Tools for Optimization of Biomass-to-Energy Conversion Processes

Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass; neural networks for higher heating value (HHV) correlations; and linear programming and its variations for achieving objectives in general, such as costs and emissions reduction. This study reinforces the progress evidenced in the literature and envisions the increasing inclusion of socio-environmental criteria as a challenge in future modeling efforts

The effect of menopause hypoestrogenism on osteogenic differentiation of periodontal ligament cells (PDLC) and stem cells (PDLCs): A systematic review

(1) Background: Menopause is a physiological condition typified by drastic hormonal changes, and the effects of this transition have long-term significant clinical implications on the general health, including symptoms or physical changes. In menopausal women, the periodontium can be affected directly or through neural mechanism by oestrogen (E2) deficiency. The majority of the biological effects of E2 are modulated via both oestrogen receptor-α (ERα) and oestrogen receptor-β (ERβ). There is evidence that hypoestrogenism has a substantial impact on the aetiology, manifestation and severity of periodontitis, via the regulation of the expression of osteoproges-terin and RANKL in human periodontal ligament cells through ERβ. However, the mechanistic understanding of oestrogen in periodontal status has been partially clarified. The aim of this paper was to synopsize the recent scientific evidence concerning the link between the menopause and periodontitis, through the investigation of physio-pathological impact of the oestrogen deficiency on osteogenic differentiation of PDLSCs and PDLSC, as well as the dynamic change of ERα and ERβ. (2) Methods: Search was conducted for significant studies by exploring electronic PubMed and EMBASE databases, and it was independently performed by two researchers. All studies on the impact of oestrogen level on alveolar bone resorption were searched from 2005 to July 2020. Data selection was in concordance with PRISMA guidelines. (3) Results: Eight studies met the criteria and were included in this systematic review. All studies reported that oestrogen deficiency impairs the osteogenic and osteoblastic differentiation of PDL cells and oestrogen affects the bone formation capacity of cells. Seven studies were conducted on animal samples, divided into two groups: the OVX animals and animals who received the sham operation. (4) Conclusions: There is a multitude of data available showing the influence of menopause on periodontal status. However, the evidence of this line to investigation needs more research and could help explain the physiological linkage between menopause state and periodontal disease

Temporomandibular disorders and fibromyalgia: A narrative review

Temporomandibular disorder (TMD) and fibromyalgia (FM) have some clinical characteristics in common, for instance the chronic evolution, the pathophysiology incompletely understood and a multifactorial genesis. The incidence and the relationship between TMD and FM patients are the aims of this review. A MEDLINE and PubMed search were performed for the key words “temporomandibular disorder” AND “fibromyalgia” from 2000 to present. A total of 19 papers were included in our review, accounting for 5449 patients. Ten studies, reporting a total of 4945 patients with TMD, showed that only 16.5% of these patients had diagnosis of FM, whereas 12 studies, reporting a total of 504 patients with FM, demonstrated that 77.0% of these patients had diagnosis of TMD. A comorbid relationship exists between TMD and FM. The complexity of both diseases shows the importance of a multimodal and interdisciplinary

Texture profile and sensory acceptance of a symbiotic diet aerated mousse containing Lactobacillus acidophilus LA-5, inulin, and fructooligosaccharide

This work dealt with the texture profile and sensory acceptance of a symbiotic diet aerated mousse containing Lactobacillus acidophilus LA-5, inulin, and fructooligosaccharid

Trattamento di inquinanti emergenti in acque da digestione anaerobica di liquami suinicoli mediante microalghe

