Hasil untuk "Physical anthropology. Somatology"

Wahiba Bel Haouz, Abdelouahed Lagnaoui, Christian Klug et al.

This article provides a description of the invertebrate trace fossils from the Hamar Laghdad region of Tafilalt in Morocco. These ichnoassemblages occur in Middle Devonian marly limestone layers, which are characterised by varying clastic content and different degrees of dolomitisation. These Eifelian and Givetian ichnoassemblages comprise: (i) Eifilian ichnoassemblages consisting of Arenicolites isp., Crescentichnus antarcticus, Selenichnites hundalensis and Osculichnus cf. labialis; (ii) a Givetian ichnoassemblage containing Arenicolites isp., Diplocraterion isp., Sinusichnus sinuosus, and Thalassinoides paradoxicus. The Eifelian strata were deposited in a moderately shallow inner shelf environment, which have been changed to a deeper inner shelf setting during the Givetian. This environment was characterised by high energy levels and a high concentration of organic matter. Some of these ichnotaxa are commonly referred to feeding of xiphosurans or trilobites, traces of suspension feeding and dwellings of polychaete worms and crustaceans, deposit-feeding non-decapod crustaceans, as well as deposit-feeding and dwelling of crustaceans. The present comprehensive ichnological analysis is important in at least three aspects: (1) it reports the oldest occurrences of Selenichnites and Sinusichnus in Africa and one of the oldest in the world; (2) It suggests a more ancient root for the represented complex behaviour of modern arthropods than previously thought; (3) It contributes to the understanding of the environmental deposit settings. The great palaeobiodiversity of Hamar Laghdad includes not only the skeletal record, but also ichnofossils. Both records indicate regional favourable environmental settings, characterised by local topography that varied over time and space, formed by several cold and hydrothermal seeps combined with sea-level fluctuations and currents.

Karishma Jagad, Dinesh Sorani

Background: Nerve conduction studies involve electrically stimulating a nerve to initiate the impulse which travels along the nerve fibre and results in an evoked potential which is recorded, analysed and interpreted. Nerve conduction studies (NCS) are a key diagnostic tool for various neurological conditions. Research findings indicate that several anthropological and physiological factors play a significant role in influencing the parameters of nerve conduction studies, ultimately influencing diagnostic precision and sensitivity. Objective: The study aims to determine if age, gender, height, weight, body temperature, wrist ratio, finger circumference and length of upper limb can be used to predict the distal latency, CMAP (Compound Motor unit Action Potential) amplitude, MNCV (Motor Nerve Conduction Velocity) values of commonly tested upper limb nerves. Method: After obtaining ethical approval, participants were screened for inclusion and exclusion criteria. A total of 101 (49 females, 52 males) participants from 20 to 60 years of age were included in the study after screening. Independent variables (IV) included – Age, height, weight, body temperature, limb length, wrist ratio, gender, finger circumference and side of the limb. Dependent variables (DV) included – DL (distal latency), CMAP amplitude, and MNCV. The study design was cross-sectional and after the measurements of IVs and DVs, the data was analysed for normality distribution. The variables that were not normally distributed, were transformed using Tukey’s ladder of powers. Correlation analysis (for continuous variable) and univariate regression analysis (for dichotomous variable) was performed for each DV with all the IVs. A multiple regression stepwise analysis was run on the significantly correlated IVs with DV. A subset of the IVs that produced the maximum R-squared 5-fold value was then selected for model preparation. Results: Temperature emerged as the significant predictor for distal motor latencies of all the tested nerves. Length of upper limb is a significant predictor for median and ulnar DLs and height is found to be significant predictor of radial DL. Gender is a significant predictor for ulnar DL. Age has been identified as a significant predictor for CMAP amplitudes across all the tested nerves. The wrist ratio is a significant predictor of median CMAP amplitude. Age and temperature have been identified as the most significant variables influencing the values of median and ulnar MNCV. None of the independent parameters examined in the current study were able to accurately predict the value of radial NCV. Conclusion: The current study emphasizes the critical role of physiological factors such as temperature, age, sex, and anatomical measurements on nerve conduction parameters. These findings highlight the necessity of adjusting nerve conduction studies for these variables to improve diagnostic accuracy. Keywords: Nerve conduction, Age, gender, height, weight, wrist ratio, limb length, temperature, finger circumference, distal latency, CMAP amplitude

