Miracle-dz/UniFAR-FTUs huggingface dataset:
Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano

The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.
GT-Bidding: Group Trust Model of P2P Network Based on Bidding

Due to the lack of trusted third parties as guarantees in peer-to-peer (P2P) networks, how to ensure trusted transactions between peers has become a research hotspot. However, the open and distributed characteristics of P2P networks have brought challenges to network security, and there are problems such as node fraud and unavailability of services in the network. To solve the problem of how to select trusted transaction peers in P2P groups, a new trust model, GT-Bidding, is proposed in this paper. This model follows the bidding process of human society. First, each service peer applies for a group of guarantee peers and carries out credit mortgages for this service. Second, based on the entropy and TOPSIS method (Technology for Order Preference by Similarity to an Ideal Solution) approaching the ideal solution, a set of ideal trading sequences is selected. Then, the transaction impact function is used to assign weights to the selected guarantee peers and service nodes, respectively; thus, the comprehensive trust of each service node can be calculated. Finally, the service peer is verified using feedback based on the specific confidence level, which encourages the reputation of the service and its guarantee peers to update. Experiments show that GT-Bidding improves the successful transaction rate and resists complex attacks.
Examining the Interplay Between Big Data and Microservices - A Bibliometric Review

Due to the ever increasing amount of data that is produced and captured in today’s world, the concept of big data has risen to prominence. However, implementing the respective applications is still a challenging task. This holds especially true, since a high degree of flexibility is desirable. One potential approach is the utilization of novel decentralized technologies, as in the case of microservices to construct such big data analytics solutions. To obtain an overview of the current situation regarding the corresponding research, using the scientific database Scopus and its provided tools for search and analytics, this bibliometric review provides an analysis of the literature and subsequently discusses avenues for future research.
Characterization of 3,4-methylenedioxypyrovalerone discrimination in female Sprague–Dawley rats

3,4-Methylenedioxypyrovalerone (MDPV), one of several synthetic cathinones, is a popular constituent of illicit ‘bath salts’. In preclinical studies utilizing drug discrimination methods with male rodents, MDPV has been characterized as similar to both cocaine and 3,4-methylenedioxymethamphetamine-hydrochloride (MDMA). Whereas few drug discrimination studies have utilized female rats, the current study evaluated the discriminative stimulus effects of MDPV in 12 adult female Sprague–Dawley rats trained to discriminate 0.5 mg/kg MDPV from saline under a fixed ratio 20 schedule of food reinforcement. Stimulus substitution was assessed with MDPV and its enantiomers, other synthetic cathinones [alpha pyrrolidinopentiophenone-hydrochloride(α-PVP), 4-methylmethcathinone (4-MMC)], other dopamine agonists (cocaine, [+)-methamphetamine] and serotonin agonists [MDMA, lysergic acid diethylamide (LSD)] Stimulus antagonism was assessed with the dopamine D1 receptor antagonist, Sch 23390 and the D2 receptor antagonist, haloperidol. Cocaine and (+)-methamphetamine engendered full stimulus generalization to MDPV with minimal effects on response rate. LSD produced partial substitution, whereas MDMA and 4-MMC produced complete substitution, and all these serotonergic compounds produced dose-dependent response suppression. (S)-MDPV and α-PVP engendered full substitution with similar potency to the racemate, while (R)-MDPV failed to substitute up to 5 mg/kg. Both Sch 23390 and haloperidol attenuated the discrimination of low MDPV doses and essentially shifted the dose-response curve to the right but failed to block discrimination of the training dose. These findings are generally consistent with previous reports based exclusively on male rodents. Moreover, they confirm the contribution of dopaminergic mechanisms but do not rule out the possible contribution of other neurotransmitter actions to the interoceptive stimulus effects of MDPV.
Behavior of Growth and Yield Bread Wheat by the Influence of Fulvic Acid and Seeing Rate

