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Based on the effect hierarchy principle in experimental design, an aliased effect-number pattern (AENP, or AP for short) is proposed to judge two-level regular designs; it contains the basic information of all effects aliased with other effects at varying severity degrees in a design. Based on the AENP, a general minimum lower-order confounding (GMLOC, or GMC for short) criterion is proposed, and several results follow. First, the word-length pattern, as the core of the minimum aberration (MA) criterion, is a function of the AENP. The same also holds for the clear effects (CE) criterion. Furthermore, the estimation capacity (EC) of a design can be also calculated as a function of the new pattern, and links between the MA and CE criteria are discovered. In addition, a concept of estimation ability is introduced, and it is concluded that a GMC design is the one with the best estimation ability. Finally, more applications of the new pattern are given. All GMC designs of 16 and 32 runs, a number of GMC designs of 64 runs, and some comparisons with the optimal designs under MA and CE criteria are tabulated.

With reference to regular fractions of general s-level factorials, we consider the design criterion of general minimum lower order confounding (GMC) that aims, in an elaborate manner, at keeping the lower order factorial effects unaliased with one another to the extent possible. Using a finite projective geometric formulation, this involves identification of the alias sets with the points of the geometry; we derive explicit formulae connecting the key terms for this criterion with the complementary set. These results are then applied to find optimal designs under the GMC criterion.

We consider regular fractions of s-level factorials arranged in block designs. Optimal designs are explored under the criterion of general minimum lower order confounding which aims, in an elaborate manner, at keeping the lower order factorial effects unaliased with one another and unconfounded with blocks. A finite projective geometric formulation, that identifies the alias sets with the points and the blocking system with a flat of the geometry, forms the mathematical basis of our approach. Theoretical results and tables are obtained in terms of complementary sets and an idea of double complementation is found to be useful in some situations.

Optimality criteria are usually used to choose fractional factorial designs in applications. Within an optimal design, the effects of factors assigned to different columns may be estimated with different precisions. Among factors to be investigated in an experiment, the user often has prior information on their relative importance. Thus, it is beneficial to assign most important factors to columns enabling most precise estimation. In this paper, we introduce a criterion to rank the columns of a regular design and use the criterion to GMC designs accordingly. We study the mathematical properties of the new ranking practice and provide concrete guidance on assigning factors in some GMC designs. Les critères d'optimalité sont généralement utilisés pour choisir les plans factoriels fractionnaires dans le cadre d'applications. Au sein d'un plan optimal, les effets des facteurs attribués à différentes colonnes peuvent être évalués à divers degrés de précision. Souvent, de l'information est disponible sur les facteurs à examiner au cours d'une expérience et sur leur importance relative. Il est alors pertinent d'assigner les facteurs les plus importants aux colonnes offrant l'estimation la plus précise. Dans cet article, les auteurs présentent un critère permettant de classer les colonnes d'un plan classique et l'utilisent dans le cadre de plans généraux minimisant les effets confondants d'ordre inférieur (GMC). Ils étudient les propriétés mathématiques de la nouvelle méthode de classement et proposent des directives concrètes à propos de l'assignation des facteurs dans certains plans GMC.

In many scientific investigations, the interest lies in the study of effects of two or more factors simultaneously. Factorial designs are most commonly used for this type of investigation. This chapter considers the important class of factorial designs for factors at two levels. It also considers the estimation and testing of factorial effects for location and dispersion models for replicated and unreplicated experiments. The chapter discusses optimal blocking schemes for full factorial designs. It describes how the factorial effects can be computed using regression analysis. The chapter also discusses three fundamental principles: effect hierarchy principle, effect sparsity principle, and effect heredity principle. These principles are often used to justify the development of factorial design theory and data analysis strategies. The chapter also describes a graphical method that uses the normal probability plot for assessing the normality assumption.

