Miracle-dz/UniFAR-FTUs huggingface dataset:
Avalanche dynamics of structural phase transitions in shape memory alloys by acoustic emission spectroscopy

In this work the avalanche dynamics of five shape memory samples has been analyzed by acoustic emission spectroscopy. The acoustic emission spectroscopy is particularly suitable for this analysis as it couples with high sensitivity to small structural changes caused by nucleation processes, interface movements, or variant rearrangements [91]. Owing to its high time resolution it provides a statistical approach to describe the jerky and intermittent character of the avalanche dynamics [20]. Rate-dependent cooling and heating runs have been conducted in order to study time-dependent aspects of the transition dynamics of the single crystals Ni63Al37, Au50.5Cd49. 5, and Fe68.8Pd single 31.2 , and the polycrystalline sample Fe68.8Pd poly 31.2 . Moreover, a ferromagnetic Ni52Mn23Ga25 single crystal has been studied by temperature cycles under an applied magnetic field and additionally by magnetic-field cycles at a constant temperature in the martensitic phase. All samples analyzed in this work show power law behavior in the acoustic emission features amplitude, energy, and duration, which indicates scale-free behavior. The access to these power law spectra allows an investigation of energy barriers separating the metastable states, which give rise to avalanche transition dynamics. By performing rate-dependent experiments the importance of thermal fluctuations and the impact of martensite respectively twin stabilization processes have been examined. In the case of the Ni52Mn23Ga25 sample, the magneticfield-induced variant rearrangement at slow field cycles leads to stronger signals than the rearrangement at quick cycles. This behavior can be explained by twin stabilization processes, which are accompanied by a reduction of the twin boundary mobility. For Ni63Al37, the combination of relevant thermal fluctuations, different involved time scales, and a high degree of intrinsic disorder leads to a lower acoustic activity and weaker signals under decreasing cooling rates. In the case of Au50.5Cd49. 5, by contrast, the low rates allow aging to become significant. This leads to higher energy barriers and, as a consequence, to stronger acoustic emission signals. The excellent agreement of this result with a model introduced by Otsuka et al. [127], suggests that aging should be included into the framework of driving rate effects in avalanche-mediated phase transitions [135]. In contrast to what has been stated earlier [20, 172], the author concludes that only the detection of rate-dependent acoustic activity or the detection of incubation times are necessary and sufficient conditions for time-dependent
A Comparative Method for Securing Internet of Things (IoT) Devices: AES vs Simon-Speck Encryptions

Delivering information from Internet of Things (IoT) devices to a cloud server possesses several security issues, e.g. information eavesdropping, modification, and theft. Therefore, communication between IoT devices and the cloud server should be protected by encryption methods. However, there are few encryption techniques options that are suitable for the need for lightweight communication as demanded by the IoT devices. Due to these circumstances, the NSA launched an encryption algorithm for IoT named Simon and Speck, which are maximally efficient while still providing the advertised level of security, as determined by the key size. This study aims to test and compare the Simon-Speck and AES encryption algorithms and their effect on networking performance on IoT devices. The parameters in this test are delay, throughput, the efficiency of memory usage from the encryption algorithm, and the value of the avalanche effect. Experimental results show that the Speck algorithm outperforms the Simon and the AES algorithms in terms of communication delay and memory usage. Regarding the avalanche effect values, the Simon algorithm possesses the highest avalanche effect value on average against the Speck and the AES algorithms.
Classification of Glaucoma in Fundus Images Using Convolutional Neural Network with MobileNet Architecture

Glaucoma is a damaged optic nerve due to increased pressure on the eyeball. The cause is a mismatch between eye fluid (aqueous humor) produced and the amount of eye fluid secreted. Ophthalmologists usually detect glaucoma using Cup to Disc Ratio or CDR parameter. However, the calculation of CDR parameters is still done manually, usually done by trained doctors and relatively expensive and limited equipment. This study proposes a system that can classify glaucoma using the Convolutional Neural Network method with MobileNet architecture. MobileNet has two convolution parts: depthwise convolution and pointwise convolution. The function of the Depthwise Convolution is to apply a single convolution filter per input channel, while the function of the pointwise convolution is to build new features by calculating the linear combination of the input channels by applying the 1x1 convolution. The data used comes from rimone-r1 database. Result accuracy of the proposed method reaches 99%. Automated glaucoma classification can assist medical staff in identifying the best treatment for their patients.
Strength and vertical jumping performance characteristics

The constructed 3D model of the lower part of human body for the simulation of high jump enables to investigate not only parameters of the jump but to analyses the forces acting in muscles and joints as well. The model enables determining the most important muscles for the jump performance and sequentially to make recommendations for the sportsmen seeking the jump height. The developed methodology can be applied for the analysis of other type movements in sports.
Critical Success Factors of Knowledge Management Implementation in BPKP

