Fundamental Types Research Articles

Pigouvian vs. Coasian approach: Ideas, values, perspectives

The paper reveals the characteristics of competing approaches — Pigouvian and Coasian — to identify the grounds for state regulation. We outline the connections between Pigouvian and Coasian approaches with the values and prospects for their advancement in the field of political decision-making in the context of demand for economic knowledge and the possibilities of organizing compensating transactions. These connections are considered in the light of the externalities problem as one of the manifestations of market failure, as well as different internalization options. We also clarify the provisions from the theory of externalities in terms of their definition, classification and correlation with the conditions for optimal allocation of resources. The key types of structural alternatives for correcting market flaws are considered, and the main properties of the Pigouvian and Coasian approaches in economics, as they relate to the problem of market and government flaws, are determined. This helps explain why the Coasian approach cannot be considered synonymous with liberal fundamentalism. Finally, we indicate the relationship between normative conclusions and prospects of functionalism and two types of fundamentalism in the field of political decision-making. Using the example of intertemporal externalities, the difference in the approaches of Coasianism and Pigouvianism to their internalization is demonstrated.

УЧЕТ РИСКОВ И НЕОПРЕДЕЛЕННОСТЕЙ ПРИ ОЦЕНКЕ ЭФФЕКТИВНОСТИ ИННОВАЦИОННЫХ ПРОЕКТОВ

The innovation process is the consistent transformation of new ideas and knowledge into an economic result. Uncertainty and risk are integral parts of innovation activity. Conducting a risk assessment is necessary to identify their origin. The results obtained provide an opportunity for their minimization. The article is devoted to the assessment of risk factors, their grouping, fundamental types and step-bystep risk management.

DIFFERENTIATION OF CLASSROOM İNSTRUCTİON AS A MEANS OF IMPLEMENTING INCLUSIVE EDUCATION

In the article the author provides information about the transition of the education system in Azerbaijan from a seg- regative model to an inclusive one. The work examines the works of researchers in the field of pedagogy and psychol- ogy, who argue the importance of systemic changes for the transformation of segregative education into inclusive. The article examines the elements of the education system, such as regulatory legal acts, pedagogical methods and competence of teachers, requirements for the infrastructure and material and technical base of the school, which should be revised for the implementation of inclusive education. The author argues about the importance of using internal classroom differentiation of instruction for the implementation of inclusive education in general schools. The article examines the fundamental foundations and types of differentiation of classroom instruction, as well as the conflicting opinions of scientists in pedagogy in this area. The study reviews the work of foreign authors in the field of differentiation of classroom instruction, outlines the main disadvantages of differentiation models in working with heterogeneous groups. At the end of the article, the author provides a result of the survey which he conducted with teachers of pilot inclusive classes for evaluation level of the differentiation of instruction in these classes.

Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation

The Fiedler vector of a connected graph is the eigenvector associated with the algebraic connectivity of the graph Laplacian and it provides substantial information to learn the latent structure of a graph. In real-world applications, however, the data may be subject to heavy-tailed noise and outliers which results in deteriorations in the structure of the Fiedler vector estimate. We design a Robust Regularized Locality Preserving Indexing (RRLPI) method for Fiedler vector estimation that aims to approximate the nonlinear manifold structure of the Laplace Beltrami operator while minimizing the negative impact of outliers. First, an analysis of the effects of two fundamental outlier types on the eigen-decomposition for block affinity matrices which are essential in cluster analysis is conducted. Then, an error model is formulated and a robust Fiedler vector estimation algorithm is developed. An unsupervised penalty parameter selection algorithm is proposed that leverages the geometric structure of the projection space to perform robust regularized Fiedler estimation. The performance of RRLPI is benchmarked against existing competitors in terms of detection probability, partitioning quality, image segmentation capability, robustness and computation time using a large variety of synthetic and real data experiments.

