Miracle-dz/UniFAR-FTUs huggingface dataset:
Breast cancer stem cell markers CD44, CD24 and ALDH1: expression distribution within intrinsic molecular subtype

Background and Aim The study of CD44/CD24 and ALDH1 expression is the most accurate method to identify cancer stem cells (CSC) from breast cancer populations. However, the overlap between CD44 + CD24 (cid:1) /low and ALDH1 high CSC phenotypes in breast cancer seems to be very small, as well as their distribution among intrinsic breast cancer subtypes. Due to this discrepancy, it is imperative to improve the understanding of breast CSC marker distribution. Methods 466 invasive breast carcinomas and eight breast cancer cell lines were analysed for the expression of CD44, CD24 and ALDH1, to evaluate their distribution among the distinct molecular subtypes. tumours ALDH1-positive basal-like tumours of breast cancer cell lines that luminal cell enriched in a CD44 cell breast cancer cell the CD44 CD24 phenotype, the basal/epithelial cell mainly positive for both ALDH1 activity in basal/epithelial breast cancer CSC subtypes be it is pivotal to translate the CSC concept to clinical practice. In the future, the recognition of reliable markers to distinguish the CSC pool in each molecular subtype will be decisive for the development of speciﬁc target therapies.
TradingStone: Benchmark for Multi-Action and Adversarial Games

This paper introduces TradingStone, a simpliﬁcation of the zero-sum game trading card game HearthStone that preserves the core elements of the game that challenge existing agents, namely: multi-action turns that require the management of a pool of resources; partially observable boards; Highly dimensional card spaces. However, it removes elements that are speciﬁc only to certain cards that render many existing approaches unusable, such as, effects that generate new arbitrary cards that lead to very sparse search spaces. We show that the core elements that can be studied with TradingStone are still unsolved by existing search and RL approaches and as such, it is a milestone that must precede solving Hearth-stone.
Do Humans Trust AI or Its Developers? Exploring Beneﬁts of Differentiating Trustees Within Trust in AI Frameworks

“Trustworthy” artiﬁcial intelligence (AI) is a globally discussed topic. Trust, distinct but related to trustworthiness, is considered a critical success factor in human-AI-interactions. However, the role of the trusted “partner’ remains unclear: Is an AI system or the designers of AI the trusted peer in this relation, and does it even matter? Since trust in technology providers is not frequently evaluated, we conducted an online survey to explore the potential beneﬁt of differentiating the actors of the trust framework in more detail. The results of this early stage research conﬁrms that trust is a relevant construct in the adoption of AI technology, and participants trusted AI in general signiﬁcantly more than technology providers that develop AI. Preliminary results, limitations and plans for future research will be discussed. We aim to reﬁne the discussion around trustworthy AI through a better understanding of the designers’ role in the trust framework with the goal to develop trust-relevant recommendations for companies designing “trustworthy’ AI.
Multi-scene small object detection with modified YOLOv4

Applications of small object detection exist in many different scenarios of our daily life, and this topic is also one of the most difficult issues in studying object detection and recognition. Therefore, improving small object detection accuracy is not only important in theory, but also significant in practice. However, current detection relevant algorithms are inefficient in this task; therefore in this study, a type of generalized improved algorithm based on YOLOv4 model is proposed. After the “ADD” and “Concat” layers of the conventional cross stage partial network (CSPNet), a type of mixed attention module (MA) is added to strengthen the spatial and channel feature information of the object; the dilated convolution module (DC) is then used to expand the receptive field information around the peripheries of target small objects. Several numerical experiments show that this modified model associated with its algorithms achieves higher mean average precision of the tests on the Pascal VOC and the VISDRONE data sets (mAP@0.5). Compared with YOLOv4, these performance assessment indices increase by 3.23% and 2.88% respectively. After refining the YOLOv4 network structure with the condition that the size of input images set as 416*416, the average accuracies of the model can reach 87.58% on Pascal VOC data set and 26.12% on VISDRONE data set. These results present the apparent better performance of this method than the original detection algorithm on recognizing small objects without affecting the real-time performance.
Analysis of Chemical and Physical Properties of Boiling Time in Kecombrang Drink (Etlingera Elatior, Jack)

