Miracle-dz/UniFAR-FTUs huggingface dataset:
SYSTEMATIC REVIEW: COCRYSTAL AS EFFORTS TO IMPROVE PHYSICOCHEMICAL AND BIOAVAILABILITY PROPERTIES OF ORAL SOLID DOSAGE FORM

Water solubility and low bioavailability of active pharmaceutical ingredients are some of the main challenges in the process of developing new drugs, especially drugs in oral solid dosage forms. One way to improve drug solubility is the principle of cocrystallization. Cocristallyzation itself is the process of combining the active ingredients of a less water-soluble drug with a coformer so that it becomes more soluble. Pharmaceutical cocrystal provides benefits to improve physicochemical properties without affecting its pharmacological properties. In this review, we have reviewed literature discussions and research that discuss co-crystallization as an aid to improve the physicochemical and bioavailability of drugs and also discuss some drugs in the form of cocrystal and their improvement in physicochemical-biopharmaceutical properties. The main references data used in this review are research journals published in the past 10 y (2010-2020) using keywords: cocrystal, physicochemistry, bioavailability, and solid dosage form, and using google scholar as a database. Discussion on the effect of cocrystal on physicochemical properties and bioavailability of drugs was produced. The method of producing cocrystal and its characterization was also discussed. Cocrystal offers a promising approach to improve the physicochemical properties of API. The benefits of cocrystal can be observed through increased solubility, dissolution rate, permeability, bioavailability, drug stability, and tabletability.
Mitigating DDoS Attacks in SDN-Based IoT Networks Leveraging Secure Control and Data Plane Algorithm

Software-Defined Networking (SDN) and Internet of Things (IoT) are the trends of network evolution. SDN mainly focuses on the upper level control and management of networks, while IoT aims to bring devices together to enable sharing and monitoring of real-time behaviours through network connectivity. On the one hand, IoT enables us to gather status of devices and networks and to control them remotely. On the other hand, the rapidly growing number of devices challenges the management at the access and backbone layer and raises security concerns of network attacks, such as Distributed Denial of Service (DDoS). The combination of SDN and IoT leads to a promising approach that could alleviate the management issue. Indeed, the flexibility and programmability of SDN could help in simplifying the network setup. However, there is a need to make a security enhancement in the SDN-based IoT network for mitigating attacks involving IoT devices. In this article, we discuss and analyse state-of-the-art DDoS attacks under SDN-based IoT scenarios. Furthermore, we verify our SDN sEcure COntrol and Data plane (SECOD) algorithm to resist DDoS attacks on the real SDN-based IoT testbed. Our results demonstrate that DDoS attacks in the SDN-based IoT network are easier to detect than in the traditional network due to IoT traffic predictability. We observed that random traffic (UDP or TCP) is more affected during DDoS attacks. Our results also show that the probability of a controller becoming halt is 10%, while the probability of a switch getting unresponsive is 40%.
Esthetic Dining Experience: The relations among aesthetic stimulation, pleasantness, memorable experience, and behavioral intentions

ABSTRACT The role of esthetic dining in the experience economy has rarely been explored by previous studies. This study examines the relationships among esthetic stimulation, pleasantness, behavioral intentions, and memorable experiences in the dining context. A total of 561 participants were recruited through Amazon Mechanical Turk (MTurk). The survey results demonstrated that the esthetic dining experience can be explained by the S-O-R model. Esthetic stimulation positively affects the most common esthetic emotion-pleasantness, in the service context and subsequently affects behavioral intentions. Moreover, memorable experiences fully mediate the relationship between pleasantness and behavioral intentions. This study identifies a model of the esthetic dining experience and contributes to the construction of an esthetic dining context for restaurant practitioners.
Security and Privacy in IoT Smart Healthcare

Patient care is a key element of healthcare practices. The use of Healthcare IoT could enhance the lives of patients and the quality of services provided by medical practitioners, nurses, clinicians, pharmaceutical companies, and government in general. Wireless healthcare monitoring systems tremendously used in hospitals and other healthcare practices and witnessed as a revolution in the medical field. Nevertheless, security and privacy for interconnected things are mostly overlooked on the Internet of Things paradigm. In the context of healthcare and remote health monitoring, it is crucial a systematic approach for security and privacy measures must be used in device manufacturing, interconnecting things, communication, data handling and storage, and destruction of such devices and data. This article examines the current state of security and privacy of the Internet of Things in the healthcare system, the challenges encountered in implementing security frameworks and advocates security and privacy solutions.
Centrifugal Microfluidic Integration of 4-Plex ddPCR Demonstrated by the Quantification of Cancer-Associated Point Mutations

