Similar Ranking Research Articles

Comparison of Criteria Weight Determination Using MEREC and CRITIC Methods in Choosing The Best Student Accommodation with the MOORA Method Case Study: Coventry University

One of the challenges faced by IISMA Awardees and students in general in Coventry University is choosing a comfortable place to live. Although various student accommodations are provided, differences in facilities and considerations from other parties such as parents and friends make the selection process complicated. This study develops a decision support system to help students choose student accommodation objectively without any intervention from others and provides a comparison of the use of different combinations of methods as additional guidance in the decision-making process. Two methods, Method Based on the Removal Effects of Criteria (MEREC) and Criteria Importance Through Intercriteria Correlation (CRITIC), are used to weight the criteria. The Multi-Objective Optimization (MOORA) method is used to determine the best alternative after the weight calculation is known. The results using a combination of the MEREC-MOORA method and a combination of the CRITIC-MOORA method place Alternative 5 (A5) in first place, while the remaining alternatives show a similar ranking order. In this study, scenario testing was also carried out by deleting and adding criteria and alternatives which then provided ranking results with a positive correlation even though different combinations of methods were used in determining the ranking.

Going circular with what we wear and how we build: parallelisms between the Dutch and French catwalks of fashion and construction

Responding to the call for circular transition, the Dutch and French governments aspire to achieve a fully circular economy by developing a transitional agenda in various sectors, including fashion and construction. The two countries are among the top 10 waste generators in the European Union (EU), while the two sectors—fashion and construction—are the largest polluters in the EU. The aim of this study, and its main contribution, is to harmonize circular design principles, which vary by sector into common types, and identify the circular design principle that balances the sustainability dimensions the most. This responds to research gaps that merely describe these design principles applicable to different sectors but which are also silent on which achieves sustainability balance. Using multicriteria decision analysis, selected case studies of companies in the two sectors and countries were scored and ranked according to environmental, economic, and social sustainability indicators. The case projects were selected based on the circular design principle that the enterprises were applying. These principles were standardized for the two sectors to come up with five distinct types, namely, design for (i) biobased materials, (ii) service/adaptability, (iii) disassembly, (iv) waste and material recovery, and (v) longevity. Three forms of triangulation were used to achieve reliability, validity, and equivalence of the findings: (i) data—by doubling the size of the sample cases to 40 establishments from 20, (ii) investigator—by having the authors score the projects separately, and (iii) method—by using two objective weighting methods in scoring the criteria. These techniques resulted in similar rankings of the cases in terms of triple bottom-line scores per design principle. Designing for biobased materials turned out to achieve the most balance. The case projects were also compared regarding performance in achieving the UN Sustainability Development Goals (SDGs), which companies use to integrate sustainability with business. Circularity in fashion and construction primarily targets responsible consumption, production, and climate action. A tertiary SDG was life on water and access to clean water for fashion and life on land and sustainable cities for construction.

Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning

Abstract Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1,000 scientific papers focused on biological materials into a comprehensive ontological knowledge graph. Through an in-depth structural analysis of this graph, we have calculated node degrees, identified communities along with their connectivities, and evaluated clustering coefficients and betweenness centrality of pivotal nodes, uncovering fascinating knowledge architectures. We find that the graph has an inherently scale-free nature, shows a high level of connectedness, and can be used as a rich source for downstream graph reasoning by taking advantage of transitive and isomorphic properties to reveal insights into unprecedented interdisciplinary relationships that can be used to answer queries, identify gaps in knowledge, propose never-before-seen material designs, and predict material behaviors. Using a large language embedding model we compute deep node representations and use combinatorial node similarity ranking to develop a path sampling strategy that allows us to link dissimilar concepts that have previously not been related. One comparison revealed detailed structural parallels between biological materials and Beethoven's 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. In another example, the algorithm proposed an innovative hierarchical mycelium-based composite based on integrating path sampling with principles extracted from Kandinsky's `Composition VII' painting. The resulting material integrates an innovative set of concepts that include a balance of chaos and order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a context-dependent heterarchical interplay of constituents. Because our method transcends established disciplinary boundaries through diverse data modalities (graphs, images, text, numerical data, etc.), graph-based generative AI achieves a far higher degree of novelty, explorative capacity, and technical detail, than conventional approaches and establishes a widely useful framework for innovation by revealing hidden connections.

