Terms Of Set Research Articles

Waste-to-energy incineration site selection framework based on heterogeneous fuzzy regret-PROMETHEE model considering life-cycle carbon emissions.

Waste incineration technology has received extensive attention for its advantages of being harmless, reducing, and recycling. However, the waste-to-energy incineration project confronts significant "not-in-my-backyard (NIMBY) concerns," and irrational location choices will have negative effects on the project's economy and sustainability; it is also a great challenge to the credibility of the government. To this end, a multi-criteria decision-making framework is constructed for the site selection of waste-to-energy incineration projects. To begin with, a site selection criteria system is established including 16 sub-criteria from four aspects, where probabilistic linguistic term sets are introduced to depict the qualitative sub-criteria and probabilistic hesitant fuzzy sets are employed to express the uncertainty of quantitative sub-criteria. An optimization model is then built to determine the weights of criteria based on the Pearson correlation coefficient and least square method. Furthermore, a regret-preference ranking organization methods for enrichment evaluations (PROMETHEE) model is presented to rank alternatives in a heterogeneous decision environment. Finally, a case study in China is conducted to validate the applicability of the proposed framework; the result of the site selection demonstrates that alternative A2 located in Miyun, Beijing, is the most optimal option. This work provides investors with scientific decision reference and also extends the methods in the decision-making field.

A new approach to neural network via double hierarchy linguistic information: Application in robot selection

Robotization is necessary to keep up with the constant changes in production, which calls for a staff with robotics expertise. A manufacturing business must also have the ability to swiftly change its production method. But today the procedure is drawn-out and complicated. In this study, inverse kinematics functionality and a machine learning model have been used to simulate an industrial robot’s movement in a digital environment. By using machine learning, less time and money must be invested in developing the procedure and determining the robot’s route. In this article, feed-forward double hierarchy linguistic neural networks with estimation information for double hierarchy linguistic term sets are proposed. First defined were the Yager operational rules and Yager aggregation operators for the double hierarchy linguistic terms set. Following that, we’ll discuss fuzzy neurons, feed-forward neural networks, simple neural networks, hybrid neural networks, and the sigmoid function. After that, explain feed-forward, double-hierarchy linguistic neural networks, including how their output is calculated. The weight vector of expert’s information is calculated by using the entropy measure with the help of Yager aggregation operators. Finally, we use the Yager t-norms to determine the output date of feed-forward double hierarchy linguistic neural networks and also find the output data. Linguistic neural network with Yager T-norms apply to the Robot selection for manufacturing bussing. The proposed approach of linguistic neural network are compared with Extended TOPSIS methods and GRA method for ranking.

Evaluation of Covid-19 Vaccine Sentiment on TikTok from 2020 to 2022

Purpose: The unprecedented spread of misinformation during the COVID-19 pandemic was largely driven by social media platforms. Public understanding of masking, viral transmission, treatment options for COVID, and vaccination were distorted by the influence of social media platforms. The pandemic saw a rise of TikTok, another social media platform, whose role in the dissemination of misinformation has been far less studied. The relatively new social medial platform boasts millions of monthly U.S. users, with a large portion of those users being impressionable children, teens, and young adults. Given its popularity, its role in the spread of misinformation is likely substantial. The purpose of this study is to assess the quantity of COVID misinformation on TikTok in the period of 2020 to 2022, looking specifically at the number of anti- and pro-vaccine videos appearing on the platform. By assessing the impact severity of the anti-vaccine content on TikTok, this study will be able to inform future public health efforts of these effects, with the goal of curtailing the spread of medical misinformation. Methods: Videos were collected from the app by the research team retrospectively using four sets of search terms that appear frequently in pro and anti-vaccine related content. Researchers judged the videos as anti- or pro-vaccine while recording metrics on the videos such as likes and bookmarks. A total of 284 videos were selected for analysis. Comparisons were made between likes and the other metrics using p-values for significance. Results: 109 anti- and 175 pro-vaccine videos were identified. A larger share of the data set was pro vaccine. There was no significant difference between the number of likes between anti- and pro-vaccine content. Conclusion: This study took a novel approach to TikTok by examining videos using multiple search terms over the course of a three-year period. The lack of statistical significance in number of likes between anti- and pro-vaccine content, despite there being more pro-vaccine videos is indicative of pervasive misinformation is on TikTok. Ambivalence over medical facts is not reassuring for any public health effort. Our analysis found a larger share of misinformation than previous studies, providing further evidence that a supra-dominant positive narrative on social media platforms is difficult to achieve, even though this is critical for health care initiatives.

