Discrete Issues Research Articles

Scaffolding decision spaces in decision support systems: Using plagiarism screening software in editorial offices

This paper explores the dynamics of algorithmic governance, decision support systems and human involvement in the context of plagiarism screening in academic publishing. While automated plagiarism screening is widespread in editorial work, critical investigations about these decision support systems remain scarce. Focusing on the issue of human autonomy and discretion in algorithmic governance, the paper investigates the complexities of the human-in-the-loop within these screening tools. Revisiting Wanda Orlikowski's conceptual metaphor of ‘scaffolding’, the study empirically analyses interactions between editors and plagiarism screening software. It traces how these tools act as scaffolds, defining plagiarism as a manageable problem while allowing editors considerable flexibility in decision-making. The software, which is both non-deterministic and powerful, transforms issues into potential decisions, shaping the decision space for human editors. Based on this investigation of screening software as scaffolding, the paper argues that the question of human involvement in systems for automated decision-making is somewhat beside the point, and that analytical attention should shift towards understanding how algorithmic systems configure decision spaces by establishing issues as decidable problems. The implications of this shift are discussed, emphasizing the need for advancing our understanding of power dynamics inherent in algorithm–human interactions within automated decision-making systems.

Automatic fault detection and stability management using intelligent hybrid controller

Microgrid allows the integration of remote sources and other flexible loads to raise security concerns. Thus, it is necessary to detect the type of fault to maintain the system's stability. Existing fault detection systems include limitations such as high detection times, inability to process noisy data and discretization issues. To address these issues, a spiking neural network with a self-organizing map is used to produce precise synaptic weights for fault detection in the microgrid. A feature exploration-based spiking neural network can accurately classify faults such as line-to-ground (LG), line-to-line (LL), double line-to-ground (DLG), and three-phase ground (TLG). To mitigate the impact of the fault, a voltage deviation estimation-based control method is used, which employs a three-degree of freedom fractional order proportional integral resonant (3DOF-FOPIR) controller. In order to stabilize the system frequency, the controller sends a control signal to the multi-level inverter based on the measured voltage deviation and fault auxiliary value. This ensures reduced distortions at the output voltage, and thus, it maintains the stability of the microgrid. As a result, when compared to graph-based convolution networks, the proposed method has a higher accuracy of 99.8 % and a lower error in system stability of 55.47 %.

SMC for discrete singular stochastic jump systems under semi-Markov kernel

In this study, the sliding mode control (SMC) is investigated for discrete singular stochastic semi-Markov jump systems under a semi-Markov kernel. Based on the remarkable application background and advantages in modeling complex dynamic systems, singular semi-Markov jump systems are utilized to address discrete SMC issues. A mode-dependent sliding function with a singular matrix is considered to reduce the effects of the numerical problems. The main aim is to use an improved approach to eliminate the matrix power terms so that the underlying augmented system can realize mean-square admissibility. An appropriate SMC law is constructed to achieve the reachability of the quasi-sliding mode, overcoming the difficulty caused by parameter uncertainty. Finally, the effectiveness of the SMC approach is verified using a DC motor model.

Variational Feature Extraction in Scientific Visualization

Across many scientific disciplines, the pursuit of even higher grid resolutions leads to a severe scalability problem in scientific computing. Feature extraction is a commonly chosen approach to reduce the amount of information from dense fields down to geometric primitives that further enable a quantitative analysis. Examples of common features are isolines, extremal lines, or vortex corelines. Due to the rising complexity of the observed phenomena, or in the event of discretization issues with the data, a straightforward application of textbook feature definitions is unfortunately insufficient. Thus, feature extraction from spatial data often requires substantial pre- or post-processing to either clean up the results or to include additional domain knowledge about the feature in question. Such a separate pre- or post-processing of features not only leads to suboptimal and incomparable solutions, it also results in many specialized feature extraction algorithms arising in the different application domains. In this paper, we establish a mathematical language that not only encompasses commonly used feature definitions, it also provides a set of regularizers that can be applied across the bounds of individual application domains. By using the language of variational calculus, we treat features as variational minimizers, which can be combined and regularized as needed. Our formulation not only encompasses existing feature definitions as special case, it also opens the path to novel feature definitions. This work lays the foundations for many new research directions regarding formal definitions, data representations, and numerical extraction algorithms.

