Observable Research Articles

Impact of artificial aging on the physical and mechanical properties of polycaprolactone under the combined effect of UV-radiation and elevated temperatures

ABSTRACT Annotation In this work the effect of long-term UV-radiation and temperature degradation on physical and mechanical properties of PCL was experimentally studied. Artificial aging of PCL at 30, 35 and 40°C and UV irradiation for 166, 360 and 554 h was carried out on a specially designed bench. Microstructure evaluation, spectroscopic studies and tensile tests were performed on aged specimens. On the surface of aged PCL there appear zones of irregular degradation with pronounced detachment of separate polymer fragments. Fragmentation occurs as a result of more intensive photodegradation and oxidative processes. According to the results of FTIR analysis, a strong effect on crystallinity is shown with two-factor exposure to PCL over time. At 30°C, with increasing UV degradation time, the crystallinity of PCL decreases by 7% in 504 hours. It was found that increasing the temperature during aging to 40°C leads to a more intense degradation of the crystalline phase of PCL and a decrease in crystallinity by 10% over the same period. Curves of changes in the mechanical properties of PCL versus aging time were obtained. The Young’s modulus is reduced by 20% from 326 to 252 MPa, the tensile strength is 2 times (from 16.1 to 8.5 MPa), and the relative elongation at break is 5 times compared to the initial PCL, from 528 to 103%. Based on physical observations of the properties of environmentally friendly biodegradable polymers, it is possible to provide controlled representations of their use for packaging and biomedical applications to replace petroleum-based plastics as a structural material.

Optimal Dynamical Gauge in the Quantum Rabi Model

Abstract We investigate the gauge dependence of physical observables in the quantum Rabi model (QRM) under di erent potential fields, arising from the Hilbert-space truncation of the atomic degrees of freedom. In both square-well and harmonic potentials, we find that the optimal gauge for the groundstate energy depends on the cavity frequency: the dipole gauge is optimal in the low-frequency limit, while the Coulomb gauge is optimal in the high-frequency limit. For a dynamical quantity such as the out-of-time-order correlator (OTOC), we demonstrate the necessity of introducing an optimal dynamical gauge. This study provides deeper insights into the intricate relationship between gauge choice and the dynamics of quantum electrodynamics (QED) systems, paving the way for more accurate theoretical frameworks.

Digital inventory audits: an alternative approach to physical observation in audit evidence gathering

PurposeThis study examines whether digital streaming and observation technologies can serve as an alternative approach for collecting inventory audit evidence, the challenges faced in their adoption and the factors contributing to their non-adoption.Design/methodology/approachThis study adopts a two-stage, mixed-method approach, beginning with pilot study interviews that informed the comprehensive survey with qualitative and quantitative data. Quantitative data were analyzed using descriptive statistics, t-tests and Pearson’s correlation coefficient, while qualitative data were analyzed using qualitative content analysis.FindingsOur findings revealed a positive perspective concerning the effectiveness and reliability of these technologies for inventory audits and the efficiency of internal controls within them, despite challenges such as obtaining a holistic view of the warehouse, observing obsolescence, ensuring inventory completeness and general reliability concerns. Additionally, preferences for physical inventory audits and skepticism about these technologies’ potential to enhance audit quality were identified as factors contributing to their non-adoption.Research limitations/implicationsThese findings have important implications for cost-conscious firms because they reveal that carefully adopting intermediate technologies can enhance the audit process. Our findings are relevant to audit regulators and firms interested in determining whether such technologies enhance audit efficiency and quality. This study highlights the need for updated auditing standards and directives and technologies that meet auditing requirements.Originality/valueThis study contributes to the literature by uncovering whether less advanced technologies can be used as an alternative approach to collect audit evidence. Consequently, the finding adds to the growing body of literature underscoring the potential of technologies, even less sophisticated ones, to enhance the efficiency and effectiveness of audits, despite their challenges. Additionally, it underscores the need for clear regulatory standards, suggests that auditors embrace emerging technologies and acquire relevant skills and offers insights for technology developers on audit firms’ concerns regarding digital technologies.

Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning

Abstract Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems and the absence of time-invariance. The nonequilibrium properties of many-body systems can be described by the dynamics of the correlator, or the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff-Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time numerical simulation. In this paper, we develop an operator-learning framework based on Recurrent Neural Networks (RNNs) to address this challenge.
We utilize RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs.
By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ 
where $N_t$ is the number of steps taken in a simulation, thereby
making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. 
Through various numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. 
Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.