Guilherme Hermanson, Serjoscha W. Evers

Abstract Ontogenetic shell shape changes of turtles are often only documented for individual species. It is currently unclear how shell shape changes during ontogeny across species, if there are common trends, and at what point in ontogeny individuals reach their adult morphology. Inspired by questions of whether some morphologies are too juvenile to be included into macroevolutionary studies of shell shape, we develop ontogenetic shell shape curves based on landmarked 3D shell shapes of turtles. Species-specific allometric shape regressions confirm that turtles show marked ontogenetic shell shape change. Geometric morphometric analysis shows that juvenile turtles have rounded shells, and ontogenetic differentiation between species increases adult turtle disparity. Disparity analysis indicates that juvenile shells across turtle clades are more similar than adult shapes, suggesting an important role of developmental constraints on early turtle shell shape, and possible adaptive post-natal ontogenetic changes that produce the observed adult shell shape disparity. Ontogenetic shell shape curves indicate when turtles converge onto adult morphologies, here quantified as 85% the distance between juvenile shape and maximum size adult shape. This happens at about 65% of the species-specific maximum carapace sizes. Sexual shell shape dimorphism is comparatively low across turtles even in the presence of pronounced sexual size dimorphism. These preliminary results provide guidance for studying shell shape macroevolution, but need to be scrutinized further in the future by data addition.

Zlatozar Boev

A first attempt has been made to systematically present the achievements of several archaeozoological fields in Bulgaria: archaeomalacology, archaeoichthyology, archaeoherpetology, archaeornithology, and archaeomammalogy. The main results and some of the more significant studies in each of these fields are presented. In summary, archaeozoological studies began in the first decade of the 20th century. A list of established authors of archaeozoological publications in Bulgaria has been compiled. Of the identified species, four species of birds and six species of mammals have disappeared from the modern fauna of the country. Two species have completely disappeared globally.

Roman M. Reida, Anatoliy V. Heiko, Sapiehin V. Sergiy

The article deals with the ethno-cultural situation in the Dnipro Left Bank Forest-Steppe region during the Hunnic period based on a consideration of material from the sites discovered in this region, primarily burial sites, that contain finds from the last quarter of the 4th – the first half of the 5th century B.C. The sites were divided into three groups: 1) burials of nomads with some elements of Cherniakhiv culture; 2) “syncretic” burials of the Cherniakhiv culture with nomadic elements; 3) sites of Cherniakhiv culture. The existence of these sites is caused by contacts between the nomadic world and the Cherniakhiv population, who may be classified as farmers. These active contacts demonstrate different degrees of incorporation of nomads into the Cherniakhiv environment. The description of the burials that belong to these groups is presented in the article. Among them, burial 124 of the Shyshaky cemetery can be mentioned here. Due to the size of the grave and individual finds, this burial complex stands out among the sites of the Cherniakhiv culture and should be classified as belonging to the burials of princes. Based on archaeological finds, it can be stated that the arrival of the Huns did not cause catastrophic consequences for the population of the Dnipro Left Bank Forest-Steppe region. At that time, it was not a decline, but a development of the culture of the nomads (the Alans) and also the settled population of the Cherniakhiv culture.

V Hewes, Adam Aurisano, Giuseppe Cerati et al.

Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. This article describes NuGraph2, a Graph Neural Network (GNN) for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multi-head attention message-passing mechanism to perform background filtering and semantic labelling on these graph nodes, identifying those associated with the primary physics interaction with 98.0\% efficiency and labelling them according to particle type with 94.9\% efficiency. The network operates directly on detector observables across multiple 2D representations, but utilizes a 3D-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12~s/event on a CPU, and 0.005s/event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this article.

Jai Bardhan, Cyrin Neeraj, Subhadip Mitra et al.

We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show that it produces decision boundaries that change according to the cross sections of the processes involved. We find that the models trained with the new loss have higher signal efficiency for similar values of estimated signal significance compared to ones trained with a cross-entropy loss, showing promise to improve sensitivity of particle physics searches at colliders.

Miklós Kázmér, Petra Jamšek Rupnik, Krzysztof Gaidzik

Searching for unknown earthquakes in Slovenia in the first millennium, we performed archaeoseismological analysis of Roman settlements. The <i>Mesto pod mestom</i> museum in Celje exhibits a paved Roman road, which suffered severe deformation. Built on fine gravel and sand from the Savinja River, the road displays a bulge and trench, pop-up structures, and pavement slabs tilted up to 40°. The city wall was built over the deformed road in Late Roman times, supported by a foundation containing recycled material (<i>spolia</i>) from public buildings, including an emperor’s statue. We hypothesize that a severe earthquake hit the town before 350 AD, causing widespread destruction. Seismic-induced liquefaction caused differential subsidence, deforming the road. One of the nearby faults from the strike-slip Periadriatic fault system was the seismic source of this event.

