bluuebunny/crossref_metadata_2025_split huggingface dataset:
Links between the sustainable development goals: An analysis for Italy
Authors: Markandya Anil , Cavalli Laura , Bhattacharya Arnab , Galinato Gregmar , Farnia Luca 
Published: August 2024

AbstractThe article reviews the links between the Sustainable Development Goals (SDGs), examining how progress in one is related to the others and what factors determine the progress. While most previous work is at the national level, this article focusses at the sub‐national level for one country, namely Italy. After reviewing the evidence from the literature, the paper utilizes an unbalanced panel data collected for Italian provinces, covering the period 2004–2022 and reports descriptive statistics. On the positive side, we see a decline in ambient air pollution, and an increase in indicators of gender equality. There are also big gains in tertiary education and a decline in occupational injuries. These are common to almost all provinces. On the health side, life expectancy has risen across the country. Less positive are the changes in endocrine, metabolic, and nutrition disease rates and in availability of hospital beds. The inter‐province indicator of inequality has also increased. Furthermore, the overall level of inequality has gone up and the difference in inequality between provinces has not fallen. There has been no increase in green spaces or efficiency of courts. There has been progress in poverty reduction but not enough to meet the SDG target by 2030 and regional differences in poverty remain stark. The Italian data are subjected to estimation of causal linkages between indicators using a system of equations. Two‐stage least squares estimation is carried out on the panel data set for provinces in Italy. The empirical modeling finds a positive impact of wealth on life expectancy and of income on poverty. There is an effect of an increase in income on poverty at the sub‐national level. There is also a negative impact of NGOs on land under construction, which is something not observed elsewhere as far as we are aware.
Unraveling the influence of convenience situational factors on e‐waste recycling behaviors: A goal‐framing theory approach
Authors: Puzzo Gabriele , Prati Gabriele 
Published: August 2024

AbstractThe surge in e‐waste production highlights the need to investigate what influences recycling behaviors, ultimately to enhance sustainable management practices. Previous research has explored its psychological antecedents, but there has been limited study on how situational factors of collection centers affect e‐waste recycling behavior. This research investigates how convenience of collection centers (i.e., proximity, availability, and user experience) influences e‐waste recycling behaviors. A survey administered to 700 citizens explores the relationship between convenience and goal‐framing theory components, namely gain, normative, and hedonic goals. Structural equation modeling is employed for analysis. Results reveal that convenience significantly affects e‐waste recycling behaviors through gain (β = −.304, p < .001) and normative goals (β = .154, p < .05), while hedonic goals show no significant association (β = −.064, p > .05). Furthermore, convenience exhibits a direct impact on e‐waste recycling behaviors (β = .166, p < .05). Implications for research and practical strategies for recycling initiatives are discussed. This study adds valuable insights to the existing literature and informs targeted interventions for sustainable e‐waste management.
Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India
Authors: Kyoi Shinsuke , Uchiyama Yuta , Mori Koichiro 
Published: August 2024

AbstractPro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.
Can income inequality reduction policies limit the disparity between urban and rural clean cooking fuel access rates?
Authors: Murshed Muntasir 
Published: August 2024

AbstractConsidering the utmost importance of adopting clean cooking fuels, especially from the viewpoint of partially realizing the targets of the 7th Sustainable Development Goal (SDG‐7), this study appraises how income inequality and other macroeconomic factors impact urban–rural disparities in clean cooking fuel accessibility across 40 developing countries from Sub‐Saharan Africa, Latin America, and the Caribbean. For analytical purposes, yearly data spanning from 2002 to 2018 is utilized for conducting advanced panel data examinations using methods that are robust against issues of cross‐sectional dependency, slope heterogeneity, and endogeneity. As a whole, the empirical findings reveal that deteriorating income inequality situations widen the gap between urban and rural clean cooking fuel access rates while good institutional quality accounts for lower disparities in urban–rural clean cooking fuel accessibility. Besides, region‐specific findings assure that this disparity is further enhanced by foreign remittances flowing into the selected Sub‐Saharan African nations while institutional quality improvement reduces disparity in the Latin America and the Caribbean countries of concern. Furthermore, the factors influencing the urban–rural disparities in clean cooking fuel accessibility are found to vary across different regional locations and national income‐based classifications of the sampled countries. Accordingly, considering the above findings, some region‐ and income‐group‐specific policy suggestions are put forward for enabling convergence between urban and rural clean fuel access rates across Sub‐Saharan Africa, Latin America, and the Caribbean.
Dear‐PSM: A deep learning‐based peptide search engine enables full database search for proteomics
Authors: He Qingzu , Li Xiang , Zhong Jinjin , Yang Gen , Han Jiahuai , Shuai Jianwei 
Published: August 2024

