Miracle-dz/UniFAR-FTUs huggingface dataset:
Melhores Práticas para sua Análise Fatorial Exploratória: Tutorial no Factor

RESUMO Contexto: a análise fatorial exploratória (AFE) é um dos métodos estatísticos mais utilizados em administração. No entanto, sua prática corrente coexiste com regras de bolso e heurísticas proferidas há meio século. Objetivo: o propósito deste artigo é apresentar as melhores práticas e recomendações recentes para uma AFE típica em administração através de uma solução prática acessível aos pesquisadores. Métodos: nesse sentido, além de serem discutidas as práticas correntes versus as práticas recomendadas, ilustra-se um tutorial com dados reais no Factor, um software ainda pouco conhecido na área de administração, porém freeware, fácil de usar (point and click) e poderoso. O passo a passo ilustrado no artigo, além das discussões levantadas e de um exemplo adicional, também é disponibilizado no formato de vídeos tutoriais. Conclusão: através da metodologia didática proposta (artigo-tutorial + vídeo-tutorial), incentivamos os pesquisadores/metodologistas que dominam alguma técnica particular a fazerem o mesmo. Especificamente sobre a AFE, esperamos que a apresentação do software Factor, como uma primeira solução, possa transcender as regras de bolso e heurísticas correntes ultrapassadas, ao tornar acessíveis as melhores práticas para os pesquisadores da administração.
Front-end electronics for large arrays of macro-bolometers

Underground bolometric experiments offer unique opportunities for the study of neutrino-less double beta decay and other rare decays. Both the current and next generation detectors consist of large arrays of macro-bolometers. In particular, CUORE is the first bolometric experiment reaching the 1-ton scale and has successfully completed the first physics run. CUPID, CUORE upgrade with particle identification, has also started its first demonstrator (CUPID-0), with very promising performance. The readout of such large scale and high sensitivity bolometric detectors poses new challenges for the front-end and detector calibration electronic systems. We will present the strategies, design choices and actual performance of the electronic instrumentation as implemented in CUORE and CUPID-0.
Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques

Background There are a myriad of language cues that indicate depression in written texts, and natural language processing (NLP) researchers have proven the ability of machine learning and deep learning approaches to detect these cues. However, to date, these approaches bridging NLP and the domain of mental health for Bengali literature are not comprehensive. The Bengali-speaking population can express emotions in their native language in greater detail. Objective Our goal is to detect the severity of depression using Bengali texts by generating a novel Bengali corpus of depressive posts. We collaborated with mental health experts to generate a clinically sound labeling scheme and an annotated corpus to train machine learning and deep learning models. Methods We conducted a study using Bengali text-based data from blogs and open source platforms. We constructed a procedure for annotated corpus generation and extraction of textual information from Bengali literature for predictive analysis. We developed our own structured data set and designed a clinically sound labeling scheme with the help of mental health professionals, adhering to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) during the process. We used 5 machine learning models for detecting the severity of depression: kernel support vector machine (SVM), random forest, logistic regression K-nearest neighbor (KNN), and complement naive Bayes (NB). For the deep learning approach, we used long short-term memory (LSTM) units and gated recurrent units (GRUs) coupled with convolutional blocks or self-attention layers. Finally, we aimed for enhanced outcomes by using state-of-the-art pretrained language models. Results The independent recurrent neural network (RNN) models yielded the highest accuracies and weighted F1 scores. GRUs, in particular, produced 81% accuracy. The hybrid architectures could not surpass the RNNs in terms of performance. Kernel SVM with term frequency–inverse document frequency (TF-IDF) embeddings generated 78% accuracy on test data. We used validation and training loss curves to observe and report the performance of our architectures. Overall, the number of available data remained the limitation of our experiment. Conclusions The findings from our experimental setup indicate that machine learning and deep learning models are fairly capable of assessing the severity of mental health issues from texts. For the future, we suggest more research endeavors to increase the volume of Bengali text data, in particular, so that modern architectures reach improved generalization capability.
TELKOMNIKA Telecommunication Computing Electronics and Control

Received Jan 31, 2022 Revised Jun 09, 2022 Accepted Jun 19, 2022 In the fifth generation of wireless communication systems (5G), the millimeter wave (mmWave) signal can be affected by many challenges in irregular environments such as dust and sand storms. In this paper, we investigate the attenuation that faces a mmWave link signal in a dusty environment for different sizes of dust particles. Further, we derive a model to calculate the total attenuation in mobile communication systems and/or internet of things (IoT) in different base stations highnesses. The obtained results show that in a dusty environment it is better to have high base station transmitters as the signal may escape the dense dust area. The paper also studies the case of the city of Mosul to find that not only the dry weather is increasing the attenuation, but wet weather also may impact more on the attenuation in the mmWave systems.
A newly identified pyroptosis-related gene signature for predicting prognosis of patients with hepatocellular cancer

