Consistent Rules Research Articles

Phenolic profiles and antioxidant activity in different organs of Sinopodophyllum hexandrum.

Sinopodophyllum hexandrum is a perennial anti-cancer medicinal plant as unique phytochemical composition podophyllotoxin, and it has special effects on the treatments of pneumonic, cervical and testicular cancers. Besides the podophyllotoxin, phenolic substances play a key role in the clinical practice. However, few reports were available in terms of the phenolic compositions and antioxidant activity. In this work, main phenolic compounds were quantified by RP-HPLC in seven organs from S. hexandrum. Simultaneously, the sodium borohydride/chloranil-based (SBC) method and the Folin-Ciocalteau colorimetric method were used to determine total flavonoids and total phenols contents, respectively. The antioxidant activity of the different organs was further assessed by three methods (DPPH method, ABTS method and FRAP method). Phenolic compositions/total flavonoids contents/total phenols contents/antioxidant activity was observed to have significant differences among different organs (P<0.05), but have a consistent changing rule viz. rhizome>root>fruit>flower>leaf>stem>petiole. Furthermore, a correlation analysis was employed and indicated that a positive correlation existed between phenolic compositions contents and antioxidant activity. Obviously, rhizome had high phenolic compositions contents and strong antioxidant activity with the low DPPHIC50 value of 23.52 μg/mL, high ABTS value of 1137.82 μmol equiv. Trolox/g and high FRAP value of 685.76 μmol equiv. Trolox/g. Therefore, rhizome is recommended as a preponderant medicinal part, and root is proposed as an alternative raw material resource for natural antioxidant agents in functional food, medicine and chemicals. This study can provide a new insight into the utilization extension of S. hexandrum resources.

E-Scooter Presence in Urban Areas: Are Consistent Rules, Paying Attention and Smooth Infrastructure Enough for Safety?

Electric micromobility represents a sustainable mobility option for specific classes of users and distance thresholds. Had this mobility solution been integrated into a comprehensive mobility framework from the beginning, it would have expanded the coverage and accessibility of urban transit services. Instead, slow and incoherent regulation has established a contrast between enthusiastic users (who consider electric micromobility vehicles “fun” and “easy to use”) and recalcitrant public opinion (wherein electric micromobility vehicles are deemed “unsafe” and “dangerous”). Beyond the few attempts made by transport experts to assess the capability of e-scooters to become a sound mobility option (through mobility surveys, pattern analysis, fleet and routing problems), safety and infrastructure design should be developed in a consistent way in order to guarantee a balanced transport setting. With respect to this challenge, a methodology framework is proposed to address the increasing proliferation of micromobility in the context of a coherent transport system. Special attention is devoted to those aspects that have received less attention from the scientific community, namely infrastructure and safe interactions at intersections. The similarities and differences between e-scooters and bikes, chosen in this study as the representative of traditional soft mobility modes, have been taken into consideration. To support the proposed approach, tests investigating e-scooter performance and the perception of both the modes at safety-critical nodes (such as intersections) under different conditions are presented, and the methodology can be applied to a variety of urban scales. The results can be adopted by local authorities, transport companies and e-mobility providers to optimize infrastructure and increase the number and quality of available mobility options.

Effect of the INSIGHT Responsive Parenting Intervention on Parenting and Child Behavior at Ages 3 and 6 Years

To examine effects of the INSIGHT study responsive parenting (RP) intervention on reported and observed general parenting and child behavior during early and middle childhood. Primiparous mother-newborn dyads (n=279) were randomized to RP intervention or a safety control, with intervention content delivered at research nurse home visits at infant ages 3-4, 16, 28, and 40weeks and research center visits at 1 and 2years. At age 3 (n=220) and 6years (n=171) parenting and child behavior were observed during dyadic interactions and coded using the Iowa Family Interaction Rating Scales. Mothers also reported on child behavior (age 3) and aspects of general parenting (age 6) via the Child Behavior Checklist and The Comprehensive General Parenting Questionnaire, respectively. RP group children had fewer mother-reported externalizing (F=8.69, P=.004) and problem behaviors at age 3 (F=4.53, P=.03), and higher observed prosocial (F=4.73, P=.03) and lower antisocial (ie, externalizing; F=4.79, P=.03) behavior at age 6 vs controls. There were no study group differences in observed maternal sensitivity at age 3 or 6years. At age 6, RP group mothers reported higher use of structure defined by establishing consistent rules and routines (F=5.45, P=.02) and organization of their child's environment (F=7.12, P=.008) compared with controls. The INSIGHT RP intervention increased parental organization of the child's environment to facilitate competence, and had beneficial impacts on child behavior at 3 and 6years. No impacts were found on maternal sensitivity in childhood. ClinicalTrials.gov: NCT01167270.