A field experiment was carried out during winter season of 2019-2020 at College of Agricultural, University of Al-Qasim Green, Hila, Babil, Iraq to study the behavior of growth and yield bread wheat (Triticum aestivum L. var. AlRasheed) by the influence of fulvic acid and seeding rate. Randomized complete block design RCBD arranged according to split plots used at three replicates. The experiment included three seeding rate (80, 120 and 160) Kg ha-1 within main plots and spraying two fulvic acid concentration (3 and 6) ml L-1 in addition to control treatment (distilled water spraying) within sub plots. The results showed that the 160 Kg ha-1 seeding rate was significantly superior and gave the highest means of flag leaf area (56.9 cm2), number of grain (44.34 grain spike-1), spike weight (3.84 g spike-1), grain yield (4.273 ton ha-1) and biological yield (17.84 ton ha-1) without significant difference on the 120 Kg ha-1 in these character except spike weight. Also, the spraying of fulvic acid at 6 mg L-1 was significantly superior and had the highest means of plant height (80.89 cm), number of grain (44.09 grain spike-1), spike weight (3.50 g spike-1), 1000 grain weight (33.60 g), grain yield (4.162 ton ha1) and biological yield (17.14 ton ha-1) without significant difference on the spraying of fulvic acid at 3 mg L-1 which had the highest mean of flag leaf area (55.8 cm2) spike weight (3.48 g). The interaction between the two factors had significant effect on all characters except spike length. We can conclude that the spraying of fulvic acid improved wheat yield by providing the necessary factors for growth and development under biotic stress conditions.
A predictive large neighborhood search for the dynamic electric autonomous dial-a-ride problem

The dynamic electric autonomous Dial-a-Ride Problem (e-ADARP) is a generalization of the dial-a-ride problem which employs electric Autonomous Vehicles (e-AVs) to provide shared rides to on-line requests. The goal of the dynamic e-ADARP is to maximize the number of served requests while minimizing operational cost and user excess ride time. To reach this goal, meta-heuristics are designed to modify vehicle-trip assignments as information reveals over time. Dif-ferently from human-driven vehicles, e-AVs can be re-routed as often as desired in the course of operations. Given the on-line nature of the problem, plan modiﬁcations need to be efﬁciently performed to timely notify users and provide new instructions to the vehicles. In this work, we present a new extension to the family of Large Neighborhood Search (LNS) metaheuristics, which employs a machine learning component to select destroy/repair couples from a pool of competing algorithms. At each iteration, the machine learning component predicts the objective function improvement that is expected to be obtained after the employment of each of the competing algorithms. The destroy/repair couple is consequently drawn according to the expected improvement proportions. Worsening solutions are also considered and drawn with the same likelihood of descent solutions. The proposed metaheuristic is denoted by Predictive Large Neighborhood Search (PLNS) and is employed to efﬁciently solve dynamic e-ADARP instances. Computational results are performed on 244 100-request dynamic instances from Uber Technologies Inc. Results show that PLNS outperforms the state-of-the art in the context of on-line operations.
Bachelor Thesis: Attention! - Learning Link State Routing Protocols

The thesis is part of a larger project with the goal, to use deep learning to learn a routing protocol in spirit of link-state routing protocols1. This protocol should be tailored to a specific network and learn specific forwarding policies through supervised-learning. The learned protocol has two goals: • Learn the link state advertisements, calledHidden Link State Advertisements (HLSAs)2 • Learn for each router, which routers in the network actually need its HLSA to make forwarding decisions. To achieve those goals, the used neural-network architecture makes heavy use of (self-) attention, multi-head attention [3] and graph-attention [4]. The remainder of this document is as follows: Sec. 2 introduces the representation of nodes in the graph. Sec. 3 describes the learning tasks. Sec. 4 introduces the different neural network architectures that should be implemented and evaluated during the thesis. Sec. 5 introduces the concrete research questions that should be answered in your thesis.
Susceptibility response of varieties and local lupine Lupinus albus L. population to Bruchus rufimanus Boheman, 1833 (Coleoptera: Chrysomeloidae)

