Processing Strategies Research Articles

Microbial production of 3-hydroxypropionic acid by acetic acid bacteria: Modeling including the buffering capacity of the biological medium enables prediction of pH and metabolite concentrations

A mathematical model of the fed-batch bioconversion of 1,3-propanediol into 3-hydroxypropionic acid using acetic acid bacteria is proposed. The model includes the microbial growth, the oxidation of 1,3-propanediol to 3-hydroxypropionaldehyde followed by a second oxidation reaction to 3-hydroxypropionic acid. The inhibitory effect of the total acid concentration upon the biological reactions was considered as well as the effect of pH on bacterial growth. A special attention was paid to make accurate pH predictions as pH is a key parameter that influences the microbial growth and bioconversion and also defines the strategy of downstream processing for acid recovery. The buffering capacity of the complex biological medium was found to change throughout the bioconversion. In addition to describing satisfactorily a set of experiments reported in the literature, the model was successfully used to predict metabolite concentrations and the resulting pH in new operating conditions with free pH dynamics. A sensitivity analysis was performed to identify the most influential parameters of the model. The proposed model represents a valuable tool for bioprocess design as it describes the detailed kinetics of 1,3-propanediol oxidation to 3-hydroxypropionic acid by acetic acid bacteria in bioreactor. Additionally, the pH prediction is a major feature of this model, which could guide the identification of optimal operating conditions for microbial activity with a simultaneous in-situ recovery process.

Overt Word Reading and Visual Object Naming in Adults with Dyslexia: Electroencephalography Study in Transparent Orthography.

The study aimed to investigate overt reading and naming processes in adult people with dyslexia (PDs) in shallow (transparent) language orthography. The results of adult PDs are compared with adult healthy controls HCs. Comparisons are made in three phases: pre-lexical (150-260 ms), lexical (280-700 ms), and post-lexical stage of processing (750-1000 ms) time window. Twelve PDs and HCs performed overt reading and naming tasks under EEG recording. The word reading and naming task consisted of sparse neighborhoods with closed phonemic onset (words/objects sharing the same onset). For the analysis of the mean ERP amplitude for pre-lexical, lexical, and post-lexical time window, a mixed design ANOVA was performed with the right (F4, FC2, FC6, C4, T8, CP2, CP6, P4) and left (F3, FC5, FC1, T7, C3, CP5, CP1, P7, P3) electrode sites, within-subject factors and group (PD vs. HC) as between-subject factor. Behavioral response latency results revealed significantly prolonged reading latency between HCs and PDs, while no difference was detected in naming response latency. ERP differences were found between PDs and HCs in the right hemisphere's pre-lexical time window (160-200 ms) for word reading aloud. For visual object naming aloud, ERP differences were found between PDs and HCs in the right hemisphere's post-lexical time window (900-1000 ms). The present study demonstrated different distributions of the electric field at the scalp in specific time windows between two groups in the right hemisphere in both word reading and visual object naming aloud, suggesting alternative processing strategies in adult PDs. These results indirectly support the view that adult PDs in shallow language orthography probably rely on the grapho-phonological route during overt word reading and have difficulties with phoneme and word retrieval during overt visual object naming in adulthood.

Developing Benzodithiophene-Free donor polymer for 19.36% efficiency Green-Solvent-Processable organic solar cells

In this work, a newly benzodithiophene-free D-A polymer donor, named PDTP-BDD, was developed for realizing green-solvent processed high-performance OSCs. By bridging two electron-rich unit of dithieno[3,2-b:2′,3′-d]pyridin-5(4H)-one (DTP) and electron-deficient benzo[1,2-c:4,5c′]dithiophene-4,8-dione (BDD) with thiophene units, PDTP-BDD possesses high absorption in the short wavelength range and a deep HOMO energy level. The rigid building blocks also make PDTP-BDD has strong aggregation and poor solubility in common halogen-free solvents (such as o-xylene) at room temperature, but it is readily dissolved and disaggregated at high temperature (120 °C). After cooling down to a lower temperature (60 °C), PDTP-BDD self-assembled and pre-aggregated slowly in the solution at a long time. By employing a delayed processing strategy in the layer-by-layer processed OSCs (LbL-OSCs), an optimized fibril network of the underling layer was realized, enabling the permeation of acceptor into the donor network. The optimized PDTP-BDD/L8-BO-based LbL-OSCs realized a high PCE of 18.42 %. By adding a small amount of D18 to further optimize the PDTP-BDD fibril network, an impressive PCE of 19.36 % was achieved finally in the resulting ternary LbL-OSCs, which is the highest value for OSCs processed by halogen-free solvents.