I liquami suinicoli post digestione anaerobica, ancora ricchi in ammonio, possono causare fenomeni di eutrofizzazione delle acque. Inoltre, per l\u2019eccessivo uso di antibiotici negli allevamenti, significative quantit\ue0 non metabolizzate di questi farmaci possono essere ritrovate nei liquami e quindi nelle acque dopo trattamento anaerobico. Crescendo in modo eterotrofico, le microalghe potrebbero offrire diversi vantaggi, essendo in grado di usare il carbonio organico cos\uec come nutrienti inorganici quali azoto e fosforo presenti nelle acque di scarico. Lo scopo di questa ricerca \ue8 stato quello di valutare l\u2019effetto di inquinati emergenti aggiunti al digestato da liquami suinicoli sulla crescita della microalga Chlorella vulgaris. A tale scopo C. vulgaris \ue8 stata fatta crescere in terreno con differenti quantit\ue0 di digestato al fine di ottenere concentrazioni iniziali di azoto ammoniacale pari a 60, 120 e 170 mgN/L. A fini comparativi, colture addizionali sono state eseguite sia in acqua arricchita con ammonio puro alle stesse concentrazioni sopra riportate, sia nel terreno classico di crescita per questa microalga (Bold Basal Medium). Alla coltura con 60 mgN/L di ammonio da liquami, dopo il raggiungimento della fase stazionaria sono stati aggiunti 14.0 mg/L di Micospectone, consistente in una miscela al 33.3 e 66.6% degli antibiotici Lincomicina e Spectinomicina. Studi preliminari hanno evidenziato che concentrazioni superiori di antibiotico (56-112 mg/L) possono portare ad un\u2019inibizione della crescita microalgale. La concentrazione della biomassa microalgale \ue8 stata quantificata giornalmente per via spettrofotometrica ad una lunghezza d\u2019onda pari a 625 nm, mentre quelle di ammonio totale e di fosfati periodicamente. La biomassa fresca ed essiccata al termine della crescita \ue8 stata osservata mediante microscopia ottica ed elettronica a scansione. In presenza di digestato e antibiotico, C. vulgaris ha raggiunto concentrazioni finali di circa 1.3 g di biomassa secca per litro di terreno. L\u2019ammonio \ue8 stato rimosso efficacemente con velocit\ue0 di rimozione pari a 5.6 and 3.4 mgN/Lgiorno partendo rispettivamente da concentrazioni iniziali di ammonio di 60 e 120 mgN/L, mentre concentrazioni superiori di ammonio (170 mgN/L) hanno comportato inibizione sia della crescita sia della sua propria rimozione

Investigation on the TransientConditions of a Rotating Biological Contactor for Bioethanol Production

Alcoholic fermentations of sucrose solutions were performed in a Rotating BiologicalContactor with immobilized-yeast cells, and the results collected during the transient conditions of start-up are presented and discussed. The analysis and modeling of data constitute a preliminary semi-empirical approach to the study of dynamics of such a bioprocess. The investigation has been developed on the observations of the responses to variations in the operating conditions of substrate, product, suspended- and immobilized- cell concentrations either in the fermentation broth or within a synthetic spongy matrix

The development of a new blood substitute

Trattasi di un sommario pubblicato online nel sito dell'Agenzia di divulgazione scientifica Atlas of Science, AoS Nordic AB, Moscow, Russia, riguardante l'impiego di nuovi sostituti del sangu

Exploring the role of Fusobacterium nucleatum in preterm birth: A narrative review

In recent years, substantive attention has been drawn to the relationship between oral microbiome homeostatic equilibrium disruption and systemic health, demonstrating the negative impacts of this reciprocal biological interplay. Increasingly, there is a concern over the potential noxious effect of oral microbiome dysbiosis on obstetric poor outcomes, focusing on preterm birth. This epidemiological observation remains unexplained, although biologically plausible mechanism has been proposed. Intrauterine infection has long been associated with adverse pregnancy, when the elicitation of an immune response is determinant. There is evidence that Fusobacterium nucleatum (FN), a Gram-negative anaerobe ubiquitous in the oral cavity, infects the mouse placenta originating in the decidua basalis. Based on the current data in literature, we performed a review to provide resources for the explanation of the potential impact of microbiome dysbiosis on poor obstetric outcomes, focusing on the role of FN

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations

Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting