Formal Framework Research Articles

Exploring the Impact of Social Media on English Language Learning: Opportunities and Challenges

This study seeks to elaborate on the influence that social media presents in the learning of the English language in Pakistan, together with the opportunities and challenges influenced by the phenomenon. The research object was to understand how media in Pakistan influence educational dynamics and language development within everyday life. The growing dominion of the English language as the lingua franca of the world would, therefore, make educators and policymakers understand the role that social media plays as host for language learning. The data collection method for this study was therefore done through a mixed-method approach across Pakistan with 30 participants from different linguistic and cultural backgrounds. Methods to use social media and keeping users engaged with English content were studied and presented as an effective tool for the most recent method of upgrading language skills. Finding out from our respondents further reiterated this, as a good number of them (97%) found social media helpful in improving their English language through substantial involvement with content in English and gains made in vocabulary and grammar. Quality of language, however, remains a persistent challenge amid informal styles of communication. In other words, the study suggests that while social media is an effective supplementary tool for language learning, yet at the same time, it should be used with a degree of caution within formal educational frameworks to optimise the benefits. It is precisely this research, then, that highlights strategies that could fully exploit such an educational potential while mitigating social media's weaknesses to improve English language education in Pakistan and other similar settings.

Estimation of mixed-gas selectivity of membranes using the linear friction-based model

The development of analytical models for predicting the transport properties of membranes in the separation of multicomponent mixtures is highly relevant because (1) measurements of permeability and sorption of gas mixtures in membrane materials are laborious and time-consuming, and (2) estimation of transport properties based on pure gas measurements is desirable, however, rather unreliable and can lead to serious errors. In this paper, within the framework of the well-known frictional-coefficient formalism, we present nonlinear equations for the fluxes of gas mixture components through the membrane, which take into account the diffusional coupling of the fluxes compared to independent gas transport. As a result, an analytical equation for the mixed-gas selectivity was obtained. This equation has an implicit form, which is not quite convenient for practical application, since it has to be solved numerically. A simple explicit expression for mixed gas selectivity was derived using the so-called linear transport model, which is a linearized version of the original nonlinear flux equations. It is important to emphasize that a comparison of the linear model with the formally more rigorous nonlinear model showed only minor quantitative differences. The input parameters for the model are the single gas transport parameters. Testing the linear model using experimental data for the separation of hydrocarbons (n-butane/methane, n-butane/i-butane, propylene/propane) revealed an acceptable fit. A comparison of the model with recent experimental data on the separation of CO2-containing mixtures using microporous polymers is also presented. It is found that the calculated CO2/N2 mixed gas selectivity is in good agreement with the experimental data, while only a semi-quantitative agreement is observed for the CO2/CH4 separation. Besides, it is shown that the model developed for binary gas separation can be extended to ternary gas mixtures.

On preradicals, persistence, and the flow of information

We use Category Theory notions to explore the conceptual axiom that, in a general sense, information flows through a neural network following a path of “least resistance”. To this end, we consider particular endofunctors, called preradicals, whereby we describe persistence in small shape diagrams defined in R-Mod. Specifically, we show that the α preradicals naturally describe persistence in commutative G-modules, for G a directed acyclic graph. Then, we use this results to generalize the notion of persistence to any diagram labeled by a quiver Q. These results, in turn, set the theoretical foundation for our formal framework that explores the notion of “paths of least resistance”. Lastly, we provide a notion of entropy for preradicals in R-Mod, and prove that it respects the order and operations between preradicals.

Gavel to Ground: Examining the Effectiveness and Efficacy of Project Karunungan in Enhancing Local Governance and Its Future Direction

In the context of local governance, careful implementation of parliamentary procedures during meetings is of great concern. In 2019, Project Karunungan, a training workshop program, was born from cooperation between the Political Science Department of Mindanao State University and the Local Government Unit of General Santos City, Philippines. The initiative's primary goal is to educate barangay officials about Robert's Rules of Order principles. This paper is qualitative and quantitative research, drawing arguments from interviews and surveys. The correct parliamentary process significantly improves local governance by providing a formal framework for decision-making and guaranteeing fairness, openness, and accountability. This paper advances the significance of Project Karunungan to new heights as a continuous program to produce better leaders.

