Miracle-dz/UniFAR-FTUs huggingface dataset:
Transactional Sex: Supply and Demand Among European Men Who have Sex with Men (MSM) in the Context of Local Laws

ABSTRACT Objectives: Transactional sex (TS) is generally defined as the trading of sex for material goods. Cast within the broader context of prostitution laws, we examined variations in the sociodemographic profile of men who have sex with men engaging in TS by payment direction (buying/selling). Methods: The data were collected as part of the 38-country European Men who have sex with men Internet Survey project, conducted in 2010. Results: About 12% of respondents reported engaging in TS in the past year. TS was associated with laws, age, education, employment, and residence. Conclusions: The striking sociodemographic differences in TS by payment direction suggest a power differential and a leading role of socioeconomic factors in TS.
A USRP based scheme for cooperative sensing networks

In this contribution, a hardware platform for cooperative sensing networks is presented with the use of Roy ́s Largest Root Test, also known as a blindly combined energy detection scheme, such that the system is configured with three ETTUS USRPs N210 and one computer for processing. The hardware experiments were validated with simulations, and the results show that the cooperative scheme outperforms the case when sensing is taken individually, keeping in mind that the number of samples collected is a key performance indicator.
At the Vanguard of Design Science: First Impressions and Early Findings from Ongoing Research

The insider threat is a security problem that is well-known and has a long history, yet it still remains an invisible enemy. Insiders know the security processes and have accesses that allow them to easily cover their tracks. In recent years the idea of monitoring separately for these threats has come into its own. However, the tools currently in use have disadvantages and one of the most effective techniques of human review is costly. This paper explores the development of an intelligent agent that uses already in-place computing material for inference as an inexpensive monitoring tool for insider threats. Design Science Research (DSR) is a methodology used to explore and develop an IT artifact, such as for this intelligent agent research. This methodology allows for a structure that can guide a deep search method for problems that may not be possible to solve or could add to a phenomenological instantiation.
Design of beam-tilted array antenna for UWB location system

In this paper, a beam-tilted array used as the reader antenna of ultra-wideband (UWB) location systems is proposed. Bow-tie antenna is adopted as element in the array to achieve a wideband performance. Feeding structure of coaxial line is employed to realize beam tilt angle. Good simulation and measurement results are presented. The antenna achieves a wide impedance bandwidth of 36.8% (3.1~4.5GHz). A stable gain of more than 4.5dBi and beam tilt angle of 30° is realized within the whole impedance bandwidth.
On-Demand Source-Tree Routing for Ad Hoc Networks

The Source-Tree On-Demand Adaptive Routing (SOAR) protocol is presented, verified and analyzed. With SOAR, a wireless router communicates to its neighbors only the link states of those links along the paths it uses to reach destinations with which it has active flows. SOAR communicates such information in route requests and route replies, and does not incur overhead messages when there are no link connectivity changes in the network. SOAR is shown to obtain correct routing information at any given router for those destinations involved with active flows. The performance of SOAR is compared through simulation experiments in ns2 with the performance of the Dynamic Source Routing (DSR) protocol and the Ad-Hoc On-Demand Distance Vector (AODV) protocol, which are two of the leading proposals for on-demand routing in ad hoc networks. The result of simulation experiments involving different node mobility, traffic load, and numbers of flows indicate that SOAR is more efficient than AODV and DSR in terms of control packet packet overhead, data delivery or path lengths. Keywords— Mobile Networks, Wireless Networks, OnDemand Routing, Ad-Hoc networks, Link-State Routing
Data Mining for Electrical Load Forecasting In Egyptian Electrical Network

The paper presents the design of a model for forecasting long-term electricity load. The model uses data mining techniques. The paper defines the load forecast and the summary of the most important factors affecting the load forecast in Egyptian electricity network. The steps needed for the knowledge discovery process is implemented to the time series data. Preprocessing the data in order to detect the missing value, odd value, outliers and normalize data. The output from the preprocessing step is fed into multiple regression or neural network to predict the coefficient parameters. Comparison between different cases using different techniques is indicated
The Negatively Charged Regions of Lactoferrin Binding Protein B, an Adaptation against Anti-Microbial Peptides

