Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation into the anti‐inflammatory properties of metformin in intervertebral disc cells

Intervertebral disc degeneration (IDD) is closely related to heightened inflammation in the annulus fibrosis (AF) and nucleus pulposus (NP) cells in the intervertebral disc. An imbalanced matrix homeostasis has been shown to contribute to disc degeneration and associated discogenic low back pain. Metformin, a diabetes medication, has been noted to exhibit anti‐inflammatory properties through upregulation of the AMPK pathway, leading to various anti‐inflammatory‐related responses in hepatocytes. However, it is still unclear how metformin influences disc cellular response to inflammatory stress and the corresponding mechanism. Hence, the objective of this study is to elucidate the effects of metformin on expression of key pro‐inflammatory, catabolic, and anabolic factors within rat AF cells in response to inflammatory stimulation and mechanical tensile stress.
Facile Microwave Synthesis of Pd-Catalyzed Suzuki Reaction for Bis-6-Aryl Imidazo[1,2-a]Pyridine-2-Carboxamide Derivatives with PEG3 Linker

Abstract An efficient and facile synthetic procedure has been developed for the synthesis of novel series of Bis-6-aryl-imidazo[1,2-a]pyridine-2-carboxamide containing 3,3'-((oxybis(ethane-2,1-diyl))bis(oxy))bis(propan-1-amine) derivatives (6a-6j). An application of the Palladium catalyzed Suzuki reaction on amine-(CH2)3-PEG3-(CH2)3-amine linked 6-bromo imidazo[1,2-a]pyridine-2-carboxamide was reported and their products are discussed. The reaction was optimized through numerous catalytic conditions for this strategy, and it was fixed with the best approach having advantages of less reaction time, higher degree of yield, most consumption of starting material, and selection of best catalyst for the success of the reaction. The ideal spectroscopic characterization (PMR, CMR, IR and MS) have been utilized for the finalization of the synthesized adducts.
‘We all have a role to play’: A comparative analysis of political speech acts on the COVID19 crisis in the South Pacific

ABSTRACT This article compares government communication on the COVID19 crisis in three countries: Australia, Fiji and New Zealand. It analyses six speeches made by each country’s leader , from March to June 2020, using speech act theory and discourse analysis. The study aimed to compare the discursive strategies used , to discuss these in relation to their respective socio-political contexts, and to examine the application of speech act theory to political crisis management, which is currently underexplored. To achieve these objectives, the research used an eclectic combination of crisis communication theory and speech act theory, examining how speech acts were used in crisis ‘base response’ strategies, such as ‘instructing’ and ‘adjusting’ information. The study found some notable differences in the degree of assertiveness, hesitation, and sympathy, which relate to differences in the political ideology of each government. It also found some similarities, especially in inclusion techniques and using media briefings as a promotional tool.
FILM: Filtering and Machine Learning for Malware Detection in Edge Computing

Machine learning with static-analysis features extracted from malware files has been adopted to detect malware variants, which is desirable for resource-constrained edge computing and Internet-of-Things devices with sensors; however, this learned model suffers from a misclassification problem because some malicious files have almost the same static-analysis features as benign ones. In this paper, we present a new detection method for edge computing that can utilize existing machine learning models to classify a suspicious file into either benign, malicious, or unpredictable categories while existing models make only a binary decision of either benign or malicious. The new method can utilize any existing deep learning models developed for malware detection after appending a simple sigmoid function to the models. When interpreting the sigmoid value during the testing phase, the new method determines if the model is confident about its prediction; therefore, the new method can take only the prediction of high accuracy, which reduces incorrect predictions on ambiguous static-analysis features. Through experiments on real malware datasets, we confirm that the new scheme significantly enhances the accuracy, precision, and recall of existing deep learning models. For example, the accuracy is enhanced from 0.96 to 0.99, while some files are classified as unpredictable that can be entrusted to the cloud for further dynamic or human analysis.
Adaptive Particle Swarm Optimization based System Identification and Internal Model Sliding Mode controller for Mass Flow System

An integrated mass flow meter and control valve forms the Mass Flow Controller (MFC) and is extensively used in semiconductor industries. In this work an Internal Model Sliding Mode Controller based on Adaptive Particle Swarm Optimization (IMSMC-APSO) is proposed to control the valve position in MFC. First, step is to identify an open loop transfer function model for MFC through system identification via the data acquired from a real time mass flow controller. Optimal transfer function parameters are estimated through APSO and compared with PSO for better estimation accuracy. In second step IMSMC is designed for the identified transfer function model and an APSO optimally tunes the control parameters off-line. The control objective is to track a multi step flow trajectory instantaneously with high precision, under model uncertainties and pressure disturbances accounting the limitation on long sensor time constant. For the purpose of comparison a classical PI, a simple IMSMC are also designed. Simulation results show that IMSMC- APSO automated the controller tuning and improved the MFC performance in terms of settling time (average) by 11.45% and accuracy (ITAE) by 16.33%, 20.24% and 18.26% in normal condition, amid pressure disturbance and model uncertainty respectively compared to simple IMSMC.
Business unit utilization in supply chain of distribution channel

