Miracle-dz/UniFAR-FTUs huggingface dataset:
Temperature Dependences and Gamma Radiation Stability of Liquid Scintillation Cocktails for n–γ Discrimination

To correctly use different devices, including liquid scintillators, operating parameters have to be known and be at disposal. Among many others, the temperature influence and radiation hardness play an important role. The liquid organic scintillators, namely, EJ-301 and EJ-309 (both Eljen Technology), and new custom-made cocktails based on 1-phenyl-3-(2,4,6-trimethylphenyl)-2-pyrazoline (PYR) and 2,5-bis(5-tert-butyl-benzoxazol-2-yl)thiophene (THIO) fluors have been tested for temperature stability and radiation resistance. The temperature dependence, in terms of neutron–gamma discrimination, was found to be very weak with the temperature coefficient in order of 10−4 MeVee/°C. As far as the gamma radiation resistance of scintillators goes, THIO proved to give consistent results almost independently of the absorbed dose. The EJ-301 exhibited the second-best result. EJ-309 was mostly comparable with PYR except for isomerization, which was observed at an absorbed dose from gamma irradiation greater than 10 kGy. In addition, the color change of borosilicate glass, serving as the casing, was noticed causing a decrease in light yield (LY).
Normative effect of economic inequality: empirical evidence about conspicuous consumption (Efecto normativo de la desigualdad económica: evidencias empíricas sobre el consumo conspicuo)

ABSTRACT Economic inequality has negative consequences at different levels. At the individual level, it may affect social perception and configure specific features that may generate a normative climate. The current research’s main goals were (a) to analyse the effects of inequality on the attributions of others’ consumer behaviour and (b) to provide empirical evidence about how inequality configures normative consumer habits focusing on luxury consumption. This work includes three preregistered experimental studies. In Study 1 (N = 276) and Study 2 (N = 301), we used a between-participants design with two experimental conditions (high vs. low inequality). In Study 3, we reversed the order of the variables to identify whether luxury consumption triggered expectations about the levels of inequality in a specific context. Results showed that in high (vs. low) economic inequality settings, participants inferred more materialism, status-seeking, status anxiety and conspicuous consumption in others. Furthermore, luxury consumption became a predictor of economic inequality. Thus, economic inequality affects attribution processes and works as a clue to infer descriptive norms in a society.
A Switching State Feedback Control of Constrained Systems with Exogenous Inputs

This paper considers switching state feedback control of systems with unknown but bounded disturbance inputs subject to state and control constraints, and resolves convergence problems which arise with the effects of unknown disturbances. This difficulties have not been handled by the switching control laws which were proposed in the literatures. The proposed switching control law assures convergence of the state and successive controller switching even in the presence of unknown disturbances. The main idea is to construct an upper bound of the state reachable set ' such that convergence of the state is assured by restricting this set to some subset of the state space. The resulting optimization problems which provide a sequence of feedback gains are more difficult to solve than those with no disturbances. However, this optimization problems may be solved with some heuristic manners. In addition, required on-line computational burden is the same as that in case of no disturbance inputs.
Transcriptional Profile of Soybean Seeds with Contrasting Seed Coat Color

Soybean is the primary source of vegetable protein and is used for various purposes, mainly to feed animals. This crop can have diverse seed coat colors, varying from yellow, black, brown, and green to bicolor. Black seed coat cultivars have already been assigned as favorable for both seed and grain production. Thus, this work aimed to identify genes associated with soybean seed quality by comparing the transcriptomes of soybean seeds with contrasting seed coat colors. The results from RNA-seq analyses were validated with real-time PCR using the cultivar BRS 715A (black seed coat) and the cultivars BRS 413 RR and DM 6563 IPRO (yellow seed coat). We found 318 genes differentially expressed in all cultivars (freshly harvested seeds and seeds stored in cold chamber). From the in silico analysis of the transcriptomes, the following genes were selected and validated with RT-qPCR: ACS1, ACSF3, CYP90A1, CYP710A1, HCT, CBL, and SAHH. These genes are genes induced in the black seed coat cultivar and are part of pathways responsible for ethylene, lipid, brassinosteroid, lignin, and sulfur amino acid biosynthesis. The BRSMG 715A gene has almost 4times more lignin than the yellow seed coat cultivars. These attributes are related to the BRSMG 715A cultivar’s higher seed quality, which translates to more longevity and resistance to moisture and mechanical damage. Future silencing studies may evaluate the knockout of these genes to better understand the biology of soybean seeds with black seed coat.
Bidirectional Collaborative Mentoring Network for Marine Organism Detection and Beyond

