Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Patterns of Platinum Resistance in Ovarian Cancer Cells with Homologous Recombination Proficient and Deficient Background

Platinum compounds are very active in first-line treatments of ovarian carcinoma. In fact, high rates of complete remission are achieved, but most patients eventually relapse with resistant disease. Many mechanisms underlying the platinum-resistant phenotype have been reported. However, there are no data in the same isogenic cell system proficient and deficient in homologous recombination (HR) on platinum-acquired resistance that might unequivocally clarify the most important mechanism associated with resistance. We generated and characterized cisplatin (DDP)-resistant murine ovarian ID8 cell lines in a HR-deficient and -proficient background. Specific upregulation of the NER pathway in the HR-proficient and -resistant cells and partial restoration of HR in Brca1−/−-resistant cells were found. Combinations of different inhibitors of the DNA damage response pathways with cisplatin were strongly active in both resistant and parental cells. The data from the ID8 isogenic system are in line with current experimental and clinical evidence and strongly suggest that platinum resistance develops in different ways depending on the cell DNA repair status (i.e., HR-proficient or HR-deficient), and the upregulation and/or restoration of repair pathways are major determinants of DDP resistance.
Novel SNP markers for flowering and seed quality traits in faba bean (Vicia faba L.): characterization and GWAS of a diversity panel

Faba bean (Vicia faba L.) is a legume crop grown in diverse climates worldwide. It has a high potential for increased cultivation to meet the need for more plant-based proteins in human diets, a prerequisite for a more sustainable food production system. Characterization of diversity panels of crops can identify variation in and genetic markers for target traits of interest for plant breeding. In this work, we collected a diversity panel of 220 accessions of faba bean from around the world consisting of gene bank material and commercially available cultivars. The aims of this study were to quantify the phenotypic diversity in target traits to analyze the impact of breeding on these traits, and to identify genetic markers associated with traits through a genome-wide association study (GWAS). Characterization under field conditions at Nordic latitude across two years revealed a large genotypic variation and high broad-sense heritability for eleven agronomic and seed quality traits. Pairwise correlations showed that seed yield was positively correlated to plant height, number of seeds per plant, and days to maturity. Further, susceptibility to bean weevil damage was significantly higher for early flowering accessions and accessions with larger seeds. In this study, no yield penalty was found for higher seed protein content, but protein content was negatively correlated to starch content. Our results showed that while breeding advances in faba bean germplasm have resulted in increased yields and number of seeds per plant, they have also led to a selection pressure towards delayed onset of flowering and maturity. DArTseq genotyping identified 6,606 single nucleotide polymorphisms (SNPs) by alignment to the faba bean reference genome. These SNPs were used in a GWAS, revealing 51 novel SNP markers significantly associated with ten of the assessed traits. Three markers for days to flowering were found in predicted genes encoding proteins for which homologs in other plant species regulate flowering. Altogether, this work enriches the growing pool of phenotypic and genotypic data on faba bean as a valuable resource for developing efficient breeding strategies to expand crop cultivation.
A 28 GHz Highly Linear Up-Conversion Mixer for 5G Cellular Communications

In this paper, we present a highly linear direct in-phase/quadrature (I/Q) up-conversion mixer for 5G millimeter-wave applications. To enhance the linearity of the mixer, we propose a complementary derivative superposition technique with pre-distortion. The proposed up-conversion mixer consists of a quadrature generator, LO buffer amplifiers, and an I/Q up-conversion mixer core and achieves an output third-order intercept point of 15.7 dBm and an output 1 dB compression point of 2 dBm at 27.6 GHz, while it consumes 15 mW at a supply voltage of 1 V. The conversion gain is 11.4 dB and the LO leakage and image rejection ratio are −56 dBc and 61 dB, respectively, in the measurement. The proposed I/Q up-conversion mixer is suitable for 5G cellular communication systems.
AN EMPIRICAL AND ECONOMIC ANALYSIS OF CASSAVA PRODUCTIVITY IN SELECTED AREAS OF SOUTHERN SIERRA LEONE

