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Background Spray drying is still the most important technique for the microencapsulation of bioactive ingredients. It makes possible to create easy-handling powdered microcapsules from liquid material in one simple and easy-scalable operation. However, the selection of proper wall material is crucial to obtain high retention of biological activity, high encapsulation efficiency, beneficial physical properties, high bioavailability and storage stability (both in terms of physical and biological properties). Scope and approach During last decade there was a trend to apply new types of biopolymers as wall materials, as additional compounds or as complete substitutions to conventionally used maltodextrins and gum arabic. Such approach provides new “clean label”, “green label” and health-promoting properties to the obtained microcapsules. Moreover, it creates also new possibilities for waste valorization. On the other hand, there is still a question about the technological suitability and properties of such biopolymers applied as wall materials. The review summarizes the investigations done in this topic during last decade. Key findings and conclusions the delivery of additional health benefits with wall materials and sustainable methods to obtain them, is a current trend in spray drying encapsulation.

Oral bioavailability is the key to the bioefficiency of food bioactive ingredients; it evaluates the relationship between foods and their health benefits. The analysis of the main factors limiting the oral bioavailability (bioaccessibility, absorption, and transformation) has led to the proposal of classification systems for pharmaceuticals and nutraceuticals (Biopharmaceuticals Classification System and Nutraceutical Bioavailability Classification Scheme). Based on the relevant studies published in the last decade, this review presents the essential aspects regarding the factors limiting the oral bioavailability of the biocomponents and different in vitro methods used to investigate the mechanisms involved in the digestion, absorption, and metabolism of biocomponents, particularly encapsulated bioactive compounds. Oral bioavailability investigated by in vitro studies provides the food and drug manufacturers with information to formulate delivery systems more efficiently and to determine the dosage of biocomponents for increase the health benefits and avoid or reduce the risk of toxicity.

In recent years, researchers' concern for the applications of nanotechnologies in the food industry has grown exponentially. This has led to a major change in both consumer and producer behaviours. Thus, recent findings have shown that consumers increasingly prefer value-added foods, which provide significant health benefits. In this context, new approaches have emerged in the design of functional foods, such as the ‘reverse engineering’ approach that ensures the necessary conditions for designing sustainable ‘nano-food’ systems, in accordance with the requirements of the consumer. The stages of nano-food design using the reverse engineering method, the factors affecting the bioavailability of nanoencapsulated biocomponents and the fortification of foods with bioactive-loaded nanocarriers are briefly analyzed in this review.

Nowadays, many consumers prefer foods with a high content of nutraceuticals that contribute to the prevention or healing of chronic diseases. Therefore, in recent years, more and more researchers have studied the bioefficiency, safety, and toxicity of nutraceutical-enriched foods. The key stage of nutraceutical bioefficiency is oral bioavailability, which involves the following processes: the release of nutraceuticals from food matrices or nanocarriers in gastrointestinal fluids, the solubilization of nutraceuticals and their interaction with other components of gastrointestinal fluids, the absorption of nutraceuticals by the epithelial layer, and the chemical and biochemical transformations into epithelial cells. These processes are endogenous factors that greatly influence the bioavailability of nutraceuticals. In addition to endogenous factors, the bioavailability of nutraceuticals is also affected by exogenous factors, such as: physicochemical properties of nutraceuticals, food matrix, food processing and storage, and so forth. Both the endogenous and exogenous factors are comprehensively analyzed in this review. Thus, the physicochemical and enzymatic processes involved in food digestion are described, highlighting the role of each stage of gastrointestinal tract (mouth, stomach, and intestine) in nutraceuticals bioaccessibility. The structure and functions of the mucus and epithelial layers, the mechanisms involved in the active and passive transport of nutraceuticals through the cell membrane, and phase I and phase II metabolism reactions are also discussed. Finally, this review focuses on several types of bioactive-loaded nanocarriers such as lipid-based, surfactant-based, and biopolymeric nanocarriers that improve the bioavailability of nutraceuticals.

During the Late Iron Age, draft animals, both oxen and horses, mules or donkeys, were harnessed on the transport vehicle by a double yoke that transmitted the traction power to the wagon through which it was fastened. Certain metallic parts were attached on these double wooden yokes, some of them having a functional role, and others an ornamental one. Such metal parts were also discovered in pre-Roman Dacia, most of them coming from fortunate discoveries, illegal detection and less in clear archaeological contexts. Some of the pieces presented in this article have been discussed and featured typologically and chronologically, but a number of differences and sub-variants require a re‑discussion of these. Also in this article will be discussed other yokes known in pre-Roman Dacia which will be typologically and chronologically framed.