AbstractPeptide spectrum matching is the process of linking mass spectrometry data with peptide sequences. An experimental spectrum can match thousands of candidate peptides with variable modifications leading to an exponential increase in candidates. Completing the search within a limited time is a key challenge. Traditional searches expedite the process by restricting peptide mass errors and variable modifications, but this limits interpretive capability. To address this challenge, we propose Dear‐PSM, a peptide search engine that supports full database searching. Dear‐PSM does not restrict peptide mass errors, matching each spectrum to all peptides in the database and increasing the number of variable modifications per peptide from the conventional 3–20. Leveraging inverted index technology, Dear‐PSM creates a high‐performance index table of experimental spectra and utilizes deep learning algorithms for peptide validation. Through these techniques, Dear‐PSM achieves a speed breakthrough 7 times faster than mainstream search engines on a regular desktop computer, with a remarkable 240‐fold reduction in memory consumption. Benchmark test results demonstrate that Dear‐PSM, in full database search mode, can reproduce over 90% of the results obtained by mainstream search engines when handling complex mass spectrometry data collected from different species using various instruments. Furthermore, it uncovers a substantial number of new peptides and proteins. Dear‐PSM has been publicly released on the GitHub repository https://github.com/jianweishuai/Dear‐PSM.
NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity
Authors: Wu Xiaoxue , Weng Shiyu , Zheng Bin , Zheng Wei , Chen Xiang , Sun Xiaobin 
Published: August 2024

AbstractAs software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data.
Good for the goose, bad for the gander? Corruption and income inequality
Authors: Pavlik Jamie Bologna , Callais Justin T. 
Published: August 2024

AbstractWe examine the relationship between corruption and income inequality across countries. While previous studies have explored this association at both an international and within‐country level, we expand on this literature in two distinct ways. First, along with the most commonly utilized measure of inequality (Gini coefficients), we also examine income per‐capita at each decile, along with top 1% and 5%, and the associated income shares. Second, we employ an empirical strategy that differs from the existing literature. Our primary results are estimated using matching methods, but we also supplement these results with a “doubly robust” difference‐in‐difference design. We find that a reduction in corruption increases incomes of the top 80% but does not significantly impact incomes of the bottom 20%, or the top 1% and 5%. We find some evidence of income growth amongst the top 1% and 5% following increases in corruption, but these results are inconsistent across estimations. Our results also suggest that accounting for the size of the informal sector matters a great deal in understanding the relationship between corruption and the distribution of income.
A systematic literature review on the mathematical underpinning of model‐based systems engineering
Authors: Wach Paul , Topcu Taylan G. , Jung Sukhwan , Sandman Brandt , Kulkarni Aditya U. , Salado Alejandro 
Published: August 2024

AbstractThe International Council on Systems Engineering (INCOSE) has initiated a Future of Systems Engineering (FuSE) program that includes a stream for advancing the theoretical foundations of the discipline of Systems Engineering (SE). A near‐term goal of FuSE is to assess the adequacy of current theoretical foundations of SE. The discipline of SE is converging toward model‐based practices (i.e., MBSE) that have not yet reached the maturity of model‐based practices in other engineering domains. For example, finite element analysis and computational fluid dynamics are grounded in mathematical theory, while, generally, MBSE is not. However, some attempts have been made to underpin MBSE with theoretical richness. This article presents a systematic literature study that surveyed state of the art on providing MBSE with mathematical foundations. Our protocol collected over 2000 publications that were reviewed for inclusion/exclusion, categorized, and analyzed. We provide insights to the type of mathematical theories used, domains of applications, and areas of SE to which the math was applied to, among other analysis. We also provide a synthesized discussion about the field moving forward, emphasizing positive trends along with the negatives and areas of concern. Overall, we found the field to be nascent.
Evaluating the tipping point of a complex system: The case of disruptive technology
Authors: Edwards Christine M. , Nilchiani Roshanak Rose , Ganguly Anirban , Vierlboeck Maximilian 
Published: August 2024

