Miracle-dz/UniFAR-FTUs huggingface dataset:
Contention-Aware Adaptive Data Rate for Throughput Optimization in LoRaWAN

In Long Range Wide Area Network (LoRaWAN), the data rate of the devices can be adjusted to optimize the throughput by changing the spreading factor. However, the adaptive data rate has to be carefully utilized because the collision probability, which directly affects the throughput, is changed according to the use of spreading factors. Namely, the greater the number of devices using the same spreading factor, the greater the probability of collision, resulting in a decrease of total throughput. Nevertheless, in the current system, the only criteria to determine the data rate to be adjusted is a link quality. Therefore, contention-aware adaptive data rate should be designed for the throughput optimization. Here, the number of devices which can use a specific data rate is restricted, and accordingly the optimization problem can be regarded as constrained optimization. To find an optimal solution, we adopt the gradient projection method. By adjusting the data rate based on the retrieved set of optimal data rate, the system performance can be significantly improved. The numerical results demonstrate that the proposed method outperforms the comparisons regardless of the number of devices and is close to the theoretical upper bound of throughput.
Solving Three Dimensional and Time Depending PDEs by Haar Wavelets Method

Haar wavelets are applied for solution of three dimensional partial differential equations (PDEs) or time depending two dimensional PDEs. The proposed method is mathematically simple and fast. Two techniques are used in numerical solution, the first based on 2D-Haar wavelets and the second based on 3D-Haar wavelets and we compare them. To demonstrate the efficiency of the method, two test problems (solution of the diffusion and Poisson equations) are discussed. Computer simulation showed that 3D-Haar wavelets are better and closer to the exact solution but it is need to more time from 2D-Haar wavelets.
Major adverse cardiac events and drug-coated balloon size in coronary interventions

Objective In-stent restenosis (ISR) is a feared complication after coronary stent implantation. Drug-coated balloon (DCB) is being promoted as a treatment option for ISR. However, the benefit-risk ratio of DCB length has not been investigated. Longer DCBs release more anti-proliferative drug to the vessel wall; however, they are associated with a higher lesion length and vessel injury. Hypothesis: DCB length is associated with clinical outcome. Methods We analyzed 286 consecutive Pantera Lux (Biotronik, active component Paclitaxel) DCB-treated patients between April 2009 and June 2012. Of them, 176 patients were treated using a 15-mm DCB and 109 were treated using a 20-mm DCB. Baseline characteristics and major adverse cardiac events (MACE; death, myocardial infarction, and target lesion revascularization) during initial hospital stay and a 2-year follow-up period were obtained. Results Patients characteristics such as cardiovascular risk factors, prior diseases, co-medication, clinical presentation, target vessel, and left ventricular function did not differ between the groups. MACE during hospital course was similar [1.7% vs. 2.8%, relative risk (RR) 1.6, 95% confidence interval (CI) 0.3-7.9, p=0.554]. Likewise, at 2-year follow-up, MACE did not differ between the groups (23.2% vs. 27.5%, RR 1.2, 95% CI 0.6-1.5, p=0.408). Conclusion DCB length was not associated with clinical outcome during a 2-year follow-up period.
The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability

Aims To determine the ranges of pre-test probability (PTP) of coronary artery disease (CAD) in which stress electrocardiogram (ECG), stress echocardiography, coronary computed tomography angiography (CCTA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and cardiac magnetic resonance (CMR) can reclassify patients into a post-test probability that defines (>85%) or excludes (<15%) anatomically (defined by visual evaluation of invasive coronary angiography [ICA]) and functionally (defined by a fractional flow reserve [FFR] ≤0.8) significant CAD. Methods and results A broad search in electronic databases until August 2017 was performed. Studies on the aforementioned techniques in >100 patients with stable CAD that utilized either ICA or ICA with FFR measurement as reference, were included. Study-level data was pooled using a hierarchical bivariate random-effects model and likelihood ratios were obtained for each technique. The PTP ranges for each technique to rule-in or rule-out significant CAD were defined. A total of 28 664 patients from 132 studies that used ICA as reference and 4131 from 23 studies using FFR, were analysed. Stress ECG can rule-in and rule-out anatomically significant CAD only when PTP is ≥80% (76-83) and ≤19% (15-25), respectively. Coronary computed tomography angiography is able to rule-in anatomic CAD at a PTP ≥58% (45-70) and rule-out at a PTP ≤80% (65-94). The corresponding PTP values for functionally significant CAD were ≥75% (67-83) and ≤57% (40-72) for CCTA, and ≥71% (59-81) and ≤27 (24-31) for ICA, demonstrating poorer performance of anatomic imaging against FFR. In contrast, functional imaging techniques (PET, stress CMR, and SPECT) are able to rule-in functionally significant CAD when PTP is ≥46-59% and rule-out when PTP is ≤34-57%. Conclusion The various diagnostic modalities have different optimal performance ranges for the detection of anatomically and functionally significant CAD. Stress ECG appears to have very limited diagnostic power. The selection of a diagnostic technique for any given patient to rule-in or rule-out CAD should be based on the optimal PTP range for each test and on the assumed reference standard.
Hybrid Destination-Based Jamming and Opportunistic Scheduling with Optimal Power Allocation to Secure Multiuser Untrusted Relay Networks

In this paper, we investigate secure communication for a dual-hop multiuser relay network, where a source communication with N ( N ≥ 1 ) destinations via an untrusted variable gains relay. To exploit multiuser diversity while protecting source’s confidential message, we first propose a joint destination-based jamming and opportunistic scheduling (DJOS) scheme. Then, we derive closed-form approximated and asymptotic expressions of the secrecy outage probability (SOP) for the considered system with DJOS. Furthermore, we determine an asymptotical optimal power allocation (OPA), which minimizes the asymptotic SOP, to further improve the secrecy performance. Our analytical results show that the achievable secrecy diversity order in terms of SOP with fixed power allocation is min ( 1 , N 2 ) , whereas, with OPA, the achievable secrecy diversity order can be improved up to min ( 1 , 2 N N + 2 ) . This interesting result reveals that OPA can improve the secrecy diversity order of the single-user network. This is intuitive since full diversity order of 1 cannot be achieved when N = 1 , thus leaving some space for OPA to improve the diversity order. Nevertheless, for N ≥ 2 , the effect of OPA is to increase the secrecy array gain rather than the secrecy diversity order since full diversity order 1 has been achieved by the OS scheme. Finally, simulation results are presented to validate our analysis.
A mapping study on living labs: Characteristics, smart cities initiatives, challenges and software architecture aspects

A Living lab (LL) allows the development of systems in a user-driven fashion in realistic environments. Despite the idea behind LL is promising, its development is still an open problem. This occurs in part because the concept of LL is still in development, does not have clear boundaries, and the discussion around the theme transcends many disciplines. We have faced some of these uncertainties when a customer asked us to develop a LL environment for Smart Cities. This scenario motivated us to perform a mapping study with the purpose of characterizing LLs, their challenges, domains where they can be applied, and software architecture aspects related to the theme. As result, 51 primary studies were identified and six aspects of them were analyzed: LL characteristics, application domains, LLs smart cities initiatives, challenges, and current evidence on software architecture for LL. The results are beneficial for both researchers and practitioners. For researchers, this work provides information about the current status of LL research, as well as topics that require further investigation. For practitioners, the paper discusses challenges that they need to be prepared to face when developing LLs, as well as aspects related to software architecture and considered application domains. The organized set of information can support development teams on the conception of new LLs.
Effect of Reactive Black 5 azo dye on soil processes related to C and N cycling