The subject of this paper is related with some solar and celestial representation discovered in the intra-Carpathian space dated from the Late Iron Age. The paper will focus on water-birds protomes discovered at Sarmizegetusa Regia and Costești-„Cetățuie”, water-birds shaped handles on some situlae and other birds representation. As well the study will focus on the association of this water-bird representation with wheels or chariots and some vessels. The research is not concerned only on discussion of the symbolism of this group of representation, but as well on understanding the origins and how the idea travelled from one community to other and finally reach the space lived by Dacian communities.

Foods contain both natural nanomaterials, like casein micelles in milk or certain organelles found in plant or animal cells (DNA, ribosomes, enzymes, antibodies), and engineered nanomaterials, which are deliberately added in order to increase food quality and safety, such as nanoparticle-based delivery systems. Other nanoparticles do not directly enter foods, but may come into contact with foods as a result of their inclusion in packaging or nanosensors. Food-grade nanomaterials are used in food processing as food additives, in the manufacture of smart and active packaging, reinforced bionanocomposites, and antimicrobial packaging through incorporation of metal nanoparticles; for nutraceutical protection, nanocarriers, and nanodelivery systems for the increase of their bioavailability; for producing nanobiosensors for detection of toxins, pathogens, and pesticides; in applications in food processing such as nanofluids, nanoadsorbents, and nanofiltration; and as nanostructures as food ingredients such as nanoemulsions. This chapter presents a brief overview of nanotechnology applications in various fields of the food industry along with characterization techniques for analysis of nanomaterials, safety and regulatory issues, and consumer expectations toward “nanofoods.”

Due to their biological properties, essential oils are used as an ingredient to enhance the functionality of various products such as foodstuff, drinks, perfumes, pharmaceuticals, cosmetics, or green pesticides. Currently, research is mainly focused on developing innovative and eco-friendly techniques to extract essential oils and subsequently stabilize them through encapsulation in order to obtain GRAS (generally recognized as safe) natural products. The emerging market of essential oils demands diverse analytical methods and improved regulations for their marketing and application.

Lipophilic biocomponents pose a real challenge to food and drug researchers because they have weak solubility in water and cannot be easily included into the food and drug matrix. Biomaterials with new functional properties, i.e. biodegradable, biocompatible and stimulus responsive, have been tested for the purpose of improving the solubility and bioavailability of lipophilic components in recent years. Also, high-performance micro- and nanotechnologies were involved. These were used to prepare microcapsules and nanocarriers with multiple applications in food industry and pharmacy. The micro- and nano-encapsulation of lipophilic components is used to increase and maintain solubility in physiological fluids or the food matrix, to improve bioavailability and to protect the bioactive compounds against the aggression of some factors such as pH, enzymes, temperature, light and oxygen. The microcapsule and nanocarrier delivery systems guarantee the controlled and targeted delivery of bioactive molecules, thus increasing the therapeutic potential of drugs, along with food safety and security. The purpose of this review is to synthesize the newest information about the lipophilic biocomponents’ encapsulation. The first part of the paper is devoted to data on the solubility and bioavailability of lipophilic components used for food and drug formulations. The second part deals with the main methods of lipophilic compounds encapsulation, evincing the preparation of nanoparticles with applicability in the food and drug industry.

