bluuebunny/crossref_metadata_2025_split huggingface dataset:
A study of behavioral decay in design patterns
Authors: Reimanis Derek , Izurieta Clemente 
Published: November 2023

AbstractDesign patterns represent a means of communicating reusable solutions to common problems, provided they are implemented and maintained correctly. However, many design pattern instances erode as they age, sacrificing qualities they once provided. Identifying such instances of pattern decay is valuable because it allows for proactive attempts to extend the longevity and quality attributes of pattern components. Apart from structural decay, design patterns can exhibit symptoms of behavioral decay. We utilized a taxonomy that characterizes these negative behaviors and designed a case study wherein we measured structural and behavioral decay, hereafter referred to as pattern grime, as well as pattern quality and size, across pattern evolutions. We evaluated the relationships between structural and behavioral grime and found statistically significant cases of strong correlations between specific types of structural and behavioral grime. Furthermore, we extended the QATCH operational software quality model to incorporate design pattern evolution metrics and measured and correlated software quality to the presence of behavioral grime in software systems. Our results suggest a strong inverse relationship between software quality and behavioral grime.
A Highly Sensitive and Specific Non‐Invasive Test through Genome‐Wide 5‐Hydroxymethylation Mapping for Early Detection of Lung Cancer
Authors: Ren Yijiu , Zhang Zhou , She Yunlang , He Yayi , Li Dongdong , Shi Yixiang , He Chuan , Yang Yang , Zhang Wei , Chen Chang 
Published: November 2023

AbstractLow‐dose computed tomography screening can increase the detection for non‐small‐cell lung cancer (NSCLC). To improve the diagnostic accuracy of early‐stage NSCLC detection, ultrasensitive methods are used to detect cell‐free DNA (cfDNA) 5‐hydroxymethylcytosine (5hmC) in plasma. Genome‐wide 5hmC is profiled in 1990 cfDNA samples collected from patients with non‐small cell lung cancer (NSCLC, n = 727), healthy controls (HEA, n = 1,092), as well as patients with small cell lung cancer (SCLC, n = 41), followed by sample randomization, differential analysis, feature selection, and modeling using a machine learning approach. Differentially modified features reflecting tissue origin. A weighted diagnostic model comprised of 105 features is developed to compute a detection score for each individual, which showed an area under the curve (AUC) range of 86.4%–93.1% in the internal and external validation sets for distinguishing lung cancer from HEA controls, significantly outperforming serum biomarkers (p < 0.001). The 5hmC‐based model detected high‐risk pulmonary nodules (AUC: 82%)and lung cancer of different subtypes with high accuracy as well. A highly sensitive and specific blood‐based test is developed for detecting lung cancer. The 5hmC biomarkers in cfDNA offer a promising blood‐based test for lung cancer.
Intravascular lobular capillary haemangioma of the external jugular vein
Authors: Derboghossian Teal , Hill Samantha , Peden Samantha , Lightfoot Tony 
Published: November 2023

AbstractA case of non‐thrombotic intravascular pathology of the external jugular vein. The patient presented with a 5‐week history of a pain‐less lump on the right side of the neck. There was no history of trauma or associated signs or symptoms. Ultrasound imaging showed an intravenous mass that was excised and histopathologically diagnosed as an intravascular lobular capillary haemangioma (IVLCH). The rare tumour most commonly effects the neck, face, and upper extremities. Ultrasound is often the first‐line imaging in such cases, and IVLCH should be considered in the differential under specific circumstances.
Reading Engagement Matters! A New Scale to Measure and Support Children's Engagement with Books
Authors: McGeown Sarah , Smith Kristin Conradi 
Published: November 2023

AbstractWhile there is a considerable body of research demonstrating benefits of book reading, the quality and depth of engagement children experience while reading is essential to ensure positive reading experiences and outcomes. In this article we describe four dimensions of reading engagement: behavioral, cognitive, affective, and social, illustrating how each dimension is essential to ensure children benefit from the rich and diverse experiences that books have to offer. We next share a new scale, developed with input from researchers, teachers, and children, for teachers to use to measure children's reading engagement and support their students to get the most out of their reading experiences. We end with practical ideas for teachers to use the scale to inform instructional practices.
Terms of recurrence sequences in the solution sets of norm form equations
Authors: Hajdu Lajos , Sebestyén Péter 
Published: November 2023

AbstractThe structure as well as several arithmetic properties of the solution sets of norm form equations are of classical and recent interest. In this paper, we give a finiteness result for terms of linear recurrence sequences appearing in the coordinates of solutions of norm form equations. Our main theorem yields a common generalization of certain recent results from the literature.
Single-firm inference in event studies via the permutation test
Authors: Nguyen Phuong Anh , Wolf Michael 
Published: November 2023

AbstractReturn event studies generally involve several firms but there are also cases when only one firm is involved. This makes the relevant testing problems, abnormal return and cumulative abnormal return, more difficult since one cannot exploit the multitude of firms (by using a relevant central limit theorem, say) to design hypothesis tests. We propose a permutation test which is of nonparametric nature and more generally valid than the tests that have previously been proposed in the literature in this context. We address the question of the power of the test via a brief simulation study and also illustrate the method with two applications to real data.
Wood-water interactions of primers to enhance wood-polyurethane bonding performance
Authors: Böger Thomas , Engelhardt Max , Suh Francis Tangwa , Richter Klaus , Sanchez-Ferrer Antoni 
Published: November 2023

AbstractToday, using one-component polyurethane (1c-PUR) adhesives in the manufacturing of engineered wood products from spruce is common practice. However, the use of other wood species can require the application of a primer to fulfill normative requirements. Previous research shows the primers’ effectiveness, especially in moist environments. However, the primers’ exact mode of action remains not yet fully understood. We hypothesize a reduction in the hygroscopic behavior of the primer-treated wood—intensity and kinetics—that could reduce the formation of stresses in the bond line region. To test this hypothesis, two commercially available primers, based on Polysorbate 20 and poly(ethylene glycol), and the hydroxymethylated resorcinol (HMR) primer are examined with wood from beech, birch, larch, and Douglas fir. Swelling experiments show that of each primer a portion infiltrates and swells the wood cell walls, affecting the wood’s hygroscopic and mechanical properties. In stepwise sorption experiments, it is seen that the primers influence differently the amount of moisture uptaken by the wood (adsorption). The rate at which the moisture spreads within the wood (diffusivity) also changed differently for the primers, while the rate at which the moisture moves through the wood (permeability) remains unchanged. The application of all primers improves the bulk flow behavior and thus the void penetration of the adhesive into the lumina in the interphase region, which in turn leads to a reduced bond line thickness. All three primers improve the tensile shear strength. The hygroscopic changes caused by the primers appear too small to be claimed as the sole and primary cause of their functionality, whereas more relevance is seen in the primers’ cell wall infiltration and the increased adhesive’s void penetration.
IS-SAR: an irrigation scheduling web application for Hass avocado orchards based on Sentinel-1 images
Authors: Erazo-Mesa Edwin , Murillo-Sandoval Paulo J. , Ramírez-Gil Joaquín Guillermo , Benavides Kevin Quiroga , Sánchez Andrés Echeverri 
Published: November 2023

AbstractAs the Hass avocado crop expands exponentially in Colombia, concern about its increasing water use is on the rise. This research aimed to develop IS-SAR, a free-access web application to schedule irrigation for Valle del Cauca’s Hass growers. We calibrated the water cloud (WCM) and artificial neural network (ANN) models using field data measurements from Hass avocado orchard plots in Valle del Cauca (Colombia) and Sentinel 1 (S1) satellite imagery measurements and evaluated their performance computing the root-mean-square error (RMSE) and Pearson correlation coefficient ($$r$$
r
). IS-SAR estimates the surface soil water content from the most recent S1 image, becomes it in water depth, and recommends to users apply irrigation according to allowable depletion limits, computed from a spatially distributed $$\theta$$
θ
at field capacity and permanent wilting point obtained from a soil database of the study area. Our results indicate that the surface soil water content was retrieved with a better performance by the ANN (RMSE = 0.05 m3 m−3, $$r=0.74$$

r
=
0.74

), compared with the WCM (RMSE = 0.06 m3 m−3, $$r=0.47$$

r
=
0.47

). IS-SAR simulations in validation orchard plots result in irrigation events of up to 107 L tree−1 for 3.4 h. The IS-SAR web application provides near-real-time irrigation information to assist Hass avocado growers in designing better irrigation routines and improving the regional understanding of water crop consumption.
Brain metastases of sarcoma: a rare phenomenon in rare tumours
Authors: Jędrys Wiktoria , Leśniak Aleksandra , Borkowska Aneta , Rutkowski Piotr , Sobczuk Paweł 
Published: November 2023

