Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifunctional Activities of Gold Nanoparticles Biosynthesized Using Bacteria Isolated from Mining Areas

Research on gold nanoparticles (AuNPs) has often focused on their physical, chemical, and crystalline characteristics. Commercial AuNPs have been applied in the diverse fields of biomedicine, catalysis, photovoltaics, and sensing. In this study, we explored the various activities of AuNPs to widen their applicability. This paper presents a simple and rapid synthesis process of AuNPs with bacteria isolated from a gold mining area. We also investigated the optimization of reaction parameters for AuNP synthesis. The study results revealed that among the isolated strains, Bifidobacterium lactis and Escherichia coli demonstrated the highest capabilities of AuNP synthesis. The optimal pH values for AuNP synthesis by B. lactis (BLAuNPs) and E. coli (ECAuNPs) were 5.0 for 72 h of incubation and 8.0 for 24 h of incubation. The average particle sizes of ECAuNPs and BLAuNPs were 4.2 and 5.6 nm, respectively. Furthermore, these biogenic AuNPs were found to be stable with no aggregation after 3 months of storage. BLAuNPs and ECAuNPs exhibited high levels of antimicrobial, antioxidant, photocatalytic, and antityrosinase activity. Moreover, they were noncytotoxic to skin cells even at 100% melanin inhibitory concentrations. Considering the demonstrated multifunctional activities of AuNPs, BLAuNPs and ECAuNPs have promising potential for commercialization.
Structure, characteristics and connectivity analysis of the Asian-Australasian cruise shipping network

ABSTRACT Cruise shipping is one of the fastest-growing sectors in the maritime industry. This is due to the growing demand of the sector and the extension of capacity and cruise service coverage. This study uses network measures and random network models to analyse the structure, characteristics and connectivity of the cruise shipping network in Asia and Australasia. An analysis of 200 cruise voyages operated by 12 international cruise lines between 139 ports across 20 countries in the Asian and Australasian regions has found that the cruise shipping network is a scale-free network. This means the degree distribution follows a power law. A few ports play a central role in connecting ports in the network. However, their importance in the network varies depending on which measure of centrality is used, i.e. the degree, betweenness, closeness and eigenvector centrality. The establishment of a link between two ports is influenced by their connections with a third one or triangle relationship.
A Flexible Multivariate Distribution for Correlated Count Data

Multivariate count data are often modeled via a multivariate Poisson distribution, but it contains an underlying, constraining assumption of data equi-dispersion (where its variance equals its mean). Real data are oftentimes over-dispersed and, as such, consider various advancements of a negative binomial structure. While data over-dispersion is more prevalent than under-dispersion in real data, however, examples containing under-dispersed data are surfacing with greater frequency. Thus, there is a demonstrated need for a flexible model that can accommodate both data types. We develop a multivariate Conway–Maxwell–Poisson (MCMP) distribution to serve as a flexible alternative for correlated count data that contain data dispersion. This structure contains the multivariate Poisson, multivariate geometric, and the multivariate Bernoulli distributions as special cases, and serves as a bridge distribution across these three classical models to address other levels of over- or under-dispersion. In this work, we not only derive the distributional form and statistical properties of this model, but we further address parameter estimation, establish informative hypothesis tests to detect statistically significant data dispersion and aid in model parsimony, and illustrate the distribution’s flexibility through several simulated and real-world data examples. These examples demonstrate that the MCMP distribution performs on par with the multivariate negative binomial distribution for over-dispersed data, and proves particularly beneficial in effectively representing under-dispersed data. Thus, the MCMP distribution offers an effective, unifying framework for modeling over- or under-dispersed multivariate correlated count data that do not necessarily adhere to Poisson assumptions.
The influence of operational conditions and the extraction techniques on the yield, kinetics and composition of aqueous-ethanolic extracts from the nettle leaf (Urtica Dioica L.)

