Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo MRS study of long-term effects of traumatic intracranial injection of a culture medium in mice

Orthotopic transplantation of glioblastoma cells in the brain of laboratory mice is a common animal model for studying brain tumors. It was shown that 1H magnetic resonance spectroscopy (MRS) enables monitoring of the tumor’s occurrence and its development during therapy based on the ratio of several metabolites. However, in studying new approaches to the therapy of glioblastoma in the model of orthotopic xenotransplantation of glioma cells into the brain of mice, it is necessary to understand which metabolites are produced by a growing tumor and which are the result of tumor cells injection along the modeling of the pathology. Currently, there are no data on the dynamic metabolic processes in the brain that occur after the introduction of glioblastoma cells into the brain of mice. In addition, there is a lack of data on the delayed effects of invasive brain damage. Therefore, this study investigates the long-term dynamics of the neurometabolic profile, assessed using 1H MRS, after intracranial injection of a culture medium used in orthotopic modeling of glioma in mice. Levels of N-acetylaspartate, N-acetylaspartylglutamic acid, myoinositol, taurine, glutathione, the sum of glycerophosphocholine and phosphocholine, glutamic acid (Glu), glutamine (Gln), and gamma aminobutyric acid (GABA) indicate patterns of neurometabolites in the early stage after intracranial injection similar to brain trauma ones. Most of the metabolites, with the exception of Gln, Glu and GABA, returned to their original values on day 28 after injection. A progressive increase in the Glu/Gln and Glu/GABA ratio up to 28 days after surgery potentially indicates an impaired turnover of these metabolites or increased neurotransmission. Thus, the data indicate that the recovery processes are largely completed on day 28 after the traumatic event in the brain tissue, leaving open the question of the neurotransmitter system impairment. Consequently, when using animal models of human glioma, researchers should clearly distinguish between which changes in neurometabolites are a response to the injection of cancer cells into the brain, and which processes may indicate the early development of a brain tumor. It is important to keep this in mind when modeling human glioblastoma in mice and monitoring new treatments. In addition, these results may be important in the development of approaches for non-invasive diagnostics of traumatic brain injury as well as recovery and rehabilitation processes of patients after certain brain surgeries.
Churn Prediction for Gym Members Using Artificial Neural Networks Assisted with The Psychological Concept of Habit Formation in The Fitness Industry

Because of the growth of the health and fitness awareness, the gym industry is booming. Consequently, the competition between gym businesses is becoming increasingly fierce. So, it is vital for gyms to develop membership retention strategies to keep their customers from switching to a competitor. An important step in such strategies is to proactively detect members who are vulnerable to ending their memberships (Churning) to make efforts into retaining them. Utilizing technology for predicting churn can be advantageous for fitness clubs that aim to stay at the top of the business. Hence, the purpose of this paper is to develop a model that predicts gym membership churn by employing multi-layer perceptron (MLP) artificial neural networks (ANN) using backpropagation with an emphasis on feature selection. Two methods were used to base feature selection on: literature review and filtering method. The results found that implementing the psychological concept of habit formation served as a link for introducing an effective ANN model into the fitness retention strategies, as the model achieved high prediction performance metrics, namely, accuracy, sensitivity, and specificity, 92.1%, 89.1% and 93.8% respectively.
AlGaN/GaN Distributed Schottky Barrier Single-Pole Single-Throw Millimeter-Wave Switches

This paper presents two designs of millimeter-wave single-pole single-throw switches based on AlGaN/GaN heterostructure. The switches are based on two approaches to the travelling wave concept with Schottky barrier shunt elements integrated into a coplanar waveguide. The first design utilizes a two-stage artificial transmission line topology with distributed shunt elements in Schottky diode configuration. The second one employs a single electrically large shunt element configured as a high electron mobility transistor. Both arrangements allow the switches to achieve very high operational bandwidth, exceeding 100 GHz, without the requirement for very high lithography resolution. Examined switches were fabricated in a 2- $\mu \text{m}$ GaN-on-SiC process. Measurement results demonstrate upper operating frequencies exceeding 114.5 GHz starting from 14 GHz or even DC, depending on the design. The W band on-off ratio over 17 dB and 21 dB is achieved by the first and second construction, respectively. Measurements of transient parameters show that high switching speed can also be achieved (rise/fall time as low as ≤17 ns for the first construction).
Analysis of Workplace Turnover Intentions in Manufacturing Industries of Laguna, Philippines