This study aimed to evaluate the susceptibility response of varieties and local populations of lupine Lupinus albus to broadbean seed beetle Bruchus rufimanus in multi-environment field tests. Seed damage rate and susceptibility index were assessed in each environment and subjected to a heritability-adjusted genotype and genotype x environment biplot analysis. It was found that the susceptibility index of damaged seeds was positively correlated to precipitation amount and humidity, and inversely to minimal and maximal temperature values. The seed damage rate was positively correlated to temperatures but negatively to rain and humidity. The local Polish population WAT and cultivars Pink Mutant, Solnechnii, and Bezimenii 1 had the lowest seed damage rate and stable position across environments. Meanwhile, these cultivars showed a low susceptibility index and low variability. The discrepancy between the early phenological development of Pink Mutant, Solnechnii, and Bezimenii 1 and the life cycle of B. rufimanus was one of the reasons for the species’ tolerance to B. rufimanus Correlations between damaged seed and susceptibility index as well as the mass of 1000 seeds and sensitivity index were strongly positive and negative, respectively. WAT, Pink Mutant, Solnechnii, and Bezimenii 1 had a clear advantage by defending itself from B. rufimanus attack, which makes them particularly interesting for breeding purposes.
Influence of pulse width on decolorization efficiency of organic dye by discharge inside bubble in water

Decolorization of an organic dye by discharge in high conductive water using a pulsed power generator and a discharge reactor was investigated. The discharge reactor consisted of a glass tube and a tungsten wire inserted into the glass tube, which was immersed in the water. Room air was injected into the glass tube to generate bubbles in the water. High voltage pulses were generated by an inductive-energy storage system using semiconductor opening switch (SOS) and by a magnetic pulse compression circuit. Fast recovery diodes were used as SOS diode in the inductive-energy storage system. The pulse width was changed in range from 10 to 1200 ns. The high voltage was applied to the tungsten wire. Indigo carmine was employed as a specimen to evaluate decolorization efficiency. Potassium nitrate was used to adjust the solution conductivity. The dye solution was successfully decolorized at 7 mS/cm conductivity. Energy efficiency for decolorization increased from 0.680 to 55.6 mg/Wh with decreasing the pulse width from 1200 to 10 ns owing to the reduction of ohmic loss.
Online Multi-Agent Supervisory Control for Warehouse Automation: Heterogeneous Payloads

—In this paper we consider a problem of controlling multi-agent discrete-event systems to serve tasks dynamically appearing in the environment, where the agents generally have different capacities (or heterogeneous payloads) to serve different numbers of tasks. To solve this problem, we propose an effective online supervisory control approach combined with optimal task assignment which minimizes the total weighted distances from the heterogeneous agents to multiple tasks. The weighted distance takes into account a realistic aspect that traveling the same distance may take different times depending on whether the agents are purely moving straight or involving making turns. We then apply this online control scheme to model and control a warehouse automation system using multiple mobile robots with heterogeneous payloads; the effectiveness of this scheme is demonstrated on a case study.
Do Donor Motives Matter? Investigating Perceptions of Foreign Aid in the Conflict in Donbas --

How do the perceived motives of donor states shape recipient attitudes toward foreign aid in a conflict zone? This research note evaluates the impact of two frames that characterize the motives of foreign powers involved in a civil conflict in the Donbas region of eastern Ukraine. These frames portray foreign actors as providing aid either to alleviate suffering during conflict (humanitarian frame) or to increase their power and influence in the recipient country (political influence frame). We demonstrate how framing impacts attitudes toward foreign assistance from the European Union and the Russian government among potential aid recipients in the Donbas. The results show that frames impact support for foreign aid from the European Union but have no effect on views of Russian aid. Counter to conventional expectations, aid provided for geopolitical, strategic reasons may be viewed as a positive, stabilizing force—even more than foreign aid provided for humanitarian reasons.
TYPE: ORIGINAL ARTICLE TITLE: KNOWLEDGE OF CHRONIC KIDNEY DISEASE AMONG NON-NEPHROLOGY NURSES IN AKURE, SOUTHWEST, NIGERIA

