Miracle-dz/UniFAR-FTUs huggingface dataset:
Steps Toward Standardized In Vitro Assessment of Immunomodulatory Equine Mesenchymal Stromal Cells Before Clinical Application

Inflammation-associated disorders are significant causes of morbidity in horses. Equine single-donor mesenchymal stromal cells (sdMSCs) hold promise as cell-therapy candidates due to their secretory nonprogenitor functions. This has been demonstrated by mononuclear cell suppression assays (MSAs) showing that sdMSCs are blood mononuclear cell (BMC) suppressive in vitro. sdMSCs derived from umbilical cord blood are of clinical interest due to their ease of procurement, multipotency, and immunomodulatory ability. Due to the inherent donor-to-donor heterogeneity of MSCs, the development of robust and easily deployable methods of potency assessment is critical for improving MSCs' predictability in treating inflammatory diseases. This study focuses on the development of robust in vitro potency assays and the assessment of potential sdMSC therapeutic end products generated from pooled sdMSCs (pMSCs). We hypothesized that, compared to MSA using only one donor, MSA using pooled BMCs (pBMCs) is a more robust sdMSC potency assay due to reduced donor BMC heterogeneity. pBMCs were generated by pooling equine BMCs isolated from peripheral blood of five donors in equal ratios. pBMCs were labeled with carboxyfluorescein succinimidyl ester (CFSE) and stored in liquid nitrogen until use. Similarly, pooling sdMSCs from multiple equine donors in equal ratios generated pMSCs. sdMSC cultures were assessed with pBMCs in MSA using Bromodeoxyuridine ELISA and CFSE. Proliferation assessment of BMCs from individual donors revealed varied responses to concanavalin A (ConA) stimulation. MSA using BMCs from single donors further demonstrated BMC donor variability. Utilizing this assay, we have also found that the immunosuppressive potencies of pMSCs are at least equal, if not more, than the calculated mean of individual cultures. MSA based on pBMCs provides a consistent and reproducible equine sdMSC potency assay. This knowledge could be used in production monitoring of cellular potency and as release criteria before clinical use.
Preparing for the Next Pandemic: Transfer Learning from Existing Diseases via Hierarchical Multi-Modal BERT Models to Predict COVID-19 Outcomes

Entity Extraction from EHRs with Assertion and Patient Attribution Extracting valuable information from EHRs with intelligent systems starts with Named entity recognition (NER), a key building block of common NLP tasks such as question answering, topic modelling, information retrieval, etc1. In the medical domain, NER plays the most crucial role by giving out the first meaningful chunks of a clinical note, and then feeding them as an input to the subsequent downstream tasks such as clinical assertion status2, clinical entity resolvers 3 and de-identification of the sensitive data4. However, segmentation of clinical and drug entities is considered to be a difficult task in biomedical NER systems because of complex orthographic structures of named entities 5. As the risk factor extraction from clinical notes would play a pivotal role in representing the patient context, doing this automatically, accurately, and at scale is highly desired. In order to ensure this, we decided to deploy Spark NLP for Healthcare library6 that has state-of-the-art NER models pretrained on curated datasets as well as well known benchmark datasets2, 7, 8. NER implementation in Spark NLP is based on the BiLSTM-CNN-Char framework, a modified version of the architecture proposed by Chiu et.al.9. It is a neural network architecture that automatically detects word and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering steps. In addition to extracting clinical entities from the text, we also constrained the assertion status for the extracted to be “present". This ensures that a patient is not associated with diabetes by processing a sentence such as “patient does not have diabetes. " Similarly, we also enforce dependency checks to ensure a condition is associated with the patient and not someone else. This would avoid associating the patient with diabetes from a sentence such as “father has diabetes. " The deep neural network architecture for assertion status detection in Spark NLP is based on a Bi-LSTM framework, and is a modified version of the architecture proposed by Fancellu et.al.10. Spark NLP has its own NER model to extract the patient risk factors such as coronary artery disase (CAD), diabetes, hyperlipidemia, hypertension, history of taking long-term medications, obesity, smoking. Named as ner risk factors, it is trained on the dataset shared during 2014 i2b2/UTHealth shared task Track 2 11. The assertion model that is fed by the out of NER model is also an out of the box solution that is pretrained on a combination of a dataset shared at 2010 i2b2/VA challenge2.
SATCOMS 2020 R&D challenges: Part I: broadband fixed communications

In this paper we describe possible application scenarios for future broadband satcom networks, together with the associated key challenges as well as critical techniques and technologies which in the authors' opinion show enough potential to justify their development in the coming years. This Part I paper is focusing on broadband fixed networks and is complemented by Part II dedicated to mobile networks. (10 pages)
Integrating Crisis Management into the Strategic Planning Process in the Service Sector Firms