Assessment of Typical Meteorological Year Data in Photovoltaic Geographical Information System 5.2, Based on Reanalysis and Ground Station Data from 147 European Weather Stations

The Photovoltaic Geographical Information System (PVGIS) is a web application that provides free access to solar radiation and temperature data, typical meteorological year (TMY) data, and to photovoltaic performance assessment tools for any place in most parts of the world. The PVGIS was originally developed over 20 years ago, and since then, it has been under continuous development. At present, there are two versions of the PVGIS online—the older version 5.1 and the newest version 5.2. PVGIS 5.2 includes substantial improvements compared to the previous version, e.g., the update of the underlying data sets both in terms of quality, resolution, and geographical coverage and the extension of the time period used. This paper focuses on comparing the TMYs (and more specifically the TMY time series of air temperature), coming from both PVGIS 5.1 and 5.2, with the TMY produced on the basis of ground station meteorological data and with the ground station data itself. The results show that whereas overall the errors and biases for most locations are within the expected range (mean stationRMSE 4.27), these differences increase in places with complicated topography, e.g., in the Alps (maximum stationRMSE 9.50).

A Systematic Decision-Making Approach for Quality Function Deployment Based on Hesitant Fuzzy Linguistic Term Sets

As a powerful tool for improving customer satisfaction, quality function deployment (QFD) can convert customer requirements (CRs) into engineering characteristics (ECs) during product development and design. Aiming to address the deficiencies of traditional QFD in expert evaluation, CRs’ weight determination and ECs’ importance ranking, this paper proposes an enhanced QFD model that integrates hesitant fuzzy binary semantic variables, the Best–Worst Method (BWM), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The objective is to determine the prioritization of product engineering characteristics. Indeed, hesitant fuzzy linguistic term sets (HFLTS) have found extensive application in decision-making problems. Compared to other fuzzy language methods, HFLTS offers greater convenience and flexibility in addressing decision-makers’ hesitations and uncertainties. Initially, the combination of hesitant fuzzy linguistic term sets with interval binary tuple language variables is employed to articulate the uncertainty in the assessment information provided by QFD team members. Subsequently, the improved BWM and TOPSIS methods based on HFLTS are used to improve the accuracy of the importance ranking of engineering characteristics by determining the weights of CRs and prioritizing ECs in two stages. Finally, the feasibility and effectiveness of the proposed method are validated through an illustrative example.

Modelling the dynamics of customer requirements considering their lability and sensitivity in product development

Enterprises are challenged with fulfilling continuously changing customer requirements (CRs) that are influenced by many uncertain factors. Existing research only modelled the dynamics of CRs (DoCRs) by focusing on the potential resultant effects of CR changes, while lacking insightful exploration of the change characteristics of CRs themselves. To fill this gap, an online review-based DoCRs modelling method is proposed considering the lability and sensitivity of CRs by analyzing the changes in customers’ perceptions of product features over time. First, CRs in online reviews are extracted through Latent Dirichlet Allocation. Second, Bidirectional Long Short-Term Memory is applied to measure customer sentiments in different periods towards specific product features. The probabilistic linguistic term sets are employed to characterize customer sentiment distributions which are then incorporated into the modelling of DoCRs. Third, a novel CRs classification cube is established by integrating the lability, sensitivity, and importance of CRs from an evolution-adapted perspective. Finally, the DoCRs of a smartphone is investigated as a case study to demonstrate and effectiveness of the proposed methodology. Several managerial implications for the selection and adjustment of product development strategy are provided.

