Strong Hypothesis Research Articles

Characterization of differential susceptibility and differential infectivity epidemic models.

Heterogeneity in susceptibility and infectivity is a central issue in epidemiology. Although the latter has received some attention recently, the former is often neglected in modeling of epidemic systems. Moreover, very few studies consider both of these heterogeneities. This paper is concerned with the characterization of epidemic models with differential susceptibility and differential infectivity under a general setup. Specifically, we investigate the global asymptotic behavior of equilibria of these systems when the network configuration of the Susceptible-Infectious interactions is strongly connected. These results prove two conjectures by Bonzi et al. (J Math Biol 62:39-64, 2011) and Hyman and Li (Math Biosci Eng 3:89-100, 2006). Moreover, we consider the scenario in which the strong connectivity hypothesis is dropped. In this case, the model exhibits a wider range of dynamical behavior, including the rise of boundary and interior equilibria, all based on the topology of network connectivity. Finally, a model with multidirectional transitions between infectious classes is presented and completely analyzed.

Autonomy as the self-realisation of an environmental identity

This article addresses the question raised by the Anthropocene of rethinking the concept of autonomy which, in the conditions of the new geological epoch, is subject to a crisis of legitimation. It explores the ‘strong hypothesis’ according to which nature is a necessary condition of our qualitative experience of the world and a constitutive relation of autonomy defined as the self-realisation of individual identity. With this aim in mind, the article attempts to rethink the concept of recognition in order to conceive a recognising attitude of nature in the form of love of nature as a form of adequate recognition. Rethinking recognition can serve as foundation of an ‘objective’ theory of autonomy, that is, a theory that explains how nature can be defined as a condition and constitutive relation of autonomy. The article, therefore, proposes to reformulate the concept of autonomy as the self-realisation of an ecological or environmental identity in the form of the self-realisation of an ecological ethos, that is, an attitude of respect and care for nature. This enquiry, ultimately, is a contribution to the task of rethinking the basis of our conceptions of autonomy and freedom in the Anthropocene, so that the idea of autonomy can legitimately constitute the central value of the social transformation that is needed to face the ecological crisis.

When may a system be referred to as complex?—an entropic perspective

Defining complexity is hard and far from unique—like defining beauty, intelligence, creativity, and many other such abstract concepts. In contrast, describing concrete complex systems is a sensibly simpler task. We focus here on such an issue from the perspective of entropic functionals, either additive or nonadditive. Indeed, for the systems currently referred to as simple, the statistical mechanics and associated (additive) entropy is that of Boltzmann–Gibbs, formulated 150 years ago. This formalism constitutes a pillar of contemporary theoretical physics and is typically grounded on strong chaos, mixing, ergodicity, and similar hypotheses, which typically emerge for systems with short-range space–time generic correlations. It fails, however, for the so-called complex systems, where generic long-range space–time correlations prevail, typically grounded on weak chaos. Many such nontrivial systems are satisfactorily handled within a generalization of the Boltzmann–Gibbs theory, namely, nonextensive statistical mechanics, introduced in 1988 and grounded on nonadditive entropies. Illustrations are presented in terms of D-dimensional simplexes such as nodes (D = 0), bonds (D = 1), plaquettes (D = 2), polyhedra (D = 3, …), and higher-order ones. A regularly updated bibliography is available at http://tsallis.cat.cbpf.br/biblio.htm.

Three-dimensional analytical solution of free vibrations of a simply supported composite plate in contact with a fluid

Background:The quantification of fluid–structure interactions is critical for the design and optimization of marine structures. As composite materials gain more traction in naval construction, there is a dire need of modeling tools to detail the dynamic response of composites in contact with fluids. Current structural theories and finite-element simulations offer promising pathways, but their strong hypotheses on through-the-thickness deformations and high computational cost challenge their practical feasibility. Methods:We propose an analytical three-dimensional solution to the free vibration of a bidirectional composite, separating a fluid column from vacuum. We leverage Pagano’s method to describe the three-dimensional displacement field in each ply of the composite plate and the fluid potential in the form of a Fourier series. Results:An analytical solution to the coupled fluid–structure problem is derived by imposing the continuity of displacement and stress at the ply interfaces, as well as boundary conditions on dry and wetted plies. We specialize the solution to sandwich structures of naval interest, offering insight on how the interactions with water can dramatically affect mode shapes and through-the-thickness profiles of displacement and stress. Discussion:The proposed approach provides the foundations for the design of thick composite plates for use in marine structures.