Ecotoxicological assessment of waste foundry sands and the application of different classification systems.

The application of a battery of bioassays is widely recognized as a useful tool for assessing environmental hazard samples. However, the integration of different toxicity data is a key aspect of this assessment and remains a challenge. The evaluation of industrial waste leachates did not initially undergo any of the proposed integration procedures. This research addressed this knowledge gap. Twenty-five samples of waste foundry sands were subjected to a leaching test (UNI EN 12457-2) to evaluate waste recovery and landfill disposal. The leachates were evaluated using a battery of standardized toxicity bioassays composed of Aliivibrio fischeri (EN ISO 11348-3), Daphnia magna (UNI EN ISO 6341), and Pseudokirchneriella subcapitata (UNI EN ISO 8692), both undiluted and diluted. Daphnia magna and P. subcapitata were the most affected organisms, with significant effects caused by 68% and 64% of undiluted samples, respectively. The dilution of samples facilitates the calculation of EC50 values, which ranged from greater than the highest concentration tested to 2.5 g/L for P. subcapitata. The data on single-organism toxicity were integrated using three methods: the Toxicity Classification System, the toxicity test battery integrated index, and the EcoScore system. The three classifications were strongly similar. According to all applied systems, three samples were clearly nontoxic (from iron casting plants) and two were highly toxic (from steel casting plants). Moreover, the similar ranking between undiluted and diluted leachates suggests the possibility of using only undiluted leachates for a more cost-effective and time-efficient screening of waste materials. The findings of this study highlight the usefulness of integrating ecotoxicological waste assessment. Integr Environ Assess Manag 2024;00:1-18. © 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Zero-Shot Content-Based Crossmodal Recommendation System

Information Recommendation (IR) systems are conventionally designed to operate within a single modality at a time, such as Text2Text or Image2Image. However, the concept of cross-modality aims to facilitate a versatile recommendation experience across different modalities, such as Text2Image. In recent years, significant strides have been made in developing neural recommender models that are multimodal and capable of generalizing across a broad spectrum of domains in a zero-shot manner, thanks to the robust representation capabilities of neural networks. These architectures enable the generation of embeddings for assets (i.e., content uploaded on a platform by users), presenting a concise representation of their semantics and allowing for comparisons through similarity ranking. In this paper, we present ZCCR, a Zero-shot Content-based Crossmodal Recommendation System that leverages knowledge from large-scale pretrained Vision-Language Models (VLMs) such as CLIP and ALBEF to redefine the recommendation task as a zero-shot retrieval task, eliminating the need for labeled data or prior knowledge about the recommended content. Furthermore, ZCCR performs crossmodal similarity search on an optimized index, such as FAISS, to improve the speed of recommendation. The goal is to recommend to the user assets that are similar to those they have previously uploaded, commonly referred to as their user profile. Within the user profile, we identify “areas of interest”—groups of assets associated with specific user interests, such as cooking, sports, or cars. To identify these areas of interest and construct the search query for the retrieval operation, ZCCR employs an innovative use of Agglomerative Clustering. This technique groups user past assets by similarity without requiring prior knowledge of the number of clusters. Once the areas of interest, or clusters, are identified, the centroid is utilized as the search seed to find similar assets. Experimental results demonstrate the efficiency of the selected components in terms of search time and retrieval performance on modified MSCOCO and FLICKR30k datasets tailored for the recommendation task. Furthermore, ZCCR outperforms both a baseline tagging system (BT) and a more advanced tag system which utilizes a Large Language Model (LLM) to extract embeddings from tags. The results show that, even compared with the latter, embeddings directly extracted from raw assets yield superior outcomes compared to relying on intermediate tags generated by other tools. The code implementation to reproduce all experiments and results shown in this paper is provided at the following link: ZCCR-experiments.