Informacja czy agitacja? Wizerunek Andrzeja Dudy w portalu Tvp.info przed I turą wyborów prezydenckich

The text analyzes the image of Andrzej Duda on the Tvp.info portal before the first round of the presidential elections in 2020. The author referred to the theory of social influence formulated by Robert Cialdini, which states that in some situations people act in a simplified manner and automatically react to specific stimulus. In the texts analyzed was found one example of the rule of involvement and consistency. Not once did the analyzed texts contain the principles of liking and inaccessibility. In seven of the studied publications was found none of the rules of exerting social influence. In the vast majority of texts which stated the existence of rules of exerting social influence, was observed agitation in favor of the president seeking re-election.

Fair and Consistent Prize Allocation in Competitions

Given the ranking of competitors, how should the prize endowment be allocated? This paper introduces and axiomatically studies the prize allocation problem. We focus on consistent prize allocation rules satisfying elementary solidarity and fairness principles. In particular, we derive several families of rules satisfying anonymity, order preservation, and endowment monotonicity, which all fall between the equal division rule and the winner-takes-all rule. Our results may help organizers to select the most suitable prize allocation rule for rank-order competitions. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: A. Y. Kondratev is supported by HSE University [Basic Research Program and “Priority 2030” Program].

Continuum mechanics modeling of complex fluid systems following Oldroyd's seminal 1950 work

A review of some key developments in the continuum mechanics – based macroscopic modelling of complex fluid flows, following the pioneering work of Oldroyd in the 1950s will be presented. We start by reviewing the major developments achieved by Oldroyd, namely how his work established rules for consistency in developing continuum stress constitutive models extending the material into objective time derivatives for tensor quantities, with first application the stress tensor in viscoelastic constitutive relationships. We then show how the impact of his work to viscoelastic fluid flows has been amplified from the kinetic theory work of Bird and others who established a firm macromolecular structural connection. The use of objective time derivatives was then extended to include second order conformation tensors and through them the connection was made to internal deformation energy by Marrucci. We then further demonstrate the impact of this work by analysing the models through the microstructural descriptions offered within the non-equilibrium thermodynamics formalism. We then conclude with a brief mentioning of direct applications of the original Oldroyd-B model (through stability analyses, etc.) towards bettering our understanding of the rheological behaviour of complex fluid systems.

Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data

In this article, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies with correlated multiple responses such as the cancer cell-line encyclopedia project. We assume that the underlying regression coefficient matrix is simultaneously low-rank and row-wise sparse. We propose an intuitively appealing selection and estimation framework based on marginal model likelihood, and we develop an efficient computational algorithm for inference. We establish a novel high-dimensional theory for this nonlinear multivariate regression. Our theory is general, allowing for potential correlations between the binary responses. We propose a new type of nuclear norm penalty using the smooth clipped absolute deviation, filling the gap in the related non-convex penalization literature. We theoretically demonstrate that the proposed approach improves estimation accuracy by considering multiple responses jointly through the proposed estimator when the underlying coefficient matrix is low-rank and row-wise sparse. In particular, we establish the non-asymptotic error bounds, and both rank and row support consistency of the proposed method. Moreover, we develop a consistent rule to simultaneously select the rank and row dimension of the coefficient matrix. Furthermore, we extend the proposed methods and theory to a joint Ising model, which accounts for the dependence relationships. In our analysis of both simulated data and the cancer cell line encyclopedia data, the proposed methods outperform the existing methods in better predicting responses. Supplementary materials for this article are available online.

Software-Defined Networking: Categories, Analysis, and Future Directions.

