bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biomass Production and Nutrient Removal by Perennial Energy Grasses Produced on a Wet Marginal Land
Authors: Cooney Danielle R. , Namoi Nictor , Zumpf Colleen , Lim Soo-Hyun , Villamil Maria , Mitchell Robert , Lee D. K. 
Published: July 2022

AbstractGrowing dedicated bioenergy crops on marginal land can provide beneficial outcomes including biomass production and energy, resource management, and ecosystem services. We investigated the effects of harvest timing (peak standing crop [PEAK] or after killing frost [KF]) and nitrogen (N) fertilizer rates (0, 56, and 112 kg N ha−1) on yield, nutrient concentrations, and nutrient removal rates of perennial grasses on a wet marginal land. We evaluated three monocultures, including switchgrass (Panicum virgatum L., SW), Miscanthus x giganteus (MG), prairie cordgrass (Spartina pectinata Link, PCG), and a polyculture mixture of big bluestem (Andropogon gerardii Vitman), Indiangrass (Sorghastrum nutans (L.) Nash), and sideoats grama (Bouteloua curtipendula Torr., MIX). Increasing the application of N did correlate with increased biomass, concentration, and subsequent removal of nutrients across almost all treatment combinations. In all grass treatments except MG, PEAK harvesting increased yield and nutrient removal. At PEAK harvest, switchgrass is ideal for optimizing both biomass production and nutrient removal. While our results also suggest short-term plasticity for farmers when selecting harvest timing for optimal nutrient removal, KF harvest is recommended to ensure long-term stand longevity and adequate nutrient removal. If the KF harvest is adopted, MG would be the ideal option for optimizing biomass yield potential. Additionally, we found that the yield of polyculture did not vary much with harvest timing, suggesting better yield stability. Future studies should give consideration for long-term evaluation of polyculture mixtures to assess their biomass yields and nutrient removal capacities.
Optimization of integrated production scheduling and vehicle routing problem with batch delivery to multiple customers in supply chain
Authors: Azad Tanzila , Rahman Humyun Fuad , Chakrabortty Ripon K. , Ryan Michael J. 
Published: July 2022

AbstractIn supply chain literature, production coordination and vehicle routing have received a lot of attention. Even though all functions in the supply chain are interrelated, they are normally handled independently. This disconnected approach might lead to less-than-ideal outcomes. Increasing total efficiency by integrating manufacturing and delivery scheduling processes is popular. This study focuses on synchronic production–distribution scheduling difficulties, particularly permutation flow shop scheduling in production and sequence-dependent setup time (SDST) and vehicle routing alternatives in distribution. To create a cost-effective distribution among the placement of geographically separated clients and hence to minimize delivery costs, batch delivery to customers employing a succession of homogenized capacity limitation vehicles is examined here. However, this might result in the failure to complete multiple client orders before their deadlines, raising the cost of lateness. As a result, the goal of this study is to lower the overall cost of tardiness and batch distribution in the supply chain. To accomplish so, a mixed-integer nonlinear programming model is developed, and the model is solved using a suggested genetic algorithm (GA). Because there is no established benchmark for this issue, a set of genuine problem scenarios is created in order to assess the proposed GA in a viable and difficult environment. Ruiz's benchmark data, which is derived from Taillard's benchmark cases of permutation flow shops, was supplemented with SDSTs in the production of test examples. In comparison to an exact method, the results show that the proposed GA can rapidly seek solutions to optimality for most small-sized instances. Furthermore, for medium and large-scale cases, the proposed GA continues to work well and produces solutions in a fair amount of time in comparison to GA without the local search.
Quantifying knapping actions: a method for measuring the angle of blow on flakes
Authors: Li Li , Reeves Jonathan S. , Lin Sam C. , Tennie Claudio , McPherron Shannon P. 
Published: July 2022

AbstractStone artifacts are critical for investigating the evolution of hominin behavior—they are among our only proxies for hominin behavior in deep time. Hominin cognition and skill are often inferred by reconstructing the technical decisions hominins made throughout the knapping process. However, despite many advancements in understanding how hominins knapped, some of the key factors involved in past flake production cannot be easily/readily derived from stone artifacts. In particular, the angle at which the knapper strikes the hammer against the core to remove the flake, or the angle of blow, is a key component of the knapping process that has up to now remained unmeasurable on archeological assemblages. In this study, we introduce a new method for estimating the angle of blow from the ventral surface of flakes. This method was derived from a controlled experiment that explicitly connects fracture mechanics to flake variability. We find that a feature of the flake’s bulb of percussion, what we call the bulb angle, is a measurable indicator of the angle of blow. Our experimental finding is further validated in two additional datasets from controlled and replicative knapping experiments. These results demonstrate the utility of continuing to link flake variation with technical decision-making to fracture mechanics. In addition, they also provide a useful and relatively simple means to capture a currently invisible aspect of hominin stone tool production behavior.
Impacts of climate-smart crop varieties and livestock breeds on the food security of smallholder farmers in Kenya
Authors: Radeny Maren , Rao Elizaphan J. O. , Ogada Maurice Juma , Recha John W. , Solomon Dawit 
Published: July 2022