Insight into emission reduction effect of coal and biomass mixed briquette fuel

The use of coal and biomass as fuel is still the dominant method for domestic heating and cooking worldwide, particularly in rural areas, leading to degenerate atmospheric quality and human health. Briquette technology is regarded as an efficient way to suppress pollutant emissions from solid fuel combustion. In this study, a novel method for coal briquette (for both bituminous and anthracite) is presented in which biomass is added as an additive (0–30%) in producing clean coals. The comprehensive evaluations of these briquettes were done by measurements of the major pollutants, including particulate matter (PM), elemental and organic carbon (EC and OC), inorganic ions, volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs). Emission factors (EFs) for fine PM (PM2.5) and total carbon exhibited similar ranks between all coal groups, while the bituminous group yielded relatively higher EFs for the pollutants. The briquette technology showed a certain reduction effect in the bituminous group. The emission of coals, biomass, and their briquettes showed a substantial difference (>0.75) according to the coefficient of divergence. The EFs for total VOCs in bituminous, anthracite, and biomass groups ranged from 4.53 ± 0.45–23.4 ± 3.84, 0.68 ± 0.02–5.05 ± 0.02, and 140 ± 1.02 g/kg, respectively. An obvious watershed occurred in bituminous briquette with 15% biomass-added that increased emission beyond the additive amount, which highlighted the importance of mixing ratios for emission reduction. Compared to estimated emissions, the mix of biomass and coals through the combustion process released inorganic and organic species with different levels than the direct biomass and anthracite combustion emission, and around 15% is the suitable amount of biomass added to achieve the desired effect. The key findings of briquette technology provide an effective way for solid fuel combustion to alleviate the associated negative impacts and promotion value in developing regions, especially in rural areas.

An integrated Bayesian-principal component approach to macroeconomic resilience: the case of the Central Europe and Baltic macro-region

PurposeIn the fashion of Martin (2012), we develop an innovative application to a standard, well-grounded methodology to investigate resilience in two critical dimensions: recovery and resistance. Our novel approach allows us to investigate the resilience performance to the 2008 financial crisis within countries of this macro-region according to their shock isolation and absorptive capacities.Design/methodology/approachBy individually estimating six open economy DSGE models within the Central Europe and Baltic macro-region, we identify the business-cycle-volatility drivers for each country. Then, we use the outcome of our six estimates to conduct a principal component analysis to determine structural common characteristics required to explain economic resilience in the CEB macro-region.FindingsIn terms of resilience, Central European economies exhibit quite similar paths in terms of recovery, meaning they have similar economic structures. By contrast, Baltic countries behave differently, being outliers in opposite extreme positions. The contrary occurs for resistance: Baltic countries share a similar ranking, whereas Central European economies exhibit substantial differences.Research limitations/implicationsIt is important to acknowledge that a limitation of the analysis is that we explicitly consider each country as a stand-alone open economy which are subject to stochastic disturbances. Precisely, we do not model trade or other interactions across countries within the CEB region and with the rest of the world. Consequently, spillover effects in the aftermath of the shock are not accounted for.Originality/valueWe estimate the relative vulnerability or sensitivity of economies within the macro-region to disturbances and disruptions (resistance) and the speed and extent of recovery from such a disruption or recession (recovery). First, we built two different kinds of measures of resilience by aggregating the estimated parameters through non-centered and centered principal component analysis. Then, we use our model to investigate the relation between financial shock and the economic resilience across the region. The approach can be applied to several case studies, parsimoniously.

Music Similarity Ranking: Case Study Fairphonic

Fairphonic Pte Ltd is a technology company operating in the music sector. Fairphonic provides services to detect content on social media that is suspected of copyright infringement. Fairphonic utilizes audio features for the detection process. The current algorithm used by Fairphonic requires pairwise comparison, and the content to be compared is collected through a scraping process immediately after the process is run. Fairphonic has hundreds of thousands of music data in their database. Fairphonic desires a more scalable algorithm to compare an input music piece with the entire Fairphonic music catalog. This research uses features such as Harmonic Pitch Class Profile (HPCP), Chroma, and Rhythm Pattern. The study compares previously researched algorithms, namely binary similarity matrix, Euclidean distance, and similarity matrix profile. The results show that the combination of HPCP with the binary similarity matrix yields the highest Mean Average Precision of 0.989. Speed testing by performing comparisons 10 times shows that the combination of Chroma and the similarity matrix profile is 72% faster compared to the combination of HPCP with the binary similarity matrix. The author recommends the Chroma and similarity matrix profile algorithm for music similarity ranking due to its faster process.

