Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Uplink NOMA in D2D Communications

In this work, we propose and investigate a novel hybrid cellular and bidirectional device-to-device (D2D) transmission scheme that exploits cooperative non-orthogonal multiple access (termed as BD2D-CNOMA). In BD2D-CNOMA, two far users can transmit their messages to the base station (BS) via a relaying node, while simultaneously they exchange D2D messages. Analytical expressions for the ergodic capacity, ergodic sum capacity (ESC) and outage probability of all data streams are derived under both perfect and imperfect successive interference cancellation (SIC). Furthermore, it is proved that ESC under perfect SIC scales as <inline-formula> <tex-math notation="LaTeX">$\frac {2}{3}\log _{2}\rho $ </tex-math></inline-formula> as transmit signal-to-noise ratio <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> increases. Simulation results validate the accuracy of the presented analysis, illustrate the superiority of BD2D-CNOMA over conventional benchmarks under perfect SIC and provide useful insights for the case of imperfect SIC.
Adaptation of an Emotional Stroop Test for Screening of Suicidal Ideation in Portugal

Cognitive instruments, especially those with emotional components, may be useful to address the limitations of self-report scales commonly used to assess suicidal ideation. The aim of this study was to develop an emotional Stroop test for screening suicidal ideation in Portugal. The project was developed in five phases using different samples for each phase. The first two phases were focused on the formulation of the potential words that would compose the slides. For this purpose, five biology teachers (neutral slide) and five mental health professionals (positive and negative slides) were invited to help choose the words that were most representative for each slide. The third phase validated the words defined in the previous phase. In this phase, 300 university students participated (Mage = 21.66; SD = 3.67; 68% female). They rated the words on a Likert scale in terms of their frequency of use, familiarity, level of understanding, and degree of image evocation. In the fourth phase, the researchers developed the complete version of the test, which consists of three slides with neutral, positive, and negative emotional stimuli, consecutively. Finally, in the fifth phase, we validated the final version of the test through a comparative study between a clinical group and a non-clinical group, each one composed by 50 participants (Mage = 32; SD = 9.70; 55% female). Results indicated that the clinical group demonstrated significantly higher scores for depression and suicidal ideation and lower scores for the three Stroop tasks. Words related to negative emotions were strongly correlated with suicidal ideation. Finally, the three Stroop slides explained 74.1% of the variance in suicidal ideation. These findings suggest that this test can be a viable complementary measure in the psychological assessment of suicide ideation, and intervention in the field of suicide prevention in Portugal.
Nutritional Support Following Traumatic Brain Injury: A Comprehensive Review

Traumatic brain injury (TBI) can contribute to extensive dysbiosis of the gastrointestinal system, leading to worsened outcomes. The importance of nutrition in recovery is underappreciated but highly important. In this focused review, we discuss the timing of nutritional interventions with supporting data. We highlight routes of administration that are important given the extent of injury often seen in TBI. The increased energy demands can be met through these approaches. Furthermore, patients need increased vitamins, minerals, and supplements. These interventions are constantly being refined. The current standards are reviewed with an emphasis on evidence-based practices.
Self-Supervised Learning for CSI Compression in FDD Massive MIMO Systems

In frequency division duplexing systems, the user equipment frequently reports the channel state information (CSI) to the base station. This information is crucial for link adaptation and beamforming tasks in massive MIMO systems. However, it consumes a large amount of bandwidth when the number of antennas and subcarriers increases. Currently, compression techniques have been used to reduce this bandwidth overhead. In this letter, we propose a novel learning-based technique for CSI compression. Our method exploits the bias/variance tradeoff for CSI compression based on a shallow neural network to approximate a sufficient statistic function for each sampled channel. Unlike prior work that adopts projection-based techniques for compression, our method interprets the model weights as the compressed representation. The experimental results confirm the advantages of the proposed method compared with other state-of-the-art models.
Towards Serendipity for Content–Based Recommender Systems

Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item. One of the fundamental recommendation methods in the content-based method that predict ratings by exploiting attributes about the users and items such as users’ profile and textual content of items. A current issue faces by recommender systems based on this method is that the systems seem to recommend too similar items to what users have known. Thus, creating over-specialisation issues, in which a self-referential loop is created that leaves user in their own circle of finding and never get expose to new items. In order for these systems to be of significance used, it is important that not only relevant items been recommender, but the items must be also interesting and serendipitous. Having a serendipitous recommendation let users explore new items that they least expect. This has resulted in the issues of serendipity in recommender systems. However, it is difficult to define serendipity because in recommender system, there is no consensus definition for this term. Most of researchers define serendipity based on their research purposes. From the reviews, majority shows that unexpected as the important aspect in defining serendipity. Thus, in this paper, we aim to formally define the concept of serendipity in recommender systems based on the literature work done. We also reviewed few approaches that apply serendipity in the content-based methods in recommendation. Techniques that used Linked Open Data (LOD) approaches seems to be a good candidate to find relevant, unexpected and novel item in a large dataset.
The Metabolic Pathway of Cardiac Troponins Release: Mechanisms and Diagnostic Role

Modern methods of cardiac troponin determination have enabled early diagnosis of acute myocardial infarction (AMI) and selection of optimal treatment tactics for patients early from admission. It has markedly improved the further prognosis of these patients. Unfortunately, there are a number of problems arising from the use of high-sensitivity cardiac troponins: frequent and unexplained increases in serum troponin levels in a number of pathological conditions not associated with AMI; insufficient study of mechanisms of release and increase, features of circulation and elimination of cardiac troponins; inconsistent data on the influence of several factors (circadian, gender and age characteristics), on cardiac troponin levels. All this may be accompanied by difficulties and errors in differential diagnosis as well as insufficient use of the diagnostic potential of cardiac troponins. In general, these problems are due to our insufficient understanding of the metabolic pathway of cardiac troponins. This review briefly discusses the main stages of the metabolic pathway of cardiac troponins and focuses in detail on the first stage of metabolism (the release of cardiac troponins).
Detection Method for Randomly Generated User IDs: Lift the Curse of Dimensionality

Internet services are essential to our daily life in these days, and user accounts are usually required for downloading or browsing for multimedia contents from service providers such as Yahoo, Google, YouTube and so on. Attackers who perform malicious actions against these services use fake user accounts to hide their identity, or use them to continue malicious actions even after being caught by the service’s detection system. Using a random string generation algorithm for user identification (ID) string is one of the common method to create and obtain a large number of fake user accounts. To detect IDs and to defend against such attacks, some researchers have proposed the models that detect randomly generated IDs. Among these detection models, the <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-gram-based using term frequency-inverse document frequency model is regarded as a state-of-the-art model to detect randomly generated IDs, but <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>-gram-based approaches have the problem of the curse of dimensionality because the sparsity of feature vector increases exponentially with the increase of size <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>. As a result, the improvement of the detection accuracy is limited since size <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> cannot be increased. This paper proposes two methods to detect randomly generated IDs more accurately. The first is to avoid the curse of dimensionality with the compression of feature dimension size. The second is a technique to reduce false positives by using pattern matching and Bhattacharyya distance. We tested our method with about 3 million normal user IDs collected from the real portal service, 1 million IDs generated by a random string generation algorithm, and 8,541 IDs found after being used for malicious behavior in real portal services. The experimental results showed that the proposed method can improve detection accuracy as well as inference performance.
Drug Resistance and Homology of Pathogens of Infective Diarrhea in East Heilongjiang, China

Objective To investigate drug resistance and homology of pathogens that can cause infective diarrhea in East Heilongjiang, China, and provide suitable references for the clinical treatment. Methods The process of isolation and identification of pathogenic bacteria in the faeces and blood of diarrhea patients was conducted. The drug resistance of the isolated pathogenic bacteria was analyzed by VITEK 2 compact drug sensitivity analysis system, and the strain homology was determined by pulsed field gel electrophoresis (PFGE). Results From 2014 to 2018, 28 different strains of pathogenic bacteria were isolated from 657 samples of faeces and blood, including 23 strains of Salmonella, 3 strains of Vibrio parahaemolyticus and 2 strains of diarrheal Escherichia coli, with detection rates of 3.5%, 0.46% and 0.30% respectively. Among them, 22 strains of Salmonella exhibited significant drug resistance, with a drug resistance rate of 95.65%. A total of 11 different PFGE fingerprints of 23 Salmonella strains were also obtained, with a homology of 56.5–100%. Conclusion Among the various pathogens causing infective diarrhea in East Heilongjiang, China, Salmonella displayed multiple drug resistance and its distribution was found to be polymorphic.
Enabling ULV Fully Synthesizable Analog Circuits: The BA Cell, a Standard-Cell-Based Building Block for Analog Design

In this brief we propose a completely novel approach to design robust analog circuits made up only of digital CMOS gates taken from conventional standard-cell libraries. The approach exploits the topology of CMOS NOT, NOR and NAND gates to derive a basic building block, namely basic amplifier (<inline-formula> <tex-math notation="LaTeX">$BA$ </tex-math></inline-formula>) cell, which is equivalent to a CMOS inverter (used as a transconductance amplifier) with an additional input which allows to accurately set its static output voltage and to obtain a balanced input-output voltage transfer characteristic. We propose also a standard-cell-based control loop based on a replica bias technique which, despite PVT variations, allows to guarantee a static output voltage equal to half the supply voltage for all the <inline-formula> <tex-math notation="LaTeX">$BA$ </tex-math></inline-formula> cells used in the analog section of the chip. The approach has been validated through parametric and corner simulations referring to a 130 nm CMOS process and the performed analyses have highlighted that the proposed BA cell attains reliable performance under PVT variations, whereas the conventional inverter gate taken from the standard-cell library performs in a very unreliable way in the same PVT conditions.
Biomimetic Rose Petal Structures Obtained Using UV-Nanoimprint Lithography

This study aims to produce a hydrophobic polymer film by mimicking the hierarchical micro/nanostructures found on the surface of rose petals. A simple and two-step UV-based nanoimprint lithography was used to copy rose petal structures on the surface of a polyurethane acrylate (PUA) film. In the first step, the rose petal was used as a template, and its negative replica was fabricated on a commercial UV-curable polymer film. Following this, the negative replica was used as a stamp to produce rose petal mimetic structures on UV curable PUA film. The presence of these structures on PUA influenced the wettability behavior of PUA. Introducing the rose petal mimetic structures led the inherently hydrophilic material to display highly hydrophobic behavior. The neat PUA film showed a contact angle of 65°, while the PUA film with rose petal mimetic structures showed a contact angle of 138°. Similar to natural materials, PUA with rose petal mimetic structures also displayed the water pinning effect. The water droplet was shown to have adhered to the surface of PUA even when the surface was turned upside down.
Modelling the influence of marine ice on the dynamics of an idealised ice shelf

Abstract Understanding the dynamic behaviour of ice shelves, specifically the controls on their ability to buttress the flow of ice into the ocean, is critical for predicting future ice-sheet contributions to sea level rise. Many large ice shelves, which are predominantly composed of meteoric ice, have a basal layer of marine ice (formed from accumulated platelets at the ice–ocean interface), comprising up to 40% of their thickness locally. Differences in temperature, chemistry and microstructure between marine and meteoric ice mean the rheological properties of the ice vary throughout the ice shelf. These differences are not explicitly accounted for in ice-sheet modelling applications, and may have an important influence on ice shelf dynamics. We tested the sensitivity of a model of an idealised ice shelf to variations in temperature distribution and flow enhancement, and found that incorporating a realistic thermal profile (where the marine ice layer is isothermal) had an order of magnitude greater effect on ice mass flux and thinning than incorporating the mechanical properties of the marine ice. The presence of marine ice at the ice shelf base has the potential to significantly increase deviatoric stresses at the surface and ice mass flux across the front of an ice shelf.
Upper Bounds for Randi´c Spread

