Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute infective endocarditis during COVID‐19 pandemic time: The dark side of the moon

The COVID‐19 pandemic has remarkably impacted the hospital management and the profile of patients suffering from acute cardiovascular syndromes. Among them, acute infective endocarditis (AIE) represented a rather frequent part of these urgent/emergent procedures. The paper by Liu et al. has clearly shown the higher risk features which patients with a diagnosis of AIE presented at hospital admission during the first part (first and second waves) of the outbreak, often requiring challenging operations, but fortunately not associated with the worse outcome if compared to results obtained before the SARS‐2 pandemic. The report discussed herein presents several other aspects worth discussion and comments, particularly in relation to hospital management and postdischarge outcome which certainly deserve to be highlighted, but also further investigations.
CAD Modeling and Optimization of the Optimal Geometric Features of a Microdrill

The objective of this paper is to construct a CAD model and to determine the optimal geometric features of a 0.1 mm diameter of microdrill. The CAD model was constructed using Pro/Engineer software by determining the flute of a microdrill mathematically. The cross section comparison between the model and fabricated microdrill was carried out by cutting at different lengths, and the results of the web thickness of the model and the fabricated microdrill were approximately the same. Hence, the CAD Modeling of a microdrill based on determining the flute mathematically by considering both setting angle and profile of a grinding wheel was feasible. The optimal geometric features of a microdrill have been determined by carrying out optimization of the stress/displacement analysis using Pro/Mechanica software so that the maximum stress Von Mises of the microdrill was minimal.
A MODEL FOR PREDICTION OF DRUG RESISTANT TUBERCULOSIS USING DATA MINING TECHNIQUE

The rate of mortality in the recent time because of tuberculosis disease is so alarming. Drug-Resistant Tuberculosis is a communicable disease very dangerous that attack lungs, many victims were not identified due to weak health systems facilities, poor doctor-patient relationship, and inefficient mechanisms for predicting of the disease. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as delay taking during prediction which reduce the treatment cost of a patience. Several data mining technique such as classification, clustering, regression, and association rule were used to enhance the prediction of tuberculosis. In this project I used Naïve Bayes Classifier to design a model for predicting tuberculosis. I considered the following parameters; Gender, Chills, Fever, Night sweat, Fatigue, Cough with Blood, Weight loss, and Loss of Appetite for classification phase 1. While Gender Chest Pain, Sputum, Contact DR, Weight Loss, In-adequate treatment for classification phase 2 as the clinical symptom. The Naïve Bayes Classifier has the advantage of attribute independency, it is easy in construction, can classify categorical data, and can work on high dimensional data effectively. The model designed using Naïve Bayes Classifier is divided o into classification phase 1 and classification phase 2 and implemented using Phython 3.2 Programing Language. The result shows that Naïve Bayes Classfier was suitable in predicting drug resistant tuberculosis with performance accuracy of 82%, 98% and area under curve (AUC) is 88%. Keywords: Model Prediction, Tuberculosis. Drug, Resistant, Data Mining.
Users’ perspectives of library space: An analysis of user satisfaction survey at the University of Malaya, Malaysia

The new millennium with rapid advancement of information and communications technology globally and the appearance of the post millennial net generation library users have created a demanding and competitive environment for academic libraries. On one hand, libraries are competing with virtual google-driven information arena while trying hard to attract users to physically visit the library. Academic libraries are moving towards electronic collection to match the needs of the Net generation. Activities, facilities, and services offered are also revamped as significant library measures to remain relevant. One of the interesting observation of the young library users are their multitasking abilities and their need for a more relaxed atmosphere in contrary to the serious, scholarly milieu of academic libraries in the past. This paper summarizes the open comments on space by library users while answering the User Satisfaction Survey at the University of Malaya, Malaysia. From the open comments, the users’ perspectives of library space can be categorized into five areas; library as a place via extended opening hours, increase in the number of plug points and sockets, high speed wireless and internet services, the need for food and cafe for relaxation, and aesthetic value and comfort while in the library. Although the University of Malaya library had been taking actions within financial constraints and policy restrictions, to meet the users’ needs, the comments and suggestions over the years nonetheless continue to be along the same lines of the categories mentioned. It can be concluded that the users’ perspectives of the academic library is moving away from the scholarly view to that of library as a third place perhaps due to the availability of electronic books, journals, databases, and resources online.
Influence of Self-efficacy Improvement on Online Learning Participation