Life cycle assessment and multi-criteria decision-making for sustainable building parts: criteria, methods, and application

PurposeSustainable building design relies heavily on building parts, with crucial consideration for climate and environmental impact. Due to numerous criteria and diverse alternatives, employing multi-criteria decision-making (MCDM) to choose the best alternative is essential. Yet, relevant criteria and suitable MCDM methods for life cycle-based building planning still need to be determined. This study highlights prevalent environmental criteria and offers guidance on MCDM approaches for sustainable building parts.MethodsThis study introduces an innovative approach by integrating life cycle assessment and MCDM. This provides comprehensive decision support for planners. A systematic literature review identifies environmental criteria for building parts and is validated in expert workshops. Thus, the relevance of criteria across the building life cycle is established. Furthermore, the study analyzes MCDM approaches in the built environment. From this, the study employs and evaluates the Analytical Network Process (ANP) and Analytical Hierarchy Process (AHP) in a case study. Thereby, it offers insights into effective decision-making methodologies for sustainable building practices.ResultsThis research categorizes environmental criteria for building parts and buildings into emissions, energy, resources, and circularity. Among 26 building part-related criteria, the global warming potential is highlighted. While the AHP is widely used in MCDM, a standardized method in planning processes is yet to emerge. Applying the ANP and AHP reveals similar rankings for the best and worst alternatives in a case study focused on selecting the optimal ceiling structure. Ribbed or box slab ceiling constructions are favored over reinforced concrete and composite timber-concrete constructions.ConclusionsThis study presents a novel method for life cycle-based MCDM challenges, identifying key environmental criteria. While material correlations exist, evaluating building parts demands simultaneous consideration of multiple criteria. Future research aims to compare further MCDM methods regarding their applicability, transparency, and ranking to enhance decision-making in sustainable construction. These investigations are essential for refining decision-making processes in the built environment, ensuring effective and transparent sustainability planning approaches.

Species-specific behavioural responses to environmental variation as a potential species coexistence mechanism in ants.

A fundamental question of ecology is why species coexist in the same habitat. Coexistence can be enabled through niche differentiation, mediated by trait differentiation. Here, behaviour constitutes an often-overlooked set of traits. However, behaviours such as aggression and exploration drive intra- and interspecific competition, especially so in ants, where community structure is usually shaped by aggressive interactions. We studied behavioural variation in three ant species, which often co-occur in close proximity and occupy similar dominance ranks. We analysed how intra- and allospecific aggression, exploration and foraging activity vary under field conditions, namely with temperature and over time. Behaviours were assessed for 12 colonies per species, and four times each during several months. All behavioural traits consistently differed among colonies, but also varied over time and with temperature. These temperature-dependent and seasonal responses were highly species-specific. For example, foraging activity decreased at high temperatures in Formica rufibarbis, but not in Lasius niger; over time, it declined strongly in L. niger but much less in F. rufibarbis. Our results suggest that, owing to these species-specific responses, no species is always competitively superior. Thus, environmental and temporal variation effects a dynamic dominance hierarchy among the species, facilitating coexistence via the storage effect.

LSAT® scores of economics majors: The 2022–23 class update and 7-year history

In this article, the author updates his prior studies of LSAT® scores (Nieswiadomy 1998, 2006, 2010, 2014, 2017) using current data for 2022–23 law school applicants, finding that economics majors remain at or near the top of all applicants. Results of the previous studies showing economics majors scored well on the LSAT® have often been posted on economics (and other) departments’ Web sites. Economics majors (mean LSAT® score of 161.7) had the highest score of the 20 largest disciplines (in terms of the number of law school applicants). Economics places second behind math/physics (164.3) out of 30 discipline groupings created to contain students with similar majors. Also, data from the past seven years (2016–17 through 2022–23) reveal similar rankings.