Characterization of student problem solving and development of a general workflow for predicting organic reactivity

A central practice in the discipline of organic chemistry is the ability to solve certain fundamental problems, including predicting reactivity, proposing mechanisms, and designing syntheses. These problems are encountered frequently by both students and practitioners, who need to utilize vast amounts of content knowledge in specific ways to generate reasonable solutions. To gain insight into how one of these major problem types can be solved, we have investigated student approaches to complex predict-the-product problems through the detailed analysis of think-aloud interviews. This work led to the creation of a general workflow model that describes the reasoning pathways of students with varying levels of expertise when attempting to predict organic reactivity. The problems used in this study were designed to be non-trivial and potentially ambiguous to elicit “true” problem solving and discourage a purely memorization-based approach, even from more experienced organic chemists. Rich descriptions of undergraduate and graduate student interviews are provided, and student thought processes are characterized in terms of common problem-solving actions. These actions were developed into the workflow model using an iterative method that combined results from our analysis with the experiences of instructors and feedback from both undergraduate focus groups and graduate students. The workflow serves as both a potential instructional tool and a model for student thinking. This model is general enough to be applied to both successful and unsuccessful solution pathways by both novice undergraduates and more expert-like graduate students. Characteristics of more successful and more experienced problem solvers are investigated, and concrete strategies that can be recommended to students are discussed. The results of this study complement existing work on other fundamental problem types in organic chemistry and suggest a variety of teaching interventions to develop students into more successful organic problem solvers.

Muzealizace hudebních nástrojů a její teoretické reflexe od 19. století do současnosti

Two dominant lines can be recognized in the music-museological thought from the end of the 18th century to the present day. Each of these two lines relates to the musealization of one of the two fundamental types of "musical things", as well as to the respective types of music-museal institutions. The first line has reflected the musealization of musicalia as musical texts (i.e. textual records of music, texts about music, and texts otherwise connected with the existence of music). In this study, however, we deal with the second line of music-museological thought, the one that reflects musical instruments. The collecting and musealization of musical instruments are old and universal phenomena, which took on the specific form of musical instrument museums in the course of the 19th century. These institutions then became the centres of nascent organology. As an auxiliary science of organology, this intellectual line has been actively developing, and from a quantitative point of view, it represents the main area of music-museological thought.

Collaborative Sensing in Internet of Things: A Comprehensive Survey

Collaborative sensing leverages the cooperation of a collection of sensors to complete a large-scale sensing task in Internet of Things (IoT). Although some previous studies have reviewed the literature of collaborative sensing in sensor networks, there still lacks a systematic and holistic overview with the consideration of practical application needs in IoT. In this article, we highlight that sensing application is coupled with sensing mechanism and sensing collaboration, and comprehensively survey the sensing mechanism, collaboration and applications in the context of IoT. Specifically, we first give an introduction of sensing technologies widely employed in IoT applications. Then, we systematically expatiate on more design issues for collaborative sensing, i.e., sensing models, deployment methods, sensing scheduling strategies and metrics for sensing quality. Next, the network-wide sensing collaboration is expatiated through three fundamental types of coverage problems as well as their solutions and insightful observations. Furthermore, we discuss promising sensing applications and elaborate how the sensing problem is established from the realistic application requirements and how the sensing quality impacts the application performance. Finally, we discuss several inspiring future research directions.

Prevalence, Reasons and Determinants of Patients' Nondisclosure to Their Doctors in Saudi Arabia: A Community-Based Study.

IntroductionPatient–doctor communication is a fundamental component of patients’ care. Withholding important information to the doctor can negatively affect the patients’ health and patient–doctor communication.AimThis study aimed to explore the fundamental types of information that patients hide from doctors, eg, the use of medication, health-related lifestyle, or disagreement with the doctor’s plan. In addition, this study examines the prevalence and reasons for this nondisclosure and factors associated with it.MethodologyAn online survey was conducted using a self-designed questionnaire, which was distributed to social media, targeting the residents of Saudi Arabia from February 1, 2021 to February 28, 2021. Respondents under 18 years of age and those who provided incomplete/incorrect data were excluded from the study. Types of nondisclosed information and their reasons were evaluated.ResultsA total of 2725 participants completed the questionnaire, and 1392 (51.1%) were males. About 43.2% of the participants were 18–29 years. Most (82%) responded “yes” to the question “Have you ever withheld any information from your doctor?” Nondisclosed information commonly involved disagreements with the recommendation (44.7%), not taking prescription medication as instructed (40.6%), and not understanding the instructions (37.4%). The most frequent reasons (68.7%) for nondisclosure were that the participants wanted to undergo further tests, did not like the doctor’s attitude (48.7%) and felt it did not matter to the doctor (43.2%). Those under 40 were more apt to withhold information (70.4%) than older participants (29.6%) p value = 0.0034. Other factors like gender, education level, and marital status were not associated with nondisclosure.ConclusionThe prevalence of nondisclosure to doctors is high. Effective communication skills and sound doctor–patient relationships may reduce this risk and improve the care delivered to the patients.