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study.

Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security. A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years. In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): "Global production," "Food insecurity and health," "Use of food banks," "Giving to food banks," "Family poverty," "Food relief provision," "Global food insecurity," "Climate change," "Australian food insecurity," and "Human rights." The topic "Giving to food banks," which focused on support and donation, had the highest proportion of positive sentiment, and "Global food insecurity," which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues. In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area.

Interfering sensed input classification model using assimilated whale optimization and deep Q-learning for remote patient monitoring

The Internet of Medical Things (IoMT)-based Remote Patient Monitoring (RPM) systems provide real-time data and insights about patients' conditions without the need for constant physical visits to healthcare facilities. The wearable sensors are responsible for sensing the psychological vitals of humans at different intervals to detect their precise health status. This article introduces an Interfering Input Classification Model (IICM) for addressing the continuous and discrete data extraction issues in RPM. The proposed model hybridizes whale optimization and deep-Q-learning for classification and connectivity identification between different interval data. The sensed information is segregated as discrete and continuous using the search process termination of the whales. Such classified data is validated for the maximum discreteness and its interconnection between the previous sequences. This interconnection is performed using Q-learning based on different state changes through continuous clinical range correlations. The proposed model exemplifies continuous and discrete signals (data) for abnormality detection and diagnosis recommendation. Thus the proposed model is reliable in improving the classification ratio and accuracy with fewer errors. The experimental values of the proposed model are as follows: IICM enhances classification by 6.65%, signal identification by 6.63%, and classification accuracy by 10.08%. This model reduces classification time by 9.51% and error by 10.14% for different ranges of inputs.

Multiplicity of Solutions for Discrete 2n-TH Order Periodic Boundary Value Problem with φp-Laplacian

The purpose of this paper is to investigate the existence and multiplicity of nontrivial solutions with the φp-Laplacian for the discrete 2n-th order periodic boundary value issue. To support these conclusions, we have employed variational techniques and contemporary critical point theory. A few new findings are expanded upon and enhanced. We give an example to show how our key findings can be applied.

Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements.

Handling the challenge of missing measurements in nonlinear systems is a difficult problem in various scientific and engineering fields. Missing measurements, which can arise from technical faults during observation, diffusion channel shrinking, or the loss of specific metrics, can bring many challenges when estimating the state of nonlinear systems. To tackle this issue, this paper proposes a technique that utilizes a robust cubature Kalman filter (RCKF) by integrating Huber's M-estimation theory with the standard conventional cubature Kalman filter (CKF). Although a CKF is often used for solving nonlinear filtering problems, its effectiveness might be limited due to a lack of knowledge regarding the nonlinear model of the state and noise-related statistical information. In contrast, the RCKF demonstrates an ability to mitigate performance degradation and discretization issues related to track curves by leveraging covariance matrix predictions for state estimation and output control amidst dynamic disruption errors-even when noise statistics deviate from prior assumptions. The performance of extended Kalman filters (EKFs), unscented Kalman filters (UKFs), CKFs, and RCKFs was compared and evaluated using two numerical examples involving the Univariate Non-stationary Growth Model (UNGM) and bearing-only tracking (BOT). The numerical experiments demonstrated that the RCKF outperformed the EKF, EnKF, and CKF in effectively handling anomaly errors. Specifically, in the UNGM example, the RCKF achieved a significantly lower ARMSE (4.83) and ANCI (3.27)-similar outcomes were observed in the BOT example.

Exploring the Subprime Crisis

This paper delves into an examination of the three primary interrelated factors that precipitated the subprime mortgage crisis: regulatory deficiencies, policy failures, and dysfunctional innovation. Furthermore, it conducts an analysis of the underlying factors that exacerbated the ramifications of this economic upheaval: the excessive proliferation of securitization, a deficiency in market transparency, inadequate risk management practices within financial institutions, and challenges associated with credit rating agencies. The subprime mortgage crisis has incited extensive discourse across diverse sectors, notably encompassing two pivotal dimensions: firstly, inquiries surrounding the transparency of assets and, secondly, predicaments pertaining to governmental intervention for market stabilization. This article undertakes a comprehensive categorization and in-depth exposition of these discrete issues, proffering meticulous explications corroborated by empirical substantiation. Culminating in its denouement, the article proffers prudent recommendations aimed at preemptively mitigating the advent of future crises. The entire discourse endeavors to maintain an objective and truthful perspective, aiming to explore the fundamental nature of the subprime mortgage crisis rigorously.

