Framework Of Principles Research Articles

Deep learning solutions for smart city challenges in urban development

In the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized. This research embarks on a multifaceted exploration that combines the power of deep learning with Bayesian regularization techniques to enhance the performance and reliability of neural networks tailored for urban planning applications. Deep learning, characterized by its ability to extract complex patterns from vast urban datasets, has the potential to offer unprecedented insights into urban dynamics, transportation networks, and environmental sustainability. However, the complexity of these models often leads to challenges such as overfitting and limited interpretability. To address these issues, Bayesian regularization methods are employed to imbue neural networks with a principled framework that enhances generalization while quantifying predictive uncertainty. This research unfolds with the practical implementation of Bayesian regularization within neural networks, focusing on applications ranging from traffic prediction, urban infrastructure, data privacy, safety and security. By integrating Bayesian regularization, the aim is to, not only improve model performance in terms of accuracy and reliability but also to provide planners and decision-makers with probabilistic insights into the outcomes of various urban interventions. In tandem with quantitative assessments, graphical analysis is wielded as a crucial tool to visualize the inner workings of deep learning models in the context of urban planning. Through graphical representations, network visualizations, and decision boundary analysis, we uncover how Bayesian regularization influences neural network architecture and enhances interpretability.

Revising the basic principles of integrated marketing communications during a health-related crisis: the case of Croatian tourism and hospitality industry

PurposeAligning corporate communications through different information sources is a great challenge for marketers, especially those operating in the tourism sector, which has been harshly affected by the recent COVID-19 pandemic. This paper provides a deep analysis of the implementation of seven basic principles of the integrated marketing communications (IMC) paradigm in a crisis situation.Design/methodology/approachIn-depth interviews with tourism and hospitality service providers were conducted in the fourth quarter of 2021 in Croatia, a destination that showed remarkable results in terms of the number of international tourist arrivals during the pandemic.FindingsMost firms successfully transitioned from tactical to strategic IMC implementation. Some problems were reported in the coordination of communication tools and channels. Whereas the use of digital technology was enhanced, database management did not receive sufficient attention. Message clarity represented the greatest challenge, while consumer-centric communication was the most neglected principle. Relationship building was pursued mainly through B2B rather than B2C communication, whereas brand equity development pursued through communication mix mostly focused on increases in awareness, perceived quality and attitudinal loyalty.Research limitations/implicationsThis research is qualitative in nature and provides opinions on IMC adoption from the managerial perspective only.Practical implicationsThis paper provides guidelines for the successful integration of marketing communications (marcom) in an extremely ambiguous and uncertain environment.Originality/valueThe contribution of this work lies in the proposal of a new refined and expanded theoretical framework of IMC principles and numerous marcom strategies for operating during the COVID-19 pandemic, thus providing relevant implications for academia and industry.

Safety and health at work as fundamental rights: A comparative‐historical study of the ILO's strategy of realistic vigilance

This article is a comparative‐historical study of ILO action on occupational safety and health (OSH) as fundamental rights. In the two decades following the adoption of the International Covenant on Economic, Social and Cultural Rights, the ILO used a realist lens and prioritized the idea that OSH were dependent upon economic preconditions for their protection. This history is revisited in the light of the new complex of global health uncertainty and the addition of OSH to the framework of fundamental principles and rights at work. The analysis highlights the applicability of a realistic vigilance approach to the ILO's current work on OSH.

India and the Artemis Accords: Need to Tread Cautiously

The Artemis Accords refer to a non-binding multilateral arrangement on use of space led by the US and signed onto by several governments. These accords have established a framework of principles, guidelines, and best practices to ensure peaceful space exploration in a transparent manner. India signed the Artemis Accords in June 2023. India becoming a part of these accords is viewed as an important step towards expanding the Indo-US strategic partnership further. However, these accords are in conflict with the concept of Common Heritage of Mankind (CHM). The final aim of the Artemis Programme is to facilitate the outreach of humans to the Moon and Mars. Some provisions in these accords are at variance with the existing norms building process in the space domain. This article identifies the limitations of these accords and argues that as a signatory nation, India needs to remain watchful and ensure that its strategic and commercial interests are well protected.

Ethical Principles in Plastic Surgery Research.