More and more online learning apps are emerging, thanks to the development of Internet plus education and online learning platforms. Learning efficacy is the leading impactor of online learning participation. To avoid inefficiency and poor effect of online learning, it is necessary to explore the theory on the relationship between self-efficacy improvement and online learning participation. This paper examines the influence of self-efficacy improvement on online learning participation. Firstly, a general normal distribution map was drawn for self-efficacy. Then, a prediction model was established for participation based on the series of online learning behaviors. In addition, the k-means clustering (KMC) algorithm was optimized by information entropy, and the flow of the improved KMC was explained. The proposed model was proved valid through experiments.
Effect of Environmental CSR Initiatives on Potential Stakeholders’ Perception of Non-environmentally Friendly Sporting Events

The protection of the natural environment is increasingly being perceived as a pillar of CSR (corporate social responsibility). Although most professional sport organizations in the United States participate in some form of philanthropic activity, little research has examined how non-environmentally friendly sport organizations can effectively communicate their environmental initiatives. Two different CSR approaches (Environmental and Nationalistic) by NASCAR were exposed to potential stakeholders to examine the changes in the perception (Attitude, Reputation, Identification, and Perceived CSR) of the organization. We analyzed the MIMIC model to compare the means on the latent constructs. Environmental approach showed positive changes in potential stakeholders’ perception toward NASCAR. Whereas, the Nationalistic CSR approach had no significant impact on potential stakeholders’ perception of the organization. The findings suggest CSR can be an opportunity for organizations like NASCAR to have a positive non-product message for potential stakeholders depending on how they communicate these programs.
Why Do People Return to Video Platforms? Millennials and Centennials on TikTok

While some social networks like Facebook are losing interest among digital influencers, TikTok continues to grow, capturing and impacting centennials and millennials alike. This situation highlights the new generations’ increasing interest in short video formats, which are also becoming a new window of communication between companies and consumers. TikTok allows users to create, share, and discover short, user-generated videos in hopes of attracting viewers. But it is necessary to understand the variables that attract and engage users of these particular social networks. This article analyses the variables of continuance motivation, video sharing behaviour, and video creation capabilities, which allow users to enjoy such networks, and service providers and companies to obtain results from them. The aim is to understand how these variables motivate social media users to return to and spend more time on this video-sharing platform. This is measured through the stickiness variable. In this context—and due to the particular relevance of the topic—the authors also aim to reveal any potential differences in the behaviour of centennials and millennials when using TikTok. Therefore, a cross-sectional study was conducted through a questionnaire answered by 2,301 millennials and centennials who use TikTok. The data were analysed through a structural equation model to measure the relevance of each of the variables to stickiness. The results provide guidelines for improving research on video social media platforms, as well as an opportunity to explore the importance of the selected variables to the stickiness variable across different user segments.
Literature Review on Trust and Current Construction Industry Trends

There is a growing body of evidence supporting the theory that project performance can be assisted by increased levels of trust. Trust in construction has received some academic attention but warrants more. This research provides additional rationale and foundational support for the advancement of knowledge regarding trust in construction. Specifically, we emphasize the role of chosen project delivery method and its impact on trust levels. We conducted an extensive literature review on trust and pertinent concepts were collected for application to the construction industry. Extant literature indicates a high level of relevance and applicability of trust research to construction issues. It seems probable that the chosen project delivery method plays a major role in the resultant levels of trust between project participants. Recent evolutions in project delivery methods and support systems, such as Integrated Project Delivery and Lean Construction are argued by their supporters to be more effective at maintaining high levels of trust than traditional methods. Results from this research suggest that academicians and industry practitioners alike who value trust should consider the ramifications of their current practices on the subject. Further research is required to determine if IPD, relational contracting, and lean construction are capable of systemically supporting higher levels of trust than traditional methods.
Performance of Optical Receivers Employing Ultralow Noise Avalanche Photodiodes

Recently there has been interest in avalanche photodiode (APD) structures in which the injected carrier can cause only one impact ionization while crossing the multiplication region, to achieve low noise and high speed detection. In this paper the performance of optical receivers incorporating these advanced APD's is investigated. The results show that the sensitivity is improved as the hole to electron ionization rate ratio "k" increases. As k tends to one these devices can offer improved sensitivity compared with conventional APDs.
Detection and Classification of Short Transients and Interruption using Hilbert Transform

Widespread use of electronics from home appliances to the control of more sophisticated and costly industrial processes has raised the awareness of power quality. Power quality disturbance is generally defined as any change in power (voltage, current, or frequency) that interferes with the normal operation of electrical equipment. The study of power quality and ways to control is a major concerned for electric utilities, large industrial companies, businesses, and even home users. The study has intensified due to equipment have become increasingly sensitive to even minute changes in the power supply voltage, current, and frequency. In electrical energy power networks, disturbances can cause problems in electronic devices so their monitoring is very fundamental. In this paper, we address the problem of disturbance detection by using Hilbert transform which is employed as an effective tool for tracking the voltage waveforms in electrical distribution systems. In addition to this classification of disturbance is carried out by using cross correlation technique. Simulation results obtained shows the accuracy and flexibility of Hilbert transform in detecting the time instants during which the disturbance has occurred. This has been tested for oscillatory transients, interruption and multiple event interruption and sag.
The Low-Temperature Photocurrent Spectrum of Monolayer MoSe2: Excitonic Features and Gate Voltage Dependence

