Miracle-dz/UniFAR-FTUs huggingface dataset:
High-order harmonic generation from twisted bilayer graphene driven by a midinfrared laser field

Theoretical calculations of high-harmonic generation (HHG) from commensurate twisted bilayer graphene (tBLG) under intense laser fields are performed. The nonlinear electron dynamics in tBLG is considered by solving the Liouville--von Neumann equation for a single-particle density matrix, which combines the full energy bands and momentum matrix elements within the framework of tight-binding approximation. We show that the pump intensity determines the relative magnitude of two components of the harmonic spectrum parallel and perpendicular to driving polarization. The important dependence of HHG on twisted angles and crystal orientations is also presented. Especially in the absence of the relaxation process, the harmonic emission for twisted angles around 10 \ifmmode^\circ\else\textdegree\fi{} exhibits an evident decrease in efficiency per layer compared to monolayer graphene (MLG), which can be interpreted according to Fermi velocity modification. Our calculation also shows that the relative emission efficiency of different harmonic orders between tBLG and MLG contains redundant information on both the dephasing time and an empirical parameter characterizing the decay of the interlayer electron hopping, thus suggesting an all-optical method for the reconstruction of the two parameters. The reconstruction feasibility is successfully demonstrated by a simple optimization algorithm even if considering the possible experimental uncertainty of both driving pulse parameters and high-harmonic signals. Our results show that HHG spectroscopic characteristics in tBLG might serve as a fingerprint to identify the geometric stacking angle and the electronic interaction between adjacent layers, as well as the strong-field laser induced ultrafast dephasing process.
Public Perceptions of Diabetes, Healthy Living, and Conversational Agents in Singapore: Needs Assessment

Background The incidence of chronic diseases such as type 2 diabetes is increasing in countries worldwide, including Singapore. Health professional–delivered healthy lifestyle interventions have been shown to prevent type 2 diabetes. However, ongoing personalized guidance from health professionals is not feasible or affordable at the population level. Novel digital interventions delivered using mobile technology, such as conversational agents, are a potential alternative for the delivery of healthy lifestyle change behavioral interventions to the public. Objective We explored perceptions and experiences of Singaporeans on healthy living, diabetes, and mobile health (mHealth) interventions (apps and conversational agents). This study was conducted to help inform the design and development of a conversational agent focusing on healthy lifestyle changes. Methods This qualitative study was conducted in August and September 2019. A total of 20 participants were recruited from relevant healthy living Facebook pages and groups. Semistructured interviews were conducted in person or over the telephone using an interview guide. Interviews were transcribed and analyzed in parallel by 2 researchers using Burnard’s method, a structured approach for thematic content analysis. Results The collected data were organized into 4 main themes: use of conversational agents, ubiquity of smartphone apps, understanding of diabetes, and barriers and facilitators to a healthy living in Singapore. Most participants used health-related mobile apps as well as conversational agents unrelated to health care. They provided diverse suggestions for future conversational agent-delivered interventions. Participants also highlighted several knowledge gaps in relation to diabetes and healthy living. Regarding barriers to healthy living, participants mentioned frequent dining out, high stress levels, lack of work-life balance, and lack of free time to engage in physical activity. In contrast, discipline, preplanning, and sticking to a routine were important for enabling a healthy lifestyle. Conclusions Participants in this study commonly used mHealth interventions and provided important insights into their knowledge gaps and needs in relation to changes in healthy lifestyle behaviors. Future digital interventions such as conversational agents focusing on healthy lifestyle and diabetes prevention should aim to address the barriers highlighted in our study and motivate individuals to adopt healthy lifestyle behavior.
Research on A Novel MPP Tracking Strategy Reaction to Rapid Change of Light Intensity

For the misjudgment and oscillation in the traditional MPP tracking process when the light intensity changes rapidly, a novel improved optimal gradient method for power prediction is proposed, based on the research of the traditional power prediction method and the optimal gradient method.In this article, the working principle, advantages and disadvantages of the traditional power prediction method and optimal gradient method are firstly analyzed; then a novel power prediction method with high-precision power calculation, as we as an improved optimal gradient method are proposed. Finally, Simulink simulation analysis is carried out on the proposed MPP tracking strategy when the light intensity changes rapidly. The simulation results show that when the light intensity changes rapidly, the novel power prediction improvement is adopted. The optimal gradient method can not only eliminate the misjudgment phenomenon and oscillation phenomenon, but also the power calculation is more accurate. The introduction of the variable step factor can make the system output more power, which can well realize the fast tracking of MPP.
Depth-Dependent C-N-P Stocks and Stoichiometry in Ultisols Resulting from Conversion of Secondary Forests to Plantations and Driving Forces