Implementing and assessing Bayesian response-adaptive randomisation for backfilling in dose-finding trials

Traditional approaches in dose-finding trials, such as the continual reassessment method, focus on identifying the maximum tolerated dose. In contemporary early-phase dose-finding trials, especially in oncology with targeted agents or immunotherapy, a more relevant aim is to identify the lowest dose level that maximises efficacy whilst remaining tolerable. Backfilling, defined as the practice of assigning patients to dose levels lower than the current highest tolerated dose, has been proposed to gather additional pharmacokinetic, pharmacodynamic and biomarker data to recommend the most appropriate dose to carry forward for subsequent studies.The first formal framework [5] for backfilling proposed randomising backfill patients with equal probability among those doses below the dose level where the study is currently at. Here, we propose to use Bayesian response-adaptive randomisation to backfill patients. This patient-oriented approach to backfilling aims to allocate more patients to dose levels in the backfill set with higher expected efficacy based on emerging data. The backfill set constitutes of the doses below the dose the dose-finding algorithm is at. At study completion, collective patient data inform the dose-response curve, suggesting an optimal dose level balancing toxicity and efficacy.Our simulation study across diverse clinical trial settings demonstrates that a backfilling strategy using Bayesian response-adaptive randomisation can result in a patient-oriented patient assignment procedure which simultaneously enhances the likelihood of correctly identifying the most appropriate dose level. This contribution offers a methodological framework and practical implementation for patient-oriented backfilling, encompassing design and analysis considerations in early-phase trials.

A stabilized state-based peridynamic heat conduction model for interface thermal resistance problems

A stabilized state-based peridynamic heat conduction model for interface thermal resistance problems

Discrete forecast reconciliation

This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data to extend forecast reconciliation to discrete-valued data, including low counts. A novel method is introduced based on recasting the optimisation of scoring rules as an assignment problem, which is solved using quadratic programming. The proposed framework produces coherent joint probabilistic forecasts for count hierarchical time series. Two discrete reconciliation algorithms are also proposed and compared against generalisations of the top-down and bottom-up approaches for count data. Two simulation experiments and two empirical examples are conducted to validate that the proposed reconciliation algorithms improve forecast accuracy. The empirical applications are forecasting criminal offences in Washington D.C. and product unit sales in the M5 dataset. Compared to benchmarks, the proposed framework shows superior performance in both simulations and empirical studies.

Logic and Law: A Matter of Values Behind Content and Form

Abstract This special issue on Logic and Law consists of four research papers and one interview focusing on epistemological reflections on relationships between logic and law, whether in a reductionist or complementary approach. Logic aims to elucidate through formal frameworks, yet it often grapples with the intricate nuances of everyday legal discourse. While law endeavors to delineate permissible conduct within defined jurisdictions, it often encounters challenges stemming from the ambiguity of terms, leading to frequent judicial interpretations and the perception that proliferating exceptions undermines the efficacy of the rule itself.

Benefits of Give Circle: Exploring the impact of collaborative redistribution platforms on user willingness to donate to charity and tendency towards consumer minimalism

This study investigated the motivations of Taiwanese consumers who choose to redistribute their possessions via Give Circle, a free collaborative redistribution platform. We aimed to understand why users prefer using a free platform to donate and solicit items over traditional second-hand exchange platforms. Additionally, we sought to identify the behavioral changes that occur from using this platform within the context of the sharing economy. A mixed-methods approach was adopted, starting with semi-structured interviews with frequent users of the platform to identify independent and dependent variables for the formal research framework. Based on this evidence, we selected the extended UTAUT2 model and gamification affordance theory to explore the factors of continued use intentions. We applied structural equation modeling to 691 valid questionnaires. Predictors of continued use intentions included performance expectations, convenience, time and effort investment, and autonomy and self-expression. Continued use intention positively influenced charitable donation behavior and consumers' minimalist tendencies. These findings represent a novel contribution to the literature on the sharing economy, filling gaps in research on non-monetary collaborative redistribution platforms, extending the application of the UTAUT2 model, and deepening our understanding of consumer psychology and behavioral patterns in Asian Chinese societies.