Azo dyes are one of the largest classes of synthetic dyes being used in textile industries. It has been reported that 15–50% of these dyes find their way into wastewater that is often used for irrigation purpose in developing countries. The effect of azo dyes contamination on soil nitrogen (N) has been studied previously. However, how does the azo dye contamination affect soil carbon (C) cycling is unknown. Therefore, we assessed the effect of azo dye contamination (Reactive Black 5, 30 mg kg−1 dry soil), bacteria that decolorize this dye and dye + bacteria in the presence or absence of maize leaf litter on soil respiration, soil inorganic N and microbial biomass. We found that dye contamination did not induce any change in soil respiration, soil microbial biomass or soil inorganic N availability (P > 0.05). Litter evidently increased soil respiration. Our study concludes that the Reactive Black 5 azo dye (applied in low amount, i.e., 30 mg kg−1 dry soil) contamination did not modify organic matter decomposition, N mineralization and microbial biomass in a silty loam soil.
Capecitabine and Temozolomide in Neuroendocrine Tumor of Unknown Primary

Incidence of low grade well-differentiated neuroendocrine tumors (NET) is on the rise. The North American Neuroendocrine Tumor Society estimates that the United States has more than 150,000 gastroenteropancreatic NET patients. About 10% of metastatic NETs can be unknown primary, and due to their rarity, dedicated treatment algorithms and regimens are not defined. Combination of capecitabine and temozolomide (CAPTEM) is one of the systemic treatments used in gastroenteropancreatic NETs. We explored clinical activity of CAPTEM in NET of unknown primary. Methods. Retrospective review of NET of unknown primary managed at the University of Kentucky over the past five years (2012–2016). Result. 56 patients with NET of unknown primary were identified; 12 patients were treated with CAPTEM. Median progression-free survival on CAPTEM in grade II and grade III NET of unknown primary was 10.8 and 7 months, respectively. Six patients showed reduction in metastatic tumor volume at three-month CT scan. Three patients had stable disease and three patients showed disease progression at the first surveillance scan. Common side-effects were as follows: four patients developed grade II thrombocytopenia, three patients developed grade I lymphocytopenia, and two patients developed hand foot syndrome (grades I and III). Six patients developed grade I fatigue. Conclusion. CAPTEM should be considered for grades I and II NET of unknown primary, especially in the case of visceral crisis or bulky disease.
The Three-dimensional Arc h itecture of the EJC Core

0022-2836/$ see front matter © 2006 E The exon junction complex (EJC) is a macromolecular complex deposited at splice junctions on mRNAs as a consequence of splicing. At the core of the EJC are four proteins: eIF4AIII, a member of the DExH/D-box family of NTP-dependent RNA binding proteins, Y14, Magoh, and MLN51. These proteins form a stable heterotetramer that remains bound to the mRNA throughout many different cellular environments. We have determined the three-dimensional (3D) structure of this EJC core using negative-stain random-conical tilt electron microscopy. This structure represents the first structure of a DExH/D-box protein in complex with its binding partners. The EJC core is a four-lobed complex with a central channel and dimensions consistent with its known RNA footprint of about ten nucleotides. Using known X-ray crystallographic structures and a model of three of the four components, we propose a model for complex assembly on RNA and explain how Y14:Magoh may influence eIF4AIII's RNA binding. © 2006 Elsevier Ltd. All rights reserved.
Comparing the Hospital-Acquired Condition Reduction Program and the Accreditation of Cancer Program: A Cross-sectional Study