Research is an integral part of medical progress that leads to better understanding of disease processes and the development of therapies to improve patient care. The medical community has an obligation and societal responsibility to review its practices and advance its knowledge to optimize care for those who entrust it with their health and well-being. While ultimately intended to benefit patients specifically and society as a whole, every laboratory and clinical investigation inherently carries an element of uncertainty and has attendant risks. These can have unintended and, at times, harmful consequences that cannot justify the knowledge gained. In order to mitigate these risks and protect human subjects involved in clinical research studies, a basic framework of ethical principles has been developed to guide responsible experimental design, execution, and data dissemination. This article provides a review of these principles and the historical context from which they were derived and explores the persistent challenges and cognitive biases that can increase susceptibility to unethical research practices.

Design principles for sustainable community currency projects

AbstractCommunity currencies (CCs) are alternative forms of money usually issued and managed by citizens, NGOs and companies as well as local public administrations. Used to trade goods and services in limited territories or among a certain community, CCs are composed of symbolic, cultural, and social dimensions and fully immersed in particular economic and institutional contexts. Despite the increasing adoption of CCs, the discussion on guiding principles and values to address their purposes, particularly in relation to environmental sustainability issues is still scarce in the literature. Given this gap, this paper relies on the following research question: what are the design principles to be considered for community currency projects oriented to sustainable development goals? We follow the design science research paradigms to describe the design principles of a CC project. We analyze the institutional and economic context in which they are implemented, the goals for what they were created, the processes and mechanisms to rely upon their operation, and how they are evaluated according to their specific goals and to their implications to the sustainable development goals. It is important to emphasize that in this article we focus on improving the understanding of CC projects and what should be considered in their evaluation, however not to the extent on how they should be implemented. The design principles framework presented in this paper specifies guidelines to discuss principles to be considered within a range of different possibilities of diversified CC implementation strategies.

Life as the Explanation of the Measurement Problem

This study argues that a biological cell, a dissipative structure, is the smallest agent capable of processing quantum information through its triangulated, holographic sphere of perception, where this mechanism has been extended by natural evolution to endo and exosemiosis in multicellular organisms and further to the language of Homo sapiens. Thus, life explains the measurement problem of quantum theory within the framework of the holographic principle, emergent gravity, and emergent dimensionality. Each Planck triangle on a black hole surface corresponds to a qubit in an equal superposition, attaining known bounds on the products of its energies and orthogonalization interval. Black holes generate entropy variation shells through the solid-angle correspondence. The entropic work introduces the bounds on the number of active Planck triangles dependent on the information capacity of the black hole generator. The velocity and dissipativity bounds and the bounds on the theoretical probabilities for active, energy-carrying Planck triangles were derived. In particular, this study shows that black holes, Turing machines, and viruses cannot assume the role of an observer. The entropy variation shells and black-body objects may hint at solutions to ball lightning and sonoluminescence unexplained physical spherical phenomena.“It is also possible that we learned that the principal problem is no longer the fight with the adversities of nature but the difficulty of understanding ourselves if we want to survive” [1].

Beauchamp and Childress’ principlism: theoretical reasons of some persistent conceptual issues

Beauchamp and Childress’ principlism is probably the most influential model for justifying bioethical judgements at the international level. Despite its widespread adoption and influence, it is frequently subject to misunderstanding and oversimplification. Given its enduring relevance, the prevalence of a superficial grasp of its current structure, and the likelihood that the latest edition will be the final one, this paper seeks to achieve the following two objectives: a) clarify the current theoretical framework of principlism, demonstrating its ability to address most criticisms effectively; b) spotlight unresolved theoretical flaws and delve into their philosophical underpinnings. I will then conclude by suggesting a research direction for overcoming these persistent issues.

Constitutional and legal principles of ensuring the independence of judges of the Constitutional Court of Ukraine

The independence of judges constitutes one of the fundamental components of the legal system in any democratic country. Within the legal framework of Ukraine, the constitutional and legal principles guaranteeing the independence of judges, specifically those of the Constitutional Court, represent a key element of the legal structure ensuring the stability of the rule of law, the protection of human rights, and the development of the rule-of-law state.
 It is emphasized that the guarantees of the independence of courts of general jurisdiction and bodies of constitutional justice play a critical role in ensuring legal stability and protecting judges from political or external influences. The independence of judges in performing their functions is crucial in upholding the principles of the rule of law and justice. Therefore, it is vital to ensure an effective mechanism to safeguard this independence from any external pressure or influence, regardless of the sphere of jurisdiction.
 It has been identified that one potential drawback associated with expressing a separate opinion of a judge of the Constitutional Court could be the blurring of the clarity of the adopted decision. Separate opinions generate significant interest among scholars and practicing lawyers, with the texts of such opinions often utilized in the educational process of legal institutions and drawing attention from judges of the Constitutional Court of Ukraine during the consideration of similar cases. As these documents contain doctrinal views on various aspects of cases reviewed by the Constitutional Court of Ukraine, they become the subject of additional analysis and consideration.
 It is noted that ensuring the independence of judges of the Constitutional Court of Ukraine is based on a comprehensive system of constitutional and legal guarantees aimed at ensuring their independence in resolving important constitutional issues and preserving the principles of the rule-of-law state in Ukraine.
 Moreover, Ukraine, as a participant in international treaties and organizations, is obliged to adhere to international standards in the field of justice and guaranteeing judicial independence. Consequently, means of protecting the rights of judges of the Constitutional Court are developed and adapted to international normative documents.

