Hessdonnelly8442

The pandemic has led to a renewed reflection on what it means to be self-reliant in terms of our everyday practices. Nations too follow this logic in their own claims of self-reliance. This paper discusses the implications in these claims of self-reliance in the context of the nation by positioning this claim within the tension between two different formulations of the self self of the nation as against the idea of national self.This paper analyses the politics that characterised the Nigeria 2019 national minimum wage negotiations and implementation, which so far is the most prolonged in Nigeria's history. Workers' welfare is the responsibility of governments across the world through fixing and regulation of the national minimum wage. But in Nigeria, this has been problematic, and the entire process is characterised by industrial actions undertaken to compel the government to commit to wage negotiations and implementation. The paper argues that the absence of functional standing machinery with a focus on labour economics, deciding the condition and time for a minimum wage review is seen as the main bane in government-labour frequent face-off in Nigeria, which has negatively impacted on harmonious industrial relations. Writing from the analytical point of view, the paper finds that industrial actions have become one action too many because of government's political approach to labour demands. Deciphered in the foregoing is that the current system of government-labour negotiation for new national minimum wage cannot guarantee workers' welfare in Nigeria. Thus, for the Nigeria government to address this perennial minimum wage problem and be seen as fulfilling its obligation to the International Labour Organisation, it must urgently put in place an acceptable mechanism for fixing and regulating the national minimum wage in Nigeria to cushion the effect of the hike in petroleum products on which the national economy largely depends.Future biorefineries are facing the challenge to separate and depolymerize biopolymers into their building blocks for the production of biofuels and basic molecules as chemical stock. Fungi have evolved lignocellulolytic enzymes to perform this task specifically and efficiently, but a detailed understanding of their heterogeneous reactions is a prerequisite for the optimization of large-scale enzymatic biomass degradation. Here, we investigate the binding of cellulolytic enzymes onto biopolymers by surface plasmon resonance (SPR) spectroscopy for the fast and precise characterization of enzyme adsorption processes. Using different sensor architectures, SPR probes modified with regenerated cellulose as well as with lignin films were prepared by spin-coating techniques. The modified SPR probes were analyzed by atomic force microscopy and static contact angle measurements to determine physical and surface molecular properties. SPR spectroscopy was used to study the activity and affinity of Trichoderma reesei cellobiohydrolase I (CBHI) glycoforms on the modified SPR probes. N-glycan removal led to no significant change in activity or cellulose binding, while a slightly higher tendency for non-productive binding to SPR probes modified with different lignin fractions was observed. The results suggest that the main role of the N-glycosylation in CBHI is not to prevent non-productive binding to lignin, but probably to increase its stability against proteolytic degradation. The work also demonstrates the suitability of SPR-based techniques for the characterization of the binding of lignocellulolytic enzymes to biomass-derived polymers.

The online version contains supplementary material available at 10.1007/s10570-021-04002-6.

The online version contains supplementary material available at 10.1007/s10570-021-04002-6.Microcrystalline cellulose (MCC) is a semi-crystalline material with inherent variable crystallinity due to raw material source and variable manufacturing conditions. MCC crystallinity variability can result in downstream process variability. The aim of this study was to develop models to determine MCC crystallinity index (%CI) from Raman spectra of 30 commercial batches using Raman probes with spot sizes of 100 µm (MR probe) and 6 mm (PhAT probe). A principal component analysis model separated Raman spectra of the same samples captured using the different probes. The %CI was determined using a previously reported univariate model based on the ratio of the peaks at 380 and 1096 cm-1. The univariate model was adjusted for each probe. The %CI was also predicted from spectral data from each probe using partial least squares regression models (where Raman spectra and univariate %CI were the dependent and independent variables, respectively). Both models showed adequate predictive power. For these models a general reference amorphous spectrum was proposed for each instrument. The development of the PLS model substantially reduced the analysis time as it eliminates the need for spectral deconvolution. A web application containing all the models was developed.

The online version contains supplementary material available at 10.1007/s10570-021-04093-1.

The online version contains supplementary material available at 10.1007/s10570-021-04093-1.Cellulose can be dissolved with another biopolymer in a protic ionic liquid and spun into a bicomponent hybrid cellulose fiber using the Ioncell® technology. Inside the hybrid fibers, the biopolymers are mixed at the nanoscale, and the second biopolymer provides the produced hybrid fiber new functional properties that can be fine-tuned by controlling its share in the fiber. In the present work, we present a fast and quantitative thermoanalytical method for the compositional analysis of man-made hybrid cellulose fibers by using thermogravimetric analysis (TGA) in combination with chemometrics. First, we incorporated 0-46 wt.% of lignin or chitosan in the hybrid fibers. Then, we analyzed their thermal decomposition behavior in a TGA device following a simple, one-hour thermal treatment protocol. With an analogy to spectroscopy, we show that the derivative thermogram can be used as a predictor in a multivariate regression model for determining the share of lignin or chitosan in the cellulose hybrid fibers. The me at 10.1007/s10570-021-03923-6.