Under the Hospital-Acquired Condition Reduction Program (HACRP), introduced by the Affordable Care Act, the Centers for Medicare and Medicaid must reduce reimbursement by 1% for hospitals that rank among the lowest performing quartile in regard to hospital-acquired conditions (HACs). This study seeks to determine whether Accredited Cancer Program (ACP) hospitals (as defined by the American College of Surgeons) score differently on the HACRP metrics than nonaccredited cancer program hospitals. This study uses data from the 2014 American Hospital Association Annual Survey database, the 2014 Area Health Resource File, the 2014 Medicare Final Rule Standardizing File, and the FY2017 HACRP database (Medicare Hospital Compare Database). The association between ACPs, HACs, and market characteristics is assessed through multinomial logistic regression analysis. Odds ratios and 95% confidence intervals are reported. Accredited cancer hospitals have a greater risk of scoring in the Worse outcome category of HAC scores, vs Middle or Better outcomes, compared with nonaccredited cancer hospitals. Despite this, they do not have greater odds of incurring a payment reduction under the HACRP measurement system. While ACP hospitals can likely improve scores, questions concerning the consistency of the message between ACP hospital quality and HACRP quality need further evaluation to determine potential gaps or issues in the structure or measurement. ACP hospitals should seek to improve scores on domain 2 measures. Although ACP hospitals do likely see more complex patients, additional efforts to reduce surgical site infections and related HACs should be evaluated and incorporated into required quality improvement efforts. From a policy perspective, policy makers should carefully evaluate the measures utilized in the HACPR.
Evolution of proteomic biomarker for chronic liver disease

Liver is the vital organ for synthesis of proteins whose concentration in blood reflects liver dysfunction. Variations in protein domain can generate clinically significant biomarkers. Biomarker pipeline includes discovery of candidates, qualification, verification, assay optimization, and validation. Advances in proteomic approach can discover protein biomarker candidates based on “up-or-down” regulation or fold change in expression which is correlated with disease state. Despite numerous biomarker candidates been discovered, only few are useful in clinical practice which indicates the need for well-established validation regimen. Hence, the main purpose of this review is to understand the protein biomarker development and pitfalls. Companion diagnostics provide insights into potential cost-effective diagnosis for chronic liver disease.
Skull Sexual Monomorphism in the Gwembe Dwarf Goat Breed from Zambia

This study involved some morphometric parameters of the skull of thirty adult Gwembe Dwarf Goats (15 males and 15 females) without any apparent skeletal disorders. Lower jaws were not included in this study. A total of 43 linear measurements were analyzed. The analysis reflected no differences between sexes, thereby indicating lack of general conformation differences between males and females. However, comparison of the parameters that exhibited most variance revealed that horncore basal circumference and length of the horn core on the front margin were strongly influenced by sex. Therefore, only the horn conformation could be used as discriminator sexual variable. This result couples with the fact that males usually have stronger and larger horns than females but skull size seeming no to be very different between sexes. The obtained results would indicate a marked sexual monomorphism in this breed.
A Novel Memristor-Based Circuit Implementation of Full-Function Pavlov Associative Memory Accorded With Biological Feature

In this paper, a memristor-based Pavlov associative memory circuit accorded with the biological feature is proposed. The proposed circuit mainly consists of neuron module, synapse module with synaptic weight measurement function and feedback module. Variety of learning and forgetting functions are considered. The condition of learning is that the food neuron and ring neuron are activated simultaneously. There are three types of forgetting conditions, which represent different forms of forgetting, respectively. The first (second) type of forgetting condition is that only food (ring) neuron is activated. The third type of forgetting condition is that neither food neuron nor ring neuron is activated. In addition, the rates of learning and forgetting are variable. After loops of learning and forgetting, further learning will be more skillful. In other words, the rate of learning a forgotten knowledge will be faster than before. This bionic feature is similar to human. It is implemented by changing learning and forgetting voltages by adding the feedback voltage from feedback module to them. Functions mentioned above are demonstrated by simulation results in PSPICE software.
Learning to Recommend Related Entities With Serendipity for Web Search Users

