Miracle-dz/UniFAR-FTUs huggingface dataset:
The Psychological Effects of Digital Companies’ Employees during the Phase of COVID-19 Pandemic Extracted from Online Employee Reviews

The ways people use words online can furnish psychological processes about their beliefs, fears, thinking patterns, and so on. Extracting from online employees’ reviews on the workplace community websites, we can quantify the psychological effects of employees during the phase of the COVID-19 pandemic. We collect the anonymous employees’ reviews of Top 100 digital companies from the Glassdoor website which allows people to evaluate and review the companies they have worked for or are working for. Here, based on the data of numerical evaluations and textual reviews, we firstly use Z-score to investigate the psychological effects of employees in digital companies during the phase of COVID-19 pandemic. Next, we use a text analysis application called Linguistic Inquiry and Word Count (LIWC), which provides an efficient and effective method for studying the various emotional, cognitive, and structural components existing in individuals’ verbal and written speech samples, to mine these reviews to obtain changes in personal pronouns and 10 dimensions of psychological processes. Finally, we use Z-score to count on all aspects of drives and personal concerns in psychological processes.
Attention Retrieval Model for Entity Relation Extraction From Biological Literature

Natural Language Processing (NLP) has contributed to extracting relationships among biological entities, such as genes, their mutations, proteins, diseases, processes, phenotypes, and drugs, for a comprehensive and concise understanding of information in the literature. Self-attention-based models for Relationship Extraction (RE) have played an increasingly important role in NLP. However, self-attention models for RE are framed as a classification problem, which limits its practical usability in several ways. We present an alternative framework called the Attention Retrieval Model (ARM), which enhances the applicability of attention-based models compared to the regular classification approach, for RE. Given a text sequence containing related entities/keywords, ARM learns the association between a chosen entity/keyword with the other entities present in the sequence, using an underlying self-attention mechanism. ARM provides a flexible framework for a modeller to customise their model, facilitate data integration, and integrate expert knowledge to provide a more practical approach for RE. ARM can extract unseen relationships that are not annotated in the training data, analogous to zero-shot learning. To sum up, ARM provides an alternative self-attention-based deep learning framework for RE, that can capture directed entity relationships.
An integrated income and management model for internationalization

Since its conception almost three decades ago, internationalization of higher education has seen the development of organization models for internationalization at institutional level. However, based on a literature analysis of the most cited models, what remains to be developed are institutional funding frameworks to support internationalization activities. This article proposes a model for the acquisition and management of funds for the internationalization of higher education institutions. It has the premise that an institutional integrative system can generate more resources than one that is not institutionally integrative. A strategic application of the model is discussed as an example for its application at institutional level. This model provides practical implications for strengthening internationalization efforts at higher education institutions.
On Computing The Behavior Of The Mind From An Eastern Philosophical Perspective

The human mind is one of the most intriguing complex systems found in nature. Research has been conducted to develop a computer model of the mind from the Theravada Buddhist description detailed in the Abhidhamma literature. Within this context, the mind is identified as a conditional flow of thoughts that is characterized by 52 mental factors, such as perception, volition, attention, anger, attachments, mindfulness and so on. Thus, mental factors are the building blocks of thoughts. Combinations of these 52 mental factors can form 89 different thoughts with characteristics such as feelings, roots, functions, doors, objects and bases. We have developed a computational ontology of Buddhist theory of mind (OntoBM) that describes the complex association among thoughts and mental factors. OntoBM can be primarily used to examine combinations of mental factors that can form thoughts, associations among mental factors and details of thoughts. More importantly, researchers and scholars can also use OntoBM to experiment with the effect of modulation of mental factors on the formation of various thoughts.
Neuromarketing unmasked: A review of current state in the field

