bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating Sustainable Development Goals to Assess Energy Transition Scenarios in Municipalities of Northern Sweden
Authors: Sobha Parvathy , Krook-Riekkola Anna 
Published: June 2024

AbstractLocal governments face significant challenges in implementing the United Nations Sustainable Development Goals (SDGs). To address these challenges, the study adopts a model-based system analysis approach for integrating sustainable development principles into the local energy transition, examining (i) what to include and vary across scenarios, (ii) what to extract from the model results, and (iii) what to discuss with the local governments. An energy system optimisation model, based on the TIMES modelling framework, is employed to represent the municipal energy system of Gällivare in northern Sweden and its potential transition pathways. The study also provides a comparative data-driven assessment to examine whether the energy transition pathways converge or diverge from the SDGs. In addition, a set of sustainability indicators has been identified/developed and are implemented in the model to assess sustainability across different pathways. The challenge inherent in such quantitative comparisons, particularly those of a complex nature such as sustainability, lies in selecting appropriate indicators for municipalities that align with the model’s framework. The analysis shows both convergent and divergent pathways. For instance, electrification in the identified transition pathways reduces fossil fuel use (converges towards SDG13 on climate), but electrification also introduces complexities with increased material usage for electric vehicles (diverges from SDG12 on responsible consumption and production).
Will the Nordics Become an Export Hub for Electro Fuels and Electricity?
Authors: Karlsson Kenneth , Radoszynski Andrea Marin , Münster Marie , Langer Lissy , Kountouris Ioannis , Giehl Johannes Felipe , Westerberg Jenny , Nissen Sidsel A. , Daabech Alexander , Bache Mathias , Hedemann Lars , Kirkegaard Julia Kirch , Hansson Julia 
Published: June 2024

AbstractThe Nordics have a huge renewable energy potential, mainly in the form of onshore and offshore wind as well as biomass potentials and can deliver some of the lowest electricity prices in Europe. They could export large amounts of electricity and hydrogen, supplying mainland Europe and abroad. But where and when should wind, PV and green fuel production capacity be built, and what kind of infrastructure is needed? Within the Nordics—who can/will become a net exporter of electricity and green fuels?To avoid sub-optimal solutions, some overall analysis and planning may be needed to secure societal benefits and reduce the overall cost. By comparing plans and visions for the build-out of electrolyser capacity, electricity and gas infrastructure, and wind and solar projects, we discuss consistency (or lack thereof) in the project pipelines. Furthermore, the future role of the Nordics is discussed by comparing scenario analysis from different modelling teams to identify robust conclusions and critical uncertainties.The impact goes beyond SDG 13 (Climate action) and supports e.g., SDG 8 (Decent work and economic growth) by implementing a more sustainable economic system and also offers the opportunity to provide affordable and clean energy (SDG 7).
Transition Pathways for a Low-Carbon Norway: Bridging Socio-technical and Energy System Analyses
Authors: Chang Miguel , Espegren Kari , Haaskjold Kristina , Rosenberg Eva , Damman Sigrid , Mäkitie Tuukka , Pisciella Paolo , Andersen Allan Dahl , Skjølsvold Tomas Moe 
Published: June 2024

AbstractThis study presents an interdisciplinary approach to analyze different transition pathways towards the sustainable development of a low-carbon society, focusing on Norway as a case. The study bridges a socio-technical perspective on sustainability transitions with techno-economic energy systems and regional-economic modelling analyses. Incorporating a socio-technical perspective in the scenario design allows us to envision pathways considering causal processes of technological and socio-institutional change, and potential transition bottlenecks. The resulting scenarios are used in the techno-economic energy system analysis to show cost-optimal energy system configurations, including varying levels of new renewable capacity needed, new conversion technologies, and fuel substitutions across all sectors leading to different decarbonization pathways for the Norwegian energy system by 2050. The regional-economy analysis addresses the impacts of these pathways on general economic growth and labor. The results show that higher levels of decarbonization are possible for Norway; however, potential bottlenecks can slow down the transition, while trade-offs in economic growth and development must be balanced out with decarbonization ambitions.
Endoscopic management of greater trochanteric pain syndrome (GTPS): a comprehensive systematic review
Authors: Giai Via Riccardo , Elzeiny Ahmed , Bufalo Marco , Massè Alessandro , Giachino Matteo 
Published: June 2024

Abstract
Background
Greater trochanteric pain syndrome (GTPS) presents challenges in clinical management due to its chronic nature and uncertain etiology. Historically attributed to greater trochanteric bursitis, current understanding implicates abductor tendinopathy as the primary cause. Diagnosis usually involves a clinical examination and additional tests such as imaging and provocative testing. Surgical intervention may be considered for cases refractory to conservative therapy, with endoscopic techniques gaining ground over open procedures.

