Miracle-dz/UniFAR-FTUs huggingface dataset:
Production of Lactic Acid by lactobacilli using Legume Flours

Due to their high nutritional value, richness in carbohydrates and good source of protein, legumes are considered essential for human diet. Legumes flours were used as fermentable material for lactic acid production. Hence, previously isolated lactobacilli strains were used in this study. The strains showed strong microbial growth and their survival in glucose-containing MRS medium and were described using a modified Gompertz equation. Lactobacilli exhibited the shortest latency phase in MRS-glucose medium. While the highest lactic acid produced was 15.40g/l by Lpb. pentosus U1 isolate after 48hours. Furthermore, these strains were evaluated in flours samples for acidification capacity. The results showed a decrease in pH values after 12hours of fermentation, indicating the fastest acidification with maximum lactic acid generation of 31.66, 25.33, 31.00 and 31.66g/l for black gram flour by Lpb. plantarum M1, Lpb. pentosus U1, Lpb. plantarum VIP1 and Lev. brevis TIP1 isolates. Black gram flour was considered the best among the used substrates for enhancing lactic acid production. Overall, this study can be termed as a preliminary step for fermentation using the desired organism in preparing legume-based food at the industrial level and benefiting consumers.
Panel unit root tests under the null hypothesis of stationarity and confirmatory analysis with applications to PPP and the convergence hypothesis

My dissertation, which is comprised of four main parts, is about the econometric analysis of unit-root nonstationary (1(1)) observations in panel data. Most existing (and popular) panel unit-root tests are built under the null hypothesis that all indi­ viduals in the panel are 1(1). A potential pitfall in the exclusive use of these tests lies in the interpretation of rejections of the null hypothesis since the rejection is consis­ tent both with the idea that the panel is stationary (1(0)) and also with the idea that the panel is a mixture of 1(0) and 1(1) series. Developing a strategy to overcome this problem is the main focus of my dissertation. The particular strategy that I adopt is to conduct confirmatory analysis by combining panel unit root tests under the 1(0) null and tests under the 1(1) null. The first problem, however, is th a t not many panel unit root tests under the 1(0) null are available and little is known about the finite sample properties of those that are. Thus in Part 1 of my dissertation, I develop two new tests for unit roots in panel data where the null hypothesis is that all individuals in the panel are 1(0). The two new panel unit root tests that I propose and study are the panel G-test and the ptmel KPSS test which are multivariate extensions of the univariate tests proposed by Park and Choi (1988) and Kwiatkowski et al. (1992), respectively. Both their asymptotic and finite sample properties are investigated and compared with an existing panel unit root tests under the 1(0) null. Among the three tests that I consider, the panel-G
«We can’t leave them alone fighting against a dragon»: Educators’ Representations through Images of Adolescents’ Wellbeing during Covid-19 Pandemic

Numerous alarming research are indicating the urgency to shed light on adolescents’ wellbeing during this pandemic crisis. Since qualitative and pedagogical literature on the topic is rather limited, this pilot study aims to investigate adolescents’ wellbeing through the eyes of education and health professionals working with them. In April 2021 three “image-elicited interviews” were conducted in order to stimulate narratives and storytelling using evocative illustrations as a stimulus. The results reveal a general tendency in adolescents towards negative emotions and self-closure, as well as difficulties in the relationship with themselves and the world around; professionals are worried about adolescents’ wellbeing.
Deep learning aids simultaneous structure–material design discovery: a case study on designing phase change material metasurfaces

Abstract. The capabilities of modern precision nanofabrication and the wide choice of materials [plasmonic metals, high-index dielectrics, phase change materials (PCM), and 2D materials] make the inverse design of nanophotonic structures such as metasurfaces increasingly difficult. Deep learning is becoming increasingly relevant for nanophotonics inverse design. Although deep learning design methodologies are becoming increasingly sophisticated, the problem of the simultaneous inverse design of structure and material has not received much attention. In this contribution, we propose a deep learning-based inverse design methodology for simultaneous material choice and device geometry optimization. To demonstrate the utility of the proposed method, we consider the topical problem of active metasurface design using PCMs. We consider a set of four commonly used PCMs in both fully amorphous and crystalline material phases for the material choice and an arbitrarily specifiable polygonal meta-atom shape for the geometry part, which leads to a vast structure/material design space. We find that a suitably designed deep neural network can achieve good optical spectrum prediction capability in an ample design space. Furthermore, we show that this forward model has a sufficiently high predictive ability to be used in a surrogate-optimization setup resulting in the inverse design of active metasurfaces of switchable functionality.
Effectiveness of personalized smoking cessation intervention based on ecological momentary assessment for smokers who prefer unaided quitting: protocol for a randomized controlled trial