Use of own technical equipment for participation in the court session outside the court in criminal proceedings

The article examines the peculiarities of remote court proceedings in the criminal process of Ukraine. In view of the fact that in the criminal procedural legislation such concepts as «video conference» and «remote proceedings» are used to denote online participation in the court session, the definition of these definitions is provided in the work. The provisions of the Criminal Procedure Code of Ukraine, which fix the procedure and features of the court's decision to conduct remote court proceedings, are analyzed, in particular, the issue of the court's discretion when making a decision to conduct a court session in video conference mode is analyzed. The judicial practice of considering motions to hold a court session in the mode of a video conference outside the court premises using its own technical means was studied. It is well-founded that the participants in the court proceedings, when making a request for a remote trial, and the court, when granting it, must cite not only the circumstances justifying the need to use a video conference, but also the evidence confirming the existence of one of the grounds provided for in Part 1 Art. 336 of the CCP of Ukraine. The publication draws attention to the latest changes in criminal procedural legislation regarding remote court proceedings under martial law. It is argued that remote participation in a court session using one's own technical means does not contradict the Criminal Procedure Code of Ukraine, if the possibility of effective participation in court proceedings is ensured. It is substantiated that the Code of Criminal Procedure of Ukraine does not contain any restrictions regarding the premises from which a person can participate in a court session in the mode of video conference. It was stated that the broadcast during a video conference in a court session can be carried out from any other place using its own technical means, provided that the appropriate technical capability is available.

From ‘I Think’ to ‘I Feel’

AbstractThis article explores what pollsters, journalists and politicians mean when they refer to the ‘mood’ of a nation, population or community. To what extent does the concept of mood resemble and differ from the notion of ‘public opinion’? It is argued that the ubiquity of mood‐talk reflects a move away from the myth that political action is motivated by rational instrumentalism. Attention to mood takes seriously the force of pre‐cognitive affectivity and its shaping of public disposition; the disorientating effects of diffuse globalism in which experiences and the feelings to which they give rise do not have obvious causes; and the emergence of new spaces in which affects travel and mutate freely, widely and rapidly. The article suggests that we are living in moody times in which attention to the public zeitgeist may be more important than polling responses to discrete issues.

Policy Escalation: Richard Nixon, Welfare Reform, and the Development of a Comprehensive Approach to Health Insurance

AbstractI argue that health insurance emerged as an important aspect of Nixon’s domestic policy agenda as a result of “policy escalation.” By policy escalation, I mean a cascading line of reasoning that causes policy makers focused on one apparently discrete issue to formulate approaches for dealing with other interconnecting policy areas. Policy escalation serves as an internal agenda-setting mechanism: as policy makers contemplate policy changes, they may attempt to imagine the ways in which change will affect the rationale, fiscal position, and execution of programs in other policy areas. In the case of health insurance, the Nixon administration’s proposal for replacing Aid to Families with Dependent Children with a guaranteed minimum income forced policy makers to consider how the new program would interact with the existing Medicaid program. Consideration of this question ultimately led them to formulate an approach to overhauling the nation’s entire health insurance system.

