Carboxymethyl cellulose (CMC) is one of the most promising cellulose derivatives. Due to its characteristic surface properties, mechanical strength, tunable hydrophilicity, viscous properties, ...availability and abundance of raw materials, low-cost synthesis process, and likewise many contrasting aspects, it is now widely used in various advanced application fields, for example, food, paper, textile, and pharmaceutical industries, biomedical engineering, wastewater treatment, energy production, and storage energy production, and storage and so on. Many research articles have been reported on CMC, depending on their sources and application fields. Thus, a comprehensive and well-organized review is in great demand that can provide an up-to-date and in-depth review on CMC. Herein, this review aims to provide compact information of the synthesis to the advanced applications of this material in various fields. Finally, this article covers the insights of future CMC research that could guide researchers working in this prominent field.
•A comparative analysis of auction mechanisms and bidding strategies is presented.•Impacts of bidding strategies & auction mechanisms on various markets is simulated.•The best-offer game theoretic ...strategy results in near-ideal economic efficiencies.•Results show that discriminatory k-DA can economically outperform uniform k-DA.•However, discriminatory k-DA is more sensitive to market conditions than uniform k-DA.
The advent of blockchain technology and the increasing penetration of rooftop photovoltaic (PV) systems have presented a new opportunity for peer-to-peer (P2P) energy trading. In such transactive markets, communities may enjoy cheaper electricity prices while supporting locally produced green energy. However, there exists a considerable knowledge gap between market mechanisms and energy exchanges. Challenges arise in the auction process to ensure individual rationality, incentive compatibility, budget balance, and economic efficiency. This paper offers insights for building the foundation of a P2P energy trading market and presents a comparative analysis of auction mechanisms and bidding strategies for P2P solar electricity exchanges in terms of market demand and supply metrics. Auction mechanisms considered in this paper are Discriminatory and Uniform k-Double Auction (k-DA). Impacts of different bidding strategies, including game theoretic approaches, on the economic efficiencies of the P2P transactive energy market are also studied. A simulation case study of 100 participants in a microgrid at various PV penetration levels is presented using typical residential load and solar PV generation profiles.
Since the introduction of the smart grid, accelerated deployment of various smart grid technologies and applications have been experienced. This allows the traditional power grid to become more ...reliable, resilient, and efficient. Despite such a widespread deployment, it is still not clear which communication technology solutions are the best fit to support grid applications. This is because different smart grid applications have different network requirements – in terms of data payloads, sampling rates, latency and reliability. Based on a variety of smart grid use cases and selected standards, this paper compiles information about different communication network requirements for different smart grid applications, ranging from those used in a Home Area Network (HAN), Neighborhood Area Network (NAN) and Wide-Area Network (WAN). Communication technologies used to support implementation of selected smart grid projects are also discussed. This paper is expected to serve as a comprehensive database of technology requirements and best practices for use by communication engineers when designing a smart grid network.
Abstract
Motivation
Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray ...crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction.
Results
We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins.
Availability and implementation
SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.
The mosquito Ae. albopictus is usually adapted to the peri-domestic environment and typically breeds outdoors. However, we observed its larvae in most containers within homes in northern peninsular ...Malaysia. To anticipate the epidemiological implications of this indoor-breeding, we assessed some fitness traits affecting vectorial capacity during colonization process. Specifically, we examined whether Ae. albopictus exhibits increased survival, gonotrophic activity and fecundity due to the potential increase in blood feeding opportunities.
In a series of experiments involving outdoors and indoors breeding populations, we found that Ae. albopictus lives longer in the indoor environment. We also observed increased nighttime biting activity and lifetime fecundity in indoor/domestic adapted females, although they were similar to recently colonized females in body size.
Taken together these data suggest that accommodation of Ae. albopictus to indoor/domestic environment may increase its lifespan, blood feeding success, nuisance and thus vectorial capacity (both in terms of increased vector-host contacts and vector population density). These changes in the breeding behavior of Ae. albopictus, a potential vector of several human pathogens including dengue viruses, require special attention.
•International aid donors have both formal and informal interests.•Aid donors have economic, political, and strategic interests in the countries they fund.•Development aid serves to advance donors’ ...informal self-interests.•The interests may vary from donor to donor.
