Miracle-dz/UniFAR-FTUs huggingface dataset:
Intercalation of argon in honeycomb structures towards promising strategy for rechargeable Li-ion batteries

High-performance rechargeable batteries are becoming very important for high-end technologies with their ever increasing application areas. Hence, improving the performance of such batteries has become the main bottleneck to transferring high-end technologies to end users. In this study, we propose an argon intercalation strategy to enhance battery performance via engineering the interlayer spacing of honeycomb structures such as graphite, a common electrode material in lithium-ion batteries (LIBs). Herein, we systematically investigated the LIB performance of graphite and hexagonal boron nitride (h-BN) when argon atoms were sent into between their layers by using first-principles density-functional-theory calculations. Our results showed enhanced lithium binding for graphite and h-BN structures when argon atoms were intercalated. The increased interlayer space doubles the gravimetric lithium capacity for graphite, while the volumetric capacity also increased by around 20% even though the volume was also increased. The ab initio molecular dynamics simulations indicate the thermal stability of such graphite structures against any structural transformation and Li release. The nudged-elastic-band calculations showed that the migration energy barriers were drastically lowered, which promises fast charging capability for batteries containing graphite electrodes. Although a similar level of battery promise was not achieved for h-BN material, its enhanced battery capabilities by argon intercalation also support that the argon intercalation strategy can be a viable route to enhance such honeycomb battery electrodes.
Contextualizing Lecturer Performance Indicators to Online Teaching and Learning Activities: Insights for Application during the COVID-19 Pandemic And Beyond

The purpose of this research study is to explore insights to create more contextual lecturer performance indicators (LPIs); and to gain a fairer and more useful evaluation tool for lecturers in the online environment during the COVID-19 pandemic. This significant change in conditions has forced higher education to understand the new requirements, especially of lecturers, who have played a central role in creating engaging moments for students in online classes. This study adopts a qualitative approach, based on the need for a more in-depth exploration of new phenomena. It explores students’ daily lives in class, which were expressed through social media, and gains further understanding through in-depth interviews with students and lecturers. The results indicate that most of the items taken from the Social-Task-Technology-Fit (STTF) framework remain the same; there are not many changes compared to the era before the pandemic. However, some aspects have new meanings that need to be revisited to capture the lecturers' performance in the new context. Three new themes emerged from the exploration study which have never been discussed in previous studies on LPIs for online teaching, especially in abnormal conditions such as a pandemic. The first theme concerns the variations in digital literacy of lecturers and the implications for lecturers' performance. The second theme is the dynamic ratio of social versus task activities, which implies the need to focus on academic (task-oriented) or non-academic (social) tasks. The third theme is the degree of controllability of each performance. This study provides awareness of the importance of weighting in evaluating lecturer performance, especially when not all lecturers are digitally literate, not all factors are controllable, and the ratios between social versus task requirements are not the same. These weighted evaluations have never been discussed in previous studies, which mainly used quantitative surveys. In addition, these insights can also be applied to online learning beyond the pandemic. The study results can be used by higher education information management to design more contextual LPIs for the early detection of issues with lecturer performance.
Toward remote and secure authentication: Disambiguation of magnetic microwire signatures using neural networks

Secure and high-throughput authentication systems require materials with uniquely identifiable responses that can be remotely detected and rapidly disambiguated. To this end, complex electromagnetic responses from arrangements of amorphous ferromagnetic microwires were analyzed using machine learning. These novel materials deliver maximal spectral dispersion when the frequency of incident electromagnetic radiation matches the microwire resonance. Utilizing data obtained from 225 unique microwire arrangements, a neural network reproduced the response distribution of unseen data to a confidence level of 90%, with a mean square error less than 0.01. This favorable performance affirms the potential of magnetic microwires for use in tags for secure article surveillance systems. Graphical Abstract
BISON: BCI-based Interaction Concepts for Operating Microscopes in Neurosurgery

Neurosurgery requires medical professionals to adjust operational microscopes according to constantly changing conditions during surgery. These adjustments are currently performed manually by the surgeon which comes with risks for the patient and a hands-free solution for this adjustment is desirable. Within this work we present the design, implementation and a first evaluation of a concept for hands-free interaction with an operating microscope based on Brain-Computer Interfaces (BCIs). The proposed concept takes into account the various requirements concerning the adjustment of microscope parameters and combines different BCI paradigms for interaction. The concept was implemented and integrated into a simulated surgical scene in which the position, zoom, light and focus of a virtual microscope could be adjusted via BCI. 14 healthy subjects participated in a study and tested the implemented interaction concept in a simulated surgical task. Results of this first evaluation show, that all participants were able to control the microscope via BCI and fulfill the given task. Although the evaluation further shows that the developed system is not yet robust enough for real-world use, the concept itself has proven to be an intuitive way to interact in the simulated surgical scene, highlighting the potential of BCIs for hands-free adjustment of operating microscopes in the future.
Diode laser measurements of the Ar 3p54s1 excited states in an inductively coupled RF plasma