AbstractThis paper analyses the impact of climate-smart agriculture (CSA) technologies on household dietary diversity and food insufficiency as indicators of food and nutrition security in Kenya. Using a combination of Propensity Score Matching and endogenous treatment effect approaches, we found that adoption of stress-tolerant varieties of several crops (such as bean, pigeon pea, cowpea, maize and sorghum) improved household dietary diversity score by 40% and reduced food insufficiency by 75%. Adoption of improved and resilient livestock breeds (including Red Maasai sheep and Galla goats) improved household dietary diversity by 38% while reducing household food insufficiency by 90%. We also found that stress-tolerant crop varieties were more effective in improving food security outcomes among households with large landholdings and with more educated and younger to middle-age heads. Effects of resilient livestock breeds on household food security were much stronger for households with large landholdings and with young and/or much older heads that have low levels of education. Given the large, demonstrated benefits from the use of the CSA technologies, policies and programs aimed at their promotion should apply appropriate targeting to ensure wider uptake of the technologies and maximum returns on investment.
Management of Aesthetical and Functional Complications after Total Parotidectomy. First Long-Term Experiences with Dermal Matrix Surgimend ® in Patient Affected by Malignant Parotid Tumors
Authors: Bonavolonta Paola , Iaconetta Giorgio , Improda Giovanni , Germano Cristiana , Borriello Gerardo , Goglia Federica , Abbate Vincenzo , Piombino Pasquale , Califano Luigi , Dell’Aversana Orabona Giovanni 
Published: July 2022

Abstract
Background
This is an observational cohort study on patients affected by malignant parotid tumors treated with total parotidectomy. The aim of our work is to analyze and compare the effects and complications after parotidectomy, using or not SurgiMend ®.

Methods
40 patients were retrospectively enrolled between September 2014 and June 2020. Basing on the placement of SurgiMend ® for parotid lodge reconstruction, the samples were divided into two groups. Thus, the incidence rate of complications after the surgical procedure was analyzed between the two groups.

Results
Patients in whom SurgiMend ® was used reported a lower rate of complications. The ANOVA test (p = 0.05) revealed a significant difference of Vancouver Scar Scale (VSS) between the two groups, representation as vascularity and pigmentation improvement, changing scar color, scar height reduction, and increased pliability.

Conclusion
Although many techniques are available to fill the parotidectomy defect, improve facial contour and prevent Frey’s syndrome, the use of SurgiMend ® matrix is one of most effective and reliable method to address these complications, with the advantage of decreased operative time due to not require an additional surgical donor site.
Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics
Authors: Sikimić Vlasta , Radovanović Sandro 
Published: July 2022

AbstractAs more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics (HEP) can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure (project duration, team number, and team size) and outcomes (citations per paper) of HEP experiments with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis (DEA) of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy (mean absolute error equal to 0.123), indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model.
Influence of order-disorder transition on the mechanical and thermophysical properties of A2B2O7 high-entropy ceramics
Authors: Zhu Jiatong , Wei Mingyue , Xu Jie , Yang Runwu , Meng Xuanyu , Zhang Ping , Yang Jinlong , Li Guangzhong , Gao Feng 
Published: July 2022

AbstractThe order-disorder transition (ODT) of A2B2O7 compounds obtained enormous attention owing to the potential application for thermal barrier coating (TBC) design. In this work, the influence of ODT on the mechanical and thermophysical properties of dual-phase A2B2O7 high-entropy ceramics was investigated by substituting Ce4+ and Hf4+ with different ionic radii on B-sites (Zr4+). The X-ray diffraction (XRD), Raman, and transmission electron microscopy (TEM) results show that $$r_{\rm{A}^{3+}}/r_{\rm{B}^{4+}}=1.47$$

r

A

3
+

/

r

B

4
+

=
1.47
is the critical value of ODT phase boundary with different doping B-site ion contents, and the energy dispersive spectroscopy (EDS) results further indicate the uniform distribution of elements. Interestingly, owing to the high intrinsic disorder derived from high-entropy effect, the A2B2O7 high-entropy ceramics exhibit unreduced modulus (E0 ≈ 230 GPa) and enhanced mechanical properties (HV ≈ 10 GPa, KIC ≈ 2.3 MPa·m0.5). A2B2O7 high-entropy ceramics exhibit excellent thermal stability with relatively high thermal expansion coefficients (TECs) (Hf0.25, 11.20×10−6 K−1, 1000 °C). Moreover, the matching calculation implied that the ODT further enhances the phonon scattering coefficient, leading to a relatively lower thermal conductivity of (La0.25Eu0.25Gd0.25Yb0.25)2(Zr0.85Ce0.15)2O7 (1.48–1.51 W/(m·K), 100–500 °C) compared with other components. This present work provides a novel composition design principle for high-entropy ceramics, as well as a material selection rule for high-temperature insulation applications.
Automated geometry measurement and deep rolling of butt welds
Authors: Heide Klaas Maximilian , Heikebrügge Steffen , Dänekas Christian , Breidenstein Bernd , Schaumann Peter 
Published: July 2022

