Miracle-dz/UniFAR-FTUs huggingface dataset:
Dry-season autotrophic and heterotrophic soil respiration in contrasting agroecological settings

ABSTRACT In arid Africa, it is unclear how the transition from grassland to cropland affects CO2 fluxes and whether these effects are climate-dependent. From November 2019 to March 2020 (dry season), we studied autotrophic (Ra) and heterotrophic (Rh) soil respiration. At highlands, grazing land (1.18 ± 0.13 µmol m−2 s−1) and teff croplands (1.36 ± 0.14 µmol m−2 s−1) had the lowest Rh, while acacia decurrens plantations (1.94 ± 0.19 µmol m−2 s−1) were the largest. When compared to teff (2.08 ± 0.20 µmol m−2 s−1) and khat (1.73 ± 0.24 µmol m−2 s−1), grazing land in the midland had a lower Rh (1.18 ± 0.13 µmol m−2 s−1). Ra on teff (1.51 ± 0.27 µmol m−2 s−1) was substantially (P < 0.05) greater than on grazing land (1.08 ± 0.32 µmol m−2 s−1) in the highlands. Grazing lands (1.54 ± 0.21 µmol m−2 s−1) have a considerably (P < 0.05) greater Ra than farmlands (teff, 0.20 ± 0.07 µmol m−2 s−1; groundnut, 0.09 ± 0.03 µmol m−2 s−1) in lowlands. Growing teff in the highlands and midlands will result in higher CO2 fluxes into the atmosphere. However, in-depth research is needed to implement effective land management that reduces CO2 emissions.
Fermi velocity renormalization in doped graphene

We present an ab initio study of the graphene quasi‐particle band structure within GW approximation. In particular we studied the renormalization of the Fermi velocity vF as function of the electrostatic doping. We show that within local density approximation (LDA) the Fermi velocity is substantially renormalized by correlation effects and that this renormalization rapidly decreases with doping. We discuss our results in the light of recent experiments on graphene and intercalate graphite.
The Color-Magnitude Diagram of the Globular Cluster NGC 6362 and the Canonical Tilt of Horizontal Branches

In this paper we present new and accurate photometry for stars in the Galactic globular cluster NGC 6362. The color-magnitude diagram discloses two peculiarities in the distribution of stars: (1) a slightly tilted horizontal branch (HB) (Δ = 0.1 mag) and (2) a clump of stars near the red edge of the HB. We perform a detailed comparison with theoretical stellar models in both the HB and red giant branch (RGB) phases. It appears that in the moderately metal rich NGC 6362 the tilted HB can be explained as a natural product of canonical evolutionary theories, being a consequence of the minimum in the bolometric correction near 7500 K. We also investigate the effect of decreasing the efficiency of convective transport in stars climbing the RGB. Adopting Z = 0.002 and Y = 0.23, and performing a global fitting with the theoretical isochrones and zero-age HB, an age of 12 ± 1 Gyr is found, together with (m - M)V = 14.68 and E(B-V) = 0.08.
β-Carboline Alkaloids in Soy Sauce and Inhibition of Monoamine Oxidase (MAO)

Monoamine oxidase (MAO) oxidizes neurotransmitters and xenobiotic amines, including vasopressor and neurotoxic amines such as the MPTP neurotoxin. Its inhibitors are useful as antidepressants and neuroprotectants. This work shows that diluted soy sauce (1/3) and soy sauce extracts inhibited human MAO-A and -B isozymes in vitro, which were measured with a chromatographic assay to avoid interferences, and it suggests the presence of MAO inhibitors. Chromatographic and spectrometric studies showed the occurrence of the β-carboline alkaloids harman and norharman in soy sauce extracts inhibiting MAO-A. Harman was isolated from soy sauce, and it was a potent and competitive inhibitor of MAO-A (0.4 µM, 44 % inhibition). The concentrations of harman and norharman were determined in commercial soy sauces, reaching 243 and 52 μg/L, respectively. Subsequently, the alkaloids 1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid (THCA) and 1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid (MTCA) were identified and analyzed in soy sauces reaching concentrations of 69 and 448 mg/L, respectively. The results show that MTCA was a precursor of harman under oxidative and heating conditions, and soy sauces increased the amount of harman under those conditions. This work shows that soy sauce contains bioactive β-carbolines and constitutes a dietary source of MAO-A and -B inhibitors.
STABILITY OF CERTAIN MINIMAL SUBMANIFOLDS OF COMPACT HERMITIAN SYMMETRIC SPACES