This case study explored the relationships of multiple variables affecting an employee’s turnover intention in the manufacturing industries of Laguna, Philippines. Data from a survey of non-managerial employees in Calamba, Laguna were used to generate a model using structural equation modelling (SEM). Turnover intentions were measured through an individual’s intent years to stay. The generated SEM model defined variables with direct effect on an individual’s intent years to stay. Variables that are influential an individual’s intent years to stay are the following; outside workplace characteristics, educational attainment, and job satisfaction. Individuals with higher educational attainment tends to jump from organizations for a shorter period. Well satisfied individuals and those with a good perception of outside workplace characteristics tend to have an extended period of intent years to stay. Variables affecting job satisfaction are job performance and job characteristics (promotion chances, competency, and salary growth). Performing better is a reward that boosts an individual’s intent years to stay. Training, recognition, and mentorship increase an individual’s capability of performing better, which indirectly affects an individual’s intent years to stay.
Evaluating the efficiency of renewable energy technologies through thermodynamic analyzes

Renewable energy systems hold the key to a sustainable energy future, and at their core lies the pivotal influence of thermodynamics. This article comprehensively explores the fundamental thermodynamic principles that underpin renewable energy technologies, providing a robust foundation for understanding and optimizing their performance. In the context of renewable energy, temperature differentials drive energy flows, pressure and volume changes play crucial roles, and the conservation of energy is paramount. The Second Law of Thermodynamics, represented by entropy, guides the direction of natural processes within these systems. Exergy, a related concept, assesses the quality of energy within a system, facilitating efficiency evaluations. Renewable energy systems often operate on thermodynamic cycles, forming the basis for technologies like solar power plants and geothermal facilities. Heat transfer mechanisms, including conduction, convection, and radiation, are integral to these systems and influence their design and operation. This article summarizes these thermodynamic fundamentals in the context of renewable energy, offering insights into the principles that drive efficiency and sustainability. Understanding these principles is crucial for harnessing renewable energy’s full potential and aligning with global efforts to transition toward cleaner and more sustainable energy sources.
Revealing the impact of TOX3 on osteoarthritis: insights from bioinformatics

Osteoarthritis, a prevalent long-term condition of the joints, primarily impacts older individuals, resulting in discomfort, restrictions in mobility, and a decrease in overall well-being. Although Osteoarthritis is widely spread, there is a lack of successful interventions to stop the advancement of the condition. Numerous signaling pathways have been emphasized in recent research on Osteoarthritis, yet the diagnostic significance of numerous genes has not been investigated. To identify genes that were expressed differently in osteoarthritis, we utilized the Gene Expression Omnibus database. To identify marker genes, we built machine learning models including Least Absolute Shrinkage and Selection Operator and Random Forest. We categorized Osteoarthritis samples and performed immune cell infiltration analysis based on the expression patterns of these characteristic genes. Both the Least Absolute Shrinkage and Selection Operator and Random Forest models selected six marker genes (TOX3, ARG1, CST7, RERGL, COL11A1, NCRNA00185) out of a total of 17 differentially expressed genes. The osteoarthritis samples were categorized into two groups, namely a high expression group and a low expression group, based on the median levels of TOX3 expression. Comparative analysis of these groups identified 85 differentially expressed genes, showing notable enrichment in pathways related to lipid metabolism in the group with high expression. Analysis of immune cell infiltration revealed noticeable differences in immune profiles among the two groups. The group with high expression of TOX3 showed a notable increase in Mast cells and Type II IFN Response, whereas B cells, Cytolytic activity, Inflammation-promoting cells, NK cells, pDCs, T cell co-inhibition, Th1 cells, and Th2 cells were significantly decreased. We constructed a ceRNA network for TOX3, revealing 57 lncRNAs and 18 miRNAs involved in 57 lncRNA-miRNA interactions, and 18 miRNA-mRNA interactions with TOX3. Validation of TOX3 expression was confirmed using an external dataset (GSE29746), revealing a notable increase in Osteoarthritis samples. In conclusion, our study presents a comprehensive analysis identifying TOX3 as a potential feature gene in Osteoarthritis. The distinct immune profiles and involvement in fat metabolism pathways associated with TOX3 expression suggest its significance in Osteoarthritis pathogenesis. The study establishes a basis for comprehending the intricate correlation between characteristic genes and Osteoarthritis, as well as for the formulation of individualized therapeutic approaches.
Relationship between climate variables... RELATIONSHIP BETWEEN CLIMATE VARIABLES, TRUNK GROWTH RATE AND WOOD DENSITY OF Eucalyptus grandis TREES1

