bluuebunny/crossref_metadata_2025_split huggingface dataset:
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
Authors: Rath Matthias , Condurache Alexandru Paul 
Published: March 2023

Abstract
Deep Neural Networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and - in the case of supervised learning - labelling the data is expensive and time-consuming. Additionally, assessing the networks' generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.
Extraction of functional natural products employing microwave-assisted aqueous two-phase system: Application to anthocyanins extraction from mulberry fruits
Authors: WANG Da-quan , ZONG Kai-li , CUI Hai-peng , HU Hong-chao , HU Jin-peng , YANG Jian-ting 
Published: March 2023

Abstract
Microwave-assisted aqueous two-phase extraction (MAATPE) was applied to obtain anthocyanins from mulberry using ethanol/ammonium sulfate ((NH4)2SO4) as the biphasic extractant. The optimal conditions for anthocyanins extraction were determined by response surface methodology (RSM), which included 39% ethanol (w/w), 13% (NH4)2SO4 (w/w), a liquid-to-solid ratio of 45:1, and microwave exposure for 3 min at 32°C and 480 W, resulting in a maximum extraction recovery of 86.35 ± 0.32%. The main components of mulberry-extracted anthocyanin were effectively enriched through MAATPE extraction, as analyzed using high-performance liquid chromatography (HPLC). Fourier transform infrared (FT-IR) spectra analysis indicated that hydrogen bonding was the primary mechanism causing the extraction behavior of MAATPE. Scanning electron microscope (SEM) showed that MAATPE was more conducive to the dissolution of mulberry anthocyanins than ethanol and ATPE extraction. Moreover, antioxidant experiments showed that the mulberry anthocyanin extract obtained employing the MAATPE method exhibited better antioxidant activity in scavenging DPPH and ABTS free radicals. These results demonstrate that the MAATPE is a promising approach for the extraction of anthocyanins from mulberry. Moreover, mulberry-derived anthocyanins may serve as novel natural antioxidants for functional food ingredients and additives.
Study on the inhibitory effect of cystine, cysteine and glutamate as inhibitors on X100 pipeline steel in 1 M HCl
Authors: Zhang Pandong , He Liang , Sun Xiaolu , Liu Xinran , Li Ping 
Published: March 2023

Abstract
Metal corrosion can cause massive economic losses and many safety hazards; thus, metal corrosion inhibition has been a major research direction worldwide. Among the many methods to stop metal corrosion, the addition of corrosion inhibitors is a common method. The use of amino acids as metal corrosion inhibitors not only has the advantage of being economical and efficient but also meets the long-term concept of being environmentally friendly. In this study, the effect on the corrosion behavior of a mild steel (X100 pipeline steel) in 1 M HCl containing the same concentration of the amino groups of cystine (Cys-Cys), cysteine (Cys) and glutamate (Glu) was investigated via density functional theory (DFT) and various characterization methods. These methods include the weightlessness method, electrochemical tests, scanning electron microscopy (SEM), and the contact angle test. The results show that the inhibition efficiency increased with increasing inhibitor concentration; Cys-Cys showed the best inhibition and Glu showed the poorest inhibition for X100 pipeline steel at the same amino group concentration. Furthermore, the results obtained from various characterization methods were generally consistent.
Investigation of Groundwater Potential within the Coastal and the Orontes Basins in Syria by Using Remote Sensing, Analytical Hierarchy Processes (AHP) and Geographic Information Systems (GIS)
Authors: BEKRI Samah , HOSKAN Nihan 
Published: March 2023

Abstract
Water is the basis of human life. The importance of water is increasing in regions with limited water resources such as Mediterranean basin countries. But unfortunately water is the most important natural resource at risk today. This is due to industrial development, climate change, population growth, continued agricultural expansion aimed at increasing production to meet the permanent demand for food products. So sustainable use of groundwater resources in an area is required and the development of a sensible groundwater potential model is needed. Such studies can provide information about the groundwater potential area and the determination of productive well locations in the study area. This can reduce the extra labor and time required. This study used remote sensing, geographic information systems (GIS), and analytical hierarchy processes (AHP) in order to map the Orontes and Coastal basins in Syria and investigate their groundwater potential. For this purpose, five groundwater occurrence and movement controlling parameters (Geology, rainfall, slope, drainage density and soil) were prepared and transformed into raster data using QGIS software. These five thematic layers were allocated weights proportional to their importance according to the expert’s opinions. Furthermore, the final normalized weights of these layers were estimated using a pairwise comparison matrix of the AHP. Weights were determined to be 22.22% for geology, 49.93% for precipitation, 10.49% for slope, 8.83% for drainage density, and finally 8.53% for soil. Using ArcGIS software, weighted overlay technique was used to combine layers to create a groundwater potential zones map of study area. Four regions were identified on the final map: very good, good, moderate, and poor, representing 6.61%, 43.97%, 35.01% and 14.41% of the study area, respectively.
Synthetic Control and Comparative Studies on Covid-19 Vaccines Enrollment and Hesitancy in Africa
Authors: LANGAT AMOS KIPKORIR , Ofori Michael , Ishag Mohamed , Bouzir Youssouf 
Published: March 2023

