Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge Matters: Modulating Secondary Interactions in Hyaluronan Hydrogels

Hyaluronic acid (HA) is a vital, functional component of the extracellular matrix (ECM). Following a synthetic biology approach, we designed polyelectrolyte hydrogels composed of HA as a polymeric backbone interconnected by short covalent crosslinkers. Utilizing a thiol-Michael addition, defined network structures are created, which can be modulated by the aromatic core and charge of the crosslinkers. With increasing negative network charge, varied both through the charge of the crosslinker and the degree of functionalization of the HA chain, the Young's modulus of the hydrogels decreases linearly as swelling ratios increase. Interestingly, secondary interactions with aromatic crosslinkers are stronger for elastin inspired crosslinkers with a pyridinium core compared to analogous triazolium crosslinkers. Based on the defined covalent interconnectivity of these HA-hydrogels, we were able to demonstrate the specific electrostatic and aromatic interactions induced by different short crosslinkers.
AN ANALYTICAL MODEL FOR ELASTO-PLASTIC BUCKLING OF COLUMNS

Abstract. The theory of buckling strength of compression members in the plastic range has been extensively studied, and numerical methods already exist which deal with such behaviour. However, there is significant research interest in developing analytical models for elasto-plastic buckling, largely driven by the need for simplified mechanics-based design tools, but also by the desire for enhanced understanding of this complex phenomenon. This paper is intended to illustrate the mechanics of the elasto-plastic buckling response of stocky columns by means of a simplified analytical model, starting from the point of buckling initiation and considering the post-buckling response. In this model, the Rotational Spring Analogy is used for formulating the geometric stiffness matrix, whereas the material stiffness matrix is obtained with due consideration for the spread of material plasticity. In addition to establishing some key features of elasto-plastic buckling, the imperfection sensitivity in the plastic range is also studied and as a result a threshold level of imperfection is identified.
Combined Caffeine and Lecithin Induces Anxiogenic-Like Behaviours via Oxidative Stress

Caffeine and lecithin were documented with many different contradictory effects in the body. In these studies, we set to investigate possible ameliorating effect of lecithin on anxiogenic effect of caffeine, possible mechanism of combined caffeine and lecithin on anxiety-like behaviours as well as potential anxiety biomarker. Thirty-six adult male Wistar rats of an average weight of 140-145g was divided into six groups (1 - 6) of six rats each and were used for these studies. Groups 1, 2 and 3 were given normal saline (10ml/kg), lecithin (20mg/kg) and caffeine (10mg/kg) respectively, while Groups 4, 5 and 6 were treated with combined caffeine and lecithin (10mg/kg + 20mg/kg), (5mg/kg + 30mg/kg) and (15mg/kg + 10mg/kg) respectively. All treatments were done orally via oral cannula, once daily and repeated for 14 days. Two hours after the last treatment, anxiety-like behaviours were evaluated on open field maze. Animals were finally sacrificed under ketamine (50mg/kg); blood and brain were harvested to quantify lactate dehydrogenase (LDH) and glucose-6-phosphate dehydrogenase (G6PD). Histological study on the amygdala was done using H&E. Combined caffeine and lecithin at the doses used significantly (p<0.05) decreased locomotor and exploratory behavioural parameters (anxiogenic effects), caused both significant increase and decrease of blood and brain LDH and significantly (p<0.05) decreased in the blood and the brain G6PD compared to the control group. Histological studies demonstrated normal cell morphology and distribution in all groups. In conclusion, combined caffeine and lecithin decreases G6PD leading to decrease antioxidants and increase oxidative stress which in turn stimulate anxiogenic-like behaviours. With the decreasing pattern of G6PD recorded in this study, G6PD might be a potential biomarker for generalized anxiety disorders.
Simple tting of subject-speci c curves for longitudinal data

We present a simple semiparametric model for tting subject-speci c curves for longitudinal data. Individual curves are modelled as penalized splines with random coe cients. This model has a mixed model representation, and it is easily implemented in standard statistical software. We conduct an analysis of the long-term e ect of radiation therapy on the height of children su ering from acute lymphoblastic leukaemia using penalized splines in the framework of semiparametric mixed e ects models. The analysis revealed signi cant di erences between therapies and showed that the growth rate of girls in the study cannot be fully explained by the group-average curve and that individual curves are necessary to re ect the individual response to treatment. We also show how to implement these models in S-PLUS and R in the appendix. Copyright ? 2004 John Wiley & Sons, Ltd.
The effects of internal and external resource management on logistics performance