A Novel Method for Mining Regulatory sRNAs Related to Rice Resistance Against Blast Fungus from Multi-Omics Data

Background: Due to infection by the rice blast fungus, rice, a major global staple, faces yield challenges. While chemical control methods are common, their environmental and economic costs are growing concerns. Traditional biological experiments are also inefficient for exploring resistance genes. Therefore, understanding the interaction between rice and the rice blast fungus is urgent and important. Objective: This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus Magnaporthe oryzae. We built a detailed, multi-layered heterogeneous interaction network, employing an innovative graph embedding feature with a cross-layer random walk algorithm to identify crucial crucial resistance factors.This could inform strategies for enhancing disease resistance in rice. objective: This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus Magnaporthe oryzae. We built a detailed, multi-layered heterogeneous interaction network, employing an innovative graph embedding feature with a cross-layer random walk algorithm, to identify crucial crucial resistance factors. This could inform strategies for enhancing disease resistance in rice. Methods: We integrated genomics, transcriptomics, and proteomics data on Magnaporthe oryzae infecting rice. This multi-omics data was used to construct a multi-layer heterogeneous network.An advanced graph embedding algorithm (BINE) provided rich vector representations of network nodes. A multi-layer network walking algorithm was then used to analyze the network and identify key regulatory small RNA (sRNAs) in rice. Results: Node similarity rankings allowed us to identify significant regulatory sRNAs in rice that are integral to disease resistance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses further revealed their roles in biological processes and key metabolic pathways.Our integrative method precisely and efficiently identified these crucial elements, offering a valuable systems biology tool. Conclusion: By integrating multi-omics data with computational analysis, this study reveals key regulatory sRNAs in rice's disease resistance mechanism. These findings enhance our understanding of rice disease resistance and provide genetic resources for breeding disease-resistant rice. Despite limitations in sRNA functional interpretation, this research demonstrates the power of applying multi- omics data to address complex biological problems.

Hospital Rating Organizations' Quality and Patient Safety Scores: Analysis of Result Discrepancies.

In the USA, multiple organizations rate hospitals based on quality and patient safety data, but few studies have analyzed and compared the rating results. Compare the results of different US hospital-rating organizations. Observational data analysis of US acute care hospital ratings. Four rating organizations: Hospital Compare® (HC), Healthgrades® (HG), The Leapfrog Group® (Leapfrog), and US News and World Report® (USN). We analyzed the level of concordance (similar ranking), discordance (difference of 1 or more rankings), and severe discordance (difference of two or more rankings), as well as differences and correlations between the scores. From Feb 1 to Oct 3, 2023, we analyzed data from 2,384 hospitals. In Leapfrog, there were 688 hospitals (29%) with Grade A, 652 (27.3%) with B, 885 (37.1%) with C, 153 (6.4%) with D, and 6 (0.3%) with F. For HC, 333 hospitals (14%) had five stars, 676 (28.4%) four, 695 (29.2%) three, 502 (21.4%) two, and 171 (7.2%) one-star. In ratings between HC and Leapfrog, discordance was 70%, and severe discordance was 25.1%. USN ranked 469 hospitals (19.7%). Within the USN-ranked hospital group, there was a 62% discordance and 19.8% severe discordance between HC and Leapfrog. The analysis of orthopedic procedures from HG and USN showed discordance ranging from 48 to 61.2%. The rating organizations' reported metrics were highly discordant. A hospital's ranking by one organization frequently did not correspond to a similar ranking by another. The methodology and included timeline and patient population can help explain the differences. However, the discordant ratings may confuse patients and customers.

SKYPER: Legal case retrieval via skeleton-aware hypergraph embedding in the hyperbolic space

Legal Case Retrieval (LCR) plays a significant role in ensuring judicial justice in various legal systems and has recently received increasing attention in Information Retrieval (IR) research. Existing LCR methods typically encode documents into low-dimensional vectors by using either bag of keywords or pretrained language models. However, such approaches generally overlook the interactions among numerous cases and the hierarchy skeleton between event and event type. In this paper, we propose to construct an event-aware knowledge hypergraph for representing legal cases. And we put forward a SKeleton-aware hYPERgraph representation framework named SKYPER, to learn the case embeddings. Concretely, a global skeleton aggregates coarse-grained type information and roughly locates a case. A local skeleton learns the fine-grained information and precisely locates a case. And SKYPER also uses hyperbolic space strengthens the hierarchy between event and event type and avoids vocabulary mismatch. At the retrieval stage, the SKYPER learned case embedding is leveraged for further case similarity matching with a few annotated similarity ranking data. Extensive experiments demonstrate that SKYPER outperforms existing models on the public LCR datasets LeCard and COLIEE20-T1 in both supervised and unsupervised settings. Especially, the retrieval efficiency is improved by a large margin.