Abstract
The COVID-19 pandemic has highlighted the importance of vaccines in the fight against infectious diseases. However, the rollout of vaccines in Africa has been slow and uneven. This study uses synthetic control and comparative analysis to examine the factors that have contributed to differences in vaccine enrollment in African countries. Using data from the World Health Organization and other sources, we constructed a synthetic control group of countries that were similar in African countries in terms of demographic, economic, and health characteristics. We then used a difference-in-differences approach to compare the vaccine enrollment rates of each African country with its synthetic control group. Our analysis showed that government support, health infrastructure, and access to vaccines were significant factors that contributed to differences in vaccine enrollment rates in African countries. Countries with stronger government support and better health infrastructure tended to have higher vaccine enrollment rates. Access to vaccines, including availability, affordability, and distribution, was also a significant factor. Additionally, the use of synthetic control and comparative analysis can provide valuable insights into the factors that contribute to differences in vaccine enrollment rates in different countries. In conclusion, the slow and uneven rollout of vaccines in Africa has been a significant challenge in the fight against COVID-19. Policymakers and public health officials should prioritize efforts to strengthen health infrastructure, provide greater government support for vaccine rollout efforts, and improve access to vaccines. The use of synthetic control and comparative analysis can provide valuable insights into the factors that contribute to differences in vaccine enrollment rates in different countries, and should be considered in future research.
Landscape configuration modulates the presence of leaf-cutting ants in eucalypt plantations
Authors: Cavigliasso Pablo , Gonzalez Ezequiel , Scherf Abel , Villacide Jose 
Published: March 2023

Abstract
Leaf-cutting ants (LCAs) are herbivorous insects endemic to the Neotropical region that cause considerable problems in agricultural and forestry production. In particular, LCAs are a major threat for non-native forest plantations in tropical and subtropical Americas. Here we evaluate the presence and abundance of LCA nests in different spatial contexts associated with young Eucalyptus plantations in the littoral region of Argentina: land uses, interfaces (pairs of neighboring land uses), agroecosystems and landscapes. We selected 30 sites representing 3 types of land uses neighboring Eucalyptus plantations: adult eucalypt plantations, citrus plantations and semi-natural habitats. At each site, we quantified and identified LCA nests on two transects (50x4 m) during the fall of 2022. In addition, we quantified landscape composition and configuration in circular landscapes of 250m radius. We found that LCA nests abundance and presence was similar across different land uses, interfaces, and agroecosystems, although semi-natural land uses and the plantation forest agroecosystem tend to have more nests. Nest presence decreased in landscapes with increasing mean perimeter/area ratio and LCAs abundance showed a similar trend, meaning that small-grain landscapes were less likely to have LCA nests. Despite that LCAs seem to be resistant to human disturbance, our results suggest that decreasing patch size within productive landscapes by planting small plots of trees surrounded by other habitats could be the simplest way to decrease damage by LCAs. Understanding the dynamics of ant populations and their complex associations with landscape components will help promoting a successful environmental management plan for eucalypts forest plantations.
A Deep Learning Approach of Blood Glucose Predictive Monitoring for Women with Gestational Diabetes
Authors: Lu Huiqi , Clifton David , Lu Ping , Hirst Jane , MacKillop Lucy 
Published: March 2023

Abstract
Gestational diabetes is a subtype of diabetes that develops during pregnancy.Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. In this paper, we developed a Stacked Long and Short-Term Memory (Stacked-LSTM) model to predict before- and after-meal BG levels for women with gestational diabetes. A total of 190396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days, and 14 days to predict 14 days. Our results show that the optimized BG schedule uses a 7-day observational window to predict the BG of the next 14 days, achieved accuracies of RMSE = 0.958±0.007, 0.876±0.003, 0.898±0.003, 0.622±0.003, 0.814±0.009, and 0.845±0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. The performance of our models is on par withthe prediction accuracies (RMSE) in benchmark BG prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the Stacked-LSTM model is a promising approach for capturing the patterns in time series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes.
Bioinspired synthesis, characterization of molybdenum (Mo5O14) nanoparticles and their impact on the germination growth of green gram (Vigna radiate)
Authors: Paulraj Suganya , Raman Krishnamoorthi , Ulagan Mahalingam Pambayan 
Published: March 2023