This study examined the productivity and economic analyses of the cassava production in three communities—Nyallay, Sammamie, and Sindeh sections in Bo districts, Southern Sierra Leone. The crop is processed into garri, fufu, akara, and other foods. Cassava can also be used to make flour, pellets, and chips for animal consumption, as well as biofuel and particle boards The data was gathered using a questionnaire and interview schedule that were both well organized. Descriptive statistics, financial techniques, and a multiple regression model were all employed in the data analysis. The main data came from a sample of 300 cassava growers from three regions. The collected data were evaluated using econometric regression analysis. The findings indicated that, the net farm income was Le 214,361 and the net return on investment was almost Le 258, 375. The findings of a regression analysis showed that the number of cassava cuttings, hired labor, farm size, farming experience, and age statistically substantially influenced the output of cassava in the research region. It is indicated that the availability of improved cassava cuttings in the area and farmers’ access to subsidized inputs like fertilizer. In the study it was suggested that farmers be prodded to invest in the cultivation of cassava because of its economic worth and profitability. Cassava growers should be encouraged to obtain instruction on correct agronomic practices and input usage, and inputs should be made available and at reasonable costs, especially improved types of cassava cuttings, this will increase productivity.
Surrogate Modeling performance for building design problems

Simulating building energy performance is a key part of design. Surrogate modeling has emerged as a method to reduce the computational load of simulating potential building designs. To better understand the relationship between problem characteristics and surrogate model performance, this study investigated two building design problems and four types of surrogate models. The study documents the strong performance of gradient boosted decision trees in predicting energy use and representing the original fitness landscape of the simulated data. These findings provide insight into the relationship between problem characteristics and surrogate model performance, and serve as a foundation for future research into surrogate modeling for building energy performance.
Enhancing Cancer Classification Through Ensemble Machine Learning and Gene Selection Approaches

The vast dimensionality of gene expression data and the limited number of relevant genes necessitate the adoption of gene selection techniques. Furthermore, the choice of an efficient classifier plays a pivotal role in achieving accurate results. In this study, we employ the Minimum Redundancy Maximum Relevance (mRMR) method for gene selection, coupled with ensemble classifiers and individual classifiers like K-Nearest Neighbors (KNN) and Decision Trees (DT). A comparative analysis between two ensemble classifiers and two individual classifiers is conducted, revealing the superior performance of the ensemble classifiers. Our investigation utilizes four distinct cancer gene expression datasets to showcase the efficacy of employing ensemble classifiers and gene selection methods for cancer classification. The ensemble classifier (Bagging Classifier), in conjunction with the MRMR method selecting only the top 30 genes, achieved an impressive overall accuracy of 94% across all four employed datasets.
Multivariable predictive models for the estimation of power consumption (kW) of a Semi-autogenous mill applying Machine Learning algorithms [Modelos predictivos multivariables para la estimación de consumo de potencia (kW) de un molino Semi - autógeno aplicando algoritmos de Machine Learning]

This research aimed to develop machine learning (ML) models to estimate power consumption (Kw) in a Semi-autogenous mill in the mining industry. Using Machine Learning algorithms considering various operating variables for the different models such as Multiple Linear Regression (RLM), Decision Tree Regression (RAD), Random Forest Regression (RBA) and Regression Artificial Neural Networks (ANN). The methodology adopted was applied, with an experimental design with a descriptive and transversal approach. The results of the application of these models revealed significant differences in terms of predictive efficiency. The RLM and RRNA stood out with coefficients of determination (R²) of 0.922 and 0.939, respectively, indicating a substantial capacity to explain the variability in power consumption. In contrast, the tree-based models (RAD and RBA) showed inferior performance, with R² of 0.762 and 0.471. When analyzing key metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Root Mean Square Error (RMSE), it was confirmed that both RLM and RRNA outperformed the tree-based models. These results support the choice of RLM and RRNA as preferred models for estimating power consumption in a Semi-autogenous mill.
Artificial Intelligence in Compulsory K-12 Computer Science Classrooms: A Scalable Professional Development Offer for Computer Science Teachers

Given the ever-growing importance of artificial intelligence in our society and daily lives, everyone needs to learn about the core ideas and principles of this technology. While there is still a lack of empirical findings on the teaching and learning about AI in K-12 education, various teaching approaches and materials have been developed in recent years, and the topic is being introduced into K-12 computer science curricula. However, qualifying CS teachers to adequately teach this new field is a significant challenge, as they require extensive content knowledge as well as pedagogical content knowledge. In this paper, we describe the conditions and challenges and the resulting design of a professional development offer to prepare teachers for the introduction of AI into mandatory K-12 CS education in Bavaria (Germany). By designing a scalable PD program in a blended learning format and building on principles such as the "pedagogical double-decker", we successfully addressed challenges such as limited resources, a large number of teachers to be trained, and the significant heterogeneity of teachers’ backgrounds. We also share the results of a formal evaluation and other lessons learned from the initial implementations, which contribute to the design of professional development for this pressing issue.
A novel approach to address spatial uncertainties and simultaneously optimize concept, scope, and equipment design in district heating system

