Effect of the Glass Fiber Content of a Polybutylene Terephthalate Reinforced Composite Structure on Physical and Mechanical Characteristics

This work presents the influences of glass fiber content on the mechanical and physical characteristics of polybutylene terephthalate (PBT) reinforced with glass fibers (GF). For the mechanical characterization of the composites depending on the GF reinforcement rate, tensile tests are carried out. The results show that increasing the GF content in the polymer matrix leads to an increase in the stiffness of the composite but also to an increase in its brittleness. Scanning Electron Microscope analysis is performed, highlighting the multi-scale dependency on types of damage and macroscopic behavior of the composites. Furthermore, flammability tests were performed. They permit certifying the flame retardancy capacity of the electrical composite part. Additionally, fluidity tests are carried out to identify the flow behavior of the melted composite during the polymer injection process. Finally, the cracking resistance is assessed by riveting tests performed on the considered electrical parts produced from composites with different GF reinforcement. The riveting test stems directly from the manufacturing process. Therefore, its results accurately reflect the fragility of the material used.

Improving Some Mechanical Properties and Thermal conductivity of PMMA Polymer by using Environmental Waste

This study was achieved to satisfy two goals, the first of which is to treat an environmental problem represented by the disposal of date seeds, and the second is the use of these wastes to improve some mechanical and thermal properties of poly methyl methacrylate PMMA through strengthening different proportions of the powder of date seeds. Particles of date seeds were used as a natural strengthening material for PMMA polymer, by mixing the matrix material (resin) with the hardener while still stirring continuously for a period of 10 min. After that, the samples of the reinforced material were prepared by adding the powder of date seeds, which is the reinforcing substance, with different percentages of weight fraction (0, 0.5, 1, 2, 3, 5 wt. %) and a grain size of <75 µm, while continuing to stir (10 min) for a second time. The composite samples were prepared by the Hand-Lay-up method and cut according to the standard ASTM. Thermal conductivity and some mechanical properties, such as impact strength, tensile strength, compressive strength, flexural strength, and hardness, were studied. An improvement was found in all properties at the reinforcement rate of 1-2 wt. %.

Navigating the manifold of skin conductance response quantification approaches

Scientific work rests fundamentally upon data, their measurement, processing, analysis, illustration and interpretation. Raw data have to be processed to be ready for statistical analyses and interpretation. While these processing pipelines can be well defined and standardized, they are often characterized by substantial heterogeneity. Here, we present results from a systematic literature search on the different SCR response quantification approaches used in the literature by using fear conditioning research as a case example. Next, we applied seven of the identified approaches (trough-to-peak scoring, script-based baseline-correction, Ledalab as well as four different models implemented in the software PsPM) to two fear conditioning datasets differing in key procedural specifications (i.e., CS duration, reinforcement rate, number of trials). This can be viewed as a set of robustness analyses (i.e., same data subjected to different methods) aiming to investigate if and to what extent these methods yield comparable results. To our knowledge, no formal framework for the evaluation of robustness analyses exists to date, but we may borrow some criteria from a framework suggested for the evaluation of ‘replicability’ in general. Our results from seven different SCR response quantification approaches applied to two datasets suggest that there may be no single approach that consistently yields larger effect sizes across both datasets. Yet, at least some of the approaches employed show consistent effect sizes within each dataset indicating comparability. Finally, we highlight substantial heterogeneity also within most quantification approaches and discuss implications and potential remedies.

Steel-Concrete Composite Beams with Precast Hollow-Core Slabs: A Sustainable Solution

Industrialization of construction makes building operation more environmental friendly and sustainable. This change is necessary as it is an industry that demands large consumption of water and energy, as well as being responsible for the disposal of a high volume of waste. However, the transformation of the construction sector is a big challenge worldwide. It is also well known that the largest proportion of the material used in multistory buildings, and thus its carbon impact, is attributed to their slabs being the main contributor of weight. Steel-Concrete composite beams with precast hollow-core slabs (PCHCSs) were developed due to their technical and economic benefits, owing to their high strength and concrete self-weight reduction, making this system economical and with lower environmental footprint, thus reducing carbon emissions. Significant research has been carried out on deep hollow-core slabs due to the need to overcome larger spans that resist high loads. The publication SCI P401, in accordance with Eurocode 4, is however limited to hollow-core slabs with depths from 150 to 250 mm, with or without a concrete topping. This paper aims to investigate hollow-core slabs with a concrete topping to understand their effect on the flexural behavior of Steel-Concrete composite beams, considering the hollow-core-slab depth is greater than the SCI P401 recommendation. Consequently, 150 mm and 265 mm hollow-core units with a concrete topping were considered to assess the increase of the hollow core unit depth. A comprehensive computational parametric study was conducted by varying the in situ infill concrete strength, the transverse reinforcement rate, the shear connector spacing, and the cross-section of steel. Both full and partial interaction models were examined, and in some cases similar resistances were obtained, meaning that the same strength can be obtained for a smaller number of shear studs, i.e., less energy consumption, thus a reduction in the embodied energy. The calculation procedure, according to Eurocode 4 was in favor of safety for the partial-interaction hypothesis.

Multiverse analyses in fear conditioning research

There is heterogeneity in and a lack of consensus on the preferred statistical analyses foranalyzing fear conditioning effects in light of a multitude of potentially equally justifiablestatistical approaches. Here, we introduce the concept of multiverse analysis for fearconditioning research. We also present a model multiverse approach specifically tailored tofear conditioning research and introduce the novel and easy to use R package ‘multifear’ thatallows to run all the models though a single line of code. Model specifications and datareduction approaches employed in the ‘multifear’ package were identified through arepresentative systematic literature search. The heterogeneity of statistical models identifiedincluded Bayesian ANOVA and t-tests as well as frequentist ANOVA, t-test as well as mixedmodels with a variety of data reduction approaches (i.e., number of trials, trial blocks,averages) as input. We illustrate the power of a multiverse analysis for fear conditioning databased on two pre-existing data sets with partial (data set 1) and 100% reinforcement rate(data set 2) by using CS discrimination in skin conductance responses (SCRs) during fearacquisition and extinction training as case examples. Both the effect size and the direction ofeffect was impacted by choice of the model and data reduction techniques. We anticipatethat an increase in multiverse-type of studies in the field of fear conditioning research andtheir extension to other outcome measures as well as data and design multiverse analyseswill aid the development of formal theories through the accumulation of empirical evidence.This may contribute to facilitated and more successful clinical translation.

Lack of effect of different pain-related manipulations on opioid self-administration, reinstatement of opioid seeking, and opioid choice in rats

ABSTRACTRationale and ObjectivePain-related factors increase risk for opioid addiction, and opioid-induced pain relief may function as a negative reinforcer to increase opioid taking and seeking. However, experimental pain-related manipulations generally do not increase opioid self-administration in rodents. This discrepancy may reflect insufficient learning of pain-relief contingencies or confounding effects of pain-related behavioral impairments. Here we determined if pairing noxious stimuli with opioid self-administration would promote pain-related reinstatement of opioid seeking or increase opioid choice over food.MethodsIn Experiment 1, rats self-administered fentanyl in the presence or absence of repeated intraplantar capsaicin injections in distinct contexts to model context-specific exposure to cutaneous nociception. After capsaicin-free extinction in both contexts, we tested if capsaicin would reinstate fentanyl seeking. In Experiment 2, rats self-administered heroin after intraperitoneal (i.p.) lactic acid injections to model acute visceral inflammatory pain. After lactic acid-free extinction, we tested if lactic acid would reinstate heroin seeking. In Experiment 3, we tested if repeated i.p. lactic acid or intraplantar Complete Freund’s Adjuvant (CFA; to model sustained inflammatory pain) would increase fentanyl choice over food.ResultsIn Experiments 1-2, neither capsaicin nor lactic acid reinstated opioid seeking after extinction, and lactic acid did not increase heroin-induced reinstatement. In Experiment 3, lactic acid and CFA decreased reinforcement rate without affecting fentanyl choice.ConclusionsResults extend the range of conditions across which pain-related manipulations fail to increase opioid seeking in rats and suggest that enhanced opioid-addiction risk in humans with chronic pain involves factors other than enhanced opioid reinforcement and relapse.

Dissociating Representations of Time and Number in Reinforcement-Rate Learning by Deletion of the GluA1 AMPA Receptor Subunit in Mice

Theories of learning differ in whether they assume that learning reflects the strength of an association between memories or symbolic encoding of the statistical properties of events. We provide novel evidence for symbolic encoding of informational variables by demonstrating that sensitivity to time and number in learning is dissociable. Whereas responding in normal mice was dependent on reinforcement rate, responding in mice that lacked the GluA1 AMPA receptor subunit was insensitive to reinforcement rate and, instead, dependent on the number of times a cue had been paired with reinforcement. This suggests that GluA1 is necessary for weighting numeric information by temporal information in order to calculate reinforcement rate. Sample sizes per genotype varied between seven and 23 across six experiments and consisted of both male and female mice. The results provide evidence for explicit encoding of variables by animals rather than implicit encoding via variations in associative strength.

Shear failure in reinforced concrete members without transverse reinforcement: analysis of model error of NBR6118:2014

This article presents an analysis of NBR6118:2014 models used to determine the one-way shear strength in reinforced concrete members without transversal reinforcement. The study compares model predictions with 751 experimental results, taken from the 2015 ACI-DAfStb database and from Quach. Model errors are quantified. Mean values observed are around unity, indicating models with no bias, but coefficients of variation are large. Model error trends are identified with respect to cross-section depth and longitudinal reinforcement rate. In elements with low rate of longitudinal reinforcement and/or large cross-section depths, the normative models provide results with low safety. This shows the need for revision of the normative models. By means of non-linear regression analysis, two correction terms are proposed to consider the longitudinal reinforcement rate and the size effect (decrease in shear strength with increase in section depth). With the proposed corrections, the observed trends are eliminated, and the design equation becomes more accurate with respect to cross-section shear.