Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical spectroscopy of interlayer excitons in TMDC heterostructures: exciton dynamics, interactions, and giant valley-selective magnetic splitting

Two-dimensional transition-metal dichalcogenides (TMDCs) have recently emerged as a promising class of materials. A fascinating aspect of these atomically thin crystals is the possibility of combining different TMDCs into heterostructures. For several TMDC combinations, a staggered band alignment occurs, so that optically excited electron-hole pairs are spatially separated into different layers and form interlayer excitons (IEX). Here, we report on time-resolved, low-temperature photoluminescence (PL) of these IEX in a MoSe2-WSe2 heterostructure. In the time-resolved measurements, we observe indications of IEX diffusion in an inhomogeneous potential landscape. Excitation-density-dependent measurements reveal a dipolar, repulsive exciton-exciton interaction. PL measurements in applied magnetic fields show a giant valley-selective splitting of the IEX luminescence, with an effective g factor of about -15. This large value stems from the alignment of K+ and K- valleys of the constituent monolayers in our heterostructure, making intervalley transitions optically bright, so that contributions to the field-induced splitting arising from electron and hole valley magnetic moments add up. This giant splitting enables us to generate a near-unity valley polarization of interlayer excitons even under linearly polarized excitation by applying sufficiently large magnetic fields.
Student Perception of Learning English Online

The aim of this research was to highlight the impact of online education on teaching and learning English as a foreign language among students aged 12-19, and to research the importance of learner autonomy and motivation in the online teaching – learning process. In order to collect data from the 81 subjects, members of five different classes and age groups, a questionnaire created in Google Forms was used. The questionnaire was designed to provide information about the students’ attitude and motivation for studying English in an online context, autonomy and effort invested in the learning process. Student progress was measured using the end of semester grades of the first semester of the 2019/2020 year compared to those of the first 2020/2021 semester. Results reveal the importance of perceived choice as a factor that supports learner autonomy and learner motivation. The limitations of the study are due to the fact that the students’ level of English and their previous knowledge of the subjects studied online had not been tested beforehand as the current pandemic situation was not foreseeable.
The effectiveness of filters on catfish Clarias gariepinus fry performance in the aquaponic system

This study aimed to determine the effectiveness of filters on the growth performances of catfish fry (Clarias gariepinus) in the aquaponic system. The study was conducted in the Ciparanje Green House, Faculty of Fisheries and Marine Sciences, Universitas Padjadjaran, Jatinangor. A completely randomized design with four treatments and three replications was applied, namely treatment A: Water circulation using bioball filter medium, B: Water circulation using water spinach filter medium, C: Water circulation using bioball and water spinach filter media, and D: Water circulation using sponge filter medium (control). The container used for the maintenance was a fiber tank at 70 cm × 70 cm × 70 cm size filled with 257 L water and a stocking density of 15 fish/fiber. The parameters observed were survival rate, growth, feed conversion, and water quality. The results showed that the use of biological filter using water spinach produced the best growth performance of catfish. The best survival rate was also obtained by using water spinach filter (100%) with the absolute growth of 98.32 ± 15.6 g. The water quality an aquaponic system with water spinach filter supported the growth performance of catfish fry. Keywords: absolute growth, aquaponic, Clarias gariepinus, filters. ABSTRAK Penelitian ini bertujuan untuk menguji efektivitas filter terhadap performa pertumbuhan benih ikan lele Clarias gariepinus dalam sistem akuaponik. Penelitian dilakukan di Laboratorium Rumah Kaca Ciparanje, Fakultas Perikanan dan Ilmu Kelautan, Universitas Padjadjaran, Jatinangor. Metode penelitian adalah rancangan acak lengkap dengan empat perlakuan dan tiga ulangan, yaitu perlakuan A: sirkulasi menggunakan media filter bioball, B: Sirkulasi menggunakan media filter bayam air, C: Sirkulasi menggunakan media filter bioball dan tanaman kangkung, dan D : Sirkulasi menggunakan media spons (kontrol). Wadah yang digunakan untuk pemeliharaan bak fiber dengan ukuran 70 cm × 70 cm × 70 cm diisi dengan air sebanyak 257 L dengan padat tebar 15 ekor / bak. Parameter yang diamati adalah kelangsungan hidup, pertumbuhan ikan, konversi pakan dan kualitas air. Hasil penelitian menunjukkan bahwa penggunaan filter biologis menggunakan tanaman kangkung menghasilkan performa pertumbuhan ikan lele yang terbaik. Tingkat kelangsungan hidup terbaik juga ditunjukkan oleh pelakuran dengan filter berupa tanaman kankung (100%) dan pertumbuhan mutlak 98,32 ± 15,6 g. Kualitas air sistem akuaponik dengan filter tanaman kangkung mendukung performa pertumbuhan benih ikan lele.
Optimizing Power Allocation in LoRaWAN IoT Applications