AbstractComplex systems often operate in equilibriums that can be disrupted under specific conditions, driving the system to undergo an irreversible phase transition (aka tipping point) into a new equilibrium. Disruptive technology, as introduced in the book Innovator's Dilemma, is a new emerging technology that can successfully displace incumbent technologies and push the market through phase transition into a new equilibrium. In this paper, the authors model the market disruption caused by a disruptive technology as a complex system, with dynamics that show a phase transition or tipping point, after which the system shifts into a new equilibrium aiming at assessing the success or failure of a disruptive technology. The purpose of this study is to mathematically model a tipping point measure of complex networks and a theoretical framework for disruptive technology dynamics. A predator–prey model is used to emulate the behavior of a disruptive technology versus an incumbent technology, and a resilience index is integrated to measure the tipping point in the market where the disruptive technology will overtake the incumbent technology. This methodology is applied to a historical case study of film versus digital versus cell phone cameras, demonstrating the application of this methodology. The study's contribution can be applied to both systems engineering and disruptive innovation management. The proposed approach can help stakeholders assess the impact of a potential new disruptive technology and use the resulting resilience index as a measurement for adjusting technology requirements and systems management approaches to achieve a desired outcome.
Detecting cancer biomarkers using electrochemical composite nanomaterials tools
Authors: Mohan Brij , Mehak  , Virender  , Kumar Ashwani , Modi Krunal , Singh Gurjaspreet , Garazade Ismayil M. , Parikh Jaymin , Pombeiro Armando J. L. , Solovev Alexander A. 
Published: August 2024

AbstractAnnually, cancer accounts for a large number of mortalities worldwide, underscoring the imperative for early‐stage detection as a public health priority. This article not only offers an in‐depth analysis of the synthesis, fabrication, design, and structural attributes of these nanomaterials but also provides a critical evaluation of their application in various electrochemical biosensing modalities, including amperometry, impedimetric, photoelectrochemical, potentiometric, and field‐effect transistor‐based sensors. A primary focus is placed on the importance of nanomaterials‐based biosensors in detecting cancer biomarkers, emphasizing their role in achieving enhanced sensitivity and specificity for detecting circulating tumor cells, nucleic acids, proteins, and exosomes, which are crucial indicators of malignancy. The article offers valuable insights into the detection of these biomarkers. It details their significance and relevance in the early diagnosis of various cancer types with nanomaterial‐enhanced electrochemical biosensors’ stability, reusability, and conductivity, shedding light on their practical applicability in real‐world scenarios. The manuscript concludes by addressing existing challenges and outlining prospective avenues for future research, emphasizing the need for continued innovation and exploration in this burgeoning field.
A review on carbonaceous materials for fuel cell technologies: An advanced approach
Authors: Joshi Harish Chandra , Bagauli Reetika , Ahmad Waseem , Bisht Bhawana , Sharma Nishesh 
Published: August 2024

AbstractIn the context of sustainable development environment‐friendly approaches, technologies have gained momentum with dual objective of achieving technological advancements and innovations through green energy source. One such approach is fuel cells which have been identified as a potential source for the production of electric power from chemical fuels. Fuel cell has become popular as they represent environmentally friendly method to generate power. The trucking industry and fleet transit networks have shown the potential of the hydrogen fuel cell despite many obstacles, such as material composition, storage, and distribution, because it can be used and moved securely, leading to a significant reduction in CO2 and particulate pollution. Proton exchange membrane fuel cells (PEMFC) are the subject of intense research in environmental friendly power transformation and storing tools as a consequence of environmental friendliness, minimal emissions, and great effectiveness. Fuel cell transportation is successful with oxygen reduction process (ORR). Graphite, a highly organized type of carbon, serves as the standard due to its durability and accessibility. This article focuses on the evolution of electrodes as well as electrolytic materials or fluid materials for various kinds of fuel cells and also explains the uses, difficulties, advancements, etc. pertaining to electrodes and fluids of diverse types of fuel cells.
First‐principles calculations to investigate electronic thermodynamic and optical properties of the RbScO2 alloy
Authors: Dahri Soukaina , Jabar Abderrahim , Bahmad Lahoucine , Drissi Lalla Btissam , Laamara Rachid Ahl 
Published: August 2024