Background Hepatocellular carcinoma (HCC) is a very heterogeneous illness, making prognosis prediction a huge problem. Pyroptosis, which has recently been shown to be an inflammatory type of programmed cell death, is involved in HCC. Nevertheless, the role of pyroptosis-related genes in HCC has not been fully elucidated. Thus, this study aimed to construct a prognostic signature based on pyroptosis-related genes for HCC. Methods The messenger RNA expression patterns of HCC patients, as well as the accompanying clinical information, were retrieved from The Cancer Genome Atlas (TCGA) database for this research. After differentially expressed pyroptosis-related Gene in tumor and normal groups were identified, Cox regression analyses were performed to construct a prognostic signature which was then assessed through independent prognostic analysis. Results A signature consisting of four genes (CASP8, GSDME, NOD2, and PLCG1) was constructed to predict overall survival (OS) for HCC. The signature was identified to be independent by the cox regression analysis and obtained the largest area under the receiver operating characteristic (ROC) curve (AUC) was 0.691, 0.628, and 0.632 for survival at 1, 2, and 3 years, respectively. Conclusions We discovered that the levels of pyroptosis-related genes expression differed across HCC patients and were associated with both survival and prognosis. This suggested that targeting pyroptosis as a treatment strategy for HCC may be a viable option.
A Robust Bayesian Optimization Framework for Microwave Circuit Design under Uncertainty

In modern electronics, there are many inevitable uncertainties and variations of design parameters that have a profound effect on the performance of a device. These are, among others, induced by manufacturing tolerances, assembling inaccuracies, material diversities, machining errors, etc. This prompts wide interests in enhanced optimization algorithms that take the effect of these uncertainty sources into account and that are able to find robust designs, i.e., designs that are insensitive to the uncertainties early in the design cycle. In this work, a novel machine learning-based optimization framework that accounts for uncertainty of the design parameters is presented. This is achieved by using a modified version of the expected improvement criterion. Moreover, a data-efficient Bayesian Optimization framework is leveraged to limit the number of simulations required to find a robust design solution. Two suitable application examples validate that the robustness is significantly improved compared to standard design methods.
Cheese Intake Exhibits an Alteration of Glycolipid Profile and Impacts on Non-Alcoholic Fatty Liver in Bahraini Older Adults

Background: Metabolic syndrome (MetS) is a multifactorial condition characterized by a complex interrelation between genetic and environmental factors that heighten the risk of cardiovascular diseases and all-cause mortality. It is hypothesized that diet may play an important role in the regulation of metabolic syndrome factors and influence the process. Therefore, the main objective of this study was to investigate the specific dietary patterns associated with metabolic syndrome markers and quantify the possible effects of dietary patterns among Bahrain older adults. Methods: This is a cross-sectional study that included 151 Bahraini patients diagnosed with MetS, 89 (58.7%) were females and 62 (41.3%) males. Results: The prevalence of Non-Alcoholic Fatty Liver was 89%. Statistically significant correlations were found between dairy products with low fat and SBP (r = 0.182, p < 0.001) body mass index (BMI) (r = −0.195; p < −0.01). Higher chicken consumption was associated with reduction of BMI (r = −0.273; p < −0.01). A higher consumption of ricotta and cheddar cheese (high in fat) was associated with higher levels of triglycerides (p < 0.01). Higher frequent consumption of rice (basmati) was associated with lower glucose levels (r = −0.200; p < −0.01). Fatty liver has been associated with high consumption of cream cheese (p < 0.01). Conclusion: In older Bahraini adults with metabolic syndrome, higher frequency of food consumption of full-fat cheese was linked with a derangement of lipid profile and Non-Alcoholic Fatty Liver. Positive effects on BMI have been recorded with higher-frequency consumption of basmati rice and chicken.
System design is an NP-complete problem: Correspondence

The system design process translates the customers' needs into a buildable system design. It requires selecting subsystems from an allowable set and matching the interfaces between them. Designs that meet the top-level input and output requirements are tested to see how well they meet the system's performance and cost goals. This paper proves that the System Design Problem is NP-complete by reduction from the Knapsack Problem, which is known to be NP-complete. The implication of this proof is that designing optimal systems with deterministic, polynomial time procedures is not possible. This is the primary reason why engineers do not try to produce optimal systems: They merely produce designs that are good enough. © 2001 John Wiley & Sons, Inc. Syst Eng 4: 222–229, 2001
The relationship between trade openness, financial development and economic growth: Evidence from Generalized method of moments