The design of district heating systems often involves the consideration of the appropriate system scope and the trade-off between decentralized and centralized systems, referred to as spatial uncertainties. This study presents a novel optimization method aimed at sustainable dimensioning of the district heating system by simultaneously optimizing the system concept, scope, and design of equipment. The method quantifies the spatial uncertainty and assists in the decision between decentralized and centralized systems by using clustering algorithms and the genetic algorithm. The proposed approach was tested in a city-scale case study, resulting in improved solutions with lower levelized energy cost and carbon dioxide emissions, and providing insights into the energy system and district heating network scope relationship.
Framework for automated IFC-based thermal comfort analysis based on IFC model maturity

Thermal comfort greatly affects the condition of occupants in buildings. Still, during building design, thermal comfort is often neglected, as the simulation of input parameters takes time and manual effort. To enable an automated IFC-based simulation set-up, we identify simulation approaches to derive the inputs required for thermal comfort analysis. A hierarchical approach selects the simulation’s input parameters if input from given IFC files is unavailable. We propose a framework that maps these simulation approaches, input requirements, and comfort metrics to maturity stages from early to detailed design. Within the design process, our framework assists 1) in defining data exchange requirements and 2) selecting comfort metrics based on given IFC data.
UAV Path Planning Based on the Average TD3 Algorithm With Prioritized Experience Replay

Path planning is one of the important components of the Unmanned Aerial Vehicle (UAV) mission, and it is also the key guarantee for the successful completion of the UAV’s mission. The traditional path planning algorithm has certain limitations and deficiencies in the complex dynamic environment. Aiming at the dynamic complex obstacle environment, this paper proposes an improved TD3 algorithm, which enables the UAV to complete the autonomous path planning through online learning and continuous trial and error. The algorithm changes the experience pool of TD3 algorithm to priority experience replay, so that the agent can distinguish the importance of empirical samples, improve the sampling efficiency of the algorithm, and reduce the training time. The average TD3 is proposed, and the average value of <inline-formula> <tex-math notation="LaTeX">$Q_{1}Q_{2}$ </tex-math></inline-formula> is taken when the target value is updated to solve the problem of overestimating the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value while avoiding underestimating the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value, so that the improved algorithm has better stability and can adapt to various complex obstacle environments. A new reward function is set up, so that each step of the UAV action can receive reward feedback, which solves the problem of sparse reward in deep reinforcement learning. The experimental results show that this method can train the UAV to reach the target safely and quickly in a multi-obstacle environment. Compared with DDPG, SAC and traditional TD3, the path planning success rate of this algorithm is higher than that of the other three algorithms, and the collision rate is lower than that of the comparison algorithm, which has better path planning performance.
Carl Jung: a life on the edge of reality with hypnagogia, hyperphantasia, and hallucinations

Whether the Swiss psychiatrist Carl Jung (1875–1961) became psychotic after his mid-thirties is much debated. His recently published Black Books, a seven-volume journal, reveal new insights into this debate. Based on a phenomenological analysis of his self-reports in these books and in other writings, we here identify several types of anomalous perceptual experiences: hypnagogic-hypnopompic experiences, hyperphantasia, hallucinations, personifications, and sensed presence. We argue that these experiences were not indicative of a psychotic disorder, but rather stemmed from extremely vivid mental imagery, or hyperphantasia, a condition Jung’s contemporaries and later biographers were unable to take into account because it had not yet been conceptualised. Recently, the degree of vividness of mental imagery and its potential to become indistinguishable from regular sense perception has been the subject of extensive studies. Unknowingly, Jung may have foreshadowed this line of research with his psychoanalytic concept of reality equivalence, i.e., the substitution of an external world for an inner mental reality that he encountered in individuals diagnosed with schizophrenia. There is a need for future research to investigate the possible role of hyperphantasia in psychotic experiences, but to Jung, psychosis was ‘a failure to contain and comprehend’ the content of one’s experiences in the context of one’s own life, whereas he himself did manage to put the content of his perceptual experiences into context, to find meaning in them, and to share them with others - to great acknowledgement and acclaim.
A surrogate CFD model using Machine Learning for fast design explorations of the indoor environment