Purpose: The Supply chain channel serves to strengthen positioning in competing to serve customers. This study examines the business unit in the supply chain channel in producing food products from Cowhide in Indonesia. There are many business units with high demand for rawhides from cattle for use as food products. Thus, this opportunity is utilized to increase the productivity of business units in Indonesia. This study aims to determine the business unit utilization in the supply chain channel by considering patterns of business processes, value-added products, and optimal supply chain strategies.Design/methodology/approach: The study interviews 18 business units of Cowhide in Indonesia, including two slaughterhouses, five cattle owners, three restaurants, two cowhide drying business units, one cowhide cracker business unit, three cowhide traders’ traditional markets, one cowhide boiler business unit, and one retail shop. The SCOR model, Hayami, and discrete event simulation are integrated to analyze business unit utilization in Cowhide's food products' supply chain channel.Findings: There are five supply chain channels from 7 business units in food products. Then, the highest value-added product ratio was the cowhide cracker business unit at 31%. Finally, Channel 1 is the highest utilization rate of 40%. Moreover, the policymaker can study non-food products to compare cowhide raw materials' business unit utilization because Indonesia has many of these raw materials and is easy to obtain.Research limitations/implications: Further study is suggested to measure each business unit's performance in the supply chain channel and then strengthen institutions to increase productivity.Originality/value: This study needs to measure each business unit's performance in strengthening its supply chain strategy to optimize the supply chain channel. This study's implication can contribute to business units in improving their business processes and stakeholders in the decision-making process, including associations, communities, cooperatives, and the Government, in increasing the potential for economic growth on SMEs in especially of Cowhide.
VoiceYourView:mapping public confidence in policing

Voice Your View (vYv) is a RCUK Digital Economy-funded project that investigates how digital technologies can be applied in public spaces to make them safer. vYv is developing a range of mobile and static devices that allow the public to voice their opinions and concerns on their environment as and when thoughts occur to them. As a simple example, a jogger might feel unsafe in his/her local park due to lack of lighting. Rather than go home and write a letter to the local council – a strategy which would likely end up with the observation being forgotten – the jogger can simply document his/her concern using a vYv mobile phone application. Crucially, vYv is developing and applying advanced natural language processing algorithms to filter, structure and make sense of the potentially vast amount of textual comments received using the system. An early trial of vYv in Lancaster had 600 users [1]; results on automatically analyzing the textual comments from these users showed that vYv identified an acceptable theme in a comment 78% of the time [2]. This paper applies the vYv text analysis engine to qualitative questionnaire responses from a Derry District Policing Partnership (DDPP) survey carried out on public confidence and satisfaction with policing in Derry in 2009. The aim of the study was to investigate the feasibility of creating public perception maps automatically – that is, given a large number of textual commentaries (such as those produced by vYv) expressing people’s perceptions of their environment, how feasible is it to automatically extract the meaning of the comments and plot them on maps? These so-called public perception maps will capture the ‘buzz’ of public feeling at a given snapshot in time, and can potentially be updated in real-time as new comments arrive.
Clinical Application of Adipose Derived Stem Cells for the Treatment of Aseptic Non-Unions: Current Stage and Future Perspectives—Systematic Review

Fracture non-union is a challenging orthopaedic issue and a socio-economic global burden. Several biological therapies have been introduced to improve traditional surgical approaches. Among these, the latest research has been focusing on adipose tissue as a powerful source of mesenchymal stromal cells, namely, adipose-derived stem cells (ADSCs). ADSC are commonly isolated from the stromal vascular fraction (SVF) of liposuctioned hypodermal adipose tissue, and their applications have been widely investigated in many fields, including non-union fractures among musculoskeletal disorders. This review aims at providing a comprehensive update of the literature on clinical application of ADSCs for the treatment of non-unions in humans. The study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Only three articles met our inclusion criteria, with a total of 12 cases analyzed for demographics and harvesting, potential manufacturing and implantation of ADSCs. The review of the literature suggests that adipose derived cell therapy can represent a promising alternative in bone regenerative medicine for the enhancement of non-unions and bone defects. The low number of manuscripts reporting ADSC-based therapies for long bone fracture healing suggests some critical issues that are discussed in this review. Nevertheless, further investigations on human ADSC therapies are needed to improve the knowledge on their translational potential and to possibly achieve a consensus on their use for such applications.
Development of magnetic resonance imaging techniques for mouse models of Alzheimer's Disease