Lactoferrin binding protein B (LbpB) is a bi-lobed membrane bound lipoprotein that is part of the lactoferrin receptor complex in a variety of Gram-negative pathogens. Despite high sequence diversity among LbpBs from various strains and species, a cluster of negatively charged amino acids is invariably present in the protein’s C-terminal lobe in all species except Moraxella bovis. The function of LbpB in iron acquisition has yet to be experimentally demonstrated, whereas in vitro studies have shown that LbpB confers protection against lactoferricin, a short cationic antimicrobial peptide released from the N- terminus of lactoferrin. In this study we demonstrate that the negatively charged regions can be removed from the Neisseria meningitidis LbpB without compromising stability, and this results in the inability of LbpB to protect against the bactericidal effects of lactoferricin. The release of LbpB from the cell surface by the autotransporter NalP reduces the protection against lactoferricin in the in vitro killing assay, attributed to removal of LbpB during washing steps, but is unlikely to have a similar impact in vivo. The protective effect of the negatively charged polysaccharide capsule in the killing assay was less than the protection conferred by LbpB, suggesting that LbpB plays a major role in protection against cationic antimicrobial peptides in vivo. The selective release of LbpB by NalP has been proposed to be a mechanism for evading the adaptive immune response, by reducing the antibody binding to the cell surface, but may also provide insights into the primary function of LbpB in vivo. Although TbpB and LbpB have been shown to be major targets of the human immune response, the selective release of LbpB suggests that unlike TbpB, LbpB may not be essential for iron acquisition, but important for protection against cationic antimicrobial peptides.
Modular Regression Testing " : Connections to Component-Based Software

Many have argued that software that is not designed to support modular reasoning about its behavior is inherently fragile and costly to maintain, and that software engineers should seek to achieve the modular reasoning property to help overcome these problems. But some people resist these claims, taking one of two contradictory positions: 1. Modular reasoning is inherently limited to impractical purely functional programs where there is no state and there are no side-effects. 2. Modular reasoning is possible for any reasonably "well-designed" software system written in a modern imperative object-oriented language that uses its sophisticated encapsulation mechanisms. Explanations of why (1) is wrong have been relatively effective. We suspect this is because both experimental and (more recently) commercial software has been built in C++ in a disciplined way that supports modular reasoning about its behavior, and it has (among other advantages) dramatically lower defect rates than "normal" software of like kind. Explanations of why (2) is wrong have been less effective. We suspect this is because they have been based on synthetic examples that appear to be pathological and therefore of little practical consequence. Using a thought experiment involving regression testing of systems having features that no one should doubt are just like "real" software, we make another stab at giving a convincing argument on this point.
Impact of depressive mood on lifestyle changes in patients with coronary artery disease.

OBJECTIVE The aims of this study were to investigate the time-course of depressive mood in patients with coronary artery disease during a secondary prevention rehabilitation program, and to analyse how different pre-treatment levels of depressive mood during a treatment phase were related to the degree of lifestyle change at 36 months follow-up. SUBJECTS The study group comprised 109 of the original 183 consecutive coronary artery disease patients (91 male and 18 female) of whom 48 recently had experienced an acute myocardial infarction, 36 had been treated with coronary bypass surgery, 13 with percutaneous transluminal coronary angioplasty, and 12 had angina pectoris that had not been invasively treated. The subjects were divided into 3 subgroups based on their pre-treatment level of depressive mood. METHODS Depressive mood was assessed at baseline, after 4 weeks and 12 months, using the depression subscale of the Hospital Anxiety and Depression scale. Lifestyle changes analysed included diet, smoking, relaxation (stress management) and exercise. RESULTS Overall depressive mood ratings were significantly lower, both at the 4-week and 12-month assessments, compared with baseline, with the greatest improvements in patients with higher Hospital Anxiety and Depression measured depression. Original levels of depressive mood were not found to influence change of lifestyle habits during a 36-month follow-up period. CONCLUSION Depressive mood might not be an obstacle to lifestyle changes when participating in a behaviourally oriented rehabilitation program including exercise-training, which might be a component important for improved depressive mood.
Quantitative Evaluation of Scintillation Camera Imaging Characteristics of Isotopes Used in Liver Radioembolization

Background Scintillation camera imaging is used for treatment planning and post-treatment dosimetry in liver radioembolization (RE). In yttrium-90 (90Y) RE, scintigraphic images of technetium-99m (99mTc) are used for treatment planning, while 90Y Bremsstrahlung images are used for post-treatment dosimetry. In holmium-166 (166Ho) RE, scintigraphic images of 166Ho can be used for both treatment planning and post-treatment dosimetry. The aim of this study is to quantitatively evaluate and compare the imaging characteristics of these three isotopes, in order that imaging protocols can be optimized and RE studies with varying isotopes can be compared. Methodology/Principal Findings Phantom experiments were performed in line with NEMA guidelines to assess the spatial resolution, sensitivity, count rate linearity, and contrast recovery of 99mTc, 90Y and 166Ho. In addition, Monte Carlo simulations were performed to obtain detailed information about the history of detected photons. The results showed that the use of a broad energy window and the high-energy collimator gave optimal combination of sensitivity, spatial resolution, and primary photon fraction for 90Y Bremsstrahlung imaging, although differences with the medium-energy collimator were small. For 166Ho, the high-energy collimator also slightly outperformed the medium-energy collimator. In comparison with 99mTc, the image quality of both 90Y and 166Ho is degraded by a lower spatial resolution, a lower sensitivity, and larger scatter and collimator penetration fractions. Conclusions/Significance The quantitative evaluation of the scintillation camera characteristics presented in this study helps to optimize acquisition parameters and supports future analysis of clinical comparisons between RE studies.
Simultaneous Voltage and Calcium Mapping of Genetically Purified Human Induced Pluripotent Stem Cell–Derived Cardiac Myocyte Monolayers

