bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flow characteristics and energy dissipation over stepped spillway with various step geometries: case study (steps with curve end sill)
Authors: Jahad Udai A. , Chabuk Ali , Al-Ameri Riyadh , Majdi Hasan Sh. , Majdi Ali , Al-Ansari Nadhir , Abed Salwan Ali 
Published: February 2024

AbstractStepped weirs are used in a wide range of applications, designed to increase energy dissipation. In this study, laboratory experiments were conducted in a flume on six stepped weir models, with a downstream angle of θ = 26.6°. The physical models used were on a scale of 10:1, and tests of discharges up to 0.055 m3/s were carried out. Several step geometries including traditional step, sill and curve geometries were used to study flow behavior and overall energy dissipation. The laboratory investigations were augmented by modelling numerically the within step flow and energy behavior using a 2-D CFD model, incorporating the k-ε model for turbulence closure. The results showed that energy dissipation was greatest for the curved steps by about 10.5%, where it was observed that the skimming flow regime was shifted to a higher discharge range. Numerical modelling results showed good agreement with the experimental results. An inspection of the modelled streamlines highlighted the increase in vortex intensity for the curve model, reflecting the strong circulation observed. The predicted stepwise energy dissipation showed the energy dissipation increase when the step number Ns increases. For the range of step height hs, tested, our results showed that energy dissipation increased with step height. The results from this study can be used to inform engineering design for steps with θ = 26.6° and provide estimates of the expected energy dissipation and residual energy.
Analysis of a class of completely non-local elliptic diffusion operators
Authors: Li Yulong , Çelik Emine , Telyakovskiy Aleksey S. 
Published: February 2024

AbstractThis work explores the possibility of developing the analog of some classic results from elliptic PDEs for a class of fractional ODEs involving the composition of both left- and right-sided Riemann-Liouville (R-L) fractional derivatives, $${D^\alpha _{a+}}{D^\beta _{b-}}$$

D

a
+

α

D

b
-

β

, $$1<\alpha +\beta <2$$

1
<
α
+
β
<
2

. Compared to one-sided non-local R-L derivatives, these composite operators are completely non-local, which means that the evaluation of $${D^\alpha _{a+}}{D^\beta _{b-}}u(x)$$

D

a
+

α

D

b
-

β

u

(
x
)

at a point x will have to retrieve the information not only to the left of x all the way to the left boundary but also to the right up to the right boundary, simultaneously. Therefore, only limited tools can be applied to such a situation, which is the most challenging part of the work. To overcome this, we do the analysis from a non-traditional perspective and eventually establish elliptic-type results, including Hopf’s Lemma and maximum principles. As $$\alpha \rightarrow 1^-$$

α
→

1
-

or $$\alpha ,\beta \rightarrow 1^-$$

α
,
β
→

1
-

, those operators reduce to the one-sided fractional diffusion operator and the classic diffusion operator, respectively. For these reasons, we still refer to them as “elliptic diffusion operators", however, without any physical interpretation.
Evaluation of the fatigue resistance of butt-welded joints in towers of wind turbines — a comparison of experimental studies with small scale and component tests as well as numerical based approaches with local concepts
Authors: Glienke R. , Kalkowsky F. , Hobbacher A. F. , Holch A. , Thiele M. , Marten F. , Kersten R. , Henkel K.-M. 
Published: February 2024

AbstractWind turbines must endure a high number of load cycles during their service lifetime. Therefore, the fatigue strength verification plays an important role. Butt-welded joints are one of the most common structural details in the tower structure of wind turbines. In general, the nominal stress method is used for their fatigue verification. The Eurocode 3 part 1–9 is the current design standard for this field of application and defines the fatigue resistance by pre-defined FAT-classes. This paper presents recent results of fatigue tests on small-scaled specimens and large components with transverse butt welds to discuss the validity of the FAT-class. In addition, results from numerical simulations for the verification with the effective notch stress and the crack propagation approach are used for comparison. For this purpose, macroscopic measurements of the weld seam geometry and a 3D scan were used for a realistic consideration of the notch effect in the simulation. Based on the consistency between the numerical results and the fatigue tests, the influence of the seam geometry on the fatigue resistance was worked out. Furthermore, a prediction of the fatigue strength of butt-welded joints with plate thicknesses up to 80 mm was carried out.
Deference or critical engagement: how should healthcare practitioners use clinical ethics guidance?
Authors: Davies Ben , Parker Joshua 
Published: February 2024

