Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband Bandstop Filter Design With High Impedance Folded Stubs

This proposed structure of Wideband Bandstop Filter has stub topology, consists of high impedance folded multiple stubs, which controls Skirt Factor and band stop range. The response of the filter can be controlled by the length, width and space between stubs, and folded stubs can control capacity of storing power between two stubs. This structure of filter has cut-off frequency of 2.14 GHz. Further, for validation of response of the filter, the transmission line model is extracted. The stopband rejection level can be controlled by stubs width, stubs length and spacing between stubs. Three Transmission Zeros (TZ) are present in rejection band region. This proposed filter has 2.14 GHz −4.26 GHz band width with greater than 18.5 dB rejection over the entire stopband range. The Skirt factor is achieved 135dB per GHz. The proposed structure has compact size of $15.5\text{mm}\times 35.8\text{mm}(0.15\lambda \mathrm{g}\times 0.35\lambda \mathrm{g})$ with port length.
Public Sentiments in User Generated Content amid COVID-19 Pandemic in Ghana

Towards post COVID-19 pandemic, a natural language processing (NLP) technique was leveraged to understand the sentiments of Ghanaians through their public discourse in tweets during the lockdown period in Ghana. With NLP resources, feature words were extracted from the tweets and fed into three machine learning algorithms to track public sentiments in the tweets. The algorithms, support vector machines (SVM), naïve-bayes (NB) and artificial neural network (ANN) were evaluated to ascertain their efficacies. Frequently occurring words used by Ghanaians during the lockdown period were extracted to provide more insight into public sentiments. The study revealed that negative sentiments prevailed throughout the COVID-19 lockdown among Ghanaians. However, positive sentiments were surprisingly high at some points during the lockdown period. The result of evaluating the machine learning classifier yielded SVM as the best performing classifier though the other classifiers performed beyond the acceptable threshold. With these findings, it is envisioned that this study will be adopted by policymakers, as a guide, towards public management of public sentiments in pandemics.
Exploring Student Challenges in an Online Project-Based Course

In addition to sound technical skills, industry jobs require soft skills such as teamwork and good organizational abilities. This is even more important in today’s landscape, where working from the office is substituted with working from home. This trend, together with the shift towards distributed teams that are separated geographically and across time zones is expected to create challenges addressed through improved management and soft skills. Organizations of higher learning include the development of capstone or team projects as part of their curricula in order to prepare students for collaborative work and improve their organizational abilities. In this paper, we present an exploratory study focused on the challenges encountered by students in two consecutive iterations of a Team Projects course. The second iteration took place fully online due to the ongoing pandemic. We discovered the lack of face to face communication and schedule synchronization as some of the important challenges reported. We show that mitigating strategies by both teaching staff and the students themselves helped keep communication open and engagement high. We provide a replication package that allows repeating or extending our study.
Intent to Use a Smartphone App as a University-Engagement Tool by Kabarole Farmers in Uganda

Smartphone apps are promising tools for engagement between universities and external stakeholders like dairy farmers. However, there is limited evidence of dairy farmers’ intent to use apps. We use the Technology Acceptance Model to assess dairy farmers’ intent to use a co-created application. A survey of 100 farmers in the Kabarole district-Uganda was conducted, which focused on variables predicting farmers’ intention to use the app. Data were analysed using SPSS and Smart PLS 3 software. Findings show that hedonistic and utilitarian benefits drive farmers’ perceived ease of use and perceived usefulness of the app. Also, the intent to use the app is driven by both types of attitudes. Furthermore, self-belief affects the perceived usefulness but was insignificant on intent to use the app. The findings contribute to practical knowledge of the factors predicting the intent to use digital tools. Future research should focus on the actual usage of the app.
Multivariate analysis of the influence of students' characteristics on academic performance

