Miracle-dz/UniFAR-FTUs huggingface dataset:
Green versus Chemical Precipitation Methods of Preparing Zinc Oxide Nanoparticles and Investigation of Antimicrobial Properties

Comparison of green and chemical precipitation method syntheses of zinc oxide nanoparticles (ZnO NPs) was performed, and antimicrobial properties were investigated. Avocado, mango, and papaya fruit extracts were carried out for the green synthesising methods, while the chemical precipitation method was chosen from chemical synthesis methods. Zinc nitrate was used as a salt precursor, whereas leaf extract was served as a reducing agent for green synthesising methods. In addition, sodium hydroxide, polyvinyl alcohol, and potassium hydroxide were used as reducing agents in the case of chemical precipitation synthesis methods. ZnO NPs were characterised by characterizing techniques such as Fourier transform infrared (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The antimicrobial activities of prepared nanoparticles were evaluated on Bacillus subtilis (B. subtilis), Staphylococcus aureus (S. aureus), and Salmonella typhimurium (S. typhimurium). The particle sizes of the prepared samples which were evaluated by the Scherrer equation were in the range of 11-21 nm for green synthesis, while 30-40 nm for chemical precipitation synthesis methods. Small agglomerations were observed from SEM results of prepared ZnO NPs from both methods. Prepared ZnO NPs were showed strong antimicrobial properties. From the result, the inhibition zone was in the range of 15-24 mm for the green route and 7–15 mm for chemical precipitation methods, where the standard drugs have 25 mm of the zone of inhibition. A green synthesised method of preparing ZnO NPs gives promising antimicrobial properties compared to chemical synthesis and is also eco-friendly and safe compared to the chemical synthesis.
AI Tools for Military Readiness

RR-A449-1_Barcode. indd 1 7/28/21 5:09 PM rr-a449-1_cover_6x9_v5. FA.indd All Pages 8/2/21 11:00 AM M ilitary readiness is a perennial priority for the United States and a cornerstone of national security. Key to managing and improving readiness is the ability to measure it. This gives leaders situational awareness and tools for exploring trade-offs with other priorities, such as modernization, force structure, and use of national resources. There are likely many ways in which artificial intelligence (AI) can improve measurement and management of military readiness. In this report, the authors discuss work that advances the capability of computers to “understand” human language describing factors that promote or impede readiness.
Warpage modeling and characterization of intelligent power modules (IPMs)

Intelligent power modules (IPMs) are widely used in the electric vehicle (and hybrid electric vehicle industry nowadays due to their high power density and ability to integrate multiple components within a single package. However, the reliability of IPMs is severely degraded by the substrate warpage effect produced during the packaging process. This study therefore develops a computational model to analyze the warpage of the IPM assembly at various stages of the packaging process. The validity of the simulation model is confirmed by comparing the numerical results for the warpage of the direct plated copper substrate with the experimental observations. Taguchi experiments are then performed to examine the effects of eight control factors on the IPM package warpage following the post-mold cure (PMC) process, namely (1) the dam bar layout, (2) the epoxy molding compound (EMC) thickness, (3) the lead frame thickness, (4) the ceramic thickness, (5) the bottom layer Cu foil thickness, (6) the top layer Cu foil thickness, (7) the ceramic material type and (8) the EMC material type. Finally, the Taguchi analysis results are used to determine the optimal packaging design that minimizes the warpage of the post-PMC package.
Simulating fish population responses to elevated CO2: a case study using winter flounder

Scaling experimentally derived effects of CO2 on marine fauna to population responses is critical for informing management about potential ecological ramifications of ocean acidification. We used an individual-based model of winter flounder to extrapolate laboratoryderived effects of elevated CO2 assumed for early life stages of fish to long-term population dynamics. An offspring module with detailed hourly to daily representations of spawning, growth, and mortality that incorporates potential elevated CO2 effects was linked to an annual time-step parent module. We calibrated the model using a 40 yr Reference simulation (1977−2016) that included gradual warming and then performed ‘Retrospective’ simulations that assumed a suite of elevated CO2 effects by changing fertilization rate, mortality rate of embryos due to developmental malformations, larval growth rate, and size-at-settlement. ‘Recovery’ simulations that started at low population size were then used to further explore possible interactions between the effects of CO2 and warming on population productivity. Warming had a major negative effect on the simulated winter flounder population abundance, and reduced larval growth had the largest single impact among the CO2 effects tested. When a combination of the assumed CO2 effects was imposed together, average annual recruitment and spawning stock biomass were reduced by half. In the Recovery simulations, inclusion of CO2 effects amplified the progressive decrease in population productivity with warming. Our analysis is speculative and a first step towards addressing the need for extrapolating from laboratory effects of ocean acidification to broader, ecologically relevant scales.
Generation Expansion Planning with Energy Storage Systems Considering Renewable Energy Generation Profiles and Full-Year Hourly Power Balance Constraints

