Miracle-dz/UniFAR-FTUs huggingface dataset:
Deterministic Lateral Displacement (DLD) Analysis Tool Utilizing Machine Learning towards High-Throughput Separation

Deterministic lateral displacement (DLD) is a microfluidic method for the continuous separation of particles based on their size. There is growing interest in using DLD for harvesting circulating tumor cells from blood for further assays due to its low cost and robustness. While DLD is a powerful tool and development of high-throughput DLD separation devices holds great promise in cancer diagnostics and therapeutics, much of the experimental data analysis in DLD research still relies on error-prone and time-consuming manual processes. There is a strong need to automate data analysis in microfluidic devices to reduce human errors and the manual processing time. In this work, a reliable particle detection method is developed as the basis for the DLD separation analysis. Python and its available packages are used for machine vision techniques, along with existing identification methods and machine learning models. Three machine learning techniques are implemented and compared in the determination of the DLD separation mode. The program provides a significant reduction in video analysis time in DLD separation, achieving an overall particle detection accuracy of 97.86% with an average computation time of 25.274 s.
Indoor Positioning with CNN and Path-Loss Model Based on Multivariable Fingerprints in 5G Mobile Communication System

Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.
Addressing Weight Bias in the Cisgender Population: Differences between Sexual Orientations

(1) Background: Weight bias (WB) is an implicit psychological construct that can influence attitudes, beliefs, body experience, and evaluation of specific psychopathology relationships. Sexual orientation has played a crucial role in developing and maintaining psychiatric conditions linked to body evaluation, but few studies have evaluated possible connected biases. Thus, the paper aims to assess potential relationships between sexual orientation and WB, looking at potential roles in specific psychopathology; (2) Methods: A total of 836 cisgender subjects participated in an online survey, aged between 18 and 42 years old. Two specific aspects of WB were evaluated with validated scales about beliefs about obese people and fat phobia. Demographic variables, as well as depression and eating concerns were evaluated; (3) Results: Gay men and bisexual women showed higher levels of fat phobia, depression, and eating concerns. Regression analysis showed that sexual orientation significantly predicted fat phobia (p < 0.001) and beliefs about obese people (p = 0.014); (4) Conclusions: This study confirms the vulnerability of gay men and bisexual women to cognitive bias about their own bodies, showing a potential vulnerability about body and weight concerns.
Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.
Power System Harmonic Analysis Using Etap

Power system study and analyses are significant parts of power system engineering. Delivering good quality power is one of the major purposes of any electrical service company. In recent times, power quality has been affected in the power network caused by increasing the use in amount of DC devices, non-linear loads. Power system harmonics are one of the main causes for incorrect power quality. This has become a major issue and needs to be solved for power quality problems and harmonic analysis by analyzing the electric power network to minimize the harmonics distortion current or voltage. Power system harmonic analysis has been completed using ETAP software, and the entire process is explained in this project. ETAP software performs mathematical calculations of huge integrated power systems with magnificent speed as well, producing output reports. As a harmonic source a general load was demonstrated to inject harmonic current from side to side within the power network. Lastly, the Harmonic Load Flow analysis was implemented and harmonic distortion identified to investigate the outcome of harmonic current.
effects of uncontrolled breeding’s on Iraqi horses conformation

Due to the great importance of digital measurements of the conformation of the motor system in horses and the lack of a specialty center for higher cases, the purpose of this research was to form a specialized center for the conformation of the motor system in horses and depended on 60 horses of both sexes between the ages of 3 to 4 and a half years were employee for the body measurement in Iraqi equestrians club in Abu Ghraib and Iraqi equestrians Federation in Al Jadriya. The parameter which were used in this project angles measurement both fore and hind limb as well as the joint inclination. Therefore there were significant changes in the thoroughbred and Iraqi horses. To be concluded that angle inclination measurement can be used for horse evaluation.
Performance of multi-parametric magnetic resonance imaging through PIRADS scoring system in biopsy naïve patients with suspicious prostate cancer

