Conventional Scenario Research Articles

CiteNet: Cross-modal incongruity perception network for multimodal sentiment prediction

Multimodal sentiment prediction poses a formidable challenge that necessitates a profound understanding of both visual and linguistic cues, as well as the intricate interactions between them. The current achievements of modern systems in this domain can plausibly be attributed to the development of sophisticated cross-modal fusion techniques. Nevertheless, such solutions often handle each modality equally, neglecting the discordant predictions arising from sentiment incongruity in unimodal sources, which may result in performance degradation in conventional extraction-fusion scenarios. In this work, we take a different route–introducing an extraction-estimation-fusion paradigm aimed at exploring more reliable multimodal representations under the supervision of unimodal sentiment prediction. To this end, we propose a Cross-modal IncongruiTy pErception NETwork, named CiteNet, for multimodal sentiment detection. In CiteNet, we initially develop a cross-modal alignment module tailored to synchronize modality-specific representations through contrastive learning. Subsequently, with a refined cross-modal integration module, CiteNet can achieve a synergistic and comprehensive multimodal representation. In addition, we explore a cross-modal incongruity learning module from an information-theoretic perspective, capable of estimating inherent sentiment disparities by analyzing modal distributions. This incongruity score is then employed as a crucial factor in the adaptive fusion of unimodal and multimodal representations, culminating in enhanced accuracy in sentiment prediction. Experimental results on two datasets demonstrate that CiteNet outperforms prior methods by a significant margin of approximately 1%–11% in accuracy.

Higgs quadruplet impact on W mass shift, dark matter, and LHC signatures

The addition of a Higgs quadruplet to the standard model (SM) of quarks and leptons would shift the W-boson mass upward. It could also facilitate the production of dark matter through the conventional thermal freeze-out scenario via Yukawa interaction with the Higgs quadruplet or freeze-in production from the decay of SM Higgs. We investigate the same-sign lepton smoking gun signature of the double-charged scalar component of the quadruplet Higgs at the LHC. Published by the American Physical Society 2024

A charge-discharge optimization strategy considering the spatiotemporal distribution of electric vehicles and the operational safety of the power distribution network in the power-transportation coupling network

An electric vehicle (EV) charge-discharge optimization (CDO) strategy that accommodates both grid-side and user-side demands is conducive to mitigating the adverse effects of disordered EV charging on the power distribution network (PDN). To tackle the issue of inaccurate estimation of the schedulable capacity of EVs in existing research, a high temporal resolution dynamic spatiotemporal distribution simulation model for EVs is developed. Furthermore, leveraging the characteristics of the power-transportation coupling network (PTCN), a sub-districted dynamic electricity pricing (DEP) is proposed to assist in improving the PDN node voltage distribution. Subsequently, an incentive coefficient is introduced to incentivize the discharge of EVs during potential peak periods. The proposed CDO strategy, considering the non-cooperative behavior of EV owners (EVOs), can provide personalized charge-discharge plans for them. The temporal sequence experiments reveal a significant decrease in the peak-to-valley disparity of the load, achieving an optimization rate of load mean square error that exceeds 80%. The sub-districted DEP exhibits a significant advantage in maintaining PDN voltage stability compared to traditional time-of-use electricity pricing (TOUEP) and DEP, while also achieving reductions in active power losses of more than 15% and 10% for PDN, respectively. Moreover, in comparison to the conventional scenario, the CDO strategy leads to a maximum reduction of up to 942.5% and 22.3% in the total economic cost for electric private cars and electric taxis respectively. Lastly, the impact of seasonal factors is discussed. Numerical results indicate effective alleviation of load peak-valley differences, load fluctuations, and voltage drop phenomena in both winter and summer. Additionally, the maximum reduction in active power losses reaches 20.1% and 21.9%, respectively, while the total economic cost for EVOs participating in the CDO strategy is reduced by up to 97.7% and 114.7%.