AD Biological Domains—an evolving computational resource for big data classification in AD from the TREAT‐AD Consortium

AbstractBackgroundAlzheimer’s disease (AD) is a highly heterogenous disease with a multitude of subtypes and interacting biological systems emerging in a complex pattern of pathology, neurodegeneration and cognitive decline. In order to help organize large scale data analysis in AD in an unbiased manner, we developed a computational model of endophenotypes associated with AD. We call these models the AD Biological Domains.MethodThe Common Alzheimer’s Disease Research Ontology (CADRO) was leveraged in association with literature mining to identify 19 parent endophenotypes that are largely sufficient to cover AD related pathology. These endophenotypes are instantiated as large sets of manually curated gene ontology (GO) terms and linked genes. This framework enables researchers to unambiguously move from an identified gene to a larger disease‐associated biological phenotype.ResultWe defined 19 biological domains in terms of constitutive sets of GO terms. A gene‐set enrichment analysis has confirmed that these 19 biological domains cover almost all AD‐enriched GO terms, barring those that fall outside the reach of the biological domains due to lack of specificity in biological implication. The size of the biological domains ranges from over a thousand GO terms for ‘Proteostasis’ and ‘Synapse’, and just over a dozen for the terms ‘APP Metabolism’ and ‘Tau homeostasis’. There is a low level of cross‐domain utilization of common GO terms, as the biological domains are effectively siloed in their definition, however, at the level of gene annotation we see high levels of promiscuity across domains, potentially providing a molecular trail to find interdependencies between domains. These biological domains are currently being used across several NIA‐funded consortia, including TREAT‐AD, AMP‐AD, MODEL‐AD, and have been adopted by partnered resources such as the AD‐Atlas and Agora. The full list of biological domains and associated GO terms and genes are openly accessible through the AD Knowledge Portal.ConclusionThe biological domains are an open resource designed to provide a community driven definition of discrete areas of AD biology, allowing AD investigators to utilize a standard set of computationally accessible resources to define emerging transcriptomic and proteomic datasets in a unified manner, making cross study comparisons more informative.

AD Biological Domains—an evolving computational resource for big data classification in AD from the TREAT‐AD Consortium

AbstractBackgroundAlzheimer’s disease (AD) is a highly heterogenous disease with a multitude of subtypes and interacting biological systems emerging in a complex pattern of pathology, neurodegeneration and cognitive decline. In order to help organize large scale data analysis in AD in an unbiased manner, we developed a computational model of endophenotypes associated with AD. We call these models the AD Biological Domains.MethodThe Common Alzheimer’s Disease Research Ontology (CADRO) was leveraged in association with literature mining to identify 19 parent endophenotypes that are largely sufficient to cover AD related pathology. These endophenotypes are instantiated as large sets of manually curated gene ontology (GO) terms and linked genes. This framework enables researchers to unambiguously move from an identified gene to a larger disease‐associated biological phenotype.ResultWe defined 19 biological domains in terms of constitutive sets of GO terms. A gene‐set enrichment analysis has confirmed that these 19 biological domains cover almost all AD‐enriched GO terms, barring those that fall outside the reach of the biological domains due to lack of specificity in biological implication. The size of the biological domains ranges from over a thousand GO terms for ‘Proteostasis’ and ‘Synapse’, and just over a dozen for the terms ‘APP Metabolism’ and ‘Tau homeostasis’. There is a low level of cross‐domain utilization of common GO terms, as the biological domains are effectively siloed in their definition, however, at the level of gene annotation we see high levels of promiscuity across domains, potentially providing a molecular trail to find interdependencies between domains. These biological domains are currently being used across several NIA‐funded consortia, including TREAT‐AD, AMP‐AD, MODEL‐AD, and have been adopted by partnered resources such as the AD‐Atlas and Agora. The full list of biological domains and associated GO terms and genes are openly accessible through the AD Knowledge Portal.ConclusionThe biological domains are an open resource designed to provide a community driven definition of discrete areas of AD biology, allowing AD investigators to utilize a standard set of computationally accessible resources to define emerging transcriptomic and proteomic datasets in a unified manner, making cross study comparisons more informative.