Neuromarketing showed up as a new interdisciplinary field that bridges neuroscience and marketing. A relatively young field that was born within the "neuroculture" matrix is covered with a veil of mystery and often misrepresented in the media as a powerful tool used by corporations to manipulate consumers' preferences, purchasing behavior, etc. In this paper, we have done an extensive literature review in order to put light on some dilemmas and take off the veil of mystery that surrounds neuromarketing. Firstly, (i) we discussed the definition and context in which neuromarketing emerged, (ii) important brain areas in consumer neuroscience which find their application in neuromarketing research, (iii) techniques used in neuromarketing (neuroimaging and non-neuroimaging), (iv) ethical issues in the field of neuromarketing (a part of neuroethics), and (v) limitations and recommendations for future development of neuromarketing.
NUMERICAL SIMULATIONS OF UNSTEADY OIL FLOWS IN THE GEAR-BOXES

This paper deals with the behavior of oil in the gear-box. For numerical simulations, the commercial software Fluent was used for relatively complicated problems of the two-phase unsteady turbulent flow. First of all, problems are focused on simplified models of one gear and set of gears, then finally on the model of complete gear-box. These cases are solved as unsteady problem - computation leads from start time, where oil pool is in calm, to the stable regime, where oil pool is in balance.The numerical study is consisted of several cases; run-up cogwhell or set of gears with the calm pool of oil, run-up with changes of oil pool sway, influence of viscosity changes on oil flows inside gear-box and run-up complete gear-box with calm pool of oil.
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well.
Cannabidiol Inhibits Tumorigenesis in Cisplatin-Resistant Non-Small Cell Lung Cancer via TRPV2

Simple Summary Drug resistance is the key factor contributing to the therapeutic failure of lung cancer and the deaths related to lung cancer. Our study demonstrated that small molecular weight non-psychotropic phytochemical, cannabidiol (CBD), inhibits growth and metastasis of drug-resistant non-small cell lung cancer cells (NSCLC) cells in-vitro and in-vivo. We further discovered that CBD mediates its anti-cancer effects in part via an ion channel receptor, TRPV2, present on lung adenocarcinoma. Moreover, we showed that CBD induces apoptosis of cisplatin-resistant cells by modulating oxidative stress pathways. Overall, these studies indicate that CBD could be used as a promising therapeutic strategy in TRPV2 expressing cisplatin-resistant NSCLC. Abstract Chemotherapy forms the backbone of current treatments for many patients with advanced non-small-cell lung cancer (NSCLC). However, the survival rate is low in these patients due to the development of drug resistance, including cisplatin resistance. In this study, we developed a novel strategy to combat the growth of cisplatin-resistant (CR) NSCLC cells. We have shown that treatment with the plant-derived, non-psychotropic small molecular weight molecule, cannabidiol (CBD), significantly induced apoptosis of CR NSCLC cells. In addition, CBD treatment significantly reduced tumor progression and metastasis in a mouse xenograft model and suppressed cancer stem cell properties. Further mechanistic studies demonstrated the ability of CBD to inhibit the growth of CR cell lines by reducing NRF-2 and enhancing the generation of reactive oxygen species (ROS). Moreover, we show that CBD acts through Transient Receptor Potential Vanilloid-2 (TRPV2) to induce apoptosis, where TRPV2 is expressed on human lung adenocarcinoma tumors. High expression of TRPV2 correlates with better overall survival of lung cancer patients. Our findings identify CBD as a novel therapeutic agent targeting TRPV2 to inhibit the growth and metastasis of this aggressive cisplatin-resistant phenotype in NSCLC.
Analysis of FPGA filter in computed tomography images for radioactive dose reduction

— X-Ray or CT (computed tomography) images may have noise due to image acquisition process. As contaminated images complicate diagnosis many filters have been developed to overcome this problem. In this work we study the behavior of a Fuzzy method called FPGA, which detect and correct impulsive and Gaussian noise, used over a medical image obtained from the mini-MIAS database that has been altered with impulsive and/or Gaussian noise. The aim of the study is verify if FPGA is a candidate to be used as a method to reduce the radiation dose in CT. Results show that FPGA outperforms the rest of the methods studied and it reveals itself as a good candidate to be employed in CT images to reduce the radiation dose.
Effect of Vacancy Defects on the Vibration Frequency of Graphene Nanoribbons