Software-defined networking (SDN) is an innovative network architecture that splits the control and management planes from the data plane. It helps in simplifying network manageability and programmability, along with several other benefits. Due to the programmability features, SDN is gaining popularity in both academia and industry. However, this emerging paradigm has been facing diverse kinds of challenges during the SDN implementation process and with respect to adoption of existing technologies. This paper evaluates several existing approaches in SDN and compares and analyzes the findings. The paper is organized into seven categories, namely network testing and verification, flow rule installation mechanisms, network security and management issues related to SDN implementation, memory management studies, SDN simulators and emulators, SDN programming languages, and SDN controller platforms. Each category has significance in the implementation of SDN networks. During the implementation process, network testing and verification is very important to avoid packet violations and network inefficiencies. Similarly, consistent flow rule installation, especially in the case of policy change at the controller, needs to be carefully implemented. Effective network security and memory management, at both the network control and data planes, play a vital role in SDN. Furthermore, SDN simulation tools, controller platforms, and programming languages help academia and industry to implement and test their developed network applications. We also compare the existing SDN studies in detail in terms of classification and discuss their benefits and limitations. Finally, future research guidelines are provided, and the paper is concluded.

A functional-group-based approach to modeling real-fuel combustion chemistry – III: Application to biodiesels

Real biodiesel fuels are mixtures comprising many high molecular weight components, making it a challenge to predict their combustion chemistry with detailed kinetic models. Our group previously proposed a functional-group approach (FGMech) to model the combustion chemistry of real gasoline and jet fuels; this approach has now been extended to model real biodiesel combustion and mixtures with petroleum fuels. As in our previous work, a decoupling philosophy is adopted for construction of the model. A lumped reaction mechanism describes the (oxidative) pyrolysis of fuels, while a detailed base chemistry model represents the oxidation of key pyrolysis intermediates. However, due to the presence of the ester group, several oxygenated species are identified as additional primary products and incorporated into the lumped reaction steps. In addition to the lumped reactions initiated by unimolecular decomposition and H-atom abstraction reactions, a lumped H-atom addition-elimination reaction is also incorporated as a new reaction class to account for the presence of double bonds. Stoichiometric parameters are obtained based on a multiple linear regression (MLR) model, which establishes relationships between the fuel's functional group distributions and the stoichiometric parameters of the lumped reactions. Global rate constants are developed from consistent rate rules obtained from pure fuels. New pyrolysis experimental data for methyl pentanoate/methyl nonanoate and methyl heptanoate/n-heptane mixtures (50%/50% in mol) are obtained in a jet-stirred reactor at atmospheric pressure. In general, kinetic models developed using the FGMech approach can reasonably reproduce all the validation targets obtained in this work, as well as those in the literature, confirming that functional-group-modeling is a promising approach to simulate combustion behavior of diesel/biodiesel surrogate fuels and real biodiesels.

"My life isn't my life, it's the systems": A qualitative exploration of women's experiences of day-to-day restrictive practices as inpatients.

WHAT IS KNOWN ON THE SUBJECT?: Sometimes someone needs to be in hospital because they are struggling with their mental health and need some extra support but being in hospital can also be a difficult experience. There are a lot of restrictions in place in hospital, like locked doors, rules to follow and not much choice about what happens to you. Other research has found that these restrictions can feel difficult and stressful for people and so more research is needed about this. We wanted to know what being in hospital felt like for women in particular. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: We interviewed women who were in hospital because of their mental health about what it was like for them in hospital. The women told us that they felt powerless while they were in hospital. They sometimes felt like they were being punished and this could affect their mood and could lead to them hurting themselves. They also said that they were not always listened to by staff, and they found it difficult being away from their family and friends while they were in hospital. The women also told us that being in hospital could sometimes help them to feel safe. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Women should be looked after in hospital in a way that helps them to feel in control of what happens to them. They should be supported to be able to go outside the hospital on leave, to keep in touch with their family and friends, and they should be listened to by staff. A project called "safewards" has suggested some ways for helping to make hospital wards safer. They have suggested that everyone should be clear about what the rules are when they go into hospital and ways that staff could communicate more clearly with the people they are working with. Our research supports using these techniques. ABSTRACT: Introduction Inpatient care often involves restrictive interventions such as seclusion and restraint and restrictive practices that limit the person's freedom, rights and daily activities. Restrictive practice has not been the explicit focus in previous research; however, it often appears as an important theme, with participants identifying it can have a detrimental effect on their well-being. More research specifically on this topic in an inpatient setting is, therefore, needed. Women might be particularly vulnerable to adverse effects of restrictive practices compared to men as women generally occupy less powerful positions in society and more often experience abuse. Aims The study aimed to explore women's experiences of routine restrictive practices in mental health inpatient settings. Methods Twenty-two women who were currently inpatients on mental health wards were interviewed about their experiences of restrictive practices in hospital. Interviews were analysed using thematic analysis. Results An overarching theme emerged of powerlessness. Four key sub-themes were also identified: restrictions perceived as punitive, having no voice, impact of restrictions on relationships and restrictions providing safety and support. Discussion Although restrictive practices were found to provide the women with a sense of safety, they were also found to impact upon the women's well-being, leading to increases in self-harm and over-reliance on restrictions. Implications for practice This research highlights the importance of gender-informed inpatient services for women that foster independence, empowerment and allow women to have their voices heard. Safewards interventions such as clear mutual expectations and soft words could contribute to mitigating the impact of restrictive practices.