Whole exome sequencing reveals diverse genomic relatedness between paired concurrent endometrial and ovarian carcinomas

IntroductionConcurrent non-serous endometrial and ovarian tumours are often managed clinically as two separate primary tumours, but almost all exhibit evidence of a genomic relationship. MethodologyThis study investigates the extent of relatedness using whole exome sequencing, which was performed on paired non-serous endometrial and ovarian carcinomas from 27 patients with concurrent tumours in a cohort with detailed clinicopathological annotation. Four whole exome sequencing-derived parameters (mutation, mutational burden, mutational signatures and mutant allele tumour heterogeneity scores) were used to develop a novel unsupervised model for the assessment of genomic similarity to infer genomic relatedness of paired tumours. ResultsThis novel model demonstrated genomic relatedness across all four parameters in all tumour pairs. Mutations in PTEN, ARID1A, CTNNB1, KMT2D and PIK3CA occurred most frequently and 24 of 27 (89 %) tumour pairs shared identical mutations in at least one of these genes, with all pairs sharing mutations in a number of other genes. Ovarian endometriosis, CTNNB1 exon 3 mutation, and progression and death from disease were present across the similarity ranking. Mismatch repair deficiency was associated with less genomically similar pairs. DiscussionAlthough there was diversity across the cohort, the presence of genomic similarity in all paired tumours supports the hypothesis that concurrent non-serous endometrial and ovarian carcinomas are genomically related and may have arisen from a common origin.

Design and Discovery of Apigenin-based Drugs as a Potential Casein Kinase II Inhibitor for Breast Cancer through Hybrid In-silico Methods

This study investigates the potential of apigenin and derivatives as casein kinase II inhibitors for breast cancer treatment by a hybrid in-silico approach. Drug-drug transcriptomic similarity analysis reveals correlations with 17 drugs, guiding the identification of perturbagens with major transcriptional impressions. Transcriptional impact analysis across cell lines underscores apigenin’s dose-dependent influence on gene expression, particularly in breast cancer cells. Protein pre-preparation and molecular docking, refined by PDB REDO, showcase improved model quality and favorable binding affinities. Notable findings include apigenin’s potency as a CK2 inhibitor and promising compounds like dextromethorphan. Drug-drug transcriptomic similarity rankings identify potential candidates for further investigation. In conclusion, this integrated approach lays the groundwork for targeted therapies in breast cancer.

Sensitivity analysis of the parameters for preference functions and rank reversal analysis in the PROMETHEE II method

The PROMETHEE II method is a classical multiple criteria decision making method. However, it also exists the rank reversal which is a highly important problem for analyzing the reliability of a MCDM method. The main objective of this study is to analyze the sensitivity of the parameters for preference functions and the rank reversal problem in the PROMETHEE II method. By analyzing the parameters for preference functions from the standpoint of theoretics, a method is proposed to calculate the ranges of the parameters for four types of preference functions to remain the ranking of all the alternatives unchanged. Second, the sufficient and necessary condition of the rank reversal is obtained in the PROMETHEE II method when there are only three types of criteria, i.e., usual criteria, U-shape criteria and level criteria. Finally, two minor modification methods for the PROMETHEE II method itself are proposed by observing the net outranking flow formula. Numerical simulations show that the occurrence of the rank reversal is clearly reduced and the ranges of fault tolerance of the parameters for preference functions are significantly larger for each new modified PROMETHEE II method. The similarity of rankings is tested by using the similarity rank coefficient WS. This indicates the rationality of the two proposed modifications.

Factors Impacting Provider Treatment Decision-Making in Early Glottic Cancer.

