Miracle-dz/UniFAR-FTUs huggingface dataset:
TiO2 Sensitization with Bi2S3 Quantum Dots: The Inconvenience of Sodium Ions in the Deposition Procedure

This paper highlights the inconvenience of sodium ions in the Bi2S3 quantum dots (QDs) deposition procedure, on the TiO2 surface, for the conversion efficiency of the sensitized solar cells derived from these materials. Porous TiO2 electrodes were sensitized with QDs obtained by the successive ion layer adsorption and reaction (SILAR) technique, using aqueous solutions of Bi(NO3)3 as the cation precursor and Na2S as the sulfide source. Independently, elemental sulfur vapor was used to replace the Na2S solution in the synthesis procedure. The obtained layers were characterized by X-ray diffraction, transmission electron microscopy, Raman scattering, and diffuse reflectance spectroscopy. We found that the presence of sodium ions in the reaction system strongly affects the formation of Bi2S3 on the porous TiO2. This result contrasts with what other authors have claimed. Using elemental sulfur vapor as a precursor, crystalline Bi2S3 nanoparticles are formed on the TiO2 surface. The sensitized solar cells prod.
Assessing offender change over treatment

The sample consisted of male offenders (n = 139) who had completed at least two EQUIPS programs, and who had completed the Measures of Criminal Attitudes and Associates (MCAA: Mills & Kroner, 2001) before and after each program. Offender responses across assessments were analysed using descriptive statistics, analyses of variance (ANOVAs) and analyses of covariance (ANCOVAs). Residual change scores were calculated as indices of individual within-treatment change. A series of cox proportional hazard models were used to assess the relationship between change in MCAA scores and recidivism.
SAVED BY THE BELL? THE EFFECT OF SCHOOLING ON CRIME AGAINST WOMEN

Brazil has a female homicides rate of 4.8 homicides per 100,000 female population. More than 1/3 of the female homicides occur inside their household suggesting that violent context and vulnerability at home. Lower levels of education are listed by WHO as a risk factor for women, increasing her probability of suffering violence from their partner. Increasing female schooling helps to improve women’s access to paid employment and to have access to and knowledge of their rights. The higher level of education would reduce the woman's vulnerability, whether expressed in her relationship with her partner or given her role in the family structure. Nevertheless, the relationship between education and criminal activity has been investigated focusing on the criminal’s point of view seeking what the effect of education has on the decision to commit a crime. Alternatively, this paper highlights the different education effects on men and women to answer the question: could education reduce the female homicides rate? To study it, we take the most desegregated level of data available on this subject to construct a panel analysis with the 645 municipalities of São Paulo state from 2003 to 2018. The impact of schooling (lag school enrolment) on female homicide rates is studied controlling for an inherited characteristic of each municipality along with following measures: the municipality economic performance, employment opportunities, level of urbanization, the percentage of youth on population, crime level and the probability to be caught (incarceration rate). Our estimative shows that an increase of one percentage point on the male enrolment tax could decrease 0.3 points the female homicide rate suggesting the importance of greater access to education.
Assessment of Diastasis Recti Abdominis using Dial Caliper in Overweight and Obese Adults (18 years and above) and Its Relationship with Lumbopelvic Pain: A Pilot Study