This paper proposes a methodology to develop generation expansion plans considering energy storage systems (ESSs), individual generation unit characteristics, and full-year hourly power balance constraints. Generation expansion planning (GEP) is a complex optimization problem. To get a realistic plan with the lowest cost, acceptable system reliability, and satisfactory CO2 emissions for the coming decades, a complex multi-period mixed integer linear programming (MILP) model needs to be formulated and solved with individual unit characteristics along with hourly power balance constraints. This problem requires huge computational effort since there are thousands of possible scenarios with millions of variables in a single calculation. However, in this paper, instead of finding the globally optimal solutions of such MILPs directly, a simplification process is proposed, breaking it down into multiple LP subproblems, which are easier to solve. In each subproblem, constraints relating to renewable energy generation profiles, charge-discharge patterns of ESSs, and system reliability can be included. The proposed process is tested against Thailand’s power development plan. The obtained solution is almost identical to that of the actual plan, but with less computational effort. The impacts of uncertainties as well as ESSs on GEP, e.g., system reliability, electricity cost, and CO2 emission, are also discussed.
Energy Management System for Hybrid PV/Wind/Battery/Fuel Cell in Microgrid-Based Hydrogen and Economical Hybrid Battery/Super Capacitor Energy Storage

The present work addresses the modelling, control, and simulation of a microgrid integrated wind power system with Doubly Fed Induction Generator (DFIG) using a hybrid energy storage system. In order to improve the quality of the waveforms (voltages and currents) supplied to the grid, instead of a two level-inverter, the rotor of the DFIG is supplied using a three-level inverter. A new adaptive algorithm based on combined Direct Reactive Power Control (DRPC) and fuzzy logic controls techniques is applied to the proposed topology. In this work, two topologies are proposed. In the first one, the active power injected into the grid is smoothened by using an economical hybrid battery and supercapacitor energy storage system. However, in the second one, the excess wind energy is used to produce and store the hydrogen, and then a solid oxide fuel cell system (SOFC) is utilized to regenerate electricity by using the stored hydrogen when there is not enough wind energy. To avoid overcharging, deep discharging of batteries, to mitigate fluctuations due to wind speed variations, and to fulfil the requirement of the load profile, a power management algorithm is implemented. This algorithm ensures smooth output power in the first topology and service continuity in the second. The modelling and simulation results are presented and analysed using Matlab/Simulink.
Currency Depreciation Nexus Country's Export: Evidence from Georgia

The recent shocks in demand and supply caused by Covid-19 influence and the current depreciation of the Georgian Lari (GEL) reduced the trust of economic agents in currency. There is no objective macroeconomic reason that would change their attitude to the future of currency for the better. Depreciation of a currency impacts all the components of GDP, leading to myriad problems in economic growth. The depreciation of the national currency is generally perceived as positively affecting the country's export. The study analyzes the impact of Georgian Lari's exchange rate depreciation on Georgia's export using monthly GEL exchange rate data from May 2006 to April 2020. The paper employs Autoregressive Distributed Lag Model (ARDL) for its advantages of measuring cointegration, usefulness in the small samples, and being unbiased in measuring a long-run relationship between variables. Outcomes indicate that the exchange rate depreciation has an inverse long-run impact on export in the long-run period. The exchange rate impact on Georgia's export shows inelastic demand for Georgia's export goods. The study contributes to the literature while providing the implication of currency depreciation on exports of the Georgian economy. The estimated value of the exchange rate has been found to exert no direct pressure on the amount of export. In the paper, possible reasons for such implications are also examined. Paper found that the control variable interest rate also has an inverse impact on Georgia's export performance in the long-run as well as in the short-run. International reserves positively influence the export in the long-run with a high significance level. The paper also discusses possible ways of stabilization of national currency. © 2020 by authors, all rights reserved.
Nano-Sized NiO Immobilized on Au/CNT for Benzyl Alcohol Oxidation: Influences of Hybrid Structure and Interface