Rationale: Human induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs) offer a powerful in vitro tool to investigate disease mechanisms and to perform patient-specific drug screening. To date, electrophysiological analysis of iPSC-CMs has been limited to single-cell recordings or low-resolution microelectrode array mapping of small cardiomyocyte aggregates. New methods of generating and optically mapping impulse propagation of large human iPSC-CM cardiac monolayers are needed. Objective: Our first aim was to develop an imaging platform with versatility for multiparameter electrophysiological mapping of cardiac preparations, including human iPSC-CM monolayers. Our second aim was to create large electrically coupled human iPSC-CM monolayers for simultaneous action potential and calcium wave propagation measurements. Methods and Results: A fluorescence imaging platform based on electronically controlled light-emitting diode illumination, a multiband emission filter, and single camera sensor was developed and utilized to monitor simultaneously action potential and intracellular calcium wave propagation in cardiac preparations. Multiple, large-diameter (≥1 cm), electrically coupled human cardiac monolayers were then generated that propagated action potentials and calcium waves at velocities similar to those commonly observed in rodent cardiac monolayers. Conclusions: The multiparametric imaging system presented here offers a scalable enabling technology to measure simultaneously action potential and intracellular calcium wave amplitude and dynamics of cardiac monolayers. The advent of large-scale production of human iPSC-CMs makes it possible to now generate sufficient numbers of uniform cardiac monolayers that can be utilized for the study of arrhythmia mechanisms and offers advantages over commonly used rodent models.
Leaving the World Behind: Supporting Group Collaboration Patterns in a Shared Virtual Environment for Product Design

Virtual reality technology is increasingly being applied to globally distributed teams engaged in collaborative product design. Observations of product design teams have suggested four distinct patterns of collaborationcomplementary, competitive, peer-to-peer, and leader-follower. Another insight from observation is that collaboration consists of fluid transitions between these patterns in the accomplishment of the design task, driven by a flexible process of subgrouping and regrouping which reflects the structure and progress of the task. Yet most collaborative virtual environment systems support only one pattern of collaborationpeer-to-peerand those that do explicitly support multiple patterns or roles do not allow fluid transitions between them in the context of the same task. In addition, no explicit support is provided to allow subgroups to be formed and dissolved. A collaborative virtual environment that supports multiple collaboration patterns and fluid transitions was developed using the Shared Simple Virtual Environment (SSVE) application framework. A novel user interface widget, the collaboration tree, was created to drive the subgrouping and regrouping process. Group experiments were performed to test the operating hypothesis that support for group collaboration patterns led to higher performance. The result was that the operating hypothesis was confirmed; however, the conceptual approach to problem solving, suggested by the presence of support for collaboration patterns, may have been more significant than the actual mechanism provided.
A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

The employ of Wireless Visual Sensor Networks (WVSNs) has grown enormously in the last few years and have emerged in distinctive applications. WVSNs-based Surveillance applications are one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy to maximize the lifetime of sensor nodes as visual sensor nodes can be left for months without any human interaction. The constraints of WVSNs such as resource constraints due to limited battery power, memory space and communication bandwidth have brought new WVSNs implementation challenges. Hence, the aim of this paper is to investigate the impact of adaptive Compressive Sensing (CS) in designing efficient target detection and tracking techniques, to reduce the size of transmitted data without compromising the tracking performance as well as space and energy constraints. In this paper, a new hybrid adaptive compressive sensing scheme is introduced to dynamically achieve higher compression rates, as different datasets have different sparsity nature that affects the compression. Afterwards, a modified quantized clipped Least Mean square (LMS) adaptive filter is proposed for the tracking model. Experimental results showed that adaptive CS achieved high compression rates reaching 70%, while preserving the detection and tracking accuracy which is measured in terms of mean squared error, peak-signal-to-noise-ratio and tracking trajectory.
Tradeoffs in the Design of On-Line Systems

