Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Plant Protein Hydrolysates as Natural Antioxidants in Fish Oil-In-Water Emulsions

In this work, we evaluated the physical and oxidative stabilities of 5% w/w fish oil-in-water emulsions stabilized with 1%wt Tween20 and containing 2 mg/mL of protein hydrolysates from olive seed (OSM–H), sunflower (SFSM–H), rapeseed (RSM–H) and lupin (LUM–H) meals. To this end, the plant-based substrates were hydrolyzed at a 20% degree of hydrolysis (DH) employing a mixture 1:1 of subtilisin: trypsin. The hydrolysates were characterized in terms of molecular weight profile and in vitro antioxidant activities (i.e., DPPH scavenging and ferrous ion chelation). After incorporation of the plant protein hydrolysates as water-soluble antioxidants in the emulsions, a 14-day storage study was conducted to evaluate both the physical (i.e., ζ-potential, droplet size and emulsion stability index) and oxidative (e.g., peroxide and anisidine value) stabilities. The highest in vitro DPPH scavenging and iron (II)-chelating activities were exhibited by SFSM–H (IC50 = 0.05 ± 0.01 mg/mL) and RSM–H (IC50 = 0.41 ± 0.06 mg/mL). All the emulsions were physically stable within the storage period, with ζ-potential values below −35 mV and an average mean diameter D[4,3] of 0.411 ± 0.010 μm. Although LUM–H did not prevent lipid oxidation in emulsions, OSM–H and SFSM–H exhibited a remarkable ability to retard the formation of primary and secondary lipid oxidation products during storage when compared with the control emulsion without antioxidants. Overall, our findings show that plant-based enzymatic hydrolysates are an interesting alternative to be employed as natural antioxidants to retard lipid oxidation in food emulsions.
Forgivingness of an Anteromedially Positioned Small Locked Plate for High Tibial Osteotomy in Case of Overcorrection and Lateral Hinge Fracture

High tibial osteotomy (HTO) represents a sensible treatment option for patients with moderate unicondylar osteoarthritis of the knee and extraarticular malalignment. The possibility of a continuously variable correction setting and a surgical approach low in complications has meant that the medial opening osteotomy has prevailed over the past decades. The objective of the present study was to determine whether anteromedially positioned small plates are nevertheless forgiving under biomechanically unfavourable conditions (overcorrection and lateral hinge fracture). In this study, a simulated HTO was performed on composite tibiae with a 10-mm wedge and fixed-angle anteromedial osteosynthesis with a small implant. Force was applied axially in a neutral mechanical axis, a slight and a marked overcorrection into valgus, with and without a lateral hinge fracture in each case. At the same time, a physiological gait with a dual-peak force profile and a peak load of 2.4 kN was simulated. Interfragmentary motion and rigidity were determined. The rigidity of the osteosynthesis increased over the cycles investigated. A slight overcorrection into valgus led to the lowest interfragmentary motion, compared with pronounced valgisation and neutral alignment. A lateral hinge fracture led to a significant decrease in rigidity and increase in interfragmentary motion. However, in no case was the limit of 1 mm interfragmentary motion critical for osteotomy healing exceeded. The degree of correction of the leg axis, and the presence of a lateral hinge fracture, have an influence on rigidity and interfragmentary motion. From a mechanically neutral axis ranging up to pronounced overcorrection, the implant investigated offers sufficient stability to allow healing of the osteotomy, even if a lateral hinge fracture is present.
Smartphone Addiction among University Students in Light of the COVID-19 Pandemic: Prevalence, Relationship to Academic Procrastination, Quality of Life, Gender and Educational Stage

Abstract: Aims The current study aims to identify the level and proportions of smartphone addiction, and academic procrastination among university students in the light of the Corona pandemic; identify the differences in smartphone addiction, academic procrastination, and quality of life according to gender and stage of study; and revealing the predictive ability of academic procrastination and quality of life for smartphone addiction. Methods: 556 male and female students from Saudi universities participated in the study, whose ages ranged from 18 to 52 years. Measures of academic procrastination and quality of life were used, in addition to the Italian scale of smartphone addiction, which was translated and checked for validity and reliability. Results: The results revealed that 37.4% of the sample were addicted to smartphone use, while 7.7% had a high level of procrastination, and 62.8% had an average level of procrastination. The results did not show statistically significant differences in smartphone addiction and quality of life according to gender and educational stage, while there were statistically significant differences in academic procrastination according to gender in favor of males, and according to stage of education in favor of undergraduate students. The results also revealed a statistically significant positive relationship between academic procrastination and smartphone addiction, and a statistically significant negative relationship between smartphone addiction and quality of life. A negative relationship between quality of life and academic procrastination was found. The results also revealed that addiction to smartphones could be predicted through academic procrastination and quality of life.
Efficient Hybrid Iterative Method for Signal Detection in Massive MIMO Uplink System over AWGN Channel