Towards an Analytic Framework for System Resilience Based on Reaction Networks

Reaction network is a promising framework for representing complex systems of diverse and even interdisciplinary types. In this approach, complex systems appear as self‐maintaining structures emerging from a multitude of interactions, similar to proposed scenarios for the origin of life out of autocatalytic networks. The formalism of chemical organization theory (COT) mathematically specifies under which conditions a reaction network is stable enough to be observed as a whole complex system. Such conditions specify the notion of organization, crucial in COT. In this paper, we show that the structure and operation of organizations can be advanced towards a formal framework of resilience in complex systems. That is, we show that there exist three fundamental types of change (state, process, and structural) defined for reaction networks, and that these perturbations not only provide a general representation of perturbations in the context of resilience but also pave the ground to formalize different forms of resilient responses. In particular, we show that decomposing the network’s operational structure into dynamically decoupled modules allows to formalize what is the impact of a perturbation and to what extent any potential compensation to that perturbation will be successful. We illustrate our approach with a toy model of a farm that operates in a sustainable way producing milk, eggs, and/or grains from other resources. With the help of simulations, we analyze the different types of perturbations and responses that the farm can undergo and how that affects its sustainable operation.

A comparative assessment and unification of bond models in DEM simulations

Bonded contact models have been increasingly used in the discrete element method (DEM) to study cemented and sintered particulate materials in recent years. Several popular DEM bond models have been proposed in the literature; thus it is beneficial to assess the similarities and differences between the different bond models before they are used in simulations. This paper identifies and discusses two fundamental types of bond models: the Spring Bond Model where two bonded particles are joined by a set of uniform elastic springs on the bond’s cross-section, and the Beam Bond Model in which a beam is used to connect the centres of two particles. A series of cantilever beam bending simulation cases were carried out to verify the findings and assess the strength and weakness of the bond models. Despite the numerous bond models described in the literature, they can all be considered as a variation of these two fundamental model types. The comparative evaluation in this paper also shows that all the bond models investigated can be unified to a general form given at a predefined contact point location.

Mean Squared Error, Deconstructed

AbstractAs science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like “season” or “variability,” and that simple decompositions can be combined to represent more complex concepts, like “seasonal variability,” creating an expressive language through which to interrogate models and data.

Seismic design of non-structural components mounted on irregular reinforced concrete buildings

The seismic response of non-structural components (NSCs) attached to irregular reinforced concrete (RC) multi-storey buildings is underestimated by current European design provisions. This paper presents a new design model for NSCs, accounting for the effect of torsion and seismic capacity of an irregular RC primary structure (P-structure). The proposed model is a modification of the current Eurocode 8 (EC8) model for the acceleration amplification factor of NSCs. It is based on the results of some 5000 nonlinear dynamic finite element analyses conducted on thirty-three building cases. The finite element analyses covered a wide range of parameters including plan layout, seismic capacity, fundamental vibration period, total height, floor rotation, ground type and eccentricity ratio. The proposed model has been demonstrated to be an improvement over EC8 model, especially for NSCs mounted on the flexible side and in tune with the fundamental vibration period of a P-structure.

Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems

Generalized formalization of recognition algorithm for specialized computer systems is presented in this paper. The structure features of image recognition methods, which have to be taken into account when developing classifiers for object recognition in specialized computer systems, are described. The fundamental types of images characteristics-features, which are used in various methods of image recognition, are discussed. Approaches to development of classifiers for recognizing robotization objects, which are implemented on basis of Haar classifiers, are discussed. The issues of using machine learning algorithm of adaptive gain AdaBoost for development of such classifiers are also considered. Utilities have been developed for implementation of classifiers for object recognition in specialized computer systems.

Synthesis and characterization of novel yellow-green Al-doped Y3Fe5O12 nano-pigments with high NIR reflectance

The pigments with high near-infrared (NIR) reflectance hold great promise toward both fundamental types of researches and applications for the realization of comfortable indoor-temperature by mitigating urban heat island effect. However, the facile yet simple method for the NIR reflective pigments with bright color and non-toxic feature remains a huge challenge, limiting their practical application. Herein, we proposed a mild and universal method of solution combustion for constructing the non-toxic and NIR reflective yellow-green pigments of Al-doped Y3Fe5O12. The Y3Fe4AlO12 pigment presented a high NIR reflectance of 87.66%, and an outstanding acid/alkali resistance. Moreover, the pigment had an impressive color performance (L* = 78.72, a* = − 4.05, b* = 40.04). Besides, the practical near-infrared reflection performance revealed that the transient surface temperature of the Y3Fe4AlO12 pigment was 7.6 °C lower than that of the pigment with similar color on the market. This research supplied high NIR reflecting pigments of Y3Fe5−xAlxO12 as promising coatings for roofs and exterior walls of buildings, acting as a favorable solution for urban heat island effect.

A unified probabilistic framework for volcanic hazard and eruption forecasting

Abstract. The main purpose of this article is to emphasize the importance of clarifying the probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting and volcanic hazard analysis seek to quantify the deep uncertainties that pervade the modeling of pre-, sin-, and post-eruptive processes. These uncertainties can be differentiated into three fundamental types: (1) the natural variability of volcanic systems, usually represented as stochastic processes with parameterized distributions (aleatory variability); (2) the uncertainty in our knowledge of how volcanic systems operate and evolve, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); and (3) the possibility that our forecasts are wrong owing to behaviors of volcanic processes about which we are completely ignorant and, hence, cannot quantify in terms of probabilities (ontological error). Here we put forward a probabilistic framework for hazard analysis recently proposed by Marzocchi and Jordan (2014), which unifies the treatment of all three types of uncertainty. Within this framework, an eruption forecasting or a volcanic hazard model is said to be complete only if it (a) fully characterizes the epistemic uncertainties in the model's representation of aleatory variability and (b) can be unconditionally tested (in principle) against observations to identify ontological errors. Unconditional testability, which is the key to model validation, hinges on an experimental concept that characterizes hazard events in terms of exchangeable data sequences with well-defined frequencies. We illustrate the application of this unified probabilistic framework by describing experimental concepts for the forecasting of tephra fall from Campi Flegrei. Eventually, this example may serve as a guide for the application of the same probabilistic framework to other natural hazards.

Bisecting Lewis X in Hybrid-Type N-Glycans of Human Brain Revealed by Deep Structural Glycomics.

The importance of protein glycosylation in the biomedical field requires methods that not only quantitate structures by their monosaccharide composition, but also resolve and identify the many isomers expressed by mammalian cells. The art of unambiguous identification of isomeric structures in complex mixtures, however, did not yet catch up with the fast pace of advance of high-throughput glycomics. Here, we present a strategy for deducing structures with the help of a deci-minute accurate retention time library for porous graphitic carbon chromatography with mass spectrometric detection. We implemented the concept for the fundamental N-glycan type consisting of five hexoses, four N-acetylhexosamines and one fucose residue. Nearly all of the 40 biosynthetized isomers occupied unique elution positions. This result demonstrates the unique isomer selectivity of porous graphitic carbon. With the help of a rather tightly spaced grid of isotope-labeled internal N-glycan, standard retention times were transposed to a standard chromatogram. Application of this approach to animal and human brain N-glycans immediately identified the majority of structures as being of the bisected type. Most notably, it exposed hybrid-type glycans with galactosylated and even Lewis X containing bisected N-acetylglucosamine, which have not yet been discovered in a natural source. Thus, the time grid approach implemented herein facilitated discovery of the still missing pieces of the N-glycome in our most noble organ and suggests itself—in conjunction with collision induced dissociation—as a starting point for the overdue development of isomer-specific deep structural glycomics.