ABSTRACT Background Use of multi-parametric magnetic resonance imaging (mp-MRI) and Prostate Imaging Reporting and Data System (PI-RADS) scoring system allowed more precise detection of prostate cancer (PCa). Our study aimed at evaluating the diagnostic performance of mp-MRI in detection of PCa. Methods Eighty-six patients suspected to have prostate cancer were enrolled. All patients underwent mp-MRI followed by systematic and targeted trans-rectal ultrasound (TRUS) guided prostate biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of mp-MRI were evaluated. Results Forty-six patients (53.5%) had prostate cancer on targeted and systematic TRUS biopsies. On mp-MRI, 96.6% of lesions with PI-RADS < 3 revealed to be benign by TRUS biopsy, 73.3% of lesions with PI-RADS 4 showed ISUP grades ≥1, whereas all PI-RADS 5 lesions showed high ISUP grades ≥ 3. For PI-RADS 3 lesions, 62.5% of them revealed to be benign and 37.5% showed ISUP grades ≥1 by TRUS biopsy. PI-RADS scores ˃3 had 69.57% sensitivity and 85% specificity for detection of PCa. On adding the equivocal PI-RADS 3 lesions, PI-RADS scores ≥3 had higher sensitivity (97.83%), but at the cost of lower specificity (32.5%). Conclusion Mp-MRI using PI-RADS V2 scoring system categories ≤3 and >3 could help in detection of PCa. PI-RADS 3 lesions are equivocal. Including PI-RADS lesions ≥3 demonstrated higher sensitivity, but at the cost of lower specificity for mp-MRI in diagnosis for Pca. Abbreviations CDR: cancer detection rates; DRE: digital rectal examination; ISUP: international society of urological pathology; mp-MRI: multi-parametric magnetic resonance imaging; NPV: negative predictive value; PCa: prosatate cancer; PI-RADS: Prostate Imaging Reporting and Data System; PPV: Positive predictive value; PSA: prostate specific antigen; TRUS: transrectal ultrasound.
Antitumor Activity and Physicochemical Properties of New Thiosemicarbazide Derivative and Its Co(II), Ni(II), Cu(II), Zn(II) and Cd(II) Complexes

A novel biologically active thiosemicarbazide derivative ligand L (N-[(phenylcarbamothioyl)amino]pyridine-3-carboxamide) and a series of its five metal(II) complexes, namely: [Co(L)Cl2], [Ni(L)Cl2(H2O)], [Cu(L)Cl2(H2O)], [Zn(L)Cl2] and [Cd(L)Cl2(H2O)] have been synthesized and thoroughly investigated. The physicochemical characterization of the newly obtained compounds has been performed using appropriate analytical techniques, such as 1H and l3C nuclear magnetic resonance (NMR), inductively coupled plasma (ICP), thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR) and magnetic measurements. In order to study the pharmacokinetic profile of the compounds, ADMET analysis was performed. The in vitro studies revealed that the synthesized compounds exhibit potent biological activity against A549 human cancer cell line.
Voltammetric Sensor Based on SeO2 Nanoparticles and Surfactants for Indigo Carmine Determination

Indigo carmine is a widely used colorant in the food and pharmaceutical industry a high concentration of which can lead to a wide range of negative effects on human health. Therefore, colorant contents have to be strictly controlled. SeO2-nanoparticle-modified glassy carbon electrodes (GCE) have been developed as a voltammetric sensor for indigo carmine. Various types and concentrations of surfactants have been used as reagents for the stabilization of SeO2 nanoparticle dispersions and as electrode surface co-modifiers. An amount of 1.0 mM cationic cetylpyridinium bromide (CPB) provides the best response of the indigo carmine on the modified electrode. The electrodes were characterized by cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy (EIS). SeO2 nanoparticle–CPB-modified electrodes show 4.2-fold higher electroactive area vs. GCE as well as a dramatic 5043-fold decrease in the electron transfer resistance indicating effectivity of the modifier developed. The surface-controlled electrooxidation of indigo carmine proceeds irreversibly (αa = 0.46) with the participation of two electrons and two protons. A linear dynamic range of 0.025–1.0 and 1.0–10 µM of indigo carmine were obtained with the detection and quantification limits of 4.3 and 14.3 nM, respectively. The practical applicability of the sensor was successfully shown on the pharmaceutical dosage forms.
Diet and Risk of Gastric Cancer: An Umbrella Review

