Real Case Research Articles

The transmedial triangulation of Dracula: how cinema turned the Gothic bloodsucker into a Gothicized serial killer

G. M. Amza and Al. Bilciurescu’s Vampirul (The Vampire), the first vampire novel in Romania, was published in 1938, a decade after the release of the first translation of Dracula into Romanian. Instead of emulating Stoker’s bloodthirsty undead aristocrat, the two authors envision a priest who exploits the community’s magical thinking, masquerading as a vampire-serial killer and haunting the increasingly industrialized community in the hopes of discouraging capitalistic ventures. Although evoking East-Central European representations of heretic vampire priests, there is textual and circumstantial evidence suggesting that the villain in Vampirul was (in)directly inspired by movies which revolutionized the Gothic trope of the vampire via exposure to real-life crime cases such as that of the Vampire of Düsseldorf. Through a close reading analysis, the article revisits Franco Moretti’s theory of the (semi)peripheries importing “foreign plots” through “local characters” and expands Andrei Terian’s concept of “cultural triangulation” to include cinema, which offers fresh insights into the evolution of literary tropes. As for the influence of this medium on the vampire myth, the article shows that the first vampire narrative in Romanian literature is the product of transmedial triangulation, a process whereby the narratives of 1930s horror cinema influenced the literary reception of the “foreign plot” in Dracula, which was, in turn, reinterpreted through a serial killer vampire priest, a “local character” who embodies the period’s concerns about a lingering feudal order that threatens to hinder the development of the then fledgling Romanian society.

SDG Innovations & Impact: Relational Approach in Enabling Innovative Solutions and Impact Creation at the Grassroots

SDG localisation is a primary goal of APPGM-SDG, and solution projects are the key component of this effort. Formed in 2019, mirroring the UK Parliament setup, the APPGM-SDG Malaysia has been the forefront organisation in localising SDGs in parliamentary constituencies. Despite Covid-19 and other practical challenges. The grounded research approach undertaken by APPGM-SDG paved the way for innovative grassroots solutions designed and undertaken by local organisations targeting vulnerable communities in their own localities. From 2020 to November 2023, APPGM-SDG has undertaken targeted solution projects at an unprecedented level, with 664 SDGs solution projects in 4 years and 364 solution providers around the country. These innovative solutions are developed through a unique relational approach between multiple stakeholders and engagements in multiple processes in the making. This approach has been the key framework in designing solution projects that have proven to have a much greater impact towards the community as a whole and personally on the livelihoods of the participants involved. This is also evident in our recent publication, SDG Solution Projects: Best Practices Impacting the Grassroots, which carries 35 best practices for solution projects and innovative stories from the ground that can be examples of model solutions for vulnerable communities. This paper will discuss the relational approach that brings out SDG innovations in finding solutions between the partners involved, the empowerment and participatory models in place, and the processes involved in curating suitable solutions. The lessons learnt from 2020 until now in crafting fine-tuned solutions will also be included in this paper, together with real-life case studies of SDG innovative solutions that have a greater relational approach between stakeholders.

SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems

SecuriDN v. 0.1 is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that may affect the asset. By merging the attack graphs according to the asset associations in the architecture, a DBN is generated. Then, the evidence-based and time-driven probabilistic analysis of the DBN permits the quantification of the system security level. Indeed, the DBN probabilistic graphical model can be analyzed through inference algorithms, suitable for forward and backward assessment of the system’s belief state. In this paper, the features and the main goals of SecuriDN are described and illustrated through a simplified but realistic case study.

Progressive and efficient verification for digital signatures: extensions and experimental results

Digital signatures are widely deployed to authenticate the source of incoming information, or to certify data integrity. Common signature verification procedures return a decision (accept/reject) only at the very end of the execution. If interrupted prematurely, however, the verification process cannot infer any meaningful information about the validity of the given signature. This limitation is due to the algorithm design solely, and it is not inherent to signature verification. In this work, we provide a formal framework to extract information from prematurely interrupted signature verification, independently of why the process halts: we propose a generic verification procedure that progressively builds confidence on the final decision. Our transformation builds on a simple but powerful intuition and applies to a wide range of existing schemes considered to be post-quantum secure, including some lattice-based and multivariate equations based constructions. We demonstrate the feasibility of our approach through an implementation on off-the-shelf resource-constrained devices. In particular, an intensive testing activity has been conducted measuring the increase of performance on three IoT boards—i.e., Arduino, Raspberry, and Espressif—and a consumer-grade laptop. While the primary motivation of progressive verification is to mitigate unexpected interruptions, we show that verifiers can leverage it in two innovative ways. First, progressive verification can be used to intentionally adjust the soundness of the verification process. Second, our transformation splits verification into a computationally intensive offline set-up (run once), and an efficient online verification that is faster than the original algorithm. We conclude showing how to tweak our compiler for progressive verification to work on a wide range of signatures with properties, on three real-life use cases, and in combination with efficient verification.