Closed string vertex operators with various ghost number

We construct closed string vertex operators with various ghost numbers in addition to the conventional ones, using the Faddeev-Popov procedure for the gauge fixing of the conformal Killing group, from matter primary fields. We find that these operators give solutions to the descent equations in the framework of the BRST formalism. Similarly, we also construct solutions to the descent equations for the dilaton vertex operator with the Lorentz covariant form. Using the unintegrated vertex operator of the dilaton with the ghost number three, we obtain the correct result of the tadpole amplitude on the disk, including a non-zero contribution from a BRST exact term which comes from a conformal transformation.

Transience of (In)Formality: The Role of the Joint Initiatives in Reforming the WTO Negotiations

Abstract This paper develops the concept of transience of (in)formality to refer to the intersection of formal and informal negotiating procedures in the WTO decision-making system. It argues that both formal and informal negotiating processes are essential for delivering negotiating outcomes in the WTO. Particularly, the article assesses how informal negotiating practices emerge within the WTO formal framework and align with it. The analysis highlights how negotiating practices (re)shape existing norms, both formal and informal, adapting the WTO to overcome challenges and meet current needs. It also disputes legal formalistic perspectives that permeate trade literature and offer limited accounts of Members' agency in the WTO. The paper uses the Joint Initiatives as a case study to illustrate the convergence between formality and informality and a possible way ahead for the WTO negotiations. By broadening the understanding of law-making, it contributes to the discussion on WTO reform. It provides alternatives that reflect the reality of WTO negotiations and underlines the institution's relevance.

THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW

This research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved.  To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
 Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems.

A general framework and decentralised algorithms for collective computational processes

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface (spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

Rethinking Informed Consent as an EPA in Surgery.

Over the past decade, entrustable professional activities (EPAs) have become an important element in the competency-based medical education movement. In this Commentary, the authors explore informed consent as an EPA within resident surgical training. In doing so, they foreground the concept of culture and reexamine the nature of trust and entrustment decisions from within a cultural framework. The authors identify role modeling and professional identity formation as core elements in the training process and suggest that faculty are sometimes better off using these tools than uncritically adopting a formal EPA framework for what is, in essence, a professionally oriented and values-based moral enterprise. They conclude that EPAs work best when they are developed at a local level, stressing the unique specialty and program culture as well as the care that must be taken when attempting to transfer notions of entrustment from the undergraduate medical education level to graduate medical education settings.

Discrete preference games with logic-based agents: Formal framework, complexity, and islands of tractability

Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via game-theoretic models in which individuals/agents hold opinions taken from a fixed set of discrete alternatives, and where the goal is to find those configurations where the opinions expressed by the agents emerge as a kind of compromise between their innate opinions and the social pressure they receive from the environments. As a matter of facts, however, these studies are based on very high-level and sometimes simplistic formalizations of the social environments, where the mental state of each individual is typically encoded as a variable taking values from a Boolean domain. To overcome these limitations, the paper proposes a framework generalizing such discrete preference games by modeling the reasoning capabilities of agents in terms of weighted propositional logics. It is shown that the framework easily encodes different kinds of earlier approaches and fits more expressive scenarios populated by conformist and dissenter agents. Problems related to the existence and computation of stable configurations are studied, under different theoretical assumptions on the structural shape of the social interactions and on the class of logic formulas that are allowed. Remarkably, during its trip to identify some relevant tractability islands, the paper devises a novel technical machinery whose significance goes beyond the specific application to analyzing opinion formation and diffusion, since it significantly enlarges the class of Integer Linear Programs that were known to be tractable so far.