We present the centrifugal microfluidic implementation of a four-plex digital droplet polymerase chain reaction (ddPCR). The platform features 12 identical ddPCR units on a LabDisk cartridge, each capable of generating droplets with a diameter of 82.7 ± 9 µm. By investigating different oil–surfactant concentrations, we identified a robust process for droplet generation and stabilization. We observed high droplet stability during thermocycling and endpoint fluorescence imaging, as is required for ddPCRs. Furthermore, we introduce an automated process for four-color fluorescence imaging using a commercial cell analysis microscope, including a customized software pipeline for ddPCR image evaluation. The applicability of ddPCRs is demonstrated by the quantification of three cancer-associated KRAS point mutations (G12D, G12V and G12A) in a diagnostically relevant wild type DNA background. The four-plex assay showed high sensitivity (3.5–35 mutant DNA copies in 15,000 wild type DNA copies) and linear performance (R² = 0.99) across all targets in the LabDisk.
Does the Design of Stablecoins Impact Their Volatility?

In this paper, we shall compare the average volatility that characterises the main stablecoin design types with a view to answering the question of whether all stablecoin designs accomplish the goal of minimising their price fluctuations to the same degree. Our research is motivated by the lack of rigorous studies comparing volatility of different stablecoin types stressed in the literature as well as the practical importance of such a comparison from the investors’ viewpoint. We opted for a standard volatility measure, i.e., standard deviation of return rates, corrected it for autocorrelation, and detected differences between distributions of the measure in three stablecoin groups using various non-parametric tests, i.e., the Kruskal–Wallis test, the bootstrap F-test, post-hoc tests and non-parametric contrasts. We proved that stablecoins do not deliver equally on the promise to provide stable market value with tokenised funds being leaders. Tokenised funds design involves complete coverage of the stablecoin supply in units of the currency of reference as well as great dependence on the trusted third-party acting as a trustee for the collateral. Our study reveals that existing complex stablecoins designs hardly compete with this simple design in terms of volatility.
Input Data Characterization Using Machine Learning and Deep Learning

Seeing the current trends in Information technology, a large volume of heterogeneous data is produced widely across the world by means of social media sites such as Facebook, Instagram, Google plus, etc. and electronic gadgets used by humans for instance sensors. The generated data act as garbage and makes no sense until they are categorized. A strong need of data analytics or data filtering came into existence to filter the data into concrete categories. The generated data contain enormous features with huge dimensions. The high dimension data must be reduced to low dimension data to avoid curse of dimensionality and to build a better machine learning (ML) model. A ML model is built to perform the classification task that result into labeling of the data. In the first phase of work, a framework has proposed to process the data having features belonging to text and images using ML and DL algorithms. In the second phase of work, we have shown data related behavior using ML and DL framework. The proposed work is generic in sense of FS techniques and FE techniques using ML classifiers and DL classifiers. After carefully implementing the algorithms on the datasets we have evaluated the results taking accuracy as our performance metric. After carefully analyzing the accuracy it can be concluded that DL algorithms give better results than ML algorithms on both text and image dataset.
Quench Propagation at Different Conditions in an HTS Pancake Coil Wound With Roebel Cable

The analysis of quench is of paramount importance for the safe operation of any superconducting magnet and this investigation is more relevant, even not fundamental, to prevent damage or burn-out in HTS magnets. Given these premises, quench tests and numerical models are indispensable. A 1D electro-thermal model was developed at the University of Bologna (Italy) and validated versus experimental quench tests performed at the University of Southampton (U.K.) on a pancake coil wound with REBCO Roebel cable in the frame of the EUCARD-2 European Project. The analysis here presented investigates quench in the Roebel-based coil in case of a thermal disturbance introduced in the coil. The quench energies measured during tests as a function of the power disturbance are compared with the computed results. The quench energy is furthermore analysed energizing the coil at different transport current. The analysis is further extended to study the quench of the coil at temperatures of 66 K and at field of 2 T.
Market Segmentation in the Banking Industry Based on Customers’ Expected Benefits: A Study of Shahr Bank