Stocks and stoichiometry of carbon (C), nitrogen (N), and phosphorus (P) in ultisols are not well documented for converted forests. In this study, Ultisols were sampled in 175 plots from one type of secondary forest and four plantations of Masson pine (Pinus massoniana Lamb.), Slash pine (Pinus elliottii Engelm.), Eucalypt (Eucalyptus obliqua L’Hér.), and Litchi (Litchi chinensis Sonn., 1782) in Yunfu, Guangdong province, South China. Five layers of soil were sampled with a distance of 20 cm between two adjacent layers up to a depth of 100 cm. We did not find interactive effects between forest type and soil layer depth on soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) concentrations and storages. Storage of SOC was not different between secondary forests and Eucalypt plantations, but SOC of these two forest types were lower than that in Litchi, Masson pine, and Slash pine plantations. Soil C:P was higher in Slash pine plantations than in secondary forests. Soil CNP showed a decreasing trend with the increase of soil depth. Soil TP did not show any significant difference among soil layers. Soil bulk density had a negative contribution to soil C and P stocks, and longitude and elevation were positive drivers for soil C, N, and P stocks. Overall, Litchi plantations are the only type of plantation that obtained enhanced C storage in 0–100 cm soils and diverse N concentrations among soil layers during the conversion from secondary forests to plantations over ultisols.
Coupling Earth observation and eddy covariance data in light-use efficiency based model for estimation of forest productivity

Abstract The light use efficiency (LUE) approach is a well-established method for estimating gross primary productivity (GPP) over large areas using Earth observation data. The present study aims to determine maximum light use efficiency (LUEmax) values specific to the northwest Himalayan foothills of India. It also aims to estimate the spatio-temporal variability of GPP from 2001 to 2020 using remote sensing data in combination with eddy covariance data in the LUE-based model. The model was parameterized using different sets of default and calculated parameters. The study showed that the use of PFT-specific LUEmax and temperatures increased the accuracy of the model predictions. On validation, the LUE-based model predicted GPP showed R2 = 0.82 for moist deciduous and R2 = 0.83 for dry deciduous PFTs. The study revealed that with rigorous model parameterization, RS data can be used in an LUE-based model to achieve accurate spatio-temporal estimates of GPP.
The Impact of COVID-19 Pandemic on Emerging Country Stock Markets: Evidence of the Value Effect

ABSTRACT We examine the impact of the COVID-19 pandemic on seven emerging stock markets by focusing on the value effect. Our results show that there are significant differences in the value premia before and during the pandemic. Furthermore, the traditional value proxies are no longer good predictors of future stock returns. To further capture the impact the pandemic’s progress on stock returns, we estimate Fama-MacBeth regressions by introducing proxies of the pandemic. We uncover heterogeneous responses of emerging markets to the pandemic. These findings provide a wealth of insights on the presence and driving force relevant to the value effect.
AN EFFICIENT ANDROID BASED SCHOOL BUS TRACKING SYSTEM

These days, guardians are concerned about the school-going youngsters on account of the expanding number of instances of missing students. The parents or guardians must patiently wait for the arrival of school transportation. The use of private vehicles aggravates the current terrible deadlock and many of the guardians consider that school transport is usually dangerous for their children. This task presents the improvement of a school transport observing framework, fit for offering profitable types of assistance through arising advances like Arduino regulator. By using geo location capabilities, our proposed Arduino regulator has the capability to provide the location of children. The proposed system employs the client-server architecture. To save the waiting time of parents, the software developed allows parents to detect the exact location of the school bus via geo location and informs them when the school bus reaches a nearby place. Admin, Driver, and Parents are the three modules included in this framework. The system allocates transportation and its related components space on Google Maps is refreshed by Cloud workers and viewable to administrators. The most up-to-date information of the bus in the form of time and location can be received by parents.
“They Didn’t See It Coming”: Green Resilience Planning and Vulnerability to Future Climate Gentrification

ABSTRACT As cities strive to protect vulnerable residents from climate risks and impacts, recent studies have identified a challenging link between these measures and gentrification processes that reconfigure, but do not necessarily eliminate, climate insecurities. Green resilient infrastructure (GRI) may especially increase the vulnerability of lower income communities of color to gentrification, an issue that remains underexplored. Drawing on the forerunner green city of Philadelphia, Pennsylvania, as our case study, this article adopts a novel intersectional approach to assess overlapping and interdependent factors in generating vulnerability and resilience using spatial quantitative data and qualitative interviews with community-based organizers, nonprofits, and municipal stakeholders. More specifically, this article develops a new methodology to assess vulnerability to future climate gentrification and contributes to debates on the role of urban development, housing, and sustainability practices in climate justice dynamics. It also informs strategies that can reduce social and racial inequities in the context of climate adaptation planning.
Neuromarketing: Marketing research in the new millennium