Diode laser absorption spectroscopy has been used to study the four lowest energy excited states of atomic argon (1s2–1s5) arising from the 3p54s1 electronic configuration and produced in an inductively coupled RF discharge. Three of the states (1s2, 1s4 and 1s5) were detected using wavelength modulation spectroscopy with diode laser radiation in the wavelength range 425–430 nm, obtained by frequency doubling the output of a near infrared diode laser in a KNbO3 crystal. The 1s3 state was probed using direct absorption spectroscopy with the fundamental output of a diode laser operating at 795 nm. The 1s5 state dominates the total population of this manifold with a proportion of around 48%, which is essentially independent of plasma power and pressure. Translational temperatures varied with power but were essentially pressure independent. For example, at 20 mTorr total pressure and 400 W applied power, the temperature was found to be 384 ±15 K. The variations in concentrations of all four 1sn states with plasma power and pressure are reported and suggests that a statistical equilibrium between them has been established. The results are rationalized by utilizing a simple kinetic model, to determine the dominant production (electron impact excitation from the ground state) and loss (intra-manifold quenching and radiative decay) processes. Fast redistribution of population in the states, mainly via electron impact excitation to higher lying electronic states and subsequent radiative decay back down, in combination with modified radiative rates for the two radiative states as a consequence of radiation imprisonment, can partially account for the observed relative trends.
Comparison of Correlated Algorithm Accuracy Naive Bayes Classifier and Naive Bayes Classifier for Classification of heart failure

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated that 17.5 million people died from cardiovascular disease, while in 2008, HF disease represented 31% of patient deaths worldwide. One of the new breakthroughs for early diagnosis is utilizing data mining techniques. In this study, the Correlated Naive Bayes Classifier (C-NBC) and Naive Bayes Classifier (NBC) algorithms are used to obtaining the best accuracy results so that they can be used for the Heart Failure dataset. Based on the results of the tests that have been carried out, it shows that the Correlated Naive Bayes Classifier (C-NBC) algorithm accuracy of 80.6% obtains higher accuracy than the Naive Bayes Classifier (NBC) algorithm of 67.5%. With the results of this study, the use of the Correlated Naive Bayes Classifier (C-NBC) algorithm can be used to diagnose patients with heart failure (heart failure) because it has a high level of accuracy and is categorized as Good Classification.
The multiplicative heteroscedastic Von Bertalanffy model

In this work, we propose a heteroscedastic Von Bertalanffy growth model considering a multiplicative heteroscedastic dispersion matrix. All estimates were obtained using a sampling based approach, which allows information to be input beforehand with lower computational effort. Simulations were carried out in order to verify some frequentist properties of the estimation procedure in the presence of small and moderate sample sizes. The methodology is illustrated on a real Kubbard female chicken corporeal weight dataset.
Deep Learning Based Infrared Thermal Image Analysis of Complex Pavement Defect Conditions Considering Seasonal Effect

Deep learning techniques underpinned by extensive data sources encompassing complex pavement features have proven effective in early pavement damage detection. With pavement features exhibiting temperature variation, inexpensive infra-red imaging technology in combination with deep learning techniques can detect pavement damages effectively. Previous experiments based on pavement data captured during summer sunny conditions when subjected to SA-ResNet deep learning architecture technique demonstrated 96.47% prediction accuracy. This paper has extended the same deep learning approach to a different dataset comprised of images captured during winter sunny conditions to compare the prediction accuracy, sensitivity and recall score with summer conditions. The results suggest that irrespective of the prevalent weather season, the proposed deep learning algorithm categorises pavement features around 92% accurately (95.18% in summer and 91.67% in winter conditions), suggesting the beneficial replacement of one image type with other. The data captured in sunny conditions during summer and winter show prediction accuracies of DC = 96.47% > MSX = 95.24% > IR-T = 93.83% and DC = 94.14% > MSX = 90.69% > IR-T = 90.173%, respectively. DC images demonstrated a sensitivity of 96.47% and 94.20% for summer and winter conditions, respectively, to demonstrate that reliable categorisation is possible with deep learning techniques irrespective of the weather season. However, summer conditions showing better overall prediction accuracy than winter conditions suggests that inexpensive IR-T imaging cameras with medium resolution levels can still be an economical solution, unlike expensive alternate options, but their usage has to be limited to summer sunny conditions.
A critical height of quiescent prominences before eruption

We discuss the problem of the limiting height of quiescent prominences and the relationship of this value to the photospheric magnetic field characteristics. Solar prominences are considered preeruptive states of coronal mass ejections (CMEs). We note that in a model of inverse-polarity filament, the equilibrium is stable only in the region where the photospheric magnetic field falls off with a height not faster than h−l. Current-free magnetic field calculations are compared with the observed prominence heights. It is shown that the maximum stable height of a prominence depends on the vertical gradient value and never exceeds the level where the power is equal to unity in the power-law decrease of the magnetic field. This is a new way to predict prominence eruptions and CMEs.
Classification of Pathological Images of Skin Diseases Based on Deep Learning

Skin cancer is one of the highest incidences of cancer, the incidence of the population covers all ages. However, the diagnosis process of skin diseases is complex, which requires doctors to observe and locate the injured sites first, then slice the living tissue under the microscope, which challenges doctors' timely diagnosis and medical treatment plan. Therefore, a more accurate classification algorithm of skin diseases has essential significance and clinical application value for timely skin cancer diagnosis. In recent years, most studies on dermatology algorithms have focused on the binary classification of benign and malignant dermatoses. However, there are many kinds of dermatoses, and each kind of dermatoses has different pathogenesis and treatment methods. Based on this, this paper applies convolutional neural network to eight classification of dermatosis. Furthermore, the size and shape of skin lesions are not the same, and the presence of artifacts such as hair and veins around the injured sites also make an accurate diagnosis more difficult. Therefore, this paper introduces the attention mechanism on the basis of the original Inception-Resnet-v2 network, and at the same time, enhances the original data. Finally, we uses the method of transfer learning to conduct experiments on the training dataset of ISIC 2019 challenge. The results show that the average classification accuracy of the method used in this paper is more than 85%, and the AUC score of each category is above 0.95, which shows that the classifier has good performance.
Induction of Multiple Immune Signaling Pathways in Gryllodes sigillatus Crickets during Overt Viral Infections

Despite decades of focus on crickets (family: Gryllidae) as a popular commodity and model organism, we still know very little about their immune responses to microbial pathogens. Previous studies have measured downstream immune effects (e.g., encapsulation response, circulating hemocytes) following an immune challenge in crickets, but almost none have identified and quantified the expression of immune genes during an active pathogenic infection. Furthermore, the prevalence of covert (i.e., asymptomatic) infections within insect populations is becoming increasingly apparent, yet we do not fully understand the mechanisms that maintain low viral loads. In the present study, we measured the expression of several genes across multiple immune pathways in Gryllodes sigillatus crickets with an overt or covert infection of cricket iridovirus (CrIV). Crickets with overt infections had higher relative expression of key pathway component genes across the Toll, Imd, Jak/STAT, and RNAi pathways. These results suggests that crickets can tolerate low viral infections but can mount a robust immune response during an overt CrIV infection. Moreover, this study provides insight into the immune strategy of crickets following viral infection and will aid future studies looking to quantify immune investment and improve resistance to pathogens.
Spatiotemporal behavior of predators and prey in an arid environment of Central Asia

Abstract The mechanisms of interactions between apex and smaller carnivores may range from competition to facilitation. Conversely, interactions between predators and prey are mainly driven by the prey reducing the likelihood of encounters with predators. In this study, we investigated (1) the spatiotemporal interactions between an apex (the snow leopard) and a meso-predator (the red fox), and (2) the temporal interactions between the snow leopard and its potential prey (Siberian ibex, argali, Asian wild ass, Tolai hare) through camera trapping in the Mongolian Great Gobi-A. The probability of occurrence for the red fox was higher in the presence of the snow leopard than in its absence. Moreover, the red fox activity pattern matched that of the snow leopard, with both species mostly active at sunset. This positive spatiotemporal interaction suggests that the presence of the snow leopard may be beneficial for the red fox in terms of scavenging opportunities. However, other explanations may also be possible. Amongst prey, the Siberian ibex and the argali were mainly active during the day, whereas the Asian wild ass and the Tolai hare were more nocturnal. These findings suggest that potential prey (especially the Siberian ibex and the argali) may shape their behavior to decrease the opportunity for encounters with the snow leopard. Our results have revealed complex interactions between apex and smaller predators and between apex predator and its potential prey.
Disease Detection System in Tobacco Leaves based on Edge Detection with Decision Tree Classification Method