As the auditor for the President of the Republic of Indonesia, the Financial and Development Supervisory Agency or abbreviated as BPKP, was charged to give a fast strategic recommendation to the President. Knowledge management (KM) became one of the vital strategies in organizations to improve the quality of recommendations and achieve organizational goals. A survey method was conducted to identify critical success factors (CSF) of KM implementation at BPKP. The structural equation modeling partial least square (SEM-PLS) method was used in this study to implement knowledge management as the latent variable. After that, the research model was described, and all critical success factors were explained clearly because of this research. The study results indicated that the critical success factors of the application of knowledge management are leadership, information technology, and rewards/motivation.
U sing strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading

Algorithmic trading has become an increasingly thriving research area and a lot of focus has been given on indicators from technical and sentiment analysis. In this paper, we examine the advantages of combining features from both analyses. To do this, we use two different genetic programming algorithms (GP). The first algorithm allows trees to contain technical and/or sentiment analysis indicators without any con-straints. The second algorithm introduces technical and sentiment analysis types through a strongly typed GP, whereby one branch of a given tree contains only technical analysis indicators and another branch of the same tree contains only sentiment analysis features. This allows for better exploration and exploitation of the search space of the indicators. We perform experiments on 10 international stocks and compare the above two GPs' performances. Our goal is to demonstrate that the combination of the indicators leads to improved financial performance. Our results show that the strongly typed GP is able to rank first in terms of Sharpe ratio and statistically outperform all other algorithms in terms of rate of return.
Learning Strategies on Scheduling Heuristics of Genetic Programming in Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling is an important combinatorial optimisation problem that covers valuable practical applications such as order picking in warehouses and service allocation in cloud computing. Machine assignment and operation sequencing are two key decisions to be considered simultaneously in dynamic flexible job shop scheduling. Genetic programming has been successfully and widely used to learn scheduling heuristics, including a routing rule for machine assignment and a sequencing rule for operation sequencing simultaneously. There are mainly two types of learning strategies to evolve scheduling heuristics, i.e., learning one rule by fixing the other rule, and learning the routing rule and the sequencing rule simultaneously. However, there is no guidance on which learning strategy to use in specific cases. To fill this gap, this paper provides a comprehensive study of learning strategies on scheduling heuristics of genetic programming in dynamic flexible job shop scheduling by comparing five learning strategies, including two strategies that are extended from the existing studies. The results show that learning two rules simultaneously, either using cooperative coevolution or multi-tree representation, is more effective than only learning one type of rule. Cooperative coevolution is recommended if an algorithm aims to handle a problem by dividing it into small sub-problems, and focuses on the characteristics of routing rule and sequencing rule. Genetic programming with multi-tree representation that treats the routing rule and the sequencing rule as an individual, is preferred to reduce the complexities of algorithms.
Comparison of coplanar waveguide models at millimetre wave frequencies

This paper investigates an improved empirical model predicting the propagation characteristics of coplanar waveguides (CPW) at G band based on a conventional analytical CPW model. A comparison with another quasi-analytic CPW model and fullwave em simulations is presented. The comparison results demonstrate that the improved CPW model shows excellent agreement with measurements on different substrate materials up to 220 GHz. This means that, for the first time, a comprehensive and efficient CPW description at higher frequency ranges up to G band is available. This improved CPW model can be applied and used during the design cycle of hybrid integrated circuits (ICs), monolithic microwave integrated circuits (MMICs) and printed circuits board (PCBs). Moreover, the enhanced accuracy of the improved CPW model can help to reduce uncertainties in on-wafer CPW-based measurements.
Design of Feature-based Underbody Image Registration Method in TFDS

As the lifeline of the national economy, railway traffic is one of important mode of transportation, it runs fast, has a large load, is safe and is not affected by weather, the rapid development of our country current implementing sustainable development strategy of critical face railway leap-forward development of vehicle safety work put forward new requirements, rail safety technical innovation planning arises at the historic moment. The dynamic image detection system of freight car running fault (TFDS) uses the real-time transmission and processing technology of mass image data of high-speed camera and high-precision positioning technology, and installs high-speed industrial camera beside the track to dynamically identify hidden faults and common faults of freight car running on the line Based on the above background, this essay will conduct an in-depth study on the intelligent image recognition method of railway operation safety and its application in the railway truck operation safety detection and monitoring system. At present, TFDS system only completes the function of image acquisition and transmission, while the automatic recognition module for image anomalies still needs to be solved urgently This project is to use digital image processing and other technologies to design the corresponding abnormal distortion image recognition algorithm, and use the algorithm to complete the correction of the distortion Figure of a train in a short time after the train passes, so as to improve the current TFDS system, conform to and promote the development of railway truck transportation safety.
Disturbance Observer Based Dynamic Output Feedback Controller Design for Linear Uncertain Systems

