Miracle-dz/UniFAR-FTUs huggingface dataset:
HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks

Sink mobility has attracted much research interest in recent years because it can improve network performance such as energy efficiency and throughput. An energy-unconscious moving strategy is potentially harmful to the balance of the energy consumption among sensor nodes so as to aggravate the hotspot problem of sensor networks. In this paper, we propose an autonomous moving strategy for the mobile sinks in data-gathering applications. In our solution, a mobile sink approaches the nodes with high residual energy to force them to forward data for other nodes and tries to avoid passing by the nodes with low energy. We performed simulation experiments to compare our solution with other three data-gathering schemes. The simulation results show that our strategy cannot only extend network lifetime notably but also provides scalability and topology adaptability.
Utilizing Page-Level Join Index for Optimization in Parallel Join Execution

This paper presents a methodology for the optimization of parallel join execution. Past research on parallel join methods mostly focused on the design of algorithms for partitioning (e.g. hash) relations and distributing data buckets as evenly as possible to the processors. Once data is distributed to the processors, it assumes that all processors will complete their tasks at about the same time. We stress that this is true if no further information such as page-level join index is available. Otherwise, the join execution can be further optimized and the workload in the processors may still be unbalanced. We study such problems that may incur in a shared-nothing architecture environment and propose algorithms for the problems. Also, a simulation study is performed to understand the characteristics of the proposed method.
Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases

Background: Data-adaptive approaches to confounding adjustment may improve performance beyond expert knowledge when analyzing electronic healthcare databases and have additional practical advantages for analyzing multiple databases in rapid cycles. Improvements seemed possible if outcome predictors were reliably identified empirically and adjusted. Methods: In five cohort studies from diverse healthcare databases, we implemented a base-case high-dimensional propensity score algorithm with propensity score decile-adjusted outcome models to estimate treatment effects among prescription drug initiators. The original variable selection procedure based on the estimated bias of each variable using unadjusted associations between confounders and exposure (RRCE) and disease outcome (RRCD) was augmented by alternative strategies. These included using increasingly adjusted RRCD estimates, including models considering >1,500 variables jointly (Lasso, Bayesian logistic regression); using prediction statistics or likelihood-ratio statistics for covariate prioritization; directly estimating the propensity score with >1,500 variables (Lasso, Bayesian regression); or directly fitting an outcome model using all covariates jointly (Lasso, Ridge). Results: In five example studies, most tested augmentations of the base-case hdPS did not meaningfully change estimates in light of wide confidence intervals except for Bayesian regression and Lasso to estimate RRCD, which moved estimates minimally closer to the expectation in three of five examples. The direct outcome estimation with Lasso performed worst. Conclusion: Overall, the basic heuristic of variable reduction in high-dimensional propensity score adjustment performed, as well as alternative approaches in diverse settings. Minor improvements in variable selection may be possible using Bayesian outcome regression to prioritize variables for propensity score estimation when outcomes are rare. See video abstract at, http://links.lww. com/EDE/B162.
On an effort to enhance lifetime of a regression based clustered network using candidate selection

Enhancement of the network lifetime and throughput of a network has become mandatory in the IoT world. Game theoretic approach used here deals with Candidate Cluster Head (CCH) selection in the network and data replication for improving the reliability of the network. In Wireless sensor Network (WSN) the Cluster Head (CH) of each cluster is responsible for aggregating data from the Cluster Members (CM) it belongs to and sending those data to the Base Station (BS). In case of energy drop of the cluster head below a threshold, the network lifetime is improved by allocating a CCH. Data replication is performed in case of link failure between CH and BS which improves the lifetime and throughput of WSN.
Expression neutralization based algorithm for expression-invariant face recognition for single training sample problem

The deformations incurred on the face during facial expressions significantly degrade the performance of the face recognition systems. It is a critical issue in many applications such as in forensic and surveillance as it is easy to spoof or fake the system using facial expression. This paper addresses this problem by proposing a method that estimates the neutral images of the subject and extracts the robust neutralized face segments for face recognition. Further, it provides a solution to the limited training sample problem encountered in many applications. A linear discriminant analysis (LDA) based approach is proposed to encode the common expression deformations that can be observed on the neutral faces across different individuals. This information is extracted in the form of vectors using a set of generic subjects that contain neutral and expressive images. Those are then used to remove the expression of the unknown test subject and estimate some neutral components of it. Further, the statistics of neutralization errors that arise due to applying general expression information for expression removal are learned from the generic subjects. It is applied to find the discriminating features of the neutralized faces. Experiments on the Extended Cohn-Kanade (CK+) database show the effectiveness of the proposed method.
Artificial neural network based design of a microstrip patch antenna for RADAR applications