CANNABIS THIN LAYER CHROMATOGRAPHY (TLC) CHEMO TYPING: HOW ACCURATE IS TLC COMPARED TO HIGH PRESSURE LIQUID CHROMATOGRAPHY?

Problem: Brazilian Cannabis Associations (BCA) search for methods of reducing their costs and increasing their quality control of Cannabis spp. Oil and extracts. The significant difficulties presented by ABRACAM (Associação Brasileira Cannabis Medicinal) to UFPI professors were to standardize cannabinoids profiles of its plants and guarantee the same levels of cannabinoids on its artisanal extracts. ABRACAM has difficulty standardizing plant cannabinoids profiles due to Cannabis spp. Phenotypic plasticity, or the ability of Cannabis spp. to change attributes like the size of leaves, levels of secondary metabolites, stems structures, flowering period, among others. The second problem is that artisanal extract combines different production batches of non-tested extractions. It is necessary to quantify and qualify each cannabinoid and how much is on artisanal Cannabis production. Aiming to solve the ABRACAM production problem, UFPI and CANNAPI suggested manual testing practices to test cannabis and its derivates. This article results from validation tests performed at ABRACAM to determine how precise manual methods are to quantify cannabinoids. Methods: This research compared the cannabinoids results of an off-the-shelf Thin Layer Chromatography (TLC) cannabinoids profile test kit with the results of a High-Pressure Liquid Chromatography (HPLC). UFPI perfect the method from the vendor manual after running sixty quantifications from one sample. Results: The TLC error compared to HPLC was between approximately 0,5% and 1%. ABRACAM differentiated the Chemotype Profile of its High THC varieties and High CBD varieties. For High CBD variety, the outdoor cultivation proportionated greater cannabinoids levels. Conclusions: The comparison pointed that TLC may be used as testing equipment for this Brazilian Association due to its artisanal production license. This article concludes that precision is the key to medication production, and the TLC error may be accepted for artisanal production but not as a standard for precision. For scientific purposes, TLC is a strong hypothesis builder, and for agriculture, purposes may be a vital tool point the right time to harvest.

Monetary Circuit Theory, Stock-Flow Consistent Modeling and Parguez’s Analysis

Stock-flow consistent (SFC) modeling goes far beyond simple formal tools. It rests on strong post-Keynesian hypotheses and proposes a new conceptual framework for formalization. This being said, if its affiliation with post-Keynesianism is obvious, SFC models are in line with many elements of the monetary circuit theory (MCT), as developed notably by Alain Parguez. Indeed, MCT has deeply studied macroeconomics and the link between money and production. More precisely, both SFC models and MCT offer an analysis in terms of national accounting, in which money is endogenized through the financing of the economy, and more precisely through the financing of production. Even if MCT has lost its vigor, its fundamental principles are still present through the SFC modeling.

Vindicate BLS and BLD Of FGD Mix-Data Census through Triangulation Convergence

Focus Group Discussion (FGD) is frequently being used as a qualitative approach to gain an in-depth understanding of social issues. It also strongly influences the quantitative method of research as the next phase of the activity. Consequently, using that both-method statistical data finding through the triangulation conclusion method is the most appropriate way to achieve the credible data result. Hence, this paper tabulates and exposes the triangulation convergence cross-finding census in order to come up with a strong theory and hypothesis prediction on the Building Leakage Syndrome (BLS) or Building Leakage Defects (BLD) problem. The report uses Excell and SPSS v27 to enhance the technical computer tabulation and records.

Priority-based bin packing with subset constraints

This paper is concerned with a new variant of bin packing called priority-based bin packing with subset constraints (PBBP-SC). PBBP-SC is a variant of traditional bin packing (TBP). In a TBP instance, we are given a collection of items {t1,t2,…tn}, where each item ti has size si∈(0,1). The goal of TBP is to pack the items in as few unit-sized bins as possible. In a PBBP-SC instance, we are given a collection of n unit-size items and a collection of m bins of varying capacities. Associated with each item is a positive integer which is called its priority. The priority of an item indicates its importance in being included in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. The first problem we study is the feasibility problem. This is the problem of checking if there is a feasible assignment to a given PBBP-SC instance. The second problem we study is the priority maximization problem. This is the problem of finding a maximum priority assignment of an infeasible PBBP-SC instance. The third problem we study is the bin minimization problem. This is the problem of finding an assignment with the fewest number of bins to pack all of the items in a feasible PBBP-SC instance. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. In particular, we show that both the feasibility and priority maximization problems are solvable in polynomial time. We also show that the bin minimization problem is log-APX-complete and cannot be solved in time o(1.41m) unless the Strong Exponential Time Hypothesis fails.