Despite various 2H-MoS 2 /carbon hybrid nanostructures have been constructed and committed to improve the performance for sodium-ion batteries (SIBs), they still show the limited cycle stability due to the relatively large volumetric expansion during the charge–discharge process. Herein, we report the construction of cobalt-doped few-layered 1T-MoS 2 nanosheets embedded in N, S-doped carbon (CMS/NSC) nanobowls derived from metal-organic framework (MOF) precursor via a simple in situ sulfurization process. This unique hierarchical structure enables the uniformly dispersed Co-doped 1T-MoS 2 nanosheets intimately couple with the highly conductive carbon nanobowls, thus efficiently preventing the aggregation. In particular, the Co-doping plays a crucial role in maintaining the integrity of structure for MoS 2 during cycling tests, confirmed by first-principles calculations. Compared with pristine MoS 2 , the volume deformation of Co-doped MoS 2 can be shrunk by a prominent value of 52% during cycling. Furthermore, the few-layered MoS 2 nanosheets with 1T metallic phase endow higher conductivity, and thus can surpass its counterpart 2H semiconducting phase in battery performance. By virtue of the synergistic effect of stable structure, appropriate doping and high conductivity, the resulting CMS/NSC hybrid shows superior rate capability and cycle stability. The capacity of CMS/NSC can still be 235.9 mAh·g −1 even at 25 A·g −1 , which is 51.3% of the capacity at 0.2 A·g −1 . Moreover, the capacity can still remain 218.6 mAh·g −1 even over 8,240 cycles at 5 A·g −1 with a low decay of 0.0044% per cycle, one of the best performances among the reported MoS 2 -based anode materials for SIBs.

Temporal network data is often encoded as time-stamped interaction events between senders and receivers, such as co-authoring scientific articles or communication via email. A number of relational event frameworks have been proposed to address specific issues raised by complex temporal dependencies. These models attempt to quantify how individual behaviour, endogenous and exogenous factors, as well as interactions with other individuals modify the network dynamics over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects. The propensity to form or receive ties can also, at least partially, be related to actor attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates. However, comprehensively capturing all personality traits is difficult in practice, if not impossible. A failure to account for heterogeneity may confound the substantive effect of key variables of interest. This work shows that failing to account for node level sender and receiver effects can induce ghost triadic effects. We propose a random-effect extension of the relational event model to deal with these problems. We show that it is often effective over more traditional approaches, such as in-degree and out-degree statistics. These results that the violation of the hierarchy principle due to insufficient information about nodal heterogeneity can be resolved by including random effects in the relational event model as a standard.

Different studies have analysed how green marketing influences the sustainable image of tourist companies or have focused on the identification and engagement between these companies and their consumers. In any case, the question of how this process influences consumers’ behaviour in the hotel industry requires even more in-depth study, with the intention of explaining the changes that occur in the current consumer and how this affects the hotel industry. This study is useful to demonstrate that beyond the direct influence of green marketing on green word of mouth indicators there are other indirect influences which are represented by other mediating variables: green attitudinal loyalty and green trust. From the literature on green marketing and the conceptual approaches offered by the Hierarchy of Effects Model and the Associate Learning Principles, this study conducted an empirical approach using a structured questionnaire. The questionnaire responses, obtained from a sample of 238 hotel users, were analysed using a Structural Equation Modelling (SEM) to test the research hypothesis related to the positive influence of green marketing on green trust, green attitudinal loyalty, and green word of mouth. This research provides theoretical and managerial implications to help executives adopt green marketing strategies, thanks to their positive effects on consumers’ recommendations, both direct and indirect, through loyalty and trust. It is concluded that green marketing actions have a greater effect on their indirect relationship with word of mouth than on their direct relationship and that loyalty is the aspect with the highest influence regarding trust.