Background: Diastasis recti abdominis is present in both gender and is associated with increasing age, activities including regular lifting of heavy weights or sit-ups, a history of midline abdominal surgery, signiﬁcant hormonal changes due to replacement therapy, menopause, congenital, chronic obstructive pulmonary disease and obesity. Studies have proven physiotherapy is beneficial in managing DRA but not many studies are done to show presence of DRA. Hence our study is a small attempt at diagnosing DRA in most vulnerable population i.e. overweight and obese population. Objective: To assess presence of diastasis recti abdominis in overweight and obese adults (18 years and above) using dial caliper. To correlate diastasis recti abdominis with lumbopelvic pain, gender and parity. Methods: 90 subjects were allocated in this study (30 overweight, 30 obese 1 and 30 obese 2). Presence of DRA was assessed using dial caliper in them and correlated with BMI. Subjects who had DRA present, their values were correlated further with Lumbopelvic pain, gender and parity. Results: Out of 90 subjects, 67 (74%) had DRA present [21 (23.3%) in overweight, 22 (24.4%) in obese 1, 24 (26.6%) in obese 2]. There is no correlation of Presence of DRA with BMI. DRA has no correlation with lumbopelvic pain and gender. DRA has moderate positive correlation with parity. Conclusion: There is Presence of Diastasis Recti Abdominis in overweight, obese 1 and obese 2 adults; however, they are at equal risk of having DRA. Subjects with DRA are not more likely to have lumbopelvic pain than subjects without DRA. DRA can occur in males as well as females and multiparous females are at increased risk of developing DRA than nulliparous and primiparous. KEY WORDS: Diastasis recti abdominis, overweight, obesity, physiotherapy, lumbopelvic pain.
A Uniﬁed Approach for Pose Graph Optimization

Pose Graph Optimization (PGO) is an important problem in Computer Vision, particularly in motion estimation, whose objective consists of ﬁnding the rigid transformations that achieve the best global alignment of visual data on a common reference frame. The majority of PGO approaches rely on iterative techniques which reﬁne an initial estimate until convergence is achieved. On the other hand, recent works have identiﬁed a global constraint which has cast this problem into the matrix completion domain. The success which both of these formulations have had in computing accurate solutions efﬁciently has been overshadowed by large-scale industrial applications such as autonomous ﬂight, self-driving cars and smart-cities, where it is necessary to fuse numerous images covering large areas but where each one of them has few pairwise observations. We developed a methodology that uniﬁes these alternative formulations, which until now, coexisted unable to share the advantages of each other, resulting in a closed-form solu-tion in the basin of attraction of the global optimum. Our formulation allows for high scalability, low computational cost and high precision, simultaneously.
Identification of the Sibling Species of the Blackfly, Simulium damnosum Complex Using their Polytene Chromosomal Banding Patterns in Oji River System, Enugu State, Nigeria

Identification of the sibling species of the blackfly, S. damnosum complex using their polytene chromosomal banding patterns in Oji river system was carried out with the aim of accurately identifying the particular sibling species of S. damnosum complex involved in the transmission of onchocerciasis in Oji-river LGA and environ. Simulium damnosum complex larvae were collected monthly in forty eight (48) breeding sites in four sampling units for three years in the Oji river system in wet and dry seasons. Collected larvae were preserved in cold Carnoy’s solution prior to processing. Salivary gland chromosomes were extracted, processed and analyzed microscopically for polytene chromosomal banding patterns. Three sibling species of Simulium damnosum complex that breed in the Oji river system were identified in varying degrees. They included S. squamosum, S. yahense and S. damnosum sensu stricto (s.s.). Variation in the occurrence of the sibling species was statistically significant. Dry and wet season catches for all the sibling species in the sampling units were found to differ statistically. The identified sibling species of S. damnosum complex are known to be vectors of Onchocerca volvulus, the causative agent of onchocerciasis, a disease highly prevalent in Oji-river LGA and environ.
Transparency or Perception Manipulation? A Study of IT Disclosure Tone in the Context of Data Breaches

Firms are increasingly engaged in voluntary disclosure to communicate critical information to external stakeholders. This study investigates how firms are motivated by the data breach events of other firms in the same industries to disclose their own IT-related information. We find that peer firms become more positive in tones to disclose their own IT-related information when other firms in their industries are affected by data breaches. We further examine the informativeness of the increased positivity in IT disclosure and how it is dependent on IT governance. We find that positive disclosure of IT information by firms with superior IT governance is mostly a transparent way to reveal strengthened IT protection. In contrast, firms with inferior IT governance are more likely to strategically use positive disclosure of IT information as a way to manipulate perception of IT robustness and safety.
Low Power Conditional-Execution Pulsed Flip-Flop