Graphene is a type of two-dimensional material with special properties and complex mechanical behavior. In the process of growth or processing, graphene inevitably has various defects, which greatly influence the mechanical properties of graphene. In this paper, the mechanical properties of ideal monolayer graphene nanoribbons and monolayer graphene nanoribbons with vacancy defects were simulated using the molecular dynamics method. The effect of different defect concentrations and defect positions on the vibration frequency of nanoribbons was investigated, respectively. The results show that the vacancy defect decreases the vibration frequency of the graphene nanoribbon. The vacancy concentration and vacancy position have a certain effect on the vibration frequency of graphene nanoribbons. The vibration frequency not only decreases significantly with the increase of nanoribbon length but also with the increase of vacancy concentration. As the vacancy concentration is constant, the vacancy position has a certain effect on the vibration frequency of graphene nanoribbons. For nanoribbons with similar dispersed vacancy, the trend of vibration frequency variation is similar.
CRISPR/Cas9-Mediated Models of Retinitis Pigmentosa Reveal Differential Proliferative Response of Müller Cells between Xenopus laevis and Xenopus tropicalis

Retinitis pigmentosa is an inherited retinal dystrophy that ultimately leads to blindness due to the progressive degeneration of rod photoreceptors and the subsequent non-cell autonomous death of cones. Rhodopsin is the most frequently mutated gene in this disease. We here developed rhodopsin gene editing-based models of retinitis pigmentosa in two Xenopus species, Xenopus laevis and Xenopus tropicalis, by using CRISPR/Cas9 technology. In both of them, loss of rhodopsin function results in massive rod cell degeneration characterized by progressive shortening of outer segments and occasional cell death. This is followed by cone morphology deterioration. Despite these apparently similar degenerative environments, we found that Müller glial cells behave differently in Xenopus laevis and Xenopus tropicalis. While a significant proportion of Müller cells re-enter into the cell cycle in Xenopus laevis, their proliferation remains extremely limited in Xenopus tropicalis. This work thus reveals divergent responses to retinal injury in closely related species. These models should help in the future to deepen our understanding of the mechanisms that have shaped regeneration during evolution, with tremendous differences across vertebrates.
New Species of Aspergillus (Aspergillaceae) from Tropical Islands of China

Aspergillus species are cosmopolitan and ubiquitous, closely related to human daily life. They are also of food, industrial and medical importance. From the examination of cultures isolated from soil samples collected on tropical islands of China, four new species of the genus were discovered based on phylogenetic analyses and morphological comparisons. Aspergillus xishaensis sp. nov. and A. neoterreus sp. nov. belong to sections Flavipedes and Terrei of subgenus Circumdati, and A. hainanicus sp. nov. and A. qilianyuensis sp. nov. are in sections Cavernicolarum and Nidulantes of subgenus Nidulantes. To accommodate A. hainanicus, a new series Hainanici was proposed. Detailed descriptions and illustrations of the new taxa were provided.
Acute postoperative pain after orthognathic surgery can be predicted by the preoperative evaluation of conditioned pain modulation and pain catastrophizing

Presurgical conditioned pain modulation and pain catastrophizing scale assessments can be used to predict acute postoperative pain in patients undergoing orthognathic surgery. Abstract Introduction: The incidence and severity of chronic postoperative pain (POP) are major clinical challenges, and presurgical conditioned pain modulation (CPM) and pain catastrophizing scale (PCS) assessments have exhibited predictive values for POP. However, whether CPM and PCS assessments are also predictive of acute POP is unknown. Objectives: We aimed to investigate the relationship between preoperative CPM and PCS and acute POP severity after orthognathic surgery by assessing preoperative CPM and PCS in 43 patients. Methods: The pressure pain threshold and tonic painful cold–heat pulse stimulation (applied with a pain intensity score of 70 on a visual analogue scale [VAS 0–100]) were used as the test and conditioning stimuli, respectively. The pain area under the postoperative VAS area under the curve (VASAUC) was estimated. The associations between CPM, PCS, and VASAUC were also analyzed. Results: No patient experienced chronic POP after 1 month. Negative and positive CPM effects (test stimulus threshold was 0% > and 0% ≤ during conditioning stimulation, respectively) were detected in 36 and 7 patients, respectively. For patients with negative CPM effects (CPM responders), multiple regression analysis revealed a prediction formula of log (VASAUC) = (−0.02 × CPM effect) + (0.13 × PCS-magnification) + 5.10 (adjusted R2 = 0.4578, P = 0.00002, CPM effect; P = 0.002, PCS-magnification; P = 0.0004), indicating that a weaker CPM and higher PCS scores were associated with more acute POP after surgery. Conclusion: CPM and PCS can predict acute POP after orthognathic surgery.
Assembly Gap Tolerance Calculation Using ANFIS and Cost Function Optimization

