Miracle-dz/UniFAR-FTUs huggingface dataset:
The optical anisotropy and orientational order parameter of two mesogens having slightly different flexible side chain – a comparative study

ABSTRACT Variation of the phase transition temperatures and existence of different number of mesophases, due to the changes of -CH2 groups at the oxygen containing flexible side chain between two Schiff’s base liquid crystals compounds p-ethoxybenzylidine p-heptylaniline (EBHA) and p-butoxybenzylidine p-heptylaniline (BBHA) have been studied by using POM and DSC. Refractive indices as a function of temperature of these two compounds have also been measured by the well-known thin prism technique. To get the best accuracy of refractive indices data a comparison between the experimental data and theoretical values which are obtained from modified four-parameter model based on Vuks’ hypothesis have been done. A comparative study of some important parameters such as, optical anisotropy, orientational order parameters of these two liquid crystal compounds have also been conducted. Graphical Abstract
Kiteboarding: a practice approach

ABSTRACT In this article we use practice theory to examine kiteboarding as a routinised type of behaviour; a social practice. By ‘zooming in’ we show how kiteboarding involves the active and performative integration of particular meanings – especially an exhilarating ‘sensation of freedom’ – materials and competences, which have to be learned in order to become a practitioner. New materials, technological innovations and changed behaviours of practitioners have made the sport more safe, popular and accessible. Furthermore, a constant interplay between producers and consumers contributed to a variation in kiteboarding styles: freeriding, hydro-foiling, waving, freestyle and big air. ‘Zooming out’ allowed us to examine the relations between kiteboarding and other practices, such as swimming, lifesaving procedures and nature conservation. Whereas lifesaving procedures to a large extent have been integrated in the practice of kiteboarding, the relation with nature conservation still is rather tense. For this reason, we suggest nature conservationists and kiteboarders learn from each other in order to give each other ‘some space’.
Stabilization of the Quadruplex-Forming G-Rich Sequences in the Rhinovirus Genome Inhibits Uncoating—Role of Na+ and K+

Rhinoviruses (RVs) are the major cause of common cold, a respiratory disease that generally takes a mild course. However, occasionally, RV infection can lead to serious complications in patients debilitated by other ailments, e.g., asthma. Colds are a huge socioeconomic burden as neither vaccines nor other treatments are available. The many existing drug candidates either stabilize the capsid or inhibit the viral RNA polymerase, the viral proteinases, or the functions of other non-structural viral proteins; however, none has been approved by the FDA. Focusing on the genomic RNA as a possible target for antivirals, we asked whether stabilizing RNA secondary structures might inhibit the viral replication cycle. These secondary structures include G-quadruplexes (GQs), which are guanine-rich sequence stretches forming planar guanine tetrads via Hoogsteen base pairing with two or more of them stacking on top of each other; a number of small molecular drug candidates increase the energy required for their unfolding. The propensity of G-quadruplex formation can be predicted with bioinformatics tools and is expressed as a GQ score. Synthetic RNA oligonucleotides derived from the RV-A2 genome with sequences corresponding to the highest and lowest GQ scores indeed exhibited characteristics of GQs. In vivo, the GQ-stabilizing compounds, pyridostatin and PhenDC3, interfered with viral uncoating in Na+ but not in K+-containing phosphate buffers. The thermostability studies and ultrastructural imaging of protein-free viral RNA cores suggest that Na+ keeps the encapsulated genome more open, allowing PDS and PhenDC3 to diffuse into the quasi-crystalline RNA and promote the formation and/or stabilization of GQs; the resulting conformational changes impair RNA unraveling and release from the virion. Preliminary reports have been published.
A method for successful collection of multicores and gravity cores from Antarctic subglacial lakes