Enhancing building robustness through a fuse-based segmentation framework

The most widely used design approaches today for improving the robustness of buildings rely on improving continuity within the structural system to ensure that loads supported by failed components can be redistributed to the rest of the system. Although this is effective for small initial failures, it can increase the risk of disproportionate collapse after larger initial failures due to collapsing elements pulling down parts of the structure that would otherwise be unaffected. This form of continuity-enabled collapse propagation can be avoided by dividing a structure into different segments. However, completely separating parts of a building results in lower performance under operational conditions, against lateral loads, and after small initial failures. In fact, the advantages of both continuity and segmentation can be combined through a fuse-based segmentation approach in which predefined segment borders ensure connectivity after small initial failures but separate to isolate collapse after larger initial failures. To ensure that this approach is used effectively to improve the robustness of building structures, a design framework is proposed in this article to systematically consider relevant structural and geometric criteria in order to define suitable segmentation configurations for reinforced concrete and steel framed building structures. An application to a realistic case study is also presented to demonstrate the effectiveness of the proposed framework in enhancing structural robustness.

Judgment Prediction Based on Tensor Decomposition With Optimized Neural Networks.

In the field of smart justice, handling legal cases through artificial intelligence technology is a research hotspot. Traditional judgment prediction methods are mainly based on feature models and classification algorithms. The former is difficult to describe cases from multiple angles and capture the correlation information between different case modules, while requires a wealth of legal expertise and manual labeling. The latter is unable to accurately extract the most useful information from case documents and produce fine-grained predictions. This article proposes a judgment prediction method based on tensor decomposition with optimized neural networks, which consists of OTenr, GTend, and RnEla. OTenr represents cases as normalized tensors. GTend decomposes normalized tensors into core tensors using the guidance tensor. RnEla intervenes in a case modeling process in GTend by optimizing the guidance tensor, so that core tensors represent tensor structural and elemental information, which is most conducive to improving the accuracy of judgment prediction. RnEla consists of the similarity correlation Bi-LSTM and optimized Elastic-Net regression. RnEla takes the similarity between cases as an important factor for judgment prediction. Experimental results on real legal case dataset show that the accuracy of our method is higher than that of the previous judgment prediction methods.

Quantifying trapping stability of optical tweezers with an external flow

Optical tweezers (OTs) can immobilize and manipulate objects with sizes that span between nano- and micro-meter scales. The manipulating ability of OTs is traditionally characterized by stability factor (S), which can only indicate an empirical “hit-or-miss” process. Additionally, the current quantitative models for trapping stability rarely consider the influence of external flow. In this paper, a comprehensive analysis to quantify the optical trapping stability in a perturbed asymmetric potential well is presented from the perspective of statistics, especially for weak trapping scenarios. Our analytical formulation takes experimentally measurable parameters including particle size, optical power, and spot width as inputs and precisely outputs a statistically relevant mean trapping time. Importantly, this formulation takes into account general and realistic cases including fluidic flow velocity and other perturbations. To verify the model, a back-focal-plane-interferometer-monitored trapping experiment in a flow is set up and the statistical characteristics of trapping time demonstrate good agreement with theoretical predictions. In total, the model quantitatively reveals the effects of external disturbance on trapping time, which will find applications where optical trapping stability is challenged by external perturbations in weak trapping conditions.

Singular effect of linkage on long-term genetic gain in Fisher's infinitesimal model.

During the founding of the field of quantitative genetics, Fisher formulated in 1918 his "infinitesimal model" that provided a novel mathematical framework to describe the Mendelian transmission of quantitative traits. If the infinitely many genes in that model are assumed to segregate independently during reproduction, corresponding to having no linkage, directional selection asymptotically leads to a constant genetic gain at each generation. In reality, genes are subject to strong linkage because they lie on chromosomes and thus segregate in a correlated way. Various approximations have been used in the past to study that more realistic case of the infinitesimal model with the expectation that the asymptotic gain per generation is modestly decreased. To treat this system even in the strong linkage limit, we take the genes to lie on continuous chromosomes. Surprisingly, the consequences of genetic linkage are in fact rather singular, changing the nature of the long-term gain per generation: the asymptotic gain vanishes rather than being simply decreased. Nevertheless, the per-generation gain tends to zero sufficiently slowly for the total gain, accumulated over generations, to be unbounded.