Materials and methods
A systematic review was conducted adhering to the PRISMA guidelines. Relevant studies were searched in four databases: Pubmed, Scopus, Embase, and Medline. The selected articles were evaluated according to the criteria of levels of evidence (LoE). The Coleman methodology score (mCMS) was used to analyze the retrospective studies. This systematic review was registered in the International Prospective Registry of Systematic Reviews.

Results
Surgical success rates ranged from 70.6–100%, significantly improving pain and function. Complications were generally mild, mainly hematomas and seromas, while recurrence rates were low. However, limitations such as the retrospective design and the absence of control groups warrant cautious interpretation of the results.

Conclusions
Endoscopic surgery emerges as a promising option for refractory GTPS, offering effective symptom relief and functional improvement. Despite limitations, these results suggest a favorable risk–benefit profile for endoscopic procedures. Further research is needed, particularly prospective randomized trials, to confirm these findings and optimize surgical techniques to improve patient outcomes.
Diagnostics and treatment delay in primary central nervous system lymphoma: What the neurosurgeon should know
Authors: Hasner M. C. , van Opijnen M. P. , van der Meulen M. , Verdijk R. M. , Maas S. L. N. , te Boome L. C. J. , Broekman M. L. D. 
Published: June 2024

Abstract

Purpose
The gold standard for diagnostics in primary central nervous system lymphoma (PCNSL) is histopathological diagnosis after stereotactic biopsy. Yet, PCNSL has a multidisciplinary diagnostic work up, which associated with diagnostic delay and could result in treatment delay. This article offers recommendations to neurosurgeons involved in clinical decision-making regarding (novel) diagnostics and care for patients with PCNSL with the aim to improve uniformity and timeliness of the diagnostic process for patients with PCNSL.

Methods
We present a mini review to discuss the role of stereotactic biopsy in the context of novel developments in diagnostics for PCNSL, as well as the role for cytoreductive surgery.

Results
Cerebrospinal fluid-based diagnostics are supplementary and cannot replace stereotactic biopsy-based diagnostics.

Conclusion
Histopathological diagnosis after stereotactic biopsy of the brain remains the gold standard for diagnosis. Additional diagnostics should not be a cause of diagnostic delay. There is currently no sufficient evidence supporting cytoreductive surgery in PCNSL, with recent studies showing contradictive data and suboptimal study designs.
Predictions of transient vector solution fields with sequential deep operator network
Authors: He Junyan , Kushwaha Shashank , Park Jaewan , Koric Seid , Abueidda Diab , Jasiuk Iwona 
Published: June 2024

AbstractThe deep operator network (DeepONet) structure has shown great potential in approximating complex solution operators with low generalization errors. Recently, a sequential DeepONet (S-DeepONet) was proposed to use sequential learning models in the branch of DeepONet to predict final solutions given time-dependent inputs. In the current work, the S-DeepONet architecture is extended by modifying the information combination mechanism between the branch and trunk networks to simultaneously predict vector solutions with multiple components at multiple time steps of the evolution history, which is the first in the literature using DeepONets. Two example problems, one on transient fluid flow and the other on path-dependent plastic loading, were shown to demonstrate the capabilities of the model to handle different physics problems. The use of a trained S-DeepONet model in inverse parameter identification via the genetic algorithm is shown to demonstrate the application of the model. In almost all cases, the trained model achieved an $$R^2$$

R
2

value of above 0.99 and a relative $$L_2$$

L
2

error of less than 10% with only 3200 training data points, indicating superior accuracy. The vector S-DeepONet model, having only 0.4% more parameters than a scalar model, can predict two output components simultaneously at an accuracy similar to the two independently trained scalar models with a 20.8% faster training time. The S-DeepONet inference is at least three orders of magnitude faster than direct numerical simulations, and inverse parameter identifications using the trained model are highly efficient and accurate.
Intergenerational transmission of maternal prenatal anxiety to infant fearfulness: the mediating role of mother-infant bonding
Authors: Rousseau Sofie , Katz Danielle , Schussheim Avital , Frenkel Tahl I. 
Published: June 2024

Abstract

Purpose
This study is the first to directly investigate the mechanistic role of maternal bonding toward her infant in the early intergenerational pathway of risk from maternal anxiety to infant fearfulness.

Methods
Mothers (N = 216; M
age=32.78) reported on their anxiety and bonding at four time-points between pregnancy and ten-months postpartum. At four and ten-months postpartum, infant temperamental precursors of anxiety were assessed through maternal report and observation.

Results
Cross-lagged longitudinal path modeling indicated a significant link between prenatal maternal anxiety and infant temperamental fearful withdrawal at 10-months postpartum (R
2 = 0.117), which was fully explained by decreased maternal bonding at one-month postpartum and increased infant temperamental negative reactivity at 4-months postpartum.