Benefits of physical and machine learning hybridization for photovoltaic power forecasting

Irradiance-to-power conversion is an essential step of state-of-the-art photovoltaic (PV) power forecasting, regardless of the source and post-processing of irradiance forecasts. The two distinct approaches for mapping the irradiance forecasts to PV power are physical and data-driven, which can also be hybridized. The contribution of this paper is twofold; first, it proposes a concept and identifies the best implementation of a hybrid physical and machine learning irradiance-to-power conversion method. Second, a head-to-head comparison of the physical, data-driven, and hybrid methods is performed for the operational day-ahead power forecasting of 14 PV plants in Hungary based on numerical weather prediction (NWP). To respect the rule of consistency but still obtain as complete picture as possible, two directives are set, namely minimizing the mean absolute error (MAE) and minimizing the root mean square error (RMSE), and separate sets of forecasts are optimized for both directives. The results reveal that for two years of training data, the hybrid method that involves the most physically-calculated predictors can reduce the MAE by 5.2% and 10.4% compared, respectively, to the optimized physical model chains and the machine learning without any physical considerations. The two most important physical modeling steps are separation and transposition modeling, and the rest of the physical PV simulation can be left to machine learning in hybrid models without a significant increase in the errors. The optimization of the physical model chains is found to be important even in the case of hybrid modeling; therefore, it should become a standard procedure in practical applications. Finally, the hybrid method is only beneficial for at least one year of training data, while in the initial period of the operation of a PV plant, it is advised to stay with optimized physical modeling. The guidelines and recommendations of this paper can help both researchers and practitioners design and optimize their power conversion model to increase the accuracy of the PV power forecasts.

Работа над новым орфографическим сводом (на материале употребления прописной буквы в официонимах)

The aim of the study is to substantiate the need to improve the rules governing the use of capital letters in official names. The article discusses the linguistic content of the current standards for the spelling of official names and proposals for updating this fragment of the norm in the manuals that claim to be called the modern code of the Russian spelling laws. The scientific novelty lies in a systematic analysis of the relevant standard, in the development of consistent rules that help streamline the spelling of the names under consideration and eliminate spelling inconsistencies in the modern practice of the Russian writing. As a result, it is proved that the rules for the use of a capital letter in official names need to be significantly improved taking into account both the historically developed tradition of using a capital letter in the Russian writing and modern writing practice.

LOGICDEF: An Interpretable Defense Framework against Adversarial Examples via Inductive Scene Graph Reasoning

Deep vision models have provided new capability across a spectrum of applications in transportation, manufacturing, agriculture, commerce, and security. However, recent studies have demonstrated that these models are vulnerable to adversarial attack, exposing a risk-of-use in critical applications where untrusted parties have access to the data environment or even directly to the sensor inputs. Existing adversarial defense methods are either limited to specific types of attacks or are too complex to be applied to practical vision models. More importantly, these methods rely on techniques that are not interpretable to humans. In this work, we argue that an effective defense should produce an explanation as to why the system is attacked, and by using a representation that is easily readable by a human user, e.g. a logic formalism. To this end, we propose logic adversarial defense (LogicDef), a defense framework that utilizes the scene graph of the image to provide a contextual structure for detecting and explaining object classification. Our framework first mines inductive logic rules from the extracted scene graph, and then uses these rules to construct a defense model that alerts the user when the vision model violates the consistency rules. The defense model is interpretable and its robustness is further enhanced by incorporating existing relational commonsense knowledge from projects such as ConceptNet. In order to handle the hierarchical nature of such relational reasoning, we use a curriculum learning approach based on object taxonomy, yielding additional improvements to training and performance.