Introductory learning of quantum probability and quantum spin with physical models and observations

Quantum probability and quantum spin are foundational concepts in quantum physics, not normally taught in pre-university education. In line with recent efforts to modernize quantum science education in schools, this paper presents learning sequences designed to provide an introductory conceptual understanding of these core physical concepts. This is achieved by choosing physical models that provide tangible classical representations that can be contrasted with quantum observations relevant to modern science and technology. This approach allows activity-based learning without the use of algebra. The concept of quantum probability is taught through activities with phasor wheels in which quantum probability is obtained graphically and interpreted using single-photon interference data. Quantum spin and spin vectors are taught by contrasting quantum spin with classical spin, explored through activities with gyroscopes and spinning tops. Quantum concepts are evoked by emphasizing how spin is different and how it manifests in real-world technologies such as magnets and MRI imaging. We describe learning sequences developed following the Model of Educational Reconstruction in which historical discoveries are combined with activities based on classical models and relevant quantum applications. We summarize test results from programs conducted with students aged 11–15. Most students connect the models and activities to quantum concepts and retain knowledge of quantum spin six months after the program. The successful learning outcomes indicate that teachers can effectively introduce quantum probability and quantum spin concepts to middle school using learning sequences such as those presented here.

Spatiotemporal variation in population size and relative abundance of Kalij pheasant (Lophura leucomelanos) in scrub forest.

Most species of pheasants (Galliformes: Phasianidae) occur in Asia. In Pakistan, pheasants occur from 245-3050 m in altitude, and one of these, the Kalij pheasant (Lophura leucomelanos) is a large-bodied, brightly-colored habitat quality indicator species. The present study was designed to determine spatiotemporal variation, population size, and the relative abundance of Kalij pheasants in Haripur, Pakistan. The line transects and point count methods were used to collect direct (physical observations) and indirect signs (feathers and calls) to avoid double counting. Twenty field surveys were conducted along 16 transects with an area of 0.811 km2 in 4 study sites to quantify habitat utilization of Kalij pheasant. Kalij pheasant activities were observed at the start of dawn (0600 hours to 0730 hours) and ended by 1630 hours to 1800 hours dusk period with only a few sightings during the middle of the day. Study sites were selected based on confirmation of presence signs. Twenty-seven individuals with direct (16) and indirect (feather = 5, calls = 6) signs were encountered while walking along line transects. Twelve signs were counted (sightings = 3, Calls = 9) within the radius of 11-50 m in the point count method. Results showed that the Kalij pheasant occupied scrub forest with dense vegetation between 500-1100m away. The mean encounter rate of the Kalij pheasant was 0.82/km, which was higher from July to September. The sex ratio (1.17) is biased toward males in the overall population of Kalij pheasants. The estimated mean density of the Kalij pheasant in Haripur was 6.97 animals/km2, and the total abundance was 142 individuals. New potential sites may be identified and declared as protected areas, and awareness and support of the local community are recommended for the protection and management of the Kalij pheasant.

Pengaruh Penambahan Bubuk Cengkeh (Syzygium aromaticum) Sebagai Pengawet Alami Terhadap Daya Simpan Selai Cempedak (Artocarpus campeden)

This study aims to determine the effect of clove powder on the shelf life of day 1 and day 5, pH levels, water content, and sensory testing of cempedak jam. This study used (CRD) with 4 formulations with different concentrations of clove powder. The formulations were P0 (0% clove powder), P1 (0.2% clove powder), P2 (0.4% clove powder), and P3 (0.6% clove powder). This study used four kinds of analysis consisting of a five-day shelf-life physical observation test, pH, moisture content, and sensory test. Sensory test analysis was carried out on samples of cempedak jam on day 0, but for physical observation analysis, pH, and water content were carried out on cempedak jam with storage on day 1 and day 5. Data analysis on pH and moisture content using One-Way ANOVA statistics was followed by the Tukey test and the sensory test using the Kruskal-Wallis statistic followed by a hypothesis test with a summary test. The use of clove powder can affect the shelf life, more the concentration of clove powder in cempedak jam, the longer shelf life of the jam. The more concentration of clove powder, the acidity (pH) and water content will increase. The sensory test results showed that the concentration of clove powder affected the parameters of color, taste and aroma of cempedak jam.

