Removal of phosphate species from solution by adsorption onto calcite used as natural adsorbent

Phosphates are very important basic materials in agricultural and other industrial applications. Phosphorus is often present in low concentrations in wastewater, almost solely in the form of organic and inorganic phosphates (ortho- and poly-phosphates). The removal of phosphates from surface waters is generally necessary to avoid problems, such as eutrophication, particularly near urban areas. The usual methods of treatment are either biological or physicochemical by sedimentation. This paper studies the removal of phosphate species by adsorption onto calcite used as natural adsorbent. The phosphate solutions were prepared artificially by adding certain quantities of K2HPO4 in water. The effect of equilibrium pH, phosphate/mineral ratio and contact time was studied. The results showed that pH plays an important role in the removal of phosphate species from solution, with removal being more efficient in the basic pH region. The experimental results also show that adsorption is also efficient for high ratios phosphate/adsorbent. Finally, the adsorption process is time dependent. Based on the experimental results a possible mechanism of phosphate removal onto calcite surface is proposed. As a general conclusion, phosphate species seem to be efficiently removed from solutions using calcite as natural adsorbent. In addition, the adsorption product can be used as fertilizer for acid soils.     

Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens.     

A <Emphasis Type="Italic">k</Emphasis>-norm pruning algorithm for decision tree classifiers based on error rate estimation

Decision trees are well-known and established models for classification and regression. In this paper, we focus on the estimation and the minimization of the misclassification rate of decision tree classifiers. We apply Lidstone’s Law of Succession for the estimation of the class probabilities and error rates. In our work, we take into account not only the expected values of the error rate, which has been the norm in existing research, but also the corresponding reliability (measured by standard deviations) of the error rate. Based on this estimation, we propose an efficient pruning algorithm, called k-norm pruning, that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly, and compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Editor: Hendrik Blockeel.     

手动式电磁定位及图像导航的短径癌症放射治疗系统

提出一个用于高剂量短径癌症放射治疗的手动式电磁定们及图像导航系统，该系统主要由4个模块组成：预处理、术前规划、术内导航和术后计划，它们分别对应高剂量短径癌症放射治疗的4个流程：在术前规划阶段，用户可交互地设计探针放置位置以及三维重建病灶轮廓；在手术进行过程中，六自由度的电磁式定位技术实时而精确地跟踪探针位置，高质量的可视化技术指引医生完成手术探针放置任务；手术探针全部放置完毕后，术后计划模板计算剂量分布并利用后装机设备进行放射治疗。初步实验表明，计算机辅助的探针定位比手工定位精度提高2-3倍，手术时间减少5倍以上，同时也减轻了病人所承受的放射剂量。     

Named-Entity Recognition from Greek and English Texts

Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.     

The Gestational Effects of Maternal Bone Marker Molecules on Fetal Growth,Metabolism and Long-Term Metabolic Health: A Systematic Review

Fetal exposure in adverse environmental factors during intrauterine life can lead to various biological adjustments, affecting not only in utero development of the conceptus, but also its later metabolic and endocrine wellbeing. During human gestation, maternal bone turnover increases, as reflected by molecules involved in bone metabolism, such as vitamin D, osteocalcin, sclerostin, sRANKL, and osteoprotegerin; however, recent studies support their emerging role in endocrine functions and glucose homeostasis regulation. Herein, we sought to systematically review current knowledge on the effects of aforementioned maternal bone biomarkers during pregnancy on fetal intrauterine growth and metabolism, neonatal anthropometric measures at birth, as well as on future endocrine and metabolic wellbeing of the offspring. A growing body of literature converges on the view that maternal bone turnover is likely implicated in fetal growth, and at least to some extent, in neonatal and childhood body composition and metabolic wellbeing. Maternal sclerostin and sRANKL are positively linked with fetal abdominal circumference and subcutaneous fat deposition, contributing to greater birthweights. Vitamin D deficiency correlates with lower birthweights, while research is still needed on intrauterine fetal metabolism, as well as on vitamin D dosing supplementation during pregnancy, to diminish the risks of low birthweight or SGA neonates in high-risk populations.     

Methods for the Assessment of NET Formation: From Neutrophil Biology to Translational Research

Several studies have indicated that a neutrophil extracellular trap (NET) formation, apart from its role in host defense, can contribute to or drive pathogenesis in a wide range of inflammatory and thrombotic disorders. Therefore, NETs may serve as a therapeutic target or/and a diagnostic tool. Here, we compare the most commonly used techniques for the assessment of NET formation. Furthermore, we review recent data from the literature on the application of basic laboratory tools for detecting NET release and discuss the challenges and the advantages of these strategies in NET evaluation. Taken together, we provide some important insights into the qualitative and quantitative molecular analysis of NETs in translational medicine today.     

Cryptographic one-way function based on boson sampling

Quantum Information Processing - Based on four-qubit cluster states, we present a new multi-party quantum key agreement (QKA) protocol. The proposed protocol makes full use of four-qubit cluster...     

Weighted voting on the blockchain: Improving consensus in proof of stake protocols

Proof of stake (PoS) protocols rely on voting mechanisms to reach consensus on the current state. If an enhanced majority of staking nodes, also called validators, agree on a proposed block, then this block is appended to the blockchain. Yet these protocols remain vulnerable to faults caused by validators who abstain either accidentally or maliciously. To protect against such faults while retaining the PoS selection and reward allocation schemes, we study weighted voting in validator committees. We formalize the block creation process and introduce validators' voting profiles which we update by a multiplicative weights algorithm relative to validators' voting behavior and aggregate blockchain rewards. Using this framework, we leverage weighted majority voting rules that optimize collective decision making to show, both numerically and analytically, that the consensus mechanism is more robust if validators' votes are appropriately scaled. We raise potential issues and limitations of weighted voting in trustless, decentralized networks and relate our results to the design of current PoS protocols.     

Numerical simulation of the spreading of buoyant gases over topographically complex terrain

Numerical analysis has been performed for predicting the dispersion of continuous released neutral gases (i.e. stack gases) from elevated or near-ground sources within regions of complex topography. The three-dimensional non-steady governing transport differential equations are solved by means of the numerical finite volume method, using a collocated variable arrangement. The turbulence effects on the flow property transport are simulated by the two-equation k-ε turbulence model. Comparisons between calculated and measured data are presented, showing good agreement between them. The method is employed to predict continuous releases within a fictitious industrial plant. The height of the source, the atmospheric stability class, the topography and the wind speed and primary direction are varied, in order to point out the effect of topography on the pollutant's dispersion.