AbstractHealthcare practitioners have access to a range of ethical guidance. However, the normative role of this guidance in ethical decision-making is underexplored. This paper considers two ways that healthcare practitioners could approach ethics guidance. We first outline the idea of deference to ethics guidance, showing how an attitude of deference raises three key problems: moral value; moral understanding; and moral error. Drawing on philosophical literature, we then advocate an alternative framing of ethics guidance as a form of moral testimony by colleagues and suggest that a more promising attitude to ethics guidance is to approach it in the spirit of ‘critical engagement’ rather than deference.
A Community-Based Reproductive Health Care Model Effectively Enhances Reproductive Health Among Lahu Women in Northern Thailand
Authors: Suratana Soontaree , Boonchiang Waraporn , Apidechkul Tawatchai , Naksen Warangkana , Mulikaburt Thanatchaporn , Chomsri Pimpisa , Matrakul Mullika 
Published: February 2024

Abstract
Background
Inadequate and delayed access to sexual and reproductive health services among the Hill Tribe population can be attributed to the intersection of socioeconomic challenges and distinct cultural practices. To address this limitation and create a tailored model capable of addressing it, this study assesses the effectiveness of the Community-Based Reproductive Health Care Model (CRHC) in enhancing reproductive health knowledge, attitudes, and practices among Lahu women, a prominent hill tribe population in Northern Thailand.

Methods
Implementing the CRHC model includes training programs for community influencers and subsequent education for Lahu women using culturally adapted courses. The effectiveness of the model is assessed through pre-test and post-test comparisons of knowledge, attitudes, and practices related to reproductive health care and analyzed using paired t-tests and repeated ANOVA.

Results
The scores for knowledge, attitudes, and practices among Lahu women changed from 8.92 ± 2.02, 52.99 ± 5.54, and 27.76 ± 6.67 to 10.47 ± 2.32 (p < 0.001), 56.61 ± 5.54 (p < 0.001), and 29.47 ± 6.76 (p = 0.030), respectively. Significant improvements are observed in these areas, particularly in maternal health practices among pregnant Lahu women (n = 11). This study additionally evaluates the model’s impact on the healthcare system by analyzing changes in government performance indexes, showing increased access to high-quality antenatal care and contraceptive usage. This study highlights the challenges faced by hill tribe populations in accessing healthcare, emphasizing the need for tailored reproductive health education and the importance of addressing health insurance barriers.

Conclusion
The CRHC model’s success illustrates the potential of community-based, culturally sensitive interventions in improving reproductive health outcomes, providing valuable insights for similar interventions in other indigenous or marginalized communities.
CL-BPUWM: continuous learning with Bayesian parameter updating and weight memory
Authors: He Yao , Yang Jing , Li Shaobo , Hu Jianjun , Ren Yaping , Ji Qing 
Published: February 2024

AbstractCatastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the parameters important to the previous task to avoid catastrophic forgetting has a positive effect; existing regularization methods cause the gradient to be near zero because the loss is at the local minimum. To solve this problem, we propose a new continuous learning method with Bayesian parameter updating and weight memory (CL-BPUWM). First, a parameter updating method based on the Bayes criterion is proposed to allow the neural network to gradually obtain new knowledge. The diagonal of the Fisher information matrix is then introduced to significantly minimize computation and increase parameter updating efficiency. Second, we suggest calculating the importance weight by observing how changes in each network parameter affect the model prediction output. In the process of model parameter updating, the Fisher information matrix and the sensitivity of the network are used as the quadratic penalty terms of the loss function. Finally, we apply dropout regularization to reduce model overfitting during training and to improve model generalizability. CL-BPUWM performs very well in continuous learning for classification tasks on CIFAR-100 dataset, CIFAR-10 dataset, and MNIST dataset. On CIFAR-100 dataset, it is 0.8%, 1.03% and 0.75% higher than the best performing regularization method (EWC) in three task partitions. On CIFAR-10 dataset, it is 2.25% higher than the regularization method (EWC) and 0.7% higher than the scaled method (GR). It is 0.66% higher than the regularization method (EWC) on the MNIST dataset. When the CL-BPUWM method was combined with the brain-inspired replay model under the CIFAR-100 and CIFAR-10 datasets, the classification accuracy was 2.35% and 5.38% higher than that of the baseline method, BI-R + SI.
TD-Net:tiny defect detection network for industrial products
Authors: Shao Rui , Zhou Mingle , Li Min , Han Delong , Li Gang 
Published: February 2024

