed in different papers. Subsequently, the analysis also focuses on various features such as web application, time series model, intelligence models and performance measure. Moreover, this survey gives the detailed study regarding the chronological review and performance achievements in each contribution. Finally, it extends the various research issues, mainly the adoption of Evolutionary algorithms, which can be useful for the researchers to accomplish further research on context-aware system.

Jednym z kluczowych elementów dla zdrowego starzenia się jest prawidłowe żywienie, w tym odpowiednia ilość i rodzaj spożywanych warzyw i owoców, jednakże seniorzy zazwyczaj nie jedzą wystarczającej ilości produktów z tych grup. W artykule przedstawiono dane na temat roli owoców i warzyw w zmniejszaniu ryzyka wystąpienia takich chorób, jak schorzenia układu krążenia, osteoporoza, przewlekła obturacyjna choroba płuc, depresja, zaburzenia funkcji poznawczych, niektóre rodzaje nowotworów, a także umieralności. W tym kontekście zaprezentowano dane dotyczące częstości spożywania warzyw i owoców przez osoby starsze, które wskazują na niezadawalającą realizację zaleceń żywieniowych. Częstsze spożywanie tych produktów zaobserwowano wśród kobiet, u osób z wyższym wykształceniem, u stosujących leki lub specjalną dietę. Posiadanie protezy zębowej istotnie zmniejszało spożycie owoców. Większa wiedza żywieniowa dotycząca owoców i warzyw związana była z częstszym spożyciem tych produktów. W związku z powyższym, prowadzenie żywieniowych programów edukacyjnych wydaje się szczególnie istotne wśród osób starszych. Słowa kluczowe: osoby starsze, owoce, warzywa, częstość spożycia, wiedza żywieniowa

The PhD project presented in this paper aims to design a trust management infrastructure that allows to assess the trustworthiness of context data in the scope of access control with the IoT. The main requirements for this design are independence from the underlying access control model and flexibility of trust calculation schemes based on application needs.

In the field of Human Activity Recognition (HAR) the majority of approaches exploit fully supervised methodologies to process inertial sensor data collected from the users’ wearable devices. Unfortunately, those solutions require users to collect a large number of annotated examples to train the recognition model, which is costly, unpractical, and time-consuming. In this paper, we propose diverse semi-supervised methodologies to tackle the data scarcity issue in the field of HAR. In particular, in Caviar and ProCaviar we introduce novel knowledge-based reasoning engines that exploiting the context data (e.g. semantic location, weather condition) allows a statistical classifier trained with a limited number of example to recognise a wide set of activities. Then, we propose FedHAR an hybrid semi-supervised and Federated-learning based system that enables distributing the training of an activity recognition model among a large number of subject, reducing the effort required from users to collect annotated data while preserving their privacy.

The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data, continuous data, and media data. We first proposed a stream data anonymisation method based on k-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.

The outcome of a process e.g., the quality of a produced part, constitutes a key performance indicator for process analysis and monitoring. Process outcomes are not only affected by process data, but also by data that is not associated with the process logic through decisions or task input. The rising temperature in a machine, for example, might cause deterioration of part quality. Assessing the impact of context data on the process outcome at runtime is particularly useful to reduce the reaction time to possible errors or deviations. However, as process models contain loops and decisions, grouping and making context data streams interpretable is not always straight-forward, especially under the condition that describing dependencies between context data and process data should be simple and flexible. The contribution of this paper is a classification of context data types, how they are connected to a process model, and how process models can be segmented into stages to group semantically related tasks. The impact of context data on the process outcome is then determined during runtime, i.e., as a process instance is progressing through these segments at runtime, impact calculations using context data can be gradually refined. The approach is prototypically implemented and applied to an artificial logistics and a real-world manufacturing data set.

In this chapter, we present the system GroupMusic that allows the generation and playback of group music playlists that are based on the musical taste of individual guests attending a meeting. In our architecture, we use mobile sensing to unobtrusively collect musical taste on smartphones for the automatized generation of group music playlists. We follow the idea of utilizing context data in a preprocessing step to generate a group music profile for the recommendation process that generates a group music playlist. The whole process is almost fully automated and the played back music automatically adapts to the users currently present. The results are relevant for researchers and developers in the fields of ubiquitous systems and group recommender systems.

At present, user researchers have problems in UX (User experience) data analysis, such as low efficiency, inaccurate context data identification, and low satisfaction of analysis process. Therefore, in order to solve these problems, this paper proposes a design context UX data analysis system to compensate for the shortcomings in the data analysis process. This paper takes the analysis of UX data collected by the CAUX (Context-Aware User Experience) tool as an example, using the relevant methods in Cognitive Task Analysis (CTA), and on the basis of sensemaking loop model, explore the data analysis process of UX researchers through experiments. And carry out demand research for each stage of the analysis process, design a context UX data analysis system according to the requirements. This thesis summarizes the model of UX data analysis process, completes the design of context UX data analysis system, and evaluation experiment proves that the system can effectively solve the problems in the UX data analysis process, and provides a new idea for the UX research practice in the mobile Internet environment.