This empirical study aims to elucidate the effects of logistics information technology (LIT), top management beliefs (TMBs), and suppliers' resources selection (SRS) on logistics performance. The extended resources-based view (ERBV) supports the development of the proposed relationships in the study model, based on previous studies measures. Structural equation modeling (SPSS and AMOS v 25) was utilized to test the validity of the model and proposed hypotheses. Based on responses from 229 surveyed companies in Sudan’s food and beverages sector, this study’ findings showed that the LIT, TMBs, and SRS influence logistic cost performance (LCP) and logistic delivery performance directly. Additionally, it showed that company size could moderate the relationship between LCP and LIT, TMBs, and SRS. This study provides several theoretical and practical implications by evaluating the ability of LIT, SRS, and TMBs to improve delivery and cost performance.
A Formal Verification Method of Compilation Based on C Safety Subset

With the rapid increase in the number of wireless terminals and the openness of wireless networks, the security of wireless communication is facing serious challenges. The safety and security of computer communication have always been a research hotspot, especially the wireless communication that still has a more complex architecture which leads to more safety problems in the communication system development. In recent years, more and more wireless communication systems are applied in the safety-critical field which tends to need high safety guarantees. A compiler is an important tool for system development, and its safety and reliability have an important impact on the development of safety-critical software. As the strictest method, formal verification methods have been widely paid attention to in compiler verification, but the current formal verification methods have some problems, such as high proof complexity, weak verification ability, and low algorithm efficiency. In this paper, a compiler formal verification method based on safety C subsets is proposed. By abstracting the concept of C grammar units from safety C subsets, the formal verification of the compiler is transformed into the verification of limited C grammar units. In this paper, an axiom system of first-order logic and special axioms are introduced. On this axiom system, the semantic consistency verification of C grammar unit and target code pattern is completed by means of theorem proving, and the formal verification of the compiler is completed.
Aspect based Sentiment Classification for Online Reviews

: The tourism and travel sector is improving services using a large amount of data collected from different sources. The easy access to comments, evaluations and experiences of different tourists has made the planning of tourism rich and complex. Therefore, a big challenge faced by tourism sector is to use the gathered data for detecting tourist preferences. Unfor-tunately, some user ’ s comments are irrelevant and complex for understanding these becomes hard for recommendation. Aspect based sentiment classification methods have shown promise in overcome the noise. In existing not much work on aspect based sentiment with classification. This paper presents a framework of aspect based sentiment classification recommendation system that will not only identify the aspects very efficiently but can perform classification task with high accuracy using machine learning na¨ıve Bayes and Decision Tree algorithms. The framework helps tourists find the best place, hotel and restaurant in a city, and performance has been evaluated by conducting experiments on Yelp and foursquare real-time datasets.
Manuscript title: Phenotyping Antidepressant Treatment Response with Deep Learning in Electronic Health Records

Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applications but remains a challenge. Increasingly, artificial intelligence methods using “deep learning” applied to clinical data have shown promise in complex classification problems. Here, we systematically evaluate the performance of eight deep-learning-based natural language processing models in classifying response to antidepressants in a large real-world healthcare setting. We obtained data spanning 1990-2018 for adults with depression and a co-occurring antidepressant prescription from the EHR data warehouse of the Mass General Brigham healthcare system (n=111,572). Clinical notes were collected for the following time windows after antidepressant initiation: (1) 2 days to 4 weeks, (2) 4–12 weeks, and (3) 12–26 weeks. A stratified random sample of these note sets (total 4,299 across time periods) were manually reviewed to classify response status as “improved” or “no evidence of improvement” in depression symptoms. All models performed well, with areas under the receiver operator curve (AUROC) of at least 0.80. Positive predictive values (PPVs) ranged from 0.72 – 0.91. In general, models incorporating more information-dense and longer text sequences performed better than others. The best performing model (Longformer-large with sliding window) had an AUROC = 0.88 and PPV = 0.84 at a specificity of 0.88. Our results indicate that deep learning methods applied to EHR data can accurately classify antidepressant response in a real-world healthcare setting. Automated treatment response classification may facilitate a range of research and clinical decision support applications. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted August 4, 2021. ; https://doi.org/10.1101/2021.08. 04.21261512 doi: medRxiv preprint
A TTENTION B ASED J OINT L EARNING FOR S UPER - VISED E LECTROCARDIOGRAM A RRHYTHMIA D IFFER - ENTIATION WITH U NSUPERVISED A BNORMAL B EAT S EGMENTATION