Kecombrang drink is created using coriander powder, lemon juice, sugar, salt, and water to improve the taste of the kecombrang drink, which has a unique smell of kecombrang flowers. The purpose of this study was to look at the chemical and physical features of kecombrang drinks with different boiling times of 15 minutes, 20 minutes, and 25 minutes. This research method is an experimental study using RAL, three treatments, and two repetitions. One-way ANOVA was used to examine the data, using the Duncan Multiple Range Test (DMRT) as a follow-up test. The highest score of physical properties in kecombrang drink with color is brightness (L) 15 minutes, redness (a+) 25 minutes, and yellowness (b+) 15 minutes. The kecombrang drink’s chemical qualities (antioxidant activity, flavonoid content, tannin content, saponin content) and physical properties are affected by the variation in boiling time (color). The impact is that the longer the kecombrang flower is boiled, the more the chemicals in the kecombrang flower are dissolved into the solvent.
A Progressive and Joint Method for Micro and Macro Architecture Search

Recently, neural architecture search (NAS) has achieved great success in design neural architectures automatically. Differentiable architecture search (DARTS) has succeeded in reducing computational cost and making the searching process efficient. Based on the cell-based search space in DARTS, we extend the search space by searching diverse cells at different stages and various connection between cells. We also use a progressive and joint method to search the micro and macro architecture together. Extensive experiments on CIFAR-10 demostrate that we can obtain a better result than the original search space.
¿PODEMOS EMPLEAR LAS WEBQUEST EN EL AULA DE GRADO EDUCACIÓN INFANTIL? CAN WE USE WEBQUESTS IN A PRESCHOOL CLASS?

Incorporating the Information and Communication Technology in the classroom is being done quickly and vertiginous. However, some of tools designed do not meet all kindergarten students’ needs, as their lack of literacy. The present paper offers the proposal made by preschool students at University of Cordoba, Spain, for the development of the curriculum throughout a webquest design. After performing such an experience, it has been confirmed that traditional technological tools still have a relevant place in the world of 2.0 Web.
The influence of the degree of climate continentality on the productivity of agricultural production

This article provides estimates of the influence of the degree of climate continentality on the yield of spring crops and on the quality of spring wheat grain. The analysis of variance was chosen as a research method, which makes it possible to assess the significance of the influence of one quantity on the variability of another. As a result of the study, it was found that the degree of climate continentality affects the formation of spring wheat yields ambiguously. For areas where there is an increase in the degree of climate continentality, there is a weak negative correlation between continentality and the yield of spring wheat. For grain-sowing areas, in which no increase in the degree of continentality was observed, there is a weak positive correlation between the yield of spring wheat and this characteristic. It was found that the increase in the degree of continentality reduces the yield, but improves the quality of the grain. Correlation dependencies are estimated at -0.25 and 0.62, respectively. It is shown that the degree of climate continentality determines the quality of grain. The contribution of the degree of continentality to the formation of protein content in spring wheat is estimated at 48%.
Improving Wheel Speed Sensing and Estiamtion

This master thesis project commissioned by Haldex Brake Products AB (in Landskrona, Sweden), and in association with the Department of Automatic Control, Lund Institute of Technology, deals with the problem of correct wheel speed measurements. Investigation into different types of sensors, Inductive-, Hall Effect- and Magneto-Resistive sensors have been carried out, which are sensors that may be used, when measuring the velocity of a wheel. It also looks into different types of methods that can be used to estimate a velocity; zero detection, estimation with few measurements, and tracking demodulation. Problems, such as noise, time demanding calculations and adapting the sensor signal so it may be used in a microcontroller, which could arise when implementing these methods into a microcontroller, are also investigated. The conclusions are that to obtain wheel speed estimates at low speed sufficiently fast, the information in the sinusoidal signal from the sensors has to be used, and therefore, if one sensor is used, the Few Measurements method is the most appropriate, and if two sensors are used the Tracking Demodulation method. Finally scheduling is suggested, which means that the normal zero detection is used at high speed and the tracking demodulation method is used at low speed. (Less)
A knowledge representation based recognition method on power grid alarm monitoring data

