Physical Causality Research Articles

A development on the interior noise prediction technology using active noise control algorithm

When developing NVH of a vehicle, identifying a noise transmission vulnerable path is a very important task. The traditional TPA(Transfer Path Analysis) is based on a physical theoretical equation and calculates the contribution of each path by multiplying the ATF(Acoustic Transfer Function) of each path by the driving load. In this case, it is often difficult to calculate the driving load, and M/H consumption is also high. OTPA(Operational Transfer Path Analysis) performs transfer path analysis only with driving signals for each route, but has a disadvantage of insufficient physical causality. In this study, we proposed an efficient indoor noise TPA method that modifies the ANC algorithm by combining the reference signals while driving with the secondary path of Filtered-X LMS. The proposed method uses the secondary transfer function of Fx-LMS as the ATF for each path. It is not necessary to obtain physical load by predicting target noise with the Ref signal for each route. Therefore, it is possible to predict indoor noise with low M/H. In this study, it can be confirmed that the proposed method shows similar results compared to the existing methods and efficiently identifies main noise path for interior noise.

Exploring the influence of self-identification on perceptual judgments of physical and social causality.

People tend to overestimate the causal contribution of the self to the observed outcome in various situations, a cognitive bias known as the 'illusion of control.' This study delves into whether this cognitive bias impacts causality judgments in animations depicting physical and social causal interactions. In two experiments, participants were instructed to associate themselves and a hypothetical stranger identity with two geometrical shapes (a circle and a square). Subsequently, they viewed animations portraying these shapes assuming the roles of agent and patient in causal interactions. Within one block, the shape related to the self served as the agent, while the shape associated with the stranger played the role of the patient. Conversely, in the other block, the identity-role association was reversed. We posited that the perception of the self as a causal agent might influence explicit judgments of physical and social causality. Experiment 1 demonstrated that physical causality ratings were solely shaped by kinematic cues. In Experiment 2, emphasising social causality, the dominance of kinematic parameters was confirmed. Therefore, contrary to the hypothesis anticipating diminished causality ratings with specific identity-role associations, results indicated negligible impact of our manipulation. The study contributes to understanding the interplay between kinematic and non-kinematic cues in human causal reasoning. It suggests that explicit judgments of causality in simple animations primarily rely on low-level kinematic cues, with the cognitive bias of overestimating the self's contribution playing a negligible role.

Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties.

Machine learning (ML) has facilitated property prediction for intricate materials by integrating materials and experimental features such as processing and measurement conditions. However, ML models designed for material properties have often disregarded a common issue of "leakage," resulting in an overestimation of model performance and a decrease in model transferability. This issue can arise from biases inherent in multiple data points obtained from the same experimental group. We provide a critical examination and prevention method of leakage in property prediction for polymer composites. Our proposed method utilizes data partitioning based on the experimental group to ensure that data from the same group are not mixed in both the training and test sets. Evaluation results highlight that the conventional random partitioning unintentionally inflates ML performance through the misuse of experimental features for leaking data bias within the same experimental group rather than explaining the physical causality. In contrast, the proposed method enables the leakage-free utilization of experimental features to improve prediction accuracy while ensuring model transferability. Specifically, when integrating experimental features with polymer and filler features, the conventional method overestimates the prediction performance of electrical conductivity in reducing RMSE by 26% depending on leakage, whereas the proposed method achieves a reduction in RMSE by 5% without leakage. These findings offer valuable guidance for the effective utilization of experimental features in data-driven materials science.