The purpose of this study is to investigate the integration of crisis management into the strategic planning process. So, by understanding this integration in the time of crises, the service sector firms could strategically deal with challenges and uncertainties. In fact, this orientation can professionally supply firms with a defensive capability for preventing crises growth or lessening their effects. The qualitative method was used for analyzing the semi structured-interviews that were conducted by senior managers who were selected from top management. Findings were empirically supported according to the study respondents who confirmed that adding the crisis management concept to the strategic planning processes, will make those processes more effective and eligible in times of crisis. Basically, the integration of crisis management into strategic planning can give a more inclusive approach to the strategic planning process, which seems to fundamentally promote the strategist’s work. This paper deepens the theoretical understanding of the consequences of strategy process.
Progressing cavity (PC) pump design optimization for abrasive applications

The progressing cavity (PC) pump is well established as the pump of choice for handling abrasive solids. PC pump design can be optimized to achieve the best wear performance available for a given size. The wear optimization of the PC pumps is achieved through geometric design for minimum internal fluid velocities and by selecting proper materials of construction. Wear causes PC pump failure by gradually reducing the volumetric efficiency and increasing pump slippage. This paper focuses on the parameters that influence pump wear, describes wear mechanisms, reviews design techniques for wear optimization, and presents field data to support some of the claims.
Challenges and Coping: Perspectives of Syrian and Iraqi Refugee Youth in Germany

Refugee youth constitute around a third of the refugee population in Germany. We studied the experiences of newly arrived Syrian and Iraqi refugee youth, aged 14 to 18 years (N = 20), in Germany. We utilized semi-structured interviews and thematic analysis to investigate (a) the main challenges faced by youth and (b) their main coping resources to deal with these challenges. We grouped challenges into three levels: the individual level, the immediate social level, and the broader societal level. The most frequently mentioned challenges in our sample related to psychological wellbeing, school, friendship, accommodation, and discrimination. Youth reported relying on social support (friends, family, social services) and on themselves (through avoidance, persistence, activity seeking, active engagement) to cope with their challenges. Our findings provide insights into refugee youth’s experiences in Germany, encompassing the acculturative, developmental, and generational aspects of their lives and demonstrating their coping and resilience. We discuss our results in relation to the literature on refugee youth in high-income countries.
Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images

Road information from high-resolution remote-sensing images is widely used in various fields, and deep-learning-based methods have effectively shown high road-extraction performance. However, for the detection of roads sealed with tarmac, or covered by trees in high-resolution remote-sensing images, some challenges still limit the accuracy of extraction: 1) large intraclass differences between roads and unclear interclass differences between urban objects, especially roads and buildings; 2) roads occluded by trees, shadows, and buildings are difficult to extract; and 3) lack of high-precision remote-sensing datasets for roads. To increase the accuracy of road extraction from high-resolution remote-sensing images, we propose a split depth-wise (DW) separable graph convolutional network (SGCN). First, we split DW-separable convolution to obtain channel and spatial features, to enhance the expression ability of road features. Thereafter, we present a graph convolutional network to capture global contextual road information in channel and spatial features. The Sobel gradient operator is used to construct an adjacency matrix of the feature graph. A total of 13 deep-learning networks were used on the Massachusetts roads dataset and nine on our self-constructed mountain road dataset, for comparison with our proposed SGCN. Our model achieved a mean intersection over union (mIOU) of 81.65% with an F1-score of 78.99% for the Massachusetts roads dataset, and an mIOU of 62.45% with an F1-score of 45.06% for our proposed dataset. The visualization results showed that SGCN performs better in extracting covered and tiny roads and is able to effectively extract roads from high-resolution remote-sensing images.
Foliar spray with rutin improves cadmium remediation efficiency excellently by enhancing antioxidation and phytochelatin detoxification of Amaranthus hypochondriacus