We propose conditional execution technique to reduce the redundant switching activity of the internal nodes in flip-flops. Double-edge clocking is utilized to further reduce the power consumption. With a data switching activity of 37.5%, the new conditional execution pulsed Flip-Flop (CEPFF) can achieve 12% improvement in terms of PDP.
Voice Conversion using Generative Techniques

Generative approaches have proven to be very effective in style transfer tasks for image data. Gatys et al. [3] demonstrated how the skill of convolutional neural networks (CNN) in extracting features could be applied to extract style and content information using pre-trained networks such as VGG19[15]. The result is a ﬂood of interesting re-imaginations of classic pieces of artwork, and interesting applications such as fun camera ﬁlters. Similarly Generative Adversarial Models (GANs)[5] and Variational Auto Encoders such as CycleGan[16] have also proven to be effective in style transfer tasks for image data. In the domain of audio, the problem of Voice Conversion could be framed similarly as a style transfer problem. The ability to speak and automatically output audio in someone else’s voice such as a famous celebrity remains a recurring theme in popular science ﬁction movies. Current voice conversion techniques often rely on parallel voice training data where two users say the same sentence, however this obviously limits the number of source and target speakers. This paper explores the feasibility of applying generative techniques to convert the voice of an unseen speaker, building on and comparing existing works including Neural Style Transfer using VGG-like networks, and a Variational Auto Encoder approach using AutoVC[13].
A Convolutional Neural Network based Approach for Recognizing Malayalam Handwritten Characters

—Optical character recognition has leveraged its capabilities to reduce tedious manual work of converting images containing characters to texts for recent decades, recognition of handwritten texts is harder than recognizing printed texts as different handwrittenscript may have different style of writings, slants etc, The Convolutional Neural Network(CNN) has been successfully used to recognize characters in many languages. This paper proposes a CNN architecture for classification of handwritten characters in Malayalam language. Malayalam is a south Indian language which is used by 33.3 million people in the state of Kerala. This CNN Model has shown a testing accuracy of 97.26% for the classification of 44 handwritten Malayalam characters by using a dataset having seventy three thousand training images and eighteen thousand testing images. In addition, this paper put forward an algorithm to process an image containing handwritten Malayalam characters and output the corresponding Malayalam characters
Using Wavelet Transform of Hyperspectral Reflectance Data for Extracting Spectral Features of Soil Organic Carbon and Nitrogen

Abstract Visible and near-infrared spectroscopy can be a powerful tool to rapidly quantify various soil characteristics. Recently, wavelet analysis has been proven to be efficient in many fields, including signal processing and digital image analysis. It can be used to smooth signals and to reduce large data sets to parsimonious representations. The objective of this study was to capture the absorption features of soil organic carbon (SOC) and total nitrogen (TN) by applying wavelet analysis to reflectance spectra. In addition, the correlation between SOC, TN contents, and soil spectra reflectance and wavelet coefficient were analyzed and compared. The results show that the maximum correlation coefficient between SOC, TN, and wavelet coefficient was more than 0.96 compared with the relationship between SOC, TN, and spectral reflectance (r = −0.79 for SOC, r = −0.40 for TN), especially for TN (the maximum negative correlation coefficient, r = −0.964). For SOC + TN and SOC/TN, because SOC contents accounted for a larger proportion of soil composition than TN, their spectral features were mainly affected by SOC in soil samples. In addition, wavelet analysis also enhanced the features of SOC + TN and SOC/TN obviously. These results suggest that wavelet analysis is a better method for capturing the absorption features of soil composition from hyperspectral data. Continuous wavelet transform was confirmed to be an essential and efficient tool used to locate the wavenumber where SOC and TN could be predicted with minimal interference from other components. The results also implied that continuous wavelet transform can be used in discriminating soil composition.
Iron-Based Metal–Organic Frameworks MIL-88B and NH2-MIL-88B: High Quality Microwave Synthesis and Solvent-Induced Lattice “Breathing”