Integrated bimodal fitting and binaural streaming technology outcomes for unilateral cochlear implant users

Objective Adults typically receive only one cochlear implant (CI) due to cost constraints, with a contralateral hearing aid recommended when there is aidable hearing. Standard hearing aids differ from a CI in terms of processing strategy and function as a separate entity, requiring the user to integrate the disparate signals. Integrated bimodal technology has recently been introduced to address this challenge. The aim of the study was to investigate the performance of unilateral CI users with and without an integrated bimodal fitting and determine whether binaural streaming technology offers additional benefit. Study sample Twenty-six CI users using integrated bimodal technology. Design Repeated measures where outcomes and user experience were assessed using a functional test battery more representative of real life listening (speech perception in noise tests, localisation test, tracking test) and the speech, spatial and qualities-of-hearing scale (SSQ). Results Bimodal outcomes were significantly better than for CI alone. Speech perception in noise improvements ranged from 1.4 dB to 3.5 dB depending on the location of speech and noise. The localisation and tracking tests, and the SSQ also showed significant improvements. Binaural streaming offered additional improvement (1.2 dB to 6.1 dB on the different speech tests). Conclusions Integrated bimodal and binaural streaming technology improved the performance of unilateral CI users.

Cognitive Strategies of Second Language Vocabulary Inferencing: An Eye Tracking Study

In contextual vocabulary acquisition, second language learners address both morphological and contextual cues to infer the meanings of novel words. The study investigated cognitive strategies and their eye movement correlates in contextual vocabulary inferencing task. A sample of 30 healthy participants with normal or corrected to normal vision read an Intermediate-level text in English, aiming to define the meaning of 10 low-frequency words embedded in the text. Based on both unstructured and structured post-hoc reports, we attributed the inferencing process either to word-level processing, context-level processing, or discourse-level processing strategy. Eye movements were recorded on the target word and on the target word clause areas of interest. Inferencing success was associated with increased first fixation duration on the target word both for word- and context-level processing strategy, and increased dwell time on the target word clause for context-level processing strategy. Our findings emphasize the role of both morphological and contextual processing in vocabulary inferencing task.

Multispectral and hyperspectral images fusion based on subspace representation and nonlocal low-rank regularization

ABSTRACT Multispectral image (MSI) and hyperspectral image (HSI) fusion is a popular method for HSI super-resolution reconstruction (HSI-SR). MSI-HSI fusion problem is ill-posed and demands several image priors or regularization terms to solve accurately, which is a challenging issue. In this paper, we propose an MSI-HSI fusion model via subspace representation and nonlocal low-rank regularization (SRNLRR). SRNLRR model incorporates the global spectral correlations and spatial nonlocal similarities of HSI to improve the fusion results, where the priors complement each other. First, we use the mode-n tensor-matrix product to project latent high spatial resolution HSI (HR-HSI) into spectral subspace, which can capture spectral low-rankness and reduce computational complexity. Then, based on low-rank representation (LRR) and nonlocal processing strategy, we design a spatial nonlocal LRR regularization (spa-NLRR) and a spectral global LRR regularization (spe-GLRR). These two regularizations analyze the spatial nonlocal similarities and spectral global correlations from intermediate-level vision. Finally, we use the residual regularization program to obtain more image information and input it into the fusion model. We use alternate minimization (AM) methods to optimize the SRNLRR model and employ the alternating direction method (ADM) on spatial/spectral LRR learning. Comparison experiments between the SRNLRR method and six advanced methods on three HSI datasets indicate the superiority of our method.

Preliminarily revealing rheological behaviors of mesoscale collagen fibrils in suspension

Using natural mesoscopic collagen as the structural unit has gradually emerged as a promising processing strategy for the preparation of high-performance collagen materials. These mesoscopic collagen fibrils (CAs) are exfoliated from biological tissues and dispersed in liquid media to prepare subsequent materials. The present work explores the effects of solid content, size, and temperature on the rheological behavior of collagen fibril suspensions under shearing through steady-state and dynamic rheological studies. Suspensions tend to exhibit shear-thinning and gel-like behavior, attributed to mesoscopic collagen forming a rigid network. Suspensions with large-size collagen fibers (aspect ratio > 100) exhibit high resistance to shear deformation at low solid contents (0.5 wt%) due to the intensive physical entanglement. The viscosity of the suspension was temperature-dependent and decreased rapidly near the hydrated thermal denaturation temperature of collagen. This work reveals the interactions between mesoscale collagen's surface interfaces and provides the necessary theoretical basis for collagen processing.