Deep learning has shown great promise in arrhythmia classification in electrocardiogram (ECG). Existing works, when classifying an ECG segment with multiple beats, do not identify the locations of the anomalies, which reduces clinical interpretability. On the other hand, segmenting abnormal beats by deep learning usually requires annotation for a large number of regular and irregular beats, which can be laborious, sometimes even challenging, with strong inter-observer variability between experts. In this work, we propose a method capable of not only differentiating arrhythmia but also segmenting the associated abnormal beats in the ECG segment. The only annotation used in the training is the type of abnormal beats and no segmentation labels are needed. Imitating human’s perception of an ECG signal, the framework consists of a segmenter and classifier. The segmenter outputs an attention map, which aims to highlight the abnormal sections in the ECG by element-wise modulation. Afterwards, the signals are sent to a classifier for arrhythmia differentiation. Though the training data is only labeled to supervise the classifier, the segmenter and the classifier are trained in an end-to-end manner so that optimizing classification performance also adjusts how the abnormal beats are segmented. Validation of our method is conducted on two dataset. We observe that involving the unsupervised segmentation in fact boosts the classification performance. Meanwhile, a grade study performed by experts suggests that the segmenter also achieves satisfactory quality in identifying abnormal beats, which significantly enhances the interpretability of the classification results.
Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems

High penetration of renewable energy such as wind power and photovoltaic (PV) requires large amounts of flexibility to balance their inherent variability. Making an accurate prediction of the future power system imbalance is an efficient approach to reduce these balancing costs. However, the imbalance is affected not only by renewables but also by complex market dynamics and technology constraints, for which the dependence structure is unknown. Therefore, this paper introduces a new architecture of sequence-to-sequence recurrent neural networks to efficiently process time-based information in an interpretable fashion. To that end, the selection of relevant variables is internalized into the model, which provides insights on the relative importance of individual inputs, while bypassing the cumbersome need for data preprocessing. Then, the model is further enriched with an attention mechanism that is tailored to focus on the relevant contextual information, which is useful to better understand the underlying dynamics such as seasonal patterns. Outcomes show that adding modules to generate explainable forecasts makes the model more efficient and robust, thus leading to enhanced performance.
GREEN SYNTHESIS OF TITANIUM DIOXIDE NANOPARTICLES (TIO 2 NPS) USING LEAF EXTRACT OF DATURA INNOXIA

Introduction: A cost effective and environment benign technique is developed for the synthesis of Titanium dioxide nanoparticles (TiO 2 NPs) using the aqueous leaf extract of Datura innoxia which act as a reducing and stabilizing agent. Materials and Method: The prepared Titanium dioxide nanoparticles were characterized using dynamic light scattering (DLS), ultraviolet – visible spectroscopy (UV-Vis), transmission electron microscopy (TEM) and Fourier-transform infrared spectroscopy (FTIR). The synthesized nanoparticles were tested against four different bacterial species. 2, 2-Diphenyl-1-Picryl-Hydrazyl-Hydrate (DPPH) assay was carried out to evaluate the antioxidant potential of the synthesized TiO 2 NPs . Results: TEM study showed the formation of hexagonal shaped TiO 2 NPs with average size ranging from 36-42 nm. The FTIR spectrum indicated the role of nitro compounds and O-H group in the synthesis of TiO 2 NPs. The synthesized TiO 2 NPs were found to be maximum effective against Bacillus cereus and Proteus vulgaris. Maximum of 81% DPPH radical scavenging activity was exhibited by the TiO 2 NPs. Conclusion: This simple, low cost, eco-friendly method for synthesizing stable TiO 2 NPs provides a valuable alternative approach for large scale synthesis.
Machine Learning Approach for WhatsApp Application Threat Detection

: Social media like WhatsApp and Facebook for news consumption, is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to search and consume social media news. On the other hand, it allows the wide dissemination of \fake news, i.e. low quality news with intentionally false information. The widespread dissemination of false news has the potential for extremely negative impacts on individuals and society. Therefore, the detection of false news in Social media has recently become an emerging research that is attracting tremendous attention. False news of Detection in social media presents unique features and challenges that make existing detection algorithms of traditional Media ineffective or not applicable. First, 's false news is intentionally written to deceive readers into believing false information, which makes it dissect and not trivial to detect based on news content; therefore, we need to include information Auxiliary, as the social commitments of users in social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as the social commitments of the users with false news produce data which are large, incomplete, unstructured and noisy. Because the issue of detecting false news in social media is both challenging and relevant, we have conducted this survey to continue facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.
The role of green human resource management in creating green supply chain culture in a service industry