The Randi´c spread of a simple undirected graph G , spr R ( G ), is equal to the maximal diﬀerence between two eigenvalues of the Randi´c matrix, disregarding the spectral radius [Gomes et al., MATCH Commun. Math. Comput. Chem. 72 (2014) 249–266]. Using a rank-one perturbation on the Randi´c matrix of G it is obtained a new matrix whose matricial spread coincide with spr R ( G ). By means of this result, upper bounds for spr R ( G ) are obtained.
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Objective To evaluate the impact of National Institute for Health and Care Excellence (NICE) head injury guidelines on deaths and hospital admissions caused by traumatic brain injury (TBI). Setting All hospitals in England between 1998 and 2017. Participants Patients admitted to hospital or who died up to 30 days following hospital admission with International Classiication of Diseases (ICD) coding indicating the reason for admission or death was TBI. Intervention An interrupted time series analysis was conducted with intervention points when each of the three guidelines was introduced. Analysis was stratiied by guideline recommendation speciic age groups
Teaching Computational Thinking via Tangible and Graphical Interfaces – An Overview Through Reflective and Critical Lenses

Deeper learning and conceptual understanding are essential and critical elements for achieving learning process improvement in Computational Thinking (CT) curricula. For successful learning outcomes the school teachers of programming most probably have to familiarise themselves with two essential learning frameworks: i) tangible programming interfaces (TUIs), where real world objects are utilized to fulfil the programming process and assist in children’s abstraction skills; ii) graphical user interfaces (GUIs) or visual programming languages that involve the task of creating a structured program through the combination of graphical elements on a program canvas. Hence, this paper introduces the importance and readiness of teaching the programming concepts comprising CT in a creative and natural way. The authors present the rationale and current formal ways for teaching programming to primary school pupils. After a critical overview of some learning programming environments, the authors present and scrutinize the results between tangible and graphical interfaces (visual learning) from comparative studies.
Efficacy of Prostaglandin F2 Alfa in Diagnosis of Early Pregnancy in Bovines