1 Received on 25.03. 2013 accepted for publication on 17.12. 2015. 2 Universidade Federal de Goiás, Escola de Agronomia, Goiânia, GO – Brasil. E-mail: <crsettejr@hotmail.com>. 3 Universidade de São Paulo, Escola Superior de Agricultura Luiz de Queiroz, Departamento de Ciências Florestais, Piracicaba, SP Brasil. E-mail: <mtomazel@usp.br>. 4 Universidade de Trás-os-Montes e Alto Douro, Departamento de Engenharia Florestal, Vila Real – Portugal. E-mail: <jlousada@utad.pt>. 5 Universidade Federal de Goiás, Escola de Agronomia, Goiânia, GO Brasil. E-mail: <dlpes@agro.ufg. br>. 6 Centre de Coopération Internationale En Recherche Agronomique Pour Le Dével, Programme Arbres Et Plantations, Esalq Usp Pesquisador Visitante, Piracicaba, SP. E-mail: <laclau@cirad.fr>. *Corresponding author.
Tail inverse regression for dimension reduction with extreme response

: We consider the problem of dimensionality reduction for prediction of a target Y ∈ R to be explained by a covariate vector X ∈ R p , with a particular focus on extreme values of Y which are of particular concern for risk management. The general purpose is to reduce the dimensionality of the statistical problem through an orthogonal projection on a lower dimensional subspace of the covariate space. Inspired by the sliced inverse regression (SIR) methods, we develop a novel framework (TIREX, Tail Inverse Regression for EXtreme response) relying on an appropriate notion of tail conditional independence in order to estimate an extreme suf-ﬁcient dimension reduction (SDR) space of potentially smaller dimension than that of a classical SDR space. We prove the weak convergence of tail empirical processes involved in the estimation procedure and we illustrate the relevance of the proposed approach on simulated and real world data.
Effective Management of Irrigation Water for Wheat Crop Under Stressed Conditions Using Simulation Modeling

The reported study was undertaken to determine an efficient strategy for management of irrigation water in case of wheat crop under water stressed conditions in a sub-tropical sub-humid region. Field experiments were conducted on wheat crop over a period of three years with five different irrigation treatments. Layer-wise soil moisture status was continuously monitored to determine the crop water extraction pattern and thereby the irrigation management depth. Irrigation treatments consisted of different levels of depletion of available soil water. The five levels of depletions considered in the study were 10%, 30%, 45%, 60% and 75%. CERES- wheat growth simulation model was calibrated, validated and used for decision-making. In order to asses the depth and time variation of soil moisture under different scheduling of irrigation, soil moisture was measured periodically in 15-30, 30-45, 45-60, 60-90 and 90-120 cm soil profiles using a neutron probe, while the soil moisture in 0-15 cm soil profile was determined by gravimetric method. It was observed that the plants extracted most of the soil moisture from 0-45 cm soil layer in case of wheat. Therefore, it was recommended that only 0-45 cm of soil profile be considered for scheduling of irrigation in case of wheat crop grown in sandy loam soil in the sub-tropical regions under water scarcity conditions. The field water use efficiency of wheat crop was found to be the highest when irrigation was scheduled at 45% depletion of ASW. It was therefore recommended that under water scarcity condition, plant extractable soil water depletion of more than 45% of ASW must be avoided even during non-critical stages of the wheat crop grown in medium sandy loam soils in subtropical regions. The calibrated CERES- wheat model was found to be quite efficient in simulation of yield parameters and layer-wise soil moisture extraction pattern. It can therefore be successfully used for decision making in the region.
Applying InSAR technique to accurately relocate the epicentre for the 1999 Ms= 5.6 Kuqa earthquake in Xinjiang province, China

SUMMARY The 1999 M s = 5.6 Kuqa earthquake occurred in a seismically active belt between the Tianshan Mountain and the Traim basin. Because of the sparse seismic network and complex crustal structure, it is very difficult to accurately locate the epicentre for this event using seismic waves. The epicentres located by different research groups vary over a spatial range of 20– 40 km. Interferometric synthetic aperture radar (InSAR) is a geodetic technique with fine spatial resolution, good precision and wide coverage. An interferometric map of the epicentral region constrains the epicentre of this event. The arid climate and sparsely vegetation in the Kuqa region provide excellent conditions for InSAR studies. In this paper, we firstly construct a interferogram to map the coseismic deformation field due to the 1999 Kuqa earthquake using a coseismic interferometric pair of radar images acquired by the ESA ERS-2 satellite. Then, we develop a new geocoding method and apply it to the interferogram. Next, we infer the geometry of the seismogenic fault according to its focal mechanism and tectonic setting. To model the interferogram, we assume a dislocation buried in a uniform elastic half-space. Finally, we infer the epicentre of this earthquake to be located at (82.80 ◦ E, 41.92 ◦ N), which is close to the results of the National Earthquake Information Center of USGS and China Earthquake Administration. The epicentre location inferred from InSAR falls in the six-level isoseismal contour described by Xinjiang earthquake administration using the field investigations.
FedLANE: a federated U-Net architecture for lane detection