Two-dimensional transition metal dichalcogenides (2D-TMDs) are among the most promising materials for exploring and exploiting exciton transitions. Excitons in 2D-TMDs present remarkably long lifetimes, even at room temperature. The spectral response of exciton transitions in 2D-TMDs has been thoroughly characterized over the past decade by means of photoluminescence spectroscopy, transmittance spectroscopy, and related techniques; however, the spectral dependence of their electronic response is still not fully characterized. In this work, we investigate the electronic response of exciton transitions in monolayer MoSe2 via low-temperature photocurrent spectroscopy. We identify the spectral features associated with the main exciton and trion transitions, with spectral bandwidths down to 15 meV. We also investigate the effect of the Fermi level on the position and intensity of excitonic spectral features, observing a very strong modulation of the photocurrent, which even undergoes a change in sign when the Fermi level crosses the charge neutrality point. Our results demonstrate the unexploited potential of low-temperature photocurrent spectroscopy for studying excitons in low-dimensional materials, and provide new insight into excitonic transitions in 1L-MoSe2.
Potassium and Humic Acid Synergistically Increase Salt Tolerance and Nutrient Uptake in Contrasting Wheat Genotypes through Ionic Homeostasis and Activation of Antioxidant Enzymes

Salinity limits the growth and nutrient uptake in crop species. Studies show that both potassium (K) and humic acid (HA) improved plant tolerance to salinity. However, the interactive effect of K and HA on plant tolerance to salinity stress remains unknown. This pot study examined the effect of application of K (0, 5 or 10 mM) and HA (0 or 2 g kg−1), alone or in combination, on the growth and physiology under salinity (100 mM NaCl) in two wheat genotypes (SARC 1, salt tolerant; and SARC 5, salt sensitive). The results revealed that salt stress reduced shoot biomass by 35% and 49% in SARC 1 and SARC 5, respectively. Salinity induced overproduction of H2O2 and lipid peroxidation in both genotypes, but the decline in pigments and stomatal conductance was more profound in SARC 5 than in SARC 1. Combined application of 10 mM K and HA was most effective in alleviating salt stress with improved plant biomass by 47% and 43% in SARC 1 and SARC 5, respectively. Combined application of 10 mM K and HA mitigated salt and induced oxidative stress with the activities of APX, CAT, POD and SOD increased by up to 2.8 folds in SARC 1, and by upto 2.5 folds in SARC 5, respectively. Root and shoot Na contents were increased, while K, Fe and Zn contents were decreased under saline conditions. HA combined with K decreased Na and increased K, Fe and Zn contents in both genotypes. Combined application of 10 mM K and HA was more promising for increasing wheat salt tolerance and nutrient uptake and genotype SARC 1 performed better than SARC 5 for cultivation on saline soils.
Komparasi Algoritma Naïve Bayes dan Algoritma K-Nearst Neighbor terhadap Evaluasi Pembalajaran Daring

Sejak merebaknya pandemi yang disebabkan oleh virus Corona di Indonesia, banyak cara yang dilakukan salah satunya menyelenggarakan pembelajaran jarak jauh dan menyarankan mahasiswa untuk belajar dari rumah masing-masing. Penggunaan teknologi digital di tengah pandemi COVID-19 memiliki kontribusi yang besar bagi institusi pendidikan yaitu dengan menerapkan pembelajaran online. Mahasiswa diharapkan mampu menerima langkah yang telah diimplementasikan oleh pemerintah. Akan tetapi, keadaan itu tidak juga menjamin bahwa mahasiswa setuju atau menerima dengan adanya langkah tersebut. Maka dari itu, diperlukan pengukuran untuk menentukan tingkat kepuasan mahasiswa dalam melakukan pembelajaran online. Dengan hal tersebut penulis melakukan uji kinerja algoritma terlebih dahulu yaitu model klasifikasi dengan algoritma naïve bayes dan algoritma K-Nearst Neighbor. Data yang digunakan data primer artinya data di ambil dari hasil sebaran kuisoner pada mahasiswa semester 3 (tiga) semester 5 (lima) dan semester 7 (tujuh) sebanyak 352 responden. Dalam pengembangan model algoritma menggunkan tools rapid miner versi 9.3 dengan operator yang digunakan yaitu retrive, multiply, cross validation, algoritma naïve bayes dan knn, apply model dan performance. Hasil akurasi Algoritma naïve bayes dengan nilai 91,45%. Hasil akurasi algoritma k-nearst neighbor dengan nilai 97,72%. akurasi algoritma k-nearst neighbor lebih besar dari algoritma naïve bayes, sehingga dapat disimpulkan algoritma k-nearst neighbor mempunyai kinerja yang baik dalam klasifikasi.
Preparation of a NaFePO4 Cathode Material via Electrochemical Sodiation of FePO4 Layers on Al Substrates