AbstractThe usual site for distant metastases of sarcoma is lungs, while brain metastasis (BM) occurs much less frequently and usually late in the disease progression. Despite the advancement in cancer treatment, the outcome for patients with brain metastasis is poor, and their lifespan is short. The frequency of BM in sarcoma seems to be affected by the location and histology of the primary tumour. Sarcoma subtypes with a high propensity for brain metastasis are ASPS, leiomyosarcoma and osteosarcoma. There are no clear guidelines for the treatment of sarcoma brain metastasis. However, therapeutic options include surgery, radiotherapy and chemotherapy, and are often combined. Targeted therapies are a promising treatment option for sarcoma but require investigation in patients with BM. The following review presents the data on sarcoma brain metastasis incidence, treatment and prognosis.
How we do it: the Zurich Microsurgery Lab technique for placenta preparation
Authors: Höbner Lara Maria , Staartjes Victor E. , Colombo Elisa , Sebök Martina , Regli Luca , Esposito Giuseppe 
Published: November 2023

Abstract
Background
Perfused placentas provide an excellent and accessible model for microvascular dissection, microsuturing and microanastomosis training — particularly in the early microsurgical learning curve. This way, a significant amount of live animals can be spared.

Method
We present the Zurich Microsurgery Lab protocol, detailing steps for obtaining, selecting, cleaning, flushing, cannulating, and preserving human placentas — as well as microsurgical training examples — in a tried-and-true, safe, cost-effective, and high-yield fashion.

Conclusion
Our technique enables highly realistic microsurgical training (microdissection, microvascular repair, microanastomosis) based on readily available materials. Proper handling, preparation, and preservation of the perfused placenta models is key.
Givens rotations for QR decomposition, SVD and PCA over database joins
Authors: Olteanu Dan , Vortmeier Nils , Živanović Ɖorđe 
Published: November 2023

AbstractThis article introducesFiGaRo, an algorithm for computing the upper-triangular matrix in the QR decomposition of the matrix defined by the natural join over relational data.FiGaRo’s main novelty is that it pushes the QR decomposition past the join. This leads to several desirable properties. For acyclic joins, it takes time linear in the database size and independent of the join size. Its execution is equivalent to the application of a sequence of Givens rotations proportional to the join size. Its number of rounding errors relative to the classical QR decomposition algorithms is on par with the database size relative to the join output size. The QR decomposition lies at the core of many linear algebra computations including the singular value decomposition (SVD) and the principal component analysis (PCA). We show howFiGaRocan be used to compute the orthogonal matrix in the QR decomposition, the SVD and the PCA of the join output without the need to materialize the join output. A suite of experiments validate thatFiGaRocan outperform both in runtime performance and numerical accuracy the LAPACK library Intel MKL by a factor proportional to the gap between the sizes of the join output and input.
Particulate emissions from biomass power plants: a practical review and measurement uncertainty issues
Authors: Steiner Dominik , Lanzerstorfer Christof 
Published: November 2023

AbstractThis paper follows a practical approach to evaluate particulate matter emissions from biomass-fuelled power plants and analysis measurement uncertainties. Sixty-seven measurement reports from 11 biomass power plants, all equipped with fabric filters as dust control device, were analysed. Particulate matter emission measurements by gravimetrical standard reference method were chosen to provide the most reliable values to investigate the influence of bag filter parameters, such as media age or bag filter length. 92.1% of total dust emission measurements were below lower BAT-associated emission levels (BAT-AELs) for waste incineration. Data also showed that bag filter media older than 6 years can still secure very low dust emission levels. This work also gives a summary of findings and problems with uncertainty of automated measuring systems for dust emissions in biomass combustion applications in practice and quality assurance level one (QAL1) process related to very low emission levels such as lower best available techniques reference document (BREF) values. Practical examples on issues of continuous dust emission monitoring devices are also given.
Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study
Authors: Solís-García Javier , Vega-Márquez Belén , Nepomuceno Juan A. , Riquelme-Santos José C. , Nepomuceno-Chamorro Isabel A. 
Published: November 2023