Introduction. In this paper we consider a compact totally real totally geodesic submanifold M of a Hermitian symmetric space (M, g) of compact type with dimM= dimcii? , and study their classification and stability. We shall show that such a submanifold M is always a symmetric R-space (cf. §1 for definition), and these pairs ((M, g), M) correspond in one to one fashion to symmetric R-spaces. Furthermore we shall prove that M is stable in (M, g) as a minimal submanifold if and only if M is simply connected. Lawson-Simons [6] proved that a compact stable minimal submanifold of the complex protective w-space Pn(C) endowed with the Kahler metric of constant holomorphic sectional curvature is always a complex submanifold. They showed also [6] that this is not true for a general Hermitian symmetric space of compact type, by giving an example of a compact stable minimal submanifold of PX(C) x Pi(C) which is not a complex submanifold. The simply connected ones among our submanifolds include the example of Lawson-Simons and provide many examples with the same properties. For example, the quaternion Grassmann manifold GPtq(H) imbedded in the complex Grassmann manifold G2P)2q(C) is minimal and stable, but not a complex submanifold. The author would like to express his hearty thanks to Professor Tadashi Nagano who gave him valuable advice during the preparation of this note.
Impact of Lumbar Surgery on Pharmacological Treatment for Patients with Lumbar Spinal Canal Stenosis: A Single-Center Retrospective Study

Treatment for lumbar spinal canal stenosis (LSCS) is mainly classified into conservative and surgical therapies. Among conservative therapies, pharmacological treatment is commonly prescribed for LSCS. Meanwhile, surgical treatment is the last option for LSCS. This study aimed to examine the impact of lumbar surgery on pharmacological treatment for patients with LSCS. Consecutive patients aged ≥ 40 years who underwent lumbar surgery for LSCS were identified. A total of 142 patients were retrospectively reviewed for preoperative and 6-month and 1-year postoperative LSCS medications. The results showed that the number of LSCS medications significantly decreased after lumbar surgery. The proportion of the patients taking non-steroidal anti-inflammatory drugs, pregabalin/mirogabalin, opioids, prostaglandin E1 analogs, and neurotropin was significantly decreased after lumbar surgery, but that of the patients taking mecobalamin, acetaminophen, and serotonin-noradrenalin reuptake inhibitors was not significantly changed. Additionally, around 15% of the participants showed an increase in LSCS medications even after lumbar surgery. Multivariable analysis revealed that individuals without improvements in walking ability (RR: 2.7, 95% CI: 1.3–5.9) or social life (RR: 2.3, 95% CI: 1.1–5.0) had a greater risk of a postoperative increase in LSCS medications. The study results may provide physicians with beneficial information on treatment for LSCS.
The impact of board-level female directors on firm performance: evidence from India

PurposeThis study investigates the impact of female directors on firms' financial performance by scrutinizing the different roles they are empowered to fulfill.Design/methodology/approachThis study examines the impact of the roles performed by female directors on firms' financial performance using a panel dataset of the top 100 listed Indian firms over a period of 5 years. The study uses an appropriate panel data model for empirical analysis. For the robustness evaluation, a two-stage least square (2SLS) with the instrumental variable model were used.FindingsThe findings reveal a significantly positive impact of the total percentage of female directors on firms' financial performance. Further, by disentangling the impact of the total percentage of female directors between independent directors and executive directors, the study shows that independent female directors make a significant positive contribution to their firms' financial performance. By contrast, the performance impact of female executive directors was insignificant. In addition, the findings reveal that firms with a higher proportion of independent female directors outperform firms with a higher percentage of female executive directors.Originality/valueThis study is the first of its kind to unravel the performance impact of female directors and distinguish between the roles of independent directors and executive directors in the context of the emerging market of India, after the imposition of a gender quota for corporate boards.
Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems

In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid’s stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers’ privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers’ activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection.
A Meta-Analysis and Quality Review of Mathematics Interventions Conducted in Informal Learning Environments with Caregivers and Children

