Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining Fracture Energy in Asphalt Mixture: A Review

One of the most common pavement distresses is related to surface cracking. Therefore, identifying and characterizing fracture properties of asphalt mixtures are significant towards a better pavement design. This study reviews four experimental methods used to determine the fracture energy in asphalt mixture. These methods include circular bending test (SCB), disc shape compact tension test, single-edge notched beam, and indirect tensile test. Each experimental method has its characteristics and advantages. These experimental methods are reviewed on the basis of their features, efficiency, and parameters measured. The coefficient of variation (COV) for the fracture tests reflects the result reliability of the test methods. Results with low COV value reflect low variance in the fracture test, whereas high COV indicates high variance. The review indicates that the SCB test is commonly used for determining the fracture energy in asphalt mixtures due to its simplicity and data reliability.
An Inclusive Survey of Machine Learning based Hand Gestures Recognition Systems in Recent Applications

Hand gestures represent one of the most prevalent types of body language which can be utilized for interaction and communication. Although the other types of body language represent a more general state of emotional, hand gestures capable of possessing specified linguistic content inside it. Because of the expressiveness and speed in interaction, hand gestures are commonly utilized in human-computer interaction systems (HCI), sign languages, virtual reality, and gaming. In the process of recognizing hand gestures, the complexity and diversity of gestures will extremely impact on the recognition rate and reliability. The existence of machine learning techniques can be effectively exploited in the task of improving the rate of hand gesture recognition. This paper inspected the performance of machine learning techniques in recognizing vision and sensors based hand gestures in the recently existing applications. Additionally, the widely used architecture applied in various datasets has been considered which includes the acquisition of data, pre-processing, the extraction of features, and classification.
Shuffled Frog Leaping Algorithm Based Neural Network and Its Application in Big Data Set

This paper studies the random optimization algorithm to reorganize the leapfrog algorithm. The big data challenge requires an effective optimization algorithm to explore potential data structures using deep neural networks. At first, we introduce the neural network classifier and compare it with the support vector machine. Neural networks are suitable for large data sets and have the complex ability to extract high-level abstract data. And then we have to introduce a large dataset covering cancer data and voice data. Both datasets have large numbers of samples with complex low-level variance. At last we have to use the reorganized leapfrog algorithm to optimize neural network parameters. The random leapfrog algorithm is efficient and robust to a local minimum. The experimental results show that the algorithm has extensive application prospects and is suitable for the classification of big dataset. The neural network parameters can effectively optimized by the improved shuffled frog leaping algorithm.
Collective Synchronization of Undulatory Movement through Contact

Many biological systems synchronize their movement through physical interactions. By far, the most well-studied examples concern physical interactions through a fluid: Beating cilia, swimming sperm and worms, and flapping wings all display synchronization behavior through fluid mechanical interactions. However, as the density of a collective increases, individuals may also interact with each other through physical contact. In the field of “ active matter ” systems, it is well known that inelastic contact between individuals can produce long-range correlations in position, orientation, and velocity. In this work, we demonstrate that contact interactions between undulating robots yield novel phase dynamics such as synchronized motions. We consider undulatory systems in which rhythmic motion emerges from time-independent oscillators that sense and respond to an undulatory bending angle and speed. In pair experiments, we demonstrate that robot joints will synchronize to in-phase and antiphase oscillations through collisions, and a phase-oscillator model describes the stability of these modes. To understand how contact interactions influence the phase dynamics of larger groups, we perform simulations and experiments of simple three-link undulatory robots that interact only through contact. Collectives synchronize their movements through contact as predicted by the theory, and when the robots can adjust their position in response to contact, we no longer observe antiphase synchronization. Lastly, we demonstrate that synchronization dramatically reduces the interaction forces within confined groups of undulatory robots, indicating significant energetic and safety benefits from group synchronization. The theory and experiments in this study illustrate how contact interactions in undulatory active matter can lead to novel collective motion and synchronization.
Finding the deconfinement temperature in lattice Yang-Mills theories from outside the scaling window with machine learning

We study the machine learning techniques applied to the lattice gauge theory's critical behavior, particularly to the confinement/deconfinement phase transition in the SU(2) and SU(3) gauge theories. We find that the neural network of the machine-learning algorithm, trained on 'bare' lattice configurations at an unphysical value of the lattice parameters as an input, builds up a gauge-invariant function, and finds correlations with the target observable that is valid in the physical region of the parameter space. In particular, if the algorithm aimed to predict the Polyakov loop as the deconfining order parameter, it builds a trace of the gauge group matrices along a closed loop in the time direction. As a result, the neural network, trained at one unphysical value of the lattice coupling $\beta$ predicts the order parameter in the whole region of the $\beta$ values with good precision. We thus demonstrate that the machine learning techniques may be used as a numerical analog of the analytical continuation from easily accessible but physically uninteresting regions of the coupling space to the interesting but potentially not accessible regions.
Sensory Characteristics of Dairy By-Products as Potential Milk Replacers in Ice Cream