Modern universities, developing a digital environment, rely on a student-centric approach relevant to pedagogical and didactic practices. Personalization of the educational environment allows improving the quality of the educational process, raising it to a new level. This article presents the usage of multivariate modeling to identify common relationships and patterns between academic performance and psychometric characteristics. The study sample includes psychometric testing data of students from Novosibirsk State Technical University (NSTU), which determines cognitive, motivational and personal characteristics, and also data on their academic performance. The results obtained are further used for diagnostic and prognostic purposes to recommend educational content depending on the student's personality.
Towards the Development of a Realistic Multidimensional IoT Profiling Dataset

The Internet of Things (IoT) is an emerging technology that enables the development of low-cost and energy-efficient IoT devices across various solutions from smart cities to healthcare domains. With such a complex and heterogeneous instance of IoT devices and their applications, numerous challenges arise in both device management and security concerns. Thus, it is essential to develop intelligent IoT identification/profiling and intrusion detection components that are tailored to IoT applications. Such systems require a realistic and multidimensional reference IoT dataset for training and evaluation. Device identification/profiling ensures the authenticity of the devices attached to the IoT network and environment which can be achieved by fingerprinting a device. Since fingerprinting is mostly examined by device network flows and device local attributes, we have proposed this study to intelligently recognize machine-to-machine communication and identify each device properly. In this paper, we analyzed the behaviour of 60 IoT devices during experiments conducted in our lab setup at the Canadian Institute for Cybersecurity (CIC). Our IoT devices include WiFi, ZigBee, and Z-Wave devices. We collected data from each device in four stages: powered on, idle, active, and interactions. Besides these stages, different scenario experiments were conducted using a microcosm of devices to simulate the network activity of a smart home. Additionally, we have generated two attack datasets, namely flood denial-of-service attack and RTSP brute-force attack. Lastly, we implement an extensive case study on the transferability of the RF classifier and train our model with the dataset from our lab, transfer the model to the dataset from a different lab and test the trained model on their dataset. This paper’s dataset materials are available on the CIC dataset page under the CIC IoT dataset 20221.
Ensemble classifier for driver's fatigue detection based on a single EEG channel

Driver's fatigue detection, based on electroencephalography (EEG) signals, is a worthy field of research to study evidence regarding how to exactly pre-warn and avoid casualties nowadays. In this study, an EEG-based system of perfect performance and good stability for evaluating driver's fatigue with only one electrode by ensemble learning method is proposed. Given that EEG signals are unstable and non-linear that using several common entropy measurements to analyse EEG signals is more appropriate including spectral entropy, approximate entropy, sample entropy and fuzzy entropy. In this study, unlike other methods using a single classifier, three ensemble approaches (bagging, random forest and boosting) based on three base classifiers were employed and compared. A driving simulator in this study was used for 12 healthy and adult subjects to perform a continuous simulated driving experiment for 1-2 h. The experimental results show that the proposed method can make use of only one electrode (T6) by gradient boosted DT for driver's fatigue detection, while the average classification accuracy is >94%. The findings of this study indicated that a single EEG channel with optimal ensemble classifier may be a good candidate for usage in the portable system for driver's fatigue detection.
May I Remain Seated: A Pilot Study on the Impact of Reducing Room-Scale Trainings to Seated Conditions for Long Procedural Virtual Reality Trainings

Although modern consumer level head-mounted-displays of today provide high-quality room scale tracking, and thus support a high level of immersion and presence, there are application contexts in which constraining oneself to seated set-ups is necessary. Classroom sized training groups are one highly relevant example. However, what is lost when constraining cybernauts to a stationary seated physical space? What is the impact on immersion, presence, cybersickness and what implications does this have on training success? Can a careful design for seated virtual reality (VR) amend some of these aspects? In this line of research, the study provides data on a comparison between standing and seated long (50–60 min) procedural VR training sessions of chemical operators in a realistic and lengthy chemical procedure (combination of digital and physical actions) inside a large 3-floor virtual chemical plant. Besides, a VR training framework based on Maslow's hierarchy of needs (MHN) is also proposed to systematically analyze the needs in VR environments. In the first of a series of studies, the physiological and safety needs of MHN are evaluated among seated and standing groups in the form of cybersickness, usability and user experience. The results (n=32, real personnel of a chemical plant) show no statistically significant differences among seated and standing groups. There were low levels of cybersickness along with good scores of usability and user experience for both conditions. From these results, it can be implied that the seated condition does not impose significant problems that might hinder its application in classroom training. A follow-up study with a larger sample will provide a more detailed analysis on differences in experienced presence and learning success.
Implementing the rotational restraint effects of surrounding support members into plate buckling strength design

