Iron(II) Sulfate(VI) from Titania Production as a Raw Material for Preparation of Hydrogen Sulfide Sorbents

A hybrid sorbent material for removal of hydrogen sulfide from air was developed. The material is based on activated carbon and iron compounds obtained from waste iron(II) sulfate(VI) heptahydrate. The iron salt is deposited on the carbonaceous support and subjected to oxidation (Fe²⁺ to Fe³⁺) using atmospheric oxygen under alkaline conditions. An effect of H₂O₂ addition to the process on the composition of the resultant material was also examined. X-ray diffraction (XRD) analyses confirmed easy conversion of waste FeSO₄·7H₂O to iron oxides Fe₃O₄ and FeOOH. The activated carbon supporting iron oxides revealed a higher efficiency in H₂S elimination from air compared to the commercial activated carbon, without any modification.     

Electrodynamic headphones and woofers for application in magnetic resonance imaging scanners

Electrodynamic speakers compatible with (functional) magnetic resonance imaging (MRI) are described. The speakers magnets are removed, their function is replaced by the scanner's magnetic field, resulting in an uncommon but efficient operation. The method can be used with headphones as well as woofers. Functional MRI is not associated with any known biological risks, but as a method for visualization of task-specific activation of brain regions it is undesirably noisy. Thus, it requires both noise protection and efficient sound transmission systems for delivering acoustic stimuli to subjects. Woofers could possibly be used in active noise-control systems. The speakers described in this paper can be used for either task.     

Phase I trial of weekly scheduling and pharmacokinetics of titanocene dichloride in patients with advanced cancer

PURPOSE: To determine the maximum-tolerated dose (MTD) and the dose-limiting toxicities (DLTs) of a weekly schedule of titanocene dichloride (TD) and to define the pharmacokinetics of titanium in plasma and urine. PATIENTS AND METHODS: Twenty patients with a median age of 58 years received 83 courses of TD. TD was given as 1-hour infusion at escalating doses from 70 to 185 mg/m2/wk. Pharmacokinetic analysis was performed in eight patients for total plasma titanium (TPTi) and in three patients for ultrafiltrable titanium (UFTi). RESULTS: At the fifth dose level (185 mg/m2/wk), a variety of DLTs were seen in five patients: fatigue in three, bilirubinemia in one, and hypokalemia in two. A further six patients were treated at 140 mg/m2; only one had dose-limiting creatinine elevation and this dose was therefore defined as the MTD. No myelosuppression or alopecia were observed. One patient with adenocarcinoma of unknown primary had a minor response. Pharmacokinetic analysis showed that TPTi maximum concentration (Cmax) values were linear with dose and elimination of TPTi was triphasic with a long terminal half-life (t1/2; median, 165 hours; range, 89 to 592). Between 7% and 24.3% of the total of administered titanium was eliminated in urine over the first 24 hours. In contrast, UFTi elimination was described by a one-compartment model with a t1/2 of 0.41 hours; peak levels of UFTi were 5.2% +/- 2.5% those of TPTi. CONCLUSION: The MTD of TD given on a weekly schedule is 140 mg/m2, with cumulative, but reversible creatinine and bilirubin elevation being the DLTs.     

Mobile context-based framework for threat monitoring in urban environment with social threat monitor

Engaging users in threat reporting is important in order to improve threat monitoring in urban environments. Today, mobile applications are mostly used to provide basic reporting interfaces. With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows for real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in the field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and performs on-line reasoning about possible threats based on the information provided by the Social Threat Monitor system developed in the INDECT project.     

Possibilities in the reconstruction of bone defects

The importance of the functional unit "bone-soft-tissue" for the treatment of bone defects is discussed. A division is made between non-vascular and vascular bone transport. The individual methods are explained and their clinical significance is illustrated partly using case material. In general, the best way to fill small defects in vital soft tissue is to use cancellous autografts. For bridging longer bone defects callus distraction is the method of first choice, but in individual cases vascularized bone transfer can be taken from the iliac crest or the fibula. Homo- or heterografts should only be used in cases where no autologic bone material is available or when there is not enough.     

Range mode and range median queries in constant time and sub-quadratic space

Given a list of n items and a function defined over sub-lists, we study the space required for computing the function for arbitrary sub-lists in constant time.For the function mode we improve the previously known space bound O(n²/logn) to O(n²loglogn/log²n) words.For median the space bound is improved to O(n²loglog²n/log²n) words from O(n²⋅log(k)n/logn), where k is an arbitrary constant and log(k) is the iterated logarithm.     

Estimating the Laplace-Beltrami Operator by Restricting 3D Functions

We present a novel approach for computing and solving the Poisson equation over the surface of a mesh. As in previous approaches, we define the Laplace-Beltrami operator by considering the derivatives of functions defined on the mesh. However, in this work, we explore a choice of functions that is decoupled from the tessellation. Specifically, we use basis functions (second-order tensor-product B-splines) defined over 3D space, and then restrict them to the surface. We show that in addition to being invariant to mesh topology, this definition of the Laplace-Beltrami operator allows a natural multiresolution structure on the function space that is independent of the mesh structure, enabling the use of a simple multigrid implementation for solving the Poisson equation.     

Modeling and simulation of hard-particle interaction in head/disk interfaces

A hard particle interacting with a slider and a disk in the head/disk interface of hard-disk drives can produce a scratch on the disk and result in data loss. Our work emphasizes modeling and simulation of slider, particle, and disk interactions, scratch generating mechanisms, and linking of scratch to interface design parameters. Two models are presented. The Monte Carlo method was used to calculate scratch probabilities and parametric studies were performed to determine the effect on scratch probability of various parameters, such as friction coefficients, particle mean size, slider wall angles, the ion-milling (IM) etch depth from the air-bearing surface (ABS) of the slider, and the efficacy of additional ABS structures acting as particle shields. Simulation results show that the friction coefficient is a dominant parameter. If the disk friction coefficient is smaller than the slider friction coefficient, scratches are not produced. Using shallow IM depths is an effective and practical way to reduce the scratch probability. Experimental results are very close to the simulation predictions.     

Decision trees for uplift modeling with single and multiple treatments

Most classification approaches aim at achieving high prediction accuracy on a given dataset. However, in most practical cases, some action such as mailing an offer or treating a patient is to be taken on the classified objects, and we should model not the class probabilities themselves, but instead, the change in class probabilities caused by the action. The action should then be performed on those objects for which it will be most profitable. This problem is known as uplift modeling, differential response analysis, or true lift modeling, but has received very little attention in machine learning literature. An important modification of the problem involves several possible actions, when for each object, the model must also decide which action should be used in order to maximize profit. In this paper, we present tree-based classifiers designed for uplift modeling in both single and multiple treatment cases. To this end, we design new splitting criteria and pruning methods. The experiments confirm the usefulness of the proposed approaches and show significant improvement over previous uplift modeling techniques.