Biological Domain Network Analysis Identifies Potential Triadic Interaction between Mitochondria, Nuclear Epigenetics, and Synaptic Viability

AbstractBackgroundThe AD endophenotypes initially described in the Common Alzheimer’s Disease Research Ontology (CADRO) were augmented and transformed into computationally implementable definitions for 19 discrete Biological Domains. These AD biological domains were employed in the development of molecular interaction networks depicting the signaling and protein‐protein interaction as a central bioinformatics resource for the TREAT‐AD consortium.Method19 AD biological domains were identified by iterative literature culling and model testing based on gene set enrichment analysis and gene ontology term identification. All 19 biological domains are defined by an exhaustive set of GO terms, allowing scientists everywhere to use the same criteria to define their large datasets in an unbiased manner. Network models were constructed from the leading‐edge genes from all AD‐risk enriched GO terms within each biological domain. The network models incorporate data from over 1700 transcriptomic brain studies and 1100 proteomic brains studies examining shifts in gene expression. These are employed as a reference tool for the formulation of specific hypotheses.ResultThe analysis of mitochondrial gene expression changes at both the transcriptomic and proteomic level identify deficits in both mitochondrial energy production and metabolism along with a decrease in acetyl‐CoA production, a necessary element for many reactions including histone acetylation. This suggests that mitochondrial metabolic tone may modify epigenetic regulation through lysine acetyltransferase (KATs) independently from ATP production. Examination of decrements in KAT activity from other model systems suggest a particularly robust impact on synaptic gene expression, implying a potential interaction between mitochondrial metabolism and synaptic gene expression independent of energy production. Intriguingly, some elements of a protein module identified by TREAT‐AD large‐scale deep proteomic analysis found to be most tightly associated with AD pathology and cognitive decline, are tightly associated with this model.ConclusionThe biological domains were constructed as a computational resource to facilitate hypothesis generation within the TREAT‐AD consortium, and here are implemented to highlight one emerging interaction between domains that may imply new mechanisms of disease regulation coupling between mitochondria and synaptic viability through shifts in epigenetic regulation.

Biological Domain Network Analysis Identifies Potential Triadic Interaction between Mitochondria, Nuclear Epigenetics, and Synaptic Viability

AbstractBackgroundThe AD endophenotypes initially described in the Common Alzheimer’s Disease Research Ontology (CADRO) were augmented and transformed into computationally implementable definitions for 19 discrete Biological Domains. These AD biological domains were employed in the development of molecular interaction networks depicting the signaling and protein‐protein interaction as a central bioinformatics resource for the TREAT‐AD consortium.Method19 AD biological domains were identified by iterative literature culling and model testing based on gene set enrichment analysis and gene ontology term identification. All 19 biological domains are defined by an exhaustive set of GO terms, allowing scientists everywhere to use the same criteria to define their large datasets in an unbiased manner. Network models were constructed from the leading‐edge genes from all AD‐risk enriched GO terms within each biological domain. The network models incorporate data from over 1700 transcriptomic brain studies and 1100 proteomic brains studies examining shifts in gene expression. These are employed as a reference tool for the formulation of specific hypotheses.ResultThe analysis of mitochondrial gene expression changes at both the transcriptomic and proteomic level identify deficits in both mitochondrial energy production and metabolism along with a decrease in acetyl‐CoA production, a necessary element for many reactions including histone acetylation. This suggests that mitochondrial metabolic tone may modify epigenetic regulation through lysine acetyltransferase (KATs) independently from ATP production. Examination of decrements in KAT activity from other model systems suggest a particularly robust impact on synaptic gene expression, implying a potential interaction between mitochondrial metabolism and synaptic gene expression independent of energy production. Intriguingly, some elements of a protein module identified by TREAT‐AD large‐scale deep proteomic analysis found to be most tightly associated with AD pathology and cognitive decline, are tightly associated with this model.ConclusionThe biological domains were constructed as a computational resource to facilitate hypothesis generation within the TREAT‐AD consortium, and here are implemented to highlight one emerging interaction between domains that may imply new mechanisms of disease regulation coupling between mitochondria and synaptic viability through shifts in epigenetic regulation.

Consensus Model of Social Network Group Decision-Making Based on Trust Relationship Among Experts and Expert Reliability

Due to people's increasing dependence on social networks, it is essential to develop a consensus model considering not only their own factors but also the interaction between people. Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making. This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making (SN-GDM). A concept named matching degree is proposed to measure expert reliability. Meanwhile, linguistic information is applied to manage the imprecise and vague information. Matching degree is expressed by a 2-tuple linguistic model, and experts' preferences are measured by a probabilistic linguistic term set (PLTS). Subsequently, a hybrid weight is explored to weigh experts' importance in a group. Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus. Finally, a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

Incomplete interval-valued probabilistic uncertain linguistic preference relation in group decision making