Danyal Rehman, John H. Lienhard

Species transport models typically combine partial differential equations (PDEs) with relations from hindered transport theory to quantify electromigrative, convective, and diffusive transport through complex nanoporous systems; however, these formulations are frequently substantial simplifications of the governing dynamics, leading to the poor generalization performance of PDE-based models. Given the growing interest in deep learning methods for the physical sciences, we develop a machine learning-based approach to characterize ion transport across nanoporous membranes. Our proposed framework centers around attention-enhanced neural differential equations that incorporate electroneutrality-based inductive biases to improve generalization performance relative to conventional PDE-based methods. In addition, we study the role of the attention mechanism in illuminating physically-meaningful ion-pairing relationships across diverse mixture compositions. Further, we investigate the importance of pre-training on simulated data from PDE-based models, as well as the performance benefits from hard vs. soft inductive biases. Our results indicate that physics-informed deep learning solutions can outperform their classical PDE-based counterparts and provide promising avenues for modelling complex transport phenomena across diverse applications.

Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan et al.

The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood. Focusing on supervised learning, we present a mathematical framework for evaluating the resolvable expressive capacity (REC) of general physical systems under finite sampling noise, and provide a methodology for extracting its extrema, the eigentasks. Eigentasks are a native set of functions that a given physical system can approximate with minimal error. We show that the REC of a quantum system is limited by the fundamental theory of quantum measurement, and obtain a tight upper bound for the REC of any finitely-sampled physical system. We then provide empirical evidence that extracting low-noise eigentasks can lead to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. The applicability of these results in practice is demonstrated with experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications.

George Manolakos, Pantelis Manousselis, Danai Roumelioti et al.

Here we present an overview on the various works, in which many collaborators have contributed, regarding the interesting dipole of noncommutativity and physics. In brief, we present the features that noncommutativity triggers both in the fields of gravity and particle physics, from a matrix-realized perspective, with the notion of noncommutative gauge theories to play the most central role in the whole picture. Also, under the framework of noncommutativity, we examine the possibility of unifying the two fields (gravity-particle physics) in a single configuration.

Tomoe Kishimoto, Masahiro Morinaga, Masahiko Saito et al.

This study aims to improve the performance of event classification in collider physics by introducing a pre-training strategy. Event classification is a typical problem in collider physics, where the goal is to distinguish the signal events of interest from background events as much as possible to search for new phenomena in nature. A pre-training strategy with feasibility to efficiently train the target event classification using a small amount of training data has been proposed. Real particle collision data were used in the pre-training phase as a novelty, where a self-supervised learning technique to handle the unlabeled data was employed. The ability to use real data in the pre-training phase eliminates the need to generate a large amount of training data by simulation and mitigates bias in the choice of physics processes in the training data. Our experiments using CMS open data confirmed that high event classification performance can be achieved by introducing a pre-trained model. This pre-training strategy provides a potential approach to save computational resources for future collider experiments and introduces a foundation model for event classification.

Stefanos Ligkovanlis

Although it is well established that Hertzian fracture characterizes stone knapping mechanics, its in-depth features on lithic products remain unclear. Observations on a basic component of the Hertzian fracture manifestation, the cone of percussion ‘system’, has previously considered to reveal knappers’ hand preference, yet offering contradictory predicting results within the context of blind tests conducted on experimental lithic products. In this study, basic features of the cone of percussion on stone flakes are re-approached in an effort to determine their exact relation to handedness manifestation during stone knapping. Experimental data analysis suggests that under certain circumstances stone knappers’ hand preference is strongly, but not absolutely, connected with the cone of percussion ‘system’ various geometrics. The pilot implementation of the suggested methodology on lithic artefacts produced by Neanderthals at Kalamakia cave-southern Greece, indicates that right-handers predominate among the flintknappers of the site.

Jayson Nissen, Ian Her Many Horses, Ben Van Dusen

The American Physical Society calls on its members to improve the diversity of physics by supporting an inclusive culture that encourages women and Black, Indigenous, and people of color to become physicists. In the current educational system, it is unlikely for a student to become a physicist if they do not share the same attitudes about what it means to learn and do physics as those held by most professional physicists. Evidence shows college physics courses and degree programs do not support students in developing these attitudes. Rather physics education filters out students who do not enter college physics courses with these attitudes. To better understand the role of attitudes in the lack of diversity in physics, we investigated the intersecting relationships between racism and sexism in inequities in student attitudes about learning and doing physics using a critical quantitative framework. The analyses used hierarchical linear models to examine students attitudes as measured by the Colorado learning attitudes about science survey. The data came from the LASSO database and included 2170 students in 46 calculus-based mechanics courses and 2503 students in 49 algebra-based mechanics courses taught at 18 institutions. Like prior studies, we found that attitudes either did not change or slightly decreased for most groups. Results identified large differences across intersecting race and gender groups representing educational debts society owes these students. White students, particularly White men in calculus-based courses, tended to have more expert-like attitudes than any other group of students. Instruction that addresses society's educational debts can help move physics toward an inclusive culture supportive of diverse students and professionals.