AbstractThe detection of tiny defects in industrial products is important for improving the quality of industrial products and maintaining production safety. Currently, image-based defect detection methods are ineffective in detecting tiny and variously shaped defects. Therefore, this paper proposes a tiny defect detection network (TD-Net) for industrial products to improve the effectiveness of tiny defect detection. TD-Net improves the overall defect detection effect, especially the detection effect of tiny defects, by solving the problems of downsampling of tiny defects, pre-filtering of conflicting deep and shallow semantic information, and cascading fusion of multi-scale information. Specifically, this paper proposes the Defect Downsampling (DD) module to realize the defect information supplementation during the backbone downsampling process and improve the problem that the stepwise convolution easily misses the detection of tiny defects. Meanwhile, the Semantic Information Interaction Module (SIIM) is proposed, which fuses deep and shallow semantic features, and is designed to interact the fused features with shallow features to optimize the detection of tiny defects. Finally, the Scale Information Fusion Module (SIFM) is proposed to improve the Path Aggregation Network (PANet) for cascading fusion and information focus on different scale information, which enables further improvement of defect detection performance of TD-Net. Extensive experimental results on the NEU–DET data set (76.8$$\%$$
%
mAP), the Peking University PCB defect data set (96.2$$\%$$
%
mAP) and the GC10-DET data set (71.5$$\%$$
%
mAP) show that the proposed TD-Net achieves competitive results compared with SOTA methods with the equivalent parameter quantity.
Why is economics the only discipline with so many curves going up and down? There is an alternative
Authors: Dosi Giovanni 
Published: February 2024

AbstractEven the most rudimentary training from Economics 101 starts with demand curves going down and supply curves going up. They are so ‘natural’ that they sound even more obvious than the Euclidian postulates in mathematics. But are they? What do they actually mean? Start with “demand curves”. Are they hypothetical ‘psychological constructs’ on individual preferences? Propositions on aggregation over them? Reduced forms of actual dynamic proposition of time profiles of prices and demanded quantities? Similar considerations apply to “supply curves” The point here, drawing upon the chapter by Kirman and Dosi, in Dosi (Dosi, Foundations of Complex Evolving Economies Innovation, Organization and Industrial Dynamics, Oxford University Press, 2023), is that the forest of demand and supply curves is basically there to populate the analysis with double axiomatic notions of equilibria, both ‘in the head’ of individual agents, and in environments in which they operate. Supply and demand “curves”, I am arguing, are one of the three major methodological stumbling blocks on the way of progress in economics, the other related ones being ‘utility functions’ and ‘production functions’. There is an alternative: represent markets and industries how they actually works, and model them both via fully fledged Agent Based Models and via lower dimensional dynamical systems.
Technology Use, Maize Productivity, and Weather in West Africa
Authors: Etwire Prince M. , Fielding David , Martey Edward 
Published: February 2024

AbstractThis paper presents estimates of the association between maize yield and weather using survey data from Ghana, Mali and Nigeria, allowing for the possibility that farmers’ choices about agricultural technology may themselves depend on weather. We find that the association between yield and weather varies substantially according to these choices. We then use our estimates to forecast the change in yield under alternative weather change scenarios. All of these scenarios envisage an increase in temperature, but some envisage a rise in rainfall while others envisage a fall. In almost all scenarios, there is a substantial fall in productivity. In the absence of adaptation measures, weather change is likely to substantially reduce farm income in all three countries.
Prediction and classification of IoT sensor faults using hybrid deep learning model
Authors: Seba Adisu Mulu , Gemeda Ketema Adere , Ramulu Perumalla Janaki 
Published: February 2024