BACKGROUND: Health workers require adequate knowledge of chronic kidney disease (CKD) to be able to play their role in reducing the burden of CKD. Most previous studies focused on assessing knowledge of doctors on CKD, however nurses are also important in primary, secondary and tertiary prevention of CKD. AIM: This study assessed the knowledge of non-nephrology nurses on CKD with the aim of identifying areas of knowledge gaps which will be targets for future educational programs. METHODS: This was a cross-sectional descriptive study carried out among nurses in Akure, Southwest Nigeria during their mandatory continuing professional development program required for practicing license renewal. Knowledge of CKD was assessed using selfadministered pretested questionnaires. P-value of <0.05 was taken as significant RESULTS: One-hundred nurses participated in the study with a male:female ratio of 1:3.7. The mean duration of their nursing experience was 14.5±9.1 years. Only 15 % had nephrology posting during their training. Six (6%) of the respondents had good knowledge of CKD, 55 (55%) had fair knowledge and 37 (37%) had poor knowledge. Only 5% was aware of renal care policy in Nigeria. Junior and intermediate cadre nurses had better knowledge of CKD than senior cadre nurses (p=0.004). Nurses who had nephrology posting during their training had significantly higher mean knowledge score than others (14.38±2.25vs12.93±3.10, p= 0.036). CONCLUSION: There were significant deficiencies in the knowledge of CKD among nonnephrology nurses who participated in the study. Junior and intermediate cadre nurses and those who had nephrology postings had better knowledge of CKD.
Eliminating Bias in Treatment Effect Estimation Arising from Adaptively Collected Data

It is well understood that bandit algorithms that collect data adaptively balancing between exploration and exploitation can achieve higher average outcomes than the "experiment first, exploit later" approach of the traditional treatment choice literature. However, there has been much less work on how data arising from such algorithms can be used to estimate treatment effects. This paper contributes to this growing literature in three ways. First, a systematic simulation exercise characterizes the behavior of the standard average treatment effect estimator on adaptively collected data: I show that treatment effect estimation suffers from amplification bias and illustrate that this bias increases in noise and adaptivity. I also show that the traditional correction method of inverse propensity score weighting (IPW) can even exacerbate this bias. Second, I suggest an easy-to-implement bias correction method: limiting the adaptivity of the data collection by requiring sampling from all arms results in an unbiased IPW estimate. Lastly, I demonstrate a trade-off between two natural goals: maximizing expected welfare and having a good estimate of the treatment effect. I show that my correction method extends the set of choices regarding this trade-off, yielding higher expected welfare while allowing for an unbiased and relatively precise estimate. ∗I thank Róbert Lieli for advice. Gábor Békés, Marc Kaufmann, Dániel Kehl, Gábor Kézdi, Miklós Koren, Sergey Lychagin, Arieda Muço, Jenő Pál, Sándor Sóvágó, Anthony Strittmatter, and participants of the Brown Bag Seminar at Central European University, Big Data Econometrics Seminar at the University of Aix-Marseilles, and the summer workshop of the Hungarian Society of Economics for doctoral students provided helpful comments. The simulation code is available on GitHub. †divenyi_janos@phd.ceu. edu
A mini review on factors affecting particle size, sphericity and pore size of mesoporous silica nanoparticles