Abstract
During the joining of two metal sheets by welding, a process-specific geometry of the weld is created. The local geometry of the created weld has a decisive influence on its fatigue strength. This is due to stress concentration at the geometric notches. In this paper, a process known from mechanical engineering called deep rolling is applied on butt welds. The influence on the local weld geometry and the local stress concentration after deep rolling is investigated. Additionally, a novel automated measurement system using optical laser line scanning is presented. The system is qualified for the evaluation of the local weld geometry regarding its flank angles and toe radii. The presented investigations show that the deep rolling process influences the stress concentrations determined by 2D-FE-simulations using real scan data. A correlation between the difference in toe radii or local notch stresses before and after deep rolling and the initial flank angle was found. This indicates that there are process and geometry specific conditions for the successful application of the deep rolling process.
A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection
Authors: Geng Huantong , Xu Ke , Zhang Yanqi , Zhou Zhengli 
Published: July 2022

AbstractAdaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during the evolutionary process. This paper proposes a novel classification tree based adaptive operator selection strategy for multi-objective evolutionary algorithm based on decomposition (MOEA/D-CTAOS). In our proposal, the classification tree is trained by the recorded data set which contains the information on the historical offspring. Before the reproduction at each generation, the classifier is used to predict each possible result obtained by different operators, and only one operator with the best result is selected to generate offspring next. Meanwhile, a novel differential evolution based on search inertia (SiDE) is designed to steer the evolutionary process in a more efficient way. The experimental results demonstrate that proposed MOEA/D-CTAOS outperforms other MOEA/D variants on UF and LZ benchmarks in terms of IGD and HV value. Further investigation also confirms the advantage of direction-guided search strategy in SiDE.
M&A and early investment decisions by digital platforms
Authors: Brutti Zelda , Rojas Luis E. 
Published: July 2022

AbstractWe propose an original theoretical framework that models early investment decisions of digital platform startups and use it to study how merger and acquisition policy affects consumer welfare by shaping such decisions. We formalize the investment options faced by digital platforms into a dual margin: investment in ‘customer engagement technology’, directed towards expanding the user base and in ‘intermediation technology’, directed towards lowering operational costs. Synergies through technological transfer and increased investment incentives in customer engagement explain consumer welfare improvements in the case of M&As occurring between platforms with disjoint user bases. On the other hand, lower competition erodes consumer welfare in the case of allowing M&As between platforms with overlapping user bases. We conclude that M&A policy guidance should depend on the relationship between the incumbent’s and startup’s target users and on the ability of the startup to catch up with the incumbent.
An equivalent approach for modelling butterfly-hysteresis passive variable friction damper
Authors: Wen Wenlu , Luo Ma , Chen Lin , Ma Zhenxiao , Pan Wen 
Published: July 2022

Abstract
This research work proposes an equivalent approach modelling the peculiar hysteretic behavior of passive variable friction damper, for engineers who call for a simplified way to simulate such damper in anti-earthquake engineering practice with acceptable accuracy. This approach models the special nonlinear behavior of the damper via paralleling 3 basic link elements that commonly built-in commercially available structural analysis software, instead of developing direct hysteretic model that demands for both post development functionality on software side and programming skill on engineer side. The mathematical derivation and analytical geometric equating work are comprehensively conducted. The numerical accuracy of analysis output yielded using equivalent model is digitally verified against the one yielded using generic FEM simulation of the same damper subject to several loading cases. Code-automated optimization workflow for yielding product-wise modelling parameters is developed that is convenient and straightforward for application by engineers without requirement of high expertise, which is demonstrated by generating optimized parameters to equivalently model tested specimen.

Article highlights

Passive variable friction damper can be modelled by assembling friction spring, elastic spring and wen model into parallel.

Code-automated optimization process can locate the most ideal modelling parameters combination in predefined numeral fields.