Iron-based MIL-88B and NH2-MIL-88B microcrystals with high dispersibility and uniform size were successfully synthesized by using a rapid microwave-assisted solvothermal method. By carefully controlling the reaction conditions, the microwave method provided superior quality MIL-88B crystals in high yields and excellent phase purity. Framework flexibility was observed for both MIL-88B-Fe and NH2-MIL-88B-Fe frameworks in various solvents, which however significantly differs between the two materials. MIL-88B-Fe shrinks reversibly by about 25% only when it is dispersed in the strongly hydrogen bonding solvents water or methanol. In contrast, NH2-MIL-88B-Fe shrinks up to 33% upon replacement of dimethylformamide (DMF) by any other solvent studied (benzene, chloroform, acetone, acetonitrile, methanol, water). The change in unit cell parameters (shortening of the a axis) can be seen macroscopically, although the overall integrity of the materials is maintained. We suggest that hydrogen bonding between the oxyge.
Scale Development and Psychometric Qualities of the Resilience Doughnut tool. a Valid, Solution­ Focused and Ecological Measure of Resilience With Australian Adolescents

The Resilience Doughnut is an ecological and Solution-Focused model outlining the seven contexts where resilience skills can be developed. The premise of this study was to test the psychometric properties of the online resilience doughnut measurement tool. The analysis contains item analysis and confirmatory factor analysis. Each context was explored as a separate subtest. The results showed the model to be a good fit with Cronbach alpha coefficient between . 63 and . 87. Cor­ relations were conducted with the subscales of the Strength and Difficulties (SDQ) questionnaire and The Resilience scale for Adolescents (READ) revealing that the stronger the resources (RD) the greater the levels of personal and social compe­ tence and the lower the emotional and behavioural difficulties experienced. Im­ plications for further study are discussed noting the validity of enhancing existing strong social resources to develop resilience.
Supplementary Material for: Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks

Overview. The PASTIS dataset is composed of 2433 square 128×128 patches with 10 spectral bands and at 10m resolution, obtained from the open-access Sentinel-2 platform. 1 For each patch, we stack all available acquisitions between September 2018 and November 2019, forming our four dimensional multi-spectral SITS: T×C×H×W . The publicly available French Land Parcel Identification System (FLPIS) allows us to retrieve the extent and content of all parcels within the tiles, as reported by the farmers. Each patch pixel is annotated with a semantic label corresponding to either the parcels’ crop type or the background class. The pixels of each unique parcel in the patch receive a corresponding instance label.
REGULARIZATION TECHNIQUES IN MULTIPLE LINEAR REGRESSION IN THE PESENCE OF MULTICOLLINEARITY

Multicollinearity has been a serious problem in regression analysis. Ordinary least square (OLS) regression may result in high variability in the estimates of the regression coefficients in the presence of multicollinearity. Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RR), and Partial Least Squares (PLS) methods are well established methods that reduce the variability of the estimates by shrinking the coefficients and at the same time produce interpretable models by shrinking some coefficients. The performances of LASSO, Ridge Regression, PLS and OLS estimators were evaluated using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in the presence of multicolinearity using Monte Carlo Simulation. The simulations were done for different sample sizes: n (10, 50, 100, 150) and levels of multicollinearity: Mild (0.1 – 0.3), Low (0.4 – 0.6) and High (0.7 0.9). OLS had poor parameters estimate and produced wrong inferences, LASSO estimator is the best, while PLS is most efficient when the number of variable is greater than sample size.
Comprehend Sentiments in Product Reviews (Natural Language Processing)