We present a general framework for analyzing tradeoffs when designing systems in which an agent with limited computational resources is required to respond in a timely manner to situations arising in a dynamic environment. These tradeoffs are characterized in terms of architectural constraints that limit the computational alternatives available to the agent. We apply the framework to a design problem in which the designer is given a set of controllers, each of which is capable of responding to a subset of situations, and a model of the run-time dynamics that can be used to predict the sets of situations that might arise over time. Exactly one controller is active at any moment, and a fixed time delay is required to activate a controller. Additional architectural constraints limit the number of controllers that are capable of being activated. The objective is to design (off-line) a strategy for activating and deactivating controllers that guarantees that there will be an active controller capable of responding no matter what situation arises. We provide an asymptotic analysis for this design problem, describe exact and approximate algorithms for the off-line design, and sketch how the framework can be applied to real-time avionics scheduling. We argue that the architectural constraints of this design problem model a rich class of planning and control systems.
AdaBoost in basketball player identification

Video materials contain huge amount of information. Their storage in databases and analysis by various algorithms represents an area that constantly develops. This paper presents the process of analysis of basketball games by AdaBoost algorithm. This algorithm is mainly used for face recognition and body parts recognition. It consists of a linear combination of weak classifiers. In this paper, stumps were used as weak classifiers. The aim of this research is to assess the accuracy of this algorithm when applied in players' identification at basketball games. Capabilities of AdaBoost were examined when applied to video footage from single moving camera, and when these footages were not previously treated by any other algorithm. The first training was performed by entire images of basketball players, whereas the second training was performed by using the images of the head and torso. By applying the algorithm to the given set of images that include head and torso, the algorithm obtained an accuracy of 70.5%. Experimental results have shown that training on the set of entire body images was not possible due to large amount of background that goes into the training, and which represents noise in training process. This accuracy could be increased by applying filters that would remove background from images and leave just basketball players. By applying those filters, the amount of noise in the training data would be significantly reduced.
A multi-user MIMO system encryption algorithm based on joint channel state matrix

The existing physical layer encryption algorithm, which is based on artificial noise, could affect legitimate receivers negatively when the number of users is no less than sending antennas in the multiuser MIMO system. In order to improve the multiuser MIMO system security under this scenario, the paper proposed a multiuser MIMO system physical layer encryption algorithm based on joint channel state matrix. Firstly, multiple users are processed together, thus a multiuser joint channel state matrix is established. After Singular Value Decomposition of the joint channel state matrix, the minimum singular value is obtained and can be utilized for pre-coding, so as to eliminate the interference of artificial noise to legitimate receivers. Further, the paper also presented an approach to optimize the power allocation. The simulation results show that the proposed algorithm can effectively increase the secrecy capacity by 0.1bit/s/HZ averagely, and improve the multiuser MIMO system security.
The Neuronal Basis of Long-Term Sensorimotor Learning

The brain has a remarkable ability to learn and adjust behavior. For instance, the brain can adjust muscle activation to cope with changes in the environment. However, the neuronal mechanisms behind this adaptation are not clear. To address this fundamental question, this study examines the neuronal basis of long-term sensorimotor learning by recording neuronal activity in the primary motor cortex of monkeys during a long-term adaptation to a force-field perturbation. For 5 consecutive days, the same perturbation was applied to the monkey's hand when reaching to a single target, whereas movements to all other targets were not perturbed. The gradual improvement in performance over these 5 days was correlated to the evolvement in the population neuronal signal, with two timescales of changes in single-cell activity. Specifically, one subgroup of cells showed a relatively fast increase in activity, whereas the other showed a gradual, slower decrease. These adapted patterns of neuronal activity did not involve changes in directional tuning of single cells, suggesting that adaptation was the result of adjustments of the required motor plan by a population of neurons rather than changes in single-cell properties. Furthermore, generalization was mostly expressed in the direction of the required compensatory force during adaptation. Altogether, the neuronal activity and its generalization accord with the adapted motor plan.
Energy Return on Investment for Algal Biofuel Production Coupled with Wastewater Treatment

This study presents a second‐order energy return on investment analysis to evaluate the mutual benefits of combining an advanced wastewater treatment plant (WWTP) (with biological nutrient removal) with algal biofuel production. With conventional, independently operated systems, algae production requires significant material inputs, which require energy directly and indirectly, and the WWTP requires significant energy inputs for treatment of the waste streams. The second‐order energy return on investment values for independent operation of the WWTP and the algal biofuels production facility were determined to be 0.37 and 0.42, respectively. By combining the two, energy inputs can be reduced significantly. Consequently, the integrated system can outperform the isolated system, yielding a second‐order energy return on investment of 1.44. Combining these systems transforms two energy sinks to a collective (second‐order) energy source. However, these results do not include capital, labor, and other required expenses, suggesting that profitable deployment will be challenging.