Introduction Ecological momentary assessment (EMA)-based smoking cessation intervention may help personalize intervention for smokers who prefer to quit smoking unaided. This study aims to evaluate the effectiveness of EMA-based phone counseling and instant messaging for smoking cessation. Methods/design This is a two-arm, accessor-blinded, simple individual randomized controlled trial (allocation ratio 1:1). Participants will be recruited from community sites and online platforms in Hong Kong. Interventions will be delivered via a phone call and instant messaging. Current adult smokers who (1) self-report no intention to use smoking cessation services and medication in the coming month and (2) have not used smoking cessation services or nicotine replacement therapy in the past 7 days will be recruited. Recruited participants will be randomized to intervention or control groups via an online randomizer. All participants will be required to complete EMAs (five times per day for 7 consecutive days). The intervention group (n = 220) will receive a nurse-led brief phone counseling immediately after the 1-week EMAs and 10-week EMA-based advice via instant messaging applications (e.g., WhatsApp, WeChat). The 10-week EMA-based advice covers a summary of the 1-week EMAs, and tailored cessation support focused on personalized smoking triggers. The control group (n = 220) will not receive any intervention during the same period. The primary outcomes are participants' progression toward smoking cessation assessed by the Incremental Behavior Change toward Smoking Cessation (IBC-S) and biochemically validated abstinence at the 3-month follow-up. Secondary outcomes include self-reported and biochemically validated tobacco abstinence at the 6-month follow-up. Discussion The findings will provide evidence that the EMA-based tailored smoking cessation intervention can be adapted as a new health promotion strategy for current smokers who are unwilling to use smoking cessation aids. Clinical trial registration https://classic.clinicaltrials. gov/ct2/show/NCT05212220, identifier: NCT05212220.
Comparative Analysis of Optimization Value Between Artificial Neural Network and Long Short-Term Memory for Prediction Particulate Matter (PM2.5) in Bangkok Thailand

The crisis that the general public is worried about is particulate matter as small as 2.5 microns, which is invisible to the naked eye, causing a great lack of awareness of health hazards. One of the key goals and visions of government leaders around the world is to tackle PM2.5 particulate matter, but without measurements, reports and predictions, how will it lead to emission reduction and remedial steps? Therefore, the prediction of PM2.5 is considered as the main factor that will help to reduce the pollution of PM2.5. So, Neural networks have been widely used in predictive research, but the problem is What type of neural network would be most suitable for predicting the value of PM2.5? In this research, the predictions were compared between Artificial Neural Network (ANN) and Long Short -Term Memory (LSTM) using values measured from the performance test results with accuracy. The results showed that when the values of similar hyperparameters were given different results, the average ANN accuracy is 91.1460%. The average accuracy LSTM is 96.8496%. The values obtained from the comparison clearly show that for the prediction of PM2.5, the LSTM neural network was significantly more suitable than the ANN neural network.
Inference for the VEC(1) model with a heavy-tailed linear process errors*

Abstract This article studies the first-order vector error correction (VEC(1)) model when its noise is a linear process of independent and identically distributed (i.i.d.) heavy-tailed random vectors with a tail index . We show that the rate of convergence of the least squares estimator (LSE) related to the long-run parameters is n (sample size) and its limiting distribution is a stochastic integral in terms of two stable random processes, while the LSE related to the short-term parameters is not consistent. We further propose an automated approach via adaptive shrinkage techniques to determine the cointegrating rank in the VEC(1) model. It is demonstrated that the cointegration rank r 0 can be consistently selected despite the fact that the LSE related to the short-term parameters is not consistently estimable when the tail index . Simulation studies are carried out to evaluate the performance of the proposed procedure in finite samples. Last, we use our techniques to explore the long-run and short-run behavior of the monthly prices of wheat, corn, and wheat flour in the United States.
The Influence of Gender as a Moderating Variable on the Decision to Contribute to ZISWAF via ZISWAF Sharing Feature: Determining Factors and Implications

The rapid development of information technology has significantly influenced the growth of the banking industry, particularly Islamic banks, by facilitating business transactions. Mobile banking has gained popularity among customers, highlighting the potential for advancements in this sector. In Ponorogo Regency, the convenience of making ZISWAF payments from home without visiting the bank has been underutilized by users. Additionally, difficulties in identifying eligible recipients (mustahiq) and insufficient user trust in the bank, along with limited understanding of BSI mobile, pose challenges. This study aims to examine the influence of convenience and trust on ZISWAF payments through the ZISWAF sharing feature in Ponorogo Regency, with gender as a moderating variable. A quantitative research approach is employed, focusing on BSI customers in Ponorogo Regency. The sample consists of 100 respondents selected using purposive sampling. Data is collected through questionnaires, and the analysis includes multiple linear tests and moderated regression analysis interaction tests. The findings reveal that convenience does not have a significant positive effect on the decision to pay ZISWAF (p-value = 0.107 > 0.05). However, trust has a significant positive effect on the decision to pay ZISWAF (p-value = 0.008 < 0.05). Simultaneously, convenience and trust exhibit a positive and significant influence on the decision to pay ZISWAF (F-test: p-value = 0.015 < 0.05). Gender does not moderate the relationship between convenience and the decision to pay ZISWAF (t-test: p-value = 0.309 > 0.05), nor does it moderate the relationship between trust and the decision to pay ZISWAF (t-test: p-value = 0.761 > 0.05).
Genie: a generator of natural language semantic parsers for virtual assistant commands