During the 2018–2019 Antarctic field season, the Subglacial Antarctic Lakes Scientific Access project team cleanly accessed Mercer Subglacial Lake, West Antarctica, to sample water and sediments beneath 1087 m of overlying ice. A multicorer was successful in sampling the sediment–water interface, with 4 deployments retrieving 10 cores between 0.3 and 0.4 m in length. Gravity coring was also successful, retrieving cores of 0.97 and 1.78 m in glacial diamict. However, sediment cores retrieved by the gravity cores were shorter than the core barrel penetration (as measured by mud streaks on the outside of the coring system), indicating that the system can likely be improved. This manuscript describes the design, implementation, successes, and lessons learned while coring sediments in a subglacial lake.
Integrating presence‐only and presence–absence data to model changes in species geographic ranges: An example in the Neotropics

Anthropogenic changes such as land use and climate change affect species' geographic ranges, causing range shifts, contractions, or expansions. However, data on range dynamics are insufficient, heterogeneous, and spatially and temporally biased in most regions. Integrated species distribution models (IDMs) offer a solution as they can complement good quality presence‐absence data with opportunistically collected presence‐only data, simultaneously accounting for heterogeneous sampling effort. However, these methods have seen limited use in the estimation of temporal change of geographic ranges and are not yet widespread as they have a steep learning curve. Here we present a generalisable model and case example.
Functioning Endocrine Outcome after Endoscopic Endonasal Transsellar Approach for Pituitary Neuroendocrine Tumors

Background: The endoscopic endonasal approach (EEA) is a well-established technique for the treatment of pituitary neuroendocrine tumor Preservation of normal gland tissue is crucial to retain effective neuroendocrine pituitary function. The aim of this paper is to analyze pituitary endocrine secretion after EEA for pituitary neuroendocrine tumor to identify potential predictors of functioning gland recovery. Methods: Patients who underwent an exclusive EEA for pituitary neuroendocrine tumors between October 2014 and November 2019 were reviewed. Patients were divided into groups according to postoperative pituitary function (Group 1, unchanged; group 2, recovering; group 3, worsening). Results: Among the 45 patients enrolled, 15 presented a silent tumor and showed no hormonal impairment, and 30 patients presented pituitary dysfunction. A total of 19 patients (42.2%) were included in group 1, 12 (26.7%) patients showed pituitary function recovery after surgery (group 2), and 14 patients (31.1%) exhibited the onset of new pituitary deficiency postoperatively (group 3). Younger patients and those with functioning tumor were more likely to have complete pituitary hormonal recovery (p = 0.0297 and p = 0.007, respectively). No predictors of functional gland worsening were identified. Conclusion: EEA for pituitary neuroendocrine tumor is a reliable and safe technique regarding postoperative hormonal function. Preserving pituitary function after tumor resection should be a primary goal in a minimally invasive approach.
Online teaching self-efficacy of Chinese university teachers amidst Covid-19: its changes and the moderation of adaptability and administration quality

ABSTRACT Teacher self-efficacy is among the most valued teacher motivational constructs. However, little is known about university teachers’ self-efficacy and even less about changes to it throughout the Covid-19-related online teaching. This study applied a retrospective pre- and post-design to investigate changes in online teaching self-efficacy (OTSE) during Covid-19. Participants included 160 Chinese university teachers, who reported their OTSE before and after the COVID-19 lockdowns, adaptability and administration quality together with demographic information. The self-efficacy for online instruction failed to increase significantly over this period (β=.21, p = .083), whereas that for online technology applications increased significantly (β=.329, p < .01). Individual adaptability and administration quality significantly moderated the changes in OTSE. The implications and limitations of the study are discussed.
Intelligent Warehouse in Industry 4.0—Systematic Literature Review