The purposes of this study included conducting a meta-analysis and reviewing the study reporting quality of math interventions implemented in informal learning environments (e.g., the home) by children’s caregivers. This meta-analysis included 25 preschool to third-grade math interventions with 83 effect sizes that yielded a statistically significant summary effect (g = 0.26, 95% CI [0.07, 0.45) on children’s math achievement. Significant moderators of the treatment effect included the intensity of caregiver training and type of outcome measure. There were larger average effects for interventions with caregiver training that included follow-up support and for outcomes that were comprehensive early numeracy measures. Studies met 58.0% of reporting quality indicators, and analyses revealed that quality of reporting has improved in recent years. The results of this study offer several recommendations for researchers and practitioners, particularly given the growing evidence base of math interventions conducted in informal learning environments.
Conjoint Analysis of Genome-Wide lncRNA and mRNA Expression during the Salicylic Acid Response in Populus × euramericana

Long noncoding RNAs (lncRNAs) participate in a wide range of biological processes, but lncRNAs in plants remain largely unknown; in particular, we lack a systematic identification of plant lncRNAs involved in hormone responses. To explore the molecular mechanism of the response of poplar to salicylic acid (SA), the changes in protective enzymes, which are closely related to plant resistance induced by exogenous SA, were studied, and the expression of mRNA and lncRNA were determined by high-throughput RNA sequencing. The results showed that the activities of phenylalanine ammonia lyase (PAL) and polyphenol oxidase (PPO), in the leaves of Populus × euramericana, were significantly increased by exogenous SA application. High-throughput RNA sequencing showed that 26,366 genes and 5690 lncRNAs were detected under the different treatment conditions: SA and H2O application. Among these, 606 genes and 49 lncRNAs were differentially expressed. According to target prediction, lncRNAs and target genes involved in light response, stress response, plant disease resistance, and growth and development, were differentially expressed in SA-treated leaves. Interaction analysis showed that lncRNA–mRNA interactions, following exogenous SA, were involved in the response of poplar leaves to the external environment. Our study provides a comprehensive view of Populus × euramericana lncRNAs and offers insights into the potential functions and regulatory interactions of SA-responsive lncRNAs, thus forming the foundation for future functional analysis of SA-responsive lncRNAs in Populus × euramericana.
Channel Modeling and Generation: Train Generative Networks and Generate 6G Channel Data

In the upcoming 6G era, with the deployment of massive Multi-input Multi-output (MIMO) systems, collecting and capturing 6G channel data through traditional channel modeling methods is very expensive. In addition, wireless communication carriers continuously propose and use artificial intelligence (AI) and deep learning (DL) based wireless communication solutions. Implementing such AI and DL based solutions requires a certain amount of high-quality channel data as a prerequisite. Traditional channel modeling methods cannot meet the requirements of simulating or collecting channel data rapidly and efficiently. In this paper, a generative network for channel modeling and signal generation, two data augmentation methods and a training technique are proposed. In short, this paper covers how to improve the performance of generative networks and how to generate high quality data with the premise that a large amount of channel samples are limited. Finally, the experimental results show that our proposed network could effectively and quickly generate 6G channel data by achieving the highest final score on both simple and complex testset. And the simulation results show that the generated data by our proposed structure has consistent normalized power with the real data. And the generated data can support a wide variety of AI-based wireless communication tasks.
Spontaneous Emission of Gravity Waves by Interacting Vortex Dipoles in a Stratified Fluid: Laboratory Experiments

Results from a new series of experiments on the geophysically important issue of spontaneous emission of internal gravity waves during unsteady interactions of vortical structures are presented. Vortex dipoles are a common element of a quasi-two-dimensional turbulent flow. Vortex dipoles perform translational motion and can collide with other vortices. During collision events the flow is unsteady and unbalanced and a further adjustment process associated with these events can therefore result in the spontaneous emission of gravity waves. Our laboratory experiments demonstrate that gravity waves are emitted when two translating vortex dipoles interact (collide) in a layered fluid, in accord with the current theoretical results. The emission was evident both in a two-layer system and in a fluid with a linear distribution of density with depth. The waves were generated during the period of deceleration of the secondary dipoles which constitute a vortex quadrupole emerging immediately after the collision of the primary dipoles.
Analysis of Work Loyalty and Work Motivation during the Covid-19 Pandemic on Employee Performance on Delivery Services in Palembang City

This study aims to determine the performance of employees who are influenced by work loyalty and work motivation at the Delivery Service Service in Palembang City during the Covid-19 Pandemic, totaling 40 respondents. Purposive sampling was used in this research sample and a survey was conducted to collect data through a questionnaire with a Likert scale and using multiple regression analysis with a significance level of 5% analysis of the data This study found that work loyalty and work motivation have a positive effect on employee performance with the F value obtained at 6.550 which is greater than F table (3.24), this is also strengthened by the significant level value of 0.004 or the significance obtained is smaller than = 0.05. The results of the t-test of work loyalty show 0.231 > 0.05, which show that work loyalty has no effect on employee performance. The results of the t-test of work motivation show a t-statistic value of 0.004 <0.05, which show motivation partially has a significant influence on employee performance. Thus the multiple regression model can be used to predict employee performance in Delivery Services in Palembang City.
Condition dependence and the paradox of missing plasticity costs