The evolution of host range drives diversification in phytophagous insects, and understanding the female oviposition choices is pivotal for understanding host specialization. One controversial mechanism for female host choice is Hopkins’ host selection principle, where females are predicted to increase their preference for the host species they were feeding upon as larvae. A recent hypothesis posits that such larval imprinting is especially adaptive in combination with anticipatory transgenerational acclimation, so that females both allocate and adapt their offspring to their future host. We study the butterfly Pieris rapae, for which previous evidence suggests that females prefer to oviposit on host individuals of similar nitrogen content as the plant they were feeding upon as larvae, and where the offspring show higher performance on the mother's host type. We test the hypothesis that larval experience and anticipatory transgenerational effects influence female host plant acceptance (no‐choice) and preference (choice) of two host plant species (Barbarea vulgaris and Berteroa incana) of varying nitrogen content. We then test the offspring performance on these hosts. We found no evidence of larval imprinting affecting female decision‐making during oviposition, but that an adult female experience of egg laying in no‐choice trials on the less‐preferred host Be. incana slightly increased the P. rapae propensity to oviposit on Be. incana in subsequent choice trials. We found no transgenerational effects on female host acceptance or preference, but negative transgenerational effects on larval performance, because the offspring of P. rapae females that had developed on Be. incana as larvae grew slower on both hosts, and especially on Be. incana. Our results suggest that among host species, preferences are guided by hard‐wired preference hierarchies linked to species‐specific host traits and less affected by larval experience or transgenerational effects, which may be more important for females evaluating different host individuals of the same species. The importance of nongenetic inheritance through transgenerational acclimation has received increasing attention. Very few studies have investigated the role of transgenerational effects in the adult host plant preference and larval performance of phytophagous insects, even though these traits are explaining host‐driven diversification in this hyperdiverse group.

Fracture mechanics concepts are applied to gain some understanding of the hierarchical nanocomposite structures of hard biological tissues such as bone, tooth and shells. At the most elementary level of structural hierarchy, bone and bone-like materials exhibit a generic structure on the nanometer length scale consisting of hard mineral platelets arranged in a parallel staggered pattern in a soft protein matrix. The discussions in this paper are organized around the following questions: (1) The length scale question: why is nanoscale important to biological materials? (2) The stiffness question: how does nature create a stiff composite containing a high volume fraction of a soft material? (3) The toughness question: how does nature build a tough composite containing a high volume fraction of a brittle material? (4) The strength question: how does nature balance the widely different strengths of protein and mineral? (5) The optimization question: Can the generic nanostructure of bone and bone-like materials be understood from a structural optimization point of view? If so, what is being optimized? What is the objective function? (6) The buckling question: how does nature prevent the slender mineral platelets in bone from buckling under compression? (7) The hierarchy question: why does nature always design hierarchical structures? What is the role of structural hierarchy? A complete analysis of these questions taking into account the full biological complexities is far beyond the scope of this paper. The intention here is only to illustrate some of the basic mechanical design principles of bone-like materials using simple analytical and numerical models. With this objective in mind, the length scale question is addressed based on the principle of flaw tolerance which, in analogy with related concepts in fracture mechanics, indicates that the nanometer size makes the normally brittle mineral crystals insensitive to cracks-like flaws. Below a critical size on the nanometer length scale, the mineral crystals fail no longer by propagation of pre-existing cracks, but by uniform rupture near their limiting strength. The robust design of bone-like materials against brittle fracture provides an interesting analogy between Darwinian competition for survivability and engineering design for notch insensitivity. The follow-up analysis with respect to the questions on stiffness, strength, toughness, stability and optimization of the biological nanostructure provides further insights into the basic design principles of bone and bone-like materials. The staggered nanostructure is shown to be an optimized structure with the hard mineral crystals providing structural rigidity and the soft protein matrix dissipating fracture energy. Finally, the question on structural hierarchy is discussed via a model hierarchical material consisting of multiple levels of self-similar composite structures mimicking the nanostructure of bone. We show that the resulting 'fractal bone', a model hierarchical material with different properties at different length scales, can be designed to tolerate crack-like flaws of multiple length scales.