Abstract
In this present study, the green synthesized molybdenum (Mo5O14) nanoparticles (NPs) were successfully synthesized using the co-precipitation method using Eisenia fetida vermiwash extract and their application to the seed germination of Vigna radiata. UV visible spectroscopy and Fourier transform infrared spectroscopy (FTIR) confirm the formation of molybdenum nanoparticles and their interaction with the functional group of enzymes present in the vermiwah. The X-ray diffraction (XRD) study reveals that the crystallinity has a tetragonal shape and is 35 nm in size. A SEM study shows the formation of Mo5O14 NPs. The TEM and SAED patterns also exhibit a spherical shape with a size of 210.85 nm and polycrystalline Mo5O14 NPs. In order to evaluate the bio-potential properties of Mo5O14 NPs, seed germination studies were carried out with green gram (V. radiata) with different concentrations of Mo5O14 NPs and showed a positive influence on the germination of green gram. However, Mo5O14 NPs are being them as nanofertilizer in agriculture.
Development and Evaluation of a Simple Load Lifting Technique Applying Japanese Ancient Martial Arts
Authors: Shiraishi Hajime , Sakaki Taisuke , Iwamura Makoto , Shiraishi Haruhiro 
Published: March 2023

Abstract
Conventional ergonomics utilizes second-order control systems and three-dimensional models, but their complexity makes them unsuitable for practical development. In this paper, a simple mechanism for lifting objects by temporarily setting the load to zero was devised by applying basic techniques of kobujutsu (Japanese ancient martial arts). The accuracy of this mechanism was confirmed by simulation and experiment. This mechanism reveals that the speed of reflection of signals by the brain is important to essentially increase the output of human muscles, enabling efficient and simple movements. It is suggested that the method of lifting objects revealed in this paper is not limited to martial arts and other sports fields but could be applied to a wide range of fields such as nursing care and agricultural work. The development and evaluation of the simple object lifting technique demonstrated in this paper is expected to activate and improve the motor skills of the public and have a positive impact on health maintenance activities.
Ultra-High-Dimensional Multi-Level Optimization Strategies for Electrical Machines
Authors: Liu Chengcheng , Zhang Hongming , Lei Gang , Zhang Shiwei , Wang Youhua , Zhu Jianguo 
Published: March 2023

Abstract
Electrical machine optimization is normally a high-dimensional nonlinear multi-objective optimization problem. To improve the efficiency, multi-level optimization strategy is currently used, where sensitivity analysis is required for dividing design parameters into different groups. However, the conventional multi-level optimization strategy cannot handle ultra-high-dimensional optimization problems. To attempt this challenge, this paper proposes a sensitivity analysis method with variable weighted intervals to calculate the sensitivity coefficient in the parameter design range. Three improved multi-level optimization strategies based on different optimization algorithms and sequential sensitivity strategy are proposed, analyzed, and compared with the conventional multi-level optimization strategy. Through a case study of a synchronous reluctance machine, it can be seen that the proposed optimization strategies can improve the optimization results and efficiency of ultra-high-dimensional optimization of electrical machines.
Research on improved Rider Optimization Algorithm based on kernel density estimation
Authors: Ling Huanzhang , Tan Xufeng , Gu Chenglei , Bi Hongjiao , Liu Shuang 
Published: March 2023

Abstract
Combining the randomness of the solution convergence process due to the random search of the solution space by the Estimation of the Distribution Algorithm and the characteristics of fast convergence and high accuracy of the Rider Optimization Algorithm, a hybrid Estimation of Distribution Algorithm combining them is proposed. In order to further accelerate the convergence speed of the algorithm, the strategy adopted by the original Rider Optimization Algorithm is adjusted. The kernel density estimation is used to properly select the iteration cycle of the two algorithms, the bandwidth adopted by the Estimation of Distribution Algorithm and other relevant parameters, and the Estimation of Distribution Algorithm is used to adjust the historical optimal position and maximum speed required in the Rider Optimization Algorithm. The numerical results of benchmark test functions show that the proposed optimization algorithm can achieve a certain balance between exploring the global optimal solution and characterizing the local optimal solution, and can quickly converge to the global optimal solution of complex optimization problems.
Nationwide projections of heat- and cold-related mortality impacts under climate change and population development scenarios in Switzerland
Authors: de Schrijver Evan , Sivaraj Sidharth , Raible Christoph C. , Franco Oscar H. , Chen Kai , Vicedo-Cabrera Ana M. 
Published: March 2023