Massive multiple input multiple output (massive MIMO) is a key technology in fifth-generation (5G) and beyond fifth-generation (B5G) networks. It improves performance metrics such as gain, energy efficiency, spectral efficiency, and bit error rate (BER). Because of the large number of users and antennas, sophisticated processing is required to detect the transmitted message signal. One of the challenges in massive MIMO systems is transmitted message signal detection. To respond to these challenges, several detection algorithms have been developed, including minimum mean squared error (MMSE), zero forcing (ZF), matched filter (MF), conjugate-gradient (CG), gauss-seidel (GS), and optimized coordinate descent (OCD). Although the ZF and MMSE algorithms perform well, their computational complexity is high due to direct matrix inversion. When the number of users is much lower than the number of antennas, the MF algorithm performs well. However, as the number of users increases, the performance of the MF algorithm degrades. Although the OCD, CG, and GS algorithms have less computational complexity than the MMSE algorithm, they perform poorly in comparison. To address and resolve the shortcomings of existing methods, an efficient iterative algorithm has been proposed in this manuscript, which is a hybrid method possessing the combination of MMSE with the alternating direction method of multipliers (ADMM) technique and Gauss-Seidel method. The initial vector has a large influence on the performance, complexity, and convergence rate of such iterative algorithms. The proposed detector’s initial solution is determined using the diagonal matrix and MMSE with the ADMM technique. The proposed algorithm’s performance and complexity are compared with existing algorithms based on BER and the real number of multiplications, respectively. The numerical results revealed that the proposed algorithm achieves the desired performance with a small number of iterations and a significant reduction in computational complexity. At 8QAM, SNR = 20 dB, 80 × 120 massive MIMO antenna configuration, and n = 2, the percentage performance improvement of the proposed detector from the GS detector is 99.82%. At 32QAM, SNR = 25 dB, 120 × 180 antenna configuration, and n = 5, performance improvement of the proposed detector is 99.89%. At 64QAM, SNR = 28 dB, 80 × 120 antenna configuration, and n = 3, performance improvement of the proposed detector is 99.93%.
Sensitivity analysis of latent variables in Variational Autoencoders for Dermoscopic Image Analysis

The advances in the deep learning field have paved the way to novel strategies to represent digital image data in the form of synthetic descriptors. Variational Auto-Encoders (VAE) architectures are generative powerful tools not only to reconstruct input images but also to extract meaningful information for the task of pattern classification. The first part of the VAE network, called encoder, aims to condense the image information into a reduced set of low-level descriptors, called latent variables. The second part, called decoder, aims to use the latent variable in a reverse process that reconstructs the original image in output. In this work, we exploited the VAE-based latent representation of colour normalized dermoscopic images for the discrimination of malignant and benign skin lesions. In particular, we investigated the sensitivity to the effect of skin colour variations over the final reconstruction error and on the discrimination capability of the VAE latent variables in terms of individual Area Under the roC curve (AUC). By exploiting and adapting state-of-the art skin colour variation models we obtained a performance worsening of about 10% either in the reconstruction error and in the discrimination capability of the latent variables. The achieved preliminary results demonstrate that, with suitable VAE adaptation, latent descriptors could be used in automatic skin lesions classification frameworks.
Nostalgia: A Review, Propositions, and Future Research Agenda

Abstract This article presents a systematic review of the nostalgia literature (205 articles) using PRISMA protocols. It dwells on three questions: What do we know about nostalgia? What do we need to know about nostalgia? and Where should we be heading? The article examines the evolution of nostalgia, analyzes the definitions of nostalgia (past–memory–yearning–ambivalent emotion), and extends the nostalgia typology of Havlena and Holak (1996). The article also delineates the nostalgic advertising literature; identifies antecedents, moderators, and consequences of nostalgic advertising; and highlights research gaps. Finally, this work offers propositions on nostalgic ad appeals (those that emphasize the attractiveness of and yearning for the past in a bittersweet way) based on four themes: self-restoration, continuity, social relationships, and culture. Specifically, the propositions offer new moderators, such as ads’ emotional flow, gender identity, purchase and consumption situation, perceived interactivity, and culture type, that could make some types of nostalgic appeals more effective than others. Our work contributes by being more comprehensive and broad based, extending the typology framework and delineating propositions that lay out a research roadmap for nostalgic advertising.
Hubungan Kondisi Rumah dan Kepadatan Lalat di Sekitar Tempat Pembuangan Akhir Sampah