In group decision making (GDM), due to complexity of various factors, decision makers (DMs) often provide incomplete preference relations (PRs) in preference matrix. Interval-valued probabilistic uncertain linguistic term set (IVPULTS) is a flexible and accurate tool to depict evaluation information of experts. In this paper, we mainly propose a new concept pertaining to interval-valued probabilistic uncertain linguistic preference relation (IVPULPR) that applies the IVPULTS to preference relations. Firstly, some new basic theoretical concepts of IVPULTS are developed including ordered IVPULTS, normalization method and new expectation function. Secondly, we establish several goal programming models to estimate the unknown elements in incomplete IVPULPR and propose the expected additive consistency of IVPULPR. To improve the consistency level, two optimization models are constructed based on the idea of minimum adjustment. Thirdly, we derive the experts’ weights in terms of information uncertainty, where a new method to measure information uncertainty of IVPULTS is proposed. For the sake of improving group consensus, we construct a group consensus index (GCI) and two optimization models depending on the adjustment mechanism of expert weight. Finally, a complete GDM framework with incomplete IVPULPR is devised based on the analysis of IVPULPR consistency and group consensus. Through an experiment analysis by using an UCI dataset, we find that the proposed GDM model can not only precisely express fuzzy preference information of DMs, but also ensure achievement of acceptable consistency and group consensus under the condition of not changing the initial preference as much as possible.

Realmind: A Prospective and Retrospective Study to Characterize Real-World Use of Tafasitamab Plus Lenalidomide in US Patients with Relapsed/Refractory Diffuse Large B-Cell Lymphoma, with a Focus on Patients from Minority Groups

Background The combination of the anti-CD19 monoclonal antibody tafasitamab and the immune modulatory drug lenalidomide (LEN) has been commercially available in the US for adult patients with relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL) not otherwise specified, including DLBCL arising from low-grade lymphoma, and who are ineligible for autologous stem cell transplant (ASCT) since August 05, 2020. It was granted accelerated FDA approval and EU conditional marketing authorization after the primary analysis of Phase II L-MIND study (NCT02399085) demonstrated safety and efficacy in this setting. To complement clinical data, it is important to assess real-world safety and effectiveness given the heterogeneity of routine clinical care. Furthermore, focusing on patients from racial and ethnic minorities, who may face challenges in receiving optimal access to care, may help to identify and address inequities. realMIND is an observational study of the real-world safety and effectiveness of tafasitamab + LEN for the treatment of R/R DLBCL among patients in the US by race and ethnicity. Using prospective and, where necessary, retrospective data collection, this non-interventional study will also provide a deeper understanding of treatment patterns of US patients with R/R DLBCL treated with tafasitamab, and its use in a real-world setting in terms of treatment duration, dose modifications, and post-tafasitamab + LEN combination partners and monotherapy use. Methods realMIND (NCT04981795) is a multicenter, observational, prospective and retrospective Phase IV trial-in-progress study targeting ≥100 US patients with R/R DLBCL treated with tafasitamab + LEN. To ensure diversity, Group 1 will enroll patients from racial and ethnic minorities (n≥50), while Group 2 will comprise non-Hispanic White (NHW) patients (n≥50) (Figure 1). Eligible patients are ≥18 years old with histologically confirmed R/R DLBCL, ≥1 prior line of treatment, and have initiated or will be initiating tafasitamab treatment (except in the context of an interventional study). Prospective data collection began at study sites in September 2021, and retrospective data (from study sites or vendor databases) may include patients with a tafasitamab start date (index date) from August 05, 2020, onwards. The primary endpoints include safety and effectiveness outcomes (Table 1). Serious adverse events (SAEs) and treatment-emergent adverse events (TEAEs) will be reported. Effectiveness will be assessed in terms of tumor response rates and time-to-event endpoints, while secondary endpoints include treatment line and dose information (Table 1). Patient data for this observational study will be collected by prospective follow-up of patients included at study sites, or by retrospective collection of data from patient records, at study sites or from vendor databases. Prospective and retrospective data will be analyzed separately, as well as pooled. No formal sample size calculation was performed due to the descriptive nature of the study and no hypothesis testing is planned. Study status The in-progress non-interventional realMIND study is expected to end by approximately December 2025, or earlier, when: ≥50 patients treated with tafasitamab + LEN have been recruited into each of Group 1 and Group 2 with either prospective or retrospective data collection; and all prospectively-followed patients have ≥6 months follow-up after starting tafasitamab treatment, or have discontinued from the study for any reason, whichever occurs earlier. The results are anticipated to describe the real-world safety and effectiveness of tafasitamab for R/R DLBCL in US patients, with a focus on underrepresented racial and ethnic minorities, to help optimize outcomes of treatment and identify opportunities for further studies.