Beyond Energy: Teleporting Current, Charge, and More

Abstract As an homage to Quantum Energy Teleportation, we generalize the idea to arbitrary physical observables, not limited to energy, and prove a rigorous upper bound on the activated (“teleported”) quantity. The essence of this protocol is a quantum feedback control applied to an entangled ground state of a quantum many-body system. To illustrate the concept, we investigate a (1+1)-dimensional Dirac system and apply feedback control based on fermion chirality to activate electric current and charge. One of the most significant results is the creation of long-range correlations across the system after applying control operations only to one local site. Consequently but surprisingly, the induced charge susceptibility fully reconstructs the phase diagram, despite the model initially having no charge. Moreover, we find an activation of novel chiral dynamics induced by feedback control operations, which can be experimentally confirmed using trapped ions and neutral atoms.

Improving Students' Physical Literacy Through an Active Learning Approach in Elementary Schools

Improving physical literacy among elementary school students is a key focus in shaping an active and healthy generation. Physical literacy encompasses the understanding, skills, and motivation to participate in physical activity throughout life. In the context of elementary education, active learning approaches are considered effective for enhancing physical literacy, as this method encourages more dynamic student engagement in the learning process. This study focuses on elementary school students and how active learning approaches are implemented to improve their physical literacy. The aim of this research is to evaluate the effectiveness of active learning approaches in enhancing physical literacy among elementary school students. This study employs a quantitative method with a quasi-experimental approach. The sample consists of 120 students divided into experimental and control groups. Data were collected through physical literacy tests and observations of student activities during the learning process. The findings indicate that students participating in active learning showed significant improvement in physical literacy compared to the control group. This improvement was evident in motor skills, understanding of physical activity concepts, and student motivation to engage in physical activities. The active learning approach proves effective in enhancing physical literacy among elementary school students. Recommendations are provided for educators to integrate this approach into physical education curricula to improve learning quality and encourage active student participation.

Quantum Reference Frames, Measurement Schemes and the Type of Local Algebras in Quantum Field Theory

We develop an operational framework, combining relativistic quantum measurement theory with quantum reference frames (QRFs), in which local measurements of a quantum field on a background with symmetries are performed relative to a QRF. This yields a joint algebra of quantum-field and reference-frame observables that is invariant under the natural action of the group of spacetime isometries. For the appropriate class of quantum reference frames, this algebra is parameterised in terms of crossed products. Provided that the quantum field has good thermal properties (expressed by the existence of a KMS state at some nonzero temperature), one can use modular theory to show that the invariant algebra admits a semifinite trace. If furthermore the quantum reference frame has good thermal behaviour (expressed in terms of the properties of a KMS weight) at the same temperature, this trace is finite. We give precise conditions for the invariant algebra of physical observables to be a type II1 factor. Our results build upon recent work of Chandrasekaran et al. (J High Energy Phys 2023(2): 1–56, 2023. arXiv:2206.10780), providing both a significant mathematical generalisation of these findings and a refined operational understanding of their model.

The limited clinical utility of a routine creatine kinase (CK) on admission to a psychiatric inpatient unit

BackgroundCreatine kinase (CK) is an intracellular enzyme expressed most commonly in tissues such as skeletal muscle. CK can be used as an investigation to support the diagnosis of conditions such as neuroleptic malignant syndrome (NMS), a rare idiosyncratic drug reaction – classically to antipsychotic medications – which can be fatal. Routine screening of CK in psychiatric inpatients is a known practice, but its value is uncertain. We aimed to ascertain whether such screening resulted in new diagnoses of NMS or other conditions, and changes in clinical management.MethodsUsing an electronic case register, we conducted a descriptive retrospective cohort study, identifying all psychiatric inpatient admissions in a South London mental health trust over a four-year period where a CK test was conducted within 48 h of admission. We extracted the demographic and clinical characteristics (e.g., diagnosis) of those who met inclusion criteria. Free-text review was performed on all those with a CK potentially suggestive of NMS (CK ≥ 4x upper limit of normal reference range (ULN)) to determine the impact of this abnormal result on subsequent management and diagnosis (including NMS if identified).ResultsOf 14,236 inpatient episodes in the specified window, 2358 (16.6%) had a CK test within 48 h of admission. This was ≥ 4x ULN in 327 (13.8%) cases (free-text successfully reviewed in 318). There were no cases of NMS identified. An abnormal CK result led to a new alternative diagnosis, such as dehydration or catatonia, in only 14 patients (4.4% raised CK sample, 0.6% total CK sample). Impact on subsequent management appeared limited, with the most common adjustment being an increase in frequency of physical observations in 47 instances (14.8%).ConclusionsThe clinical utility of untargeted screening using a serum CK for psychiatric inpatients appears limited, with poor specificity in detection of NMS and a minimal impact on subsequent clinical management.