Novel strategies for wheys application, decreasing its negative environmental impact, represent a challenge for dairy industries. The use of wheys as replacers may lead to adverse sensory quality changes in conventional dairy products. This study aimed to investigate the sensory effects of the use of ricotta whey (RW), cheese whey (CW), and butter whey (BW) in replacement to whole milk (WM) at different ratios (0:100%, 25:75%, 50:50%, 75:25%, and 100:0%, w/w) in chocolate ice creams. All formulations, including a commercial sample used as a reference, were analyzed for overall liking, purchase intention, just-about-right (JAR), and survival analysis. RW, CW, and BW up to 75%, 100%, and 25% did not affect (p > 0.05) the overall liking, respectively. Purchase intention was only decreased (p < 0.05) by BW at 50%, 75%, and 100% compared to a commercial sample. Considering data from PCA and PLSR associated with hedonic scores and penalty analysis, the main adverse sensory effects on overall liking were: increased perception (p < 0.05) of chocolate flavor by RW, CW, and BW, melting velocity by RW, sweetness by CW and BW, and chocolate aroma by BW. Survival analysis indicated that BW could be added up to a maximum of 84.15%, while no rejection was detected for RW and CW. Based on results, RW, CW, and BW represent potential milk replacers to be used by the dairy industry, decreasing the environmental impact of these dairy by-products.
Chinese Event Subject Extraction in the Financial Field Integrated with BIGRU and Multi-head Attention

Event subject extraction was to extract subjects of specific event types. For the traditional BiLSTM network, the threshold is complicated, the required parameters are many, and the time cost is high. This paper is oriented to the financial field and proposes a method of introducing a multi-head attention mechanism based on the BIGRU network to extract event subjects. First, the text is vectorized, and then the word vector obtained is input into the BIGRU network to learn the context features, and introduce a multi-head attention mechanism to extract the depth feature values of the text. Finally, a comparative experiment is conducted on the data set. The method in this paper achieves an Accuracy value of 80.47% and an F1 value of 89.18%. The result is better than the control group, indicating that the model proposed in this paper can effectively improve the accuracy of extracting Chinese event subjects.
Using a robotic platform to study the influence of relative tailbeat phase on the energetic costs of side-by-side swimming in fish

A potential benefit of swimming together in coordinated schools is to allow fish to extract energy from vortices shed by their neighbours, thus reducing the costs of locomotion. This hypothesis has been very hard to test in real fish schools, and it has proven very difficult to replicate the complex hydrodynamics at relevant Reynolds numbers using computational simulations. A complementary approach, and the one we adopt here, is to develop and analyse the performance of biomimetic autonomous robotic models that capture the salient kinematics of fish-like swimming, and also interact via hydrodynamic forces. We developed bio-inspired robotic fish which perform sub-carangiform locomotion, and measured the speed and power consumption of robots when swimming in isolation and when swimming side-by-side in pairs. We found that swimming side-by-side confers a substantial increase in both the speed and efficiency of locomotion of both fish regardless of the relative phase relationship of their body undulations. However, we also find that each individual can slightly increase their own power efficiency if they change relative tailbeat phase by approximately 0. 25π with respect to, and at the energetic expense of, their neighbour. This suggests the possibility of a competitive game-theoretic dynamic between individuals in swimming groups. Our results also demonstrate the potential applicability of our platform, and provide a natural connection between the biology and robotics of collective motion.
Birth timing generates reproductive trade-offs in a non-seasonal breeding primate

The evolutionary benefits of reproductive seasonality are often measured by a single-fitness component, namely offspring survival. Yet different fitness components may be maximized by different birth timings. This may generate fitness trade-offs that could be critical to understanding variation in reproductive timing across individuals, populations and species. Here, we use long-term demographic and behavioural data from wild chacma baboons (Papio ursinus) living in a seasonal environment to test the adaptive significance of seasonal variation in birth frequencies. We identify two distinct optimal birth timings in the annual cycle, located four-month apart, which maximize offspring survival or minimize maternal interbirth intervals (IBIs), by respectively matching the annual food peak with late or early weaning. Observed births are the most frequent between these optima, supporting an adaptive trade-off between current and future reproduction. Furthermore, infants born closer to the optimal timing favouring maternal IBIs (instead of offspring survival) throw more tantrums, a typical manifestation of mother–offspring conflict. Maternal trade-offs over birth timing, which extend into mother–offspring conflict after birth, may commonly occur in long-lived species where development from birth to independence spans multiple seasons. Our findings therefore open new avenues to understanding the evolution of breeding phenology in long-lived animals, including humans.
Flow increases tolerance of heat and hypoxia of an aquatic insect