Abstract: Tolerance plays a crucial role in the quality of a product. When different parts of an assembly are assembled, gap arises due to variation in dimensions. The tolerance for the gap is calculated using two methodologies: ANFIS and cost function optimization. In ANFIS the network is trained using mean dimensions, standard deviations as inputs and tolerances as output. The tolerances are predicted from the trained network. In cost function optimization, a cost function is formulated. The machinability of a part is calculated using Fuzzy Comprehensive Evaluation method. The tolerances are calculated by optimizing the cost function. The tolerances calculated using the two methods are compared and optimal tolerance is considered for manufacturing. Keywords: Tolerance, ANFIS, Assembly gap, Cost function, Optimization.
Sistem Pemantauan dan Pengendalian Sepeda Listrik Berbasis Internet of Things

Sepeda listrik kini telah beredar cukup banyak di Indonesia. Akan tetapi, rata-rata sepeda listrik yang dijual saat ini belum terintegrasi dengan smartphone, sehingga belum mampu dipantau dan dikendalikan dari jarak jauh. Pada makalah ini dikembangkan sistem pemantauan dan pengendalian sepeda listrik berbasis internet of things (IoT). Mikrokontroler berbasis ESP32 digunakan sebagai perangkat IoT: untuk mengukur jarak tempuh menggunakan sensor GPS dengan menerapkan metode Haversine, mengukur kecepatan sepeda, merancang sistem keamanan sepeda, dan merancang sistem pengukuran kalori saat sepeda dikayuh. Modul SIM800L digunakan sebagai perangkat komunikasi. Modul ini mampu melakukan komunikasi internet melalui jaringan 2G. Controller sepeda listrik dimodifikasi agar dapat terintegrasi dengan ESP32, sedangkan sebagai penggerak sepeda listrik, digunakan motor tipe BLDC dengan tegangan 36 V. Raspberry Pi digunakan sebagai server web penyimpan dan pengolah data, sedangkan perhitungan kalori terbakar dilakukan menggunakan metode metabolic equivalent of task (MET). Proses pemantauan dan pengendalian sepeda listrik dilakukan dengan membangun aplikasi berbasis smartphone Android dengan menggunakan aplikasi Kodular, dengan fitur layanan peta yang digunakan berbasis OpenStreetMap. Aplikasi ini mampu menyalakan dan mematikan sepeda listrik dari jarak jauh, mengatur posisi gear kecepatan, mengatur kecepatan, menyalakan alarm, melacak lokasi terakhir, melakukan pelacakan riwayat lokasi, dan melakukan pengukuran kalori. Proses pengendalian tersebut dapat dilakukan dengan menekan tombol dan menggunakan perintah suara dalam bahasa Indonesia. Aplikasi ini telah diuji coba dengan metode black box dengan hasil 100% berhasil dengan delay waktu sebesar 8,82 s. Pengukuran kalori memiliki akurasi 94,24% dibandingkan dengan peralatan pengukur kalori yang berada di pasaran. Pengendalian kecepatan dapat dilakukan secara linear dengan R2 sebesar 0,9984.
COINTEGRA TION AND UNIT ROOTS