Abstract
Sepsis is a life-threatening condition whose early recognition is key to improving outcomes for patients in intensive care units (ICUs). Artificial intelligence can play a crucial role in mining and exploiting health data for sepsis prediction. However, progress in this field has been impeded by a lack of comparability across studies. Some studies do not provide code, and each study independently processes a dataset with large numbers of missing values. Here, we present a comparative analysis of early sepsis prediction in the ICU by using machine learning (ML) algorithms and provide open-source code to the community to support future work. We reviewed the literature and conducted two phases of experiments. In the first phase, we analyzed five imputation strategies for handling missing data in a clinical dataset (which is often sampled irregularly and requires hand-crafted preprocessing steps). We used the MIMIC-III dataset, which includes more than 5,800 ICU hospital admissions from 2001 to 2012. In the second phase, we conducted an extensive experimental study using five ML methods and five popular deep learning models. We evaluated the performance of the methods by using the area under the precision-recall curve, a standard metric for clinical contexts. The deep learning methods (TCN and LSTM) outperformed the other methods, particularly in early detection tasks more than 4 hours before sepsis onset. The motivation for this work was to provide a benchmark framework for future research, thus enabling advancements in this field.

Graphical Abstract
Effect of stocking density on growth, survival and cannibalism of juvenile pikeperch, Sander lucioperca (L.), in a recirculating aquaculture system
Authors: Kozłowski Michał , Piotrowska Iwona 
Published: November 2023

AbstractThe effect of stocking density on the rearing performance of pikeperch juveniles was studied. Three separate experiments (I, II and III) were conducted with fish of an initial average body weight of 1.3, 6.7 and 19.2 g, respectively. Each experimental design consisted of three treatments (three replicates) with different initial stocking densities (low, medium and high). Experiments lasted 28 days in a recirculating aquaculture system (21°C, 24L:0D) with an initial stocking density of 0.78, 1.04 and 1.30 kg m−3 in experiment I, 2.68, 4.02 and 5.36 kg m−3 in experiment II, and 3.84, 7.68 and 11.52 kg m−3 in experiment III. The results of our study showed that in experiment I, the use of a stocking density of 1.04 kg m−3 resulted in the highest body weight and survival, as well as the lowest feed conversion ratio and cannibalism. In experiments II and III, the pikeperch growth rate decreased, and their feed conversion ratio increased gradually with increasing stocking density. Our study demonstrated that based on growth parameters, densities of 1.04, 2.68 and 3.84 kg m−3 can be used for pikeperch with an initial body weight of 1.3, 6.7 and 19.2 g, respectively.
A parsimonious approach to predict regions affected by sewer-borne contaminants in urban aquifers
Authors: Rojas-Gómez Karen L. , Binder Martin , Walther Marc , Engelmann Christian 
Published: November 2023

AbstractLeaky urban drainage networks (UDNs) exfiltrating wastewater can contaminate aquifers. Detailed knowledge on spatiotemporal distributions of water-dissolved, sewer-borne contaminants in groundwater is essential to protect urban aquifers and to optimize monitoring systems. We evaluated the effect of UDN layouts on the spreading of sewer-borne contaminants in groundwater using a parsimonious approach. Due to the UDN’s long-term leakage behavior and the existence of non-degradable sewer-borne contaminants (equivalent to a conservative and constant contaminant source), we employed a concept of horizontal line sources to mimic the UDN layout. This does not require the consideration of bio-degradation processes or temporal delay and effectively bypasses the vadose zone, thus reducing computational requirements associated with a full simulation of leakages. We used a set of synthetic leakage scenarios which were generated using fractals and are based on a real-world UDN layout. We investigated the effects of typical leakage rates, varying groundwater flow directions, and UDN’s layouts on the shape of the contaminant plume, disregarding the resulted concentration. Leakage rates showed minimal effects on the total covered plume area, whereas 89% of the variance of the plume’s geometry is explained by both the UDN’s layout (e.g., length and level of complexity) and groundwater flow direction. We demonstrated the potential of applying this approach to identify possible locations of groundwater observation wells using a real UDN layout. This straightforward and parsimonious method can serve as an initial step to strategically identify optimal monitoring systems locations within urban aquifers, and to improve sewer asset management at city scale.
Synthesis of prussian blue analog (Co/TCNi/HCCr) nanoparticles using a facile co-precipitation approach and evaluation of their dielectric characteristics for electronic applications
Authors: Mosaad S. , Ibrahim Ahmed H. , Elesh E. , El-Damhogi D. G. , Elgazzar Elsayed 
Published: November 2023