Applying sequence analysis to uncover ‘real-world’ clinical pathways from routinely collected data: a systematic review

ObjectivesThis systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data. Study Design and SettingWe conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways. Results19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n = 7) and France (n = 5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward's linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA. ConclusionThis review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

Using citation networks to evaluate the impact of text length on keyword extraction.

The identification of key concepts within unstructured data is of paramount importance in practical applications. Despite the abundance of proposed methods for extracting primary topics, only a few works investigated the influence of text length on the performance of keyword extraction (KE) methods. Specifically, many studies lean on abstracts and titles for content extraction from papers, leaving it uncertain whether leveraging the complete content of papers can yield consistent results. Hence, in this study, we employ a network-based approach to evaluate the concordance between keywords extracted from abstracts and those from the entire papers. Community detection methods are utilized to identify interconnected papers in citation networks. Subsequently, paper clusters are formed to identify salient terms within each cluster, employing a methodology akin to the term frequency-inverse document frequency (tf-idf) approach. Once each cluster has been endowed with its distinctive set of key terms, these selected terms are employed to serve as representative keywords at the paper level. The top-ranked words at the cluster level, which also appear in the abstract, are chosen as keywords for the paper. Our findings indicate that although various community detection methods used in KE yield similar levels of accuracy. Notably, text clustering approaches outperform all citation-based methods, while all approaches yield relatively low accuracy values. We also identified a lack of concordance between keywords extracted from the abstracts and those extracted from the corresponding full-text source. Considering that citations and text clustering yield distinct outcomes, combining them in hybrid approaches could offer improved performance.

Analysing the Daylighting Performance of the Main Prayer-hall in the Great Mosque of Hama, Syria

Daylighting and solar availability at urban scale has come to play a crucial role in the perception of discomfort conditions for people, both in outdoor and indoor spaces, and on the energy consumption of buildings. Daylighting and solar analyses are typically done separately. The paper presents a novel method, called the ‘sunlight-daylight signature’ (SDS), which allows the qualitative analysis of urban settings with respect to sunlight and daylight. The method can be used to classify different urban settings in terms of daylight/sunlight access or to test new development proposals by referring to existing locations and confirm whether a certain daylight quality is met. The SDS relies on a new analysis tool, called ‘sunlight-daylight wedge’ (SDW), which combines obstruction (through the vertical sky component VSC) and sunlight access (through the annual probable sunlight hours PASH and the winter probable sunlight hours PWSH). The orientation of the façade at each point is also included as it will affect the times of the day when the sun-hours from PASH and PWSH occur, thus affecting the character of the corresponding sunlight. The SDS approach is based on a clustering technique to subdivide large datasets (in this case, daylight data points across entire cities or major urban areas) into smaller groups, using machine learning by way of the k-medoids clustering technique. This is used to derive typical daylight and sunlight scenarios representing groups of data points with similar conditions. Additional data is included to account for urban density and daylight availability in public areas. Final output of the clustering process consists of a map showing areas with the same daylight signature (SDS), which means areas with the same sunlight and daylight conditions. The SDS can be useful for urban planners and building practitioners to predict the access to both daylight and sunlight of large urban settings to optimize comfort for people and energy usage.