ABSTRACT Plate buckling is a primary criterion for plated-structural design. In current structural design practice, the edges of plate elements are assumed to be simply supported. In reality, however, the plate edges are supported by longitudinal stiffeners and transverse frames, and thus there are some degrees of rotational restraints along the plate edges that are neither zero nor infinite but rather dependent on the torsional rigidity of the support members. The objective of this paper is to develop a method for implementing the rotational restraint effects into the plate buckling strength design. It aims to minimise the structural weight while maximising the plate buckling strength. In this paper, the buckling strength of plate elements subjected to compressive loads is calculated using design formulations. Examples are shown for the buckling strength design of steel plates in a Very Large Crude Carrier class tanker and a naval ship, demonstrating that the structural weight can be reduced by implementing the rotational restraint effects while meeting the plate buckling strength requirements associated with the structural safety design criteria.
LETTER TO THE EDITOR: Lifetime of excitations in a clean Luttinger liquid

Non-linear band dispersion in the Luttinger model of one-dimensional interacting electrons gives rise to collisions between the bosonic sound-like excitations. The decay rate is calculated beyond a plain perturbation theory both at T = 0 and . A self-consistent approach reveals a non-analytical dependence of the lifetime of excitations on the coupling constant, wave vector and temperature.
A Spatially Resolved Study of Hard X-Ray Emission in Kepler’s Supernova Remnant: Indications of Different Regimes of Particle Acceleration

Synchrotron X-ray emission in young supernova remnants (SNRs) is a powerful diagnostic tool to study the population of high-energy electrons accelerated at the shock front and the acceleration process. We performed a spatially resolved spectral analysis of NuSTAR and XMM-Newton observations of the young Kepler’s SNR, aiming to study in detail its nonthermal emission in hard X-rays. We selected a set of regions all around the rim of the shell and extracted the corresponding spectra. The spectra were analyzed by adopting a model of synchrotron radiation in the loss-limited regime, to constrain the dependence of the cutoff energy of the synchrotron radiation on the shock velocity. We identify two different regimes of particle acceleration, characterized by different Bohm factors. In the north, where the shock interacts with a dense circumstellar medium (CSM), we found a more efficient acceleration than in the south, where the shock velocity is higher and there are no signs of shock interaction with the dense CSM. Our results suggest an enhanced efficiency of the acceleration process in regions where the shock–CSM interaction generates an amplified and turbulent magnetic field. By combining hard X-ray spectra with radio and γ-ray observations of Kepler’s SNR, we modeled the spectral energy distribution. In the light of our results we propose that the observed γ-ray emission is mainly hadronic and originates in the northern part of the shell.
Overview on the progress of the conceptual studies of a gamma ray spectrometer instrument for DEMO