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie’s generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.
Modeling and Implementation of a Joint Airborne Ground Penetrating Radar and Magnetometer System for Landmine Detection

We modeled and implemented a joint airborne system integrating ground penetrating radar (GPR) and magnetometer (MAG) models specifically for landmine detection applications. We conducted both simulations and experimental analyses of the joint airborne GPR and MAG models, with a focus on detecting the metallic components of different types of landmines, including antitank (AT) M15 metallic, antipersonnel (AP) M16 metallic, and AT M19 plastic (minimum-metal) landmines. The GPR model employed the finite-difference time-domain (FDTD) method and was evaluated using a singular value decomposition (SVD) and Kirchhoff migration (KM) with matched filtering (MF). These advanced techniques enabled the automatic identification and precise focusing of the reflected hyperbolic signals emitted by the landmines while considering cross-range resolution. Additionally, the MAGs were utilized based on the magnetic dipole model with a de-trend and a spatial median filtering method to estimate the magnetic anomaly of the landmines while considering various data spatial intervals. The joint airborne GPR and MAG system was implemented by combining and integrating the GPR and MAG models for experimental validation. Through this comprehensive approach, which included experiments, simulations, and data processing, the design parameters of the final system were obtained. These design parameters can be used in the development and application of landmine detection systems based on airborne GPR and MAG technology.
Unraveling the Effects of Hurricane Maria on the Abundance and Composition of Harvestmen (Arachnida: Opiliones) Species Along an Elevational Gradient in Puerto Rico

Abstract Harvestmen exhibit substantial responses to environmental conditions, disturbances, and modifications of their habitat. We examined the abundance and species distribution of harvestmen along an elevational gradient in the Luquillo Experimental Forest (LEF) on Puerto Rico 6–12 weeks before and 9–10 months after Hurricane Maria passed over the island in September, 2017. This provided a unique opportunity to examine the effects of a major storm on litter-dwelling arthropod populations, as the storm led to a much greater abundance of leaf litter and downed branches. The abundance of harvestmen was measured in quadrat samples from 300 to 1,000 m elevation using Winkler samplers. We observed greater harvestmen abundance post-storm, which was similar to the results for most other arthropod groups. Pre-storm, harvestmen showed greater abundance in palm habitat, as compared to forests that were a mix of broadleaf and palm vegetation. Across all sampling periods, harvestmen were most abundant at low to medium elevations (300–600 m), especially for the two dominant taxa (Stygnomma spinula and Metacynortoides obscura obscura). Our findings are similar to results obtained from manipulative studies in the LEF several years prior to the hurricane. A challenge for the future is to understand the ways that the projected increase in storm severity due to climate change can affect various invertebrate groups such as harvestmen, and how tropical forests can remain resilient to such disturbances.
The Noisy Ageing of Slow Pulsars: New Thoughts on the Evolution of the Pulsar Population

Abstract Over the last decade or so, it has become clear that pulsars exhibit sudden and significant changes in their spin properties. At the same time, a better understanding of the geometry of young and older pulsars, is providing clues about the long-term evolution of the magnetic inclination angle. In this talk, we present a simple simulation of the pulsar population that takes into account current observational facts. We show how, with very few assumptions, the observed P-Ṗ diagram can be reproduced for a synthesized population. The implications are interesting and testable.
Validity and Reliability of the Adoption Questionnaire of Agricultural Mechanization in the Food Estate Area of Central Kalimantan Indonesia

The use of agricultural mechanization has greatly contributed to the modernization of agricultural systems in many developing countries, including Indonesia. One implementation of agricultural modernization is the application of agricultural mechanization in the food estate program in Central Kalimantan, Indonesia. The adoption of agricultural mechanization is a determinant of the implementation of agricultural mechanization in the food estate area. However, the problem is that the speed of adoption of agricultural mechanization by farmers in the food estate area is still varied. The purpose of this research is to test the validity and reliability of the questionnaire in the study of agricultural mechanization adoption to support agricultural modernization in the food estate area of Central Kalimantan. This research was conducted using a quantitative research design with rice farmers who received the food estate program as the unit of analysis in the Pulang Pisau and Kapuas districts, Central Kalimantan. Data was collected purposively from 42 rice farmers in the food estate area by providing a questionnaire containing five indicators of agricultural mechanization adoption. The results showed that the questionnaires were valid and reliable with correlation values (Pearson Correlation) for the five indicators of awareness, interest, evaluation, trial, and adoption with fifteen questions showing the value of R count > R table and declared valid. While Cronbach's Alpha value is 0.794-0.951. Meanwhile, Cronbach's Alpha value was 0.794-0.951. Based on the discussion, it can be concluded that the questionnaire is valid and reliable.
Public Awareness of Healthy Lifestyle among Iraqi Population