Tiny gold nanoparticles were successfully anchored on carbon nanotubes (CNT) with NiO decoration by a two-step synthesis. Characterizations suggested that Ni species in an oxidative state preferred to be highly dispersed on CNT. During the synthesis, in situ reduction by NaBH4 and thermal treatment in oxidation atmosphere were consequently carried out, causing the formation of Au-Ni-Ox interfaces and bimetal hybrid structure depending on the Ni/Au atomic ratios. With an appropriate Ni/Au atomic ratio of 8:1, Ni atoms migrated into the sub-layers of Au particles and induced the lattice contraction of Au particles, whilst a higher Ni/Au atomic ratio led to the accumulation of NiO fractions surrounding Au particles. Both contributed to the well-defined Au-Ni-Ox interface and accelerated reaction rates. Nickel species acted as structure promoters with essential Au-Ni-Ox hybrid structure as well as the active oxygen supplier, accounting for the enhanced activity for benzyl alcohol oxidation. However, the over-layer of unsaturated gold sites easily occured under a high Ni/Au ratio, resulting in a lower reaction rate. With an Au/Ni atomic ratio of 8:1, the specific rate of AuNi8/CNT reached 185 μmol/g/s at only 50 °C in O2 at ordinary pressure.
Continuous improvement implementation models: a reconciliation and holistic metamodel

Abstract The purpose of this paper is to review and aggregate the guidance for continuous improvement (CI) implementation from existing implementation models. A sample of ultimately 27 implementation models is collected from the practitioner and academic literature. The models are assessed on quality and completeness using a research framework comprising organizational dimensions, phases in time, readiness factors, activities, and sustainability factors, leading to 415 coded observations. Subsequently, these 27 implementation models are integrated with one holistic metamodel, providing a detailed account of the existing CI deployment guidance to date. Based on the metamodel, knowledge gaps about implementation processes are identified and detailed needs for future research are presented. Thereby, repeated scholarly calls for better and more scientifically proven implementation guidance is addressed.
Effective Government and Evaluations of Democracy

Ineffective governance is known to weaken support for governments and leaders. However, it is less clear whether these effects spill over to the regime and erode support for the democratic system. This article returns to this classic question, now using time-series, cross-sectional data to test whether the effectiveness of governments in sustaining economic growth, providing quality healthcare, and tackling violent crime affects popular attitudes to democracy. We find that satisfaction with democracy is driven by fluctuations in economic performance and violent crime (but not healthcare quality). Diffuse support for democracy, in contrast, remains relatively impervious to changes in government effectiveness. Violent crime is the only indicator of effectiveness which has an impact on democratic support, and does so indirectly, via its influence on democratic satisfaction. These findings confirm that democratic support—which, unlike democratic satisfaction, is thought to help sustain democracy—is mostly immune to crises of performance.
Verification of Consistency Between Process Models, Object Life Cycles, and Context-Dependent Semantic Specifications

Process models in general, and those specifying process-oriented software in particular, should be formally verified. While activity-oriented process models have been verified against object life cycles, formally specified semantic specifications of actions were not involved. Hence, previous approaches for the verification of process models did not make use of declaratively represented knowledge of actions. This paper presents a new approach for verification of consistency between process models, object life cycles, and context-dependent semantic action specifications. This approach involves declarative specifications of all the actions of a process, which also depend on the context of use of the actions. These context-dependent specifications define the ‘logic’ of the process flow, which is grounded in (extended) object life cycles. Since a subtyping relation is enforced, reuse is facilitated through substitutability. Our extension of object life cycles makes them applicable to processes including non-monotonicity, and even to model communication based on physical interaction in cyber-physical systems. As a consequence, this formal consistency verification ensures that all the involved specifications ‘fit together’, both procedurally and logically.
Solar-Driven Desalination Using Nanoparticles

Due to the high light absorption and the possibility of localizing boiling to the interior of the receiver, nanoparticles are promising for solar-driven desalination. The paper presents an experimental study of the nanoparticle-based photothermal boiling of water with sea salt. The experiments were carried out using a laboratory-scale system with a transparent photothermal receiver of light and a closed condensate cycle. In this study, we tested three types of nanoparticles: multiwall carbon nanotubes with two main sizes of 49 nm and 72 nm, 110 nm iron oxide particles Fe3O4, and a commercial paste based on carbon nanotubes. The concentration of nanoparticles was varied up to 10% wt. We found that the nanoparticles enhance the steam generation by 23%, relative to a conventional desalinator with a black-body receiver. The best result was obtained for the 5% wt. concentration of carbon nanotubes.
Modeling the Interaction Between Perception-Based and Production-Based Learning in Children’s Early Acquisition of Semantic Knowledge