Long-range wide-area network (LoRaWAN) is one of the most promising IoT technologies that are widely adopted in low-power wide-area networks (LPWANs). LoRaWAN faces scalability issues due to a large number of nodes connected to the same gateway and sharing the same channel. Therefore, LoRa networks seek to achieve two main objectives: 1) successful delivery rate and 2) efficient energy consumption. This article proposes a novel game-theoretic framework for LoRaWAN named best equal LoRa (BE-LoRa), to jointly optimize the packet delivery ratio and the energy efficiency (bit/Joule). The utility function of the LoRa node is defined as the ratio of the throughput to the transmit power. LoRa nodes act as rational users (players) which seek to maximize their utility. The aim of the BE-LoRa algorithm is to maximize the utility of LoRa nodes while maintaining the same signal-to-interference-and-noise-ratio (SINR) for each spreading factor (SF). The power allocation algorithm is implemented at the network server, which leads to an optimum SINR, SFs, and transmission power settings of all nodes. Numerical and simulation results show that the proposed BE-LoRa power allocation algorithm has a significant improvement in the packet delivery ratio and energy efficiency as compared to the adaptive data rate (ADR) algorithm of legacy LoRaWAN. For instance, in very dense networks (624 nodes), BE-LoRa can improve the delivery ratio by 17.44% and reduce power consumed by 46% compared to LoRaWAN ADR.
Direct measurement of ATP in seawater and application of ATP to monitor bacterial growth potential in SWRO pre-treatment systems

The use of adenosine triphosphate (ATP) to monitor bacterial growth potential of seawater is currently not possible as ATP cannot be accurately measured at low concentration in seawater using commercially available luciferase-based ATP detection. The limitation is due to interference of salt with the luciferin–luciferase reaction, which inhibits light production. This research demonstrates that new reagents developed for (i) ATP extraction from microbial cells and (ii) ATP detection in seawater are able to reliably detect Microbial ATP as low as 0.3 ng L–1 in seawater. The luminescence signal of the new detection reagent is significantly higher (>20 times) than the luminescence signal of the freshwater reagent, when applied in seawater. ATP can now be used to monitor bacterial growth potential (BGP) through pre-treatment trains of seawater reverse osmosis (SWRO) plants. The level of detection of the new BGP test is significantly lower than the estimated threshold value required to prevent biofouling in SWRO systems. The new reagents have been used to monitor Microbial ATP in coastal North Sea water. Moreover, Microbial ATP has been applied to monitor the bacterial growth potential (using indigenous bacteria) through the pre-treatment train of an SWRO desalination plant. A significant reduction (>55%) of the bacterial growth potential was found through the dual media filtration with 4.5 mg-Fe(III) L–1 coagulant. Overall, the new reagents can detect low Microbial ATP concentrations in seawater and can be used to monitor bacterial growth potential in seawater desalination plants.
‘A constant reminder of what we had to forfeit’

This article examines the intergenerational effects of hydropower expansion on Sámi living conditions. In-depth conversations were conducted with five research participants from three different generations living in a hydropower impacted area on the Swedish side of Sápmi. The aim is to analyse how natural resource extraction has affected living conditions for the Indigenous Sámi people, using an intergenerational approach. The questions cover how to deal with the consequences and how coping strategies have affected the living conditions for the research participants and the participants’ families: older and younger generations. Historical unresolved grief connected to large-scale resource extraction is an important component for understanding experiences of colonialism in a Nordic Indigenous context. Furthermore, an intergenerational approach is essential for studying long-term impacts on Indigenous communities. From the conversations, four main themes are crystallized: bereavement, fear and worries, agreements with the energy company, and reconciliation and strategies for the future.
Masuklah dalam Islam Secara Kāffah: Analisis atas Tafsir Q 2: 208 dalam Ceramah Ustadz Adi Hidayat di Youtube