For linear multiple input multiple output (MIMO) systems with mismatched parameter uncertainties and matched nonlinear perturbations, a compensator‐based controller based on the disturbance estimation algorithm is considered in this paper. We design a reduced‐order high‐gain observer to estimate the effect of matched perturbation, and introduce the estimation information into the controller design. Using singular perturbation theory, the proposed control law can guarantee robust stability of the closed‐loop system. Moreover, the peaking phenomenon in the control input during the transient time can be effectively avoided using our method. Finally, the feasibility of the proposed method is illustrated by a numerical example.
LITAR

An accurate understanding of omnidirectional environment lighting is crucial for high-quality virtual object rendering in mobile augmented reality (AR). In particular, to support reflective rendering, existing methods have leveraged deep learning models to estimate or have used physical light probes to capture physical lighting, typically represented in the form of an environment map. However, these methods often fail to provide visually coherent details or require additional setups. For example, the commercial framework ARKit uses a convolutional neural network that can generate realistic environment maps; however the corresponding reflective rendering might not match the physical environments. In this work, we present the design and implementation of a lighting reconstruction framework called LITAR that enables realistic and visually-coherent rendering. LITAR addresses several challenges of supporting lighting information for mobile AR. First, to address the spatial variance problem, LITAR uses two-field lighting reconstruction to divide the lighting reconstruction task into the spatial variance-aware near-field reconstruction and the directional-aware far-field reconstruction. The corresponding environment map allows reflective rendering with correct color tones. Second, LITAR uses two noise-tolerant data capturing policies to ensure data quality, namely guided bootstrapped movement and motion-based automatic capturing. Third, to handle the mismatch between the mobile computation capability and the high computation requirement of lighting reconstruction, LITAR employs two novel real-time environment map rendering techniques called multi-resolution projection and anchor extrapolation. These two techniques effectively remove the need of time-consuming mesh reconstruction while maintaining visual quality. Lastly, LITAR provides several knobs to facilitate mobile AR application developers making quality and performance trade-offs in lighting reconstruction. We evaluated the performance of LITAR using a small-scale testbed experiment and a controlled simulation. Our testbed-based evaluation shows that LITAR achieves more visually coherent rendering effects than ARKit. Our design of multi-resolution projection significantly reduces the time of point cloud projection from about 3 seconds to 14.6 milliseconds. Our simulation shows that LITAR, on average, achieves up to 44.1% higher PSNR value than a recent work Xihe on two complex objects with physically-based materials.
Psychological Contributions to the Explanation and Prevention of Offending

The main aims of this article are to review evidence about the role of individual and family factors in offending, and to review methods of reducing offending based on individual and family factors. Psychological explanations of offending focus on individual difference factors and family influences, which have been investigated in prospective longitudinal surveys. The most important individual factors are hyperactivity and impulsivity, and low intelligence and attainment. The most important family influences are parental supervision and discipline, disrupted families, criminal parents, and large family size. The most important prevention techniques target individual and family risk factors, and have been evaluated in experimental and quasi-experimental research. Cognitive-behavioral skills training programs target impulsivity, while preschool intellectual enrichment programs target low school attainment. Home visiting programs, parent training programs, Functional Family Therapy, Treatment Foster Care, and Multisystemic Therapy target poor parental supervision and discipline. It is concluded that more early intervention efforts are urgently needed to tackle the roots of crime.
Institutional Change by Imitation: Introducing Western Governance Practices in Congolese Villages

We design a system of observations of informal governance practices and elite capture in 456 villages of the Democratic Republic of the Congo, where the state has largely withdrawn. We use these measurements to estimate the effect of exposure to Western governance practices, marketed as “good governance,” on the adoption of these foreign practices. To achieve this goal, we exploit a randomized intervention that provided a four year long incentivized exposure to participatory practices of governance transplanted from the West to 1,250 villages, and covered and 1.78m people. Our results suggest that such intentional, incentivized efforts to induce cultural change do not affect local decision making in the ways currently still advocated by many governments and donors. The results cast doubt on the assumptions and goals of attempts to export Western governance practice. JEL Codes: D72; P48; D02; O17
MCM6 is a critical transcriptional target of YAP to promote gastric tumorigenesis and serves as a therapeutic target