This paper provides an updated survey of a burgeoning literature on testing, estimation and model speciftcation in the presence of integrated variables. Integrated variables are a speciftc class of non-stationary variables which seem to characterise faithfully the properties of many macroeconomic time series. The analysis of cointegration develops out of the existence of unit roots and offers a generic route to test the validity of the equilibrium predictions of economic theories. Special emphasis is put on the empirical researcher's point of view.
BnaA03.MKK5-BnaA06.MPK3/BnaC03.MPK3 Module Positively Contributes to Sclerotinia sclerotiorum Resistance in Brassica napus

Brassica napus (oilseed rape) is one of the most important oil crops worldwide, but its growth is seriously threatened by Sclerotinia sclerotiorum. The mechanism of oilseed rape response to this pathogen has rarely been studied. Here, it was identified that BnaA03.MKK5 whose expression was induced by S. sclerotiorum infection was involved in plant immunity. BnaA03.MKK5 overexpression lines exhibited decreased disease symptoms compared to wild-type plants, accompanied by the increased expression of camalexin-biosynthesis-related genes, including BnPAD3 and BnCYP71A13. In addition, two copies of BnMPK3 (BnA06.MPK3 and BnC03.MPK3) were induced by Sclerotinia incubation, and BnaA03.MKK5 interacted with BnaA06.MPK3/BnaC03.MPK3 in yeast. These interactions were confirmed using in vivo co-immunoprecipitation assays. In vitro phosphorylation assays showed that BnaA06.MPK3 and BnaC03.MPK3 were the direct phosphorylation substrates of BnaA03.MKK5. The transgenic oilseed rape plants including BnaA06.MPK3 and BnaC03.MPK3 overexpression lines and BnMPK3 gene editing lines mediated by CRISPR/Cas9 were generated; the results of the genetic transformation of BnaA06.MPK3/BnaC03.MPK3 indicate that BnMPK3 also has a positive role in Sclerotinia resistance. This study provides information about the potential mechanism of B. napus defense against S. Sclerotiorum mediated by a detailed BnaA03.MKK5-BnaA06.MPK3/BnaC03.MPK3 module.
"From Invisible Hand to Visible Hand: Platform Governance and Institutional Logics of Independent Mac Application Developers, 2001-2012"

Within the field of study in institutional logics, the identification of field-level logics has gained considerable interest among management scholars. A cultural emergence model of field-level logics was proposed in the latest development of the institutional logics perspective (Thornton et al. 2012). This study aims to validate a section of the model: the relationship between the resource environment and emergence / evolution of field-level logics, and do so in the context of Apple’s independent Mac OS X software developers, commonly referred to as Mac indies. Through a qualitative interpretive study, and a combination of narrative and content analysis, we first identify the critical components of the resource environment for Mac indies - platform governance and developers’ own economy. Subsequently, we examine how a critical platform governance change, the introduction of the Mac App Store, causes changes in the field-level logics observed within the Mac indie field. Specifically, as Apple shifts its r.
Titanium Cholla: Lightweight, High- Strength Structures for Aerospace Applications

Aerospace designers seek lightweight, high-strength structures to lower launch weight while creating structures that are capable of withstanding launch loadings. Most 'light-weighting' is done through an expensive, time-consuming, iterative method requiring experience and a repeated design/test/redesign sequence until an adequate solution is obtained. Little successful work has been done in the application of generalized 3D optimization due to the difficulty of analytical solutions, the large computational requirements of computerized solutions, and the inability to manufacture many optimized structures with conventional machining processes. The Titanium Cholla LDRD team set out to create generalized 3D optimization routines, a set of analytically optimized 3D structures for testing the solutions, and a method of manufacturing these complex optimized structures. The team developed two new computer optimization solutions: Advanced Topological Optimization (ATO) and FlexFEM, an optimization package utilizing the eXtended Finite Element Method (XFEM) software for stress analysis. The team also developed several new analytically defined classes of optimized structures. Finally, the team developed a 3D capability for the Laser Engineered Net Shaping{trademark} (LENS{reg_sign}) additive manufacturing process including process planning for 3D optimized structures. This report gives individual examples as well as one generalized example showing the optimized solutions and an optimized metal part.