IS PSYCHOLOGY ALREADY CONCEPTUALLY COMPLETED, OR ABOUT INFORMATIONAL ACTIVITY OF THE BRAIN WHICH IS “OBSESSIVELY” ACCOMPANIED BY MENTAL ACTIVITY

The article considers a “paradoxical” situation of general scientific significance, which can be formulated in the question “Why does the human brain, being a physical entity, nevertheless process information biologically or socially expediently?”, although no physical law supports this. A way out of this situation is offered. It consists in indicating that the limbic region of the brain realizes the ability of a person to subjectively evaluate the biological (social) value of external objects and phenomena. Such an explanatory concept, which is, in fact, a “product” of both humanitarian and natural knowledge, allows, in our opinion, to “cross” the line of conceptual and categorical misunderstanding between these “irreconcilable” branches of knowledge. And this intersection is carried out through understanding the essence of the information activity of the brain, which occurs with the help of mental phenomena.
 It is argued that in the neural networks of the brain, along with the usual physical causality, there is an informational causality that orchestrates this physical causality. But it acts due to the presence in the brain, again, of mental phenomena. And above all, the ability of a person to subjectively evaluate (either positively or negatively) the surrounding world, and therefore, to be motivated to eliminate states of uncertainty through the process of information integration (unification, summation, accumulation), which in the language of psychology is mainly a thinking process.
 The leading role of psychology in solving this general scientific problem is considered – primarily because of the scale of its research object, which is the psyche. Psyche, which, it is believed, is an evolutionarily mature “tool” of direct human existence and adaptation to constantly changing world – to uncertainty, to information deficit.

Mind

This paper correlates Bahá’í concepts of the mind with insights from philosophy. It presents arguments from both sources for a non-reductive understanding of the human mind and argues that, although science can help us advance our understanding of the mind, it is not sufficient in this pursuit, as it cannot capture fully how the human mind experiences reality. The paper reviews the mind’s conceptual way of knowing, explores the implications of language for philosophy of mind, and considers how the pursuit of science and the phenomenon of religion both shed light on the capacities and nature of the mind. After suggesting that the process of learning in which the global Bahá’í community has embarked may serve as a model for engaging the human mind in a collective enterprise for the betterment of the world, it turns back to philosophy to submit that, while many contemporary philosophers persuasively argue that the human mind is not reducible to physical causality, the philosophical resistance to a spiritual dimension of the human mind is excessively limiting. The minds of human beings demonstrate capacities that lie beyond nature, and a conception of the mind as “the power of the human spirit” or “rational soul” can not only be a fruitful way of understanding the mind, but lead to an orientation by human beings in the world, demonstrated through the learning process discussed earlier in the paper, that holds promise for the future of humanity.

Punching Shear Response Theory (PSRT) – A two degree of freedom kinematic theory for modeling the entire punching shear vs. deformation response of RC slabs and footings

Punching failures of reinforced concrete flat slabs and footings without shear reinforcement have been responsible for a number of catastrophic collapses. Despite intense scientific efforts in recent decades that have led to failure criteria-based phenomenological models, our current mechanistic understanding is still not sufficiently advanced to fathom the physical background of punching phenomena in RC structures. That is why, this paper proposes a novel two degree of freedom mechanical theory - called Punching Shear Response Theory (PSRT) - that interconnects all relevant effects of punching shear behavior, namely strutting action within a conical concrete shell at the slab-column interface, shear transfer actions elicited along the circumferential punching shear crack, including cohesive fracture process zone behavior, aggregate interlock and dowel action of reinforcement, as well as transfer of normal forces within tangential and radial cracks between slab segments in one all-encompassing algorithm. The PSRT can thoroughly mimic and efficiently reproduce the entire punching failure process of flat slabs and squat members (footings, pile caps, column bases) including prediction of pre- and post-peak punching shear vs. deformation response, quantification of different punching shear contributions and analysis of their evolution and mutual interaction within the failure process. The paper illustrates the analytical development of the PSRT, exemplifies its ability to reproduce the punching shear response of selected benchmark problems, proves its validity by database evaluations (incl. punching strength and deformation capacity) and eventually discloses the physical causalities of punching failures in RC structures.