The future DEMO tokamak will be equipped with a suite of diagnostics which will operate as sensors to monitor and control the position and operation parameters of DT plasmas. Among the suite of sensors, an integrated neutron and gamma-ray diagnostic system is also studied to verify its capability and performance in detecting possible DEMO plasma position variations and contribute to the feedback system in maintaining DEMO DT plasma in stable conditions. This work describes the progress of the conceptual study of the gamma-ray diagnostic for DEMO reactor performed during the first Work-Package contract 2015–2020. The reaction of interest for this Gamma-Ray Spectrometer Instrument (GRSI) consists of D(T, γ)5He with the emission of 16.63 MeV γ rays. Due to DEMO tokamak design constraints, the gamma and neutron diagnostics are integrated, both featuring multi-line of sight (camera type), viewing DEMO plasma radially with vertical (12) and horizontal (13) viewing lines to diagnose the γ and neutron emission from the DT plasma poloidal section. The GRSI design is based on the investigation of the reaction cross sections, on the calculations performed with GENESIS and MCNP simulation codes and on the physics and geometry constrains of the integrated instrument. GRSI features long collimators which diameters are constrained by the neutron flux at the neutron detectors of the Radial Neutron Camera (RNC) system placed in front, which are key to control DEMO DT plasma position. For these reasons, only few GRSI parameters can be independently selected to optimize its performance. Among these, the choice of the collimator diameters at the back side of the neutron detector box up to the GRSI detector, the use of LiH neutron attenuators in front of the GRSI detectors, the GRSI detector material and shielding. The GRSI detector is based on commercial LaBr3(Ce) inorganic scintillating crystal coupled with a photomultiplier tube or a silicon photomultiplier. They are designed to operate at high count rate although GRSI geometry constraints severely impact on this feature. The GRSI can also provide an independent assessment of DEMO DT fusion power and T burning.
Formation of CO through C (2s 22p 2 3P) and O (2s 22p 4 3P) Radiative Association

The formation of CO through the radiative association of the carbon (C, 2s 22p 2 3P) and oxygen (O, 2s 22p 4 3P) atoms is investigated. The corresponding cross sections and rate coefficients for temperatures T = 10–10,000 K are calculated using the quantum-mechanical approach based on ab initio potential energy curves, permanent dipole moments, and transition dipole moments, which are obtained by the internally contracted multi-reference configuration interaction method with the Davidson correction and aug-cc-pwCV5Z-DK basis set. All dipole-allowed transitions between singlet, triplet, and quintet states converging to the C (2s 22p 2 3P) + O (2s 22p 4 3P) dissociation limit are considered. Compared to the previous results that only contain the X1Σ+ → X1Σ+, A1Π → X1Σ+, and B1Σ+ → X1Σ+ transitions, our results suggest that the a′3Σ+ → a3Π and d3Δ → a3Π transitions make significant contributions to the radiative association for T = 10–30 K. The total rate coefficient at low temperatures is estimated to be about 10−18 cm3 s−1, which shows significant deviation from the previous results, where only three transitions were considered. New rate coefficients may improve the chemical modeling of CO in the low-density region of the interstellar medium.
Comparing the Safety and Immunogenicity of homologous (Sputnik V) and heterologous (BNT162B2) COVID-19 prime-boost vaccination

Studies have shown increased immunogenicity from heterologous boosting. This study specifically assessed boosting with Pfizer-BioNTech in Sputnik V vaccination regimens. Reactogenicity was assessed through adverse events. Immunogenicity was assessed by comparing serum anti-Spike (Anti-S) protein antibody and neutralizing antibody titers before booster administration and after 30 days. A total of 428 participants were recruited in the heterologous arm and 351 in the homologous arm. Adverse events were more frequent in the heterologous arm (p<0.001). No serious adverse events were reported in either arm. Amongst 577 individuals who completed the study, Anti-S antibodies were 14.8 times higher after heterologous boosting, and 3.1 times higher after homologous boosting (p<0.001). Similarly, heterologous boosting showed a 2 fold increase in neutralizing antibodies, compared to a 1.6 fold increase in homologous boosting (p<0.001). In conclusion, both boosting regimens elicited an immunological response, nonetheless heterologous Pfizer-BioNTech showed a higher immunological response, with more adverse effects.
Visual Outcome of Anti-Vascular Endothelial Growth Factor Injection at the University of Gondar Hospital Tertiary Eye Care and Training Centre, North-West Ethiopia