The mediating role of engagement in the relationship between equal opportunities and employee performance

This study aimed to explore the relationship between employee engagement, equal opportunities, and employee performance in Bahraini retail businesses. Using a cross-sectional survey, data were collected from 231 employees and analyzed via SPSS and Smart-PLS, uncovering significant results. Out of the 249 participants invited to the survey, 231 responded. All the variables of the study showed different effects, with equal opportunity and employee engagement (EO → EE) considered the strongest hypotheses in this study at the significance level of 0.01 (β = 0.722, = 0.724, SD = 0.070, t = 10.324, p = 0.000), with f2 = 1.087 above 0.15 and less than 0.35, which was considered a high effect size and strongly supported the hypothesis proposed by the researcher. All hypotheses were supported. The R2 values of independent variables were above 38% for the variance of employee performance, which is considered an excellent fit to the data as evidenced by the squared multiple correlation R2 values for the dependent variables. Finally, EO → EE → JP mediating role indirectly affects JP through EO = 24%, which is considered an excellent effect size.The recent study confirmed a strong positive link between equal opportunities and employee performance, with employee engagement acting as a significant mediator. This suggests that promoting equal opportunities can boost performance, especially when coupled with employee involvement. These findings are important for HRM experts and executives, as they indicate that prioritizing fairness and fostering participation can enhance productivity.

Environmental taxes, enterprise innovation, and environmental total factor productivity-effect test based on Porter's hypothesis.

Under the increasingly severe environmental constraints, improving environmental total factor productivity (ETFP) is the fundamental way for the sustainable development of heavily polluting enterprises. Based on 3463 panel data of A-share listed companies in China from 2011 to 2019, this paper employs Porter's hypothesis (PH) framework to explore the impact of environmental tax (EN_T) on enterprise innovation and environmental total factor productivity for the heavily polluting manufacturing industry using the propensity score matching (PSM) method. The empirical results show the following. (i) Environmental taxes positively affect enterprise innovation (EI) and environmental total factor productivity (ETFP). (ii) Mechanism analysis verifies a partial mediating effect for EI between EN_T and ETFP. (iii) Regional heterogeneity analysis illustrates the differences in the impact of environmental taxes on innovation quality. (iv) Individual heterogeneity analysis shows that the "strong Porter hypothesis" is only valid for large-scale enterprises. The results are of great importance for both government and enterprises to improve the EN_T system and optimize the allocation of resources in realistic practice.

Graphs cannot be indexed in polynomial time for sub-quadratic time string matching, unless SETH fails

The string matching problem on a node-labeled graph G=(V,E) asks whether a given pattern string P equals the concatenation of node labels of some path in G. This is a basic primitive in various problems in bioinformatics, graph databases, or networks, and it was recently proven that solving it in time O(|E|1−ϵ|P|) or O(|E||P|1−ϵ) is hard under OVH, the Orthogonal Vectors Hypothesis, and thus under SETH, the Strong Exponential Time Hypothesis (Equi et al., 2019 [11]). We consider its indexed version, where the graph is indexed to support string queries. We show that, under OVH, no polynomial-time index of the graph performed in time O(|E|α) can support querying P in time O(|P|+|E|δ|P|β), with either δ<1 or β<1.We present our techniques as a general framework, introducing the notion of linear independent-components (lic) reduction, from which we derive our result. This allows us to also translate the quadratic conditional lower bound of Backurs and Indyk (2015) [48] for the problem of matching a query string inside a text, under edit distance, into an analogous tight quadratic lower bound for its indexed version. This improves the recent result of Cohen-Addad, Feuilloley and Starikovskaya (2019) [49], with a slightly different boundary condition. We also apply our technique to obtain the first quadratic indexing lower bounds for Fréchet distance and rooted unlabeled subtree-isomorphism queries.