Rationale: Hyperactivation of Hippo-Yes-associated protein (YAP) signaling pathway governs tumorigenesis of gastric cancer (GC). Here we reveal that minichromosome maintenance complex component 6 (MCM6) is a critical transcriptional target of YAP in GC. We aim to investigate the function, mechanism of action, and clinical implication of MCM6 in GC. Methods: The downstream targets of YAP were screened by RNA sequencing (RNA-seq) and microarray, and further validated by chromatin immunoprecipitation PCR and luciferase reporter assays. The clinical implication of MCM6 was assessed in multiple GC cohorts. Biological function of MCM6 was evaluated in vitro, in patient-derived organoids, and in vivo. RNA-seq was performed to unravel downstream signaling of MCM6. Potential MCM6 inhibitor was identified and the effect of MCM6 inhibition on GC growth was evaluated. Results: Integrative RNA sequencing and microarray analyses revealed MCM6 as a potential YAP downstream target in GC. The YAP-TEAD complex bound to the promoter of MCM6 to induce its transcription. Increased MCM6 expression was commonly observed in human GC tissues and predicted poor patients survival. MCM6 knockdown suppressed proliferation and migration of GC cells and patient-derived organoids, and attenuated xenograft growth and peritoneal metastasis in mice. Mechanistically, MCM6 activated PI3K/Akt/GSK3β signaling to support YAP-potentiated gastric tumorigenicity and metastasis. Furthermore, MCM6 deficiency sensitized GC cells to chemo- or radiotherapy by causing DNA breaks and blocking ATR/Chk1-mediated DNA damage response (DDR), leading to exacerbated cell death and tumor regression. As there are no available MCM6 inhibitors, we performed high-throughput virtual screening and identified purpureaside C as a novel MCM6 inhibitor. Purpureaside C not only suppressed GC growth but also synergized with 5-fluorouracil to induce cell death. Conclusions: Hyperactivated YAP in GC induces MCM6 transcription via binding to its promoter. YAP-MCM6 axis facilitates GC progression by inducing PI3K/Akt signaling. Targeting MCM6 suppresses GC growth and sensitizes GC cells to genotoxic agents by modulating ATR/Chk1-dependent DDR, providing a promising strategy for GC treatment.
Employing Augmented Reality Throughout a Civil Engineering Curriculum to Promote 3D Visualization Skills

Use of Augmented Reality (AR) sandboxes has gained popularity in “please touch” science museums and introductory earth and environmental science courses to facilitate student visualization of concepts in three dimensions. University faculty who have access to AR sandboxes typically utilize them in discrete learning experiences (i.e., one specific activity, module, or course), rather than a series of interactions with the technology to introduce and then reinforce 3D visualization skills. A case study is presented herein of how student learning experiences with the AR sandbox have been woven throughout the undergraduate civil engineering curriculum at Villanova University. A series of progressive AR sandbox learning activities have been implemented in freshmen and junior-level civil engineering courses, and additional modules are planned for other courses in the sophomore and senior years. Planning and implementation of these modules has been a collaborative effort between faculty across multiple disciplines within the department (geotechnical, water resources, and structural engineering). The purpose of the AR sandbox interventions is to facilitate student engagement and improve understanding and ability to apply basic civil engineering concepts that require three-dimensional visualization skills, supporting the goal to graduate students who are professionally prepared to serve as effective engineers. Details are provided for three of the modules to allow for adaptation by faculty at other universities with access to similar technology. In the freshman-level course, Civil Engineering Fundamentals, students insert Plexiglas sheets in the sandbox and then create plan, profile, and cross-sectional views of elevation data. In the junior-level Soil Mechanics course taught in the fall, students build upon their prior experience with the AR sandbox and apply new concepts from lecture to evaluate slope stability and calculate earthwork volumes. In Hydraulic Engineering and Hydrology, taught in the spring of junior year, the learning module includes creating a watershed landscape in the sandbox and simulating rainfall to visualize flow patterns, directions, and travel time through the basin. Based on positive faculty and student feedback regarding the current AR learning experiences included in civil engineering courses at Villanova University, additional modules are planned for sophomore and senior-level courses to further enrich 3D-visualization outcomes from a curriculum-wide approach.
Epiphytes influence the transformation of nitrogen in coniferous forest canopies

The transformation of atmospherically-deposited nitrogen (N) by coniferous forest canopies has primarily been attributed to epiphytic lichens, despite microbes being present on leaf surfaces. We examined the relative contribution of live foliage with its epiphytic microbes and epiphytic lichen on N transformations in black spruce canopies. The epiphytic microbes and live foliage intercepted 27% of rainwater, 72% of incident nitrite + nitrate, and 63% of incident ammonium, while enriching throughfall with organic N by 52%. Epiphytic lichens on dead lower branches intercepted 60% of rainwater and depleted absolute amounts of inorganic and organic N, even though solute concentration increased as water passed through the lichens. Relationships were found between the number of colony forming units of epiphytic fungi and the depletion and enrichment of different forms of N in throughfall. Intensive forest management practices could result in the canopy being a net sink for N as a result of higher lichen biomass.