Artikel ini bertujuan untuk menjelaskan kecenderungan penafsiran atas makna Islam kaffah yang dilakukan oleh Adi Hidayat di media Youtube. Makna Islam kaffah yang dipahami oleh banyak kalangan berpotensi menimbulkan perdebatan dan keraguan atas makna yang sebenarnya atas seruan berislam secara totalitas. Untuk mencapai tujuan tersebut, penelitian ini menggunakan metode kualitatif dengan analisa menggunakan content analysis. Penelitian ini menyimpulkan bahwa penafsiran al-Qur’an diperlukan munāsabah dengan rumus 2S 1L (siyāq, sibāq dan lihāq). Dalam aspek penafsiran tentang Islam kāffah dalam Q 2: 208 dipahami melalui siyāq-nya. Dengan metode ini, kāffah dalam al-Qur’an memiliki tiga term yang berbeda; al-Islām, al-Silm dan al-Salām. Al-Islām bermakna frame dan tatanannya, al-silm bermakna tuntunannya dan al-salām sebagai hasilnya. Adi Hidayat memaknai kāffah dalam konsep menyeluruh yang sesuai dengan batas maksimal kemampuan manusia. Jadi, untuk mencapai Islam kāffah, sesorang harus masuk ke dalam Islam sebagai al-dīn, kemudian beriman dan siap mengamalkan tuntunan (al-silm) secara kāffah, sehingga akan mendapatkan hasil berupa salām. Konsep ini berlaku dalam ranah kehidupan individu dan sosial kemasyarakatan.
Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.
A structural constraint on neural correlates of consciousness

Researchers on the neural correlates of consciousness (NCC) need to distinguish mere statisticalNCCs from NCCs proper. Some neural events may be co-occurrent, probabilistically coupled,or coincidental with a type of conscious experience but lack any deeper connection to it, whilein other cases, the relation between neural states and a type of experience hints at a strongmetaphysical relation, which distinguishes such NCCs proper from mere statistical NCCs. In orderto address this issue of how to distinguish NCCs proper from mere statistical NCCs, we proposea position we call neurophenomenal structuralism. The position hinges on the uncontroversialidea that phenomenal experiences relate to each other in degrees of similarity and difference.These complex structures are used to identify and individuate experiences in the methods ofneuroscience, psychophysics, and phenomenology. Such individuation by structure leads to phenomenalholism, which has implications for how to investigate consciousness neuroscientificallyand generates a constraint by which we can distinguish NCCs proper from mere statistical NCCs:the structural similarity constraint. Neural activation must preserve the structure governing thedomain of experiences it is associated with in order to count as that domain’s NCC proper. Anyactivation that fails to preserve phenomenal structure fails to be an NCC proper. We illustratehow this constraint works with a study by Brouwer & Heeger (2009) as an example.
Accurate Indoor Visible Light Positioning Using a Modified Pathloss Model With Sparse Fingerprints

Visible light communications (VLC) based indoor positioning is a promising approach to serve an increasing need for location-aware services. Received signal strength (RSS) fingerprints based visible light positioning (VLP) was shown to achieve highly accurate, yet simple, VLP systems. It needs, however, large training datasets; whose collection is labor intensive. In this work, an artificial dataset generation methodology based on a modified pathloss model is proposed, where the pathloss exponent is assumed to be variable. Four interpolation techniques were investigated for the estimation of the variable pathloss exponent, where bicubic interpolation showed the best results in the simulation using a uniform sparse grid. The method is specially conceived to address the site surveying demand while attaining high positioning accuracy under non-line-of-sight (NLOS) conditions with sparse offline measurements. The technique is not limited to visible light applications since it depends only on the RSS and the pathloss model. Moreover, by using the weighted <italic>k</italic>-nearest-neighbor (<italic>k</italic>-NN) machine learning technique, this work designs an accurate VLP system based on sparse fingerprints. Simulation results show an average positioning error of 2.48 cm using weighted <italic>k</italic>-NN (W<italic>k</italic>-NN) with only 53 offline measurements in a 25 m<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> area. Furthermore, experimental results show an average positioning error of 3.04 cm with only 18 offline measurements in an area of approximately 9 m<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>, and an average positioning error of 1.92 cm using only 16 offline measurements in an area of 6 m<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>.
The Team Handball Game-Based Performance Test Is Better than the Yo-Yo Intermittent Recovery Test to Measure Match-Related Activities in Female Adult Top-Elite Field Team Handball Players