Gwforge: a user-friendly package to generate gravitational-wave mock data

Abstract Next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary coalescence signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge, a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. gwforge allows users to seamlessly simulate data while abstracting away technical complexities, enabling more efficient testing and development of analysis pipelines. Additionally, the package’s data generation process is optimized using high-throughput systems like HTCondor, significantly speeding up the simulation of large populations of gravitational-wave events. We demonstrate the package’s capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation.

Vertical nitrate flux fuels new production over summertime Northeast U.S. Shelf

AbstractIn aquatic ecosystems, allochthonous nutrient transport to the euphotic zone is an important process that fuels new production. Here, we use high‐resolution physical and biogeochemical observations from five summers to estimate the mean vertical nitrate flux, and thus new production over the Northeast U.S. Shelf (NES). We find that the summertime nitrate field is primarily controlled by biological uptake and physical advection–diffusion processes, above and below the 1% light level depth, respectively. We estimate the vertical nitrate flux to be 8.2 ± 5.3 × 10−6 mmol N m−2 s−1 for the mid‐shelf and 12.6 ± 8.6 × 10−6 mmol N m−2 s−1 for the outer shelf. Furthermore, we show that the new production to total primary production ratio (i.e., the f‐ratio), consistently ranges between 10% and 15% under summer conditions on the NES. Two independent approaches—nitrate flux‐based new production and O2/Ar‐based net community production—corroborate the robustness of the f‐ratio estimation. Since ~ 85% of the total primary production is fueled by recycled nutrients over sufficiently broad spatial and temporal scales, less than 15% of the organic matter produced in summer is available for export from the NES euphotic zone. Our direct quantification of new production not only provides more precise details about key processes for NES food webs and ecosystem function, but also demonstrates the potential of this approach to be applied to other similar datasets to understand nutrient and carbon cycling in the global ocean.

A framework for simultaneous fit of QCD and BSM parameters with xFitter

An extension of the xFitter open-source program for QCD analyses is presented, allowing for a polynomial parameterization of the dependence of physical observables on theoretical parameters. This extension enables simultaneous determination of parton distribution functions (PDFs) and new physics parameters within the framework of the Standard Model Effective Field Theory (SMEFT). The functionalities of the code are illustrated in a sensitivity study, where a simultaneous determination of the PDFs, top quark mass and the couplings of selected dimension-6 SMEFT operators is addressed using projections for measurements of top quark-antiquark pair production at the High-Luminosity LHC. The importance of considering all the correlations of the parton distributions, top quark mass and the SMEFT parameters in a simultaneous QCD+SMEFT analysis is demonstrated. This work serves as a new platform for simultaneous extraction of the PDFs and the SM/SMEFT parameters based on xFitter.

Tensionless AdS3/CFT2 and single trace TT¯

One of the few cases of AdS/CFT where both sides of the duality are under good control relates tensionless k = 1 strings on AdS3 to a two-dimensional symmetric product CFT. Building on prior observations, we propose an exact duality between string theory on a spacetime which is not asymptotically AdS and a non-conformal field theory. The bulk theory is constructed as a marginal deformation of the k = 1 AdS3 string while the spacetime dual is a single trace TT¯-deformed symmetric orbifold theory. As evidence for the duality, we match the one-loop bulk and boundary torus partition functions. This correspondence provides a framework to both learn about quantum gravity beyond AdS and understand how to define physical observables in TT¯-deformed field theories.

