bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effectiveness of the Forest Pests and Diseases Control Methods on the Amount of Industrial Wood Production: A Deep Learning Analysis
Authors: Sevinç Volkan 
Published: November 2023

Abstract
Industrial wood production is a critical component of many countries, providing raw materials for a range of products like construction materials, paper, and pulp. However, the industry faces various challenges, including the impact of forest pests and diseases on timber quality and yield. These threats can lead to significant economic losses for the wood products industry. Thus, effective pest and diseases control strategies are crucial for ensuring sustainable industrial wood production. These strategies typically involve a combination of preventative and control measures, including the use of mechanical, chemical, biotechnical, and biological control methods. The constructed deep learning model shows that all methods have enhancer effects on the amount of industrial wood, albeit at different levels. Thus, the most effective methods in terms of increasing industrial wood production are chemical control methods, while the second most effective methods are mechanical control methods. The third effective methods, on the other hand, are biological control methods. However, biotechnical methods were found to be the least effective methods compared to the other ones.
AMP-RNNpro: A two-stage approach for identification of antimicrobials using probabilistic features
Authors: Shaon Md. Shazzad Hossain , Karim Tasmin , Hasan Md. Zahid , Moustafa Ahmed , Sultan Md. Fahim 
Published: November 2023

Abstract

Background
The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency, accuracy, and the ability to process and analyze large and complex datasets. These tools can complement experimental approaches, accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance.
Results
This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative and efficient model for detecting AMPs, which has been constructed based on eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs by computational methods. In our framework, two-stage predictions have been conducted. At first, an analysis is performed using 33 baseline machine-learning models based on these features. Six models have been selected for further study through performance comparisons using rigorous performance metrics. In the second stage, probabilistic features are generated by deploying these models based on each feature and they are aggregated to be fed into our final meta-model, leading to the precise and time-effective prediction of AMPs. The top 20 features that played greater importance in our model's outcome included AAC, ASDC, and CKSAAGP features that were significantly related to detection and drug discovery. Compared to other state-of-the-art methods, the proposed framework, AMP-RNNpro excels in the indentation of novel AMPs in accuracy and precision, achieving 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity.
Conclusions
Consequently, our approach can identify AMPs more accurately and rapidly, along with identifying features that could influence the effectiveness of potential treatment discoveries. We built a user-friendly website for the accurate prediction of AMPs based on the proposed approach which can be accessed at AMP-RNNproWebsite.
Analyzing CO2 Emissions Effect of Financial Development while Accounting for Common Factors in the GCC. Is the Effect Asymmetric?
Authors: Hasanov Fakhri J. , Shannak Sa’d , Mukhtarov Shahriyar , Suleymanov Elchin 
Published: November 2023

Abstract

Ignoring common factors can lead to erroneous findings and misleading policy recommendations, according to panel time-series literature. The same consequences occur if the nature of a factor’s effects is incorrectly considered. We investigated CO2 emission effects of financial development (FD) alongside income and international trade in Gulf Cooperation Council (GCC) countries. The Asymmetric Pooled Mean Group augmented with common unobserved factors - a cutting-edge method allowing to discover not only the features of the pooled panel but also the characteristics of each country - was applied to data for 1992–2021. There is no such application for GCC countries, and only one internationally to our knowledge. We show that accounting for factors common to GCC countries is important, as ignoring them makes the CO2 emissions effects of FD flawed. The effects are asymmetric: upturn of FD leads to lower emissions than its downturn. Another key finding is that CO2 emissions in GCC countries may converge to an identical relationship. In designing CO2 emissions mitigation policies, the following points should be considered: directing financial resources toward eco-friendly technologies, energy transitions, and sectors; accounting for global and regional common factors; close cooperation across the region countries due to common factors and convergence.
Study on the Inclusion Mechanism of Sarafloxacin/β-cyclodextrin Inclusion Complex
Authors: xingcan  , Bing  , Min  , Jiyu  
Published: November 2023