Conclusion
Results support the need to foster maternal bonding in preventive perinatal care, particularly in the context of maternal anxiety.
Stratified steady inviscid water flows with effects of surface tension and constant non-zero vorticity
Authors: Kolun Nataliia 
Published: June 2024

Abstract
In this paper we consider steady inviscid three-dimensional stratified water flows of finite depth with a free surface and an interface. The interface plays the role of an internal wave that separates two layers of constant and different density. We study two cases separately: when the free surface and the interface are functions of one variable and when the free surface and the interface are functions of two variables. In both cases, considering effects of surface tension, we prove that the bounded solutions to the three-dimensional equations are essentially two-dimensional. More specifically, assuming that the vorticity vectors in the two layers are constant, non-vanishing and parallel to each other we prove that their third coordinate vanishes in both layers. Also we prove that the free surface, the interface, the pressure and the velocity field present no variations in the direction orthogonal to the direction of motion.
A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges
Authors: Chen Binhe , Cao Li , Chen Changzu , Chen Yaodan , Yue Yinggao 
Published: June 2024

Abstract The application of optimization theory and the algorithms that are generated from it has increased along with science and technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial optimization issues. Swarm intelligence optimization algorithms have been successful in machine learning, process control, and engineering prediction throughout the years and have been shown to be efficient in handling combinatorial optimization issues. An intelligent optimization system called the chicken swarm optimization algorithm (CSO) mimics the organic behavior of flocks of chickens. In the benchmark problem's optimization process as the objective function, it outperforms several popular intelligent optimization methods like PSO. The concept and advancement of the flock optimization algorithm, the comparison with other meta-heuristic algorithms, and the development trend are reviewed in order to further enhance the search performance of the algorithm and quicken the research and application process of the algorithm. The fundamental algorithm model is first described, and the enhanced chicken swarm optimization algorithm based on algorithm parameters, chaos and quantum optimization, learning strategy, and population diversity is then categorized and summarized using both domestic and international literature. The use of group optimization algorithms in the areas of feature extraction, image processing, robotic engineering, wireless sensor networks, and power. Second, it is evaluated in terms of benefits, drawbacks, and application in comparison to other meta-heuristic algorithms. Finally, the direction of flock optimization algorithm research and development is anticipated.
A novel lifetime analysis of repairable systems via Daubechies wavelets
Authors: Wu Jingchi , Dohi Tadashi , Okamura Hiroyuki 
Published: June 2024

AbstractIt is well-known that minimally repaired systems are characterized by non-homogeneous Poisson processes. When the underlying lifetime distribution function or its associated intensity function is completely known, the statistical inference of the cumulative number of failures/repairs is made through some standard techniques. On the other hand, when the parametric form of the lifetime distribution or the intensity function is not known, non-parametric inference techniques such as non-parametric maximum likelihood estimate (NPMLE) and kernel-based estimates are employed. Since these techniques are based on the underlying lifetime data, the resulting estimates of the cumulative number of failures/repairs with respect to the system operation time are discontinuous and troublesome in assessing the reliability measures such as reliability function and mean time to failure, etc. In this paper, we propose a novel lifetime analysis of the repairable systems via Daubechies wavelets. More specifically, we generalize the seminal Daubechies wavelet estimate by Kuhl and Bhairgond (Winter Simul Conf Proc 1:562–571, 2000) by combining the ideas on NPMLE and KMBs. In both simulation experiments and real data analyses, we investigate applicability of our Daubechies wavelet-based approaches and compare them with the conventional non-parametric methods.
Quantifying and reducing the complexity of multi-line charts as a visual aid in multi-criteria decision-making
Authors: Huang He , Siraj Sajid 
Published: June 2024

AbstractMulti-line charts are commonly used in multi-criteria decision-making (MCDM) to represent multiple data series on the same graph. However, the presence of conflicting criteria or divergent viewpoints introduces the challenge of accurately interpreting these charts, necessitating thoughtful design to improve their comprehensibility. In this paper, we model these multi-line charts as connected perfect matching bipartite graphs. We propose a metric called the Coefficient of Complexity (CoC) to quantify the complexity of these multi-line charts. In order to reduce the visual complexity of these charts, we propose to minimize the CoC by modeling it as an integer linear optimization problem (reminiscent of the traveling salesman problem). We demonstrate our techniques through multiple real-life case studies, wherein multi-line charts serve as data visualization across various MCDM software tools. Additionally, multi-line charts with specific requirements have been optimized using our approach, showcasing the adaptability and efficacy of our technique. We also formulate the radar chart as a specialized form of the multi-line chart, and adapt our technique to improve its comprehensibility. The proposed CoC and its optimization are important contributions to the field of analytics, as a number of methods use multi-line charts for visual aid. Consequently, enhancing their comprehensibility can facilitate the decision-making process and help decision-makers gain insights.
Sequential learning of climate change via a physical-parameter-based state-space model and Bayesian inference
Authors: Lai Yuchuan , Pozzi Matteo 
Published: June 2024