Children learn the meaning of words and sentences in their native language at an impressive speed and from highly ambiguous input. To account for this learning, previous computational modeling has focused mainly on the study of perception-based mechanisms like cross-situational learning. However, children do not learn only by exposure to the input. As soon as they start to talk, they practice their knowledge in social interactions and they receive feedback from their caregivers. In this work, we propose a model integrating both perception- and production-based learning using artificial neural networks which we train on a large corpus of crowd-sourced images with corresponding descriptions. We found that production-based learning improves performance above and beyond perception-based learning across a wide range of semantic tasks including both word- and sentence-level semantics. In addition, we documented a synergy between these two mechanisms, where their alternation allows the model to converge on more balanced semantic knowledge. The broader impact of this work is to highlight the importance of modeling language learning in the context of social interactions where children are not only understood as passively absorbing the input, but also as actively participating in the construction of their linguistic knowledge.
CONTENT OF OMEGA-3, OMEGA-6 AND CHOLESTEROL INUF-WHITE SOFT CHEESE CONTAINING VEGETABLE AND MARINE OILS

UF soft cheese was made either traditionally or by replacing 50% of the milk fat with olive, sesame or marine oils, replacing 100% of the milk fat with coconut oil or replacing 75% of milk fat with corn oil to reach 15% fat in the final UF milk. Control cheese had the highest content of saturated fatty acids, while marine oil cheese had the lowest. Coconut oil cheese had the highest ratio of mono-unsaturated fatty acids. Corn oil had the highest polyunsaturated fatty acids. Marine oil cheese had the lowest Omega-6 content, while corn oil cheese had the highest. Corn oil cheese had the highest ratio of Omega-6/Omega-3, while marine oil cheese had the lowest. Control cheese had the highest cholesterol content and marine oil cheese had the lowest while corn oil, marine oil and coconut oil cheese had very low ratio (LDL/HDL).
Spark‐based parallel processing whale optimization algorithm

Swarm intelligence meta‐heuristic optimization algorithms for optimizing engineering applications have become increasingly popular. The whale optimization algorithm (WOA) is a recent and effective swarm intelligence optimization algorithm that mimics humpback whales' behaviors when optimizing a problem. Applying the algorithm to achieve optimal solutions has shown good results compared to most meta‐heuristic optimization algorithms. However, complex applications might require the processing of large‐scale computations, which results in down‐scaling computational throughput of WOA. Apache Spark, a well‐known parallel data processing framework, is the most recent distributed computing framework and has been proven to be the most efficient. In this article, we propose a WOA implementation on top of Apache Spark, represented as SBWOA, to enhance its computational performance while providing higher scalability of the algorithm for handling more complex problems. Compared with the recently reported MapReduce WOA (MR‐WOA), and serial implementation of WOA, our approach achieves significant enhancements with respect to computational performance for the highest population size with the maximum number of iterations. SBWOA successfully handles higher‐complexity problems which require complex computations.
Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO Systems

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion.
Plant Leaf Disease Detection using Convolutional Neural Network and Random Forest Classifier

-Plant diseases represent adverse factors that cause a serious reduction in the quality and quantity of agricultural crops. Commonly, expert biologists or farmers observe plants with the naked eye for disease, but this method is often inaccurate and can take a great deal of time. In this study, we use artificial intelligence and computer vision techniques to achieve the objective of designing and developing an intelligent mechanism for the classification of leaf diseases. In this paper, data augmentation is performed on the Plant Village dataset images (for apple, potato and rice plants) and their deep features are extracted using Convolutional Neural Network (CNN). These features are classified by Random Forest classifier and result is attained in terms of f-score and accuracy.
Looking at faculty writing groups from within: some insights for their sustainability and future implementations

ABSTRACT Scholarly publications are a key component of academics’ development in their roles as teachers and researchers. Writing groups seem to be effective to accompany this process as participants share their texts to improve them through peer feedback. To help academic developers in the understanding and implementation of faculty writing groups, a detailed analysis of what members talk about during their meetings was carried out with three Ecuadorian writing groups, complemented by in-depth interviews. Results show that in all groups most interactions focused on their common goal, the text and comments, and the organization of their meetings, supported by the facilitator.