Literature exists granted electronic service quality delivery through Websites is a fundamental approach to success. Toward providing superior service quality, decision-makers of organizations with Web presences must first realize how customers develop online customer loyalty. In-formation about this topic limited from both academic and practitioner sources; however, this information should be examined in multi-context by using multiple e-SQ dimensions. This study employs the systematic review approach to review and synthesize the literature of electronic service quality and its effects on e-satisfaction, e-trust, e-shopping, and e-loyalty, illustrate what is known about the topic, and provide a set of recommendations and future research avenues. Moreover, discussion for how artificial intelligence Technology guided e-services among online shopping websites.

Objectives: to map the evidence in relation to the maximum backpack weight recommended for children in school context. Methodology: suggested by the Joanna Briggs Institute. The relevance analysis of the articles, the extraction and synthesis of the data was carried out by two independent reviewers. Starting question: what is the maximum backpack weight recommended for children, from 6 to 12 years old, in school context Data sources: primary studies published in scientific databases, international guidelines and gray literature. Summary of the data: 353 publications were identified, of which 28 were included. The recommended weight percentage is divided into two categories: with recommendation (ranging from 5% -20%) and without recommendation. Conclusions: This scoping review allowed to map the evidence in relation to the maximum weight of the backpack recommended for children in school, where the value of 10% was the one that obtained the greatest consensus., Objetivos: mapear a evidência em relação ao peso máximo da mochila recomendado para crianças em contexto escolar. Metodologia: sugerida pelo Joanna Briggs Institute. A análise de relevância dos artigos, a extração e síntese dos dados desenvolveu-se por dois revisores independentes. Questão de partida: qual o peso máximo da mochila recomendado para crianças, dos 6 aos 12 anos, em contexto escolar? Fontes de dados: estudos primários publicados em bases de dados científicas, diretrizes internacionais e literatura cinzenta. Síntese dos dados: identificaram-se 353 publicações, onde foram incluídas 28. A percentagem de peso recomendada subdivide-se em duas categorias: com recomendação (que oscila entre 5%-20%) e sem recomendação. Conclusões: Esta scoping review permitiu mapear a evidência em relação ao peso máximo da mochila recomendado para crianças em contexto escolar, onde o valor de 10% foi o que obteve maior consenso., Objetivos: mapear la evidencia en relación con el peso máximo de mochila recomendado para niños en el contexto escolar. Metodología: sugerida por el Instituto Joanna Briggs. El análisis de relevancia de los artículos, la extracción y síntesis de los datos fue realizado por dos revisores independientes. Pregunta inicial: ¿cuál es el peso máximo de la mochila recomendado para niños de 6 a 12 años en un contexto escolar? Fuentes de datos: estudios primarios publicados en bases de datos científicas, directrices internacionales y literatura gris. Resumen de los datos: se identificaron 353 publicaciones, de las cuales se incluyeron 28. El porcentaje en peso recomendado se divide en dos categorías: con recomendación (que va del 5% al 20%) y sin recomendación. Conclusiones: Esta revisión de alcance permitió mapear la evidencia en relación con el peso máximo de la mochila recomendado para niños en la escuela, donde el valor del 10% fue el que obtuvo el mayor consenso.

In this paper, we focus on realizing a zero-energy consumption scheme based on the hybrid memory with limited size of NVM. To achieve minimized NVM requirement, three techniques are proposed to solve the conflict between the context data size and NVM capacity during the suspend process. We build a system suspend/resume mode to evaluate data size and timing. The experiment results show that the amount of data which needs to be saved is reduced by 59% and the demand for NVM size is reduced by 51%, with little impact on system resuming time.

Recognizing the activities of daily living (ADLs) in multi-inhabitant settings is a challenging task. One of the major challenges is the so-called data association problem: how to assign to each user the environmental sensor events that he/she actually triggered? In this paper, we tackle this problem with a contextaware approach. Each user in the home wears a smartwatch, which is used to gather several high-level context information, like the location in the home (thanks to a micro-localization infrastructure) and the posture (e.g., sitting or standing). Context data is used to associate sensor events to the users which more likely triggered them. We show the impact of context reasoning in our framework on a dataset where up to 4 subjects perform ADLs at the same time (collaboratively or individually). We also report our experience and the lessons learned in deploying a running prototype of our method.