High-resolution transdimensional geoacoustic inversion using autonomous underwater vehicle data

We invert reflection coefficient measurements of muddy sediment layers along a 12-km seabed transect on the Malta Plateau in the Mediterranean Sea using 1711 source transmissions recorded on a 32-element linear hydrophone array with both source and array towed by an autonomous underwater vehicle. Trans-dimensional Bayesian inference using reversible jump Markov chain Monte Carlo sampling is applied to obtain posterior probability densities of the number of homogeneous sediment layers, their depths, and their geoacoustic parameters. The forward sediment acoustics model is based on the grain-shearing model which obeys physical causality and provides correlation between important geoacoustic properties. Each dataset was treated as one-dimensional seabed structure inversion carried out on high performance clusters, and inversion results for multiple data sets were combined to yield a two-dimensional subsurface profile including full uncertainty analysis. Comparisons of inversion results to piston and gravity core estimates show agreement in both geoacoustic parameter values and depths of discontinuities. In the range-dependent model constructed from inverting the entire data set, dipping and terminating layers are observed along the track with high vertical resolution on the order of 10 cm.

An improved method to establish continuous relaxation spectrum of asphalt materials

The relaxation spectrum plays a vital role in the study of linear viscoelastic (LVE) properties of asphalt materials; however, the current developed using methods are not accurate enough to characterize the LVE properties of asphalt materials. This paper aims to develop an improved method to determine the continuous relaxation spectrum of asphalt materials. A modified Christensen–Anderson–Marasteanu (CAM) model is first selected as the master curve model of the storage modulus, upon which the master curve model of loss modulus is then established using the numerical form of the exact Kramers-Kronig relation between the storage modulus and loss modulus. According to LVE conversions between the relaxation spectrum and storage modulus, the model of the continuous relaxation spectrum is subsequently derived and finally determined based on the same model parameters of the storage modulus and loss modulus models. This study demonstrates that the relaxation spectrum of both mixtures and binders constructed by the proposed method is compliant with LVE theory and physical causality. The generated relaxation spectrum is verified to be accurate enough to compare the storage modulus and loss modulus values recalculated by the relaxation spectrum and the corresponding values predicted by master curve models. The results also show that the approximate Kramers-Kronig relation between the storage modulus and loss modulus cannot be used to establish the relaxation spectrum of asphalt materials, especially for asphalt binders. In addition, the generated relaxation spectrum was utilized to construct the relaxation modulus of mixtures and to develop the stiffness modulus of binders, respectively. Since the relaxation modulus and creep stiffness are determined from the relaxation spectrum, both are consistent with LVE theory and physical causality.

Determining with High Accuracy the Relaxation Modulus and Creep Compliance of Asphaltic Materials in the Form of Sums of Exponential Functions from Mathematical Master Curves of Dynamic Modulus

According to the physical causality principle, the two components of complex modulus are not independent yet interrelated to each other via Kramers-Kronig relationships. Using two mathematical functions to predict simultaneously the dynamic modulus (DM) and phase angle (PA) generally leads to the violation of the causality principle. Therefore, the calculation of one component based on the other one is a vital task for using mathematical models to predict the linear viscoelastic (LVE) behavior of asphaltic materials. In addition, the relaxation modulus (RM) or the creep compliance (CC) in the form of sums of exponential functions (SEFs) is necessary for the efficient resolution of the stress–strain relationship in the time domain. This paper presented a novel method for calculating with high accuracy the RM and CC in the form of SEFs based on the input DM master curve. First, the midpoint integration scheme was used to calculate the PA based on the exact relationship between the PA and DM. After that, the storage modulus and compliance were computed and decomposed with high accuracy into SEFs using a special technique. Then, the closed-form formula of the RM and CC were derived thanks to the special form function of the storage modulus and compliance. Finally, the special technique was used once again to decompose with high accuracy the RM and CC into SEFs for possible uses in finite-element method (FEM) analysis in the time domain. The results showed that the relative errors of all LVE properties computed were lower than 0.1 percent at all calculating points.