The identification of power grid monitoring and alarm information is an essential guarantee for the stable operation of the power grid. Identifying current power grid alarms is based on an expert rule system. This method has the problems of cumbersome rule matching, difficulty in maintaining the rule base, and low matching efficiency. In order to solve the above problems, this paper proposes a method of identifying power grid monitoring and warning information based on knowledge representation. The method first uses grid topology information to construct a knowledge graph. It then uses knowledge representation learning algorithms to represent the power knowledge graph which digitally represents the textual warning information, and finally uses a series of deep learning models to identify ”alarm-event. ” This paper uses six algorithms, such as TransE for knowledge representation, combined with three deep learning models such as ConvLSTM for identification. The verification shows that this method has specific ”alarm-event” identification capabilities based on actual power grid monitoring data.
Effect On The Variation Of Irradiation Time and Power Of UVC Lamp On Photocatalyst Degradation Of Iron (III) Sulfate Dye Waste

This study aimed to determine the effect of variation in irradiation time and UVC lamp power on the degradation of iron (III) sulfate dye waste with the help of photocatalyst TiO2-Ag-Zeolite nanocomposite. TiO2 used was technical TiO2, Ag precursor was AgNO3 and, zeolite powder. The method used was sol-gel for the synthesis of TiO2-Ag-Zeolite. A photoreactor with irradiation with some UVC lamps (6,8,10 W) o in the catalyst test. The experimental result of the reduction process of iron (III) sulfate with the addition of TiO2-Ag-Zeolite catalyst and variations in reaction time (10,20,30,40,50 and 60 min) obtained the highest peak concentration of about 51% at 60 min with 6 and 8 W for pH > 4.0.
Microsatellite Marker Mining Using PCR-Based Isolation of Microsatellite Arrays (PIMA) Method on Blue-Spotted Mudskipper, Boleophthalmus Boddarti

Microsatellites are small and are codominant markers that can be amplified with polymerase chain reaction. Both prokaryotic and eukaryotic organisms possess large amounts of the microsatellites repeat. Many microsatellites have high mutation rates that generate the high levels of allelic diversity necessary for genetic studies of processes acting on ecological time scales. The high variability of microsatellites provided the foundation for their successful application in a wide range of fundamental and applied fields of biology. However, de novo isolation is needed for most species hence in this study we tried to mine the microsatellite marker using PCR-based isolation of microsatellite arrays (PIMA) on Blue spotted mudskipper, Boleophthalmus boddarti a fish uniquely restricted to coastal and estuarine habitat was also commercially important. Out of three trials, seven microsatellite repeats were detected but only three repeat types (AAG)4, (TCAG)3 and (CT)4 can be useful as microsatellite marker following PHOBOS V3.3. 12 analysis. Meanwhile, the detection of octa (AATACAT)2, penta (TGACA)2 and heptanucleotides (GGAGATA)2 were unable to be continued as functional microsatellite marker as there were missense variants and interruptions detected either on forward or reverse strand of the repeat. Thus, PIMA method could be considered as tedious and detected low yields of microsatellite markers. Nevertheless, the conventional method for generating microsatellite markers from PCR based methods could be done with in silico mining of microsatellite sequences from DNA sequence databases or next generation sequencing (NGS).
Distances to Local Group Galaxies via Population II, Stellar Distance Indicators. II. The Fornax Dwarf Spheroidal