Dramatic Lockdown Fossil Fuel CO2 Decrease Detected by Citizen Science-Supported Atmospheric Radiocarbon Observations.

COVID-19 lockdowns resulted in dramatic changes to fossil fuel CO2 emissions around the world, most prominently in the transportation sector. Yet travel restrictions also hampered observational data collection, making it difficult to evaluate emission changes as they occurred. To overcome this, we used a novel citizen science campaign to detect emission changes during lockdown and engage youth in climate science. Citizen scientists collected grass samples from their garden or local park, from which we analyzed the radiocarbon content to infer the recently added atmospheric fossil fuel CO2 mole fraction at each sampling location. The local fossil fuel CO2 mole fractions during lockdown were compared with a “normal” nonlockdown period. Our results from 17 sites in five cities around New Zealand demonstrate dramatic reductions in traffic emissions of 75 ± 3% during the most severe lockdown restriction period. This is consistent with sparse local traffic count information and a much larger decrease in traffic emissions than reported in global aggregate estimates of emission changes. Our results demonstrate that despite nationally consistent rules on travel during lockdown, emission changes varied by location, with inner-city sites typically dominated by bus traffic showing smaller decreases in emissions than elsewhere.

Characteristics of Chinese Weathered Coal from Six Geographical Locations and Effects on Urease Activity Inhibition

Weathered coal is known to have potential inhibitory effects on urease activity, thus reducing the loss of nitrogen from fertilizer such as ammonia. This means that it can be used as a urea enhancer to promote urea utilization efficiency. However, the variation in its composition and structure has impeded the optimal utilization of this resource. In this study, we collected Chinese weathered coal from six representative geographical locations and analyzed its elemental and substance composition, as well as determined its chemical structure via Fourier transform infrared spectroscopy and investigated its effects on urease (soybean meal) activity. The results showed evident variation in the composition and structure among the different weathered coal samples, especially in the pH values, humic acid and ash content, and aromaticity. All six weathered coal samples significantly inhibited urease activity, and the inhibitory effect was enhanced with the elevated proportion of weathered coal introduced to urea. When the additive proportion of weathered coal increased, the weathered coal, characterized as having a higher humic acid content and a more aliphatic structure, showed a more rapid increase in the urease activity inhibition rate, while there was only a slight effect when the weathered coal had a low humic acid content and high atomicity. Therefore, the former type of weathered coal was more sensitive to the additive proportion. Furthermore, there was no consistent rule when the same proportion of weathered coal from different geographic locations was blended into urea, which might be attributable to other unexplored factors.

Hostile social media harassment: A machine learning framework for filtering anti-female jokes

Drawing motivation from the non-existing computational criteria that blocks abusive contents targeted at female folks on social media from weird contents capable of hurting, offending, or intimidating the feelings; we present a machine learning framework for an intelligent filtering of information regarding anti-female jokes on social media discourse using multi-class classification. Our framework trains, validates and test neural network algorithm with data set sourced from chat rooms on social media handles with the goal of detecting and filtering expression(s) available in online jokes defined in terms of harsh, mild and neutral anti-female jokes. During implementation, the prototyped model recorded a performance accuracy of 92.9 %. This performance though high with the available limited data set is yet to attain the desired level of accuracy due to lack of consistent rules that distinguish various classes of anti-female jokes. In future, this model will be deployed to achieve unhindered discourse on social media by implementing an intelligent filtering system that could identify, classify and block unwanted contents.