External clustering validity index based on extended similarity measures

Clustering is one of the most important unsupervised approaches to organizing data in many practical applications. The application of different methods to clustering one dataset generally results in different clusterings of that dataset; for that reason, there is a need for evaluating the similarities of various clustering methods. This can be accomplished with the help of external validity indices. Although many external validity measures exist in the literature, most of them suffer from some drawbacks: they apply very strong null hypotheses, which lead to the independence of the clusterings, fixed number of clusters, and fixed cluster sizes. In addition, an acceptable measure should have some properties to be a metric. If a measure does not qualify for being a metric, the interpretation will not be easy. In this study, a novel Set Matching Index (SMI) is proposed based extended derivation of Jaccard, Dice, and Cosine measures. Thus, three new indices are introduced in this paper, i.e., Set Matching Index based on Extended Jaccard similarity measure (SMI-EJ), Set Matching Index based on Extended Dice similarity measure (SMI-ED) and Set Matching Index based on Extended Cosine similarity measure (SMI-EC) as a set matching metric. All of the metric properties are proved in the present research. For the assessment of the proposed indices performance quality, they are experimented on ten real-world and Synthetic datasets, and then the obtained results are compared with those of four popular external indices (NMI, RI, ARI and Purity) and a new index (PSI). Also to verify the performance of three proposed similarity indices, the experiments develop. We apply the proposed indices to the generated clustering solutions and compare the results. We show that the proposed indices achieve desirable results based on the external validity index properties.Moreover, SMI has simple formula with moderate and appropriate results that can be easily interpreted.

Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach.

The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.

A combinatorial multi-armed bandit approach to correlation clustering

Given a graph whose edges are assigned positive-type and negative-type weights, the problem of correlation clustering aims at grouping the graph vertices so as to minimize (resp. maximize) the sum of negative-type (resp. positive-type) intra-cluster weights plus the sum of positive-type (resp. negative-type) inter-cluster weights. In correlation clustering, it is typically assumed that the weights are readily available. This is a rather strong hypothesis, which is unrealistic in several scenarios. To overcome this limitation, in this work we focus on the setting where edge weights of a correlation-clustering instance are unknown, and they have to be estimated in multiple rounds, while performing the clustering. The clustering solutions produced in the various rounds provide a feedback to properly adjust the weight estimates, and the goal is to maximize the cumulative quality of the clusterings. We tackle this problem by resorting to the reinforcement-learning paradigm, and, specifically, we design for the first time a Combinatorial Multi-Armed Bandit (CMAB) framework for correlation clustering. We provide a variety of contributions, namely (1) formulations of the minimization and maximization variants of correlation clustering in a CMAB setting; (2) adaptation of well-established CMAB algorithms to the correlation-clustering context; (3) regret analyses to theoretically bound the accuracy of these algorithms; (4) design of further (heuristic) algorithms to have the probability constraint satisfied at every round (key condition to soundly adopt efficient yet effective algorithms for correlation clustering as CMAB oracles); (5) extensive experimental comparison among a variety of both CMAB and non-CMAB approaches for correlation clustering.

Correction to: A Line Search Based Proximal Stochastic Gradient Algorithm with Dynamical Variance Reduction

This file contains a revised version of the proofs of Theorems 3 and 4 of the paper [1]. In particular, a more correct argument is employed to obtain the inequality (A11) from (A10), provided that a stronger hypothesis on the sequence $$\{\varepsilon _k\}$$ is included. The practical implementation of the algorithm (Section 3) remains as it is and all the numerical experiments (Section 4) are still valid since the stronger hypothesis on $$\{\varepsilon _k\}$$ was already satisfied by the selected setting of the hyperparameters.

Mutual-Assistance Learning for Standalone Mono-Modality Survival Analysis of Human Cancers.