We determine three independent Population II distance moduli to the Fornax dwarf spheroidal (dSph) galaxy, using wide-field, ground-based VI imaging acquired with the Magellan-Baade telescope at Las Campanas Observatory. After subtracting foreground stars using Gaia EDR3 proper motions, we measure an I-band tip of the red giant branch (TRGB) magnitude of I0TRGB=16.753±0.03stat±0.037sys mag, with a calibration based in the LMC giving a distance modulus of μ0TRGB=20.80±0.037stat±0.057sys mag. We determine an RR Lyrae (RRL) distance from template mean magnitudes, with periods adopted from the literature. Adopting a Gaia DR2 calibration of first overtone RRL period–luminosity and period–Wesenheit relations, we find μ0PLZ=20.74±0.01stat±0.12sys mag and μ0PWZ=20.68±0.02stat±0.07sys mag. Finally, we determine a distance from Fornax’s horizontal branch (HB) and two galactic globular cluster calibrators, giving μ0HB=20.83±0.03stat±0.09sys mag. These distances are each derived from homogeneous IMACS photometry, are anchored to independent geometric zero-points, and utilize different classes of stars. We therefore average over independent uncertainties and report the combined distance modulus 〈μ 0〉 =20.770 ± 0.042stat ± 0.024sys mag (corresponding to a distance of 143 ± 3 kpc).
Preliminary Investigation on the Processability of Low-Fat Herbal Cheese Manufactured with the Addition of Moringa, Bidara, and Bay Leaves Extracts

This study was conducted to investigate the processability of herbal cheese made from low-fat milk with herbs extract: moringa, bay, and bidara leaves, and their combination. Herbal cheeses were manufactured from commercial low-fat milk with a fat content of approximately 1%, and the percentage of herbs added was 20%. Nine treatments applied in this research were (P1) Full fat milk without herbs as control; (P2) Low-fat milk without herbs; (P3) low-fat milk + 20 % bidara leaves extract; (P4) low-fat milk + 20 % bay leaves extract; (P5) low-fat milk + 20% moringa leaves extract; (P6) low-fat milk + mixes of bidara leaves and bay leaves extracts (10:10%); (P7) low-fat milk + mixes of bidara leaves and moringa leaves extracts (10:10%); (P8) low-fat milk + mixes of bay leaves and moringa leaves extracts (10:10%); and (P9) low-fat milk + mixes of bay; moringa and bidara leaves extracts (6.67:6.67:6.67%). A completely randomized design was used with three replicates. Variables measured were instrumental total solids, acidity, and color of the cheese. A colorimeter estimated cheese color as L* (brightness), a* (green to red), and b* (blue to yellow). Results showed that, total solids, L*, and b* values were significantly different; however pH and a* values were similar. The low-fat cheese pH ranged from 6.12-6.63 and the total solid ranged from 36.82 to 55.26 %. Herbal cheese showed L* value 74.21-90.86; b* value 14.47-22.98. The pH and a* value of all low-fat cheeses were not significantly different to control. From this preliminary experiment, it can be concluded that the processability of low-fat cheeses with the addition of 20% moringa leaves extract produces the highest yellowness. In contrast, full cream milk produces fresh cheeses with highest total solid and brightness.
Energy Audit of a Classroom in the Hot and Humid Region: Effect of Mechanical Dehumidification on Energy Consumption

Air conditioning (AC) systems are the most energy-consuming systems in buildings in hot and humid climates. This study investigates the effect of mechanical dehumidification on the energy apportioning of the conventional cooling system in hot and humid regions. An energy audit was carried out in a classroom with a fan coil unit as a case study. Air characteristics, cooling load, and cooling capacity of the case study were measured. According to the energy audit results, for a room with 51% latent load and 49% sensible load, the mechanical dehumidification consumed 56% of cooling capacity, while 44% of cooling capacity was used for sensible cooling. It has been shown that mechanical dehumidification consumes the majority of the energy used by AC systems in hot and humid areas.
What does understanding look like? : Performance and assessment in using an Understanding by Design framework for professional development

This paper relates a crucial aspect of a two-year professional development innovation/research project proposed to improve Singapore‟s poorest performing grade 7 and 8 students‟ learning in English language, mathematics, and science as a consequence of their teachers‟ developing capacity to plan and teach. The instrument and process particularly designed as a pre and post innovation moderation exercise to find “articulations of understanding”, which served as evidence of change in pedagogy and learning is described. Its set of dimensions of understanding can be used to plan and assess for cognitive depth and engaged learning.
H6 Non-isolated Full Bridge Grid-connected PV Inverters With Low Leakage Currents