Background: A healthy lifestyle is a way of life that lowers the risk of being seriously ill or dying early. It includes making wise choices that can help people thrive as they move through their life's journey, like eating a healthy diet and being physically active. Objective: This study aims to assess public knowledge and awareness regarding healthy lifestyles. Methods: A cross-sectional study was conducted among 128 people relative to primary healthcare center visitors in Wasit province in Iraq. The data was collected using a self-administered questionnaire derived from a previously published article consisting of two parts. Results: In this study, around 66.4% of the sample knew about the food pyramid, and about 44.5% of them used it in their daily lives. The majority (60.6%) were eating three meals during the day, and only 44.6% skipped some meals during the day. Only one (0.8%) mentioned drinking alcohol, and 46.1% have a smoking habit. The majority (64.8%) of the sample never exercised for at least 30 minutes a day. A significant association was detected between gender and knowledge about the food pyramid (p=0.001), and a significant association was also found between smoking and the presence of chronic disease (p=0.029). Conclusion: It is important for the public to be aware of healthy lifestyles. Iraqi people still need to increase their knowledge and awareness by implementing health education.
Metasurface for Reciprocal Spin-Orbit Coupling of Light on Waveguiding Structures

Metasurfaces exploiting optical spin Hall effects present an efficient means to control light, and suggest new functionalities in integrated optics. The authors show that an array of metallic nanoantennas can be used not only to tailor the polarization of light extracted from a waveguide with the direction of the wave, but also to control the number of output directions and their polarizations. This general approach can be extended to various frequencies and applied to other systems like silicon waveguides or photonic platforms, and could be a building block for multiplexing, chiral sensing, and polarization-encoded optical quantum computing.
Longitudinal resistance performance of granular ballast beds under cyclic symmetric displacement loading

The longitudinal resistance performance of a granular ballast bed under cyclic symmetric displacement loading was studied based on a full-scale test model of ballast track structures. The change law of the longitudinal resistance characteristics of the ballast bed under variable displacement amplitudes was analyzed. The results show that: the resistance-displacement curves of a granular ballast bed are a set of closed hysteretic curves, indicating obvious energy consumption; a granular ballast bed softens gradually during the cyclic process with constant displacement amplitude, and the residual deformation rate increases nonlinearly with increasing cycle number; the peak value of the longitudinal resistance of lines decreases with increasing cycle number; the cyclic softening of a granular ballast bed is dependent on the displacement amplitude–the higher the displacement, the more severe the cyclic softening will become; after cyclic displacement loading is applied several times, the longitudinal resistance of the bed will degenerate obviously, and the higher the displacement amplitude, the higher the longitudinal resistance attenuation rate of the ballast bed will become. 中文概要目 的研究散粒体道床在纵向反复荷载下的阻力性能及变化规律是深入理解有砟轨道无缝线路动态服役性能和辨识无缝线路在循环荷载作用下的受力变形机理的基础。 本文旨在利用室内足尺试验模型及专用加载系统, 分析散粒体道床受循环位移荷载时的纵向阻力性能, 探索不同位移加载幅值对有砟道床纵向阻力的影响。 创新点1. 利用有砟轨道结构足尺试验模型及循环加载装置, 测试循环荷载下的道床纵向阻力-位移滞回曲线; 2. 根据循环加载试验曲线, 构建滞回模型, 刻划散粒体道床的纵向承载和传力性能。 方 法1. 通过试验分析, 得到散粒体道床在周期性荷载作用下的力-位移曲线(图6、8 和10); 2. 基于试验数据, 分析散粒体道床在周期性荷载下的滞回准则, 得到不同位移幅值下滞回曲线的演化规律(图7、9 和11); 3. 通过图像识别技术, 对周期性荷载作用下道砟颗粒的运动规律进行分析, 从散粒体道床的细观作用机理分析宏观力学表现(图16~18)。 结 论1. 散粒体道床在循环往复荷载下的纵向阻力-位移曲线为一条封闭的滞回曲线, 且存在明显的耗能现象; 2. 在位移幅值保持不变的循环过程中, 散粒体道床表现出一种循环软化行为; 3. 位移幅值不同, 道床纵向阻力的衰减率不同, 且位移越大, 退化效应越明显。