The influence of operational extraction conditions (the concentration of ethanol, solvomodule, temperature) and extraction techniques (maceration, Soxhlet, Tillepape and ultrasonic extraction) on the yield, composition and kinetics of total extractive matter from the nettle leaf (Urtica dioica L.) was investigated in this paper. The total phenolic content was determined by using the Folin-Ciocalteu assay and the total flavonoids content was measured spectrophotometrically by using AlCl3. The extraction kinetics models (extraction by maceration, Tillepepe, Soxhlet and ultrasonic extraction) for the extraction of total extractive matter (TEM) were defined: model Ponomaryov (A) and non-stationary diffusion model (B). The optimal operational extraction conditions by maceration of total extractive matter were defined (30 % v/v ethanol, solvomodule 1:15 m/v and solvent boiling point). The extraction techniques used have the influence on the yield of total extractive matter, extraction kinetics and the composition (the content of total phenolics and flavonoids) of the extracts obtained from the nettle leaf. The higest yield of total extractive matter, total phenols and flavonoids were achieved by using Soxhlet extraction, for the time period of 240 minutes. Compared to the classical maceration, the ultrasound significantly reduced the time duration of the extraction process (60 minutes for the ultrasonic extraction versus 120 minutes for maceration). Aqueou-sethanolic extracts of the nettle leaf have a high content of total phenols, while the content of total flavonoids was significantly lower.
Falling Behind and Feeling Bad: Unmet Expectations and Mental Health during the Transition to Adulthood

How do perceived expectations of what it means to be an adult affect mental health? We draw from life course and social psychological literature to argue that falling behind perceived expectations for reaching markers of adulthood is associated with depression and anxiety. We test predictions with data from an original sample of more than five hundred 18- to 29-year-olds in the United States. Consistent with predictions, we find a positive relationship between falling behind expectations and both anxiety and depression even while controlling for own expectations about the accomplishment of markers of adulthood. In particular, we find that falling behind perceived expectations of peers regarding markers of adulthood is associated with anxiety and that falling behind perceived expectations of parents and society regarding markers of adulthood is associated with depressive symptoms.
An Overview of the Sorption Studies of Contaminants on Poly(Ethylene Terephthalate) Microplastics in the Marine Environment

Marine pollution is one of the biggest environmental problems, mainly due to single-use or disposable plastic waste fragmenting into microplastics (MPs) and nanoplastics (NPs) and entering oceans from the coasts together with human-made MPs. A rapidly growing worry concerning environmental and human safety has stimulated research interest in the potential risks induced by the chemicals associated with MPs/NPs. In this framework, the present review analyzes the recent advances in adsorption and desorption studies of different contaminants species, both organic and metallic, on MPs made of Poly(Ethylene terephthalate). The choice of PET is motivated by its great diffusion among plastic items and, unfortunately, also in marine plastic pollution. Due to the ubiquitous presence of PET MPS/NPs, the interest in its role as a vector of contaminants has abruptly increased in the last three years, as demonstrated by the very high number of recent papers on sorption studies in different environments. The present review relies on a chemical engineering approach aimed at providing a deeper overview of both the sorption mechanisms of organic and metal contaminants to PET MPs/NPs and the most used adsorption kinetic models to predict the mass transfer process from the liquid phase to the solid adsorbent.
Design of Hybrid Neural Controller for Nonlinear MIMO System Based on NARMA-L2 Model

ABSTRACT This paper introduces a nonlinear adaptive controller of unknown nonlinear dynamical systems based on the approximate models using a multi-layer perceptron neural network. The proposal of this study is to employ the structure of the Multi-Layer Perceptron (MLP) model into the NARMA-L2 structure in order to construct a hybrid neural structure that can be used as an identifier model and a nonlinear controller for the MIMO nonlinear systems. The big advantage of the proposed control system is that it doesn’t require previous knowledge of the model. Our ultimate goal is to determine the control input using only the values of the input and output. The developed NARMA-L2 neural network model is tuned for its weights employing the backpropagation optimizer algorithm. Nonlinear autoregressive-moving average-L2 (NARMA-L2) neural network controller, based on the inputs and outputs from the nonlinear model, is designed to perform control action on the nonlinear for the attitude control of unmanned aerial vehicles (UAVs) model. Once the system has been modeled efficiently and accurately, the proposed controller is designed by rearranging the generalized submodels. The controller performance is evaluated by simulation conducted on a quadcopter MIMO system, which is characterized by a nonlinear and dynamic behavior. The obtained results show that the NARMA-L2-based neural network achieved good performances in modeling and control.
HOMOLOGY MODELLING AND DOCKING STUDIES OF FGFR3 PROTEIN TO SEARCH MOST EFFECTIVE DRUG AGAINST ACHONDROPLASIA