The development of Industry 4.0 (I4.0) and the digitization and automation of manufacturing processes have created a demand for designing smart warehouses to support manufacturing processes. Warehousing is one of the fundamental processes in the supply chain, and is responsible for handling inventory. Efficient execution of warehouse operations often determines the effectiveness of realized goods flows. Therefore, digitization and its use in exchanging information between partners, especially real-time inventory levels, is critical. For this reason, the digital solutions of Industry 4.0 have quickly found application in internal logistics processes and enabled the design of smart warehouses, also known as Warehouse 4.0. The purpose of this article is to present the results of the conducted review of publications on the design and operation of warehouses using the concepts of Industry 4.0. A total of 249 documents from the last 5 years were accepted for analysis. Publications were searched for in the Web of Science database using the PRISMA method. The article presents in detail the research methodology and the results of the biometric analysis. Based on the results, a two-level classification framework was proposed, which includes 10 primary categories and 24 subcategories. Each of the distinguished categories was characterized based on the analyzed publications. It should be noted that in most of these studies, the authors’ attention primarily focused on the implementation of (1) Industry 4.0 technological solutions, such as IoT, augmented reality, RFID, visual technology, and other emerging technologies; and (2) autonomous and automated vehicles in warehouse operations processes. Critical analysis of the literature also allowed us to identify the current research gaps, which will be the subject of further research by the authors.
A Nested Memetic Algorithm and its Application on Numerical Optimization Problems

Nowadays, many researchers have focused on the optimization topic and numerical optimization. Evolutionary optimization algorithms have been the most important and the most widely used optimization tools in the past few decades. So in this paper we introduce and study a new Memetic Algorithm (MA) based on the particle swarm optimization algorithm. In the proposed algorithm, the particle swarm optimization algorithm has combined with a nested local search, which runs at each step of the algorithm. The local search part is also included two sub-sectors: the part of local search on the chosen particles, and the part of local search on the chosen particles among the pre-selected particles. Therefore, at each stage the best particles are selected with the two steps of searching and somore accurate and better updates are achieved. Simulation result demonstrates the good performance of the proposed memetic optimization algorithm and also shows its superiority over the other algorithms in the numerical optimization problems.
Impact of Adipose Tissue Depot Harvesting Site on the Multilineage Induction Capacity of Male Rat Adipose-Derived Mesenchymal Stem Cells: An In Vitro Study

Recently, substantial attention has been paid toward adipose-derived mesenchymal stem cells (AdMSCs) as a potential therapy in tissue engineering and regenerative medicine applications. Rat AdMSCs (r-AdMSCs) are frequently utilized. However, the influence of the adipose depot site on the multilineage differentiation potential of the r-AdMSCs is still ambiguous. Hence, the main objective of this study was to explore the influence of the adipose tissue harvesting location on the ability of r-AdMSCs to express the stem-cell-related markers and pluripotency genes, as well as their differentiation capacity, for the first time. Herein, we have isolated r-AdMSCs from the inguinal, epididymal, peri-renal, and back subcutaneous fats. Cells were compared in terms of their phenotype, immunophenotype, and expression of pluripotency genes using RT-PCR. Additionally, we investigated their potential for multilineage (adipogenic, osteogenic, and chondrogenic) induction using special stains confirmed by the expression of the related genes using RT-qPCR. All cells could positively express stem cell marker CD 90 and CD 105 with no significant in-between differences. However, they did not express the hematopoietic markers as CD 34 and CD 45. All cells could be induced successfully. However, epididymal and inguinal cells presented the highest capacity for adipogenic and osteogenic differentiation (21.36-fold and 11.63-fold for OPN, 29.69-fold and 26.68-fold for BMP2, and 37.67-fold and 22.35-fold for BSP, respectively, in epididymal and inguinal cells (p < 0.0001)). On the contrary, the subcutaneous cells exhibited a superior potential for chondrogenesis over the other sites (8.9-fold for CHM1 and 5.93-fold for ACAN, (p < 0.0001)). In conclusion, the adipose tissue harvesting site could influence the differentiation capacity of the isolated AdMSCs. To enhance the results of their employment in various regenerative cell-based therapies, it is thus vital to take the collection site selection into consideration.
Efficacy of brief cognitive behavioural intervention for the treatment of psychosis: A case report