AbstractFlexible decision-making strategies provide an alternative option for climate adaptation by considering future learning of climate change. A physical-parameter-based state-space model (SSM) with Bayesian inference is developed in this work to investigate reduction of uncertainty from more observations and facilitate flexible adaptation strategies. This SSM method integrates a two-layer, energy-balance model to describe global mean temperature response, models multiple sources of uncertainty such as climate sensitivity and aerosol forcing, and uses the informative priors from processing Global Climate Model simulations. Focusing on global mean temperature anomaly, which has important implications on policies and related impacts, the SSM is assessed by applying it to both historical and pseudo-observations (i.e., model simulations used as observations), assessing the posterior probabilities of physical parameters, and evaluating reduction of projection uncertainty. Some limitations of the method are observed, such as the sensitivity related to the adopted forcing time series. Comparing the end-of-the-century projections of global mean temperature sequentially made at year 2020, 2050, and 2080 using pseudo-observations, the reduction of uncertainty from the SSM is evident: the range of 95% prediction intervals on average decreases from 1.9°C in 2020 to 1.0°C in 2050, and to 0.6°C in 2080 under the Shared Socioeconomic Pathway (SSP) 2–4.5 (or from 2.7°C, to 1.2°C and to 0.7°C under SSP5-8.5). These results illustrate how the SSM framework provides probabilistic projections of climate change that can be sequentially updated with more observations, and this process can facilitate flexible adaptation strategies.
Nexus between regulatory pressure, eco-friendly product demand and sustainable competitive advantage of manufacturing small and medium-sized enterprises: the mediating role of eco-innovation
Authors: Mady Khalid , Anwar Imran , Abdelkareem Reda Shaker 
Published: June 2024

AbstractAlthough previous research endeavours have extensively explored the environmental pressure-performance relationship, their findings have been inclusive. Therefore, this paper examines the direct and indirect (through the mediating role of eco-innovation) impact of regulatory pressure and eco-friendly product demand on sustainable competitive advantage. The research model was examined using Structural Equation Modeling methodology, analyzing data gathered from 183 senior managers within Egyptian SMEs. Several findings have been yielded, which are: (1) eco-innovation is positively motivated by regulatory pressure and eco-friendly product demand; (2) the direct link between environmental pressures (regulatory pressure, eco-friendly product demand) and sustainable competitive advantages is not statistically significant; and (3) eco-innovation acts as a mediator in the relationship between these environmental pressures and sustainable competitive advantage. This research theoretically contributes to the institutional theory of the firm and competitive advantages theory. Particularly, the findings of this research theoretically emphasize that eco-innovation plays a prominent role in alleviating and translating environmental pressures exerted on manufacturing SMEs into sustainable competitive advantages. Furthermore, the research provides practical implications, policy recommendations, limitations, and further research avenues.
Genetic diversity of the Turkish accessions of two progenitor species, Triticum baeoticum Boiss. and Triticum urartu Thum. ex Gandil., using DArTSeq markers
Authors: Özsoy Ata Umut , Tarikahya Hacioğlu Burcu 
Published: June 2024

Abstract
The aim of this study was to reveal the intra and interspecies differences between Triticum baeoticum and Triticum urartu using Diversity Arrays Technology sequencing (DArTseq) on 94 accessions representing Turkish populations. Seeds were gathered from the US Department of Agriculture, and from the Turkish Seed Gene Bank. Isolated and purified DNA samples were sent to Diversity Arrays Technologies for DArTseq. After the necessary quality filtering, a total of 16,898 and 100,103 loci were obtained respectively from the single nucleotide polymorphism (SNP) and SilicoDArT datasets. ADMIXTURE software was used to reveal the intra and interspecies population structures. Analysis of molecular variance was carried out to reveal the variance between the populations of the T. urartu and T. baeoticum species. Principal coordinate analysis was conducted to visualize the main sources of variation between the populations on a 2-dimensional plane. To reveal the evolutionary relationship, SNP dataset was used to reconstruct the phylogenetic dendrograms by using the maximum likelihood statistical method and the unweighted pair group method with arithmetic mean clustering algorithm. As a result of this study, the accessions of T. urartu and T. baeoticum species formed separate clusters and revealed as two different species. In line with the results obtained, it is obvious that the identification of some accessions should be re-evaluated. The results demonstrated that DArTseq, is a fast, low-cost, and high-accuracy method that can be used in species and population discrimination and an effective tool for Gene Bank management.