Whether aid serves the development needs of a recipient country rather than the interests of donors has been a topic of much debate and research in the field of development studies. Donor agencies have interests, as does any political actor, and bureaucratic politics theory states that any bureaucracy has a dual interest, consisting of delivering on its formal mandate as well as informally increasing its power by maximizing budgets, staff, and fields for political responsibility. This study aims to conceptualize the formal and informal interests of bilateral foreign donor bureaucracies in allocating aid, using Bangladesh forest development aid by USAID, GIZ, and the EU as a case study. Quantitative analysis of documents on actual spending in the context of forest development projects and qualitative analysis from detailed interviews with development aid experts are employed. Important informal interests of donor agencies were observed as follows: (1) drawing on consultants as well as products and services from the donor’s country; (2) expanding favorable markets for the donor’s economy; (3) increasing the donor’s geopolitical as well as policy influence in recipient countries; (4) obtaining information that is independent from the recipient government; and (5) shaping good governance as a prerequisite for investment from donor countries. Of the three donor organizations, USAID was found to have allocated extensive aid to two activities—consultancy, and collaboration and networking—that advance USAID’s informal economic and political interests. GIZ allocated major aid to recipient developmental interventions; it also advanced its informal economic and political interests (albeit to a smaller extent). The EU allocated the largest amount of aid to developmental interventions, though its informal economic and political interests were also served, even if only to a limited extent. This study concludes with key points regarding informal interests of donor bureaucracies as well as on future research fields.
In order to support the growing interest in demand response (DR) modeling and analysis, there is a need for physical-based residential load models. The objective of this paper is to present the ...development of such load models at the appliance level. These include conventional controllable loads, i.e., space cooling/space heating, water heater, clothes dryer and electric vehicle. Validation of the appliance-level load models is carried out by comparing the models' output with the real electricity consumption data for the associated appliances. The appliance-level load models are aggregated to generate load profiles for a distribution circuit, which are validated against the load profiles of an actual distribution circuit. The DR-sensitive load models can be used to study changes in electricity consumption both at the household and the distribution circuit levels, given a set of customer behaviors and/or signals from a utility.
Depression is one of the most serious yet understudied issues among Bangladeshi nurses, bringing health dangers to this workforce. This study aimed to investigate how workplace violence (WPV), ...bullying, burnout, and job satisfaction are correlated with depression and identify the factors associated with depression among Bangladeshi nurses. For this cross-sectional study, data were collected between February 26, 2021, and July 10, 2021 from the Bangladeshi registered nurses. The Workplace Violence Scale (WPVS), the Short Negative Acts Questionnaire S-NAQ, the Burnout Measure-Short version (BMS), the Short Index of Job Satisfaction (SIJS-5), and the Patient Health Questionnaire (PHQ-9) were used to measure WPV, bullying, burnout, job satisfaction, and depression, respectively. Inferential statistics include Pearson's correlation test, t-test, one-way ANOVA test, multiple linear regression, and multiple hierarchal regression analyses were performed. The study investigated 1,264 nurses (70.02% female) with an average age of 28.41 years (SD = 5.54). Depression was positively correlated with WPV, bullying, and burnout and negatively correlated with job satisfaction (p 48 hours) had a significantly higher depression score (beta = 1.490, 95% CI = 0.511 to 2.470) than those who worked less than or equal to 36 hours. Depression was found to be significantly higher among those who did not receive a timely salary (beta = 2.136, 95% CI = 1.138 to 3.134), rewards for good works (beta = 1.862, 95% CI = 1.117 to 2.607), and who had no training on WPV (beta = 0.895, 95% CI = 0.092 to 1.698). Controlling burnout, bullying, and workplace violence, as well as improving the work environment for nurses and increasing job satisfaction, are the essential indicators of reducing depression. This can be accomplished with integrative support from hospital executives, policymakers, and government officials.
False news articles pose a serious challenge in today's information landscape, impacting public opinion and decision-making. Efforts to counter this issue have led to research in deep learning and ...machine learning methods. However, a gap exists in effectively using contextual cues and skip connections within models, limiting the development of comprehensive detection systems that harness contextual information and vital data propagation. Thus, we propose a model of deep learning, FakeStack, in order to identify bogus news accurately. The model combines the power of pre-trained Bidirectional Encoder Representation of Transformers (BERT) embeddings with a deep Convolutional Neural Network (CNN) having skip convolution block and Long Short-Term Memory (LSTM). The model has been trained and tested on English fake news dataset, and various performance metrics were employed to assess its effectiveness. The results showcase the exceptional performance of FakeStack, achieving an accuracy of 99.74%, precision of 99.67%, recall of 99.80%, and F1-score of 99.74%. Our model's performance was extended to two additional datasets. For the LIAR dataset, our accuracy reached 75.58%, while the WELFake dataset showcased an impressive accuracy of 98.25%. Comparative analysis with other baseline models, including CNN, BERT-CNN, and BERT-LSTM, further highlights the superiority of FakeStack, surpassing all models evaluated. This study underscores the potential of advanced techniques in combating the spread of false news and ensuring the dissemination of reliable information.
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