This article investigates the supply chain analysis of the role of green human resource management including green behavior on the green work environment by emphasizing on the effect of direct supply chain analysis relation to green organizational culture and moderation of individual green values. This type of research is quantitative research. Primary data obtained from questionnaires distributed to 145 respondents. The collected data were analyzed using SEM-PLS (Structural Equation Model- Pear Least Square). Green organizational culture has a positive but insignificant effect on the work environment, green organizational culture has a positive and significant effect on green behavior, green behavior has a positive and significant effect on the green work environment, individual green values have a positive and significant effect on green work environments, green values individuals do not moderate the relationship between green organizational culture and green work environment, green behavior mediates the relationship between green organizational culture and green work environment. This research provides insight to leaders that to create a green work supply chain environment requires the role of green behavior as an intervening variable. However, the finding does not show the effect of individual green values as a moderating variable, so that green organizational culture can affect the green work supply chain environment. The existence of a green behavior variable that mediates the relationship between green organizational culture and green work environment is the originality of this study.
Concepts and Consciousness

I. The Conscious Mind is a hugely likable book. Perceptive, candid, and instructive page by page, the work as a whole sets out a large and uplifting vision with cheeringly un-Dover-Beach-ish implications for "our place in the universe. " A book that you can't helping wanting to believe as much as you can't help wanting to believe this one doesn't come along every day. It is with real regret that I proceed to the story of why belief would not come. Almost everything in The Conscious Mind turns on a single claim. The claim is that there can be zombie worlds: worlds physically like our own but devoid of consciousness. Zombie worlds provide a counterexample to psychophysical supervenience; which refutes physicalism; which sets the stage for Chalmers's "naturalistic dualism" with its contingent correlational laws; which comes scarily close to casting consciousness as an epiphenomenal byproduct of its physical basis; which startles us into a measure of open-mindedness about Chalmers's "way out," in which the by-product role is abandoned for a powerful new position as the physical's intrinsic realizer; which sets up the panpsychic speculations of the final chapters. Zombie worlds are possible because they seem possible; one can conceive a scenario in which, with no physical provocation whatever, the phenomenal lights blink out. Any argument in this style has got, of course, to come to terms with Kripke's treatment of conceivability in Naming and Necessity. But Kripke can seem to be teaching a "good news/bad news" lesson about modal intuition with favorable implications for Chalmers's case.
Intensification of continues biodiesel production process using a simultaneous mixer- separator reactor

ABSTRACT Nowadays, Biodiesel as an alternative, sustainable and less toxic fuel has been accepted by both researchers and industry. Developing process intensification reactors with the aim of reaching more efficient process has captured the attention of many researchers recently. In order to examine a novel reactor for biodiesel production using Waste Cooking Oil as a cost-effective feedstock, and KOH as an efficient homogeneous catalyst, the present study was developed to investigate three effective parameters (Oil flow rate, catalyst concentration and reaction temperature) focusing on transesterification reaction yield in the Simultaneous Mixer-Separator (SMS) reactor, designed and fabricated exclusively for biodiesel production at Tarbiat Modares University (TMU). As the findings indicated, rising the flow rate presented an increasing trend up to 15 mL/min and a decreasing trend was found after this level. Also, catalyst concentration up to 1% w/w showed an increasing trend which was significant. Analysis of reaction temperature showed that at 60°C the maximum yield is obtained. Furthermore, 15 mL/min oil flow rate, 1% w/w KOH concentration and 60◦C were selected as the optimal reaction conditions for continuous biodiesel production. At this point, the produced biodiesel followed by the purification step reached the yield of 96%. The produced biodiesel physicochemical properties were found to meet ASTM D6751 standard. All in all, continuous production capability, higher productivity, simultaneous separation of products, and the successful handling of waste resources distinguish the SMS reactor as a potential and efficient process intensification reactor.
Kinetic and thermodynamic studies of the adsorption of dye on to saccharum officinarum waste adsorbents

This work investigated the potential use of activated carbon produced from saccharum officinarum waste as an adsorbent for the removal of dye stuff from aqueous solution. The activated carbon was impregnated using Orth-phosphoric acid for chemical activation. Some physicochemical parameters of the activated carbon studied include, pH, bulk density, ash and moisture content. Adsorption experiments were conducted at varying contact times, initial dye concentration and temperature. Adsorption equilibrium was achieved in less than 40 minutes of contact time. Adsorption of dyes increased with increasing temperature. Thermodynamic characteristics which include free energy, enthalpy and entropy values were assessed using van’t Hoff equation. Negative free energy and positive enthalpy values indicate the feasibility as well as endothermic nature of the adsorption event. Positive entropy suggests increased disorderliness at the solid/solution interface during adsorption process. The kinetic study shows that pseudo-second-order model is best fit for the experimental data obtained.