In team handball, suitable tests determining the match-related physical performance are essential for the planning of optimal physical training regimens. Thus, the aims of the present study were (a) to determine the relationships between the physical and physiological test results from a team handball game-based performance test (GBPT), the Yo-Yo intermittent recovery test, level 1 (Yo-Yo IR1 test) and a separate linear 30-m single sprint performance test (SSPT) in female adult top-elite field team handball players, in order to establish the significance (validity) of tests for measuring relevant elements for team handball match-play; and (b) to compare and evaluate the results from the aforementioned tests for the same players in relation to the different playing positions. Twenty-three female adult top-elite field team handball players from the Danish Premier Female Team Handball League performed the GBPT, the Yo-Yo IR1 test and the 30-m SSPT test on separate days. As main findings, significant correlations between the GBPT and the Yo-Yo IR1 test in about 1/3 of the variables were found, indicating that the Yo-Yo IR1 test is reflecting most of the locomotive match activities in female adult top-elite team handball. However, the Yo-Yo IR1 test results were not correlated to the GBPT in any of the match-related activities in the team handball GBPT that included technical playing actions such as tackles, passes, jumping and shooting during specialized movements in offence and defence. Overall, the results revealed that the GBPT is better than the Yo-Yo IR1 test to evaluate female adult top-elite field team handball players’ ability to perform physical match-related activities including both locomotive and technical playing actions executed as during competitive match-play. Similar to the Yo-Yo IR1 test, the 30-m SSPT was not correlated to any of the team handball GBPT activities, which included technical playing actions. These data suggest that the SSPT only to a certain extent can measure the individual sprint capacity of elite team handball players. In addition, unexpectedly no significant differences between the various playing positions were found neither for the GBPT, the SSPT nor the Yo-Yo IR1 test. However, several effects sizes indicated that the lack of positional differences primarily was due to the relatively small sample size in each playing position and the composition of the specific group of players. In conclusion, this study clearly indicated that team handball specific physical performance, as measured by the GBPT, and general physical performance, as measured by the Yo-Yo IR1 test and the 30-m SSPT, are different components. This must be taken in consideration when using physical test results for the planning of optimal physical training regimens in elite team handball.
Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change

Significance The COVID-19 pandemic and associated lockdowns caused significant changes to human activity that temporarily altered our imprint on the atmosphere, providing a brief glimpse of potential future changes in atmospheric composition. This event demonstrated key feedbacks within and between air quality and the carbon cycle: Improvements in air quality increased the lifetime of methane (an important greenhouse gas), while unusually hot weather and intense wildfires in Los Angeles drove poor air quality. This shows that efforts to reduce greenhouse gas emissions and improve air quality cannot be considered separately. The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NOx emissions. Second, the response of O3 to decreased NOx emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.
Uma Abordagem baseada em Redes Neurais, Multiple Instance Learning e PCA para Detecção de Anomalias em Videovigilância

Multiple Instance Learning (MIL) tem se tornado uma solução atrativa na literatura de videovigilância por permitir lidar com bases fracamente rotuladas. Este trabalho propõe e avalia uma abordagem para detecção de anomalias em vídeo baseada em classificação binária com redes neurais Multilayer Perceptron (MLP) e paradigma MIL. Os experimentos foram conduzidos a partir de um conjunto de atributos I3D (Inflated 3D) referentes ao dataset de benchmark ShanghaiTech. Explora-se ainda o efeito da compacticidade dos dados e representação de informação essencial com a técnica de extração de atributos Principal Component Analysis (PCA). Os resultados alcançados foram competitivos quando comparados com o estado da arte.
The Capillary Rise in Fine and Coarse-Grained Soils Considering the Matric Suction

Few pieces of research have been conducted on the phenomenon of capillary rise in the field of soil for agriculture and geotechnical engineering. The rate of capillary rise of water in fine and granular soil is one of the major challenges for rising experiments in vertical open-tubes, as the time required for the water to reach the maximum height of capillary rise (hc) can vary from 50 to 400 days. The control variables during the capillary experiment are mainly: saturated and unsaturated hydraulic conductivity, soil density, water content, soil column height, and velocity of capillary rise. Thus, this paper presents theoretical and experimental studies of capillary rise in several soils based on matric suction models. Results were gathered by comparing the behavior of capillary rise using the analytical solutions developed by Lu (2016), Lu and Likos (2004), and by Terzaghi (1943). On analysis of the results, it was concluded that the equation proposed by Lu and Likos (2004) is the most suitable to predict the capillary rise velocity for the fine-coarse soils and the equation proposed by Lu (2016) is more suitable to predict the matric suction. Other mathematical model developed by Liu et al. (2014) is also suitable to estimate the hc but don’t consider the velocity of the water. The capillary rise method to measure the matric suction must be more applicable in sandy soil than clayey soils.