International audience; This paper considers generalizing context reasoning capabilities through a context mining facility offered to all Information System applications. This facility requires mining context data at the system scale, which raises several challenges for Machine Learning approaches used for such mining. Through a detailed literature review, we analyze these approaches with regard to the requirements of such a context mining facility at the Information System level, pointing to the potential and to the challenges raised by this perspective.

Biometric technology based on the human gait identifies people even if a person's face is covered, hidden or invisible to cameras in a dark environment. This paper presents a method of human motoric feature identification based on image recognition. One way of image recognition is described – the Haar Cascade method in conjunction with the classifier training process. Classifiers trained on MPII Human Pose and Microsoft Common Objects in Context data were used to recognize a human figure in an image. In the identification method described, joint movement parameters and characteristic body parts were analyzed. Five people were surveyed and recorded twice. The data obtained after the analysis of the first recordings, made with a camera placed at the front, served as benchmarks in the process of comparison with data from the second recordings (from behind the identified person). Data analysis was performed using a Microsoft Excel spreadsheet.

We present a sleep timing estimation approach that combines data-driven estimators with an expert model and uses smartphone context data. Our data-driven methodology comprises a classifier trained on features from smartphone sensors. Another classifier uses time as input. Expert knowledge is incorporated via the human circadian and homeostatic two process model. We investigate the two process model as output filter on classifier results and as fusion method to combine sensor and time classifiers. We analyse sleep timing estimation performance, in data from a two-week free-living study of 13 participants and sensor data simulations of arbitrary sleep schedules, amounting to 98280 nights. Five intuitive sleep parameters were derived to control the simulation. Moreover, we investigate model personalisation, by retraining classifiers based on participant feedback. The joint data and expert model yields an average relative estimation error of -2±62 min for sleep onset and -5±70 min for wake (absolute errors 40±48 min and 42±57 min, mean median absolute deviation 22 min and 15 min), which significantly outperforms data-driven methods. Moreover, the data and expert models combination remains robust under varying sleep schedules. Personalising data models with user feedback from the last two days showed the largest performance gain of 57% for sleep onset and 59% for wake up. Our power-efficient smartphone app makes convenient everyday sleep monitoring finally realistic.

Modern smartphones and ubiquitous computing systems collect a wealth of context data from users. Conditional action rules, as popularized by the IFTTT (If-This-Then-That) platform, are a popular way for users to automate frequently repeated tasks or receive smart reminders, due to the intelligibility and control that rules provide to users. A key drawback of IFTTT systems is that they place the burden of manually specifying action rules on the user. While multiple rule mining algorithms have been proposed in existing work to automatically discover action rules, they generate hundreds of action rules, and the problem of how to present a small subset of rules to smartphone users and allow them to interactively select action rules remains unsolved. In this work, we take the first step towards solving this problem by designing and implementing RuleSelector, the first interactive rule selection tool to allow smartphone users to browse, modify, and select action rules from a small set of summarized rules presented to the user. We propose novel rule selection metrics to address the needs of smartphone users, and analyze the performance of RuleSelector using data from 200 users. We also perform a qualitative user study in order to evaluate how users use the RuleSelector tool and perceive the selected rules, and present the insights gained and design recommendations for future rule selection systems. Our users rated the selected rules from useful to very useful, and an important finding of our study is that users prefer an interactive rule selection system such as RuleSelector that automatically suggests rules, but allows users to select and modify the suggested rules. Finally, we examine the promise of RuleSelector in other ubiquitous computing systems such as smart homes and smart TVs by applying our tool to public context datasets from these domains.

The advantages of using intelligent transport systems (ITS) in urban conditions which enable secure road traffic, reducing the number of road traffic accidents and death rate on roads are considered in this article. The structure and architecture of the urban ITS systems are described: they enable automatizing the processes of gathering context data on road accidents and performing their processing in real time scale with the purpose of dynamic response on changes in transport situation. The description of the theory of cognitive transport systems which is being developed in ITP RAS, which enables increasing the capabilities of traditional ITS with the help of introducing the mechanisms of constant research and self-teaching both into separate transport systems and also into transport infrastructures.