Current survival analysis of cancers confronts two key issues. While comprehensive perspectives provided by data from multiple modalities often promote the performance of survival models, data with inadequate modalities at the testing phase are more ubiquitous in clinical scenarios, which makes multi-modality approaches not applicable. Additionally, incomplete observations (i.e., censored instances) bring a unique challenge for survival analysis, to tackle which, some models have been proposed based on certain strict assumptions or attribute distributions that, however, may limit their applicability. In this paper, we present a mutual-assistance learning paradigm for standalone mono-modality survival analysis of cancers. The mutual assistance implies the cooperation of multiple components and embodies three aspects: 1) it leverages the knowledge of multi-modality data to guide the representation learning of an individual modality via mutual-assistance similarity and geometry constraints; 2) it formulates mutual-assistance regression and ranking functions independent of strong hypotheses to estimate the relative risk, in which a bias vector is introduced to efficiently cope with the censoring problem; 3) it integrates representation learning and survival modeling into a unified mutual-assistance framework for alleviating the requirement of attribute distributions. Extensive experiments on several datasets demonstrate our method can significantly improve the performance of mono-modality survival model.

Longing to act: Bayesian inference as a framework for craving in behavioral addiction

Traditionally, craving is considered a defining feature of drug addiction. Accumulating evidence suggests that craving can also exist in behavioral addictions (e.g., gambling disorder) without drug-induced effects. However, the degree to which mechanisms of craving overlap between classic substance use disorders and behavioral addictions remains unclear. There is, therefore, an urgent need to develop an overarching theory of craving that conceptually integrates findings across behavioral and drug addictions. In this review, we will first synthesize existing theories and empirical findings related to craving in both drug-dependent and -independent addictive disorders. Building on the Bayesian brain hypothesis and previous work on interoceptive inference, we will then propose a computational theory for craving in behavioral addiction, where the target of craving is execution of an action (e.g., gambling) rather than a drug. Specifically, we conceptualize craving in behavioral addiction as a subjective belief about physiological states of the body associated with action completion and is updated based on both a prior belief (“I need to act to feel good”) and sensory evidence (“I cannot act”). We conclude by briefly discussing the therapeutic implications of this framework. In summary, this unified Bayesian computational framework for craving generalizes across addictive disorders, provides explanatory power for ostensibly conflicting empirical findings, and generates strong hypotheses for future empirical studies. The disambiguation of the computational components underlying domain-general craving using this framework will lead to a deeper understanding of, and effective treatment targets for, behavioral and drug addictions.

On the problem of data interpretation in the analysis of iron-containing GSR particles

Many forensic experts hypothesize that iron found in gunshot residues (GSRs) derive from melted iron particles because the steel of the barrel consists mainly of iron. This is a strong hypothesis that deserve careful investigation. To this purpose, we focused our attention on erosion and corrosion mechanism occurring in the barrel that can influence GSRs. A preliminary physicochemical interpretation consistent with the analytical results was presented. Moreover, spectra and images of GSRs originated from the firing of ammunition by firearms having rusty barrels and of iron oxide particles not originated from the firing were analyzed. The obtained results are discussed, taking into account the chemical content and morphology of the observed particles taking in account the context both of the erosion and of the corrosion and of the GSRs formation process.

Evolution and Kantian morality: A correction and addendum

Theorem 1 in Alger and Weibull (2016, Games and Economic Behavior) consists of two statements. The first establishes that Homo moralis with the right degree of morality is evolutionarily stable. The second statement is a claim about sufficient conditions for other goal functions to be evolutionarily unstable. However, the proof given for that claim presumes that all relevant sets are non-empty, while the hypothesis of the theorem does not guarantee that. We here prove instability under a stronger hypothesis that guarantees existence, and we also establish a new and closely related result. As a by-product, we also obtain an extension of Theorem 1 in Alger and Weibull (2013, Econometrica).

The impact of disposability characteristics on carbon efficiency from a potential emissions reduction perspective

Carbon emission efficiency is the key to measuring the overall level of carbon emissions. Carbon emissions in actual production activities are uncertain, and the measurement results are biased from the perspective of direct carbon emissions. Based on the perspective of implied emission reduction potential, this study differentiates the carbon emission efficiency and emission reduction potential of different disposability, under the semi-disposability hypothesis, the interval number E-DEA model of the undesirable output is proposed. Then a new production possibility set is constructed to solve the overlapping problem of interval planning. Finally, the proposed method is applied to measure the emission reduction potential of 30 provinces in China. The results indicate that the weak disposability hypothesis always underestimates the emission reduction potential. The strong disposability hypothesis always overestimates the emission reduction potential. While the semi-disposability hypothesis can evaluate the emission reduction potential more scientifically and reasonably, taking into account the influence of different technical characteristics or the managerial capacity of decision-makers.