Dacian weapons discovered at Ardeu(Hunedoara county)- Abstract -A. Emplacement of the siteArdeu village is located in South-Western Transylvania (Fig. 1/3),on the Ardeu Valley (Fig. 1/2). From a geographical point of view, the arreabelongs to the Metaliferi Mountains – a small group in the Southern part ofthe Romanian Western Carpathians.The “CetăŃuie” Hill is placed right near the road named DJ 705, witha good positon on the shortest way from the Dacian Kingdom’s capital (Fig.1/1) area to the region rich in golden minerals in the Apuseni Mountains(Romanian Western Carpathians).The hill was inhabited during several historical periods.B. The discoveriesThis ancient pieces that we intent to present in this paper werefound fortunately, but also through archaeological investigations.C. The weapons1. Iron shield boss - MCDR-Deva nr. inv. 40993 (Fig. 2/2), fragmentary.2. Iron curved-dagger scabbard - MCDR-Deva nr. inv 40599 (Fig. 3/3),fragmentary.3. Bone dagger handle - MCDR-Deva nr. inv. 40608 (Fig. 3/2), fragmentary.4. Iron lance-head - MCDR-Deva nr. inv. 40950 (Fig. 3/5), fragmentary.5. Iron lance-butt - MCDR-Deva, nr inv. 23387 (Fig. 3/4), fragmentary.6. Iron lance-butt - MNIR, box 157, without inventory number (Fig. 3/1).D. Analogies and chronologyThree of the pieces which we present here (Fig. 2/2-3, Fig. 3/2) were discovered in the same complex, partially investigated, but very probably dated at the end of the Ist century B.C. or, at the begining of the Ist century A.D. The other objects are dated, based on the analogies, during the Ist century B.C. - Ist century A.D.

This paper describes the motivation, design and implementation of a Perception Validation System (PVS), a system for measuring the outdoor perception performance of an autonomous vehicle. The PVS relies on using large amounts of real world data and ground truth information to quantify performance aspects such as the rate of false positive or false negative detections of an obstacle detection system. Our system relies on a relational database infrastructure to achieve a high degree of flexibility in the type of analyses it can support.

Real-world applications of mobile robotics call for increased autonomy, requiring reliable perception systems. Since manually tuned perception algorithms are difficult to adapt to new operating environments, systems based on supervised learning are necessary for future progress in autonomous navigation. Data labeling is a major concern when supervised learning is applied to the large-scale problems occuring in realistic robotics applications. We believe that algorithms for automatically selecting important data for labeling are necessary, and propose to employ active learning techniques to reduce the amount of labeling required to learn from a data set. In this paper we show that several standard active learning algorithms can be adapted to meet specific constraints characteristic to our domain, such as the need to learn from data with severely unbalanced class priors. We validate the solutions we propose by extensive experimentation on multiple realistic data sets captured with a robotic vehicle. Based on our results for the task of obstacle detection, we conclude that active learning techniques are applicable to our domain, and they can lead to significant reductions in the labeling effort required to use supervised learning in outdoor perception.

This paper describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation. We are focusing on outdoor obstacle detection, and we present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems. We are combining color and IR imagery with range information from a laser range finder. We show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and non-obstacle for example). We train classifiers on different subsets of the features we extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input. We present experimental results we obtained on data collected with both the Experimental Unmanned Vehicle (XUV) and a CMU developed robotic vehicle.

This work describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation. We are focusing on outdoor obstacle detection, and we present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems. We are combining color and infrared (IR) imagery with range information from a laser range finder. We show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and non-obstacle for example). We train classifiers on different subsets of the features we extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input. We present experimental results we obtained on data collected with both the experimental unmanned vehicle (XUV) and a CMU developed robotic tractor.

Autonomous navigation in outdoor, off-road environments requires solving complex classification problems. Obstacle detection, road following and terrain classification are examples of tasks which have been successfully approached using supervised machine learning techniques for classification. Large amounts of training data are usually necessary in order to achieve satisfactory generalization. In such cases, manually labeling data becomes an expensive and tedious process. This work describes a method for reducing the amount of data that needs to be presented to a human trainer. The algorithm relies on kernel density estimation in order to identify "interesting" scenes in a dataset. Our method does not require any interaction with a human expert for selecting the images, and only minimal amounts of tuning are necessary. We demonstrate its effectiveness in several experiments using data collected with two different vehicles. We first show that our method automatically selects those scenes from a large dataset that a person would consider "important" for classification tasks. Secondly, we show that by labeling only few of the images selected by our method, we obtain classification performance that is comparable to the one reached after labeling hundreds of images from the same dataset.

Describes the design and implementation of an algorithm for improving the performance of stereo vision in environments presenting repetitive patterns or regions with relatively weak texture. The proposed algorithm makes use of the common assumption that the disparities corresponding to continuous surfaces in the world vary smoothly; we use this assumption to alleviate the correspondence problem for pixels that cannot be reliably matched by the stereo algorithm. Our approach can be described as a reliability based filtering of the disparity image followed by a recursive propagation step. It can be applied to the output of almost any "standard" stereo algorithm with minimal modifications, and is computationally efficient.