Due to increasing life expectancy in western societies, a rise in the prevalence of Alzheimer’s Disease (AD) is expected to have adverse social and economic consequences. The success of emerging treatments for AD relies heavily on the ability to test their efficacy. Sensitive biomarkers are required that provide information specific to the therapeutic targets. Through manipulation of the genome, transgenic mice have been bred to exhibit particular pathological features of AD in isolation. Magnetic Resonance Imaging (MRI) of these mouse models can be used to observe phenotypic abnormalities in-vivo in a controlled environment. As summarised in the introductory chapter, the aim of this work was to develop MRI techniques for inclusion in multi-parametric protocols to characterise AD models in-vivo. Structural MRI has become an increasingly popular tool in the measurement of atrophy of brain tissue over time and requires both accuracy and stability of the imaging system. In chapter 3, a protocol for the calibration of system gradients for high resolution, pre-clinical MRI is described. A structural phantom has been designed and 3D printed for use in a 9.4T small bore MRI and micro CT system. Post processing software is used to monitor gradient stability and provide corrections for scaling errors and non-linearity. Diffusion Tensor Imaging (DTI) and Quantitative Susceptibility Mapping (QSM) are MRI techniques that have shown sensitivity to changes in white matter regions of the brain. QSM may also provide a non invasive method for measurement of increased iron concentration in grey matter tissue observed in AD. Chapters 4 and 5 evaluate the utility of these measurements as imaging biomarkers in a mouse model that exhibits tau pathology associated with AD. Discrepancies between transgenic and wild-type groups were identified for both MRI techniques indicating the potential benefit of their inclusion in a multi-parametric in-vivo protocol.
Drivers of unique and asynchronous population dynamics in Malagasy herpetofauna

A long history of isolation coupled with complex landscapes makes Madagascar ideal for exploring the historical factors that have shaped patterns of population diversity and endemism. Many species‐level studies have suggested Late Quaternary climate change may have influenced population dynamics in the tropics, but unique biomes and individual species properties may have driven idiosyncratic responses to these shifts. Here, we use community‐scale population genetic data to investigate the impact of Quaternary climate cycles on shared demographic response and investigate the contributions of both biotic and abiotic factors which shape these demographic trajectories.
Polyp detection in video colonoscopy using deep learning

Video colonoscopy automatic processing is a challenge and further development of computer assisted diagnosis is very helpful in correctness assessment of the exam, in e-learning and training, for statistics on polyps’ malignity or in polyps’ survey. New devices and programming languages are emerging and deep learning begun already to furnish astonishing results, in the quest for high speed and optimal polyp detection software. This paper presents a successful attempt in detecting the intestinal polyps in real time video colonoscopy with deep learning, using Mobile Net.
All-Ceramic Passive Wireless Temperature Sensor Realized by Tin-Doped Indium Oxide (ITO) Electrodes for Harsh Environment Applications

In this work, an all-ceramic passive wireless inductor–capacitor (LC) resonator was presented for stable temperature sensing up to 1200 °C in air. Instead of using conventional metallic electrodes, the LC resonators are modeled and fabricated with thermally stable and highly electroconductive ceramic oxide. The LC resonator was modeled in ANSYS HFSS to operate in a low-frequency region (50 MHz) within 50 × 50 mm geometry using the actual material properties of the circuit elements. The LC resonator was composed of a parallel plate capacitor coupled with a planar inductor deposited on an Al2O3 substrate using screen-printing, and the ceramic pattern was sintered at 1250 °C for 4 h in an ambient atmosphere. The sensitivity (average change in resonant frequency with respect to temperature) from 200–1200 °C was ~170 kHz/°C. The temperature-dependent electrical conductivity of the tin-doped indium oxide (ITO, 10% SnO2 doping) on the quality factor showed an increase of Qf from 36 to 43 between 200 °C and 1200 °C. The proposed ITO electrodes displayed improved sensitivity and quality factor at elevated temperatures, proving them to be an excellent candidate for temperature sensing in harsh environments. The microstructural analysis of the co-sintered LC resonator was performed using a scanning electron microscope (SEM) which showed that there are no cross-sectional and topographical defects after several thermal treatments.
Improvement of Enzymatic Saccharification and Ethanol Production from Rice Straw Using Recycled Ionic Liquid: The Effect of Anti-Solvent Mixture