In order to meet the limit for common-mode leakage currents in grid-connected photovoltaic(PV) generation systems,a H6 non-isolated full bridge PV grid-connected inverter is proposed.In the power processing period,the grid-tied current flows through three power switches during one of half line cycles,while flows through only two power switches during another half line cycle.As a result,the conduction losses are reduced and the heat-stress balance is benefited with the proposed H6 topology,comparing with the H5 topology.The operation mode of the proposed topology is analyzed in detail and another three topologies of the H6 non-isolated full bridge grid-connected inverters has been derived.The power losses and costs are fairly compared among the H5 topology,Heric topology and the H6 topology.Finally,both of the conversion efficiency and common-mode performance of these topologies are compared with a universal prototype inverter rated at 1 kW.The experimental results show that the efficiency of the proposed H6 topology is higher than that of the H5 topology,while slightly being lower than that of the Heric topology.And the common-mode performance of the H6 topology is superior to that of Heric topology,while slightly being inferior to that of the H5 topology.
Educational data mining: collection and analysis of score matrices for outcomes-based assessment

In this dissertation we provide an overview of the nascent state of Educational Data Mining (EDM). EDM is poised to leverage an enormous amount of research from the data mining community and apply that research to educational problems in learning, cognition, and assessment. Similar problems have been researched in the educational community for over a century, but the enormous computing power and algorithmic maturity brought to bear by data mining has proven to be more successful at many of these educational statistics problems. The timing of these developments could not be better, given the current rising importance of assessment throughout education, particularly in the United States. After determining the structural commonalities between EDM projects, I detail my own EDM assessment project, covering tools for collecting, strategies for storing and archiving, and new techniques for analyzing matrices of student scores. I also detail issues of real-world deployment and adoption of this assessment system. After examining the state of EDM, both in the abstract and with my own implementation and deployment, I predict near-term trends in EDM and assessment, and conclude with thoughts on the implications of this work, both for pedagogy and for the data mining community as a whole.
Performance evaluation of a magnetic refrigeration system

This work presents an evaluation of the thermodynamic performance of a magnetic refrigerator prototype developed at the Federal University of Santa Catarina. Experiments were carried out to determine the power consumption of the system as a whole, and of its main components (sub-systems) individually. The transmission powers associated with the system components were computed from the torques required to drive each sub-system at frequencies between 0.2 and 1.6 Hz. Thermodynamic efficiency parameters, namely the coefficient of performance and the second-law efficiency were computed. Maximum values of the coefficient of performance and second-law efficiency of 2.5 and 3.7%, respectively, were obtained at system temperature spans of 4 and 6 K. The maximum zero-span cooling capacity was approximately 150 W. The major limitations to enhancing the system performance were the losses in the flow management sub-system (pumping power and valve losses). Improving the operation of the components in this system would lower their power requirements for a given fluid flow rate, which would increase the overall efficiency of the system.
MENTAL HEALTH RECOVERY: WHAT HELPS AND WHAT HINDERS? A NATIONAL RESEARCH PROJECT FOR THE DEVELOPMENT OF RECOVERY FACILITATING SYSTEM PERFORMANCE INDICATORS PHASE ONE RESEARCH REPORT: A NATIONAL STUDY OF CONSUMER PERSPECTIVES ON WHAT HELPS AND HINDERS RECOVERY

Suggested Citation: Onken, S.J., Dumont, J.M. , Ridgway, P., Dornan, D.H. , & Ralph, R.O. (2002, October). Mental Health Recovery: What Helps and What Hinders? A National Research Project for the Development of Recovery Facilitating System Performance Indicators. Phase One Research Report: A National Study of Consumer Perspectives on What Helps and Hinders Mental Health Recovery. National Association of State Mental Health Program Directors (NASMHPD) National Technical Assistance Center (NTAC), Alexandria, VA.