Organism detection plays a vital role in marine resource exploitation and marine economy. How to accurately locate the target organism object within the camouflaged and dark light oceanic scene has recently drawn great attention in the research community. Existing learning-based works usually leverage local texture details within a neighboring area, with few methods explicitly exploring the usage of contextualized awareness for accurate object detection. From a novel perspective, we in this work present a Bidirectional Collaborative Mentoring Network (BCMNet) which fully explores both texture and context clues during the encoding and decoding stages, making the cross-paradigm interaction bidirectional and improving the scene understanding at all stages. Specifically, we first extract texture and context features through a dual-branch encoder and attentively fuse them through our adjacent feature fusion (AFF) block. Then, we propose a structure-aware module (SAM) and a detail-enhanced module (DEM) to form our two-stage decoding pipeline. On the one hand, our SAM leverages both local and global clues to preserve morphological integrity and generate an initial prediction of the target object. On the other hand, the DEM explicitly explores long-range dependencies to refine the initially predicted object mask further. The combination of SAM and DEM enables better extracting, preserving, and enhancing the object morphology, making it easier to segment the target object from the camouflaged background with sharp contour. Extensive experiments on three benchmark datasets show that our proposed BCMNet performs favorably over state-of-the-art models. The code will be made available at https://github.com/chasecjg/BCMNet.
Recent progress in transparent memristors

Memristors are one of the most promising next-generation nonvolatile storage devices because of their unique structure and excellent performance. Transparent memristors with multifunctional coupling are one of the future development directions for functionally integrated electronics. Compared to other memristors, transparent memristors have enormous advantages in stability and optoelectronic devices due to their transparency. These advantages will enable transparent memristors to have more potential applications, such as in optoelectronic neuromorphic systems, artificial intelligence, and human visual systems. Therefore, it is necessary to analyze trends in transparent memristors in order to realize these applications. Here, we review three application areas : nonvolatile memory, artificial synapses, and neural networks. The resistive switching mechanism of transparent memristors is introduced through the overall and local effects. Finally, application prospects and challenges of transparent memristors are summarized, which provides an insightful guide to developing transparent electronic devices further.
Developing PMMA/Coffee Husk Green Composites to Meet the Individual Requirements of People with Disabilities: Hip Spacer Case Study

When replacing a damaged artificial hip joint, treatment involves using antibiotic-laced bone cement as a spacer. One of the most popular materials used for spacers is PMMA; however, it has limitations in terms of mechanical and tribological properties. To overcome such limitations, the current paper proposes utilizing a natural filler, coffee husk, as a reinforcement for PMMA. The coffee husk filler was first prepared using the ball-milling technique. PMMA composites with varying weight fractions of coffee husk (0, 2, 4, 6, and 8 wt.%) were prepared. The hardness was measured to estimate the mechanical properties of the produced composites, and the compression test was utilized to estimate the Young modulus and compressive yield strength. Furthermore, the tribological properties of the composites were evaluated by measuring the friction coefficient and wear by rubbing the composite samples against stainless steel and cow bone counterparts under different normal loads. The wear mechanisms were identified via scanning electron microscopy. Finally, a finite element model for the hip joint was built to investigate the load-carrying capacity of the composites under human loading conditions. The results show that incorporating coffee husk particles can enhance both the mechanical and tribological properties of the PMMA composites. The finite element results are consistent with the experimental findings, indicating the potential of the coffee husk as a promising filler material for enhancing the performance of PMMA-based biomaterials.
Knowledge Transfer through Public-Private Development Partnerships

This article aims to add to the body of literature on knowledge transfer in Technical and Vocational Education and Training (TVET) focusing on the core stakeholders’ experiences in Public-Private Development Partnership (PPDP) in Ethiopia and Zambia. The qualitative case study comparatively examines practices and barriers to promoting knowledge transfer in two TVET programmes in Ethiopia and Zambia— both applied a similar PPDP model. The study draws on data generated through semi-structured interviews based on narratives perspective. Key informants were purposively selected from among stakeholders who were directly involved in the two initiatives. The data analysis process follows thematic analysis. The results show that anchoring the PPDP initiatives with the nationally mandated Ministry and broadening coalition with the private sector are critical in transferring knowledge into the national TVET systems. Knowledge transfer through PPDPs is challenged by difficulties of retaining staff the initiatives have invested and partnering with a model school isolated from a TVET system that the PPDP intends to influence. This article points to consideration of local schools’ prior experience of delivering similar programmes in order to insert new programmes into TVET systems. In Ethiopia, new TVET interventions are installed in a country and school not familiar with the knowledge field contrary to the programme in Zambia which was built on the existing programme. The successful transfer of PPDP interventions and their sustainability, it is argued, requires implementation of the initiatives with sound knowledge of the local context, and application of adaptive practices that encourage collaboration with diversified and committed local stakeholders.
A NOTE ON A GENERAL STOPPING RULE IN DYNAMIC PROGRAMMING WITH FINITE HORIZON