Entity recommendation, providing entity suggestions to assist users in discovering interesting information, has become an indispensable feature of today’s Web search engine. However, the majority of existing entity recommendation methods are not designed to boost the performance in terms of serendipity, which also plays an important role in the appreciation of users for a recommendation system. To keep users engaged, it is important to take into account serendipity when building an entity recommendation system. In this article, we propose a learning to recommend framework that consists of two components: related entity finding and candidate entity ranking. To boost serendipity performance, three different sets of features that correlate with the three aspects of serendipity are employed in the proposed framework. Extensive experiments are conducted on large-scale, real-world datasets collected from a widely used commercial Web search engine. The experiments show that our method significantly outperforms several strong baseline methods. An analysis on the impact of features reveals that the set of interestingness features is the most powerful feature set, and the set of unexpectedness features can significantly contribute to recommendation effectiveness. In addition, online controlled experiments conducted on a commercial Web search engine demonstrate that our method can significantly improve user engagement against multiple baseline methods. This further confirms the effectiveness of the proposed framework.
Overview and Critical Appraisal of Arterial Spin Labelling Technique in Brain Perfusion Imaging

Arterial spin labelling (ASL) allows absolute quantification of CBF via a diffusible intrinsic tracer (magnetically labelled blood water) that disperses from the vascular system into neighbouring tissue. Thus, it can provide absolute CBF quantification, which eliminates the need for the contrast agent, and can be performed repeatedly. This review will focus on the common ASL acquisition techniques (continuous, pulsed, and pseudocontinuous ASL) and how ASL image quality might be affected by intrinsic factors that may bias the CBF measurements. We also provide suggestions to mitigate these risks, model appropriately the acquired signal, increase the image quality, and hence estimate the reliability of the CBF, which consists an important noninvasive biomarker. Emerging methodologies for extraction of new ASL-based biomarkers, such as arterial arrival time (AAT) and arterial blood volume (aBV), will be also briefly discussed.
Study and analysis of feature based automatic music genre classification using Gaussian mixture model

Music genre classification is a very popular tool used extensively in music industry all over the world. The information of rhythmic structure, instrumentation, and harmonic content about the music signal is visualized with the aid of genre. The task of genre classification was performed manually in early days. With the advancements in technology and signal processing algorithms, this task can now be done automatically. This paper reports the genre classification of audio signal by using multiple feature sets. Mel-Frequency Cesptral Coefficients (MFCCs), Spectral Rolloff, Time Domain Zero Crossings and Flux are the features used in this work. Gaussian mixture model (GMM) based classifier is employed for the final classification task. Simulation results are provided based on standard genre database using MATLAB. It is observed that the percentage classification improves when more number of features are used.
A Neural Approach to Cross-Lingual Information Retrieval

With the rapid growth of world-wide information accessibility, cross-language information retrieval (CLIR) has become a prominent concern for search engines. Traditional CLIR technologies require special purpose components and need high quality translation knowledge (e.g. machine readable dictionaries, machine translation systems) and careful tuning to achieve high ranking performance. However, with the help of a neural network architecture, it’s possible to solve CLIR problem without extra tuning or special components. This work proposes a bilingual training approach, a neural CLIR solution allowing automatic learning of translation relationships from noisy translation knowledge. External sources of translation knowledge are used to generate bilingual training data then the bilingual training data is fed into a kernel based neural ranking model. During the end-to-end training, word embeddings are tuned to preserve translation relationships between bilingual word pairs and also tailored for the ranking task. In experiments we show that the bilingual training approach outperforms traditional CLIR techniques given the same external translation knowledge source and it’s able to yield ranking results as good as that of a monolingual information retrieval system. In experiments we investigate the source of effectiveness for our neural CLIR approach by analyzing the pattern of trained word embeddings. Also, possible methods to further improve performance are explored in experiments, including cleaning training data by removing ambiguous training queries, exploring whether more training data will improve the performance by learning the relationship between training dataset size and model performance, and investigating the affect of English queries’ text-transform in training data. Lastly, we design an experiment that analyzes the quality of testing query translation to quantify the model performance in a real testing scenario where model takes manually written English queries as input.