One of the major concerns for utilizing ionic liquid on an industrial scale is the cost involved in the production. Despite its proven pretreatment efficiency, expenses involved in its usage hinder its utilization. A better way to tackle this limitation could be overcome by studying the recyclability of ionic liquid. The current study has applied the Box–Behnken design (BBD) to optimize the pretreatment condition of rice straw through the usage of 1-ethyl-3-methylimidazolium acetate (EMIM-Ac) as an ionic liquid. The model predicted the operation condition with 5% solid loading at 128.4 °C for 71.83 min as an optimum pretreatment condition. Under the optimized pretreatment condition, the necessity of the best anti-solvent was evaluated among water, acetone methanol, and their combinations. The study revealed that pure methanol is the suitable choice of anti-solvent, enhancing the highest sugar yield. Recyclability of EMIM-Ac coupled with anti-solvent was conducted up to five recycles following the predicted pretreatment condition. Fermentation studies evaluated the efficacy of recycled EMIM-Ac for ethanol production with 89% more ethanol production than the untreated rice straw even after five recycles. This study demonstrates the potential of recycled ionic liquid in ethanol production, thereby reducing the production cost at the industrial level.
BOOK REVIEWS STATE SPACE MODELING OF TIME SERIES

Methods for system identification (in the broad sense, i.e., in the sense of fitting a system to data), in particular the case of linear dynamic systems and time series data, have been developed, analyzed and used in a number of different and widely separated areas. In the past, econometricians have made seminal and substantial contributions to this subject; however, in the reviewer's opinion, the relative importance of their contributions has declined during the last 20 years. One of the main reasons for this is the growing number of important contributions coming from certain parts of engineering, in particular from modern system and control theory and from signal processing (but also from "noneconometric" statistics). Important developments like Kalman filtering, order estimation by the use of information criteria, or the structure theory for multi-input, multi-output systems (in ARMA(X) or state space form) and its relation to identification, have penetrated economic time-series analysis relatively slow and to a rather limited extend. Clearly, in economic time-series analysis, special features and problems, arising for instance from small sample sizes, structural breaks or nonstationarities are of particular interest. Nevertheless, economic time-series analysis without any doubt would benefit from paying more attention to system theory. In a certain sense, time-series analysis is based on statistics and on the study of dynamics and many econometricians tend to neglect the importance of this second aspect. Aoki's book fills a gap in this respect. The main goals of the book are:
PNEUMONIC NODULE EFFECTUAL DISCLOSURE USING THORACIC CT IMAGES

: Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement stage. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented. There are three main components: multichannel super pixel level feature extraction and fusion, kernel sparse representation, and segmentation. In this method, sparse coding and dictionary learning, both part of kernel sparse representation, are implemented in a high-dimensional feature space F with the help of the kernel trick. Fusion system passes information within each decomposition level so that the details of the source image is preserved expressing the artifacts. It is difficult to determine whether narrowing of a spinal canal. In Proposed, Qualitative analysis like Clustering and quantitative analysis like feature extraction and image quality assessment is used to segment the cancer detected portion in lung scanned images. To segment the portion, have to filter out the acquired image based upon the masking methodology. The Clustering function will be applied extracted throughout the filtered image. After filtering and contrast enhancement, image quality assessment (Mean Square Error, Peak Signal to Noise Ratio etc.) is calculated to compare other techniques. After segmenting cancer region, the patient’s caretaker can receive the details through E-mail as report. Finally Accuracy estimation will be done for algorithm efficient level. After result, Feature Extraction and Image quality values are plotted and the message is sent to the caretaker through GSM.
Effect of covid-19 confinement on gender stereotypes and health Efecto del confinamiento por la COVID-19 en los estereotipos de género y la salud

Resumen Son varios los efectos que la naturaleza pandémica de la COVID-19 y el consiguiente confinamiento forzado han tenido en la población en general y especialmente en las mujeres. Se ha hecho necesario conciliar las obligaciones laborales con el cuidado de los hijos, las personas a cargo y el trabajo doméstico. Estas tareas están tradicionalmente relacionadas con los estereotipos de género asignados a las mujeres. Este estudio se centra en analizar el impacto que puede haber tenido el encierro en las mujeres en relación con la internalización de las normas de género y si esta situación ha aumentado la ansiedad y la depresión en estas mismas mujeres. Participaron un total de 502 mujeres de 18 a 71 años, de las cuales 251 completaron los tres instrumentos de medición antes del confinamiento y las otras 251 los completaron tres meses después del inicio de la pandemia. Los resultados muestran una clara influencia del encierro sobre la ansiedad y depresión de las mujeres participantes, así como cambios en la percepción de algunos de los estereotipos de género tradicionalmente asignados a las mujeres.