The online version contains supplementary material available at 10.1007/s10570-021-03923-6.Cationization of cotton fabrics was performed by exhaustion procedure utilizing four different reagents provided with quaternary ammonium groups poly diallyldimethylammonium chloride (PDDACl), poly acrylamide-co-diallyldimethylammonium chloride (PAcD), poly[bis(2-chloroethyl) ether-alt-1,3-bis[3-(dimethylamino)propyl]urea] quaternized (P42) and 3-chloro-2-hydroxypropyl trimethyl ammonium chloride (CHPTAC). Pretreated samples were dyed using Reactive Red 195 dye. The cationic fabrics were analyzed by colorimetric and fastness properties, zeta potential, SEM, FTIR and an estimate of the bactericidal effect. Cationic cotton treated with PDDACl and CHPTAC showed a higher affinity for the reactive dye, with color strength (K/S) values varying from 41 to 48, against 32 for conventional dyeing. P42 presented competitive results with K/S of 27-28. The cationic dyeing considerably reduced the amount of effluent, especially for the CHPTAC samples, which requires a single washing bath for complete removal of unfixed dye. The PDDACl and P42 samples presented bactericidal activity.

The online version contains supplementary material available at 10.1007/s10570-021-04260-4.

The online version contains supplementary material available at 10.1007/s10570-021-04260-4.Substance use recovery homes represent the largest residential, community-based post-treatment option for those with substance use disorders in the United States. It is still unclear what unique factors predict relapse after residents leave such homes. This study presents results of a longitudinal study of 497 residents who departed from 42 Oxford House recovery houses. We hypothesized that the predictors of post-departure relapse would be a multi-item measure of latent recovery, length of stay, and reason for departure from the home (voluntary vs. involuntary). Predictor effects were estimated as part of a two-step model with two outcomes (a) lack of follow-up data after departure from the house, and (b) the likelihood of relapse. Determinants of missing follow-up data included less education, less time in residence, and involuntary departure. Relapse was more likely for individuals who were younger, had involuntarily left the house, and had lower values on the latent recovery factor. The implications of these important factors related to relapse following departure from residential recovery home settings are discussed.During the COVID-19 pandemic, the collapse of the public transit ridership led to significant budget deficits due to dramatic decreases in fare revenues. Additionally, public transit agencies are facing challenges of reduced vehicle capacity due to social distancing requirements, additional costs of cleaning and protective equipment, and increased downtime for vehicle cleaning. Due to these constraints on resources and budgets, many transit agencies have adopted essential service plans with reduced service hours, number of routes, or frequencies. This paper studies the resiliency during a pandemic of On-Demand Multimodal Transit Systems (ODMTS), a new generation of transit systems that combine a network of high-frequency trains and buses with on-demand shuttles to serve the first and last miles and act as feeders to the fixed network. It presents a case study for the city of Atlanta and evaluates ODMTS for multiple scenarios of depressed demand and social distancing representing various stages of the pandemic. CIA1 The case study relies on an optimization pipeline that provides an end-to-end ODMTS solution by bringing together methods for demand estimation, network design, fleet sizing, and real-time dispatching. These methods are adapted to work in a multimodal setting and to satisfy practical constraints. In particular, a limit is imposed on the number of passenger transfers, and a new network design model is introduced to avoid the computational burden stemming from this constraint. Real data from the Metropolitan Atlanta Rapid Transit Authority (MARTA) is used to conduct the case study, and the results are evaluated with a high-fidelity simulation. The case study demonstrates how ODMTS provide a resilient solution in terms of cost, convenience, and accessibility for this wide range of scenarios.The analysis of longitudinal, heterogeneous or unbalanced clustered data is of primary importance to a wide range of applications. The linear mixed model (LMM) is a popular and flexible extension of the linear model specifically designed for such purposes. Historically, a large proportion of material published on the LMM concerns the application of popular numerical optimization algorithms, such as Newton-Raphson, Fisher Scoring and expectation maximization to single-factor LMMs (i.e. LMMs that only contain one "factor" by which observations are grouped). However, in recent years, the focus of the LMM literature has moved towards the development of estimation and inference methods for more complex, multi-factored designs. In this paper, we present and derive new expressions for the extension of an algorithm classically used for single-factor LMM parameter estimation, Fisher Scoring, to multiple, crossed-factor designs. Through simulation and real data examples, we compare five variants of the Fisher Scoring algorithm with one another, as well as against a baseline established by the R package lme4, and find evidence of correctness and strong computational efficiency for four of the five proposed approaches.