An ML‐Based P3‐Like Multimodal Two‐Moment Ice Microphysics in the ICON Model

AbstractMachine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super‐particle model McSnow are used as training data. The ML performs a coarse‐graining of the particle‐resolved microphysics to multi‐category two‐moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML‐based bulk model. The ML‐based scheme is tested with simulations of increasing complexity. As a box model, the ML‐based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML‐based P3‐like scheme provides a more realistic extended stratiform region when compared to the standard two‐moment bulk scheme in ICON. In a realistic case study, the ML‐based scheme runs stably, but can not significantly improve the results. This shows that ML can be used to coarse‐grain super‐particle simulations to a bulk scheme of arbitrary complexity.

Enhancing clinical law education through immersive virtual reality: A flow experience perspective

BackgroundThe existing literature lacks experimental studies that provide a genuine virtual reality experience, particularly within the context of the flow experience, as it relates to clinical legal education. AimsThis study investigates the impact of immersive Virtual Reality (VR) on clinical law education, with a particular focus on the flow experience. SampleEighty-three law students participated in case practices. MethodThe study adopted an experimental design that included a random post-test control group, two distinct VR environments, immersive VR and desktop VR, were employed to create authentic scenarios mirroring real-life legal cases, and differences between the groups were tested using One-Way Multivariate Analysis of Variance (MANOVA). ResultsThe results revealed that immersive VR significantly enhances the flow experience of law students compared to desktop VR. Participants in the immersive VR group demonstrated higher levels of focused attention, telepresence, time distortion, and interaction, indicating a more profound immersion. Surprisingly, there was no significant difference in terms of enjoyment between the two groups, contradicting previous research in this area. ConclusionsThis study is pioneering in its approach, offering a unique contribution to the field of law education. It highlights the potential of immersive VR as a valuable tool for enhancing clinical legal education and creating more engaging learning experiences. Expanding the use of these virtual environments to both practical and theory-based learning methods presents an exciting avenue for further exploration in the field of legal education.

Axial Disease in Psoriatic Arthritis: A Challenging Domain in Clinical Practice.

Psoriatic arthritis (PsA) is a chronic inflammatory condition affecting about one-third of individuals with psoriasis. Defining axial involvement in PsA (axPsA) remains debated. While rheumatologists guide clinical practice, consensus on axPsA is still lacking. This paper explores historical and upcoming definitions from the Axial Involvement in Psoriatic Arthritis (AXIS) study, which aims to establish a validated axPsA definition. Epidemiological data reveal diverse axPsA prevalence rates, emphasizing its complex relationship with peripheral arthritis and enthesitis. Unique genetic, clinical, and radiological features differentiate axPsA from ankylosing spondylitis (AS), necessitating refined classification criteria. The recommendations from the Assessment of Spondylarthritis international Society (ASAS) provide valuable guidance due to the limited direct evidence. Emerging therapies, including interleukin-23 (IL-23) inhibitors or Janus kinase (JAK) inhibitors, are under investigation for axPsA. Currently, secukinumab, an interleukin-17 (IL-17) inhibitor, is an evidence-based option for axPsA management. However, given the variability in individual patient responses and disease manifestations, personalized, evidence-based treatment approaches remain essential for optimizing patient outcomes. In the final section, two real-life cases illustrate the challenges in managing axPsA, emphasizing the importance of tailored therapies. Achieving precision in defining axPsA remains a formidable task, making detailed criteria essential for effective strategies and improving patient outcomes.

Congestion Transition on Random Walks on Graphs.

The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities. In this work, we propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand. Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow. In the approximation of the single-step process, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle. Our results explain how congested nodes emerge as the total traffic load increases, analogous to a percolation transition where the appearance of a congested node is an independent random event. However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes. Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network.