AbstractThe quality and reliability of internet of thing (IoT) ecosystems heavily rely on accurate and dependable sensor data. However, resource limited sensors are prone to failure due to various factors like environmental disturbances and electrical noise in which they can produce erroneous and faulty measurements. These can have significant consequences across different domains, including a threat to safety in critical systems. Though many researches have been conducted, the existing literature primarily focuses on fault detection in the sensor data, while fault detection is useful, it is still a reactive approach that identifies the faults after they have occurred, meaning that actions are taken after the fault has already impacted the system, potentially leading to negative consequences. In this study, a proactive approach has been proposed by developing a two-stage solution. In the first stage, a hybrid convolutional neural network-long short term memory (CNN-LSTM) model was trained to forecast sensor measurements based on historical data, while in the second stage, the forecasted measurements were passed to a hybrid convolutional neural network-multi layer perceptron (CNN-MLP) model that has been trained to recognize different types of sensor faults and classify the new measurements accordingly. By passing the forecasted sensor values as input to the classification model and categorizing them as normal, bias, drift, random or poly-drift, anticipated the potential faults before they manifest. The publicly available Intel Lab data raw dataset is used, which has been annotated and fault-injected. For regression, gated recurrent unit (GRU), Long short term memory (LSTM), bidirectional long short term memory (BiLSTM), convolutional neural network-gated recurrent unit (CNN-GRU), convolutional neural network-long short term memory (CNN-LSTM), and convolutional neural network-bidirectional long short term memory (CNN-BiLSTM), were evaluated and compared their performance using root mean squared error (RMSE), mean squared error (MSE) and mean absolute error (MAE) with 2-split time series cross-validation. CNN-LSTM outperformed the other models with a Mean Absolute Error of 2.0957 for a 45 time steps forecast. For the classification task, convolutional neural network (CNN), multi-layer perceptron (MLP), and convolutional neural network-multi layer perceptron (CNN-MLP) evaluated using the metrics accuracy, precision, recall, and F1-score with 5 and tenfold cross-validations. CNN-MLP outperformed the others with accuracy of 96.11% for bias, 99.33% for drift, and 98.61% for random and 98.81% for poly-drift. The average accuracy across the 4 faults is 98.21%, which is a 0.3% increase from the baseline work 97.91%. By adopting a proactive approach to sensor fault prediction and classification, this research aims to enhance the reliability and efficiency of IoT systems, allowing for preventive measures to be taken before faults have a detrimental impact.
Impact of transparent conducting substrates on structural and optical of Titanium(IV) phthalocyanine dichloride (TiPcCl2) thin films
Authors: Abdelhamid Sabrien M. , Dongol M. , Elhady A. F. , Abuelwafa Amr Attia 
Published: February 2024

AbstractThis study investigates the impact of transparent conducting substrates, specifically indium-doped tin oxide (ITO) and fluorine-doped tin oxide (FTO), on the structural and optical characteristics of thermally evaporated Titanium phthalocyanine chloride (TiPcCl2) thin films. The crystalline structure of TiPcCl2/quartz thin films was verified by X-ray powder diffraction (XRD) patterns while amorphous phases were detected in TiPcCl2/ITO and TiPcCl2/FTO thin films. Field emission scanning electron microscopy (FESEM) images provided evidence that the morphology of TiPcCl2 thin films may be affected by a lattice mismatch between TiPcCl2 and substrates. Fourier-transform infrared spectroscopy (FT-IR) also showed that the substrates did not change the molecular structure of TiPcCl2. UV–Vis-NIR spectroscopy demonstrated that the substrates had a significant influence on the optical gap, optical constants, dielectric constant dielectric loss functions and optical conductivity of TiPcCl2 thin films. Lastly, the nonlinear susceptibility χ(3), the nonlinear refractive index $${{\varvec{n}}}_{2}$$