Background: People who live in a poor condition of house or in a bad environment quality can attract flies to thrive and transmit disease. In the protection against disease, the house should have safe and clean facilities and being prevented from vector that has role in transmitting the disease, such as housefly. Residential area in Cipayung village, especially around final waste disposal (TPA) Cipayung, Depok with a radius of 200 meters from the landfill of final waste disposal has the potential to become perching place of housefly, because of flight range of housefly can reach around 200-1000 meters. Method: This research is done by a cross sectional study design that aims to determine relation between house condition with the density of housefly. Results: The results showed that there was no significant relation between house criteria with housefly density inside house with p-value 0.659 (OR 0.7; 95% CI: 0.136 to 3.920), however there is a significant relation between house criteria with housefly density outside house, house with unhealthy house criteria has risk 4.2 times higher houseflies density rather than house with healthy house criteria p-value 0.011, (OR 4.273; 95% CI: 1.414 -12.909). Conclusion: Housefly control can be executed through making healthy housing and environmental.
Compact and high‐isolation microstrip diplexer using distributed coupling feeding line

A compact and high‐isolation microstrip diplexer using the distributed coupling feeding line is presented. The lower frequency channel filter is based on a short stub‐load resonator which has a transmission zero at the center frequency of the upper channel. A pair of hairpin line resonators with the pseudo‐interdigital structure are applied to design the upper frequency channel filter which creates a transmission zero at the center frequency of the lower channel. The center frequencies and bandwidths of both channel filters can be controlled independently. The obtained transmission zeros lead to high isolation. A distributed coupling feeding line is utilized to combine the lower and upper channel filters. Taking advantages of the distributed coupling feeding line, no extra matching network is required and each channel filter can be designed independently. Finally, a diplexer working at 3 and 5 GHz with over 40 dB isolation is fabricated and tested. The simulated and measured results are in good agreement.
Identity Authentication in Two-Subject Environments Using Microwave Doppler Radar and Machine Learning Classifiers

Identity authentication based on Doppler radar respiration sensing is gaining attention as it requires neither contact nor line of sight and does not give rise to privacy concerns associated with video imaging. Prior research demonstrating the recognition of individuals has been limited to isolated single-subject scenarios. When two equidistant subjects are present, identification is more challenging due to the interference of respiration motion patterns in the reflected radar signal. In this research, respiratory signature separation techniques are functionally combined with machine learning (ML) classifiers for reliable subject identity authentication. An improved version of the dynamic segmentation algorithm (peak search and triangulation) was proposed, which can extract distinguishable airflow profile-related features (exhale area, inhale area, inhale/exhale speed, and breathing depth) for medium-scale experiments of 20 different participants to examine the feasibility of extraction of an individual’s respiratory features from a combined mixture of motions for subjects. Independent component analysis with the joint approximation of diagonalization of eigenmatrices (ICA-JADE) algorithm was employed to isolate individual respiratory signatures from combined mixtures of breathing patterns. The extracted hyperfeature sets were then evaluated by integrating two different popular ML classifiers, k-nearest neighbor (KNN) and support vector machine (SVM), for subject authentication. Accuracies of 97.5% for two-subject experiments and 98.33% for single-subject experiments were achieved, which supersedes the performance of prior reported methods. The proposed identity authentication approach has several potential applications, including security/surveillance, the Internet-of-Things (IoT) applications, virtual reality, and health monitoring.
Forest Fire Effects on Landscape Snow Albedo Recovery and Decay

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA).
Causes of decadal distributions of Indian summer monsoon rainfall during the last 1500 years

Understanding the distribution of Indian summer monsoon rainfall (ISMR) and the relationships with external forcing factors (EXFs) and internal climate variability (ICV) is crucial for improving the prediction accuracy of ISMR. Here, we explore and quantify the influence of EXFs and ICV on the distribution of ISMR at decadal timescales over the past 1500 years using the Community Earth System Model (CESM). We identified the major ICV and EXF modes of ISMR distributions in simulations from 501 to 1850 ad and 1851 to 2000 ad. The results show that ICV plays a dominant role in decadal ISMR changes during the pre‐industrial and the current periods. Simulations show east–west contrasts over the Indian subcontinent, with a center of rainfall variability in the high‐latitude regions around 28°N. The EXF mode, calculated from the leading mode of the singular value decomposition analysis, exhibits a nearly uniform increase (or decrease) in the distribution of ISMR over the study region. From 501 to 1850 ad, the signals of greenhouse gases (GHGs), and land use and land cover (LULC) in the ISMR at the decadal timescale are weak and difficult to isolate from the ICV. Our numerical analyses show that natural forcing was a dominant contribution (~64.1%) to the forced ISMR distribution. From 1851 to 2000 ad, natural forcing, GHGs and LULC all contributed to the forced ISMR distribution. GHGs represent a key external forcing factor affecting the distribution of ISMR and could explain 22.3% of the total variance. LULC and natural forcing factors contribute 17.5 and 16.4% to the total variance, respectively. Our results contribute to a better understanding of the mechanisms controlling decadal variability of the Indian summer monsoon.
General Construction of Amine via Reduction of N=X (X = C, O, H) Bonds Mediated by Supported Nickel Boride Nanoclusters

Amines play an important role in synthesizing drugs, pesticides, dyes, etc. Herein, we report on an efficient catalyst for the general construction of amine mediated by nickel boride nanoclusters supported by a TS-1 molecular sieve. Efficient production of amines was achieved via catalytic hydrogenation of N=X (X = C, O, H) bonds. In addition, the catalyst maintains excellent performance upon recycling. Compared with the previous reports, the high activity, simple preparation and reusability of the Ni-B catalyst in this work make it promising for industrial application in the production of amines.
Empowering Green Development: How Social Media Interaction Influences Environmental Information Disclosure of High-Polluting Firms

While social media has become increasingly prevalent in recent years, few studies have examined the role of social media in regulating the environmental information disclosure (EID) of high-polluting enterprises. Using a sample of 2, 211 A-share listed firms in China from 2010 to 2019, this study empirically tests the relationship between firm–investor social media interactions and the EID of high-polluting firms. The results show that social media interaction not only relieves information asymmetry in the capital market, but also triggers market and regulatory pressure for management, ultimately contributing to high-quality EID. The results are robust to a series of alternative estimation approaches and alternative measurements of core variables. Moreover, we found that the positive effect of social media interaction on EID is stronger for enterprises that receive a high level of analyst coverage and for state-owned enterprises (SOEs), but weaker for enterprises whose CEO holds a chairman position (i.e., CEO duality). In addition, further testing shows that social media interaction promotes hard EID to a larger extent than soft information, and the promotion effect is more pronounced for environment-related posts. This study deepens our understanding of how social media supplements formal regulations in the supervision of corporate EID behavior and offers important practical implications for prompting enterprises to achieve high-quality green development.
Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models

This paper reviews the development of discrete mixture distributions as approximations to priors and posteriors in Bayesian analysis focusing on the development of simulation based techniques in sequential updating and the analysis of dynamic models Adaptive density estimation techniques enable construction of mixtures of elliptical distributions useful as direct approximations and as importance sampling functions Illustrations in sequential modelling are discussed Adaptive mixture modelling Importance sampling and mixtures With the recent mushrooming application of simulation based methods of numerical analysis Bayesian analyses often involves the approximation of continuous prior and posterior distributions using discrete sets of points and associated weights based on a Monte Carlo approximation West a introduced an adaptive importance sampling scheme to develop such discrete approximations and methods to provide smooth reconstructions in cases when the prior and likelihood functions separately or the posterior directly may be at least approximately evaluated up to irrelevant constants Suppose p is the continuous posterior density function for a continuous parameter vector An approximating density g is used as an importance sampling function Geweke as follows Let f j j ng be a random sample from g and de ne weights fwj j ng by wj p j kg j for each j where k Pn j p j g j The weights are evaluated via wj p j g j and then normalised to unit sum Then inference under p is approximated using the discrete distribution having masses wj at j for each j n The conditions on g required to achieve reasonable approximations typically reduce to requiring g have the same support as p and that the tails of g be heavier than those of p so that variations on multivariate T distributions have become popular Geweke In West a mixtures of T distributions are proposed one additional reason being that mixtures have the exibility to represent the possibly quite complex and varied forms of posterior densities This is done using kernel density estimation techniques With an importance sampling function g close to the true density p kernel density estimation or other smoothing techniques provides continuous estimates of joint and marginal densities In simple univariate random sampling problems kernel type density estimates have direct Bayesian interpretation as approximate predictive distributions in models based on mixtures of Dirichlet processes West multivariate analogues are similarly derivable Erkanli M uller and West West a uses weighted variations on multivariate kernel estimates as importance sampling functions and with some modi cation to more directly estimate marginal densities of p The basic idea is as follows Given a chosen importance sampling density g the sample of size n and associated weights the exact density p may be approximated by a weighted kernel estimate of the form