Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling Methods for Efficient Stamping Operations at an Automotive Company

We consider scheduling issues at Beyçelik, a Turkish automotive stamping company that uses presses to give shape to metal sheets in order to produce auto parts. The problem concerns the minimization of the total completion time of job orders (i.e., makespan) during a planning horizon. This problem may be classified as a combined generalized flowshop and flexible flowshop problem with special characteristics. We show that the Stamping Scheduling Problem is NP‐Hard. We develop an integer programming‐based method to build realistic and usable schedules. Our results show that the proposed method is able to find higher quality schedules (i.e., shorter makespan values) than both the company's current process and a model from the literature. However, the proposed method has a relatively long run time, which is not practical for the company in situations when a (new) schedule is needed quickly (e.g., when there is a machine breakdown or a rush order). To improve the solution time, we develop a second method that is inspired by decomposition. We show that the second method provides higher‐quality solutions—and in most cases optimal solutions—in a shorter time. We compare the performance of all three methods with the company's schedules. The second method finds a solution in minutes compared to Beyçelik's current process, which takes 28 hours. Further, the makespan values of the second method are about 6.1% shorter than the company's schedules. We estimate that the company can save over €187,000 annually by using the second method. We believe that the models and methods developed in this study can be used in similar companies and industries.
Long‐term hyperexcitability in the hippocampus after experimental head trauma

Head injury is a causative factor in the development of temporal lobe epilepsy. However, whether a single episode of concussive head trauma causes a persistent increase in neuronal excitability in the limbic system has not been unequivocally determined. This study used the rodent fluid percussion injury (FPI) model, in combination with electrophysiological and histochemical techniques, to investigate the early (1 week) and long‐term (1 month or longer) changes in the hippocampus after head trauma. Low‐frequency, single‐shock stimulation of the perforant path revealed an early granule cell hyperexcitability in head‐injured animals that returned to control levels by 1 month. However, there was a persistent decrease in threshold to induction of seizure‐like electrical activity in response to high‐frequency tetanic stimulation in the hippocampus after head injury. Timm staining revealed both early‐ and long‐term mossy fiber sprouting at low to moderate levels in the dentate gyrus of animals that experienced FPI. There was a long‐lasting increase in the frequency of spontaneous inhibitory postsynaptic currents in dentate granule cells after FPI, and ionotropic glutamate receptor antagonists selectively decreased the spontaneous inhibitory postsynaptic current frequency in the head‐injured animals. These results demonstrate that a single episode of experimental closed head trauma induces long‐lasting alterations in the hippocampus. These persistent structural and functional alterations in inhibitory and excitatory circuits are likely to influence the development of hyperexcitable foci in posttraumatic limbic circuits.
Blood biomarker discovery in drug-free schizophrenia: the contribution of proteomics and multiplex immunoassays

ABSTRACT Introduction: Recent evidence supports an association between systemic abnormalities and the pathology of psychotic disorders which has led to the search for peripheral blood-based biomarkers. Areas covered: Here, we summarize blood biomarker findings in schizophrenia from the literature identified by two methods currently driving biomarker discovery in the human proteome; mass spectrometry and multiplex immunoassay. From a total of 14 studies in the serum or plasma of drug-free schizophrenia patients; 47 proteins were found to be significantly altered twice or more, in the same direction. Pathway analysis was performed on these proteins, and the resulting pathways discussed in relation to schizophrenia pathology. Future directions are also discussed, with particular emphasis on the potential for high-throughput validation techniques such as data-independent analysis for confirmation of biomarker candidates. Expert commentary: We present promising findings that point to a convergence of pathophysiological mechanisms in schizophrenia that involve the acute-phase response, glucocorticoid receptor signalling, coagulation, and lipid and glucose metabolism.
New luminescent mycenoid fungi (Basidiomycota, Agaricales) from São Paulo State, Brazil