Redshift: Manipulating Signal Propagation Delay via Continuous-Wave Lasers

We propose a new laser injection attack Redshift that manipulates signal propagation delay, allowing for precise control of oscillator frequencies and other behaviors in delay-sensitive circuits. The target circuits have a significant sensitivity to light, and a low-power continuous-wave laser, similar to a laser pointer, is sufficient for the attack. This is in contrast to previous fault injection attacks that use highpowered laser pulses to flip digital bits. This significantly reduces the cost of the attack and extends the range of possible attackers. Moreover, the attack potentially evades sensor-based countermeasures configured for conventional pulse lasers. To demonstrate Redshift, we target ring-oscillator and arbiter PUFs that are used in cryptographic applications. By precisely controlling signal propagation delays within these circuits, an attacker can control the output of a PUF to perform a state-recovery attack and reveal a secret key. We finally discuss the physical causality of the attack and potential countermeasures.

Attribution of intentions in autism spectrum disorder: A study of event-related potentials.

Autism spectrum disorder (ASD) is characterized by social cognition deficits, including difficulties inferring the intentions of others. Although deficits in attribution of intentions (AI) have been consistently replicated in ASD, their exact nature remains unexplored. Here we registered the electrophysiological correlates of a nonverbal social cognition task to investigate AI in autistic adults. Twenty-one male autistic adults and 30 male neurotypical volunteers performed a comic strips task depicting either intentional action (AI) or physical causality with or without human characters, while their electroencephalographic signal was recorded. Compared to neurotypical volunteers, autistic participants were significantly less accurate in correctly identifying congruence in the AI condition, but not in the physical causality conditions. In the AI condition a bilateral posterior positive event-related potential (ERP) occurred 200-400 ms post-stimulus (the ERP intention effect) in both groups. This waveform comprised a P200 and a P300 component, with the P200 component being larger for the AI condition in neurotypical volunteers but not in autistic individuals, who also showed a longer latency for this waveform. Group differences in amplitude of the ERP intention effect only became evident when we compared autistic participants to a subgroup of similarly performing neurotypical participants, suggesting that the atypical ERP waveform in ASD is an effect of group, rather than a marker of low-task performance. Together, these results suggest that the lower accuracy of the ASD group in the AI task may result from impaired early attentional processing and contextual integration of socially relevant cues. LAY SUMMARY: To understand why autistic people have difficulties in inferring others' intentions, we asked participants to judge the congruence of the endings of comic strips depicting either intentional actions (e.g., fetching a chair to reach for something) or situations solely following physical rules (e.g., an apple falling on someone's head), while their electrical brain activity was recorded. Autistic individuals had more difficulties in inferring intentions than neurotypical controls, which may reflect impaired attention and contextual integration of social cues.

Internal causality in agri-food Life Cycle Assessments: Solving allocation problems based on feed energy utilization in egg production

Physical allocation in Life Cycle Assessment (LCA) should, ideally, be based on underlying causal relationships. While both cause-oriented and effect-oriented causality referred to in LCA literature are forms of external causality, internal causality addresses the actual flow of materials and inputs in a system – in other words, the real behaviour of the system under study. While a number or examples of allocation based on physical causality have been used in poultry LCAs, none of these represent the internal causality (the actual biological processes) in egg production. The current study remedies that gap by proposing such a method. Agri-food LCAs, in particular LCAs of livestock production, were used to identify existing physical allocation approaches consistent with internal causality. The most commonly used approach was found to be based on the allocation of feed energy to support the various physiological functions of the livestock species. A feed energy – Metabolizable Energy (ME) – utilization model for allocation in egg production LCAs is hence similarly proposed. Using the inventory of a previous LCA study of egg production in Canada, allocation ratios for eggs and spent hens were developed. Feed utilization models specific to each unit process were identified. The overall differences between ME utilization (∼95% eggs, 5% spent hens) and gross chemical energy content (92% eggs, 8% spent hens) for allocation were relatively small. Scenario analysis, however, showed that the allocation ratios can be considerably different if the causal relationship is interpreted differently. Differences over ∼20% was seen in a scenario which did not allocate between the co-products of each unit process in the system, but rather to the products at the end of a biological causal chain straddling multiple unit processes. The proposed approach is consistent with the interpretation of LCA as a natural sciences framework, and with the ISO 14044 multi-functionality hierarchy, because it reflects actual biological causality in egg production systems. The study results also underscore that practitioners should not only clearly justify their choice of allocation strategy, but also describe its application in detail, since small differences in methods can result in divergent outcomes.