Abstract Rutin is a flavonoid with strong antioxidative effects on plant metabolism that facilitates resistance to environmental stress. The effect of foliar rutin on cadmium (Cd) uptake in Amaranthus hypochondriacus (K472) was studied. The results showed that a foliar spray of rutin alleviated Cd toxicity, promoted plant growth, improved Cd transfer to and storage in aerial plant parts and Cd accumulation with positive effects over time. A rutin concentration of 1.5 mg/mL showed the strongest promotion effect: the biomass and Cd content were increased at 13 days by 68.62% and 405.54% compared to 3 days, respectively, whereas a high concentration of rutin (5 mg/mL) inhibited plant growth and hindered Cd absorption. Two stages of Cd detoxification were identified in K472 after appropriate rutin application. First, an antioxidant system including an enzymatic antioxidant (superoxide dismutase [SOD]) and nonenzymatic antioxidants (glutathione [GSH] and flavonoids) was activated to enhance plant stress resistance. Quercetin and phytochelatin (PC) synthesis were then enhanced to perform detoxification synergistically with the antioxidant system to improve stress tolerance and achieve stable Cd detoxification. The results demonstrated that appropriately prolonging the application time of exogenous rutin to K472 is an effective way to improve the Cd remediation efficiency. Novelty statement The application of exogenous rutin to regulate the growth and Cd absorption of grain amaranth is reported for the first time. A foliar spray of rutin enriches Cd by regulating the metabolism of flavonoids and enhancing antioxidation and phytochelatin detoxification under Cd stress. Properly prolonging the harvest time after rutin treatment can greatly improve the Cd remediation efficiency of soil. The findings of the present study would be helpful for the remediation of Cd-contaminated soils. Graphical Abstract HIGHLIGHTS Rutin promotes the growth and Cd uptake of Amaranthus hypochondriacus. Extending the treatment time of rutin can improve Cd remediation efficiency. Rutin achieves stress detoxification for Cd by activating antioxidation. The synthesis and transfer of flavonoids and phytochelatin can reduce Cd toxicity.
Estimating the oscillation frequency of a payload mass on quadcopter in non-autonomous flight

Unmanned aerial vehicles (UAVs) have been employed in several engineering applications, such as aerial photography, environmental surveillance, delivery tasks, and others. Most of these applications have attracted increasing attention due to their ability to carry different payloads, which can change the dynamic of flight. The present article investigates the dynamics of a quadcopter with a payload mass including the stiffness of the attachment to the aerial vehicle. An approach to obtain non-dimensional equations of motion is introduced, and as a consequence, it is possible to determine the frequency of vibration of the payload mass during the flight. The results are presented for non-autonomous flight, and they establish a simple equation to estimate the oscillation frequency depending on the relation between the quadcopter and the payload masses, also considering the stiffness of attachment, without requiring a solution to the equation of motion in the time domain.
Effects of column and superstructure stiffness on the seismic response of bridges in the transverse direction1

The transverse seismic responses of continuous 4-span bridges designed based on the 2006 Canadian Highway Bridge Design Code were studied using inelastic time history analyses. A total of 648 bridge configurations were considered in which the column heights, column diameters, superstructure stiffness and mass as well as abutment restraint conditions were studied. The maximum ductility demands obtained using elastic and inelastic analyses were compared to study the influence of the degree of irregularity. The effects of column stiffness ratios and superstructure to substructure stiffness ratios on the maximum ductility demands and concentration of ductility demands were investigated. A number of different regularity indices were compared to determine the suitability of these different indices in predicting the influence of irregularity. This study demonstrates the conservative nature of the 2006 Canadian Highway Bridge Design Code and provides some guidance on factors for determining the degree of irregula.
Comparison of gas-oil and bio-oil spray performance for use in a gas turbine

This study forms part of an ongoing program to investigate the feasibility of substituting a bio-oil for gas-oil in gas turbine applications. In this paper, the influence of the change of fluid properties on spray characteristics is presented and analyzed. It was not feasible to characterize the spray at full-scale operating conditions, and so a scaling methodology was adopted using published correlations. The study was conducted in three stages; a scaled nozzle was selected utilizing a published correlation to ensure the spray characteristics match the critical characteristics of the full-scale nozzle. The scaled nozzle was then characterized with a gas-oil as a benchmark for comparison with the bio-oil tests. Finally bio-oil tests were conducted at elevated fuel preheat temperatures and the spray characteristics compared to the benchmark case. The objective of the program was to provide an insight into the effect of fuel preheat on spray characteristics. The results of this program helped determine scaled operating conditions for a subsequent atmospheric combustion program, not presented in this paper. The spray characterization was conducted in the atmospheric spray rig (ASR) located at Cardiff University’s GTRC, at Port Talbot, UK. The experiments presented were conducted using bio-oil at elevated fuel preheat temperatures. The spray structure and mean droplet sizes are measured for a bio-oil at various initial pre-heat temperatures and compared with the benchmark gas-oil case. Operating conditions were chosen to maintain an equivalent thermal load of approximately 1.5 MW. Good quality PDA data were recorded for all cases and demonstrated that primary spray characteristics for bio-oil could be optimized and matched to those of gas-oil utilizing the same fuel delivery system. This was achieved by implementing a fuel pre-heat of 80 °C, thereby changing the delivery conditions of the bio-oil. © 2010 ASME
A Systematic Review of Human-Computer Interaction and Explainable Artificial Intelligence in Healthcare with Artificial Intelligence Techniques

Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual support, healthcare, and security. Human-Computer Interaction (HCI) is a field that has been combining AI and human-computer engagement over the past several years in order to create an interactive intelligent system for user interaction. AI, in conjunction with HCI, is being used in a variety of fields by employing various algorithms and employing HCI to provide transparency to the user, allowing them to trust the machine. The comprehensive examination of both the areas of AI and HCI, as well as their subfields, has been explored in this work. The main goal of this article was to discover a point of intersection between the two fields. The understanding of Explainable Artificial Intelligence (XAI), which is a linking point of HCI and XAI, was gained through a literature review conducted in this research. The literature survey encompassed themes identified in the literature (such as XAI and its areas, major XAI aims, and XAI problems and challenges). The study’s other major focus was on the use of AI, HCI, and XAI in healthcare. The poll also addressed the shortcomings in XAI in healthcare, as well as the field’s future potential. As a result, the literature indicates that XAI in healthcare is still a novel subject that has to be explored more in the future.
Parameters Influencing the Response of a Base-Isolated Building

Abstract In order to illustrate the effect of damping on the response of a base-isolated building, a large investigation was undertaken. It consists of a parametric study, which takes into account the progressive variation of the damping ratio (10% to 30%) under different seismic excitations. Using a time history analysis of displacements and accelerations at various levels of the building, the results show that the efficiency of the isolator increases with the assumed damping ratio, provided that the latter is less than or equal to 20%. Beyond this value, the isolator becomes less useful.
Metformin treatment in old rats and effects on mitochondrial integrity.

Metformin, a commonly used, well-tolerated treatment for type 2 diabetes, is being deployed in clinical trials to ameliorate aging in older non-diabetic humans. Concerningly, some experiments in model organisms have suggested that metformin use at old ages shortens lifespan and is toxic to mitochondria. The demonstrated safety of metformin therapy in humans and the conflicting data from model organisms compelled us to test the hypothesis that metformin treatment would be toxic to older rats. To define an effective dose in 30-month-old hybrid rats, we evaluated two doses of metformin (0.1%, 0.75% of the diet) and treated the rats for four months. Body mass decreased at the 0.75% dose. Neither dose affected mortality between 30- and 34-months of age. We assessed mitochondrial integrity by measuring mitochondrial DNA copy number and deletion mutation frequency, and mitochondrial respiration in skeletal muscle and the heart. In skeletal muscle, we observed no effect of metformin on quadriceps mass, mtDNA copy number, or deletion frequency. In the heart, metformin-treated rats had higher mtDNA copy number, lower cardiac mass, with no change in mtDNA deletion frequency. Metformin treatment resulted in lower mitochondrial Complex I-dependent respiration in the heart. We found that, in old rats, metformin did not compromise mitochondrial DNA integrity, did not affect mortality, and may have cardiac benefits. These data provide some reassurance that a metformin intervention in aged mammals is not toxic at appropriate doses.
PENERAPAN FRAMEWORK COBIT 2019 UNTUK PERANCANGAN TATA KELOLA TEKNOLOGI INFORMASI PADA PERGURUAN TINGGI

Berkembangnya teknologi informasi memaksa perguruan tinggi untuk dapat mengikuti dan meningkatkan sumber daya dengan teknologi informasi untuk menghadapi persaingan dan perkembangan zaman. Framework Cobit 2019 menjadi salah satu panduan yang dapat digunakan untuk menerapkan Teknologi Informasi untuk tata kelola Teknologi Informasi pada Institusi Teknologi Bisnis dan Dinniyah Lampung karena dapat memberikan masukan dalam membantu pengelolaan kerangka keja manajemen Teknologi Informasi Penelitian ini bertujuan menerapkan Framework Cobit 2019 untuk membangun rancangan tata kelola teknologi informasi dalam pengelolaan bisnis dan teknologi sehingga didapatkan informasi berhubungan dengan tatakelola yang telah berjalan.
Robust Hybrid Control Using Recurrent Wavelet- Neural-Network Sliding-Mode Controller for Two- Axis Motion Control System

In this paper, a robust hybrid control system (RHCS) for achieving high precision motion tracking performance of a two-axis motion control system is proposed. The proposed AHCS incorporating a recurrent wavelet-neuralnetwork controller (RWNNC) and a sliding-mode controller (SMC) to construct a RRWNNSMC. The two-axis motion control system is an x-y table of a computer numerical control machine that is driven by two field-oriented controlled permanent-magnet synchronous motors (PMSMs) servo drives. The RWNNC is used as the main motion tracking controller to mimic a perfect computed torque control law and the SMC controller is designed with adaptive bound estimation algorithm to compensate for the approximation error between the RWNNC and the ideal controller. The on-line learning algorithms of the connective weights, translations and dilations of the RWNNC are derived using Lyapunov stability analysis. A computer simulation and an experimental are developed to validate the effectiveness of the proposed RHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results using star and four leaves contours are provided to show the effectiveness of the RHCS. The motion tracking performance is significantly improved using the proposed RHCS and robustness to parameter variations, external disturbances, cross-coupled interference and frictional torque can be obtained as well for the two-axis motion control system.