Abstract Four species of mycenoid fungi are reported as luminescent (or putatively luminescent) on the basis of specimens collected from São Paulo State, Brazil. Two of them represent new species (Mycena oculisnymphae, Resinomycena petarensis), and two represent new reports of luminescence in previously described species (M. deformis, M. globulispora). Comprehensive descriptions, illustrations, photographs, and comparisons with phenetically similar species are provided. Sequences of nuc rDNA internal transcribed spacer regions were generated for barcoding purposes and for comparisons with similar species.
REVIEW ON THE PHARMACEUTICAL APPLICATIONS OF HOT MELT EXTRUDER

Compared to single-unit dosage forms, oral multi particulate drug-delivery systems like pellets, granules offer advantages in terms of a more even and predictable distribution and transportation in the gastro-intestinal tract. As the number of poorly soluble drugs has risen sharply in recent years, techniques for improving the solubility, and potentially, the bioavailability have become increasingly important. Melt extrusion technology represents an efficient pathway for increasing the solubility of poorly soluble drugs. In the drug the active forms a solid dispersion where the drug is presented in an amorphous state and molecularly dispersed in the carrier. This leads to an increase in solubility, as no lattice energy has to be overcome during dissolution. The particle size is reduced to the molecular level and the carrier increases the wetting of the drug. Scientists have tried to address such issues by various pharmaceutical interventions like various pelletizations, granulation and hot melt extrusion techniques available to prepare drug loaded spherical particles or granules. Amongst all extrusion is the most common method which utilized in formulation of beads and pellets. Limitations related to bioavailability and site specific drug delivery can be over come by this technique. This technology has gained focus because of its easy of operation and fast processing. Extrusion is widely utilized in formulation of sustained release, controlled release delivery system. Melt extrusion is considered to be an efficient technology with particular advantages over solvent processes like co-precipitation for the manufacture of a variety of dosage forms and formulations such as granules, pellets, tablet s, suppositories, implants, stents, transdermal systems and ophthalmic inserts. This process is also useful from an ecological standpoint to produce solid dispersions efficiently without degradation. Potential drawbacks like the influence of heat stress and shear forces on the drug needs attention while doing extrusion. This review article describes the melt extrusion equipment, process and polymers used for formulation of various API using various pharmaceutical-grade polymers.
A Delayed Projection Neural Network for Solving Linear Variational Inequalities

In this paper, a delayed projection neural network is proposed for solving a class of linear variational inequality problems. The theoretical analysis shows that the proposed neural network is globally exponentially stable under different conditions. By the proposed linear matrix inequality (LMI) method, the monotonicity assumption on the linear variational inequality is no longer necessary. By employing Lagrange multipliers, the proposed method can resolve the constrained quadratic programming problems. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed neural network.
Single-Exposure HDR Technique Based on Tunable Balance Between Local and Global Adaptation

This brief describes a high-dynamic-range technique that compresses wide ranges of illuminations into the available signal range with a single exposure. An online analysis of the image histogram provides the sensor with the necessary feedback to dynamically accommodate changing illumination conditions. This adaptation is accomplished by properly weighing the influence of local and global illuminations on each pixel response. The main advantages of this technique with respect to similar approaches previously reported are as follows: 1) standard active-pixel-sensor circuitry can be used to render the pixel values and 2) the resulting compressed image representation is ready either for readout or for early vision processing at the very focal plane without requiring any additional peripheral circuit block. Experimental results from a prototype smart image sensor achieving a dynamic range of 102 dB are presented.
Neglected filoviruses.

Eight viruses are currently assigned to the family Filoviridae Marburg virus, Sudan virus and, in particular, Ebola virus have received the most attention both by researchers and the public from 1967 to 2013. During this period, natural human filovirus disease outbreaks occurred sporadically in Equatorial Africa and, despite high case-fatality rates, never included more than several dozen to a few hundred infections per outbreak. Research emphasis shifted almost exclusively to Ebola virus in 2014, when this virus was identified as the cause of an outbreak that has thus far involved more than 28 646 people and caused more than 11 323 deaths in Western Africa. Consequently, major efforts are currently underway to develop licensed medical countermeasures against Ebola virus infection. However, the ecology of and mechanisms behind Ebola virus emergence are as little understood as they are for all other filoviruses. Consequently, the possibility of the future occurrence of a large disease outbreak caused by other less characterized filoviruses (i.e. Bundibugyo virus, Lloviu virus, Ravn virus, Reston virus and Taï Forest virus) is impossible to rule out. Yet, for many of these viruses, not even rudimentary research tools are available, let alone medical countermeasures. This review summarizes the current knowledge on these less well-characterized filoviruses.
Supplementing Intensive Targeted Computerized Cognitive Training With Social Cognitive Exercises for People With Schizophrenia: An Interim Report