Optical Detection of Marine Debris Using Deep Knockoff

This article investigates the use of deep knockoff, a modern statistical variable selection methodology, to uncover the spectral signatures of marine debris. This method uses a generative model by leveraging deep neural networks to learn the high dimensional distribution of reflectance in visible to near infrared wavelengths. To that end, a public dataset obtained from ground-based labeling of the observations by the Multispectral Instrument (MSI) on-board Sentinel-2 satellite is used. Through controlling the false discovery rate, consistent with the known physical causalities, the results indicate that the near infrared (band 8, 833 nm) and red (band 4, 665 nm) are the most important bands, respectively for discrimination of marine (plastic) debris from the background water. In the presence of dense <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sargassum</i> macroalgae, the deep knockoff isolates the green (band 3, 560 nm) and the narrow NIR (band 8a, 864.7 nm) as another important band.

Order relations are Ubiquitously fundamental: Alexandrov(-Zeeman) theorem Extended from space-time physics to logical reasoning and decision making

In all areas of human activity, there are natural ordering relations: causality in space-time physics, preference in decision making, and logical inference in reasoning. In space-time physics, a 1950 theorem by A.D. Alexandrov proved that causality relation is fundamental: many other features, including numerical characteristics of time and space, can be reconstructed from this relation. In this paper, we provide simple proofs that, similarly, the corresponding ordering relations are fundamental in decision making and in logical reasoning.

Kramers-Kronig Relation for Attenuated Total Reflection from a Metal-Dielectric Interface Where Surface Plasmon Polaritons Are Excited.

The applicability of the Kramers–Kronig relation for attenuated total reflection (ATR) from a metal–dielectric interface that can excite surface plasmon polaritons (SPP) is theoretically investigated. The plasmon-induced attenuation of reflected light can be taken as the resonant absorption of light through a virtual absorptive medium. The optical phase shift of light reflected from the SPP-generating interface is calculated using the KK relation, for which the spectral dependence of ATR is used at around the plasmonic resonance. The KK relation-calculated phase shift shows good agreement with that directly obtained from the reflection coefficient, calculated by a field transfer matrix formula at around the resonance. This indicates that physical causality also produces the spectral dependence of the phase of the leakage field radiated by surface plasmons that would interfere with the reflected part of light incident to the interface. This is analogous with optical dispersion in an absorptive medium where the phase of the secondary field induced by a medium polarization, which interferes with a polarization-stimulating incident field, has a spectral dependence that stems from physical causality.

Causality in Discrete Time Physics Derived from Maupertuis Reduced Action Principle

Causality describes the process and consequences from an action: a cause has an effect. Causality is preserved in classical physics as well as in special and general theories of relativity. Surprisingly, causality as a relationship between the cause and its effect is in neither of these theories considered a law or a principle. Its existence in physics has even been challenged by prominent opponents in part due to the time symmetric nature of the physical laws. With the use of the reduced action and the least action principle of Maupertuis along with a discrete dynamical time physics yielding an arrow of time, causality is defined as the partial spatial derivative of the reduced action and as such is position- and momentum-dependent and requests the presence of space. With this definition the system evolves from one step to the next without the need of time, while (discrete) time can be reconstructed.