Radars demand low profile and light weight antenna subsystems. Microstrip antennas possess these characteristics and serve as an alternative for the bulky and heavy weight reflector/slotted waveguide array antennas, thus an ideal choice for radars. Here, a single line fed microstrip antenna with pierced corners is designed. This antenna has improved parameters compared to the basic square microstrip antenna. The main problem encountered is in designing the patch antenna with optimum values for various antenna parameters. In order to overcome this, an alternative solution used is artificial neural networks (ANN). The antenna is also optimized using Particle Swarm Optimization (PSO). The parameters considered in all the cases are return loss (S11) and VSWR which was designed using FEKO software. The designed antennas are found to radiate in the C-band, which covers frequencies in the range 5–8GHz, applicable in most of the modern radars. The Simulation design is carried out using CADFEKO suite.
Alcohol, caffeine, and nicotine consumption in adolescents: hair analysis versus self-report

ABSTRACT Background: Clinical reliability of self-reported data for alcohol, caffeine, and nicotine consumptions is lacking, particularly in adolescents. Objectives: To compare a self-report questionnaire and hair analysis to assess the reliability and effectiveness of the self-report. Methods: A cross-sectional study on 14–15-year-old Italian students (n = 874, 38% males, 62% females) was performed comparing self-reported data to hair analysis. The latter quantified hair concentrations of caffeine, nicotine, cotinine, ethyl glucuronide (EtG), and fatty acid ethyl esters (FAEEs) using mass spectrometry. Results: Concordance between self-report and hair testing ranged from good to poor across substances and levels of use: poor for heavy alcohol intake (EtG: k = 0.36, 20 positive cases by hair analysis, false negative by self-report, 2.3% of total sample; FAEE k = 0.31, 25 positive cases, 2.9% of total sample); fair to poor for active smokers (k = 0.40, 125 positive cases, 14.3% of total sample); and moderate for caffeine (k = 0.57, 56 positive cases, 6.4% of total sample). Conclusions: Epidemiological studies on alcohol, caffeine, and nicotine consumption in adolescents may benefit from the inclusion of toxicological analysis on hair samples to overcome the under-reporting phenomenon of questionnaires and detect more cases of problematic substance use.
Learning Patient Similarity Using Joint Distributed Embeddings of Treatment and Diagnoses

We propose the use of vector-based word embedding models to learn a cross-conceptual representation of medical vocabulary. The learned model is dense and encodes useful knowledge from the training concepts. Applying the embedding to the concepts of diagnoses and medications, we then show that they can then be used to measure similarities among patient prescriptions, leading to the discovery of in- formative and intuitive relationships between patients.
Efficient Modular Squaring Algorithms for Hardware Implementation in GF(p)

ABSTRACT Some of the most popular public key encryption algorithms use exponentiation as their core operation, which can be mostly broken into several modular squaring operations. In this paper, we present GF(p) modular squaring algorithms and efficiently implement them on hardware. We present different algorithms, two for squaring and one for reduction combined with the squaring, to provide a general modular squaring algorithm. The algorithms are implemented through datapaths that uses redundant Carry-Save Adders, making the computation time independent form the operands precision. The proposed algorithms are compared with each other as well as with the existing modular squaring algorithms. The experimental results are obtained by synthesizing the hardware designs for FPGA Virtex5 chip (xc5vlx50 – ff1153 technology), which showed interesting results and made our ideas very attractive.
Neurogenesis during Abstinence Is Necessary for Context-Driven Methamphetamine-Related Memory

Abstinence from methamphetamine addiction enhances proliferation and differentiation of neural progenitors and increases adult neurogenesis in the dentate gyrus (DG). We hypothesized that neurogenesis during abstinence contributes to context-driven drug-seeking behaviors. To test this hypothesis, the pharmacogenetic rat model (GFAP-TK rats) was used to conditionally and specifically ablate neurogenesis in the DG. Male GFAP-TK rats were trained to self-administer methamphetamine or sucrose and were administered the antiviral drug valganciclovir (Valcyte) to produce apoptosis of actively dividing GFAP type 1 stem-like cells to inhibit neurogenesis during abstinence. Hippocampus tissue was stained for Ki-67, NeuroD, and DCX to measure levels of neural progenitors and immature neurons, and was stained for synaptoporin to determine alterations in mossy fiber tracts. DG-enriched tissue punches were probed for CaMKII to measure alterations in plasticity-related proteins. Whole-cell patch-clamp recordings were performed in acute brain slices from methamphetamine naive (controls) and methamphetamine experienced animals (+/−Valcyte). Spontaneous EPSCs and intrinsic excitability were recorded from granule cell neurons (GCNs). Reinstatement of methamphetamine seeking enhanced autophosphorylation of CaMKII, reduced mossy fiber density, and induced hyperexcitability of GCNs. Inhibition of neurogenesis during abstinence prevented context-driven methamphetamine seeking, and these effects correlated with reduced autophosphorylation of CaMKII, increased mossy fiber density, and reduced the excitability of GCNs. Context-driven sucrose seeking was unaffected. Together, the loss-of-neurogenesis data demonstrate that neurogenesis during abstinence assists with methamphetamine context-driven memory in rats, and that neurogenesis during abstinence is essential for the expression of synaptic proteins and plasticity promoting context-driven drug memory. SIGNIFICANCE STATEMENT Our work uncovers a mechanistic relationship between neurogenesis in the dentate gyrus and drug seeking. We report that the suppression of excessive neurogenesis during abstinence from methamphetamine addiction by a confirmed phamacogenetic approach blocked context-driven methamphetamine reinstatement and prevented maladaptive changes in expression and activation of synaptic proteins and basal synaptic function associated with learning and memory in the dentate gyrus. Our study is the first to demonstrate an interesting and dysfunctional role of adult hippocampal neurogenesis during abstinence to drug-seeking behavior in animals self-administering escalating amounts of methamphetamine. Together, these results support a direct role for the importance of adult neurogenesis during abstinence in compulsive-like drug reinstatement.
A cross-layer protocol evaluation framework on ESB nodes