In this research, a cyclic voltammetry (CV) method was applied to intercalate Na + into an FePO 4 /Al substrate to produce NaFePO 4 /Al as a potential cathode material. The sodiation was conducted directly to FePO 4 instead of applying delithiation to LiFePO 4 followed by sodiation, as was done in previous research. CV was conducted within a potential window of 2.0 – 4.0 V using a scan rate of 0.05 mVs -1 . The result was compared to LiFePO 4 /Al treated with a similar method. The various scan rate was then applied to understand its effect on the electrochemical activity recorded in the voltammogram and its impedance profile. The results show that the CV product of FePO 4 /Al (NFP(A)) was crystallized in an orthorhombic olivine NaFePO 4 , as a result of Le Bail refinement. Orthorhombic Na 0.7 FePO 4 , trigonal FePO4, and monoclinic FePO4 were presented as secondary phases. Meanwhile, the CV product of LiFePO 4 /Al (NFP(B)) was also crystallized in olivine NaFePO 4 and possessed secondary phases similar to NFP(A) with an additional Fe 2 O 3 phase. NFP(A) showed two significant peaks at 2.442 V and 3.534 V, confirming sodiation/de-sodiation and Fe 3+ /Fe 2+ activity, respectively. Meanwhile, NFP(B) showed two peaks at 3.183 V and 3.04 V, corresponding to de-lithiation and sodiation, respectively. The Nyquist plots of both materials show a similar profile, with the impedance value of NFP(A) being lower than that of NFP(B). This confirms that the CV treatment of FePO 4 /Al is more facile than the treatment of the LiFePO 4 layer, while also producing a cathode with higher electrical conductivity. Scan rate reduction to 0.04 mVs -1 produced a much lower impedance value, confirming higher electrical conductivity.
40 Years of Models of Business Insolvency: A Proposal for its Taxonomy

During the last 40 years a large number of articles, books and working papers have focused on insolvency prediction models. From the first Altman's study [1968] to the most recent contributions is possible to find some common characteristics. Starting from these features this article gives a simple and open frame in order make a taxonomy according these characteristics. The criteria we used for this classification are: the objective of the work, the definition of insolvency, the variables used, the techniques applied and finally, the results. We outlined the heterogeneity of techniques and also, how results are difficult to compare among works.
Sex-specific transcriptome of spinal microglia in neuropathic pain due to peripheral nerve injury

Recent studies have suggested a sexually dimorphic role of microglia in the maintenance of neuropathic pain in rodents. Here, we utilized RNA sequencing analysis and in vitro primary cultures of microglia to characterize the sex differences in microglia in pain-related regions in nerve injury and chemotherapy-induced peripheral neuropathy mouse models. Whilst mechanical allodynia and behavioral changes were observed in all models, transcriptomic analysis revealed a substantial change in microglial gene expression only within the ipsilateral lumbar spinal cord 7-days after nerve injury. Both sexes upregulated genes associated with inflammation, phagosome, and lysosome activation, though males revealed a prominent global transcriptional shift not observed in female mice, reflecting acute activation. Further, in vitro studies revealed that only male microglia from nerve-injured mice developed a reactive phenotype with increased phagocytotic activity. This study indicates distinct sex differences in spinal microglia and suggests they contribute to the sex-specific pain processing following nerve injury.
Enhancing the Quality of Planning of Software Development Projects

As business competition gets tougher, there is much pressure on software development projects to become more productive and efficient. Previous research has shown that quality planning is a key factor in enhancing the success of software development projects. The research method selected for this study was design science research (DSR), and the design science research process (DSRP) model was adopted to conduct the study. This research describes the design and development of the quality of planning (QPLAN) tool and the quality of planning evaluation model (QPEM), which are two innovative artefacts that evaluate the quality of project planning and introduce best planning practices, such as providing references from historical data, suggesting how to manage in an appropriate way and including lessons learnt in the software development process. In particular, the QPEM is based on cognitive maps that represent the project manager’s know-how, project manager’s characteristics and technological expertise, as well as top management support, enterprise environmental factors and the quality of methods and tools in a form that corresponds closely with humans’ perceptions. Data were collected from 66 projects undertaken in 12 organisations from eight types of industries in six countries. The results show that the QPLAN tool has been significantly contributing to enhancing the success rate of projects.