Detection and analysis of real-time anomalies in large-scale complex system

For data-driven anomaly detection, it is difficult to model a prediction model with high accuracy and sensitivity to anomalous states. In order to solve the above problems, this paper proposes an improved multivariate Transfer Entropy method to judge the physical causality between high-dimensional time series. According to the causality, combined with the LSTM (Long Short-Term Memory) model, the CF-LSTM (causality features-LSTM) model is proposed. The CF-LSTM model uses the causality features of the parameter to make predictions. Compared with other models, the CF-LSTM model improves the prediction accuracy and it is sensitive to anomalies. Based on this, an anomaly detection algorithm is proposed. Case analysis based on the actual data shows that the detection precision, recall, and F1-score (a measure of classification problem) of this method are improved compared with other anomaly detection models, which illustrates the effectiveness of this method.

Evaluation of prosocial actions performed by dynamic shapes at 17 months of age

ABSTRACT This research investigated whether the perception of social-causal relation alone triggers both infants’ evaluation processes and expectations about the social preferences of informed third parties. Three experiments were carried out, using the violation of expectation (VoE) paradigm. During the familiarization phase, infants saw events in which negative interactions involving two spheres as third parties were presented, while another shape intervened (intervening event) or not (non-intervening event), and a bystander watched these interactions. In Experiment 1, a sphere moved towards another sphere that started to move before the contact (social causality); in Experiment 2, a sphere moved towards another sphere that started to move after the contact (physical causality); in Experiment 3, a sphere moved towards fixed objects (physical causality). In the two test trials, either the intervening or the non-intervening agent needed help to enter into a box, and the bystander helped either agent. Later, a manual choice task assessed infants’ preferences. The results demonstrated that only in the social condition infants expected a bystander to help an intervening agent, and preferred the intervening agent. The findings provided evidence that infants’ evaluations are triggered by the perception of social causality.

Infant perception of causal motion produced by humans and inanimate objects

Both the movements of people and inanimate objects are intimately bound up with physical causality. Furthermore, in contrast to object movements, causal relationships between limb movements controlled by humans and their body displacements uniquely reflect agency and goal-directed actions in support of social causality. To investigate the development of sensitivity to causal movements, we examined the looking behavior of infants between 9 and 18 months of age when viewing movements of humans and objects. We also investigated whether individual differences in gender and gross motor functions may impact the development of the visual preferences for causal movements. In Experiment 1, infants were presented with walking stimuli showing either normal body translation or a “moonwalk” that reversed the horizontal motion of body translations. In Experiment 2, infants were presented with unperformable actions beyond infants’ gross motor functions (i.e., long jump) either with or without ecologically valid body displacement. In Experiment 3, infants were presented with rolling movements of inanimate objects that either complied with or violated physical causality. We found that female infants showed longer looking times to normal walking stimuli than to moonwalk stimuli, but did not differ in their looking time to movements of inanimate objects and unperformable actions. In contrast, male infants did not show sensitivity to causal movement for either category. Additionally, female infants looked longer at social stimuli of human actions than male infants. Under the tested circumstances, our findings indicate that female infants have developed a sensitivity to causal consistency between limb movements and body translations of biological motion, only for actions with previous visual and motor exposures, and demonstrate a preference toward social information.

Is Causality a Necessary Tool for Understanding Our Universe, or Is It a Part of the Problem?

In this paper, the concept of causality in physics is discussed. Causality is a necessary tool for the understanding of almost all physical phenomena. However, taking it as a fundamental principle may lead us to wrong conclusions, particularly in cosmology. Here, three very well-known problems—the Einstein–Podolsky–Rosen paradox, the accelerating expansion and the asymmetry of time—are discussed from this perspective. In particular, the implications of causality are compared to those of an alternative approach, where we instead take the probability space of all possible developments as the starting point.