A genetic algorithm-based optimal selection and blending ratio of plastic waste for maximizing economic potential

Pyrolysis of plastic waste presents an innovative solution to convert plastics into valuable fuels like oil and gas, thereby contributing to circular economy and sustainability. The efficiency of this process highly depends on the types and compositions of plastics utilized, as each type reacts uniquely during pyrolysis, affecting fuel yield and quality. However, previous studies have not thoroughly examined the impact of plastic selection and blending ratios on the pyrolysis outcome, as this requires extensive experimentation due to the numerous possible combinations. Optimizing these factors, along with operational conditions, is essential for enhancing the economic viability of plastic recycling via pyrolysis. This study introduces an optimal selection and blending ratio for plastic waste in pyrolysis fuel production to enhance economic viability using the surrogate model-based genetic algorithm (GA). The optimization process involves (1) generating data via a process model of the plastic waste pyrolysis reactor, (2) preprocessing data, (3) developing a surrogate model based on deep neural networks (DNNs), and (4) deriving the optimal solution using GA. The results reveal an optimal blending ratio: 4 wt% LDPE, 30 wt% HDPE, 43 wt% PP, and 24 wt% PS. Additionally, the optimal pyrolysis temperature is 607 ℃. By adopting this optimal solution, the net profit could increase by 20% compared to the conventional scenario, resulting in an additional annual net profit of US$ 407,811, thus highlighting the potential this approach offers to attain maximum economic feasibility in industrial applications.

Treatment of aggression regulation problems with virtual reality: study protocol for a randomized controlled trial.

Aggressive conduct among delinquents presents a pervasive issue, bearing substantial implications for not only society at large but also for the victims and the individuals displaying the aggression. Traditional approaches to treating aggression regulation deficiencies generally employ Cognitive Behavioral Therapy (CBT) in conjunction with analog role-playing exercises. A body of research supports the efficacy of various therapeutic models for aggression regulation, including Responsive Aggression Regulation Therapy (Re-ART). Role-playing within a therapeutic context has been shown to contribute significantly to reductions in violent reoffending. However, the practical application of these skills in real-world settings remains challenging due to the inherent risk of aggressive outbreaks. Additionally, the conventional role-playing scenarios, often conducted in a therapy room, lack contextual realism and may induce role confusion between the patient and the therapist. Virtual Reality (VR) technology could offer a viable solution to these limitations by allowing for skill training in both behavioral and cognitive domains within a realistic yet safe and controlled setting. The technology also facilitates real-time awareness of emotional states and tension levels in the patient. This paper describes the study protocol of a randomized controlled trial in which Re-ART offered in a virtual environment (Re-ART VR) is compared to Re-ART offered as treatment as usual. Adult forensic outpatients with aggression regulation problems are randomly assigned to either Re-ART VR or Re-ART. The Controlling Skills, Influence of Thinking and Handling Conflicts modules will be offered to both groups during 3-6 months. Pre- and post-intervention measurements are performed. The primary outcome measurement is the degree of aggression regulation, while secondary outcome measurements include impulsivity and cognitive biases. Additionally, patient motivation and therapist motivation are expected to act as moderating factors. To date, scarcely previous research has been done on the effectiveness of VR in treatment of aggression regulation problems in forensic outpatients. Forensic outpatients who do not benefit sufficiently from mainly CBT-based interventions may benefit more from experiential learning. The unique capabilities of VR in this regard have the potential to enhance the treatment effect.Clinical trial registration: [https://clinicaltrials.gov/], identifier [NL78265.018.21].

Enhancing structural battery performance: Investigating the role of conductive carbon additives in LiFePO4-Impregnated carbon fiber electrodes

This study centers on investigating the influence of conductive additives, carbon black (Super P) and graphene, within the context of LiFePO4 (LFP)-impregnated carbon fibers (CFs) produced using the powder impregnation method. The performance of these additives was subject to an electrochemical evaluation. The findings reveal that there are no substantial disparities between the two additives at lower cycling rates, highlighting their adaptability in conventional energy storage scenarios. However, as cycling rates increase, graphene emerges as the better performer. At a rate of 1.5C in a half-cell versus lithium, electrodes containing graphene exhibited a discharge capacity of 83 mAhgLFP−1; those with Super P and without any additional conductive additive showed a capacity of 65 mAhgLFP−1 and 48 mAhgLFP−1, respectively. This distinction is attributed to the structural and conductivity advantages inherent to graphene, showing its potential to enhance the electrochemical performance of structural batteries. Furthermore, LFP-impregnated CFs were evaluated in full cells versus pristine CFs, yielding relatively similar results, though with a slightly improved outcome observed with the graphene additive. These results provide valuable insights into the role of conductive additives in structural batteries and their responsiveness to varying operational conditions, underlining the potential for versatile energy storage solutions.

Gravitational waves in the circular restricted three body problem

The prospect of unprecedented high-quality data of gravitational waves in the upcoming decades demands a theoretical effort to optimally study and analyze the signals that next generation detectors will provide. Here we study the gravitational wave emission and related dynamics during the inspiralling phase of the Circular Restricted Three Body Problem, a modification of the conventional binary scenario in which a small third object co-rotates with the parent binary system. Specifically, we obtain analytic expressions for the emitted power, frequency variation and other dynamical variables that describe the evolution of the system. As a key highlight, we find that the presence of the third body actually slows down the coalescence of the binary, which can be partially interpreted as an effective rescaling of the binary’s chirp-mass. Our analysis assumes semi-Keplerian orbits for the particles and a highly mass asymmetric parent binary needed for the stability of orbits.

Stochastic optimization of solar-based distributed energy system: An error-based scenario with a day-ahead and real-time dynamic scheduling approach

Distributed energy systems (DES) have garnered global attention as a promising solution for the expansion of renewable energy sources. However, stochastic uncertainty in renewable sources poses significant challenges in the collaboration optimization of system design and operation. In this study, a comparison analysis was conducted to assess the effectiveness of the conventional distribution-based scenario generation (DS) method for stochastic optimization of a distributed energy system in residential buildings. The results revealed that the DS method inaccurately captured extreme scenarios and exhibited limitations in operation optimization, leading to significant performance evaluation bias. To address these challenges, a novel stochastic optimization approach was developed based on error-based scenarios and a day-ahead and real-time dynamic scheduling strategy (ES-DRS). This approach incorporated solar energy prediction errors to more accurately characterize extreme scenarios, while also considering dynamic dual-scale meteorological boundary condition for rolling operation optimization. Furthermore, the buffer storage and coverage periods in ES-DRS were investigated and discussed during dynamic scheduling. Results demonstrated that when the buffer storage and coverage period were set at 36.22 kWh in summer and 24 h, respectively, the DES with ES-DRS achieved optimal multi-objective performance. This resulted in an annual total cost of 3.21 × 104 USD and CO2 emission of 5.82 tons, representing reductions of 26.45% and 61.06%, respectively, compared to the conventional strategy. Overall, this research contributes to advancing uncertainty analysis and scenario-based optimization in DES, highlighting the potential benefits of adopting the ES-DRS approach to maximize overall performance from both economic and environmental perspectives.

Partial replacement of mineral fertilisers with animal manures in an apple orchard: Effects on GHG emission

Partial replacement of mineral fertilisers (MF) with animal manures is a good alternative to reduce MF use and increase both nutrient cycling in agriculture and soil organic matter. However, the adoption of this practice must not lead to increased environmental impacts. In this two-year study conducted in an apple orchard, MF were partially replaced with various animal manures, including cattle slurry (CS), acidified cattle slurry (ACS), solid cattle manure (CsM), or poultry manure (PM), and their impacts on greenhouse gas emission (GHG: CO2, N2O and CH4) were examined. A control (CTRL) receiving only MF served as the baseline, representing the conventional scenario in orchard fertilisation. Overall, replacing MF with manures increased GHG emissions, with the magnitude of the impacts depending on the specific characteristics of the manures and the amount of nutrients and organic matter applied. Comparing to the CTRL, application of ACS and CS led to higher CH4 and N2O emissions, while PM application increased both N2O and CO2 emissions. In contrast, replacement with PM and CsM decreased CH4 emissions. Nevertheless, results varied between the two years, influenced by several factors, including soil conditions. While acidification showed potential to mitigate CH4 emissions, it also led to increased N2O emissions compared to CS, particularly in 2022, suggesting the need for further investigation to avoid emission trade-offs. Replacement with CS (20.49 t CO2-eq ha−1) and CsM (20.30 t CO2-eq ha−1) showed comparable global warming potential (GWP) to the conventional scenario (CTRL, 19.49 t CO2-eq ha−1), highlighting their potential as viable MF substitutes.

Chiral symmetry restoration and the ultraquantum limit of axionic Charge Density Waves in Weyl Semimetals

A new mechanism for chiral symmetry restoration at extreme high magnetic fields is proposed in the context of the Magnetic Catalysis scenario in Weyl Semimetals. Contrary to previous proposals, here we show that, at very large magnetic fields, the transverse velocity of the axion field, the phase mode of the chiral condensate \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\langle \\overline{\\Psi }\\Psi \\rangle $$\\end{document}, becomes effectively one-dimensional and its fluctuations destroy a possible nonzero value of this fermionic condensate. We also show that, despite of the U(1) chiral symmetry not being broken at extremely large magnetic fields, the spectrum of the system is comprised by a well defined gapless bosonic excitation, connected to the axion mode, and a correlated insulating fermionic liquid that is neutral to U(1) chiral transformations. When the theory is supplemented with the inclusion of dynamical electromagnetic fields, the chiral symmetry is broken again, and the conventional scenario of magnetic catalysis can be recovered.

The explanatory function of metaphor scenario in the Serbian pro-vaccine discourse

Metaphor has been established and extensively examined as one of the tools experts deploy to explain, simplify and transform complex scientific discourse into the knowledge suitable for the audience of non-experts. However, relatively little research has been conducted on metaphor scenario (Musolff 2006, 2016a) and its role in this process. Therefore, in this paper we explore how metaphor scenario is used to explain Covid-19 vaccines’ safety and effectiveness to the population in an understandable manner in order to speed up the immunization process in Serbia. By analysing a data set gathered from various Serbian electronic news media sources ( NovaS , N1 , Danas , Vreme , Večernje novosti , Mondo , Politika , Telegraf , Krug ) published from January to December 2021, we aim to explore (1) how the three metaphor scenarios, combat, container and movement, may help simplify complex scientific concepts in the pro-vaccine discourse; and (2) how the conceptual elements of these scenarios and their interconnected relations are used for this purpose. The findings showed that these conventional scenarios manifest their explanatory potential by means of several sub-scenarios, whose conceptual elements establish useful mappings relying on rarely used components of source domains. The results confirm that metaphor scenarios may be used strategically by medical experts as an apt explanatory tool to simplify challengingly complex scientific concepts to the general public. The paper contributes to current research on the role that metaphor and other cognitive instruments play in science popularization.

Sustainability analysis of primary wastewater treatment by willow plantations in Québec

Wastewater treatment is a necessary step to avoid environmental impacts of water consumption and usage. Traditional approaches are expensive and are limited to developed countries. Phytofiltration using fast-growing trees and shrubs like willows potentially offer an alternative. This paper aims to determine if wastewater treatment using phytofiltration can provide complementary environmental and economic benefits for rural communities in a Nordic climate such as the province of Québec, Canada. It looks at different perspectives of the wastewater treatment solution in a local and rural context. Based on life cycle analysis (LCA) and life cycle cost analysis (LCC), we found that, for an exemplar Québec municipality, the conventional wastewater treatment scenario impacted more on climate change, ecosystem quality and human health than the two phytofiltration of wastewater scenarios studied, where impact is highly dependant on the biomass valorization. The net present cost of the phytofiltration scenarios were lower than typical conventional treatment in Québec. For a biomass producer, conventional biomass production had the highest environmental impact on ecosystem quality, while biomass production from phytofiltration had the highest environmental impact on climate change, human health, and resources. We demonstrate that the phytofiltration is a viable and multifunctional technology that could provide good incentives for a local biomass value chain. it allows to both alleviate wastewater treatment burden and provide affordable biomass for bioenergy development for rural communities. Mobilizing local stakeholders will be key to make phytofiltration an alternative solution for both environmental burden alleviation and rural economic development.

Application of a novel geometric seismic attribute for enhancing fault visualization in areas of potential carbon capture and storage

Seismic fault interpretation is a critical task for any type of energy industry. Correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes, such as coherence and curvature, are routinely employed to enhance fault visualization in seismic data. However, they can show limitations for subseismic faulting. In this study, we highlight the usefulness of including novel aberrancy attributes for fault identification in multiattribute analysis and unsupervised machine learning (ML) techniques. We compare broadband coherence, curvature, multispectral coherence, and aberrancy when trying to map faults in a potential CO2 storage location. We also compare self-organizing maps and generative topographic mapping techniques when including and excluding aberrancy attributes. Our results show that integrating aberrancy attributes during multiattribute analysis and ML steps considerably enhanced the visualization of lineaments with strikes similar to those of fracture sets seen only with well-log data and that were not clearly captured by the conventional seismic attributes and ML scenarios excluding aberrancy attributes. We demonstrate the potential of these novel geometric seismic attributes to map subseismic faults. We also provide an example that can encourage interpreters to include them in their interpretation workflows.

Agent-Based Modelling for Scenario Analysis in Management Consulting

This paper investigates the modernization of a corporate practice called scenario analysis. It is widely used across the globe, originating from World War II, helping companies to gain a better understanding of potential future outcomes. In order to modernize scenario analysis, this paper will explore the novel approaches; of a hybrid development between agent-based modelling (ABM) and scenario analysis, the utilization of bottom-up and industry view approaches and introducing elements of human social behaviour. All these features are integrated in a single scenario analysis ABM that will model the fundamental philosophy behind the daily operations of a large corporation, such as Deloitte. This paper addresses more natural evolutions for scenarios, by looking at the firms' fundamental building blocks, rather than just an objective view based on the firms' financial statements. This model will focus upon accurate representations of the daily interactions of an enterprise, especially improving the explainability and transparency of causality in scenario analysis generated by the models, in order to achieve enduring agreements among executives about the firm's future. With this novel approach to scenario analysis, the modelling of a firm with the use of an ABM, there is the potential to provide more in-depth micro-observations of the future, rather than the conventional scenario analysis methods performed today.

Facilitating (Further) Inhumanity: On the Prospect of Losing Article 3 echr, a Vital Guarantee for the Under-Protected

Abstract Article 3 of the echr encompasses an absolute right not to be subjected to torture or to inhuman or degrading treatment or punishment. This right proscribes, and demands protection from, treatment which is antithetical to human dignity. Over the years, what amounts to such treatment – and the obligations flowing therefrom – has been the subject of extensive interpretation by the ECtHR and domestic courts. Applicable in many contexts beyond the conventional scenario of interrogational torture, the right’s significance – and much of the backlash it has attracted – lies chiefly in the protections it demands for persons who fall through the cracks of the political process. This comment contemplates what is at stake, in terms of Article 3’s protections, in a potential UK departure from the echr, and concludes that a loss of the protection offered under Article 3 echr would facilitate (further) inhumanity, particularly against persons who are already othered, under-protected, and victimised.

Environmental consequences of shifting to timber construction: The case of Denmark

Many life cycle assessments (LCA) studies on wooden buildings show potential to decarbonise the building industry, though often neglecting to consider the systemic changes of such a shift at the building stock scale. This study applies a consequential LCA to evaluate the transition from conventional construction to increased wood-based construction in Denmark from 2022 to 2050. The assessment models a material flow analysis of the two construction scenarios, incorporating an area forecast and case buildings. By that, we assessed suppliers' capacity to likely meet the demand for wood, steel, and concrete, employed an input-output model to enhance completeness and country representativeness for other materials' markets, and considered the competition for land by indirect land use change. We implemented a dynamic IPCC-based assessment of GHG-emissions concurrently with a carbon forest model to anticipate the relationship between the delayed carbon storage resulting from using wood in buildings and forest regrowth management. The findings indicate wood construction is the most climate-friendly option for multifamily houses. In contrast, single-family houses (SFH) and office buildings (OB) exhibit the lowest climate impacts in the conventional scenario. The SFH result could be credible due to the sizable GWP impact gap between construction scenarios despite uncertainties related to the weight proportion of sedum roofs. The less conclusive OB findings relate to the substantial steel quantities in the wood case buildings, requiring further investigation. Generally, metals, cement-based- and biobased materials demonstrate the largest climate impact among the material categories. Across all three building typologies, the change to timber construction increased the impact on nature occupation (biodiversity). In conclusion, this study emphasises the need for further research on forest management model inputs, land use change approaches, potential steel suppliers' impact, and a broader array of case studies. It is because these are influential factors in facilitating informed decision-making of the increased implementation of wood in buildings. As the first study to integrate these modelling characteristics, it contributes to the research gap concerning geographical circumstances, forestry, and markets relevant to decision support for increased wood utilisation in Europe's building industry.

Closed-form bound states for two Dunkl–Liénard oscillator systems

In this paper, we construct the general Hamiltonian for position-dependent mass systems within the Dunkl formalism. The associated Schrödinger equation is shown to admit closed-form solutions of bound state type for two particular Dunkl–Liénard systems, given by the Dunkl–Higgs oscillator, and a spiked mass Dunkl oscillator. Differences to the conventional scenario are discussed.

Peridynamic Simulation of the Penetration of an Ice Sheet by a Vertically Ascending Cylinder

The vertical ice breaking of marine structures in ice-covered areas involves the deformation and failure of an ice sheet. Different from the existing conventional scenarios where the ice sheet is used as a transportation and support medium, the damage to the ice sheet will be more severe when a structure penetrates the ice sheet from below, due to the lack of elastic support from the fluid above the ice sheet. In order to investigate the failure mode of the ice sheet and the ice load characteristics during vertical penetration, a mesh-free bond-based peridynamic method is used in this paper to simulate the mechanical behaviors of the ice sheet. The cracks simulated in this study exhibit a higher level of similarity to experimental results, which improves the accuracy of the ice load. The numerical model established shows satisfactory applicability for the analysis of penetration failure of an ice sheet. In addition, the influence of ice thickness, impact velocity, and cylinder diameter on the failure characteristics of the ice sheet and breakthrough load are analyzed. The results of a parametric study indicate that the relationship between ice thickness and breakthrough load, as well as the relationship between load area and breakthrough pressure, can both be fitted using quadratic functions.

Conventional metaphorical scenarios of humor in Romanian

Abstract The way humorous verbal communication is construed in a linguaculture can be analyzed within the framework of cognitive metaphor theory and its more discursive and cultural developments. Cognitive/conceptual metaphors are instrumental for framing humor as a communicative form which goes beyond mere aesthetic experience. This article focuses on the conceptualization of verbal forms of humor in Romanian. The examples are retrieved mainly from internet mediated communication, backed by corpus analysis, and compared to older literary examples (19th–early 20th century). The examples illustrate the stability of various metaphorical scenarios in the Romanian linguaculture. The analysis reveals that several conventional metaphorical scenarios exist in parallel, displaying what are the functions of humor in an emic perspective. In the case of the Romanian examples, the list comprises the disciplinary, aesthetic, and therapeutic functions. The main metaphorical scenarios are subsumed to (physical) aggression, food, and health domains. The scenarios associated with each domain emphasize a certain function: for example, the disciplinary function of humor relates to physical aggression, while the aesthetic function relates to food. However, the conventional scenarios also show combinations between these functions.

An efficient approach for safety factor-based and reliability-based design of tunnel face support pressure using BP Neural Network

ABSTRACT This paper develops an efficient approach for safety factor-based design and reliability-based design of tunnel face support pressure by constructing a surrogate model to predict limit support pressures. The Back Propagation Neural Network is utilised to fit a surrogate model based on a set of training samples with limit support pressures computed by a proposed numerical method. Several scenarios are used to demonstrate the applications after the prediction performance of the model has been evaluated. A safety factor-based design is implemented by predicting the limit support pressure of a scenario with strength parameters divided by the target safety factor from those of the original scenario, whereas a reliability-based design is performed by predicting the limit support pressures of a certain number of Monte Carlo simulation samples and then choosing a quantile value from the sorted limit support pressures. The design results are checked by direct numerical simulations, demonstrating that the model is valid for conventional scenarios within the specified design domain. The approach is convenient and efficient for applicable scenarios because the most complex and time-consuming process of constructing a surrogate model is no longer needed.