Transoral laser microsurgery (TLMS) and radiotherapy (XRT) are mainstays of treatment for early glottic carcinoma (EGC). Here, we investigated case-dependent provider treatment preferences and identify factors which impact decision-making in EGC. This cross-sectional survey of laryngologists, head-and-neck surgeons, and radiation oncologists presented five diagrammatic cases of progressively advanced EGC (T1/2, N0). Respondents indicated preference for TLMS or XRT and ranked factors which influenced their recommendation for each case. Analysis utilized descriptive statistics, Fischer's exact tests, and Kruskal-Wallis tests for nonparametric data. A total of 141 complete responses (69.5% laryngologists) were received. Most respondents practiced in academic settings (93.5%) and within multidisciplinary teams (94.0%). Anterior commissure involvement was the most important a priori tumor factor for case-independent treatment recommendation (Likert Scale: 4.22/5), followed by Laterality (Likert Scale: 4.02/5). Across all specialties, TLMS was recommended for unilateral T1a lesions. Laryngologists continued recommending TLMS in T2 lesions (41.0%) more than head-and-neck surgeons (5.0%) and radiation oncologists (0.0%). Across all cases, survival and voice outcomes were the most important clinical factors impacting treatment decisions. Radiation oncologists weighed voice more heavily than laryngologists in more complex presentations of EGC (rank: 1.6 vs. 2.7, Kruskall-Wallis: p < 0.05). In more complex clinical presentations of EGC, preference for TLMS compared to XRT differed across specialists, despite similar rankings of factors driving these treatment recommendations. This may be driven by differing experiences and viewpoints on case-dependent voice outcomes following TLMS versus XRT, suggesting a need for increased understanding of how tumor location and depth impact voice outcomes. 5 Laryngoscope, 134:3686-3694, 2024.

Strategic Decision-Making in Swiss-System Chess Tournaments

This study delves into the decision-making strategies of chess players in the final round of Swiss-system chess tournaments. This paper first introduces the basic rules and characteristics of Swiss-system tournaments, as well as the objectives of players in the competition. Subsequently, this paper analyzes the strategies adopted by players in the final round under different circumstances, including scenarios without tied players, with tied players but leading or trailing in rankings, and situations where players are tied and have similar rankings. Through theoretical analysis and validation with empirical data, this paper derives optimal strategies for players in different situations to ensure achieving the highest possible ranking in the final round. Starting with a simple four-player championship model, the study provides important insights and guidance for player decision-making in Swiss-system chess tournaments, while also offering valuable exploration into understanding the decision-making process of players. The strategies are well discussed in Section 3.

New information measures for linear Diophantine fuzzy sets and their applications with LDF-ARAS on data storage system selection problem

In addition to membership and non-membership degrees, linear Diophantine fuzzy set (LDFS) incorporates two reference parameters in fuzzy set definition to represent human judgment more comprehensively. It also provides flexibility in updating the decision setting by changing the meaning of the reference parameters when required by the decision-maker. Information measures in fuzzy sets have many application areas in the literature. Especially, distance and entropy measures are utilized in multiple attribute decision-making (MADM) field. Distance measures are needed by distance-based methods like TOPSIS and EDAS because their alternative ranking procedure is based on measuring the distances between alternatives and some artificial ideal or average solutions. Entropy measure is a very important tool being used in objective attribute weighting procedures of MADM applications. Thus, new distance and entropy measure propositions for LDFS are the first contribution of the study. Also, Additive Ratio Assessment (ARAS) method is extended into LDFS (LDF-ARAS) for the first time in the literature as a second contribution. In this extension, we selected ARAS as one of the most cited tools because it is found flexible and easily adaptable by researchers who do not have any background in MADM. The proposed entropy measure is integrated with the objective attribute weighting procedure of the novel LDF-ARAS approach. The third contribution is the newly developed decision model proposed for a real-life data storage system (DSS) selection problem. Three DSS options were evaluated with respect to thirteen attributes by five skilled and well-educated experts and LDF-ARAS was performed to rank alternatives. The entropy-based LDF-ARAS approach was validated by comparing the results of six different applications. In the first three, different entropy formulas are used and in the last three, these entropy measures are integrated with LDF-TOPSIS. All six comparisons gave similar alternative rankings.