Towards Optimal Problem Dependent Generalization Error Bounds in Statistical Learning Theory

We study problem-dependent rates, that is, generalization errors that scale near-optimally with the variance, effective loss, or gradient norms evaluated at the “best hypothesis.” We introduce a principled framework dubbed “uniform localized convergence” and characterize sharp problem-dependent rates for central statistical learning problems. From a methodological viewpoint, our framework resolves several fundamental limitations of existing uniform convergence and localization analysis approaches. It also provides improvements and some level of unification in the study of localized complexities, one-sided uniform inequalities, and sample-based iterative algorithms. In the so-called “slow rate” regime, we provide the first (moment-penalized) estimator that achieves the optimal variance-dependent rate for general “rich” classes; we also establish an improved loss-dependent rate for standard empirical risk minimization. In the “fast rate” regime, we establish finite-sample, problem-dependent bounds that are comparable to precise asymptotics. In addition, we show that iterative algorithms such as gradient descent and first order expectation maximization can achieve optimal generalization error in several representative problems across the areas of nonconvex learning, stochastic optimization, and learning with missing data. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2021.0076 .

Understanding and responsiveness in the trauma-informed adult ESL classroom

This paper reports the findings of a critical qualitative study on trauma-informed teaching of English as a second language (ESL) at Australian universities. Post-traumatic stress affects verbal learning, yet most ESL teachers do not receive training in trauma-informed teaching. The field has suffered from a dearth of empirical studies and absence of student voice. This study used a validated tool to measure the post-traumatic stress of 39 participants, including international students and former refugees. Twenty of these completed semi-structured interviews about the ESL learning environment, based on a framework of trauma-informed principles. Data were analysed using critical, qualitative methods through a trauma-informed lens. A major theme in the findings was the importance of ESL teachers’ understanding of students. Within this theme, four sub-themes are explored: personal engagement and attention, acceptance and understanding of the learner role, understanding the lives of students outside the classroom and an understanding of students’ cultures.

Coarser Equivalences for Causal Concurrency

Trace theory (formulated by Mazurkiewicz in 1987) is a principled framework for defining equivalence relations for concurrent program runs based on a commutativity relation over the set of atomic steps taken by individual program threads. Its simplicity, elegance, and algorithmic efficiency makes it useful in many different contexts including program verification and testing. It is well-understood that the larger the equivalence classes are, the more benefits they would bring to the algorithms and applications that use them. In this paper, we study relaxations of trace equivalence with the goal of maintaining its algorithmic advantages. We first prove that the largest appropriate relaxation of trace equivalence, an equivalence relation that preserves the order of steps taken by each thread and what write operation each read operation observes, does not yield efficient algorithms. Specifically, we prove a linear space lower bound for the problem of checking, in a streaming setting, if two arbitrary steps of a concurrent program run are causally concurrent (i.e. they can be reordered in an equivalent run) or causally ordered (i.e. they always appear in the same order in all equivalent runs). The same problem can be decided in constant space for trace equivalence. Next, we propose a new commutativity-based notion of equivalence called grain equivalence that is strictly more relaxed than trace equivalence, and yet yields a constant space algorithm for the same problem. This notion of equivalence uses commutativity of grains , which are sequences of atomic steps, in addition to the standard commutativity from trace theory. We study the two distinct cases when the grains are contiguous subwords of the input program run and when they are not, formulate the precise definition of causal concurrency in each case, and show that they can be decided in constant space , despite being strict relaxations of the notion of causal concurrency based on trace equivalence.