n

2

and the nonlinear absorption coefficient β(c) for TiPcCl2/ITO, TiPcCl2/FTO, and TiPcCl2/quartz were obtained using a semi-empirical approach. The changes seen in optical properties could be due to changes in the crystal structure and morphology, which are closely connected to the properties of the substrate material. Overall, the results for TiPcCl2 thin films are promising, and they are appropriate for novel optoelectronic device applications.
Exploring the biochemical constituents, medicinal properties and potential commercialization of Ximenia caffra Sond. for enhancing human health: a comprehensive review
Authors: Maluleke Mdungazi K. , Boitumelo Lekoba , Marokane-Radebe Cynthia K. 
Published: February 2024

AbstractIn the realm of sustainable development, universal access to safe and nutritious food and medicine is paramount, particularly championed by the “Zero Hunger” initiative. This article comprehensively reviews the status of research on Ximenia caffra Sond, commonly known as Wild sour plum, an underutilized plant offering versatile applications in both food and medicine. The study delves into X. caffra’s phytochemistry, drawing from peer-reviewed articles sourced from Scopus, Web of Science, Google Scholar, and ScienceDirect. The findings illuminate the plant’s richness in diverse bioactive compounds such as polyphenols, flavonoids, tannins, and terpenoids, underpinning its multifaceted medicinal and nutritional properties. The nutritional and phytochemical abundance of X. caffra underscores a compelling case for advancing from sporadic tribal consumption to full-scale commercialization, necessitating further research to optimize cultivation techniques and extraction methods for commercial products.
RETRACTED ARTICLE: Intelligent dynamic spectrum
access using fuzzy logic in cognitive radio networks
Authors: Kishore K. Krishna , Rajasekaran Arun Sekar , Keshta Ismail , Byeon Haewon , Bhatt Mohammed Wasim , Irshad Azeem , Ofori Isaac 
Published: February 2024

AbstractCognitive radio networks have emerged as a promising solution to the
growing problem of spectrum scarcity and inefficient spectrum utilization. By enabling
secondary users to opportunistically access underutilized frequency bands while avoiding
harmful interference to primary users, cognitive radio networks offer the potential for
more efficient and dynamic spectrum allocation. In this paper, we propose an intelligent
approach to dynamic spectrum access using fuzzy logic in cognitive radio networks. The
key challenge in cognitive radio networks lies in spectrum sensing, decision-making, and
channel selection, where uncertainties and variations in the radio frequency spectrum
are prevalent. To address these challenges, we employ fuzzy logic as a powerful tool to
handle the inherent imprecision and ambiguity in the decision-making process.
Fuzzy-based spectrum sensing algorithms allow for robust detection of spectrum
opportunities by considering the “fuzziness” in the received signal strength and noise
conditions.
Can sensors be used to measure the Arm Curl Test results? a systematic review
Authors: Matos Tomás , Vornicoglo Daniel , Coelho Paulo Jorge , Zdravevski Eftim , Albuquerque Carlos , Pires Ivan Miguel 
Published: February 2024

AbstractThere is growing interest in the automated measurement of physical fitness tests, such as the Arm Curl Test, to enable more objective and accurate assessments. This review aimed to systematically analyze the types of sensors and technological methods used for automated Arm Curl Test measurement and their benefits for different populations. The search consisted of the search related to the possibilities to measure the Arm Curl Test results with sensors in scientific databases, including PubMed Central, IEEE Explore, Elsevier, Springer, MDPI, ACM, and PMC, published from January 2010 to October 2022. The analysis included 30 studies from 15 nations with diverse populations analyzed. According to data extraction, the most prevalent sensors were chronometers, accelerometers, stadiometers, and dynamometers. In the investigations, statistical analysis predominated. The study shows how automated sensor technologies can objectively measure the Arm Curl Test. The detected sensors combined with statistical analysis techniques can enhance assessments. Applications for the Arm Curl Test may be improved even more with more research on cutting-edge sensors and algorithms. This evaluation offers insightful information about utilizing sensor-based automation to enhance Arm Curl Testing.