AbstractIn this study, we explore the electronic, thermodynamic, and optical properties of the compound RbScO₂ using density functional theory (DFT) and incorporating a U‐correction term, known as the Hubbard correlation energy. This approach enables us to gain an in‐depth understanding of the band structure, density of states, and specific optical properties of RbScO₂. The results reveal that RbScO₂ exhibits an indirect band gap measuring 3.68 eV for GGA+SOC+U. Importantly, this indirect band gap is located at the Γ‐VBM and M‐CBM points of the Brillouin zone. The optical properties of RbScO₂ were studied in the xx, yy, and zz directions, revealing a significant increase in the absorption coefficient with higher photon energies, including a distinct peak in the UV range. Increased absorption was observed along the xx and yy axes under a pressure of 50 GPa. Additionally, the thermodynamic attributes of RbScO₂, including volume, Debye temperature, heat capacity, and entropy, were examined. It was observed that the Debye temperature increases proportionately to the tensile strain but decreases with increasing temperature, suggesting that the expansion strain may increase the compound's mechanical and thermal stability. This study provides valuable information on the characteristics of RbScO2, enriching our understanding of its potential applications.
Predictors of distant metastatic recurrence in intermediate‐risk papillary thyroid carcinoma
Authors: Onoda Naoyoshi , Ito Yasuhiro , Miya Akihiro , Kihara Minoru , Miyauchi Akira 
Published: August 2024

AbstractBackgroundPatients with intermediate‐risk papillary thyroid carcinoma (PTC) have a favorable prognosis with standard treatment of total thyroidectomy (TT) and adjuvant radioactive iodine therapy (RAIT). However, the benefits of TT or adjuvant RAIT remain undetermined, and they are often omitted in Japan. We investigated risk factors for life‐threatening distant recurrence in patients with intermediate‐risk PTC who are optimal candidates for adjuvant RAIT.Patients and MethodsOutcomes without RAIT were retrospectively examined in 4030 intermediate‐risk conventional PTC cases underwent initial surgery from 2005 to 22 (IRB approval 20200709‐1).ResultsLobectomy (LT) and TT was performed in 11.5% and 88.5%, respectively. Recurrent laryngeal nerve paralysis and hypoparathyroidism was less commonly observed in LT (1.3% and 0%) than TT (2.4% and 3.5 %). Fifty‐six cases (1.4%) had distant recurrence. Recurrence‐free survival rates at 10 years was 93.5%. There was no significant difference in recurrence rate between LT and TT. Age ≥55, cN1b, and tumor diameter >30 mm significantly associated with distant recurrence. There was a strong relationship between the number of positive risk factors and recurrence; the distant recurrence rate in cases of 0, 1, 2, and 3 positive factors was 0.3% (4/1203), 1.3% (25/1889), 2.7% (23/830) and 7.1% (4/52) (HR 6.46 (2.34–17.86), Log‐rank <0.001).ConclusionFor intermediate‐risk conventional PTC, there is no difference in prognosis even if LT was selectively conducted. However, in patients with risk factors for distant metastatic recurrence, such as age ≥55 years, cN1b, and tumor size >30 mm, adjuvant RAIT was considered eligible.
Results from the Bivariate and Multivariate Analysis of Homonegativity
Authors: Tausch Arno 
Published: August 2024

AbstractOur estimates of global homonegativity based on rates of disapproval of homosexual neighbours cover more than 90% of the world's population and are based on research results from 88 countries covert in the World Values Survey and the European Values Study. These data give the population-weighted result of a global percentage of homonegative people of around 55%. We present our research findings on the drivers of homonegativity using correlations, partial correlations and factor analysis, examining the effects of individual global attitudes on secularism, democracy, tolerance and religious particularism, as well as on gender equality, religion, political violence and national resilience on homonegativity, as evidenced in the global attitude data from the World Values Survey. We also analyse the relationship between homophobia and cross-social science data in the countries of the world.