Neuromarketing (NM) uses neuroscience tools, for example, but not limited to functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to study, explore, analyze, and explain the neural correlates of consumer’s behavior (i.e., decision-making processes), the cognitive processes (i.e., attention and memory) and emotional processes (i.e., emotion) of interest for marketing research (e.g., advertising, brand, product, price). This study analyzes the relevant literature and sheds light on the triune brain of humans, the dimensions of NM such as emotion, attention, and memory. Currently, emotional and cognitive processes have remarkably received attention from academic and industrial environments. Thus, NM presents unrivalled possibilities to record the activity regions in consumers' brains and provide precise information about which neurons are active when consumers are exposed to marketing stimuli. To best our knowledge, this will aid in shaping and understanding the central theme and set the future research directions for the researchers.
Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation

Recommender systems, which aim to provide personalized suggestions for users, have proven to be an effective approach to cope with the information overload problem existing in many online applications and services. In this paper, we target two specific sequential recommendation tasks, next music recommendation and next new music recommendation, to predict the next (new) music piece that users would like based on their historical listening records. In current music recommender systems, various kinds of auxiliary/side information, e.g., item contents and users’ contexts, have been taken into account to facilitate user/item preference modeling and have yielded comparable performance improvement. Despite the gained benefits, it is still a challenging and important problem to fully exploit sequential music listening records due to the complexity and diversity of interactions and temporal contexts among users and music, as well as the dynamics of users’ preferences. To this end, this paper proposes a novel Attentive Temporal Point Process (ATPP) approach for sequential music recommendation, which is mainly composed of a temporal point process model and an attention mechanism. Our ATPP can effectively capture the long- and short-term preferences from the sequential behaviors of users for sequential music recommendation. Specifically, ATPP is able to discover the complex sequential patterns from the interaction between users and music with the temporal point process, as well as model the dynamic impact of historical music listening records on next (new) music pieces adaptively with an attention mechanism. Comprehensive experiments on four real-world music datasets demonstrate that the proposed approach ATPP outperforms state-of-the-art baselines in both next and next new music recommendation tasks.
Pro‐Healing Zwitterionic Skin Sensor Enables Multi‐Indicator Distinction and Continuous Real‐Time Monitoring

In both artificial electronic and ionic skins, the distinction of multi‐stimuli response in a single sensing unit is challenging, due to unavoidable mutual signal interference. Here, a zwitterionic skin sensor system that can continuously monitor and differentiate three‐stimuli‐responsive information in real‐time is designed. This sandwich‐structured sensor system is based on a zwitterionic thermo–glucose‐sensitive skin‐like hydrogel at its upper and lower layers with a middle isolation elastomer layer, enabling monitoring and distinction of temperature, mechanical and glucose information without signal interference: 1) the capacitance of the upper/lower layers as glucose–temperature‐insensitive is variable to measure strain; 2) the resistance of the upper hydrogel as glucose‐insensitive is variable to measure strain and temperature; and 3) the resistance of the lower hydrogel can detect three indicators. Based on the skin sensor system, a smart wound dressing is developed to pro‐heal chronic diabetic wounds and enable continuous real‐time monitoring of three indicators—infection, swelling, and blood glucose. This work provides a new method of real‐time monitoring and the distinction of multi‐stimuli response in a wearable device.
Persistant Organic Pollutants in Soil and Its Phytoremediation

Persistent organic pollutants (POPs) of soil mainly exhibit toxic characteristics that posses hazard to whole mankind. These toxic pollutants includes several group of compound viz., polychlorinated biphenyls, polybrominated biphenyls, polychlorinated dibenzofurans, polycyclic aromatic hydrocarbons, organophosphorus and carbamate insecticides, herbicides and organic fuels, especially gasoline and diesel. They can also be complex mixture of organic chemicals, heavy metals and microbes from septic systems, animal wastes and other sources of organic inputs. Phytoremediation is an emerging technology which can be used for remediation of soil from organic pollutants. In this chapter an attempt has been made to discuss about the sources of organic pollutants, factors that influenced the uptake of organic pollutants by plants, the different mechanism responsible for organic pollutants, phytoremediation of organic pollutants and their advantages and limitation.
Functional and Rheological Profile of LAB-fermented Bambara Groundnut (Vigna subterranean (L)) Flour