AbstractThe Prussian blue analog Co0.4/(TCNi)0.2/(HCCr)0.6 (PBA) nanoparticles were synthesized via a chemical co-precipitation approach. The structural characteristics of the nanocomposite (NCp) were investigated using the XRD technique, which revealed that the synthetic PBA has a tiny crystallite size and poor crystallinity. The XRD analysis suggests that the fabricated NCp has a face-centered cubic structure with space group Fm-3 m. For deep microstructure analysis, SEM, EDX, and AFM were employed to describe the topological nature and surface architecture. It is observed that the PBA nanocomposite has a small crystallite size of ~13.66 ± 0.32 nm. The particles’ surface displays a high degree of homogeneity, while the grain boundaries are clearly discernible, presenting a spherical morphology with an average diameter of 22 ± 2.1 nm. The porosity was ascertained from an AFM image of approximately 140. Furthermore, the value of the average roughness (Rs) is 6.86 nm. The AC conductivity of PBA in disc form was calculated at various temperatures and frequencies. The results indicate that the dielectric constant (ɛ′) of PBA nanoparticles exhibits a rise from 163 to 3464 throughout the frequency range of 10–100 kHz at ambient temperature and from 27 to 6000 at 293 K. This finding provides confirmation that the dielectric characteristics of PBA nanoparticles are enhanced during the process of annealing. The values of the exponent factor (s) range from 0 to 1; this behavior is connected to the correlated barrier hopping model (CBHM) for AC conductivity. The value of WM was ascertained to be 1.69 eV. Furthermore, the values of activation energy were calculated, and these values decreased from 0.20 to 0.17 eV with annealing. The change in loss tangent (tanδ) as a function of angular frequency at various temperatures is also shown. The investigation of the dielectric characteristics was measured over a wide range of frequencies and temperatures to get valuable data about the dissipation properties of the prospective electronic applications.
Mixed integer programming with kriging surrogate model technique for dispersion control of photonic crystal fibers
Authors: Hammad Ahmed E. , Hameed Mohamed Farhat O. , Obayya S. S. A. , Etman Ahmed S. 
Published: November 2023

AbstractIn this paper, mixed integer nonlinear programming (MINLP) optimization algorithm integrated with kriging surrogate-model is newly formulated to optimize the dispersion characteristics of photonic crystal fibers (PCFs). The MINLP is linked with full vectorial finite difference method (FVFDM) to optimize the modal properties of the PCFs. Through the optimization process, the design parameters can take real and/or integer values. The integer values can be used to selectively fill the PCF air holes to control its dispersion characteristics. However, the other optimization techniques deal with real design parameters where the PCF can be optimized using none or predefined infiltrated air holes. The MINLP algorithm is used to obtain an ultra-flat zero dispersion over a broadband of wavelength range from 1.25 to 1.6 μm using silica PCF selectively infiltrated with Ethanol material. To show the superiority of the proposed algorithm, nematic liquid crystal selectively infiltrated PCFs are also designed with high negative flat dispersion over wide range of wavelengths from 1.25 to 1.6 μm for the quasi transverse magnetic (TM) and the quasi transverse electric (TE) modes. Such designs have negative flat dispersions of − 163 ± 0.9 and − 170 ± 1.2 ps/Km nm, respectively over the studied wavelength range. Therefore, the MINLP algorithms could be used efficiently for the design and optimization of selectively filled photonic devices.