The use of Computational Fluid Dynamics (CFD) to evaluate indoor air quality and thermal comfort performance has been widely adopted. However, it remains a computationally demanding process that requires prior experience in commercially available codes. Thus, it cannot be used adequately in a fast-paced early HVAC design stage. This paper explores the efficacy of a surrogate model for real-time prediction of steady turbulent flow within the context of the indoor environment. The proposed approach employs Convolutional Neural Networks (CNNs) to predict physical properties of the environment such as air velocity, dry-bulb temperature and age of air on a plane positioned at 1.2m above ground. The proposed surrogate model is capable of predicting results with low mean average errors (0.315, 0.931, 0.017, 0.016 for temperature, age of air, Ux and Uy respectively) and four orders of magnitude faster than a standard CFD simulation for a similar scenario, thereby enabling fast explorations of design options, especially useful in the early stage of the design.
Trajectory curves and surfaces: A new perspective via projective geometric algebra

The aim of this work is to define quaternion curves and surfaces and their conjugates via operators in Euclidean projective geometric algebra (EPGA). In this space, quaternions were obtained by the geometric product of vector fields. New vector fields, which we call trajectory curves and surfaces, were obtained by using this new quaternion operator. Moreover, dual quaternion curves are determined by a similar method and then their generated motion is studied. Illustrative examples are given.
Magnetic position sensing for self-calibration and image registration of scanning fiber endoscopes

We present a magnetic position sensor for scanning fiber endoscopes to address their inherent short-and long-term position instability, which is a major hurdle before their widespread clinical deployment. The position sensor uses a ring-shaped micro-magnet at the tip of the fiber cantilever, producing a dynamic magnetic field as the scanner resonates. A miniaturized three-dimensional Hall sensor accommodated within the endoscopic probe housing measures the magnetic field vector, which is then mapped directly to the beam position using closed-form calibration curves empirically obtained through a one-time calibration step using a position sensitive detector. By integrating the sensor into an OCT-endomicroscope recently developed in our group, we demonstrate an average position resolution of 20 μ m over a field-of-view of 2.1 mm field-of-view and distortion-free OCT images recorded with various scan parameters. We also discuss how sub-pixel (e.g., better than half the diffraction-limited spot size) position resolution can be attained with the new sensing scheme.
Triggered and maintained engagement with learning among gifted children in primary education

Motivating gifted children to become and stay engaged with classroom tasks can be a huge struggle for primary education teachers. This concurrent parallel mixed method design study investigates the relative differences in the importance of the basic psychological needs between gifted children and children with an average IQ with respect to triggered and maintained engagement to learn. A total of 1,017 students (512 girls and 505 boys) from 35 schools participated in this study. The qualitative analysis showed that children identify a wide array of themes related to both the context of their learning experiences and to themselves. In addition, statistical analyses showed that while all children benefit from the fulfillment of their basic psychological needs, gifted children experience a greater need for autonomy support to become engaged in school tasks. To maintain engagement, even in the face of resistance, all children, regardless of their IQ, indicate that structure is important. Possible implications for teachers are addressed in the discussion.
Enhancing biofuel production from a marine microalgae- constraints of cultivation scale-up

xvi Chapter 1 General Introduction . 1 1.1. Global Energy Challenge . 1 1.2. Biofuels . 2 1.3. Algal biofuels . 3 1.3. 1. Overview . 3 1.3. 2. Energy balance . 4 1.4. The microalgae biofuel production process . 7 1.4. 1. Microalgal cultivation . 7 1.4. 2. Downstream processing . 8 1.5. Constraints in large-scale microalgae cultivations . 10 1.5. 1. Scale . 10 1.5. 2. Reactor design and mode of operation constraints . 10 1.5. 3. Algal strain selection . 11 1.5. 4. External abiotic environmental factors . 12 1.5. 5. Nutrient supply . 15 1.5. 6. Carbon supply . 16 1.6. Summary . 17 1.7. Thesis outline . 17 1.8. General references . 19
Temporal-Spatial Distribution Characteristics of Quantitative Precipitation Estimation Accuracy using X-band Multi-Parameter Radar over the Hokuriku Region

This study was designed to clarify the accuracy of X-band multi-parameter radar (X-MP) over the Hokuriku region using rain gauge data and quantitative precipitation estimation (QPE) by C-band radar. Moreover, this study used results of the correlation coefficient and estimate error for QPE accuracy analysis. Temporal analysis revealed that X-MPs provide observation data with high accuracy for short-term downpours, but results for long-term and light rainfall or regions distant from the radar site were less accurate. Spatial analysis showed the X-MP data accuracy was observable with sufficient accuracy within a range of 2.25 × 2.25 km as the center of each rain gauge station. The X-MP data accuracy has less than the C-band radar data accuracy beyond a 20 km radius from the radar site. Quantitative analysis of the rainy season and snowy season, total precipitation of X-MP and C-band radar were overestimated from that of the rain gauge station, it was especially overestimated significantly during the snowy season.