Cognitive behavioural therapy (CBT) has been found to have strong empirical evidence for the treatment of anxiety and mood disorders. However, its applicability to schizophrenia and other psychotic disorders have posed as a challenge to clinicians and researchers for a long time. In the recent years, there has been a growing body of literature on the effectiveness of CBT on psychosis. The present study attempts to highlight the role of brief CBT in the management of a 23-year-old Bengali speaking, unmarried male, hailing from a rural extended family, with a diagnosis of schizophrenia with prominent symptoms of hallucinations and delusions for the past three years. He was treated with brief CBT approach, resulting in improvement after eight sessions.
A Compliant Self-Stabilization Nanopositioning Device With Modified Active–Passive Hybrid Vibration Isolation Strategy

Micro/mini light-emitting diodes (LEDs) display panel inspection and repairs have a high demand for vibration isolating devices to protect industrial-level atomic force microscopes (AFM scanning head) against vibrations. The motivation of this work is to combine the advantages of both passive and active vibration isolation strategies to improve inspection performance. The developed self-stabilization device achieves this objective with a design that incorporates a suspension-type passive vibration isolation unit and integrates it with the modified active–passive hybrid (MAPH) vibration isolation strategy using piezoelectric ceramics (PZT) and voice coil motors (VCM) as compensators. First, the design, modeling, and optimization of a self-stabilization device are presented based on the MAPH vibration isolation strategy. To satisfy the requirements of vibration isolation performance and a lightweight design, a multiobjective optimization task was conducted. Next, a tailor-made double compensating PID controller was designed to allow this mechanism to run in the MAPH method to effectively isolate vibrations. Finally, a series of validation experiments, including passive vibration isolation performance tests and MAPH closed-loop tests, were applied. From 1 to 500 Hz, more than 98% frequency domain achieved a vibration isolation rate of 90%, the vibration amplification effect of the passive vibration isolation was significantly suppressed, the steady-state positioning accuracy reached $\pm 0.1\;\mu$m, load capacity was up to 2.5 kg, the attenuation ratio of the disturbances reached up to 70%, and the heat of the VCM was effectively reduced. All results comprehensively confirmed that the developed compliant MAPH vibration isolation system has achieved a satisfactory self-stabilization function.
Service Satisfaction-Oriented Task Offloading and UAV Scheduling in UAV-Enabled MEC Networks

With the development of computation-intensive applications, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) provides task offloading service for the users with or without terrestrial infrastructure support. Meanwhile, the next generation of UAVs communication systems are expected to be user-centric. Therefore, more attention should be paid to users’ satisfaction with the offered service. In this paper, we study the service satisfaction-oriented task offloading and UAV scheduling problem for UAV-enabled MEC networks, where the task priorities are considered based on the delay requirements of users’ tasks and the remaining energy status of users. Specifically, we firstly divide the users into different groups via the K-means-based grouping algorithm. Then, we develop a novel user satisfaction model by jointly considering the task processing delay and energy saving, based on which a total user satisfaction maximization problem is formulated to jointly optimize the task offloading decisions and UAV scheduling strategy. To solve the formulated problem, we decompose it into two sub-problems, i.e., the UAV scheduling sub-problem and the task offloading sub-problem. To solve the first sub-problem, we develop a genetic algorithm (GA)-based UAV scheduling algorithm through dealing with multiple balanced assignment problems. To address the second sub-problem, a GA-based task offloading algorithm is developed. Then, we propose a joint task offloading and UAV scheduling optimization algorithm to solve the original optimization problem. Finally, simulation results demonstrate that the proposed optimization algorithm not only converges fast, but also improves the total users’ satisfaction greatly. The total users’ satisfaction improved by the proposed scheme is up to 16.9% in the case of 170 users.
Scheduling for Minimizing the Age of Information in Multisensor Multiserver Industrial Internet of Things Systems