Implicit sequence learning using auditory cues leads to modality-specific representations.

The implicit acquisition of statistical information from the environment is considered a fundamental type of human learning. Paradigms using visually cued sequences have been frequently used to study implicit learning. However, learning sequences of auditory cues is likely to be important in domains such as language or music. In three experiments, we established a novel auditorily cued implicit perceptual-motor sequence learning paradigm to compare to traditional visually cued sequence learning and identify whether this type of learning generalizes across cue modality. Participants exhibited reliable sequence-specific learning to auditory cues in all three experiments (Experiments 1-3), which was generally not influenced by explicit knowledge (Experiment 2). In Experiment 3, a large drop in knowledge expression in the novel cue modality was observed, suggesting that the acquired implicit sequence knowledge depended largely on sensory-specific representations. Overall, auditorily cued learning was similar to, though proceeded faster than, learning in comparable visually cued sequence learning paradigms. Similarity between learning from cues in different sensory modalities suggests that there may be a common process for the automatic extraction of sequential statistical structure. However, the lack of robust transfer sequence knowledge across modalities argues against a purely domain-general learning mechanism for all kinds of sequences. By expanding quantitative methodologies to characterize sequence learning in the auditory domain, these findings illustrate the possibility of bridging research in sequence and statistical learning domains to identify common mechanisms of complex cognitive skill and language learning.

Evaluation of an Assertiveness Training Based on the Social Learning Theory for Occupational Health, Safety and Environment Practitioners

Assertiveness is a fundamental type of behavior for the creation and maintenance of positive relationships at work and the facilitation of team functioning. Therefore, the promotion of assertiveness contributes to improving work performance, preventing harassment at work, facilitating the adoption of safe behaviors and making critical decisions in terms of occupational health and safety. However, few studies have evaluated the effectiveness of assertive training to train occupational health, safety and environment (OHSE) technicians to facilitate their work as agents of change in the attitudes and behaviors of other workers. Therefore, an assertive training was carried out to increase assertiveness and decrease social anxiety in this type of professional. The training effectiveness was evaluated following a pretest–posttest group design. The results from both a pilot study in a sample of 328 undergraduate students and a study in a sample of 155 OHSE technicians indicated that the training was effective in achieving both objectives. Moreover, Cohen’s d statistics suggest that the effect size was intermediate. These results are discussed with respect to their role in contributing to occupational health safety and environment practices as well as to the organizations’ sustainability.

Signaling Information Management in Entrepreneurial Firms' Financing Acquisition

Research has identified the significant effect of new ventures’ signaling information management on their ability to secure private equity financing. This study adopts an integrated signaling and screening perspective to investigate investors’ differing perceptions of signals from ventures, across early financing stages. It proposes a three-step interpretation process. Based on an inductive multiple case study of signaler‒receiver dyads, it finds that to reach a financing decision, angel investors extract a characteristic signal as the fundamental type, orchestrate an acting signal as a supplementary type, and scrutinize the consistency between both. However, venture capital investors extract an action signal as the fundamental type, orchestrate characteristic and endorsement signals as complementary types, and scrutinize the consistency among all three types.

The Role of Different Types of Conversations for Meeting Success

While current meeting tools are able to capture key analytics from both text and voice (e.g., meeting summarization), they do not often capture important types of conversations (e.g., a heated discussion resulting in a conflict being resolved). We developed a framework that not only analyzes text and voice, but also quantifies fundamental types of conversations. Upon analyzing 72 hours of conversations from 85 real-world virtual meetings together with their 256 self-reported meeting success scores, we found that our quantification of types of conversations (e.g., social support, conflict resolution) was more predictive of meeting success than traditional voice and text analytics. These new techniques will be essential to uncover patterns in online meetings that might otherwise go unnoticed.