Combination of waste processing between MASARO plastic refinery unit and maggots technology

The evaluation of MASARO plastic refinery unit and maggots waste processing strategies’ efficacy, environmental impact, and economic analysis is at the center of the issue formulation in this study. Waste processing through MASARO plastic refinery unit requires incinerators, pyrolysis, and wet scrubber units. On the other side, maggots are used in waste processing because of their capacity to swiftly degrade the waste without negative impacts on the environment. Waste reduction is a parameter that can be used to measure the performance of waste processing. The percentages of waste reduction achieved by using MASARO plastic refinery unit can surpass 90% whereas maggots can range from 65.5% to 78.9%. The environmental impacts of MASARO plastic refinery unit and maggots technology are low. The flue gas qualities from MASARO plastic refinery unit are almost below threshold limits so it does not harm the environment. Likewise, waste processing using maggots has almost no negative impacts due to low emissions of CO2, CH4, SO2, and N-element. From an economic point of view, the MASARO process can provide an annual revenue of $3,840,000. Meanwhile, the anticipated annual revenue from waste processing using maggots is $55,000 if they are sold for $1000 per ton.

Exploring the journey of influencers in shaping social media engagement success

This study unfolds nuanced insights into the diverse dimensions dictating the success of social media influencers. Analyzing more than 210,000 social media posts and utilizing the Heuristic-Systematic Model of Information Processing (HSM), this study explores diverse factors, including individual appearance characteristics, depth of persuasive power, and various influencer types. The findings of this study shed light on the distinct impacts of varying influencer archetypes, such as celebrities and micro-celebrities, on user engagement and reveal the nuanced moderating effects of these archetypes on the relationships intertwined with personal attributes, persuasive potency, and influencer success. The proposed model advocates that influencers who leverage more profound, systematic processing strategies, marked by detailed information analysis and conveyance, are poised to experience elevated user engagement compared to counterparts employing heuristic modalities, distinguished by practical mental shortcuts and superficial examinations. This elucidation accentuates the imperative of harmonizing heuristic and systematic methodologies for emerging influencers and brands aspiring to optimize user engagement and efficaciously mold consumer behavior. This paper encapsulates a comprehensive exploration of the dynamic landscapes of influencer marketing via the HSM prism, delivering profound insights and practical ramifications for scholars, marketers, and influencers aiming to navigate and exploit the intricate networks of influential determinants in the ever-evolving digital marketing domain.

Efficient Edge Computing: A Survey of High-Throughput Concurrent Processing Strategies for Graph Data

This paper reviews the strategies for high-throughput concurrent processing of graph data in edge computing environments. As information technology rapidly advances, particularly in areas such as the Internet of Things (IoT), smart cities, and autonomous driving, the need for real-time and efficient data processing continues to grow. Edge computing is a distributed computing paradigm that can process data at or near its origin, thereby reducing network latency, improving application performance, and reducing the load on central data centers. The discussion includes the application of edge computing in graph data processing, highlighting parallel computing models such as the Bulk Synchronous Parallel (BSP) model and other parallel optimization techniques like data partitioning and task scheduling. The paper also addresses specific challenges of parallel processing in edge computing, such as resource constraints, data communication delays, and strategies for security and privacy protection. Despite the significant potential edge computing has demonstrated in processing graph data, numerous challenges remain. Future research directions involve the development of new resource optimization algorithms, low-latency communication protocols, and technologies to enhance data security, aiming to achieve more efficient and secure graph data processing. This paper provides clear directions and a foundation for the efficient implementation of graph data processing in edge computing environments, positioning edge computing to play an increasingly significant role in real-time data analysis and intelligent applications.

Etching of quartz crystals in liquid phase environment: A review

Quartz crystals are the most widely used material in resonant sensors, owing to their excellent piezoelectric and mechanical properties. With the development of portable and wearable devices, higher processing efficiency and geometrical precision are required. Wet etching has been proven to be the most efficient etching method for large-scale production of quartz devices, and many wet etching approaches have been developed over the years. However, until now, there has been no systematic review of quartz crystal etching in liquid phase environments. Therefore, this article provides a comprehensive review of the development of wet etching processes and the achievements of the latest research in this field, covering conventional wet etching, additive etching, laser-induced backside wet etching, electrochemical etching, and electrochemical discharge machining. For each technique, a brief overview of its characteristics is provided, associated problems are described, and possible solutions are discussed. This review should provide an essential reference and guidance for the future development of processing strategies for the manufacture of quartz crystal devices.

HORTICULTURAL FIELD IS EXPLORING WITH ITS VARIOUS UTILIZATION

Horticulture farming also aims to enhance the quality of life, as well as the beauty, sustainability, and recovery of our ecosystem and the human condition. These days, a crucial concerning issue is arising globally to ensure nutrition security for huge population that leads to focus on production increase, quality improvement, food safety assurance, and processing strategies. Consequently, a large amount of waste generates in the processing industries, household kitchen, and supply chain of horticultural commodities that has led to a significant nutrition and economic loss, consequently creating environment pollution with extensive burden of landfills. However, these wastes showed magnificent potentiality of re-utilization in several industries owing to as rich source of different bioactive compounds and phytochemicals. Therefore, sustainable extraction methods and utilization strategies deserve the extensive investigations.

Quartz Crystal Microbalance as a Holistic Detector for Quantifying Complex Organic Matrices during Liquid Chromatography: 1. Coupling, Characterization, and Validation.

A matrix in highly complex samples can cause adverse effects on the trace analysis of targeted organic compounds. A suitable separation of the target analyte(s) and matrix before the instrumental analysis is often a vital step for which chromatographic cleanup methods remain one of the most frequently used strategies, particularly high-performance liquid chromatography (HPLC). The lack of a simple real-time detection technique that can quantify the entirety of the matrix during this step, especially with gradient solvents, renders optimization of the cleanup challenging. This paper, along with a companion one, explores the possibilities and limitations of quartz crystal microbalance (QCM) dry-mass sensing for quantifying complex organic matrices during gradient HPLC. To this end, this work coupled a QCM and a microfluidic spray dryer with a commercial HPLC system using a flow splitter and developed a calibration and data processing strategy. The system was characterized in terms of detection and quantification limits, with LOD = 4.3-15 mg/L and LOQ = 16-52 mg/L, respectively, for different eluent compositions. Validation of natural organic matter in an environmental sample against offline total organic carbon analysis confirmed the approach's feasibility, with an absolute recovery of 103 ± 10%. Our findings suggest that QCM dry-mass sensing could serve as a valuable tool for analysts routinely employing HPLC cleanup methods, offering potential benefits across various analytical fields.

Relationships Between Anxiety, Attention, and Reading Performance in Children.

Many studies link anxiety in children with reading difficulties, but some facets of anxiety have been found to be positively associated with reading achievement. Attentional Control Theory offers a potential explanation for these seemingly contradictory findings, positing that anxiety can both interfere in attentional processes and enhance effort and use of compensatory processing strategies. The current study examines the relationships between anxiety, attentional control, and reading performance (word reading/decoding and passage comprehension) in a racially-diverse sample of 251s-grade students, 152 of whom had not met reading benchmarks using screening measures. Results showed that harm avoidance was positively associated with reading performance and physical symptoms of anxiety were negatively associated with reading performance. These links were attenuated when including attentional control in the model, suggesting mediation and lending support to Attentional Control Theory. Further research is needed to confirm causal mediation effects between anxiety, attentional control, and reading performance.

Photovoltaic power prediction system based on multi-stage data processing strategy and improved optimizer

Building a reliable forecasting system can quantify future fluctuations in short-term photovoltaic output power, which is essential for optimizing grid configuration and reducing operating costs. However, most of the existing studies only use denoising technology to preprocess data, which results in the elimination of some key information. And the traditional optimizer cannot meet the parameter optimization requirements of the prediction system because of its limited search capacity and search space. Based on the above problems, a combined system based on multi-stage data processing strategy and improved optimizer is proposed, which solves the tradeoff problem between prediction accuracy and stability. Firstly, the multi-stage processing strategy effectively improves the signal-to-noise ratio and preserves more implicit information. Then, the optimal sub-model determination strategy extends the structural framework of model selection and improves the flexibility of the prediction system. Finally, three improved strategies are introduced to improve the optimization ability and convergence speed of the optimizer, which magnifies the advantages of the prediction system. An empirical study using Safi-Morocco data shows that the symmetric mean absolute percentage errors of three photovoltaic modules are 4.777%, 4.755% and 6.033%, respectively, which implies that the system can not only achieve accurate prediction of photovoltaic output power, but also help to balance supply and demand and improve the overall sustainability and stability of the grid-connected power system.

Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach

In this editorial, we comment on the article by Wang and Long, published in a recent issue of the World Journal of Clinical Cases . The article addresses the challenge of predicting intensive care unit-acquired weakness (ICUAW), a neuromuscular disorder affecting critically ill patients, by employing a novel processing strategy based on repeated machine learning. The editorial presents a dataset comprising clinical, demographic, and laboratory variables from intensive care unit (ICU) patients and employs a multilayer perceptron neural network model to predict ICUAW. The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW. This editorial contributes to the growing body of literature on predictive modeling in critical care, offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.