The present investigation was carried out with the aim of modeling the 3D structure of FGFR3 protein and predicting the most effective drug using SU5402 and its analogues. FGFR3 protein, responsible for long bone growth, causes Achondroplasia in H. sapiens when it becomes mutated. When mutated, the dimmer of FGFR3 stabilizes without interacting with its ligand results in constitutive activation of downstream pathway and inhibits the bone growth. No known structure of FGFR3 was available. The 3D modeling of FGFR3 was done using Robetta server and from the various model predicted, model 5 was selected as the best model after evaluating the models using PROCHECK. The total number of residues in selected model was found as: 589 (86.5%) residues in most favored region, 84 (12.3%) in additional allowed region, 8 (1.2%) in generously allowed region and 0 (0.0%) in disallowed region of the Ramachandran plot. Three analogues were constructed by using the existing FGFR3 specific inhibitor SU5402. Receptor-analogue interaction study was performed in FlexX3 docking software. Ligand 3 (IUPAC Name: 3-{2-(Z)-(4-hydroxy-2-oxo-1,2-dihydro-3H- indol-3-ylidene) methyl)-4-oxo-4,5-dihydro-1H-pyrrol-3-yl} propanoic acid) was showing the best binding energy (-10.458 KJ/Mol) that can be predicted the most effective inhibitor for FGFR3. It should be noted that these predicted data should be validated using suitable assays for further consideration.
Small Cogeneration Unit with Heat and Electricity Storage

A micro cogeneration unit based on a three-cylinder internal combustion engine, Skoda MPI 1.0 L compressed natural gas (CNG), with an output of 25 kW at 3000 RPM is proposed in this paper. It is a relatively simple engine, which is already adopted by the manufacturer to operate on CNG. The engine life and design correspond to the original purpose of use in the vehicle. A detailed dynamic model was created in the GT-SUITE environment and implemented into an energy balance model that includes its internal combustion engine, heat exchangers, generator, battery storage, and water storage tank. The 1D internal combustion engine model provides us with information on engine start-up time, actual effective power, friction power, and the amount of heat going to the cooling system and exhaust pipe. The catalytic converter was removed from the exhaust pipe, and the engine was always operating at full load; thus, engine power control is not considered. An energy storage system for an island operation of the entire power unit for a large, detached house was designed to withstand accumulated energy for a few days in the case of a breakout. To reach a low initial system cost, the possible implementation of worn-out battery packs toward emission reduction in terms of the second life of the battery is proposed. The energy and emission balance are carried out, and the service life of the engine is also discussed.
Testimonio and Counterstorytelling by Immigrant-Origin Children and Youth: Insights That Amplify Immigrant Subjectivities

This article seeks to amplify our scholarly view of immigrant identity by centering the first-person narratives of immigrant-origin children and youth. Our theoretical and methodological framework centers on testimonio—a narrative practice popularized in Latin American social movements in which an individual recounts a lived experience that is intended to be representative of a collective struggle. Our goal is to foreground first-person narratives of childhood as told by immigrant-origin children and youth in order to gain insight into what they believe we should know about them. We argue for the power of testimonio to communicate both extraordinary hardship and everyday experiences and that—through this storytelling—immigrant-origin children and youth also express imagined futures for themselves and their loved ones. Through our analysis of ethnographic recordings of testimonio shared by Latin American immigrant children and multimedia testimonios created by immigrant-origin adolescents with roots in the Caribbean and West Africa, we gain a fuller understanding of immigrant subjectivities and push the boundaries of “the immigrant experience” still prevalent in mainstream discussions today.
An efficient hash‐based authentication protocol for wireless sensor networks in Internet of Things applications with forward secrecy

Wireless sensor networks (WSNs) play an influential role in the advancement of Internet of Things (IoT) because the infrastructure of WSNs consists of lots of sensors, which can be used to collect online data by the users or service providers; however, in the process of collecting the data, the users and service providers have to communicate with the sensors through an unprotected channel, so the confidentiality and integrity of the transmitted messages might be threatened by an adversary. Consequently, several authentication protocols have been proposed to provide a secure authentication process for IoT‐based WSNs. In this paper, we analyze Ghani et al.'s protocol and demonstrate that their protocol is vulnerable to user impersonation attack, malicious gateway attack, and traceability attack. Furthermore, it suffers from some design weaknesses. To fix these drawbacks, we propose a new hash‐based authentication protocol for IoT‐based WSNs. We analyze our protocol with both formal and informal methods to show that our protocol is secure against various known attacks such as sensor and user trace, sensor capture, off‐line password guessing, and replay attacks. Finally, we evaluate our protocol in terms of security features and communication and computation costs. The results show that not only the proposed protocol is more secure than other existing protocols but also reduces 60% of the execution time of the user authentication process in comparison with Ghani et al.'s protocol.
Remarks on asymptotic regularity in A-metric spaces

The purpose of this paper is to present a potential generalizations of celebrated theorems due to Kannan, Reich, Bianchini and Chatterjee in the set-up of an A-metric space. We unify these well-known fixed point theorems in A-metric space and give the simple proof of the result presented herein. For this purpose, we use concept of asymptotic regularity. Also, we give most general form of Kannan, Reich, Bianchini and Chatterjee Fixed Point Theorems in A-metric spaces, and prove these fixed point theorems using asymptotically regular sequences and maps rather than Picard iteration, respectively. Furthermore, we give some examples in support of our results.
The effects of blue spaces on mental health and associated biomarkers

Abstract Green spaces have been shown to have many benefits for physical and mental health. Recently, blue spaces have been introduced as another field of research and have been suggested to have similar health effects. This scoping review summarized the current state of the knowledge on how blue space exposure (views and visits) affects mental health (depression, anxiety, stress, and associated biomarkers). A keyword search was developed for PubMed, Web of Science, Embase, and PsycINFO. Identified articles were then imported into Rayyan and put through a title review, an abstract review, and finally a full-text review. Five articles were found to have direct relevance to the study. Two articles showed that blue space visits had benefits to general mental health and possibly depression. Two studies investigated blue space views, one showing benefits for general mental health and the other showing benefits for depression. One study showed that both blue space views and visits had positive impacts on mental health and that blue space visits had a stronger effect than merely viewing blue spaces. However, no articles discussed mental health-associated biomarkers. These studies all provide evidence that blue space exposure has a positive impact on mental health, but based on the small number of articles there is not enough evidence to conclude measurable changes in biomarker responses. Further areas of research include man-made/urban blue spaces, other mental health outcomes, virtual reality, and their effects on biomarkers. Highlights Views of and visits to blue space improve mental health. Visiting blue space has greater mental health benefits than blue space views. Blue space results in lower depression symptoms.
Accelerating Bayesian estimation for network Poisson models using frequentist variational estimates

This work is motivated by the analysis of ecological interaction networks. Poisson stochastic block models are widely used in this field to decipher the structure that underlies a weighted network, while accounting for covariate effects. Efficient algorithms based on variational approximations exist for frequentist inference, but without statistical guaranties as for the resulting estimates. In the absence of variational Bayes estimates, we show that a good proxy of the posterior distribution can be straightforwardly derived from the frequentist variational estimation procedure, using a Laplace approximation. We use this proxy to sample from the true posterior distribution via a sequential Monte Carlo algorithm. As shown in the simulation study, the efficiency of the posterior sampling is greatly improved by the accuracy of the approximate posterior distribution. The proposed procedure can be easily extended to other latent variable models. We use this methodology to assess the influence of available covariates on the organization of several ecological networks, as well as the existence of a residual interaction structure.
Investigation of structural properties of melt-spun graded-index polymer optical fibers made from PMMA

The structural properties of PMMA, which has been melt-spun and treated using a specific cooling profile, is investigated in order to evoke desired optical and mechanical properties. Several PMMA fibres, which had been melt spun and subsequently processed with different temperature profiles, were analysed by small-angle X-ray scattering (SAXS) measurements. These results will be compared to a combination of numerical models, which consider the quenching of a filamentary PMMA polymer melt in water. This multi-scale simulation considers macroscopically the cooling process in the water and within the fiber. The spatially resolved cooling rates, which have been simulated at different locations serve as input for a 3D-Monte-Carlo polymer simulation model, which takes, among others, the Lennard-Jones, the bending and bond potentials into account in order to predict the resulting PMMA structure of the fabricated fiber These simulated structures are then evaluated in order to analyse their macroscopic properties. These comprise for instance the polymer entanglement, which describes the interaction of neighboring polymer chains leading to stronger, but stiffer fibers. Entanglement will also affect the glass-transition temperature, which determines the maximum operation temperature. But this can also lead to increased optical scattering, which will be subject to investigations, as well.
An Automatic Abstractive Text Summarization Model based on Hybrid Attention Mechanism

Attentional sequence-to-sequence models based on RNN have achieved promising performances in the automatic abstractive summarization technology. However, there are still some shortcomings, including the inaccuracies and lack of key information. In this paper, an abstractive text summarization model based on the hybrid attention mechanism is presented. The model aims at adopting sentence-level attention mechanism to guide the word-level attention distribution. Moreover, this model modulates the weight of sentence-level attention value to alleviate the adverse effect of high variance on word-level attention distribution for short documents. The experimental results on the LCSTS dataset show that the presented model can effectively improve the ROUGE scores and can better summarize the source document while retaining the important information. An example in this paper shows that the model can generate summaries that are similar to human-written ones.