Continuing the Discussion on Administrative Discretion

The article continues the discussion on the topical issue of administrative discretion for Russian administrative-legal theory and legal practice, organized by the editors of the journal “Siberian Law Review” on the pages of two previous issues of the journal with the participation of P. P. Serkov and Yu. P. Solovey. The Author focuses on the state of the modern domestic doctrine of administrative discretion, the contribution of Russian legal scholars to its development, the need and possibility (including criteria and limits) of delimiting administrative discretion from other types of discretion. It is proved that the Russian jurisprudence demonstrates, contrary to the assertions of some experts, not confusion in the face of the problematic category of discretion, but ontological and methodological certainty, although sometimes reaching extremes. At the same time, there is no single Russian doctrine of discretion, there are many such doctrines, and some scholars have the right to claim that specific doctrines are associated with their names. The Author draws attention to the fact that discretion in general and administrative discretion in particular are interdisciplinary (interscientific) categories, so they must first of all be rid of the semantic and meaningful “layers” of other sciences. The sooner a pure theory of discretion appears, the more mistakes and risks will be insured against by legal science and law enforcement practice. In order to avoid terminological confusion and preserve the subject matter of the study, it is absolutely important to distinguishfour concepts: 1) administrative discretion; 2) judicial control over administrative discretion; 3) judicial discretion; 4) judicial discretion in the exercise of judicial control over administrative discretion. These concepts have a certain connection with each other, however, they designate different (partly even by their branch affiliation) categories, phenomena, processes and institutions. As a conclusion, it is indicated that the motives of each discretionary decision of the public administration must sooner or later (better sooner than later) be made public. To make this a reality, legal science needs to develop and offer effective legal guarantees for ensuring the rights of citizens and their associations when public authorities exercise their discretionary powers.

Discretion in Terms of Digitalization

Currently, information technologies are being developed and introduced into various spheres of public relations. State regulation and the judicial system are no exception. In this regard, not only the formats of legal relations are changing, but also traditional approaches in justice and administrative regulation. Therefore, it is necessary to consider the influence of discretion in various kinds of relations and the political and legal system. Separately, the issues of judicial discretion and internal conviction of the judge in the conditions of digitalization are investigated. The purpose of this work is to determine legal decisions based on information technology and the implementation of judicial discretion in modern conditions. The above goal is realized by identifying the reasons for the lack of an effective assessment of discretion in various areas of public administration. The research methods are formal-legal, logical, and systematic. Brief conclusions are the need to develop legal discretion in modern national realities. The authors distinguish between the content of “judicial discretion” and “internal conviction” of a judge from the perspective of the implementation of professional activity in the conditions of digitalization and technologization of justice. The necessity and efforts of the leading role of the court (in the person of a particular judge) from the perspective of the emergence of various kinds of risks with the widespread use of digital products, as this contributes to the abuse of the law, are noted.

The Power in Rural Place Stigma

The phenomenon and implications of stigma have been recognized across many contexts and in relation to many discrete issues or conditions. The notion of spatial stigma has been developed within stigma literature, although the importance and relevance of spatial stigma for rural places and rural people have been largely neglected. This is the case even within fields of inquiry like public and rural health, which are expansively tasked with addressing the socio-structural drivers of health inequalities. In this paper, we argue that developing a better understanding of rural place stigma is critical for addressing contemporary patterns of spatial injustice and health inequalities affecting rural communities globally. Drawing on international literature and examples from the reported experiences of rurally living Australians and news and other media, we present an analysis highlighting the power in rural place stigma. In doing so, we build a case for the relevance and importance of interrogating rural place stigma, especially in the fields of public and rural health, for changing the conditions within—and the broader positioning of—the rural in the public and political landscapes.

Policy supports for the deployment of solar fuels: islands as test-beds for a rapid green transition

Coastal areas, particularly islands, are especially vulnerable to climate change due to their geographic and climate conditions. Reaching decarbonisation targets is a long process, which will require radical changes and ‘out of the box’ thinking. In this context, islands have become laboratories for the green transition by providing spaces for exploring possibilities and alternatives. Here we explore how hydrogen (H2) energy technologies can be a critical ally for island production of renewable electricity in part by providing a storage solution. However, given the abundance of sunlight on many islands, we also note the huge potential for a more profound engagement between renewables and hydrogen technologies via the co-generation of ‘green hydrogen’ using solar fuels technology. Solar hydrogen is a clean energy carrier produced by the direct or indirect use of solar irradiation for water-splitting processes such as photovoltaic systems coupled with electrolysers and photoelectrochemical cells. While this technology is fast emerging, we question to what extent sufficient policy support exists for such initiatives and how they could be scaled up. We report on a case study of a pilot H2 plant in the Canary Islands, and we offer recommendations on early-stage policy implications for hydrogen and other solar fuels in an island setting. The paper draws on the literature on islands as policy laboratories and the multi-level perspective on energy transitions. We argue that particular attention needs to be given to discrete issues such as research and planning, and better synchronising between emerging local technology niches, the various regulatory regimes for energy, together with global trends.