Several dietary exposures have been associated with gastric cancer (GC), but the associations are often heterogenous and may be afflicted by inherent biases. In the context of an Umbrella Review (UR), we provide an overview and a critical evaluation of the strength and quality, and evidence classification of the associations of diet-related exposures in relation to the risk of GC. We searched PubMed and Scopus for eligible meta-analyses of observational studies published in English from inception to 12 December 2021, and for any identified association, we applied robust epidemiological validity evaluation criteria and individual study quality assessment using AMSTAR. We screened 3846 titles/abstracts and assessed 501 full articles for eligibility, of which 49 were included in the analysis, investigating 147 unique exposures in relation to GC, cardia (GCC) or non-cardia (GNCC) cancer. Supported by suggestive evidence, positive associations were found comparing the highest vs. lowest categories for: heavy (>42 g/day) alcohol consumption (Relative Risk (RR) = 1.42, 95% Confidence Interval (CI): 1.20–1.67), salted fish consumption (RR = 1.56, 95% CI:1.30–1.87) and waist circumference (RR = 1.48, 95% CI:1.24–1.78) and an inverse association for the healthy lifestyle index (RR = 0.60, 95% CI:0.48–0.74) in relation to GC. Additionally, a positive association was found comparing obese individuals (Body Mass Index (BMI) ≥ 30) to normal-weight individuals (BMI: 18.5–25) (RR = 1.82, 95% CI:1.32–2.49) in relation to GCC. Most of the meta-analyses were of medium-to-high quality (median items: 7.0, interquartile range: 6–9). Maintaining a normal body weight and adopting healthy dietary choices, in particular, limiting the consumption of salt-preserved foods and alcohol, can reduce the risk of gastric cancer.
Knowledge production, agriculture and commons : the case of generation challenge programme

Keywords: Knowledge Production; Agrarian Research; Research Networks; Research Policy; (non)-instrumentality; CBPP; Commons; GCP; Drought; Sahbhagi Dhan; India Knowledge Production, Agriculture and Commons: The Case of Generation Challenge Programme Soutrik Basu The discourse on knowledge production is in constant transformation: on the one hand, there is the emergence of instrumental knowledge production based on scientific utility and socio-economic relevance and marked by property regimes, while on the other hand, there is another form of knowledge production based on cooperation, communication and the sharing of knowledge often entitled the open-source production or commons-based peer production (CBPP) mode. Both these trends are reflected partially or in full measure within the agrarian knowledge production programme called Generation Challenge Programme (GCP) of the Consultative Group of International Agricultural Research (CGIAR). GCP is an international knowledge production platform that aims to use plant genetic diversity to develop technologies to support plant breeders in developing countries. This research aims at understanding the dynamics of the knowledge production in the GCP drought-tolerant rice research network in Indian context to reflect on the ways in which its knowledge production evolves and the implications of this for agrarian knowledge production and agrarian knowledge producing institutions. This research applied a critical-constructivist research methodology to analyse the GCP knowledge production (drought tolerant rice research in Indian context) in which the three different paradigms of knowledge production (non-instrumental and instrumental, CBPP and Commons) come together. This research shows that an alternative hybrid discourse of knowledge production has emerged within the knowledge production process of GCP drought tolerant rice research in India that has elements from all the three paradigms (mentioned above) alongside some elements that belongs to neither of these paradigms. Further, this research also reflects on the broader implications of this hybrid discourse of knowledge production for agrarian knowledge production and agrarian knowledge producing institutions such as CGIAR, NARS, and ICAR.
Educator Sentiments on How Primary School Staff Development Programmes could be Made More Effective: In Quest for Educational Excellence

This study explored the sentiments of educators on how primary school staff development programmes could be made more effective; in pursuit of educational excellence. The case study design within the qualitative research paradigm was adopted. A sample of 42 educators in Zimbabwean primary schools; comprising 36 teachers and 6 head teachers was used. Focus Group Discussions and Interviews were utilised to generate data. The major findings were that school management should consider teachers’ staff development needs as the major priority and that the relevant ministry should also support staff development programmes in order to promote their effectiveness. Accordingly, the researcher concluded that the efficacy of primary school staff development could be enhanced if all stakeholders would work together in order to ensure their effectiveness while educators should be made to realise the importance of staff development programmes as being part of their professional development.
Diagnóstico de las causas de rezago y deserción en alumnos de la Facultad de Ciencias de la UNAM

Debido a los históricos índices de rezago y deserción que se presentan en las carreras de la Facultad de Ciencias de la Universidad Nacional Autónoma de México (UNAM), la dirección conformó una comisión para que se realizara un estudio de las causas de dicho fenómeno. El objetivo de este trabajo es exponer los resultados del diagnóstico mencionado, así como hacer una serie de propuestas para mejorar los índices de rezago y deserción de los estudiantes de la Facultad de Ciencias. Este diagnóstico se realizó por medio de una encuesta y el análisis de sus respuestas durante las sesiones de los grupos focales que se conformaron para el estudio. Los resultados obtenidos muestran, en términos generales, que, en opinión de los profesores de la UNAM, sus estudiantes no cuentan con buenos hábitos de estudio; no vienen suficientemente bien preparados del bachillerato, y cuentan con habilidades de escritura y comprensión lectora deficientes. Por otro lado, 64 % de los profesores no conocen la oferta anual de cursos de actualización didáctica, y casi 60 % de los profesores considera estos cursos muy necesarios. Al final del estudio, con base en los resultados recogidos, se presentan propuestas para abatir el rezago escolar como alternativas a las clases magistrales que actualmente son la metodología didáctica preponderante en los cursos.
A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments

Acoustic source localization in the spherical harmonic domain with reverberation has hitherto not been extensively investigated. Moreover, deep learning frameworks have been utilized to estimate the direction-of-arrival (DOA) with spherical microphone arrays under environments with reverberation and noise for low computational complexity and high accuracy. This paper proposes three different covariance matrices as the input features and two different learning strategies for the DOA task. There is a progressive relationship among the three covariance matrices. The second matrix can be obtained by processing the first matrix and it effectively filters out the effects of the microphone array and mode strength to some extent. The third matrix can be obtained by processing the second matrix and it further efficiently removes information irrelevant to location information. In terms of the strategies, the first strategy is a regular learning strategy, while the second strategy is to split the task into three parts to be performed in parallel. Experiments were conducted both on the simulated and real datasets to show that the proposed method has higher accuracy than the conventional methods and lower computational complexity. Thus, the proposed method can effectively resist reverberation and noise.
Computational models of natural gas markets for gas-fired generators

Climate change is a major factor reforming the world's energy landscape today, and as electricity consumes 40% of total energy, huge efforts are being undertaken to reduce the carbon footprint within the electricity sector. The electric sector has been taking steps to reform the grid, retiring carbon-intensive coal plants, increasing renewable penetration, and introducing cyber elements end-to-end for monitoring, estimating, and controlling devices, systems, and markets. Due to retirements of coal plants, discovery of shale gas leading to low natural gas prices, and geopolitical motives to reduce dependence on foreign oil, natural gas is becoming a major fuel source for electricity around the United States. In addition, with increasingly intermittent renewable sources in the grid, there is a need for a readily available, clean, and flexible back-up fuel; natural gas is sought after in New England to serve this purpose as a reliable and guaranteed fuel in times when wind turbines and solar panels cannot produce. While research has been conducted advocating natural gas pipeline expansion projects to ensure this reliability, not enough attention has been paid to the overall market structure in the natural gas and electricity infrastructures which can also impact reliable delivery of gas and therefore efficient interdependency between the two infrastructures. This thesis explores the market structures in natural gas and electricity, the interdependence of natural gas and electricity prices with increasing reliance on natural gas as the penetration of renewable energy resources (RER) increases in order to complement their intermittencies, possible volatilities in these prices with varying penetration rates in RER, and alternatives to existing market structures that improve reliability and reduce volatility in electricity and gas prices. In particular, the thesis will attempt to answer the following two questions: What will the generation mix look like in 2030 and how will this impact gas and electricity prices? How do Gas-Fired Generator (GFG) bids for gas change between 2015 and 2030? In order to answer these questions, a computational model is determined using regression analysis tools and an auction model. Data from the New England region in terms of prices, generation, and demand is used to determine these models. Thesis Supervisor: Dr. Anuradha Annaswamy Title: Senior Research Scientist, MIT Department of Mechanical Engineering
Analysis of the In Vitro Toxicity of Nanocelluloses in Human Lung Cells as Compared to Multi-Walled Carbon Nanotubes

Cellulose micro/nanomaterials (CMNM), comprising cellulose microfibrils (CMF), nanofibrils (CNF), and nanocrystals (CNC), are being recognized as promising bio-nanomaterials due to their natural and renewable source, attractive properties, and potential for applications with industrial and economical value. Thus, it is crucial to investigate their potential toxicity before starting their production at a larger scale. The present study aimed at evaluating the cell internalization and in vitro cytotoxicity and genotoxicity of CMNM as compared to two multi-walled carbon nanotubes (MWCNT), NM-401 and NM-402, in A549 cells. The exposure to all studied NM, with the exception of CNC, resulted in evident cellular uptake, as analyzed by transmission electron microscopy. However, none of the CMNM induced cytotoxic effects, in contrast to the cytotoxicity observed for the MWCNT. Furthermore, no genotoxicity was observed for CNF, CNC, and NM-402 (cytokinesis-block micronucleus assay), while CMF and NM-401 were able to significantly raise micronucleus frequency. Only NM-402 was able to induce ROS formation, although it did not induce micronuclei. Thus, it is unlikely that the observed CMF and NM-401 genotoxicity is mediated by oxidative DNA damage. More studies targeting other genotoxicity endpoints and cellular and molecular events are underway to allow for a more comprehensive safety assessment of these nanocelluloses.