Recent experiments support the idea that upper thermal limits of aquatic insects arise, at least in part, from a lack of sufficient oxygen: rising temperatures typically stimulate metabolic demand for oxygen more than they increase rates of oxygen supply from the environment. Consequently, factors influencing oxygen supply, like water flow, should also affect thermal and hypoxia tolerance. We tested this hypothesis by measuring the effects of experimentally manipulated flows on the heat and hypoxia tolerance of aquatic nymphs of the giant salmonfly (Plecoptera: Pteronarcys californica), a common stonefly in western North America. As predicted, stoneflies in flowing water (10 cm s−1) tolerated water that was approximately 4°C warmer and that contained approximately 15% less oxygen than did those in standing water. Our results imply that the impacts of climate change on streamflow, such as changes in patterns of precipitation and decreased snowpack, will magnify the threats to aquatic insects from warmer water temperatures and lower oxygen levels.
The mechanics of brittle granular materials with coevolving grain size and shape

The influence of particle shape on the mechanics of sand is widely recognized, especially in mineral processing and geomechanics. However, most existing continuum theories for engineering applications do not encompass the morphology of the grains and its evolution during comminution. Similarly, the relatively few engineering models accounting for grain-scale processes tend to idealize particles as spheres, with their diameters considered as the primary and sole geometric descriptor. This paper inspires a new generation of constitutive laws for crushable granular continua with arbitrary, yet evolving, particle morphology. We explore the idea of introducing multiple grain shape descriptors into Continuum Breakage Mechanics (CBM), a theory originally designed to track changes in particle size distributions during confined comminution. We incorporate the influence of these descriptors on the elastic strain energy potential and treat them as dissipative state variables. In analogy with the original CBM, and in light of evidence from extreme fragmentation in nature, the evolution of the additional shape descriptors is postulated to converge towards an attractor. Comparisons with laboratory experiments, discrete element analyses and particle-scale fracture models illustrate the encouraging performance of the theory. The theory provides insights into the feedback among particle shape, compressive yielding and inelastic deformation in crushable granular continua. These results inspire new questions that should guide future research into crushable granular systems using particle-scale imaging and computations.
Parthenogenesis is self-destructive for scaled reptiles

Parthenogenesis is rare in nature. With 39 described true parthenogens, scaled reptiles (Squamata) are the only vertebrates that evolved this reproductive strategy. Parthenogenesis is ecologically advantageous in the short term, but the young age and rarity of parthenogenetic species indicate it is less advantageous in the long term. This suggests parthenogenesis is self-destructive: it arises often but is lost due to increased extinction rates, high rates of reversal or both. However, this role of parthenogenesis as a self-destructive trait remains unknown. We used a phylogeny of Squamata (5388 species), tree metrics, null simulations and macroevolutionary scenarios of trait diversification to address the factors that best explain the rarity of parthenogenetic species. We show that parthenogenesis can be considered as self-destructive, with high extinction rates mainly responsible for its rarity in nature. Since these parthenogenetic species occur, this trait should be ecologically relevant in the short term.
Should Fishing Quotas Be Measured in Terms of Numbers?

Whereas rights-based catch regulations such as individual transferable quotas (ITQs) are gaining traction as the key management instrument in many fisheries, most fisheries are additionally regulated by gear restrictions, minimum landing sizes, and similar measures that intend to protect young fish from being caught. Here we study the incentives to fish selectively in a second-best setting, where the regulator issues quotas of different types and fishers choose the size of the fish they catch. We find that if quotas are specified in terms of the number of fish, rather than biomass or weight, fishers have substantial incentives to target larger fish. Thus juvenile fish are protected without need for gear restrictions. We develop the economic principles in an analytical model and quantify results for empirical examples. We find that steady-state profits under second-best deregulated number quota management are only 0.1%–2.1% below the first-best optimum.
Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models

A general-purpose planning agent requires an open-scope world model: one rich enough to tackle any of the wide range of tasks it may be asked to solve over its operational lifetime. This stands in contrast with typical planning approaches, where the scope of a model is limited to a specific family of tasks that share significant structure. Unfortunately, planning to solve any specific task using an open-scope model is computationally intractable - even for state-of-the-art methods - due to the many states and actions that are necessarily present in the model but irrelevant to that problem. We propose task scoping: a method that exploits knowledge of the initial state, goal conditions, and transition system to automatically and efficiently remove provably irrelevant variables and actions from a planning problem. Our approach leverages causal link analysis and backwards reachability over state variables (rather than states) along with operator merging (when effects on relevant variables are identical). Using task scoping as a pre-planning step can shrink the search space by orders of magnitude and dramatically decrease planning time. We empirically demonstrate that these improvements occur across a variety of open-scope domains, including Minecraft, where our approach leads to a 75x reduction in search time with a state-of-the-art numeric planner, even after including the time required for task scoping itself.
What is Sumak Kawsay? A Qualitative Study in the Ecuadorian Amazon