Lane detection is a crucial module for today’s autonomous driving cars. Detecting road lanes is a challenging task as it varies in color, texture, boundaries and markings. Traditional lane detection techniques detect the lane by applying a model trained with centralized data. As roads vary in urban and rural areas, a more localized and decentralized training technique is desired for accurate and personalized lane detection. Federated learning has recently proved to be a promising technology that trains and prunes the model using local data. Applying federated learning-based lane detection improves the accuracy of detection and also ensures the security and privacy of autonomous cars. This paper proposes FedLANE, a federated learning-based lane detection technique. U-Net, U-Net long short-term memory (LSTM) and AU-Net architectures were explored using a federated learning approach. Experimental analysis using TuSimple and CuLane dataset shows that the FedLANE based lane detection performs similar to that of the traditional deep learning lane detection models.
A Review of DC-DC Resonant Converter Topologies and Control Techniques for Electric Vehicle Applications

Resonant converters are attractive for DC-DC converter applications of electric vehicles (EVs) due to their wide range of soft switching and less output filter requirements compared to dual active bridge (DAB) converters. However, the design and control implementation of resonant converters is comparatively challenging due to the nonlinearities introduced with the resonant tank. LLC and CLLC resonant converters are popular among the resonant converter topologies, while new topologies are being derived based on the basic resonant converter topologies. This paper conducts a review on the basic resonant converter topologies, modes of operation of the resonant converter, modeling and control techniques, and special design considerations for resonant converter design. Existing topologies and control techniques of resonant converters enabling the derivation of new converter topologies and control methodologies, are investigated.
Advancing CO2 Storage Monitoring via Cross-Borehole Apparent Resistivity Imaging Simulation

Conventional resistivity inversion methodologies encounter constraints in perpetual monitoring owing to the necessity for recurrent measurements. In response, this research leverages a 3-D finite element method to formulate an approximate geometry imaging of cross-borehole resistivity during forward modeling, circumventing the direct computation of Jacobian matrix equations in the electric field. This study meticulously explores the complex relationship among apparent resistivity (<inline-formula> <tex-math notation="LaTeX">$\rho _{a}$ </tex-math></inline-formula>), carbon dioxide (CO2) resistivity (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {CO2}}$ </tex-math></inline-formula>), and the volume of the CO2 storage area (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {CO2}}$ </tex-math></inline-formula>). Remarkably, the impact of <inline-formula> <tex-math notation="LaTeX">$\rho _{\mathrm {CO2}}$ </tex-math></inline-formula> on <inline-formula> <tex-math notation="LaTeX">$\rho _{a}$ </tex-math></inline-formula> is found to be more pronounced than that of <inline-formula> <tex-math notation="LaTeX">$V_{\text {CO2}}$ </tex-math></inline-formula>, attributed to the repulsion effect emanating from the high-resistance storage area. A robust linear correlation between <inline-formula> <tex-math notation="LaTeX">$\rho _{a}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V_{\text {CO2}}$ </tex-math></inline-formula> is identified across various multihorizontal layer models, while the relationship between <inline-formula> <tex-math notation="LaTeX">$\rho _{a}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\rho _{\mathrm {CO2}}$ </tex-math></inline-formula> adheres to a rational function. The intricate correlation between <inline-formula> <tex-math notation="LaTeX">$\rho _{a}$ </tex-math></inline-formula> and CO2 concentration is dissected, offering a quantitative perspective for inferring the resistivity of the CO2 storage area. These findings are further validated through field formation models featuring salt caverns, highlighting the effectiveness of cross-borehole resistivity imaging for CO2 storage monitoring. Beyond enhancing our understanding of subsurface geological behavior, our study underscores the feasibility of using salt caverns for CO2 storage, presenting a pioneering approach towards navigating the monitoring of subsurface CO2 storage.
Policy Analysis of Multi-Actor Systems

Policy of Multi-Actor Systems is an introduction into the art of craft of problem exploration and problem structuring. It positions policy analysis as a scientific discipline focused on systems analysis in a multi-actor context to support better informed decision-making. The approach presented in this book is considered to be the cornerstone of the curricula of the Faculty of Technology, Policy and Management of Delft University of Technology and underlies the research on (the governance of) socio-technical systems. Systems thinking applied in a multi-actor environment and its inherent multi-disciplinary character is what makes this work stand out from traditional hard- and soft systems approaches. The core of the book is dedicated to systems analysis, actor- or stakeholder-analysis and discusses methods for dealing with uncertainty. These analytical activities combined lead to a rich problem description and to plans for further research. Due to the stepwise approach this book serves as a basis for any problem analysis both for our bachelor and master students, our alumni worldwide and any interested practitioners.