: The objective of this study was to determine the efficacy and accuracy of a non- luteolytic dose of PGF2 injection on the diagnosis of early pregnancy in Holstein-Friesian cross breed cows. The diagnosis of early pregnancy depended on the increased intra-mammary pressure response due to release of luteal oxytocin induced by intravenous administration of non-luteolytic dose of PGF2 . The study was performed on 32 lactating cows from 18-22 days post insemination, 3 hours after morning milking. The cows were divided into 3 groups, group I (N=11) and II (N=11) were administered with 125µg and 250µg of PGF2 intra venous, respectively, while group III (N=10) was administered with 2ml of normal saline intra jugular and served as control. The pregnancy status was confirmed by rectal examination on 45-60 days post AI. The positive responses of the test were 54.54% (6/11) for group I, 72.72% (8/11) for group II and 0.00% (0/10) for group III. While the negative responses were 45.45% (5/11), 27.27% (3/11) and 100% (10/10) in group I, II and III, respectively. The time interval for test response were 153.91 ± 44.45 and 85.00 ± 16.73 seconds in groups I and II, respectively, which did not differ significantly (P>0.05). Overall test sensitivity and specificity were 75% and 66.67%, respectively. Accuracy of predicting pregnancy and non-pregnancy were 85.71% (12/14) and 50% (4/8), respectively; with an overall diagnostic accuracy of 72.72% (16/22). It was found that the accuracy of the test varied between groups. The test being inexpensive, rapid, easy to interpret, field friendly and does not require highly qualified personnel to perform, can be used for early pregnancy diagnosis with reasonable accuracy.
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*These authors contributed equally to this work Purpose: To investigate the effect of astragaloside IV (AS-IV) on mitochondrial-dependent apoptosis in the dorsal root ganglion of diabetic peripheral neuropathy (DPN) rats through the SIRT1/p53 pathway. Methods: Diabetic rat model was induced by high-carbohydrate/high-fat diet and intraperitoneal injection of STZ. Diabetic rats were divided into three groups (n =16 per group): DPN group, AS-IV group (60mg/kg/d) and α-lipoic acid (ALA) group (60mg/kg/d). Weight and blood glucose levels were monitored every 4 weeks for 12 weeks. DPN was evaluated using the Von Frey Filaments Test and nerve conduction velocity. The dorsal root ganglia of rats were isolated and the pathological changes of mitochondria were observed by electron microscopy. The activity of mitochondrial electron transport chain complex, mitochondrial membrane potential, malonaldehyde (MDA) and glutathione (GSH) levels were measured. Neural apoptosis was detected using the Terminal Deoxynucleotidyl Nick-End Labeling (TUNEL) assay kit. The cleaved caspase-3, major proteins in the SIRT1/p53 pathway, including SIRT1, acetyl p53, Drp1, BAX, and BCL-2, were detected using immunohistochemistry and Western blot. Gene expression of major proteins in the SIRT1/ p53 pathway was also detected. Results: After 12 weeks of treatment, AS-IV and ALA did not significantly affect body weight or fasting glucose levels, but reduced mechanical abnormal pain in DPN and improved nerve conduction velocity. AS-IV and ALA increased the level of GSH and decreased the level of MDA. Both AS-IV and ALA can reduce mitochondrial damage, improve mitochondrial electron transport chain complex activity and mitochondrial membrane potential, and reduce the percentages of positive cells with DNA fragmentation and the expression of cleaved caspase-3 protein. AS-IV and ALA up-regulated the expression of SIRT1 and down-regulated the expression of acetyl-p53, Drp1 and the ratio of BAX to BCL-2. Changes in gene expression were similar. Conclusion: AS-IV can reduce the occurrence of mitochondrial-dependent apoptosis by regulating the SIRT1/p53 pathway. It has a similar therapeutic effect as ALA and is therefore a promising drug for the potential treatment of DPN.
Efficient Optimization for Multi-Objective Decision-Making on Civil Systems Using Discrete Influence Diagram

: The breakdown of civil systems, e.g. bridge networks and water distribution networks, has a significant social and economic impact, highlighting the importance of optimal decision-making on such systems. Modeling and optimization of probabilistic decision-making problems for civil systems, can be facilitated by graphical methodologies such as influence diagram (ID). However, the converging structure in IDs representing civil systems, which relates the random variables standing for component events and that for system event, results in the exponential increase in the number of modeling parameters and variables to be optimized as that of component events increases. In order to address these challenges, in this paper, the recently proposed matrix-based Bayesian network (MBN) is employed to quantify the IDs. To facilitate the optimization process, a proxy objective function is also proposed. The proxy function not only significantly reduces the number of variables to be optimized, but also allows an efficient framework for multi-objective optimization in which the weighted sum of the objectives is optimized to obtain a set of non-dominated solutions. Three numerical examples demonstrate the performance of the proposed methodology.
Regularization of end-to-end learning for cardiac diagnosis by multitask learning with segmentation

Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and management of cardiovascular disease. Deep learning methods have proven to deliver segmentation results comparable to human experts in CMR imaging, however, no successful attempts have been made at fully automated diagnosis. This has been contributed to a lack of sufficiently large datasets required for end-to-end learning of diagnoses. Here we propose to exploit the excellent results obtained in segmentation by jointly training with diagnosis in a multitask learning setting. We hypothesize that segmentation has a regularizing effect on learning and promotes learning of features relevant for diagnosis. Results show a three-fold reduction of the classification error to 0.12 compared to a baseline without segmentation, both results are obtained by training on just 75 cases in a dataset (ACDC) that is equally distributed over 5 classes.