Linear Programming for Influence Maximization Problems in Social Networks Based on Diffusion Models

Influence maximization is a challenging problem that combines optimization and the study of social sciences. The purpose is to find the most influential individual within a society with a limited number of initial nodes. With the increasing use of social networks these years, Understanding and using forceful diffusion will spread social events or innovations far more widely than the conventional perspective of communication. Many studies have focused on providing algorithmic methods, ignoring the study of finding the optimal point. Although it is still difficult for computer community to settle massive influence maximization issues computationally, it presents a significant opportunity to build operations research-based models, approaches, and analyses at this interface. In the current research, the influence maximization problem is investigated as a discrete optimization issue on social networks. It focuses on the creation of precise techniques for resolving massive cases near the optimal and will provide two classical diffusion models describing the propagation of influence in networks. Finally, given the additional constraints considered, an extension of the trigger model with effective analysis is proposed.

Frontline discretion from a Bourdieu-inspired field perspective: an ethnographic case study of a Norwegian activation measure for sick-listed employees

ABSTRACT Frontline work is increasingly undertaken in multi-stakeholder settings, indicating a need to revisit issues of frontline discretion. This ethnographic case study, based on an activation measure for sick-listed employees in Norway, applies a Bourdieu-inspired field analysis of frontline discretion. We approach frontline discretion in the Norwegian Labour and Welfare Administration (NAV) as comprised by ‘position-takings’ in an available space of possibilities in the field of work reintegration. A key feature of this field is that stakeholders employ opposing forms of capital: the NAV caseworker employs activation capital; sick-listed employees use individual or client capital; client employers draw on economic capital; and the clients’ general practitioners employ medical expertise capital. We find that NAV caseworkers undertake three specific position-takings in manoeuvring the other forms of stakeholder capital: nudging and negotiating with and between stakeholders; legitimising NAV’s interests and demands; and making other stakeholders accountable. Notably, frontline discretion is constituted in the configuration of the forms of stakeholder capital, including that of their own organisation, which requires administration and compromises between them. Our theoretical contribution is that an administrative form of capital becomes symbolic, potentially incorporating frontline workers’ future professional habitus.

Cross-mode stabilized stochastic shallow water systems using stochastic finite element methods

The development of surrogate models to study uncertainties in hydrologic systems requires significant effort in the development of sampling strategies and forward model simulations. Furthermore, in applications where prediction time is critical, such as prediction of hurricane storm surge, the predictions of system response and uncertainties can be required within short time frames. Here, we develop an efficient stochastic shallow water model to address these issues. To discretize the physical and probability spaces we use a Stochastic Galerkin method and an Incremental Pressure Correction Scheme to advance the solution in time. To overcome discrete stability issues, we propose cross-mode stabilization methods which employs existing stabilization methods in the probability space by adding stabilization terms to every stochastic mode in a modes-coupled way. We extensively verify the developed method for both idealized shallow water test cases and hindcasting of past hurricanes. We subsequently use the developed and verified method to perform a comprehensive statistical analysis of the established shallow water surrogate models. Finally, we propose a predictor for hurricane storm surge under uncertain wind drag coefficients and demonstrate its effectivity for Hurricanes Ike and Harvey.

Monetary policy, fiscal policy and cross signal jamming

This paper considers the issue of rule versus discretion when the central bank and the government share private information but have different preferences over inflation and output. We demonstrate that if the monetary policy is rule-based, Intuitive Criterion selects the unique separating equilibrium in which the central bank signals a low supply shock by a low interest rate. Interestingly, discretion may be better than the rule for the central bank, contrary to the case of complete information. Also, we examine the effect of information asymmetry on the monetary and fiscal policy mix. We show that cross signal jamming whereby the monetary authority and the fiscal authority successfully jams an unfavorable signal of each other does not occur in equilibrium.