Social distancing during the current pandemic has prompted consumers to buy products online. One of the important factors that help consumers judge quality of a product or service without being able to tangibly review the product is customer reviews. In addition, reviews also help product seller to get feedback from their customers. We propose using Deep Learning to analyze sentiments of product reviews that can help product sellers and consumers to take focused approach on the reviews that matter to them. The input to our deep learning system will be product review comprised of product rating and text description of the review. The input will be processed to generate embedding, which will be passed to Neural Network model. The output will be rating in the range of 1-5 signifying score of the review.
Does Mobile App Failure Impact Online and In-Store Shopping

Mobile devices account for a majority of transactions between shoppers and marketers. Branded retailer mobile apps have been shown to increase purchases across channels (e.g., online and brick-and-mortar store). However, mobile apps are also prone to failures or disruptions in use. Does a failure in an omnichannel retailer’s branded app impact shoppers’ purchases? Are there cross-channel effects of these failures? Does the impact of failure vary across shoppers? These questions, although important, are challenging to answer because field experiments inducing failures are infeasible, and observational data suffer from selection issues. We identify a natural experiment involving an exogenous two-hour failure in a large omnichannel retailer’s mobile app during which the app became unavailable to all the shoppers. We examine the short-term impact of app failures on purchases in both online and store channels using a difference-in-differences approach. We investigate two potential mechanisms behind these effects, channel substitution and brand preference dilution. We also analyze shopper heterogeneity in the effects based on shoppers’ relationship with the retailer, past digital channel use, and other characteristics identified using machine-learning approaches. Our analysis reveals that an app failure has a significant overall negative effect on shoppers’ frequency, quantity, and monetary value of purchases across channels. The effects are heterogeneous across channels and by shoppers. The decreases in purchases across channels are driven by purchase reductions in stores and not in the online channel. The fall in purchases in brick-and-mortar stores is consistent with the brand preference dilution mechanism, whereas the preservation of purchases in the online channel is in line with the channel substitution mechanism. Furthermore, shoppers with a higher monetary value of past purchases and less recent purchases are less sensitive to app failures. The results suggest that app failures lead to an annual revenue loss of about $0.97-$1.36 million for the retailer in our data. About 47% shoppers contribute to about 70% of the loss. We outline targeted failure prevention and service recovery strategies that retailers could employ.
SYNTHESIS AND MAGNETIC PROPERTIES OF NICKEL NANO PARTICLES

Nickel nano particles around 12nm were prepared by chemical reduction of Nickel Chloride dissolved in Ethylene glycol, in which hydrazine act as a reducing agent and NaOH as stabilizer. The powder XRD study confirms that the average size of the particle is 12nm.The Ni particles show the characteristic of super paramagnet with saturation magnetization (M s ) and remanent magnetization (M r ) at 5K is 2.55 emu/g and 1.11 emu/g, respectively.
A photoautotrophic platform for the sustainable production of valuable plant natural products from CO2

Many plant natural products have remarkable pharmacological activities. They are mainly produced directly by extraction from higher plants, which can hardly keep up with the surging global demand. Furthermore, the over-felling of many medicinal plants has undesirable effects on the ecological balance. In this study, we constructed a photoautotrophic platform with the unicellular cyanobacterium Synechococcus elongatus PCC7942 to directly convert the greenhouse gas CO2 into an array of valuable healthcare products, including resveratrol, naringenin, bisdemethoxycurcumin, p-coumaric acid, caffeic acid, and ferulic acid. These six compounds can be further branched to many other precious and useful natural products. Various strategies including introducing a feedback-inhibition-resistant enzyme, creating functional fusion proteins, and increasing malonyl-CoA supply have been systematically investigated to increase the production. The highest titers of these natural products reached 4.1–128.2 mg L−1 from the photoautotrophic system, which are highly comparable with those obtained by many other heterotrophic microorganisms using carbohydrates. Several advantages such as independence from carbohydrate feedstocks, functionally assembling P450s, and availability of plentiful NADPH and ATP support that this photosynthetic platform is uniquely suited for producing plant natural products. This platform also provides a green route for direct conversion of CO2 to many aromatic building blocks, a promising alternative to petrochemical-based production of bulk aromatic compounds.