An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images

This paper proposes an edge-preserving regularization technique to solve the color image demosaicing problem in the realistic case of noisy data. We enforce intra-channel local smoothness of the intensity (low-frequency components) and inter-channel local similarity of the depth of object borders and textures (high-frequency components). Discontinuities of both the low-frequency and high-frequency components are accounted for implicitly, i.e., through suitable functions of the proper derivatives. For the treatment of even the finest image details, derivatives of first, second, and third orders are considered. The solution to the demosaicing problem is defined as the minimizer of an energy function, accounting for all these constraints plus a data fidelity term. This non-convex energy is minimized via an iterative deterministic algorithm, applied to a family of approximating functions, each implicitly referring to geometrically consistent image edges. Our method is general because it does not refer to any specific color filter array. However, to allow quantitative comparisons with other published results, we tested it in the case of the Bayer CFA and on the Kodak 24-image dataset, the McMaster (IMAX) 18-image dataset, the Microsoft Demosaicing Canon 57-image dataset, and the Microsoft Demosaicing Panasonic 500-image dataset. The comparisons with some of the most recent demosaicing algorithms show the good performance of our method in both the noiseless and noisy cases.

Importance of artificial intelligence in achieving sustainable development goals through financial inclusion

Purpose The purpose of this paper is to provide evidence on the relationship between artificial intelligence (AI) and financial inclusion to achieve sustainable development goals (SDGs), an agenda set by United Nations for 2030. Financial inclusion is an enabler of 8 of the 17 SDGs. This paper emphasizes the introduction of AI in the financial sector, which is indispensable for achieving financial inclusion and plays a crucial role in the achievement of SDGs. Design/methodology/approach This study adopts qualitative research methodology to highlight the significance of AI in achieving high levels of financial inclusion in an economy. Both narrative and comparative approaches are used to provide empirical evidence for reaching the UN SDGs target through AI-assisted financial inclusion. Findings AI implementation in finance enables people to take part in the formal financial sector and thus, enhances economic growth and reduces poverty. Research limitations/implications This research is limited in its data. Only five top AI applications are chosen and comparison is made between two countries only. Future research should consider it as an established concept and include more data to strengthen the evidence. Practical implications The results of this paper will help policymakers convince governments and institutions to put their efforts toward AI implementation in financial infrastructure of countries. Originality/value This research is unique in providing real-life examples and cases demonstrating the significance of AI implementation in the financial sector. Recent literature lacks evidence on the relationship of AI, financial inclusion and SDGs. This study adds to the existing literature by compiling data on top AI applications and comparing the performance of countries in achieving financial inclusion with the help of AI.

Introducing a comprehensive methodology for optimizing the mass of realistic roofing structural systems using cold-formed steel profiles

This study aims to introduce a comprehensive methodology for optimizing complete real structural systems for roofs involving trusses, purlins, and bracing systems jointly, taking into account realistic loads and constraints dictated by technical codes, thereby offering a more accurate representation of practical scenarios. The objective is to achieve the minimum mass through size, shape, and topology optimization of both the main truss and purlin structural subsystems. To achieve this goal, the Enhanced Particle Swarm Optimization (EPSO) algorithm is implemented. An example of a realistic case, which takes into account multiple actual constraints such as stress, displacement, buckling, and natural frequency limits, is thoroughly evaluated. After that, 144 other interactions among dimensions of the building and loads applied are simulated, and the mass of the system is obtained for each one of them. The results indicated that the graphs generated from the various simulations allow for the determination of the optimized mass for different building dimensions. Consequently, the cost and raw material consumption can be estimated for common applications. Therefore, it is concluded that this work presents a significant contribution to structural designers, as the proposed methodology enables structural optimization quickly and easily for practical engineers.

Mutual Expected Rationality in Online Sharing: An Agent-Based Model Study

AbstractModels of content-sharing behavior on online social media platforms typically represent content spread as a diffusion process modeled on contagious diseases; users’ behavior is modeled with single-agent decision theory. However, social media platforms are interactive spaces where users care about reactions to, and further spread of, the content they post. Thus, social media interaction falls under the intended use cases for game theory. In contrast to existing models leaving strategic reasoning out, we capture agents’ social media decisions within a cognitive hierarchy framework, which can be interpreted as making formally precise how agents make strategic choices based on mutual expectations of rationality. Analytically, we identify limit cases in which a platform can be swamped with content that no agents personally like but all expect to elicit reactions (think obvious fake-news). We then use agent-based simulations to show that a range of more realistic cases give rise to similar outcomes.