A Framework for Innovation in Obesity

The intersection of innovation and obesity presents a significant opportunity for businesses to address a pressing public health challenge while capitalizing on a growing market demand. This abstract explores the potential for businesses, both existing and new, to innovate within sectors such as food, healthcare, technology, and education to promote healthier lifestyles and combat obesity. With the obesity market estimated at $80 billion annually, entrepreneurs, healthcare providers, and technology developers have ample room for growth and innovation. Existing businesses can reformulate products, offer new services, and advocate for supportive policies, while new ventures can enter the market with innovative solutions targeting obesity-related services and products. Moreover, the DACMAR (Disrupt, Adopt, Collaborate, Manage, Adapt, and Resource) framework provides a structured approach for innovators to navigate the complexities of obesity management. Through the DACMAR framework, stakeholders can drive meaningful progress and innovation in addressing obesity. This framework emphasizes the importance of challenging the status quo, fostering partnerships, effective resource management, and dynamic adaptation to changing circumstances. Similarly, by applying the DACMAR framework's principles, investors can systematically rank different innovations in the obesity domain, ensuring market-readiness, identify go-to-market gaps and align investments with investors’ strategic objectives. This enables objective de-risking to improve investment returns. Furthermore, effective leadership is crucial in driving innovation and change, particularly in complex issues like obesity. Various leadership styles, including transformational, innovative, collaborative, servant, adaptive, and strategic, play essential roles in shaping the direction and impact of obesity prevention and treatment efforts. By leveraging diverse leadership approaches, stakeholders can work together to develop innovative solutions that improve outcomes for individuals and communities affected by obesity. Keywords: obesity, innovation, leadership, business

Effect of project-based learning method for improving students problem-solving ability in the experiment of power electronics course

Abstract— This paper examines the integration of the CDIO (Conceive, Design, Implement, Operate) framework in Mechatronics curriculum. CDIO revolutionizes technical education by emphasizing project-based learning (PBL) and practical experiences, fostering skills, attitudes, and problemsolving abilities for real-world challenges. The Power Electronics course for fourth-semester students, aligned with CDIO, covers semiconductor devices, converters, rectifiers, choppers, inverters, and specialized machine switching circuits. Being an outcomebased education (OBE) curriculum, two of its outcomes focuses on applying gained knowledge. PBL was implemented to achieve the outcome of the course among students. During the process of implementation, students demonstrated diverse outcomes. In the proposed case study, Set A effectively accomplished problem identification, simulation, and hardware implementation. In contrast, Set B had difficulties pertaining to the integration of hardware components. This study highlights the varied dynamics observed in student involvement and academic achievement within the context of project-based learning. By doing a comparative analysis of the outcomes between Set A and Set B, valuable insights may be gained to enhance instructional techniques. This statement highlights the significance of utilizing the CDIO framework's principles to improve project-based learning and better the overall educational experience in future deployments. Keywords— Power Electronics, CDIO, Project Based Assignment, T-test.

Savremene iregularne migracije u Republici Srbiji - etički normativni aspekti

This paper examines global and regional trends in contemporary irregular migration, which reflect complex economic, political, social, and security factors, which, as such, reflect on the Republic of Serbia. Analyzing the ethical norms of migration, the author starts from the theoretical framework of Kant's principle of general hospitality, which dictates openness to foreigners, emphasizing that in practice this principle has limitations due to real security and economic reasons. Through the ethical aspects of irregular migration, the author analyzes two categories of mandatory admission - family reunification and asylum seekers. It is shown that states have a moral obligation to enable family reunification, as well as to ensure a fair process for considering asylum applications. When it comes to discretionary admission, the author identifies permitted exclusion criteria (threat to national security, criminal history) and selection criteria (family ties, economic potential, cultural affiliation). Serbia has established a national legislative framework and is implementing best practices, continuing the trend of improving migration management policy. The paper presents current statistical indicators analyzing the current situation on the Western Balkan migration route. Migration ethics requires a complex balancing act between the sovereign rights of states, moral principles, and practical limitations, which is also reflected in the actual policies of liberal-democratic states. Through the analysis of these aspects, the paper provides insights into the ethical and normative aspects of contemporary irregular migration in the Republic of Serbia and lays the groundwork for further research and understanding of this phenomenon.