Indonesia has long been known as an agricultural country, one of Indonesia's best agricultural products is tobacco. Tobacco with good quality can be seen from the leaves, in fact the diseases that attack tobacco are of various types which can be seen from the changes in the tobacco leaves starting from the seeding and planting period. In the tobacco growing period, it is divided into two major parts, namely seeding period and the planting period, so that diseases that attack tobacco are also divided into two, namely diseases that attack during seeding and planting. This research is limited to diseases that attack tobacco at the time of planting, because at the time of seedling the tobacco has not yet produced leaves. When tobacco enters the planting period, at this time tobacco leaves begin to form. Good care is needed at this time such as fertilization, nutrition, vitamins, and pest control in order to obtain healthy tobacco so that tobacco is not susceptible to disease. Tobacco that lacks nutritional intake will be susceptible to diseases including fungi, bacteria, and viruses. The disease attack on tobacco has its own characteristics that appear on tobacco leaves. Early detection of the disease is very important so that disease control can be precise and the spread of the disease can be prevented so as not to cause endemic. In this research, an early detection system of tobacco leaf disease based on image processing will be designed. Normalization image, grayscale technique, folllowed by edge detection will be applied in these image so that from here the entropy, energy, and inertia values of the image can be obtained using statistical measures, and the last one using the decision tree classification technique can be classified as uninfected leaf or infected leaf. In this study, feature extraction from images of tobacco leaves that are not infected with the virus using grayscale techniques followed by edge detection produces an average statistical measure with entropy (h) values between 2,341 to 2,676, energy (e) values between 6,112 to 6,665, and inertia values. (i) between 3,322 to 3,576, while for leaves infected with the virus the average value of entropy (h) is between 4,543 to 5,576, the average value of energy (e) is between 12,212 to 13,455, and the average value of inertia (i) between 5,343 to 6,597.
Millimeter Wave Compact Branch Line Coupler Using Metal–Air–Metal Capacitors

In this paper, a compact branch line coupler fabricated on a GaAs substrate using surface micromachining. It is composed of a dielectric-supported air-gapped microstrip line (DAML) structure, which is a low-loss transmission line with an air-post and is useful for integration with other micro electro-mechanical system (MEMS) and/or MMIC components. The size reduction of the branch line coupler is accomplished by the insertion of metal–air–metal (MAM) capacitors within the structure. The coupler shows an isolation characteristic of approximately 21 dB and differences in magnitude and phase of less than 0.3 dB and 2°, respectively.
Chemistry of Therapeutic Oligonucleotides That Drives Interactions with Biomolecules

Oligonucleotide therapeutics that can modulate gene expression have been gradually developed for clinical applications over several decades. However, rapid advances have been made in recent years. Artificial nucleic acid technology has overcome many challenges, such as (1) poor target affinity and selectivity, (2) low in vivo stability, and (3) classical side effects, such as immune responses; thus, its application in a wide range of disorders has been extensively examined. However, even highly optimized oligonucleotides exhibit side effects, which limits the general use of this class of agents. In this review, we discuss the physicochemical characteristics that aid interactions between drugs and molecules that belong to living organisms. By systematically organizing the related data, we hope to explore avenues for symbiotic engineering of oligonucleotide therapeutics that will result in more effective and safer drugs.
New Digisonde for research and monitoring applications

The new Digisonde‐4D, while preserving the basic principles of the Digisonde family, introduces important hardware and software changes that implement the latest capabilities of new digital radio frequency (RF) circuitry and embedded computers. The “D” refers to digital transmitters and receivers in which no analog circuitry is used for conversion between the baseband and the RF. In conjunction with the new hardware design, new software solutions offer significantly enhanced measurement flexibility, enhanced signal selectivity, and new types of data, e.g., the complete set of time domain samples of all four antenna signals suitable for independent scientific analysis. With the new method of mitigating in‐band RF interference, the ionogram running time can be made as short as a couple of seconds. The h′(f) precision ranging technique with an accuracy of better than 1 km can be used on a routine basis. The 4D model runs the new ARTIST‐5 ionogram autoscaling software which reports in real time the required data for assimilation in ionospheric models. The paper highlights technical advances of the new Digisonde for research and monitoring applications.