Abstract
Climate change and progressive population development (i.e., ageing) are altering the temporal patterns in temperature-related mortality in Switzerland. However, limited evidence exists on how current trends in heat- and cold-related mortality would evolve in future decades under composite scenarios of global warming and population development. Moreover, the contribution of these drivers to future mortality impacts is not well-understood. Therefore, we aimed to project heat- and cold-related mortality in Switzerland under various warming scenarios based on a set of emission and population development pathways and to disentangle the contribution of each of these two drivers using high-resolution mortality and temperature data. We combined age-specific (< 75 and ≥75years) temperature-mortality associations in each district in Switzerland (1990–2010), estimated through a two-stage time series analysis, with 2-km downscaled CMIP5 temperature data and population and mortality rate projections under two scenarios: RCP4.5/SSP2 and RCP8.5/SSP5. We derived heat and cold-related mortality for different warming scenarios (1.5°C, 2.0°C and 3.0°C) using different emission and population development trends and compared this to the historical period (1990–2010). We estimated that heat-related mortality will increase from 312 (116; 510) in the 1990–2010 period to 1,274 (537; 2284) annual deaths under 2.0°C scenario (RCP4.5/SSP2) and to 1,871 (791; 3,284) under 3.0°C scenario (RCP8.5/SSP5). Cold-related mortality will substantially increase from 4069 (1898; 6016) to 6558 (3223; 9589) annual deaths under 2.0°C scenario (RCP4.5/SSP2) and to 5997 (2951; 8759) under 3.0°C scenario (RCP8.5/SSP5). Moreover, while the increase in cold-related mortality is solely driven by population development, for heat, both components (i.e., changes in climate and population) have a similar share of around 50% to the projected heat-related mortality trends. In conclusion, our findings suggest that both heat- and cold-related mortality will substantially increase under all scenarios of climate change and population development in Switzerland. Population development will lead to an increase in cold-related mortality despite the decrease in cold temperature under warmer scenarios. Whereas the combination of the progressive warming of the climate and population development (i.e., ageing) will further exacerbate heat-related mortality, leading to a substantial increase in the total temperature-related mortality burden in Switzerland.
Reference-free Bayesian model for pointing errors-of-type in neurosurgical planning
Authors: Baxter John S.H. , Croci Stéphane , Delmas Antoine , Bredoux Luc , Lefaucheur Jean-Pascal , Jannin Pierre 
Published: March 2023

Abstract
Purpose: Many neurosurgical planning tasks rely on identifying points of interest in volumetric images. Often, these points require significant expertise to identify correctly as, in some cases, they are not visible but instead inferred by the clinician. This leads to a high degree of variability between annotators selecting these points. In particular, errors-of-type are when the experts fundamentally select different points rather than the same point with some inaccuracy. This complicates research as their mean may not reflect any of the experts’ intentions nor the ground truth.
Methods: We present a regularised Bayesian model for measuring errors-of-type in pointing tasks. This model is reference-free, in that it doesn’t require a priori knowledge of the ground truth point but instead works on the basis of the level of consensus between multiple annotators. We apply this model to simulated data and clinical data from transcranial magnetic stimulation for chronic pain.
Results: Our model estimates the probabilities of selecting the correct point in the range of 82.6-88.6% with uncertainties in the range of 2.8-4.0%. This agrees with the literature where ground truth points are known. The uncertainty has not previously been explored in the literature, and gives an indication of the dataset’s strength.
Conclusions: Our reference-free Bayesian framework easily models errors-of-type in pointing tasks. It allows for clinical studies to be performed with a limited number of annotators where the ground truth is not immediately known, which can be applied widely for better understanding human errors in neurosurgical planning.
Proximate and Elisa Determination of Aflatoxin B1 Levels in Chrozophora Tinctoria from Malakand, Khyber Pakhtunkhwa Pakistan
Authors: Ullah Farhan , Khan Babar Ali , Hussain Altaf 
Published: March 2023

Abstract
To investigate the proximate and Aflatoxin B1 analysis in Chrozophora Tinctoria leaves were shade dried in the lab. The proximate composition of leaves was analyzed by standard methods. Mineral contents in this plant were analyzed by using atomic absorption spectrophotometer. The result revealed that Chrozophora Tinctoria leaves contained 86.68% dry matter, 9.80% moisture, 10.01% ash, 21.05% crude protein, Aflatoxin 14%, crude fibers, 14.05% carbohydrate, 44.87% crude fats and energy (243.65 kcal/100g). The analysis of Chrozophora Tinctoria revealed that this plant contains high amount of mycotoxin The type of mycotoxin in present in thise plant is known as Aflatoxin B1. Aflatoxin caused by high doses in short intervals or low doses in high intervals 1961 caused the death of over 100000 Turkey poults. Naturally produesd aflatoxins-B1,B2,G1,G2. Chrozophora Tinctoria also contained a high amount of aflatoxin (ppb). Aflatoxin a various poisous carcinogens and mutagens that are produeced by certain molds particularly aspergillus species the fungi grow in soil, decaying vegetation and various staple foodstuffs and commodities such as hay, sweetcorn, wheat ,millet, sorghum, and various species.This plant contains a huge amount of carbohydrates, and a good source of proteins and fibers.