Coupled support tensor machine classification for multimodal neuroimaging data

AbstractMultimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities. Multimodal modeling helps to explain the interdependence between heterogeneous data sources, discovers new insights that may not be available from a single modality, and improves decision‐making. Recently, coupled matrix–tensor factorization has been introduced for multimodal data fusion to jointly estimate latent factors and identify complex interdependence among the latent factors. However, most of the prior work on coupled matrix–tensor factors focuses on unsupervised learning and there is little work on supervised learning using the jointly estimated latent factors. This paper considers the multimodal tensor data classification problem. A coupled support tensor machine (C‐STM) built upon the latent factors jointly estimated from the advanced coupled matrix–tensor factorization is proposed. C‐STM combines individual and shared latent factors with multiple kernels and estimates a maximal‐margin classifier for coupled matrix–tensor data. The classification risk of C‐STM is shown to converge to the optimal Bayes risk, making it a statistically consistent rule. C‐STM is validated through simulation studies as well as a simultaneous analysis on electroencephalography with functional magnetic resonance imaging data. The empirical evidence shows that C‐STM can utilize information from multiple sources and provide a better classification performance than traditional single‐mode classifiers.

Compromising as an equal loss principle

A social choice rule aggregates the preferences of a group of individuals over a set of alternatives into a collective choice. The literature admits several social choice rules whose recommendations are supposed to reflect a compromise among individuals. We observe that all these compromise rules can be better described as procedural compromises, i.e., they impose over individuals a willingness to compromise but they do not ensure an outcome where everyone has effectively compromised. We revisit the concept of a compromise in a collective choice environment with at least three individuals having strict preferences over a finite set of alternatives. Referring to a large class of spread measures, we view the concept of compromise from an equal loss perspective, favoring an outcome where every voter concedes as equally as possible. As such, being a compromise may fail Pareto efficiency, which we ensure by asking voters to concede as equally as possible among the Pareto efficient alternatives. We show that Condorcet consistent rules, scoring rules (except antiplurality) and Brams-Kilgour compromises (except fallback bargaining) all fail to ascertain an outcome which is a compromise. A slight restriction on acceptable spread measures suffices to extend the negative result to antiplurality and fallback bargaining. This failure also prevails for social choice problems with two individuals: all well-known two-person social choice rules of the literature, namely, fallback bargaining, Pareto and veto rules, short listing and veto rank, fail to pick ex-post compromises. We conclude that there is a need to propose and study rules that satisfy this equal loss, or outcome oriented, notion of a compromise.

A coupled plasticity-damage cohesive-frictional interface for low-cycle fatigue analysis

A novel thermodynamically consistent cohesive-frictional model for the analysis of interface degradation and failure under either monotonic quasi-static loading or cyclic loading in low-cycle fatigue problems is proposed.Starting from the definition of a suitable Helmholtz energy density function, a phenomenological interface model is developed in the framework of plasticity and damage mechanics. In particular, a coupled plasticity-damage activation function is defined and employed together the consistent evolution rules to capture the evolution of damage and plasticity under the action of the external loads. Due to the specific features of such threshold and flow rules, the initiation and accumulation of damage under monotonic increasing loads is captured and accompanied by negligible plastic evolution, allowing to approximate pure damage-based cohesive laws. On the other hand, coupling associative plasticity and damage evolution allows linking the interface degradation in low-cycle fatigue processes to plastic hysteresis, on the basis of the phenomenological assumption that no infinite plastic flows may happen without microstructural transformation leading to loss of load bearing capability. The model also embodies a smooth transition from an initially cohesive to a residual frictional interface behaviour, governed by a Coulomb frictional activation function.The developed formulation has been implemented and assessed for individual interfaces, highlighting consistent phenomenological behaviour. It has been then applied to the analysis of delamination and de-bonding in composite test cases, showing accuracy against experimental data and confirming its potential.

Composition of Multipartite Quantum Systems: Perspective from Timelike Paradigm.

Figuring out the physical rationale behind natural selection of quantum theory is one of the most acclaimed quests in quantum foundational research. This pursuit has inspired several axiomatic initiatives to derive a mathematical formulation of the theory by identifying the general structure of state and effect space of individual systems as well as specifying their composition rules. This generic framework can allow several consistent composition rules for a multipartite system even when state and effect cones of individual subsystems are assumed to be quantum. Nevertheless, for any bipartite system, none of these compositions allows beyond quantum spacelike correlations. In this Letter, we show that such bipartite compositions can admit stronger-than-quantum correlations in the timelike domain and, hence, indicates pragmatically distinct roles carried out by state and effect cones. We discuss consequences of such correlations in a communication task, which accordingly opens up a possibility of testing the actual composition between elementary quanta.