Hysteretic behaviors yielded from equivalent modelling approach match well corresponding ones from generic FEA simulation.
A preliminary study on wellbore flow interpretation of fiber optic vibration signals based on K-means clustering algorithm
Authors: Wu Xianzu , Gan Lixiong , Yuan Shixiong , Rui Deng 
Published: July 2022

AbstractThe wellbore flow analysis of optical fiber vibration signal depends on distributed optical fiber logging. Distributed optical fiber logging technology identifies the fluid in the well through distributed optical fiber acoustic sensor (DAS) and distributed optical fiber temperature sensor (DTS). Distributed optical fiber sensor has the advantages of small underground interference, high efficiency and low cost. In this paper, the wellhead data extracted by the distributed optical fiber acoustic sensor is used to calculate the upper bound of the fluid sound frequency band in the pipe by nonlinear least squares fitting. The K-means clustering algorithm is used to cluster the optical fiber vibration signals in the low frequency band. According to the clustering results, the ratio of the optical fiber signal eigenvalues of each production layers is obtained, and the trend of the ratio of the optical fiber signal eigenvalues of each production layers is judged to be close to the trend of the water absorption intensity. Compared with traditional acoustic logging, the wellbore flow analysis using distributed optical fiber acoustic sensor can quickly determine the production contribution of each layer and the change of fluid phase state in the production cycle. Combined with traditional production logging technology, distributed optical fiber logging shows its reliability and accuracy in data collection, logging interpretation and production application. Starting from the principle of distributed optical fiber acoustic sensing technology, this paper briefly expounds the properties of distributed optical fiber acoustic sensor and the principle of injection profile logging, systematically introduces the processing of distributed optical fiber acoustic data, and emphatically introduces the accuracy of K-means clustering algorithm for analyzing distributed optical fiber acoustic signal and qualitative judgment of production layer, which provides a new idea for judging the accuracy of production layers.
Type D Personality Independently Predicts Erectile Dysfunction in Taiwanese Young Men
Authors: Fan Yu-Hua , Liou Ying-Jay , Cheng Wei-Ming 
Published: July 2022

ABSTRACT

Background
Studies have demonstrated that erectile dysfunction has a well-established bidirectional relationship with depression and have indicated an independent association of type D personality (TDP) with depression. Nevertheless, the relationship of erectile dysfunction with TDP has not been sufficiently examined.

Aim
To examine the associations among depression symptoms, TDP, and erectile dysfunction.

Methods
The cross-sectional study recruited 1740 sexually active Taiwanese men (age: 20–40 years) with erectile dysfunction. Participants completed an online questionnaire collecting general demographic information and containing the International Index of Erectile Function-5, Type D Scale-14, and Depression and Somatic Symptom Scale. Pearson’s chi squared or Student’s t’test was conducted for comparisons between participants with vs without TDP. We conducted multivariate and univariate logistic regression analysis to investigate the predictors of moderate/severe erectile dysfunction.

Outcomes
The prevalence of TDP and moderate/severe erectile dysfunction, the associations between TDP and the severities of depression symptoms and erectile dysfunction, and independent risk factors for moderate/severe erectile dysfunction.

Results
A total of 360 (15.9%) and 941 (54.08%) men had moderate/severe erectile dysfunction and TDP, respectively. Men with TDP reported significantly higher total and subscale scores in the International Index of Erectile Function-5 and the Depression and Somatic Symptom Scale; this group also exhibited higher prevalence of moderate or severe erectile dysfunction. According to the univariate analysis, all variables significantly predicted moderate or severe erectile dysfunction except for age and body mass index. A multivariate analysis revealed TDP status and depression symptoms to be independent predictors of moderate or severe erectile dysfunction. With regard to subscales of the Type D Scale-14, we discovered that social inhibition had a greater influence on moderate or severe erectile dysfunction than had negative affectivity. A mediation analysis indicated that the relationship between TDP and erectile dysfunction was mediated by depressive symptoms.

Clinical Implications
Research has suggested that compared with the general population, individuals with TDP are less willing to seek medical consultation, have lower medication adherence, and have heightened risk of depression; urologists should strive to identify patients with TDP.

Strengths & Limitations
This study is the first to investigate the association of TDP with erectile dysfunction in a large population of young men by using validated instruments. Conclusions on causality cannot be drawn due to the study’s cross-sectional nature.

Conclusion
This research revealed relationships among TDP, depression symptoms, and erectile dysfunction in Taiwanese young men.
Is Law Like Social Sciences? On ‘New Private Law Theory’ and the Call for Disciplinary Pluralism
Authors: Resta Giorgio 
Published: July 2022

AbstractThis article reflects upon the challenges arising from a “pluralistic” approach to private law theory. Having distinguished the different meanings of the notion of “pluralism”, it focuses on epistemic pluralism and stresses the importance of an interdisciplinary stance. At the same time, it criticizes the emphasis put mainly on the dialogue with social sciences, clearly mirrored by the traditional organization of the ERC panels. Finally, it advocates a deeper and more critical engagement with the economic approaches to law.