Due to the fact that the MAC layer of commodity 802.11 wire-less network devices is usually realized in a proprietary firmware module, it is difficult to conduct real world evaluations of novel MANET cross-layer protocols that require a modified MAC layer. We demonstrate a testbed framework based on ESB sensor nodes. Due to their open firmware these nodes allow for the implementation of arbitrary MAC modifications and cross-layer interactions. This provides an opportunity to test MANET and Mesh network protocols with this kind of modifications in the real world on commercially available hardware. Our framework contains utilities and modules which automatically detect the network topology for a given node placement, construct and deploy static routing tables along predefined paths and allow an analysis of the network traffic by logging the packet transmissions. The demo points out the feasibility of experiments and cross-layer implementations with our framework on the ESB nodes.
Global flow instability in a lid‐driven cavity

The stability of flow in a lid‐driven cavity is investigated using an accurate numerical technique based on a hybrid scheme with spectral collocation and high‐order finite differences. A global stability analysis is carried out and critical parameters are identified for various aspect ratios. It is found that while there is reasonable agreement with the literature for the critical parameters leading to loss of stability for the square cavity, there are significant discrepancies for cavities of aspect ratios 1.5 and 2. Simulations of the linearized unsteady equations confirm the results from the global stability analysis for aspect ratios A = 1, 1.5 and A = 2. Copyright © 2009 John Wiley & Sons, Ltd.
Bayesian Channel Estimation for Massive MIMO Communications

In this paper, we derive the Bayes-Optimal estimator based on approximate message passing (AMP) algorithm in massive multiple-input multiple-output (MIMO) systems, which requires statistical channel state information (CSI). According to the analysis of channel model in beam domain, the covariance matrix is derived for CSI acquisition. With the aid of statistical CSI, the convergence of the proposed algorithm has significant improvement in comparison with which use the expectation-maximization (EM) algorithm to fit the statistical CSI. Simulations show great mean squared error (MSE) performance that approximates the Minimum Mean Square Error (MMSE) estimator, and better convergence performance than other AMP algorithm can be achieved. Besides, the results prove that performance of the random pilot in this algorithm is close to that of the orthogonal pilot based on Zadoff-Chu sequences.
Microwave heating on outside of biliary stent by coaxial probe

Microwave thermal therapy is one of the modalities for cancer treatment. One example is microwave hyperthermia which is a thermal therapy for carcinoma based on the decrease of the survival ratio of cancer cells by heating. In particular, we have been studying the intracavitary heating using the implanted biliary stent and a thin coaxial microwave probe combined with endoscope which is an effective and low-invasive treatment for deep-seated carcinoma such as a bile duct carcinoma. This paper describes the heating characteristics around the biliary stent by conducting both the heating experiments using muscle-equivalent phantom and the numerical simulations using the finite-difference time-domain (FDTD) method.
Low Duty-Cycling MAC Protocol for Low Data-Rate Medical Wireless Body Area Networks

Wireless body area networks (WBANs) are severely energy constrained, and how to improve the energy efficiency so as to prolong the network lifetime as long as possible is one of the most important goals of WBAN research. Low data-rate WBANs are promising to cut down the energy consumption and extend the network lifetime. Considering the characteristics and demands of low data-rate WBANs, a low duty-cycling medium access control (MAC) protocol is specially designed for this kind of WBAN in this paper. Longer superframes are exploited to cut down the energy consumed on the transmissions and receptions of redundant beacon frames. Insertion time slots are embedded into the inactive part of a superframe to deliver the frames and satisfy the quality of service (QoS) requirements. The number of the data subsections in an insertion time slot can be adaptively adjusted so as to accommodate low data-rate WBANs with different traffic. Simulation results show that the proposed MAC protocol performs well under the condition of low data-rate monitoring traffic.
Cyber Physical Systems for User Reliability Measurements in a Sharing Economy Environment