A qualitative study of the main characteristics of sumak kawsay (buen vivir, living well/good life) in the Ecuadorian Amazon shows that it has four constitutive elements of which a multitude of interpretations coexist: an indigenous and nature-focused worldview, community, an economy based on solidarity, and ancestral knowledge. Understandings of sumak kawsay are rooted in the practices and beliefs of the communities interviewed rather than in theoretical constructions of idyllic community forms, and in this connection differences can be observed between the academic “indigenist” view of it and local discourses. Una aproximación cualitativa a las características principales del sumak kawsay (buen vivir, living well/good life) en la Amazonía ecuatoriana muestran la existencia de cuatro elementos constitutivos del sumak kawsay en los que colindan multitud de interpretaciones: cosmovisión indígena y naturaleza, comunidad, economía solidaria, y conocimiento ancestral. Las comprensiones del sumak kawsay enraízan con prácticas y creencias de las personas y no con construcciones teóricas de formas comunitarias idílicas, y al respecto, apreciamos diferencias entre la visión académica “indigenista” del sumak kawsay y los discursos locales.
GRACEfully assessing the timeline for noninfarct‐related artery intervention in ST‐elevation myocardial infarction patients

• A large registry study of ST-elevation myocardial infarction (STEMI) patients with multivessel disease and without cardiogenic shock demonstrated that staged in-hospital intervention of the noninfarctrelated artery was associated with a reduced risk of all-cause mortality when compared to culprit-only PCI or immediate multivessel intervention. • A subgroup analysis stratified by the Global Registry of Acute Coronary Events (GRACE) score, showed a mortality benefit in high-risk patients (GRACE score > 140) undergoing staged in-hospital intervention of the noninfarct-related artery with a median interval between the two procedures of 5 days. • Further large, randomized trials are needed to help clarify appropriate timing for nonculprit staged intervention in STEMI patients.
Prevalence of Depression, Anxiety and Stress Among Patients Discharged from Critical Care Units

Abstract Introduction The widespread use of advanced technology and invasive intervention creates many psychological problems for hospitalized patients; it is especially common in critical care units. Methods This cross-sectional study was conducted on 310 patients hospitalized in critical care units, using a non-probability sampling method. Data were collected using depression, anxiety, and stress scale (DASS-21) one month after discharge from the hospital. Data analysis was performed using descriptive and inferential statistics. Results 181 males and 129 females with a mean age (SD) of 55.11(1.62) years were enrolled in the study. The prevalence of depression, anxiety and stress were 46.5, 53.6 and 57.8% respectively, and the depression, anxiety and stress mean (SD) scores were 16.15(1.40), 18.57(1.46), 19.69(1.48), respectively. A statistically significant association was reported between depression, anxiety and stress with an increase in age, the number of children, occupation, education, length of hospital stay, use of mechanical ventilation, type of the critical care unit, and drug abuse. Conclusion The prevalence of depression, anxiety and stress in patients discharged from critical care units was high. Therefore, crucial decisions should be made to reduce depression, anxiety and stress in patients discharged from critical care units by educational strategies, identifying vulnerable patients and their preparation before invasive diagnostic-treatment procedures.
Contrast Medium-Induced Encephalopathy After Coronary Angiography– Case Report

Abstract Introduction Contrast-induced encephalopathy represents a rare, reversible complication that appears after intravenous or intra-arterial exposure to contrast agents. There is no consensus in the literature regarding the mechanism of action. However, the theoretical mechanism is set around the disruption of the blood-brain barrier and the contrast agents’ chemical properties. Case report The case of a 70-year-old patient, known to have hypertension and type 2 diabetes mellitus is reported. The patient had undergone a diagnostic coronary angiography during which he received 100ml of Ioversol (Optiray 350™). Soon after the procedure, the patient began experiencing a throbbing headache, followed by intense behavioural changes and aggressive tendencies. He was transferred to the Neurology Clinic. The neurological examination was without focal neurological signs; however, the patient was very aggressive and uncooperative. The CT scan revealed a mild hyper-density in the frontal lobes. MRI scan revealed no pathological changes. Conservative treatment with diuretics and hydration was administered, and the patient experienced a complete resolution of symptoms in 72 hours. Conclusion Contrast-induced encephalopathy is a possible secondary complication to contrast agents and a diagnostic challenge, and it should not be overlooked, especially following procedures that use contrast agents.