Superconformal monodromy defects in ABJM and mABJM theory

We study D = 11 supergravity solutions which are dual to one-dimensional superconformal defects in d = 3 SCFTs. We consider defects in ABJM theory with monodromy for U(1)4 ⊂ SO(8) global symmetry, as well as in \U0001d4a9 = 2 mABJM SCFT, which arises from the RG flow of a mass deformation of ABJM theory, with monodromy for U(1)3 ⊂ SU(3) × U(1) global symmetry. We show that the defects of the two SCFTs are connected by a line of bulk marginal mass deformations and argue that they are also related by bulk RG flow. In all cases we allow for the possibility of conical singularities at the location of the defect. Various physical observables of the defects are computed including the defects conformal weight and the partition function, as well as associated supersymmetric Renyi entropies.

Expansion in low calcium fly ash-based geopolymer concrete: chemical factors influenced by silica fume and NaOH concentration

Geopolymer technology offers a sustainable alternative to energy-intensive cement production, potentially reducing dependence on natural resources and mitigating environmental impact. Silica fumes (SF) are mostly used to enhance the fresh, hardened, and durable performance of geopolymers. However, their influence on geopolymerization may result in the expansion of the geopolymer mortar (GPM) due to reaction with alkaline liquid. In this study, the effect of NaOH concentration, particle size and dosage of SF, alkaline liquid-to-binder ratio, Na2SiO3-to-NaOH ratio, and sand-to-binder ratio on the expansion behavior of a low calcium fly ash (FA)-based GPM was investigated. FA-based GPMs were tested using two distinct SFs with varying particle sizes (SF1 = 9.5 µm and SF2 = 16.2 µm) and two varying dosages of SF (2.5% and 5%). Physical observations of GPMs revealed that NaOH concentration, particle size and dosage of SF are crucial factors that significantly impact the expansion which is concomitant with a reduction in compressive strength. Analytical techniques such as X-ray diffraction (XRD), Rietveld analysis, Fourier transform infrared spectroscopy (FTIR), and Thermo Gravimetric Analysis (TGA) were employed to comprehensively investigate reaction product(s) that led to the expansion of such GPMs. The chemical analysis confirmed the presence of excess analcime in the mortars fabricated by blending SF1 and FA at 12 M NaOH concentration. This study is the first to report the expansion of low calcium FA-based GPMs blended with a SF of particle size 9.5 µm. These findings emphasize the significance of selecting the appropriate SF particle size to prevent expansion and ensure optimal compressive strength in geopolymer applications.

Decay Bc+→D(s)(*)+ℓ+ℓ− within covariant confined quark model

We study the rare semileptonic decays of Bc mesons within the effective field theoretical framework of covariant confined quark model. The transition form factors corresponding to Bc+→D(*)+ and Bc+→Ds(*)+ are computed in the entire q2 range. Using form factors, we compute the branching fractions and compare them with the available theoretical results. We also compute various physical observables such as forward-backward asymmetry, longitudinal and transverse polarizations, as well as clean angular observables. Published by the American Physical Society 2024

Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects

Representation learning for the electronic structure problem is a major challenge of machine learning in computational condensed matter and materials physics. Within quantum mechanical first principles approaches, density functional theory (DFT) is the preeminent tool for understanding electronic structure, and the high-dimensional DFT wavefunctions serve as building blocks for downstream calculations of correlated many-body excitations and related physical observables. Here, we use variational autoencoders (VAE) for the unsupervised learning of DFT wavefunctions and show that these wavefunctions lie in a low-dimensional manifold within latent space. Our model autonomously determines the optimal representation of the electronic structure, avoiding limitations due to manual feature engineering. To demonstrate the utility of the latent space representation of the DFT wavefunction, we use it for the supervised training of neural networks (NN) for downstream prediction of quasiparticle bandstructures within the GW formalism. The GW prediction achieves a low error of 0.11 eV for a combined test set of two-dimensional metals and semiconductors, suggesting that the latent space representation captures key physical information from the original data. Finally, we explore the generative ability and interpretability of the VAE representation.

Quantifying entanglement for unknown quantum states via artificial neural networks

Quantum entanglement acts as a crucial part in quantum computation and quantum information, hence quantifying unknown entanglement is an important task. Due to the fact that the amount of entanglement cannot be achieved directly by measuring any physical observables, it remains an open problem to quantify entanglement experimentally. In this work, we provide an effective way to quantify entanglement for the unknown quantum states via artificial neural networks. By choosing the expectation values of measurements as input features and the values of entanglement measures as labels, we train artificial neural network models to predict the entanglement for new quantum states accurately. Our method does not require the full information about unknown quantum states, which highlights the effectiveness and versatility of machine learning in exploring quantum entanglement.