An optimization model for determining cost-efficient maintenance policies for multi-component systems with economic and structural dependencies

In most multi-component systems, the cost-efficiency of maintenance policies depends on technical structural dependencies. Motivated by the recognition that these dependencies must be accounted for in the development of optimal maintenance policies, we develop an optimization model to determine cost-efficient maintenance schedules for multi-component systems. Our main contribution is twofold. First, we introduce directed graphs as an expressive tool to represent the economic and structural dependencies of the system, including situations in which the maintenance of a given component may require other components to be disassembled or maintained. Second, we formulate a Markov Decision Process model, which is solved through the modified policy-iteration algorithm to determine the most cost-efficient policy. This policy indicates which maintenance actions consisting of disassembly and component replacement decisions are optimal when mandatory replacements must be made whenever the system fails, or the reliability of the system falls below a predefined reliability threshold. To our knowledge, this is the first model that provides optimal maintenance policies that comply with reliability requirements in the presence of constraints arising from technical structural dependencies. We illustrate the model with a realistic case study on the development of cost-efficient maintenance policies and show that its results compare favorably with heuristic maintenance policies.

Accuracy of plant identification applications to identify plants in suspected poisoning cases referred to the Queensland Poisons Information Centre

Accidental exposure to potentially poisonous plants is a common reason for contacting poisons information centres. Although most exposures are mild, early identification can be critical for severe cases. Smartphone apps that use image recognition can serve as an alternative to traditional botanist identification. This study investigates the accuracy of smartphone applications compared to botanist identification in real-life plant exposure cases referred to the Queensland Poisons Information Centre. This prospective observational study analysed data from 25 plant exposure cases between February 2022 and January 2023. In these cases, images were referred to a botanist for identification and reviewed by six researchers using six different plant identification applications (Google Lens™, PictureThis, PlantSnap, LeafSnap, Plant Identifier, and Pl@ntnet™). The researchers’ identifications were then compared to the botanist’s identification. The accuracy of the applications varied considerably, PictureThis was the best, with 74% correct identification per exposure, followed by Plant Identifier and Pl@ntnet™ (both 72%). PlantSnap had the lowest accuracy (38%). Agreement between researchers using the same application also showed variation. Current smartphone plant identification applications are not reliable enough for poison information centres to use in guiding advice for plant exposure calls. Botanist identification remains the gold standard. Clinicians should be wary of their use in real-world scenarios.

Beyond profit margins: Orchestrating social, economic, and environmental sustainability within the Norwegian Salmon Food Supply Chain

Food Supply Chains (FSCs) have become increasingly complex with the average distance between producers and consumers rising considerably in the past two decades. Consequently, FSCs are a major source of carbon emissions and reducing transportation costs a major challenge for businesses. To address this, we present a mathematical model to promote the three core dimensions of sustainability (economic, environmental, and social), based on the Mixed-Integer Linear Programming (MILP) method. The model addresses the environmental dimension by intending to decrease the carbon emissions of different transport modes involved in the logistics network. Several supply chain network characteristics are incorporated and evaluated, with a consideration of social sustainability (job generation from operating various facilities). The mathematical model's robustness is demonstrated by testing and deploying it to a variety of problem instances. A real-life case study (Norwegian salmon supply chain) helps to comprehend the model's applicability. To understand the importance of optimizing food supply networks holistically, the paper investigates the impact of multiple supply chain permutations on total cost, demand fluctuations and carbon emissions. To address fluctuations in retail demand, we undertook sensitivity analysis for variations in demand, enabling the proposed model to revamp Norway's salmon supply chain network. Subsequently, the results are thoroughly examined to identify managerial implications.

Thermal pairing treatment within the path integral formalism

Abstract A method for the treatment of the pairing correlations at finite temperature is proposed within the path integral formalism. It is based on the square root extraction of the pairing term in the Hamiltonian of the system. Gap equations, as well as expressions of the pairing gap parameter Δ, the energy E and the heat capacity C are established. The formalism is first tested using the Richardson model which enables comparison with exact solution. The results obtained using the present formalism are also compared to the Finite Temperature BCS (FTBCS) ones. An improvement compared to the FTBCS model is noted especially at low temperature. Indeed, it is shown that the agreement between the Δ values of the present work and the exact ones is very good at low temperature. This leads to a better agreement between the values of E and C of the present model and the exact values than with the FTBCS values. However, a critical value of the temperature still exists.
Realistic cases are then considered using single-particle energies of a deformed Woods-Saxon mean-field for the nuclei ¹⁶²Dy and ¹⁷²Yb. It is shown that in the framework of the present approach, the pairing effects persist beyond the FTBCS critical temperature. Moreover, at low temperature, a good agreement between the present model results and semiexperimental values of the heat capacity is observed. A clear improvement compared to the FTBCS method is noted. It is no more the case at higher temperature.