Background: The management of macular edema and ocular neovascularization is changing and includes a new group of drugs called anti-vascular endothelial growth factor (anti-VEGF). Intra-vitreal injection of anti-VEGF agents has become the new standard of care for macular edema. However, data on their real world effectiveness and safety of these drugs in African eye care settings are very scarce. Objective: To assess the visual outcome of intravitreal Avastin (IVA) injection at University of Gondar hospital tertiary eye-care and training center. Methods: A retrospective analysis of medical records of patients who received IVA at the center was done. The main outcome measure was visual acuity (VA). Results: The study included 37 eyes of 34 study participants with macular edema secondary to diabetic retinopathy, retinal vein occlusions, and neovascular age related macular degeneration (AMD). Mean VA improved from 6/60 (approximate 35 ETDRS letters) at baseline to 6/24 (approximate 55 ETDRS letters) at 2 months follow-up (p=0.0045) and this improvement was maintained at 6 months of follow up. This happened after mean injection of 2.5 times per eye over 6 months period. No major ocular or systemic treatment related adverse events were observed. Conclusion: Patients who received IVA as initial therapy for macular edema from diabetic retinopathy, retinal vein occlusions, and neovascular AMD has a significant mean VA improvement which was maintained up to 6 months. Short term results show that IVA is effective and safe.
Prevalence of carbapenem-resistant and extended-spectrum beta-lactamase-producing Enterobacteriaceae in a teaching hospital in Ghana

Background: Carbapenem-resistant Enterobacteriaceae (CRE) and Extended-spectrum beta-lactamase (ESBL) production among Gram-negative Enterobacteriaceae is an increasing global challenge because of the high morbidity and mortality associated with their infections, especially in developing countries where there may not be many antibiotic treatment options. Despite these challenges, there are few studies in Ghana that have described the burden of CRE. This study aimed to determine the prevalence of carbapenem-resistant Enterobacteriaceae isolated from patients at the Cape Coast Teaching Hospital (CCTH) in the Central region of Ghana. Methodology/Principal findings : Enterobacteriaceae isolates were collected from April to July 2019 at the bacteriology unit of CCTH using a consecutive sampling method. Isolates were identified by standard microbiological techniques and confirmed using API 20E. Kirby Bauer disc diffusion method was used to determine the antibiogram of isolates. Isolates were also subjected to ESBL testing using the single-disc combination method. Carbapenem resistant isolates were identified by the Kirby Bauer disc diffusion method and then subjected to genetic confirmation using polymerase chain reaction (PCR). Of the 230 isolates comprising E. coli (40.9%), Citrobacter spp. (32.6%), K. pneumoniae (9.1%), P. mirabilis (6.1%), P. vulgaris (5.2%), Enterobacter spp (3.5%). , K. oxytoca (2.2%) , and Serratia marcenses (0.4%). Majority of the isolates were from urine 162(70.4%) and wound samples. The isolates showed high resistance to ampicillin 171 (74.3%) and cefuroxime 134(58.3%). The prevalence of MDR was 35.2% (81) with E. coli 40(42.6%) being the majority that exhibited MDR. Out of the 230 isolates, 113(49.1%) were ESBL producers with E. coli 54(57.5%) accounting for the majority while Serratia marcenses was the least. Out of the 13 ( 5.7%) CRE isolates which showed resistance towards carbapenem in the disc diffusion method, 11 expressed the blaNDM-1 gene whilst all isolates which showed resistant CRE expressed the blaOXA-48 gene. Conclusion: The prevalence of carbapenem resistance and ESBL- producing Enterobacteriaceae pathogens among patients at the CCTH, Cape coast of Ghana is high, and effective infection prevention and control measures should be implemented at the hospital to prevent the rapid spread of these dangerous organisms.
Uric acid lowering for slowing CKD progression after the CKD-FIX trial: a solved question or still a dilemma?

ABSTRACT Hyperuricemia has been associated with several cardiovascular risk factors and is a well-known predictor of kidney disease. In vitro studies as well as animal models highlighted a role for uric acid in the development and progression of haemodynamic and tissue damage at the renal level leading to glomerular and tubulointerstitial abnormalities. Urate-lowering treatment, especially by xanthine oxidase inhibitors, has been proposed in order to improve kidney outcomes. However, recent randomized controlled trials failed to demonstrate a beneficial effect of allopurinol or febuxostat on renal disease, casting doubts on the role of this therapeutical approach to improve nephroprotection. We provide a critical overview of current literature on this topic and offer a possible interpretation of results from recent intervention trials with urate-lowering treatment on renal outcomes.