Real-time data delivery is significant for the Industrial Internet of Things (IIoT). Age of information (AoI), a popular real-time metric, is usually used to measure the data freshness of the IIoT systems. If the data most recently received by the destination at time <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> was generated at time <inline-formula><tex-math notation="LaTeX">$t_{1}$</tex-math></inline-formula>, then the AoI is <inline-formula><tex-math notation="LaTeX">$t-t_{1}$</tex-math></inline-formula>. In this article, we consider a multisensor multiserver IIoT system and develop scheduling algorithms to minimize the average AoI. The challenge lies in the strong coupling between link scheduling, server selection, and service preemption. To address this issue, we propose a guided exploration-based deep <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Network (GE-DQN) algorithm utilizing a fixed advantage policy, which has a faster learning speed compared to classical deep <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-network. Moreover, we use a shared decision module followed by several network branches to transform the structure of GE-DQN and propose a guided exploration-based branching dueling <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-network (GE-BDQN) algorithm. Since the branch structure of GE-BDQN can decompose the high-dimensional action, GE-BDQN can reduce the approximate exponential growth of the number of output neurons with the increase of the number of sensors to linear growth compared to GE-DQN, ensuring the applicability of the algorithm under large-scale systems. From the simulation results, it can be found that the proposed two algorithms can achieve better average AoI compared to the advanced algorithms, and the GE-BDQN algorithm can achieve up to 36% performance gain.
Constructing Approximately Standard Normal Pivots from Signed Roots of Adjusted Likelihood Ratio Statistics

For inference about a scalar parameter *I in the presence of nuisance parameters, several authors have suggested that the usual log profile likelihood function M(41) be replaced by an objective function of the form M(*) = M(*) + B(*), where the derivatives of the adjustment function B(*) are of order Op(l). An adjusted likelihood ratio statistic W(4/) can be defined in terms of M(*). The distribution of R(qlD, the signed root of W(4), is typically standard normal to error of order O(n- /2). This paper concerns the use of mean and variance corrections to construct approximate pivots based on R(f) that have the standard normal distribution to error of order 0(n-3/2). General formulae for the mean and variance corrections are provided, and these formulae are evaluated for specific adjustment functions B(q) that have been proposed in the literature. Use of the corrections is illustrated in numerical examples.
Mentalizing, Resilience, and Mental Health Status among Healthcare Workers during the COVID-19 Pandemic: A Cross-Sectional Study

The COVID-19 pandemic has caused unprecedented stress on healthcare professionals worldwide. Since resilience and mentalizing capacity play very important preventive roles when it comes to mental health, the main goal of this study was to determine whether the capacity for mentalizing and resilience could explain the levels of depression, anxiety, and stress among healthcare workers during the COVID-19 pandemic. The study was conducted in Serbia on a sample of 406 healthcare workers (141 doctors and 265 nurses) aged 19 to 65 (M = 40.11, SD = 9.41). The participants’ mental health status was evaluated using the Depression, Anxiety, and Stress Scale—DASS-42. The Reflective Functioning Questionnaire was used to evaluate the capacity for mentalizing. Resilience was assessed using the Brief Resilience Scale. The results of the correlation analysis showed that there were negative correlations between resilience and all three dimensions of mental health status: depression, anxiety, and stress. Hypermentalizing was negatively correlated with depression, anxiety, and stress, while hypomentalizing was positively correlated. Hierarchical linear regression analysis showed that both resilience and hypermentalizing were significant negative predictors of depression, anxiety, and stress, and that hypomentalizing was a significant positive predictor of depression, anxiety, and stress. Furthermore, socioeconomic status was a significant negative predictor of depression, anxiety, and stress. Marital status, number of children, and work environment were not statistically significant predictors of any of the three dimensions of mental health status among the healthcare workers in this study. There is an urgent need to establish and implement strategies to foster resilience and enhance the capacity for mentalizing among healthcare workers in order to minimize the devastating effects of the COVID-19 pandemic on mental health.