Objective: Individuals with schizophrenia demonstrate cognitive, social cognitive, and motivational deficits that contribute to impairment in real-world functioning. In the current study, we investigated the effects of supplementing computerized neurocognitive training with social cognitive exercises, as compared with neurocognitive training alone. Method: In this ongoing, double-blind, randomized controlled trial of 111 participants with psychosis, we compare the effects of supplementing intensive targeted cognitive training with social cognitive training exercises (TCT + SCT) with the effects of targeted cognitive training alone (TCT-only). Participants were assessed on cognition, symptoms, functional capacity, and functional outcomes, as well as social cognition and measures related to reward processing. Results: Both treatment groups showed significant improvement in multiple cognitive domains and improvement in functional capacity. However, as predicted, TCT + SCT group participants showed significant improvement in prosody identification and reward processing relative to TCT-only participants. Conclusions and Implications for Practice: Our findings indicate that supplementing intensive computerized cognitive training with social cognitive exercises in people with psychosis confers greater benefits in prosody identification and reward processing relative to cognitive training alone, even though both approaches drive significant improvements in cognition and functional capacity. Impairments in both prosody identification and reward processing have been associated with greater negative symptoms and poorer functional outcomes in schizophrenia, raising the possibility that this form of treatment may lead to better long-term outcomes than traditional cognitive training approaches. Follow-up assessments will determine whether results are durable and generalize over time to improvements in symptoms and functioning.
Effects of Moisture and Particle Size on Quantitative Determination of Total Organic Carbon (TOC) in Soils Using Near-Infrared Spectroscopy

Near-Infrared Spectroscopy is a cost-effective and environmentally friendly technique that could represent an alternative to conventional soil analysis methods, including total organic carbon (TOC). Soil fertility and quality are usually measured by traditional methods that involve the use of hazardous and strong chemicals. The effects of physical soil characteristics, such as moisture content and particle size, on spectral signals could be of great interest in order to understand and optimize prediction capability and set up a robust and reliable calibration model, with the future perspective of being applied in the field. Spectra of 46 soil samples were collected. Soil samples were divided into three data sets: unprocessed, only dried and dried, ground and sieved, in order to evaluate the effects of moisture and particle size on spectral signals. Both separate and combined normalization methods including standard normal variate (SNV), multiplicative scatter correction (MSC) and normalization by closure (NCL), as well as smoothing using first and second derivatives (DV1 and DV2), were applied to a total of seven cases. Pretreatments for model optimization were designed and compared for each data set. The best combination of pretreatments was achieved by applying SNV and DV2 on partial least squares (PLS) modelling. There were no significant differences between the predictions using the three different data sets (p < 0.05). Finally, a unique database including all three data sets was built to include all the sources of sample variability that were tested and used for final prediction. External validation of TOC was carried out on 16 unknown soil samples to evaluate the predictive ability of the final combined calibration model. Hence, we demonstrate that sample preprocessing has minor influence on the quality of near infrared spectroscopy (NIR) predictions, laying the ground for a direct and fast in situ application of the method. Data can be acquired outside the laboratory since the method is simple and does not need more than a simple band ratio of the spectra.
Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems

Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs. york.ac. uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.
XSS Vulnerability Detection Using Model Inference Assisted Evolutionary Fuzzing

We present an approach to detect web injection vulnerabilities by generating test inputs using a combination of model inference and evolutionary fuzzing. Model inference is used to obtain a knowledge about the application behavior. Based on this understanding, inputs are generated using genetic algorithm (GA). GA uses the learned formal model to automatically generate inputs with better fitness values towards triggering an instance of the given vulnerability.
Experimental Determination of Cooling Rate and its Effect on Microhardness in Submerged Arc Welding of Mild Steel Plate (Grade c-25 as per IS 1570)