Este artigo apresenta uma revisao dos aspectos basicos do processo de producao de conhecimento arqueologico a partir de problemas sugeridos pelos dados de contexto arqueologico da cultura material, artefatos, ecofatos e biofatos remanescentes de ocupacoes humanas holocenicas dos litorais sul e norte do Brasil. Sao apresentados aspectos historicos da pesquisa sobre sambaquis; caracteristicas funerarias de suas populacoes; as contribuicoes da Etnologia, Bioarqueologia, Zooarqueologia e Geoarqueologia; temas sobre complexidade social, continuidade sociocultural, hierarquia, organizacao social, visibilidade na paisagem costeira, area funeraria no contexto monumental e territorial; e atividades de subsistencia e adaptacao ambiental essas surpreendentes e diversificadas sociedades costeiras. HOLOCENE HUNTER-GATHERER-FISHERS OF THE SOUTHERN AND NORTHERN BRAZILIAN COAST Considerations about sambaquis ABSTRACT This article presents a review of the basic aspects of the process of production of archaeological knowledge from problems suggested by the archaeological context data of the material culture, artifacts, ecofacts and biofacts remnants of Holocene human occupations of the southern and northern coasts of Brazil. Historical aspects of the research on sambaquis are presented; funerary features of their populations; the contributions of ethnology, bioarchaeology, zooarchaeology and geoarchaeology; themes on social complexity, sociocultural continuity, hierarchy, social organization, visibility in the coastal landscape, funeral area in the monumental and territorial context and subsistence activities and environmental adaptation of these surprising and diversified coastal societies. Keywords: Pre-historic hunter-gatherers; Archaeology of Death; Sambaquis.

This article presents the results of a bioarchaeological analysis of 39 human remains recovered from Late and Terminal Formative burials excavated from residential and civic-ceremonial contexts at Cerro Jazmin, a site that emerged in the Early Ramos period (Late Formative, 300 BCE) and thrived until the end of the Late Ramos period (Terminal Formative, 200–300 CE). We evaluate the osteological evidence to characterize the skeletal health of the ancient population at various points of the city's occupation and consider the bioarchaeological data along with their archaeological context—associated offerings and location—to identify broader patterns of skeletal health and mortuary treatment. The skeletal health and burial context data are used as proxies to assess the population's quality of life and determine whether that quality of life deteriorated in the centuries leading up to the city's abandonment at the start of the Early Classic period (300–500 CE). The late Terminal Formative burial sample suggests an increase of infant mortality rates prior to the city's abandonment, while adult burials display overall good skeletal health in the population. We discuss whether this pattern may have been a factor in the city's demise.

Piglet crushing can be detected online using vocalisation analysis and context information.Fundamental crushing context information can be obtained by tracking the posture of the sows.Spatial event filtering is a prerequisite for a high precision of the crushing detection.Active measures against piglet crushing could replace passive measures like the farrowing cage. Fatal piglet crushing by the mother sow is a pervasive economic and animal welfare issue in piglet production. To keep the mother sow in a farrowing cage is the established countermeasure. This facility is a compromise that results in an impairment of the sows welfare to the benefit of her piglets and the farmer. A natural behaviour pattern which is demonstrated by most but not all sows is to free the trapped piglet by a posture change. Promoting this behaviour through aversive stimulations is an alternative approach to reduce piglet mortality. This approach requires an identification and localisation of ongoing piglet trapping in real-time. The present study investigates the online analysis of piglet vocalisation for this purpose. The results show, that trapping related stress articulations are outnumbered by other stress related articulations by a factor of 1:140 in a farrowing compartment with only 4 sows. Theoretical calculations for larger compartments indicate that this ratio becomes even worse due to an increasing influence of vocalisation from neighbouring pens. However, the specificity could be increased to more than 95% and precision to approximately 30% while maintaining a sensitivity of approximately 70% by retrospectively applying context based event filters. This specificity would be sufficient to limit the average number of erroneous trapping detections to one detection per sow within 3days without a substantial loss of sensitivity. Effective parameters for filtering were the age of the piglets and the sows body posture history. Calculations with hypothetical spatial event filters showed that this classification performance could be maintained even in much larger farrowing compartments. Combined with an aversive stimulation principle that can be applied to a whole region, this detection technology could be useful to reduce piglet mortality in loose farrowing applications. An already known and effective stimulation principle of this type is floor vibration. Such an active piglet rescue system would allow limiting the impairment of welfare to only those sows that actually crush piglets and to the time when piglets are being crushed.

Context. Several physical properties of solar polar plumes have been identified by different published studies, however such studies are rare and sometimes in disagreement. Aims. The purpose of the present work is to analyze a set of SOHO/UVCS data dedicated to the observation of plumes and to obtain a picture of the physical properties of plumes in the intermediate solar corona through a self-consistent analysis. Methods. We applied the Doppler Dimming technique to data acquired by SOHO/UVCS in April 1996, which was during the very early phases of the mission. From this we derived outflow speeds and electron densities. We used SOHO/LASCO images as context data in order to better identify plume and interplume regions in the UVCS field of view. Results. The results we obtain demonstrate that in three cases out of four plumes expand with outflow speeds comparable to those of interplumes, and in a single case with lower speeds. We estimate that the contribution of plumes to the wind coming from the solar poles is about 20%, and that different plumes provide a different contribution, possibly according to different stages of their evolution. Conclusions. We conclude that plumes are not static structures, and that they contribute significantly to the wind coming from the solar poles., Comment: Astronomy & Astrophysics in press