Lactic fermentation is commonly employed to improve protein digestibility and overall nutritional quality of grains foods. This study evaluated the functional and rheological properties of flour samples from Bambara groundnut fermented with Lactobacillus plantarum [NRRL B-4306] and Lactobacillus fermentum [NRRL B-1932] obtained from the United States Department of Agriculture. Functional profile such as particle size index, water absorption capacity, swelling capacity, and least gelation concentration of the flour were determined; as well, amylograph and maturograph evaluations were used to determine the rheological properties and the results presented as average, minimum, and maxi­mum values. Particle size determination observed that 150, 125, 105 µ orifice did not readily accommodate particles from the non-inoculated samples while the inoculated samples passed through 150 and 125 µ but did not readily pass through 105 µ orifice. Bioprocess with lactic acid bacteria increased the water absorption capacity of the flour samples from 346.5 to 386.4%, the least gelation concentration decreased from 5.3 to 4.1%, while swelling capacity increased from 14.9 to 23.2 mg/100 g for non-inoculated and inoculated flours, respectively. Rheological investigations show evaluations for amylograph and maturogram determinations. Values obtained for amylograph indicate that temperature at start of gelatinization was peak at 63.8 and 63.00C for non-fermented and LAB-fermented flour with no significant (p ˂ 0.05) difference, while temperature at maximum viscosity and maximum viscosity had significant (p ˂ 0.05) values of 92.1 and 76.00C, as well as 730 and 265 brabender units (BU), respectively. Thus, amylograph quality of the fermented flour sample was indicated by the maximum viscosity which is significantly higher in the case of non-inoculated flour sample. The maturograph evaluation also recorded the maturation behavior of the dough prepared from the test flours after the proofing time (fermentation rest) by means of a sensing probe which records the elasticity of the mature dough every 2 min and produces the typical zigzag form of the maturogram. This action was recorded in maturograph units (MU) on the strip-chart with values of 44 and 28 min for final proof time, 750 and 610 MU for dough level, 210 and 220 MU for dough elasticity, as well as 10 and 12 min for proofing stability, determined respectively for non-fermented and LAB-fermented flour samples. LAB-fermentation demonstrated to improve the functionality and rheology of Bambara groundnut flour and the production process could be further controlled to achieve products of optimal quality.
Modelling dissolved phosphorus losses from accumulated soil phosphorus and applied fertilizer and manure for a national risk indicator

Abstract: Balancing the weighting of various components of phosphorus loss in models is a critical but often overlooked step in accurate estimation of risk of P loss under field conditions. This study compared the P loss coefficients used to predict dissolved P losses from desorption from accumulated P in the soil, and those incidental to applications of P as fertilizer or manure, with extraction coefficients determined from actual P losses reported in literature for sites in Canada, with the addition of some sites with similar soils and climate from some northern states. The extraction coefficients for dissolved P measured in runoff water were 6.5× greater in year-round edge-of-field (EoF) measurements than in runoff boxes, indicating that models using P extraction coefficients derived from runoff box experiments will be underestimating the magnitude of losses from P accumulation in soil. Differences among the measurement methods (runoff box, rainfall simulator, or EoF) were not evident for incidental losses from applied P, but current models appear to overpredict the losses of applied P. Good agreement between measured and predicted dissolved P (DP) concentrations using the equations in the Annual Phosphorous Loss Estimator model were achieved by applying coefficients of 0.275 to the fertilizer equations and 0.219 to the manure equations, implying that 72.5% of fertilizer P and 78% of manure P are not available for runoff. This study underlines the importance of considering the relative weights of the various components of P loss as new models are developed and validated.
PDRNet: A Deep-Learning Pedestrian Dead Reckoning Framework

Pedestrian dead reckoning is a well-known approach for indoor navigation. There, the smartphone’s inertial sensors readings are used to determine the user position by utilizing empirical or bio-mechanical approaches and by direct integration. In this paper, we propose PDRNet, a deep-learning pedestrian dead reckoning framework, for user positioning. It includes a smartphone location recognition classification network followed by a change of heading and distance regression network. Experimental results using a publicly available dataset show that the proposed approach outperforms traditional approaches and other deep learning based ones.
Food pantry use and intake of foods with added sugar, sugar sweetened beverages and fruits and vegetables among low-income parents in a large Midwestern city

ABSTRACT This study examined consumption of fruits and vegetables, foods with added sugar and sugar-sweetened beverages (SSBs) based on the frequency of food pantry use. A repeated cross-sectional study was conducted among food pantry clients (n = 563) in Omaha, Nebraska. Daily intake of individual dietary items was combined into each final dietary outcome measure and dichotomized: <1 time per day or ≥ times per day. Descriptive statistics were produced, and a logistic regression model was fit. More frequent pantry use was associated with 2.1 times greater odds of consuming foods with added sugar one or more times per day compared to non-pantry users.