FINANCIAL SUSTAINABILITY OF THE BANKS: AN EXPERTS’ ASSESSMENT

The rise of sustainable finance, which involves financial tools operating within the framework of environmental, social, and governance principles adopted by companies and financial institutions, is gaining traction in major global markets. According to the Global Alliance for Sustainable Investments, these approaches already account for 35.9% of total invested assets. This trend is driven by a new global reality where long-term development cannot take place without addressing deepening environmental and social contradictions. The article aims to assess the priority of ESG of the banks as sustainable finance factors for the development of the recommendations, as the assessment of the ESG principles in the banks of the country allows for the identification of further courses of sustainable finance development. Furthermore, the investigation of leading countries' experiences in environmentally and socially responsible investment and its role in the banking system reveals both common and specific features that could be effectively implemented in Kazakhstan. The research methodology entails experts’ assessment from the middle management level of the banks of ESG impact of 9 factors by AHP method. The AHP method allows investors to systematically compare and prioritize used ESG factors, that relates to their significance as the indicators for assessment the state and prospects for the ESG principles implementation in the bank’s activity in Kazakhstan. The obtained results allow to suggest that priority instruments for ESG promotion in the country are a legislative framework and the state program. The study illustrates that the integration of ESG factors into the strategy of sustainable development of banks contributes to the growth of their assets by increasing demand for sustainable products and attracting new customers. The findings have practical implications for banks related to sustainable finance and ESG criteria-based investing.

Facilitation of caring through the development of mindfulness − A concept analysis

Caring is central to nursing. Many relevant studies have been conducted on caring however, clarity is needed on the concept ‘facilitation of caring through the development of mindfulness’. Thus, the need to conduct the concept analysis on ‘facilitation of caring through the development of mindfulness’. To provide an in-depth analysis of the concept ‘facilitation of caring through the development of mindfulness’. Walker and Avant’s framework was used. Literature was searched electronically using databases such as Africa-wide Information, Business Source Ultimate, CINAHL, Medline, Psych INFO, ScienceDirect, EBSCO (Health Source: Nursing and Academic Edition), Sage, Ujoogle and Google Scholar. Articles from 2013 to 2020 were searched to target recent and up-to-date information about the definitions, defining attributes, antecedents, and consequences of the concept ‘facilitation of caring through the development of mindfulness’. Forty-eight English sources, including research articles and other sources, were retained from a pool of 15000. The results of the concept analysis identified seven essential attributes. ‘Facilitation of caring through the development of mindfulness’ is a process implemented by the nurse educator to assist and empower student nurses to love and show a spirit of ubuntu. This is done through a process of becoming that enhances empathy for self and others and focusing on the present moment when caring for patients. The results of the concept analysis present an understanding of the concept ‘facilitation of caring through the development of mindfulness’. The framework of mindfulness principles derived from the concept analysis could guide nurse educators in facilitating caring among student nurses.

Managing Vocal Endurance Through Active Recovery

Abstract: Author Justin John Moniz addresses the evolving demands of operatic, musical theatre, and popular styles of singing, and the need for active recovery both during and after vocal activities. A discussion surrounding rate of perceived exertion as it relates to a singer’s heart rate, breath rate, sweating, and fatigue, provides the framework for self-assessment principles which serve as tools to assist in cultivating longevity and success.

A Particle Swarm Optimised Independence Estimator for Blind Source Separation of Neurophysiological Time Series.

The decomposition of neurophysiological recordings into their constituent neural sources is of major importance to a diverse range of neuroscientific fields and neuroengineering applications. The advent of high density electrode probes and arrays has driven a major need for novel semi-automated and automated blind source separation methodologies that take advantage of the increased spatial resolution and coverage these new devices offer. Independent component analysis (ICA) offers a principled theoretical framework for such algorithms, but implementation inefficiencies often drive poor performance in practice, particularly for sparse sources. Here we observe that the use of a single non-linear optimization function to identify spiking sources with ICA often has a detrimental effect that precludes the recovery and correct separation of all spiking sources in the signal. We go on to propose a projection-pursuit ICA algorithm designed specifically for spiking sources, which uses a particle swarm methodology to adaptively traverse a polynomial family of non-linearities approximating the asymmetric cumulants of the sources. We robustly prove state-of-the-art decomposition performance on recordings from high density intramuscular probes and demonstrate how the particle swarm quickly finds optimal contrast non-linearities across a range of neurophysiological datasets.

Exploiting Hierarchical Interactions for Protein Surface Learning.

Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, existing works only consider handcrafted or individually learned chemical features from the atom type and extract geometric features independently. Here, we identify two key properties of effective protein surface learning: 1) relationship among atoms: atoms are linked with each other by covalent bonds to form biomolecules instead of appearing alone, leading to the significance of modeling the relationship among atoms in chemical feature learning. 2) hierarchical feature interaction: the neighboring residue effect validates the significance of hierarchical feature interaction among atoms and between surface points and atoms (or residues). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. Extensive experiments demonstrate that our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2 available at https://github.com/lyqun/HCGNet.