Symmetry and symmetric transformations in mathematical imaging

The article delves into the intricate relationship between symmetry and mathematical imaging, spanning various mathematical disciplines. Symmetry, a concept deeply ingrained in mathematics, manifests in art, nature, and physics, providing a powerful tool for understanding complex structures. The paper explores three types of symmetriesreflection, rotational, and translationalexemplified through concrete mathematical expressions. Evariste Galoiss Group Theory emerges as a pivotal tool, providing a formal framework to understand and classify symmetric operations, particularly in the roots of polynomial equations. Galois theory, a cornerstone of modern algebra, connects symmetries, permutations, and solvability of equations. Group theory finds practical applications in cryptography, physics, and coding theory. Sophus Lie extends group theory to continuous spaces with Lie Group Theory, offering a powerful framework for studying continuous symmetries. Lie groups find applications in robotics and control theory, streamlining the representation of transformations. Benoit Mandelbrots fractal geometry, introduced in the late 20th century, provides a mathematical framework for understanding complex, self-similar shapes. The applications of fractal geometry range from computer graphics to financial modeling. Symmetrys practical applications extend to data visualization and cryptography. The article concludes by emphasizing symmetrys foundational role in physics, chemistry, computer graphics, and beyond. A deeper understanding of symmetry not only enriches perspectives across scientific disciplines but also fosters interdisciplinary collaborations, unveiling hidden order and structure in the natural and designed world. The exploration of symmetry promises ongoing discoveries at the intersection of mathematics and diverse fields of study.

Genetically guided precision medicine clinical decision support tools: a systematic review.

Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM). A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients. The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy. Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes. We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.

A framework for formal verification of robot kinematics

As robotic applications continue to expand and task complexity increases, the adoption of more advanced and sophisticated control algorithms and models becomes critical. Traditional methods, relying on manual abstraction and modeling to verify these algorithms and models, may not fully encompass all potential design paths, leading to incomplete models, design defects, and increased vulnerability to security risks. The verification of control systems using formal methods is crucial for ensuring the safety of robots. This paper introduces a formal verification framework for robot kinematics implemented in Coq. It constructs a formal proof for the theory of robot motion and control algorithms, specifically focusing on the theory of robot kinematics, which includes the homogeneous representation of robot coordinates and the transformation relations between different coordinate systems. Subsequently, we provide formal definitions and verification for several commonly used structural robots, along with their coordinate transformation algorithms. Finally, we extract the Coq code, convert the functional algorithms into OCaml code, and perform data validation using various examples. It is worth emphasizing that the framework we have built possesses a high level of reusability, providing a solid technological foundation for the development of kinematics theorem libraries.

Differentially Private Ad Conversion Measurement

In this work, we study ad conversion measurement, a central functionality in digital advertising, where an advertiser seeks to estimate advertiser website (or mobile app) conversions attributed to ad impressions that users have interacted with on various publisher websites (or mobile apps). Using differential privacy (DP), a notion that has gained in popularity due to its strong mathematical guarantees, we develop a formal framework for private ad conversion measurement. In particular, we define the notion of an operationally valid configuration of the attribution rule, DP adjacency relation, contribution bounding scope and enforcement point. We then provide, for the set of configurations that most commonly arises in practice, a complete characterization, which uncovers a delicate interplay between attribution and privacy.

Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

In this work, we attempt to bring together the origins, main results, and recent advances on discrete-event simulation of continuous-time systems. Starting from the early approaches that aimed to represent continuous-time dynamics within the discrete-event system specification (DEVS) formalism framework, the work shows how these ideas gave place to the formalization of the quantized state system (QSS) family of numerical integration algorithms. Then, we describe the QSS algorithms, their properties, their extensions, and the main practical software tools implementing them. We also present a selection of simulation examples illustrating the main features and advantages through comparisons with state-of-the-art continuous-time simulation solvers.