In Situ TEM Tracking of Disconnection Motion and Atomic Cooperative Reorientation in the Oriented Attachment of Pt Nanoparticles.

The oriented attachment (OA) of nanoparticles (NPs) is an important crystal growth mechanism in many materials. However, a comprehensive understanding of the atomic-scale alignment and attachment processes is still lacking. We conducted in situ atomic resolution studies using high-resolution transmission electron microscopy to reveal how two Pt NPs coalesce into a single particle via OA, which involves the formation of atomic-scale links and a grain boundary (GB) between the NPs, as well as GB migration. Density functional theory calculations showed that the system energy changes as a function of the number of disconnections during the coalescence process. Additionally, the formation and annihilation processes of disconnection are always accompanied by the cooperative reorientation motion of atoms. These results further elucidate the growth mechanism of OA at the atomic scale, providing microscopic insights into OA dynamics and a framework for the development of processing strategies for nanocrystalline materials.

Stepwise processing strategy for efficient and comprehensive utilization of sugarcane bagasse via enzymatic hydrolysis and catalytic conversion to produce 5-hydroxymethylfurfural

To achieve the goal of “no waste” in circular bioeconomy on the basis of waste-to-energy conversion, the development of efficient technology for sustainable treatment and utilization of lignocellulosic biomass is significant yet challenging. In this study, a stepwise processing strategy based on enzymatic hydrolysis and catalytic conversion was proposed for comprehensive utilization of sugarcane bagasse (SCB) to produce value-added products. Mechanical activation (MA) pretreatment was adopted to disrupt the recalcitrant structure of SCB for improving enzymatic hydrolysis of polysaccharides to produce reducing sugars, obtaining a high yield (392 mg/g SCB) of target sugars (TS; i.e., cellobiose and glucose). The enzymolysis residue and Al(NO3)3·9 H2O were treated by MA and then calcinated to construct the biochar (BC)-based catalyst (denoted as MA-Al/BC) with Brønsted–Lewis dual-acidic sites, which effectively catalyzed the conversion of TS to produce 5-hydroxymethylfurfural (5-HMF). A TS conversion of 94.5% and a 5-HMF yield of 77.6% were achieved at 190 °C for 4 h in the biphasic system. MA-Al/BC exhibited excellent universality for efficient catalytic conversion of various carbohydrates. This study offers a promising approach for value-added exploitation of agricultural wastes within circular bioeconomy principles.

Enhancing the 3D printing fidelity of vat photopolymerization with machine learning-driven boundary prediction

Like many pixel-based additive manufacturing (AM) techniques, digital light processing (DLP) based vat photopolymerization faces the challenge that the square pixel based processing strategy can lead to zigzag edges especially when feature sizes come close to single-pixel levels. Introducing greyscale pixels has been a strategy to smoothen such edges, but it is a challenging task to understand which of the many permutations of projected pixels would give the optimal 3D printing performance. To address this challenge, a novel data acquisition strategy based on machine learning (ML) principles is proposed, and a training routine is implemented to reproduce the smallest shape of an intended 3D printed object. Through this approach, a chessboard patterning strategy is developed along with an automated data refining and augmentation workflow, demonstrating its efficiency and effectiveness by reducing the deviation by around 30%.

Flexible and stretchable photodetectors constructed by integrating photosensor, substrate, and electrode in one polymer matrix

Flexible photodetector (FPD) is a crucial component in modern wearable/flexible systems and plays a vital role in healthcare, environmental monitoring, flexible display, and other fields. However, achieving satisfactory interfacial adhesion among the electrode, photoactive material, and flexible substrate at the same time to fabricate FPDs with high flexibility, stretchability, stability, and mechanically compliance is still a challenge. In this study, we propose a green and eco-friendly water-system processing strategy to construct all-in-one FPDs, using borate ester polymers (BPs) as both the co-matrix of the photoactive layer substrate and the conductive ink. The flexible substrate exhibits a high adaptation to the conductive ink, and thus, efficient interface charge transfer with the complicated pattern electrode. The resulting FPD exhibits stable photoresponse under different bending angles and large mechanical strains. Even after 2000 cycles of light stimulation test at a whole device elongation of 50%, the FPD exhibits no significant decrease in the photoresponse performance. Such devices may provide new opportunities for multifunctional electronic skin and wearable electronic devices.