A consumers’ Kansei needs mining and purchase intention evaluation method based on fuzzy linguistic theory and multi-attribute decision making method

Numerous previous researches have demonstrated that consumers are increasingly prioritizing their Kansei needs, with the development of technology and social economy. Moreover, whether consumers' Kansei needs (CKN) can be satisfied greatly affects their purchase intention (PI). Despite the substantial impact of CKN on PI, there remains a paucity of research on this subject. To bridge this gap, this paper proposes a CKN mining and PI evaluation method. Firstly, the double hierarchy hesitant fuzzy linguistic term set is employed to enhance the semantic differential model, facilitating the acquisition of product Kansei features evaluation and PI information. Subsequently, grey correlation analysis is applied to calculate the correlation coefficient between them, enabling quantitative CKN mining. Then, cluster consumers based on the correlation coefficient, and analyze the difference of CKN. Thirdly, construct the PI evaluation model based on the multi-attribute decision-making method, taking CKN into consideration, to rank the alternative product. Finally, the proposed method is implemented in the armchair evaluation problem. Comparative experimental results affirm the improved semantic differential model has higher reliability, which can be used to deal with the subjectivity and complexity of product evaluation information. Additionally, the feasibility and effectiveness of the PI evaluation model is validated through eye movement experiments, which can predect the consumers’ Kansei perference and rank the alternative products.

Sustainability assessment of small hydropower from an ESG perspective: A case study of the Qin-Ba Mountains, China

Small hydropower (SHP) has made significant contributions to economic and social development in rural and remote mountainous regions. However, the adverse ecological-environmental impacts resulting from the SHP sector and challenges in hydropower management have become major areas of concern. From an Environmental, Social, and Governance (ESG) perspective and using three SHP stations (GXD, WZL, and SJB) in the Qin-Ba Mountains as case studies, we constructed a sustainability assessment system comprising 18 indicators across three dimensions. The hesitant fuzzy linguistic term sets (HFLTSs) and cloud models were employed to determine the sustainability level of SHP by characterizing the hesitancy of the evaluator and the uncertainty of the evaluated data. (1) The ecological-environmental protection (E) dimension was assigned the greatest weight, followed by the dimensions of social responsibility contribution (S) and corporate governance management (G). The weights of certain indicators, including the water qualification rate, river morphology maintenance, guaranteed rate of instream flow, comprehensive utilization, and production safety standardization grade were relatively high, conforming to the current context of green development prioritization in which ecological-environmental protection is of the utmost importance. (2) The overall sustainability levels of all three SHP stations were “good”, with the E-dimension contributing the most and the G-dimension contributing the least to the sustainability goal. (3) The GXD, WZL, and SJB stations were ranked first, second, and third, respectively, in terms of their sustainability scores. This study provides an innovative perspective for the sustainability assessment of SHP. The evaluation method can be generalized to encompass multi-attribute decision-making problems. The findings of this study can aid in addressing the shortcomings associated with SHP development and promote sustainability within the SHP industry.

Eco‐sustainable Synthesis of N‐containing Heterocyclic Systems Using Porous Carbon Catalysts

AbstractFrom the most classic carbon materials (CMs) to the advanced ones, all of them integrate a promising catalyst set in terms of sustainability and energy efficiency for a greener future. Different synthetic strategies concerning to the catalytic synthesis of relevant N‐containing heterocycles are herein described to address the great potential of the referred catalysts flying over what has been done and all that remains to be done. Current trends in this field involve structure‐activity relationships establishment also considering the reaction mechanisms understanding and the identification of active catalytic sites, as function of both experimental datasets, emphasizing on operando characterization techniques, and theoretical studies which will significantly contribute to the design of custom‐made catalysts as a new horizon.

Consumer awareness and search trends for agrifood products certified with European Quality Schemes: PDO, PGI, and TSG - An analysis through Google Trends

This groundbreaking study leverages Google Trends to probe into consumer awareness of Romanian food products certified under European quality schemes, Protected Designation of Origin (PDO), Protected Geographical Indication (PGI), and Traditional Specialty Guaranteed (TSG), delivering prompt insights and addressing an existing research void. By adopting an innovative methodology through big data analytics - Google Trends and analyzing 12 sets of search terms, this study brings fresh insights to academic discourse and provides valuable information for marketers and policymakers. Prominent search interest was detected for 'Salam de Sibiu', ‘Plăcintă dobrogeană', and 'Magiun de prune Topoloveni', pointing to significant consumer familiarity. The study also unveiled regional search patterns, signifying location-based preferences, and potential marketing prospects. However, a marked contrast was found between the high interest in the products and the relatively low awareness of their European quality labels, highlighting a need for enhanced communication initiatives around these certifications. The study could be limited by its focus on Romanian certified food products, which may not be applicable to other geographical contexts. Future research endeavours may also seek to integrate data from Google Trends data with additional indicators of consumer behaviour.