This study examines the effect of trade openness and financial development on economic growth four South Asian countries for the period of 1980-2017 using static and dynamic models. The results indicates that stock market development positively effect economic growth. The results validate that all three proxies of stock market perform a significant and positive role in augmenting economic growth in the sample countries. Trade openness, inflation and real interest rate significantly reduce economic growth while saving rise economic growth. The findings of this study have important policy implication for the sample countries regarding rising stock market in order to strengthen economic growth.
REAP-IIoT: Resource-Efficient Authentication Protocol for the Industrial Internet of Things

With the widespread utilization of Internet-enabled smart devices (SDs), the Industrial Internet of Things (IIoT) has become prevalent in recent years. SDs exchange information through the open Internet, which creates security and privacy concerns for the exchanged information. To address these concerns, various solutions exist in the literature which, because of high computational and communication overheads, are not appropriate for the resource-constricted IIoT environment. This article proposes a resource-efficient authentication protocol for the IIoT, called REAP-IIoT, which employs a lightweight cryptography (LWC)-based authenticated encryption with associative data (AEAD) primitive AEGIS along with hash function. LWC-based AEAD primitives are suitable for resource constraint SDs because they require fewer computational resources. Moreover, REAP-IIoT renders the privacy-preserving user authentication functionality and establishes a session key (SK) between SDs deployed in the IIoT environment and users. Both user and SD utilize the established SK for encrypted communication. The security of SK, established during the authentication and key exchange (AKE) process of REAP-IIoT, is validated through the broadly accepted random or real model. Besides, Scyther-based security verification is conducted to illustrate that REAP-IIoT is secure and can protect the man-in-the-middle and replay attacks. Additionally, the informal security analysis is carried out to show that REAP-IIoT is protected against various covert security risks. A thorough comparison reveals that REAP-IIoT renders enhanced security characteristics apart from its low communication, storage, and computational overheads than the relevant AKE protocols.
Treatment Planning in Intraoperative Radiation Therapy (IORT): Where Should We Go?

Simple Summary Intraoperative radiation therapy is evolving towards new treatment regimens, including ultrahigh dose rates, while in-room imaging systems are increasingly being used for treatment planning and verification. Furthermore, the combination of multiple treatment modalities is being investigated and suggested in some studies, with the aim to improve clinical outcomes. This evolution calls for newly designed treatment planning systems in which several features shall be integrated. In this article, an attempt is made to identify emerging needs and foresee the possible evolution of treatment planning technology and strategies for intraoperative radiation therapy in the near future. Abstract As opposed to external beam radiation therapy (EBRT), treatment planning systems (TPS) dedicated to intraoperative radiation therapy (IORT) were not subject to radical modifications in the last two decades. However, new treatment regimens such as ultrahigh dose rates and combination with multiple treatment modalities, as well as the prospected availability of dedicated in-room imaging, call for important new features in the next generation of treatment planning systems in IORT. Dosimetric accuracy should be guaranteed by means of advanced dose calculation algorithms, capable of modelling complex scattering phenomena and accounting for the non-tissue equivalent materials used to shape and compensate electron beams. Kilovoltage X-ray based IORT also presents special needs, including the correct description of extremely steep dose gradients and the accurate simulation of applicators. TPSs dedicated to IORT should also allow real-time imaging to be used for treatment adaptation at the time of irradiation. Other features implemented in TPSs should include deformable registration and capability of radiobiological planning, especially if unconventional irradiation schemes are used. Finally, patient safety requires that the multiple features be integrated in a comprehensive system in order to facilitate control of the whole process.
Resilient iterative learning control for a class of discrete‐time nonlinear systems under hybrid attacks

The security control problem for a class of unknown nonlinear systems is considered in this paper. For the nonlinear system running in the network environment, the measurement channel is subjected to hybrid attacks. Intermittent denial of service attacks and false data injection attacks are modeled as the hybrid attacks. According to the characteristics of the repetitive system, a resilient iterative learning control (ILC) algorithm under hybrid attacks is devised. Subsequently, the stability of the system is proved by mathematical derivation and theoretical analysis in the sense of mathematical expectation. The theoretical analysis results indicate that the resilient ILC algorithm can ensure the stability of the system, and the tracking error converges with the increased number of iterations. Finally, the validity of the algorithm is illustrated by numerical simulation and mobile robot simulation.
TikTok and YouTube Videos in the Flipped Classroom Model to Improve the Learning Process and Motivate Students