Nowadays, the analysis of customer behavior is necessary for active organizations in the field of banking which deal with many customers with different characteristics. In recent years, Shahr Bank of Iran has faced many problems because of poor customer-orientedness and customer services. Therefore, to solve the existing problem, the current study concentrated on segmenting the customers of Shahr Bank based on their expected benefits. This study is applied in terms of purpose and descriptive-survey research in terms of data collection and analysis. To achieve the research objectives, through field studies and exploratory interviews with customers and banking experts, 165 benefits were extracted. Then, using expert questionnaires, the number of these benefits was reduced, and through factor analysis, nine factors were identified as the most important expected benefits. Moreover, using cluster analysis, four customer segments were extracted, namely benefit-oriented, peace-oriented, interest-oriented, and moderate ones. Finally, a suitable marketing solution was provided to the bank consistent with the most important features of the segments. For bank managers, this paper provides an appropriate view to identify customers' preferencesas an important factor in customer-orientedness and bank profitability.
Development of microsatellite loci and population genetics in the bumblebee catfish species Pseudopimelodus atricaudus and Pseudopimelodus magnus (Siluriformes: Pseudopimelodidae)

The Neotropical freshwater catfish Pseudopimelodus atricaudus and Pseudopimelodus magnus are two recently discovered species endemic to the Colombian Magdalena-Cauca River basin. In this study, a set of 13 microsatellite loci were developed by using next generation sequence technology to assess the genetic diversity and population structure in P. atricaudus and test for crossspecies amplification in P. magnus. Both species exhibited high genetic diversity (P. atricaudus: Na: 9.000 – 9.769 alleles/locus, Ho: 0.760 – 0.804, HE: 0.804 – 0.840; P. magnus: Na: 12.8 – 5.4 alleles/locus, Ho: 0.638 – 0.683, HE: 0.747 – 0.755) compared to the mean levels of genetic diversity reported for Neotropical Siluriformes, and lack of genetic differentiation among sampling sites within the Cauca River (P. atricaudus: F’ST=0.013 – 0.017, P > 0.05, D’est= -0.004 – 0.023, P > 0.05; P. magnus: F’ST= 0.031, P= 0.055; D’est= 0.045, P= 0.058). This work is the first insight on the diversity and the population genetics of species of the family Pseudopimelodidae and provides a framework to further population genetic and conservation analyses needed in this poorly studied family at the microevolutionary level.
Prevalence of nomophobia and its association with stress, anxiety and depression among students

Introduction and Aim: The change in human behaviour has created a social phobia wherein a human use either computers or any other electronic gadgets to defend himself from social contact. Nomophobia is fear of not having mobile phones and the stress associated with it. Increased use of mobile phones among students has changed the scenario and it is no more a luxury but a necessity. Social interaction has decreased and electronic gadgets have become the master. This has led to development of neuropsychological issues. The purpose of the study is to find out the prevalence of nomophobia among college students and to explore the association between nomophobia and stress, anxiety and depression. Materials and Methods: Randomly selected 167 medical and dental students were given questionnaires to assess the prevalence of nomophobia. Separate questionnaires were given to assess stress, anxiety and depression. Results: Results showed a wide prevalence of nomophobia (59%) among students. 14%, 29%, 8% of students were seen having severe/ extreme depression, anxiety and stress respectively. Conclusion: Nomophobia was significantly associated with stress and depression (P ? 0.05). Students have to be periodically screened to identify stress, anxiety and depression and counselled about the constructive usage of mobile phones.
Response of Spring Wheat (Triticum aestivum) to Deficit Irrigation Management under the Semi-Arid Environment of Egypt: Field and Modeling Study

In many areas of the world, water shortages prevail and threaten food production. Deficit irrigation was commonly investigated in dry areas as a precious and sustainable production approach. Using the CropSyst model to simulate the effects of different deficit irrigation treatments could help draw conclusions and save time, effort, and money. Therefore, the aims of this research were (i) to calibrate and validate the CropSyst model for wheat under different sustained and phenological stage-based deficit irrigation treatments, (ii) to simulate the impacts of the latter treatments on limiting wheat yield reduction. Two field experiments were conducted in Nubaria (Egypt), representing an arid environment. They included seven irrigation treatments: (1) 100%, (2) 75%, or (3) 50% of crop evapotranspiration (ETc) during the whole crop cycle; (4) 50% ETc at tillering only, or (5) at booting only, or (6) at grain filling only, or (7) at both tillering and grain filling, with the replenishment of 100% ETc to the treatments (4) to (7) in the remaining phenological stages. The results revealed that phenological stage-based deficit irrigation of wheat resulted in lower yield reduction compared to sustained deficit irrigation treatments, with a 6% yield reduction when 50% ETc was applied at the booting stage. Wheat yield loss was reduced to 4 or 6% when 95 or 90% of ETc were applied, respectively. The CropSyst model accurately simulated wheat grain and total dry matter under deficit irrigation with low RMSE value. In conclusion, the CropSyst model can be reliably used for evaluating the strategy of planned deficit irrigation management in terms of wheat production under the arid environment.
Exploring Ethnic Marginalisation and Indigene-Settler Problems in University Life in Nigeria