An experiment based thermal analysis has been performed to obtain the thermal histories, which can be applied to determine cooling rate of weldment. During fusion welding process it is possible to determine temperature at any point by using thermocouple and from such data it is possible to draw thermal histories for any point of interest, such thermal histories can be utilized to determine cooling rate. The temperature distribution and cooling rate during the welding process have significant effect on the mechanical and metallurgical properties of a weldment. The changes in microstructure, grain growth and hardness in a weldment are very dependent on the temperature distribution and cooling rate. These studies are utilized to investigate the micro-structure and microhardness of the heat affected zone (HAZ) and weldment. In present work, the influence of heat input and cooling rate on microhardness has been investigated. Full factorial design is used to conduct the experiment with three factors and two levels. Eight combinations and two set of heat input are designed with different combinations of SAW welding parameters. Temperature distribution curves and cooling rate have been drawn. The effect of selected welding parameters (Wire feed rate, Open circuit Voltage and welding Speed) on the microhardness have been investigated.
Reduced nuclear translocation of serum response factor is associated with skeletal muscle atrophy in a cigarette smoke-induced mouse model of COPD

Skeletal muscle atrophy and dysfunction are common complications in the chronic obstructive pulmonary disease (COPD). However, the underlying molecular mechanism remains elusive. Serum response factor (SRF) is a transcription factor which is critical in myocyte differentiation and growth. In this study, we established a mouse COPD model induced by cigarette smoking (CS) exposure for 24 weeks, with apparent pathophysiological changes, including increased airway resistance, enlarged alveoli, and skeletal muscle atrophy. Levels of upstream regulators of SRF, striated muscle activator of Rho signaling (STARS), and ras homolog gene family, member A (RhoA) were decreased in quadriceps muscle of COPD mice. Meanwhile, the nucleic location of SRF was diminished along with its cytoplasmic accumulation. There was a downregulation of the target muscle-specific gene, Igf1. These results suggest that the CS is one of the major causes for COPD pathogenesis, which induces the COPD-associated skeletal muscle atrophy which is closely related to decreasing SRF nucleic translocation, consequently downregulating the SRF target genes involved in muscle growth and nutrition. The STARS/RhoA signaling pathway might contribute to this course by impacting SRF subcellular distribution.
Etched and Nanocoated SMS Fiber Sensor for Detection of Salinity Concentration

An optical fibre refractometer has been developed by etching and deposition of a thin film of indium tin oxide (ITO) on a single-mode-multimode-single-mode (SMS) fibre structure. The interference between modes in this structure is sensitive to the refractive index changes of the surrounding medium, achieving sensitivities of up to 7000 nm/RIU in the 1.333–1.338 RIU range. A salinity sensor has been implemented as a practical application of this proposed structure. Fast Fourier transform (FFT) analysis and tracking of an interference dip were used to monitor the interference between modes obtaining sensitivities of 0.99 nm/PSU and 0.025 rad/PSU, respectively.
Multiple kinases inhibit origin licensing and helicase activation to ensure reductive cell division during meiosis

Meiotic cells undergo a single round of DNA replication followed by two rounds of chromosome segregation (the meiotic divisions) to produce haploid gametes. Both DNA replication and chromosome segregation are similarly regulated by CDK oscillations in mitotic cells. Yet how these two events are uncoupled between the meiotic divisions is unclear. Using Saccharomyces cerevisiae, we show that meiotic cells inhibit both helicase loading and helicase activation to prevent DNA replication between the meiotic divisions. CDK and the meiosis–specific kinase Ime2 cooperatively inhibit helicase loading, and their simultaneous inhibition allows inappropriate helicase reloading. Further analysis uncovered two previously unknown mechanisms by which Ime2 inhibits helicase loading. Finally, we show that CDK and the polo–like kinase Cdc5 trigger degradation of Sld2, an essential helicase–activation protein. Together, our data demonstrate that multiple kinases inhibit both helicase loading and activation between the meiotic divisions, thereby ensuring reductive cell division.
Structure Prediction of RNA Loops with a Probabilistic Approach

The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction.