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Marketers today are increasingly using storytelling to engage their audiences. However, the design of narrative visuals is often inspired by a text-centric understanding of narratives. Despite the fast increase in visual content and the distinct processing it induces, extant research on visual narrativity remains fragmented, lacking a comprehensive framework to explain how a single still image can convey a narrative. Our literature review addresses this gap through the lens of narrative transportation theory. Based on a systematic review of 64 articles from marketing and adjacent disciplines, the authors propose that an image must narrate, act, and resonate (NAR) to stimulate narrative processing and transport viewers into its narrative. They also identify specific visual features that can facilitate this process and explore how characteristics of the storyteller, story receiver, and story settings can influence the strength of visual narrative transportation (VNT). Finally, the authors highlight affective, cognitive, and behavioral responses of transported viewers. This research extends narrative transportation theory to the visual domain, offering practical design principles that can be easily applied by marketing professionals. It also outlines an actionable research agenda for marketing scholars to further explore visual narrativity. [ABSTRACT FROM AUTHOR]

The two vegetation transfer parameters of $\tau $ τ (Vegetation Optical Depth,VOD) and $\omega $ ω (Omega) could vary significantly across microwave channels in terms of frequencies, polarizations, and incidence angles, and their channel-dependent characteristics have not yet been fully investigated. In this study, we investigate the channel dependence of vegetation effects on microwave emissions from soils using a higher-order vegetation radiative transfer model of Tor Vergata. Corn was selected as the subject of investigation, and a corn growth model was developed utilizing field data collected from the multifrequency and multi-angular ground-based microwave radiation experiment from the Soil Moisture Experiment in the Luan River (SMELR). Upon compilation of the simulation dataset of microwave emissions of the corn field, the effective scattering albedo across different channels were calculated using the Tor Vergata model. Results show that vertical polarization of the vegetation optical depth is more affected by incidence angle changes, while horizontal polarization exhibits lower variations in vegetation optical depth due to incidence angle adjustments. The channel dependence of vegetation optical depth can be described as the polarization dependence parameter (${C_P}$ C P ) and the frequency dependence parameter (${C_f}$ C f ). These two parameters enable the calculation of vegetation optical depth at any channel under three adjacent frequencies (L-band, C-band and X-band). The effective scattering albedo of vegetation does not vary significantly with vegetation height or angle. It primarily depends on frequency and polarization, showing an overall increasing trend with increasing frequency. The effective scattering albedo with vertical polarization is slightly higher than that with horizontal polarization at higher frequencies, while both are lower in the L-band. This investigation is helpful for understanding the vegetation effects on microwave emissions from soils, ultimately advancing the accuracy of large-scale soil moisture retrieval in vegetated areas. [ABSTRACT FROM AUTHOR]

The paper examines the rhetorical imagination of the Catholic speaker in public discourse on the basis of the parliamentary discourses of Pope Benedict XVI. According to this researcher, Pope Benedict XVI's rhetorical model in the speeches is an extended version of Michael J. Hostetler's critical distance model supported by the translation idea of Jurgen Habermas. This conclusion is reached by analysis of the constituent elements of the arguments in three parliamentary speeches given by Pope Benedict XVI. James B. Freeman's updated version of Stephen Toulmin's argumentation model is used to discern the constituent element of the argument. [ABSTRACT FROM AUTHOR]

References to the divine have been frequent in the history of Christian art. As examples, this article surveys the symbolism of light and gold, the rhetoric of gesture, and the theatricality of the pictorial space. Sacred art is closely linked to biblical, liturgical and theological texts which are full of metaphorical expressions of transcendent realities. Since artists, patrons and viewers considered these texts authoritative, they must be consulted to interpret artworks correctly. This study argues that Catholic theology and the magisterium have constantly called for references to the divine. As a theoretical framework, this image theology helps to interpret the art-historical data with new questions that tourists and communicators today usually do not ask. This is because Christian art mediates the meaning of divine revelation, which is not self-explanatory. It needs to be decoded with a method interested in both images and texts. Erwin Panofsky has presented such a method. His 'iconology' consists of three steps for describing, analyzing and interpreting images. This article claims that the interpretation of Christian art is a prime example of the ongoing applicability of Panofsky's method as a tool for decoding iconography in pastoral, academic and social media communications, and examines a selection of best practices in such communications. [ABSTRACT FROM AUTHOR]

Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases' spatial distribution, the VGI sources utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the "VGICED Atlas", a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps. [ABSTRACT FROM AUTHOR]

Multicast services provide an efficient way of conserving resources and reducing network traffic for multicast senders. However, ensuring security, particularly source authentication, is crucial for applications such as online news, IPTV, video streaming, and stock quote distribution. Previous research efforts have attempted to provide source authentication for multicast applications, but they often struggle to handle the dynamic nature of multicast mobile receivers and multi-domain networks. This paper provides an analysis of research conducted over 22 years on source authentication mechanisms with non-repudiation. Most of these mechanisms fail to address the dynamic nature of mobile users and multi-domain networks. To address this issue, a new multicast source authentication scheme for multi-domain Software Defined Mobile Networks (SDMN) is proposed. This approach uses the global view of the controller in SDMN to operate in dynamic environments, provide non-repudiation, and tolerate packet loss. Simulation results indicate that the proposed mechanism uses resources efficiently, reduces communication and delay overhead, and performs well in multi-domain and dynamic networks. [ABSTRACT FROM AUTHOR]

This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into subcategories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models' performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA). [ABSTRACT FROM AUTHOR]

In this article, a higher-order degree of freedom (DOF) proportional-integral-derivative (PID) (3DOFPID) controller optimized with the meta-heuristic method of the water cycle algorithm (WCA) is proposed. Initially, WCA-tuned 3DOFPID is designed to the widely accepted power system model of two-area simple hydro-thermal (TASHT) system and the presented controller efficacy is revealed with other techniques discussed in the literature. Later, another interconnected power system model of two areas nonlinear realistic hydro thermal (TANRHT) system is considered for the investigative purpose by subjugating area-1 with step load perturbation (SLP) of 10%. The proposed controller is implemented to TANRHT without and with deliberating the integration of a geothermal power plant (GTPP). Dynamical responses of TANRHT reveal the efficacy of 3DOFPID over PID and 2DOFPID. Further, the impact of GTPP integration on the performance of the TANRHT system is demonstrated. Furthermore, the coordinated territorial strategy of superconducting magnetic energy storage (SMES) and unified power flow controller (UPFC) is enacted with GTPP-integrated TANRHT system for performance improvement. Finally, the sensitivity analysis is conducted to reveal the robustness of proposed secondary and territorial control strategies. [ABSTRACT FROM AUTHOR]

The m-polar fuzzy set (mFs) algorithm is a novel approach to solving multi-polar uncertainty problems. Combining other multi-criteria decision-making techniques can improve this algorithm in efficient ways. This work examines the ELimination Et Choice Translating Reality-I (ELECTRE-I) approach. The input data given to any MCDM technique will change the rank of alternatives based on the method of normalization used and the weight calculation method utilized for the criteria weight calculation purpose. Various normalization approaches and criteria weight methods are required to transform real data into normalized data for MCDM method implementation. This study uses a framework to implement the mFs ELECTRE-I algorithm to investigate the effect of AHP weight and Shannon's entropy weight (SEW) calculation combined with normalization strategies. In this paper, selecting nontraditional machining (NTM) processes is evaluated to assess the impact of the created framework on choices and rank performance analysis. The study results in linear sum and vector normalization are suitable normalization techniques with AHP as well as Shannon's entropy weight (SEW) criteria weight methodology with Spearman's rank correlation coefficients as 0.8333 and 0.9285, respectively, which shows consistency of the rank of alternatives with the results obtained by previous researchers. This study can be used to select suitable normalization and criteria weight methods for the m-polar fuzzy set ELECTRE-I algorithm while solving multi-criteria decision-making problems. The framework is intended to address various industrial problems with many criteria, including the selection of robots, non-traditional machining techniques, and various industrial challenges without considering their subgroups. [ABSTRACT FROM AUTHOR]

In multi-antenna systems, advanced techniques such as massive multiple-input multiple-output (MIMO), beamforming, and beam selection depend heavily on the accurate acquisition of the channel state. However, pilot contamination (PC) can be a major source of interference which degrades they are performance. Moreover, the severity of PC increases as more pilots are reused between users in the wireless systems. Researchers have shown that PC can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing PC, the examination that identifies the applications and factors that distinguish these DL approaches is still limited. This paper reviews these DL approaches and the improvements needed to enhance their performance. Simulation results confirm that DL networks that learn to predict the channels directly have superior performance under PC. [ABSTRACT FROM AUTHOR]

The launch of large language models (LLMs) like ChatGPT in late 2022 and the anticipated arrival of future GPT-x iterations have marked the beginning of the generative artificial intelligence (GAI) era. We conducted a systematic review of how to integrate LLMs including GPT and other GAI models into geospatial science, based on 293 papers obtained from four databases of academic publications -- Web of Science (WoS), Scopus, SSRN and arXiv -- 26 papers were eventually included for analysis. We statistically outlined the share of domains where LLMs and other GAI models, the type of data that have been used for these models, and the modelling tasks and roles that they play. We also pointed out the challenges and future directions for the next research agenda -- along with which we could better position ourselves in the mainstream of science and the cutting-edge research paradigm as others leverage insights from the growing data deluge. [ABSTRACT FROM AUTHOR]

Background: Recent individual studies have indicated that ultra-processed food (UPF) consumption may be associated with the incidence of chronic kidney disease (CKD). We conducted a systematic review and meta-analysis based on those longitudinal studies evaluating the relationship between UPF consumption and the risk of incident CKD, and synthesizing the results. Method: PubMed, Embase, The Cochrane Library, Web of Science, and Scopus were searched from inception through 22 March 2023. Any longitudinal studies evaluating the relationship between UPF consumption and the risk of incident CKD were included. Two researchers independently conducted the literature screening and data extraction. RR and its 95% CI were regarded as the effect size. The Newcastle–Ottawa Scale (NOS) was applied to assess the quality of the studies included, and the effect of UPF consumption on the risk of incident CKD was analyzed with STATA version 15.1. This study’s protocol was registered in PROSPERO (CRD42023411951). Results: Four cohort studies with a total of 219,132 participants were included after screening. The results of the meta-analysis suggested that the highest UPF intake was associated with an increased risk of incident CKD (RR = 1.25; 95% CI: 1.18–1.33). Conclusions: High-dose UPF intake was associated with an increased risk of incident CKD. However, the underlying mechanisms remain unknown. Thus, more standardized clinical studies and further exploration of the mechanisms are needed in the future. [ABSTRACT FROM AUTHOR]

Objective: Shenkang injection (SKI) has been widely used in China for many years for the treatment of kidney disease. The objective of this systematic review was to assess the efficacy of Shenkang injection for the treatment of acute kidney injury (AKI). Methods: A search was conducted across seven databases, encompassing data from the inception of each database through October 8th, 2023. Randomized controlled trials comparing SKI-treated AKI patients with control subjects were extracted. The main outcome measure was serum creatinine (SCr) levels. Secondary outcomes included blood urea nitrogen (BUN), serum cystatin C (CysC), 24-h urine protein (24h-Upro) levels, APACHE II score and adverse reactions. Results: This meta-analysis included eleven studies, and the analysis indicated that, compared with the control group, SKI significantly decreased SCr [WMD=−23.31, 95% CI (-28.06, −18.57); p<0.001]; BUN [WMD=−2.07, 95% CI (-2.56, −1.57); p<0.001]; CysC [WMD=−0.55, 95% CI (-0.78, −0.32), p<0.001]; 24-h urine protein [WMD=−0.43, 95% CI (-0.53, −0.34), p<0.001]; and the APACHE II score [WMD=−3.07, 95% CI (-3.67, −2.48), p<0.001]. There was no difference in adverse reactions between the SKI group and the control group [RR = 1.32, 95% CI (0.66, 2.63), p=0.431]. Conclusion: The use of SKI in AKI patients may reduce SCr, BUN, CysC, 24-h Upro levels, and APACHE II scores in AKI patients. The incidence of adverse reactions did not differ from that in the control group. Additional rigorous clinical trials will be necessary in the future to thoroughly evaluate and establish the effectiveness of SKI in the treatment of AKI. [ABSTRACT FROM AUTHOR]