As the sharing economic market grows, the number of users is also increasing but many problems arise in terms of reliability between providers and users in the processing of services. The existing methods provide shared economic systems that judge the reliability of the provider from the viewpoint of the user. In this paper, we have developed a system for establishing mutual trust between providers and users in a shared economic environment to solve existing problems. In order to implement a system that can measure and control users’ situation in a shared economic environment, we analyzed the necessary factors in a cyber physical system (CPS). In addition, a user measurement system based on a CPS structure in a sharing economic environment is implemented through analysis of the factors to consider when constructing a CPS.
A comprehensive secure email transfer model

Simple Mail Transfer Protocol has been used since 1980 to send and receive Emails. Initially, this protocol did not have security controls implemented as it was not a need at that time. Therefore, the first version had many weaknesses. To address those weaknesses, many solutions have been proposed. However, there are still security weaknesses in it. The main weakness in the protocol is that it does not address security considerations like Confidentiality, Integrity, Authentication and Authorization. To bring in those security considerations into message transfer, we have proposed a new Email transfer model. That model has a central server to authenticate and authorize Email traffic between Email servers. Moreover, this model provides confidentiality and integrity to email messages with the help of encryption.
Energy Borrowing for Energy Harvesting Wireless Communications

For the wireless communication systems with joint energy harvesting (EH) and grid power supply, a novel technique, namely, energy borrowing, is proposed to establish a cooperation relationship between EH devices and the power grid. Specifically, in the proposed EB-aided EH systems, EH nodes could borrow energy from the power grid, and is requested to return the borrowed energy back with additional energy interest. A joint optimization of both EB policy and power scheduling for maximizing the system throughput is formulated, and the numerical solutions are developed. Simulation results, which are exemplified with real solar data records, show that the proposed EB-aided EH system provides considerable throughput gain compared with conventional EH systems while achieving extra energy interest for the power grid.
A Self-Organizing Storage Cluster for Parallel Data-Intensive Applications

Cluster-based storage systems are popular for data-intensive applications and it is desirable yet challenging to provide incremental expansion and high availability while achieving scalability and strong consistency. This paper presents the design and implementation of a self-organizing storage cluster called Sorrento, which targets data-intensive workload with highly parallel requests and low write-sharing patterns. Sorrento automatically adapts to storage node joins and departures, and the system can be configured and maintained incrementally without interrupting its normal operation. Data location information is distributed across storage nodes using consistent hashing and the location protocol differentiates small and large data objects for access efficiency. It adopts versioning to achieve single-file serializability and replication consistency. In this paper, we present experimental results to demonstrate features and performance of Sorrento using microbenchmarks, application benchmarks, and application trace replay.
CHARACTERISATION OF PARTICULATE EPOXY COMPOSITES

Among composites, the polymer matrix ones are the cheapest and the easiest to form but they show major disadvantages such as poor electrical and thermal conductivity, low fire resistance etc. In the case of any composite, some of the properties may be designed, some of them may be obtained by using an appropriate forming technique and, at least, some of them may be improved by special treatments. In the case of polymer matrix composites the first two ways are recommended if we are taking into account the polymers’ properties while the last one will turn the PMC into an expensive material due to the costs of metal or oxide thin film deposition on polymeric surface. This study is about changing the polymer properties using appropriate fillers.
Production of polyethylene/carbon nanotube nanocomposite using mechanical milling process and investigation of its microstructure Abstract No:107

In the current research polyethylene/carbon nanotube nanocomposite produced using mechanical milling method. For doing this a high energy ball mill with 70 mm in diameter and steel balls with different diameters were employed. The medium density polyethylene (MDPE) and multi wall carbon nanotube (MWCNT) at constant weight ratio of ball/powders (i.e. 10) were added to the ball mill and milled 10 hours. To clarify the role of both MWCNT content and milling time on morphology of MDPE some ball milled nanocomposites samples were investigated using scanning electron microscope (LEO 1450 VP). To evaluate the role of milling time on the microstructure of produced nanocomposites, a very thin film of MDPE/MWCNTs was prepared and studied by transmission electron microscope (LEO 912 AB). The results of the study of microscopic structure of the MDPE/CNTs nanocomposite materials produced by mechanical milling method show that milling process can be a simple method for producing MDPE/MWCNTs nanocomposite and addition carbon nanotube to MDPE causes to change its morphology at constant milling parameters.