Chinmaya Mahesh, Kristin Dona, David W. Miller et al.

Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being collected. This is especially true at the energy and intensity frontiers of particle physics where bandwidths of raw data can exceed 100 Tb/s of heterogeneous, high-dimensional data sourced from hundreds of millions of individual sensors. In this paper, we introduce a new data-driven approach for designing and optimizing high-throughput data filtering and trigger systems such as those in use at physics facilities like the Large Hadron Collider (LHC). Concretely, our goal is to design a data-driven filtering system with a minimal run-time cost for determining which data event to keep, while preserving (and potentially improving upon) the distribution of the output as generated by the hand-designed trigger system. We introduce key insights from interpretable predictive modeling and cost-sensitive learning in order to account for non-local inefficiencies in the current paradigm and construct a cost-effective data filtering and trigger model that does not compromise physics coverage.

Aritra Biswas, Lopamudra Mukherjee, Soumitra Nandi et al.

We constrain the parameters of a representative new physics model with possible dark matter (DM) signature from a global CKM fit analysis. The model has neutral quark current interactions mediated by a scalar, impacting the semileptonic and purely leptonic meson decays at one-loop. We take this opportunity to update the fit results for the Wolfenstein parameters and the CKM elements with and without a contribution from the new model using several other updated inputs. Alongside, we have analyzed and included in the CKM fit the $B\to D^*\ellν_{\ell}$ decay. The newly available inputs on the relevant form factors from lattice are included, and the possibility of new physics effects in $B\to D^*\ellν_{\ell}$ is considered. We obtain tight constraints on the relevant new physics parameters. We have studied the possible implications of this constraint on DM phenomenology. Apart from DM, the bounds are also applicable in other relevant phenomenological studies.

Kétévi Adiklè Assamagan, Mounia Laassiri

The African School of Fundamental Physics and Applications is a biennial school in Africa. It is based on the observation that fundamental physics provides excellent motivation for students of science. The aim of the school is to build capacity to harvest, interpret, and exploit the results of current and future physics experiments and to increase proficiency in related applications. The participating students are selected from all over Africa. The school also offers a workshop to train high school teachers, an outreach to motivate high school pupils and a physics conference to support a broader participation of African research faculties. Support for the school comes from institutes in Africa, Europe, USA and Asia. In this paper, we will present the school and discuss strategies to make the school sustainable.

Louis Vervoort

Probability theory as a physical theory is, in a sense, the most general physics theory available, more encompassing than relativity theory and quantum mechanics, which comply with probability theory. Taking this simple fact seriously, I argue that probability theory points towards superdeterminism, a principle that underlies, notably, 't Hooft's Cellular Automaton Interpretation of quantum mechanics. Specifically, I argue that superdeterminism offers a solution for 1) Kolmogorov's problem of probabilistic dependence; 2) the interpretation of the Central Limit Theorem; and 3) Bell's theorem. Superdeterminism's competitor, indeterminism ('no hidden variables'), remains entirely silent regarding 1) and 2), and leaves 3) as an obstacle rather than a solution for the unification of quantum mechanics and general relativity. This suggests that, if one wishes to stick to the standard position in physics and adopt the principles with the highest explanatory power, one should adopt superdeterminism and reject indeterminism. Throughout the article precise questions to mathematicians are formulated to advance this research.

Laureano Ramírez-Piscina, José M. Sancho

Experiments on single ionic channels have contributed to a large extent to our current view on the function of cell membrane. In these experiments the main observables are the physical quantities: ionic concentration, membrane electrostatic potential and ionic fluxes, all of them presenting large fluctuations. The classical theory of Goldman--Hodking--Katz assumes that an open channel can be well described by a physical pore where ions follow statistical physics. Nevertheless real molecular channels are active pores with open and close dynamical states. By skipping the molecular complexity of real channels, here we present the internal structure and calibration of two active pore models. These models present a minimum set of degrees of freedom, specifically ion positions and gate states, which follow Langevin equations constructed from an unique potential energy functional and by using standard rules of statistical physics. Numerical simulations of both models are implemented and the results show that they have dynamical properties very close to those observed in experiments of Na and K molecular channels. In particular a significant effect of the external ion concentration on gating dynamics is predicted, which is consistent with previous experimental observations. This approach can be extended to other channel types with more specific phenomenology.