Social media has become a very useful tool for learning in recent years. Millions of people access YouTube , TikTok , Instagram and other social media every day to learn. At the same time, more and more teachers are using these platforms to share their knowledge with the general public and bring it into the classroom. Materials of this type offer the advantage of supplementing various didactic techniques; one of them is the Flipped Classroom. This article presents the use of videos deployed in the TikTok and YouTube platforms with the Flipped Classroom model to develop grammatical competency in Spanish. The results showed a considerable score improvement among high school students attending the Tecnologico de Monterrey Mexico City campus. The entire sample, 140 students, increased their scores between 12 and 56 points out of 100 in the results of grammatical performance in Spanish. Additionally, the student satisfaction survey showed that 98% felt that the Flipped classroom methodology with TikTok and YouTube videos motivated them to learn grammar in Spanish. This study demonstrates the potential of the TikTok and YouTube platform resources to benefit the development of the grammatical competency in Spanish with this population. This study’s methodology employing these resources could be replicated in various educational scenarios and different areas of knowledge. Moreover, it could be used for promoting complex thinking.
A 0.5–1.7 V Efficient and PVT-Invariant Constant Subthreshold gm Reference Circuit in CMOS

This paper presents a low-power constant subthreshold transconductance reference circuit that produces a constant transconductance by subtracting the output currents of two independent transconductance references. By taking the difference between the output currents of the two independent transconductance references, process, voltage and temperature variations are reduced by minimizing the effects of channel length modulation and drain-induced barrier lowering without relying on feedback to regulate the drain voltage. The proposed reference, fabricated in TSMC’s 130 nm process and testing with off-chip temperature-insensitive resistors, can provide a constant transconductance over a temperature range of −30 to 120°C, and a supply voltage range of 0.5 to 1.7 V. The experimental result of the proposed transconductance reference shows an average variation of ±0.8% over temperature for the entire operating voltage range and an average variation of ±1.97% over voltage for the entire operating temperature range. Through the subtraction, the proposed circuit also shows less variation across process corners compared to the conventional constant transconductance reference. The proposed constant transconductance reference exhibits the highest power efficiency (transconductance over power consumption) amongst the reported constant transconductance references. At 0.5 V, the proposed transconductance reference produces a transconductance of 21.46 $\mu \text{S}$ , while consuming 1.95 $\mu \text{W}$ .
Performance‐based seismic analysis on a super high‐rise building with improved viscously damped outrigger system

To investigate the seismic performance of a kind of improved viscously damped outrigger (IVDO) system, 6 elastoplastic models of a super high‐rise building (729 m) with multi‐IVDOs located at different heights have been established using PERFORM‐3D. Then, elastoplastic analysis on these models is carried out under 3 levels of earthquakes, namely, frequent, basic, and rare earthquakes. Comparison results of interstory drift, top displacement, base shear and overturning moment, additional damping ratio, maximum ratio of demand to capacity and other related indexes, and seismic performance assessments show that structure with IVDOs put at its lower height has larger additional damping ratio, smaller drift, and slighter degree of damage when it suffers from frequent and basic earthquakes. Reasonable damping ratio of building with IVDOs locating at lower height can also be achieved when rare earthquakes happen. Additionally, as belt truss is usually set at the same story where outrigger truss locates at to form closed spatial structure for better integrity, it is suggested belt truss at story where the IVDO locates or between 2 IVDOs should be optimized in case of premature bulking during earthquakes. It should also be noted that the IVDO can well reduce base overturning moment and have satisfactory control on base shear force and top displacement under basic and rare earthquakes, which is especially true in structure with IVDO installed at its lower height.
Evidence of distant hybridization within Central Asian feather grasses (Poaceae: Stipa)

The genus Stipa is one of the largest genera of Poaceae. The phylogeny of the genus is still poorly resolved, and one of the main problems is linked to the extensive inter and intrasectional hybridization. Disclosure of hybridization processes is a key to understanding relationships between species within the genus. During a floristic study in feathergrass steppe ecosystems of central Asia, we found challenging specimens of Stipa with an interesting combination of morphological characters suggesting their origination through hybridization between morphologically and phylogenetically distant species. To confirm our hypothesis, we applied a combination of classical morphological and genome-wide SNP genotyping methods. Using such an approach, we determined that the new taxon named Stipa × smelanskyi arose from crossing S. richteriana and S. drobovii and confirmed that it is an F1 hybrid. Moreover, we found a S. drobovii specimen with a minor admixture of S. richteriana loci that may indicate putative introgression events among these taxa.
ClockViz: Designing Public Visualization for Coping with Collective Stress in Teamwork

The intervention solutions for coping with collective stress have been neglected in interaction design because of limited scalability of the physiological measuring methods. This paper focuses on exploring visual biofeedback design for collective stress in the context of teamwork. We design ClockViz, an augmented reality installation overlaid with static or dynamic projection to visualize three different extents of collective stress on a clock.Results of a 16-participant study show that ClockViz is useful to provide biofeedback data, change their internal status, and increase their mindfulness. Based on the results, we also discussed the potential solutions to collective stress sensing for designers to apply into their interactive design intervention.