Abstract Phenotypic plasticity plays a key role in adaptation to changing environments. However, plasticity is neither perfect nor ubiquitous, implying that fitness costs may limit the evolution of phenotypic plasticity in nature. The measurement of such costs of plasticity has proved elusive; decades of experiments show that fitness costs of plasticity are often weak or nonexistent. Here, we show that this paradox could potentially be explained by condition dependence. We develop two models differing in their assumptions about how condition dependence arises; both models show that variation in condition can readily mask costs of plasticity even when such costs are substantial. This can be shown simply in a model where plasticity itself evolves condition dependence, which would be expected if costly. Yet similar effects emerge from an alternative model where trait expression itself is condition-dependent. In this more complex model, the average condition in each environment and genetic covariance in condition across environments both determine when costs of plasticity can be revealed. Analogous to the paradox of missing trade-offs between life history traits, our models show that variation in condition can mask costs of plasticity even when costs exist, and suggest this conclusion may be robust to the details of how condition affects trait expression. Our models suggest that condition dependence can also account for the often-observed pattern of elevated plasticity costs inferred in stressful environments, the maintenance of genetic variance in plasticity, and provides insight into experimental and biological scenarios ideal for revealing a cost of phenotypic plasticity.
Increasing the RF energy per pulse of an RKO

The Air Force Research Laboratory RKO source has recently demonstrated the ability to convert electron beam power to RF power until the termination of the electron beam pulse, achieving a power of 1.5 GW at an energy of 170 J. These results represent an increase in power of 25-30% in power and energy extracted from this source. This paper discusses the principal research areas encountered in lengthening the RF pulse (FWHM) from 50 ns to the present 120 ns and the associated increase in the RF energy.
Confidence Screening Detector: A New Method for Detecting Test Collusion

Test collusion (TC) is a form of cheating in which, examinees operate in groups to alter normal item responses. TC is becoming increasingly common, especially within high-stakes, large-scale examinations. However, research on TC detection methods remains scarce. The present article proposes a new algorithm for TC detection, inspired by variable selection within high-dimensional statistical analysis. The algorithm relies only on item responses and supports different response similarity indices. Simulation and practical studies were conducted to (1) compare the performance of the new algorithm against the recently developed clique detector approach, and (2) verify the performance of the new algorithm in a large-scale test setting.
Venetoclax resistance induced by activated T cells can be counteracted by sphingosine kinase inhibitors in chronic lymphocytic leukemia

The treatment of chronic lymphocytic leukemia (CLL) patients with venetoclax-based regimens has demonstrated efficacy and a safety profile, but the emergence of resistant cells and disease progression is a current complication. Therapeutic target of sphingosine kinases (SPHK) 1 and 2 has opened new opportunities in the treatment combinations of cancer patients. We previously reported that the dual SPHK1/2 inhibitor, SKI-II enhanced the in vitro cell death triggered by fludarabine, bendamustine or ibrutinib and reduced the activation and proliferation of chronic lymphocytic leukemia (CLL) cells. Since we previously showed that autologous activated T cells from CLL patients favor the activation of CLL cells and the generation of venetoclax resistance due to the upregulation of BCL-XL and MCL-1, we here aim to determine whether SPHK inhibitors affect this process. To this aim we employed the dual SPHK1/2 inhibitor SKI-II and opaganib, a SPHK2 inhibitor that is being studied in clinical trials. We found that SPHK inhibitors reduce the activation of CLL cells and the generation of venetoclax resistance induced by activated T cells mainly due to a reduced upregulation of BCL-XL. We also found that SPHK2 expression was enhanced in CLL cells by activated T cells of the same patient and the presence of venetoclax selects resistant cells with high levels of SPHK2. Of note, SPHK inhibitors were able to re-sensitize already resistant CLL cells to a second venetoclax treatment. Our results highlight the therapeutic potential of SPHK inhibitors in combination with venetoclax as a promising treatment option for the patients.
Mental Stress Detection Using a Wearable In-Ear Plethysmography

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual’s health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color–word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.