This study investigates ethnic marginalisation and indigene-settler problems at selected universities in Nigeria. Three universities and 12 participants were purposively selected for the study. Face-to-face, semistructured, and individual interviews were utilised to obtain information from the participants regarding their various universities. The data were uploaded on ATLAS.ti 8, qualitative data analysis software for proper management and grouping of transcribed data for further data analysis. Four themes emerged: (a) contextualising ethnic marginalisation and indigene-settler issues; (b) emotional bonds and determination to preserve ancestral land; (c) cultural bonds and determination to preserve traditional practices; and (d) effects of ethnic marginalisation and indigene-settler issues. Further interpretation of the themes revealed that attachment to cultural heritage such as landed properties, cultural life, and practices and economic and indigene dominance were factors driving ethnic marginalisation. The study further found that ethnic marginalisation and indigene-settler problems influenced the emotional and psychological wellbeing and functioning of university lecturers in the selected institutions. The findings also revealed that protests and problems among ethnic groups resulted from the activities of indigenes and settlers when placed in positions of authority (e.g., biased way of recruiting, removing, and promoting ethnic members). The study concludes that there is a need for an integration programme with a practical implementation strategy to ensure peaceful coexistence of ethnic groups within the universities.
Modular-based modelling of protein synthesis regulation

A variable-volume modelling framework has been used to build-up modular structures that can reproduce complex protein syntheses inside cells. Methodology is based on a modular kinetic representation of the homeostatic regulatory network that control the metabolic processes, and on a globally efficient module-linking rule that optimise the chain performance indices. The paper exemplifies, at a generic level, how this methodology can be applied to: i) characterize the module efficiency, species connectivity, system stability, based on proposed regulatory indices vs. dynamic and stationary environmental perturbations; ii) build-up modular regulatory chains of various complexity; iii) prove feasibility of the cooperative vs. uncooperative construction that ensures gene expression, system homeostasis, proteic functions, and an equilibrated cell growth during the cell cycle. The more realistic variable-cell-volume dynamic modelling allows an accurate evaluation of individual effector/unit efficiency and perturbation propagation inside a cell, pointing out the influence of module type and enzyme activity allosteric control on regulatory indices.
Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.
Growth mindset development in design-based makerspace: a longitudinal study

Abstract Growth mindset plays an important role in students’ motivation for learning. Students who believe that their abilities are malleable are more likely to set mastery goals and to persist when encountering difficulties. Design-based makerspace – or a makerspace structured with design-based learning components – has many features known to improve growth mindset. The present study examined how growth mindset of 3rd–6th grade students developed over 2 years in a design-based makerspace course. Growth modeling was used to examine trajectories of students’ growth mindset as well as its two associative factors – self-efficacy and creative mindset. Results showed that students’ growth mindset initially decreased slightly in the first year, but significantly improved during the intervening period and remained high throughout the second year. Students with higher creative mindset showed more adaptive growth mindset trends. Our study contributes to an understanding about how makerspace experiences may contribute to growth mindset development.
Peer assisted academic support: a comparison of mentors’ and mentees’ experiences of a drop-in programme

ABSTRACT In Peer Assisted Learning (PAL) programmes, students provide academic and social support to fellow students. Recent PAL iterations include drop-in programmes to improve higher education students’ general academic literacy in an open, non-judgemental, learning environment led by experienced students. The majority of the PAL literature, however, focuses on curricular programmes, with limited understandings on processes enabling positive outcomes for students. This study contributes to the PAL literature through the investigation of factors and mentoring strategies underpinning effective peer learning in drop-in PAL programmes. Participants were students attending an academic drop-in PAL programme, and the peer mentors employed by the programme. Results suggest that students’ expectations about the type of support provided by PAL mentors may jeopardise the learning objectives of the programme. The analysis of PAL mentors’ experiences reveals how the use of strategies to enhance social and cognitive congruence between peers supports the development of effective peer learning partnerships. Mentors’ approachability alongside their ability to provide practical strategies and academic examples tailored to students’ learning needs characterised peer learning interactions between students. Mentors also encouraged students to engage in active help-seeking behaviours. This paper examines strategies to enhance peer learning interactions that support the development of independent and transferable academic skills in students seeking peer support.