There are two main classes of mesoporous silica nanoparticles (MSN) and these are M41S and SBA. They have diverse fields of applications such as drug delivery, catalysis, separation and sorption. In their synthesis the major types of reagents used are silica precursor, structure directing agent/surfactant, catalyst and water. This mini review elaborates on the conditions which could affect particle size, sphericity, pore size and pore wall thickness of MSN in relation to molar ratios of the reagents, synthesizing conditions, use of cosurfactants and cosolvents.
Root proteome alterations in sugarcane promoted by the regrowth cycle in commercial production

The decrease in agricultural productivity in successive cutting of sugarcane plants is associated with several extrinsic and intrinsic factors. However, no studies have focused on the physiological potential of sett roots in successive cuts in sugarcane culture. There have been no proteomic studies on sugarcane sett roots at different stages of cutting. In this study, the UPLC-ESI-TOF-MS system and bioinformatics tools were used to identify proteins of sett roots in the first and fifth cuts of sugarcane cultivar RB966928 in the sprouting stage. Differences in the proteome of sett roots of RB966928 in the first and fifth cuts detected in this study supports the hypothesis that the proteome of sett roots may change after successive cuts in sugarcane culture. A reduction in the number of proteins was observed in the roots of the fifth cut, whereas 34% of proteins, identified exclusively in the first cut, were absent in the fifth cut. Proteome analysis of sett roots in the first and fifth cuts showed that the changes after successive cuts were quantitative (number of proteins) and mainly qualitative. In this study, the detailed list of proteins identified in the first cut but absent in the fifth cut is relevant. The findings of this study may aid further research that employ biotic or abiotic elicitors to induce gene expression of essential proteins absent in sett roots of the fifth cut, and thus increasing the agricultural productivity and longevity of cane fields
Measuring urban dynamics in port-cities using an agent-based cellular automata land-use and transport interaction model

The urban environment is a complex system where general urban morphology patterns emerge from the local interactions of many agents. Examination on these interactions are crucial to enable successful transport and urban planning. While cellular automata (CA) models are suitable for modelling this self-organisation behaviour, measuring individual interactions from empirical data is still complicated due to their auto-correlation and to path-dependence in urban systems evolution. Due to this, manual methods are more often used in the calibration of urban land-use and transport interaction (LUTI) models based on CA, limiting the results to a few explored settlements. This paper therefore presents a method for applying the gradient-descent algorithm with momentum to calibrate an agent-based LUTI-CA model by representing the model as an optimisation problem. This process allowed efficient calibration for 46 port-related settlements and 10 non-port settlements across Great Britain, enabling cross-sectional analysis of whether observed relationships are unique to a study area due to its path-dependence, or if they have similarities with results from other study areas. We found unique characteristics of urban dynamics separating port from non-port settlements in smaller urban systems, while the distinctions between the two groups were less prominent in larger settlements. However, there were variations in urban dynamics between larger settlements with respect to the effects of their services and manufacturing activities on other land-uses. The automated calibration of agent-based urban CA models in this paper enabled the quantification of these urban dynamics in multiple settlements, providing the ability to look beyond the uniqueness of individual case studies to find general patterns of interaction. This paper therefore provides improvement in the calibration of LUTI models based on CA and contributes to the better understanding of the dynamics between port and urban systems.
The importance of sensory perception in an elephant's cognitive world

The three extant species of the family Elephantidae — Asian (Elephas maximus), African savanna (Loxodonta africana), and African forest (L. cyclotis) elephants — have long trajectories of independent evolution. The African species diverged from the Asian species more than 6 million years ago (Rogaev et al., 2006), and the two African species diverged from each other more than 1.9 million years ago (Rohland et al., 2010). Although these species inhabit somewhat different ecosystems, they are all generalist feeders and consume a wide variety of grasses and browse depending on resource availability (Hatt & Clauss, 2006). All species are social, with the main social unit centering on adult female relatives and their offspring (Buss & Smith, 1966; McKay, 1973). The oldest female leads the group consistently in African savanna elephants (Douglas-Hamilton & Douglas-Hamilton,