We consider a general dynamic programming model. Assume that there is an action a^ under which a State s^ of the system remains unchanged. If in a model with finite time horizon â ^ is optimal for ŝ ^ then once the system has reached State s^ we can apply a^ for the rest of the time. This property of the model is a kind of stopping rule reducing the effort of optimization. If the time horizon is finite this property need no longer hold. Instead it may happen that after a^ has been used as an optimal action it may become nonoptimal at some later point of time. However, the desired stopping rule holds in the finite horizon case if the total expected rewards are convex with respect to the time parameter. Sufficient conditions for convexity are given. In particular these results can be applied to the so-called k-armed bandit with one arm known. AMS 1979 subject Classification: 90 C 39, 90 C 40, 62 L 05
The Visual Footsteps Planning System for Exoskeleton Robots Under Complex Terrain

The lower limb power-assist exoskeletons are expected to help paraplegic people to walk again in daily life. However, most of these exoskeletons deal with walking in the scene that has been seen or has an external vision sensor, rather than in the unknown environment. It is a great challenge to understand the wear’s intention and plan the footstep sequence in an unknown scene. Moreover, the traditional visual footstep planning is dominated by the robot, which can lead to an awkward trajectory plan. Therefore, we construct a visual footstep planning system and propose an onboard vision planning algorithm based on the Bezier curve to address the previous two problems. Specially, our human–computer interaction system understands the environment and the wearer’s behavior intention by integrating Hololens and Realsense. Then, we apply the Bezier curve to plan footsteps for the first time in the field of the exoskeleton and define two parameters of the Bezier curve, which are more suitable for our exoskeleton system and could increase the planning speed. Finally, we add the tracking feature cost in the cost function, which could better fit the planned footprints to the planned path and make the gait smoother. Extended experimental results show that the average planning time is 67.46% less than that of the traditional search algorithm. Moreover, the effectiveness of our system is also verified on the visual interaction platform.
Brain Activity Indexing via Network Clustering

Brain activity can be indirectly reflected by the fluctuation of blood oxygenation level dependent (BOLD) signal captured by functional magnetic resonance imaging (fMRI). Due to many factors, like imaging-system-related instability and subject motion, raw signals are noisy, which impedes the analyses and interpretations of fMRI data. We propose a networkclustering-based indexing method to extracted key information from noisy BOLD signals. To evaluate the effectiveness, we train classifiers to discriminate patients with Alzheimer’s disease and normal controls using the linear correlations of indices as features. Results show satisfying classification accuracy (as high as 100%) when different machine learning methods are used, which indicates that the proposed method can be used to find potential fMRI biomarkers.
Share Issuance and Factor Timing

We show that characteristics of stock issuers can be used to forecast important common factors in stocks returns such as those associated with book-to-market, size, and industry. Specifically, we use differences between the attributes of stock issuers and repurchasers to forecast characteristic-related factor returns. For example, we show that large firms underperform following years when issuing firms are large relative to repurchasing firms. While our strongest results are for portfolios based on book-to-market, size (i.e., we forecast the HML and SMB factors), and industry, our approach is also useful for forecasting factor returns associated with distress, payout policy, and profitability.
Social Functions of Machine Emotional Expressions

Virtual humans and social robots frequently generate behaviors that human observers naturally see as expressing emotion. In this review article, we highlight that these expressions can have important benefits for human–machine interaction. We first summarize the psychological findings on how emotional expressions achieve important social functions in human relationships and highlight that artificial emotional expressions can serve analogous functions in human–machine interaction. We then review computational methods for determining what expressions make sense to generate within the context of interaction and how to realize those expressions across multiple modalities, such as facial expressions, voice, language, and touch. The use of synthetic expressions raises a number of ethical concerns, and we conclude with a discussion of principles to achieve the benefits of machine emotion in ethical ways.
Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification

In the context of machine learning for uncertainty quantification (UQ) of inverse problems: we propose to first transform input observations using the adjoint. We demonstrate with two imaging examples (photoacoustic imaging and CT) that firstly: the adjoint partially undoes the physics of the problem resulting in faster convergence of the learning phase and secondly: the final algorithm is now robust to changes in observations such as changing transducer subsampling in photoacoustic imaging and angles in CT. Our adjoint-based method gives point estimates faster than traditional baselines and with higher SSIM metrics, while also providing validated UQ.
Features of dislocation images reconstructed from step‐scanned white X‐ray section topographs

We have observed dislocations in a CZ‐silicon crystal using step‐scanning white X‐ray section topography and examined the feature of the dislocation images reconstructed by stacking 200 shots of sec‐ tion topographs. These topographs were obtained from symmetric 004‐reflection in the Laue case using 20, 30, and 60 keV X‐rays. Direct images, intermediary images, and dynamical images of dislocations were observed in a tomogram, i.e., a sliced image obtained from the stacked section topographs, and the contrast of the dynamical images was reduced with increasing X‐ray energy. The stacked section topographs provided three‐dimensional images of the dislocations based on the direct images, and the sliced image enabled us to observe the dislocations separately in a highly dislocated region of the CZ‐silicon crystal. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Phylogenetic systematics, diversification, and biogeography of Cerurinae (Lepidoptera: Notodontidae) and a description of a new genus

We present the first dated molecular phylogeny of the Cerurinae moths (Notodontidae), based on sequence data for 666 loci generated by anchored hybrid enrichment. Monophyly of Cerurinae is corroborated, which includes the following genera: Pararethona Janse, Pseudorethona Janse, Oreocerura Kiriakoff, stat. rev., Cerurella Kiriakoff, Notocerura Kiriakoff, Hampsonita Kiriakoff, Afrocerura Kiriakoff, Cerurina Kiriakoff, Neoharpyia Daniel, Furcula Lamarck, Neocerura Matsumura, Americerura St Laurent and Goldstein, gen. nov., Cerura Schrank, and Kamalia Koçak & Kemal. The type species of the Neotropical genus Tecmessa Burmeister, T. annulipes (Berg), which had been incorrectly assigned to Cerurinae, is recovered in Heterocampinae; and Americerura gen. nov. is proposed to receive 17 unambiguously cerurine species transferred from Tecmessa. Divergence time estimates recover a crown age of Notodontidae roughly coincident with the K-Pg boundary, and a late-Oligocene crown age for Cerurinae. An African origin is inferred for Cerurinae, followed by colonization of the Palearctic, the Americas, Indomalaya, and Australasia during the Miocene. At least three independent colonizations of the Americas are inferred, one in the mid-Miocene associated with ancestral Americerura gen. nov. and two in the Pliocene and Pleistocene within Furcula. We hypothesize that the global spread of Cerurinae was enabled by that of its primary caterpillar foodplants in the Salicaceae. State-dependent diversification analyses suggest that cerurines diversified most rapidly in temperate climates.
Centenary of Acta Societatis Botanicorum Poloniae

is year marks the 100th anniversary of the Acta Societatis Botanicorum Poloniae (ASBP) establishment, and it is an opportune time to reflect on its contributions to the field of botany. e journal was founded in 1923 by a group of Polish botanists who sought to create a platform for publishing high-quality research in the field. Since then, the journal has published numerous seminal papers that have shaped the course of botany. Currently, the journal covers a wide range of topics, including plant anatomy, ecology, genetics, molecular biology, taxonomy, and systematics. Over the years, the journal has maintained high standards of quality and has been a go-to resource for botanists around the world. e ASBP has published papers by some of the most eminent botanists in the field, including Władysław Szafer, a pioneering Polish botanist and one of the founders of modern plant geography Jan Kornaś, a distinguished phytogeographer and plant ecologist.
Improving the Physics Learning Outcomes of Engineering Students Through Just in Time Teaching (JiTT)

In this work, we estimate the impact over five semesters of a methodology based on Just in Time Teaching in Physics for Engineering laboratory courses. The research question explored here is as follows: what is the impact of Just in Time Teaching (JiTT) implementation on student academic achievements as measured by the grades obtained by engineering students in physics laboratories? We examined the research question by statistical analysis of the grades obtained by students before and after the implementation of the JiTT strategy. The statistical analysis showed an increase in the percentage of students with grades over 4.0/5.0 upon the implementation of this strategy. A significant difference between the grades obtained before and after implementation in the Physics I and Physics II laboratories was confirmed by ANOVA (p < 0.005); in contrast, in Physics III laboratories, we observed similar grades before and after the implementation. We conclude that the implementation of JiTT improves the grade distribution over higher values, suggesting that students have better learning outcomes; more efforts are needed in Physics III laboratories to achieve a similar (or greater) increase in grades than in Physics I and II.
Transfer learning from synthetic to routine clinical data for motion artefact detection in brain T1-weighted MRI

Clinical data warehouses (CDWs) contain the medical data of millions of patients and represent a great opportunity to develop computational tools. MRIs are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are unusable because corrupted by these artefacts. Since their manual detection is impossible due to the number of scans, it is necessary to develop a tool to automatically exclude images with motion in order to fully exploit CDWs. In this paper, we propose a CNN for the automatic detection of motion in 3D T1-weighted brain MRI. Our transfer learning approach, based on synthetic motion generation, consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the manual labelling of 5500 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy<80%). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and to highlight the importance of a clinical validation of models trained on research data.