Abstract

The purpose of this study is to explore the inclusion mechanism of sarafloxacin/β- cyclodextrin inclusion complex and explain the fundamental reasons for the improvement of physical and biological properties of cyclodextrin inclusion complex. Through phase solubility analysis, continuous change techniques of chemometrics, nuclear magnetic resonance(NMR) and molecular dynamics simulation techniques, the inclusion complex of sarafloxacin/β-cyclodextrin was studied by chemometrics, molecular docking and one-dimensional and two-dimensional nuclear magnetic resonance spectroscopy. The characterization experiment proved that the inclusion complex of sarafloxacin/β-cyclodextrin was successfully prepared. Finally, the dissolution efficiency of sarafloxacin/β-cyclodextrin inclusion complex was determined in different media. According to the correlation classification, the phase solubility analysis curve is AL. in the continuous change analysis results of chemometrics, when the molar ratio of sarafloxacin is 0.5, the solubility of sarafloxacin is the highest. The result of one-dimensional nuclear magnetic resonance analysis showed that the proton integral area ratio of sarafloxacin to β-cyclodextrin was 1：1. The results showed that the stoichiometric ratio of sarafloxacin/β-cyclodextrin inclusion complex was 1：1. The results of two-dimensional nuclear magnetic resonance spectroscopy and molecular dynamics simulation show that in the structure of sarafloxacin/β-cyclodextrin inclusion complex, several hydrogen bonds are formed between the drug molecule of sarafloxacin and the inner wall of β-cyclodextrin. And sarafloxacin enters into β-cyclodextrin from the side of the larger cavity, its material structure is stable, the matching of surface hydrophobicity and electrostatic force is reasonable, and the overall repulsion is low. The dissolution efficiency of sarafloxacin/β-cyclodextrin inclusion complex in different media is significantly higher than that of its physical mixture and has excellent properties. Through the explanation of the package and mechanism, this study plays a positive role in improving the dissolution efficiency of many insoluble drugs and the promotion and application of fluoroquinolones.
Optimal Control for Fractional Order Dynamics of Tumor Growth
Authors: Nortey Solomon , Fellah Mohammed A. , Akindeinde Saheed O. , Moore Stephen E. 
Published: November 2023

Abstract
This paper concerns the analysis and optimal control of fractional order model of tumor cells and the body’s immunological response using Atangana-Baleanu-Caputo derivative fractional operator. The model consists of spawning cells (S), reposing cells (R) and tumor cell (T) where we have used the Hattaf-Yousfi functional response for the activation of the reposing cells in the presence of tumor cells. We investigate the model’s existence and stability and present numerical results using a modified predict-evaluate-correct-evaluate (PECE) method of Adams-Bashforth-Moulton. We also study the Fractional optimal control problem (FOCP) in order to minimize tumor cell density. The Fractional Pontryagin maximum principle (FPMP) is used to characterize the fractional optimal control problem and we implement the forward-backward PECE method to determine the extremals of the problem. We study the optimal control dynamics of tumor growth via several numerical simulations.
Stress or failure? An experimental protocol to distinguish the environmental determinants of decision-making
Authors: Vecchi Martina , Vitt Nicolai 
Published: November 2023

Abstract
Are economic decisions affected by short-term stress, failure, or both? Such effects have not been clearly distinguished in previous experimental research, and have the potential to worsen economic outcomes, especially in disadvantaged socioeconomic groups. We validate a novel experimental protocol to examine the individual and combined influences of stress, failure, and success. The protocol employs a 2x3 experimental design in two sessions and can be used online or in laboratory studies to analyse the impact of these factors on decisionmaking and behaviour. The stress protocol was perceived as significantly more stressful than a control task, and it induced a sizeable and significant rise in state anxiety. The provision of negative feedback (“failure”) significantly lowered participants’ assessment of their performance, induced feelings of failure, and raised state anxiety.
JEL Classification code: C9, D91.
Cu isotope fractionation between Cu-bearing phases and hydrothermal fluids: Insights from ex situ and in situ experiments
Authors: Qi Dongmei , Zhang Chao , Lazarov Marina 
Published: November 2023

Abstract
Cu isotope ratios have been widely applied to fingerprinting metal sources, tracking fluid pathways, and tracing mineralization processes, for which knowledge of isotope fractionation is required. This paper presents new experimental calibration data of Cu isotope fractionation between Cu-bearing minerals (native Cu and cuprite) and Cl-bearing hydrothermal fluids at 25–800 °C and 0.1–200 MPa. The experiments were performed either using a polytetrafluoroethylene (Teflon) beaker on a hotplate, a rapid heat/rapid quench argon cold seal pressure vessel (RH/RQ Ar-CSPV), or a large volume fluid reactor, which enabled fluid sampling ex situ (the former two) and in situ (the latter). Three setups were designed to investigate Cu isotope fractionation (Δ65Cufluid-mineral): (1) associated with Cu(I) dissolution, Cu(I) disproportionation as well as oxidation of Cu(0) (native copper) and Cu(I) in Cu ± Cu2O + HCl systems at temperatures of up to 800 °C and pressures of up to 200 MPa; (2) related to Cu-Au alloying processes in the Cu + NaCl/HCl system at 600 °C and 150 MPa; (3) to evaluate the influences of different sampling techniques (ex situ vs. in situ) and superimposed cooling processes. The selected system is Cu2O + NaCl + 0.2 m HAc/NaAc (pH-buffer), and runs were conducted at 200–250 °C and 20 MPa.
Δ 65Cufluid-mineral shows the least variation during solid separation from source fluids, generally &lt;0.16 ± 0.03‰. Positive Δ65Cufluid-mineral values are found in most runs, excluding cuprite dissolution in NaCl/HCl solutions at 200–250 °C, 20 MPa, and 800 °C, 200 MPa. Cu oxidative leaching into Cu(I) results in decreasing Δ65CuCu(I)-Cu(0) values from 0.12 ± 0.05‰ to 0.07 ± 0.05‰, and –0.30 ± 0.03‰ at 25, 50, and 200 °C, respectively. In contrast, Cu dissolution at high temperatures (600–800 °C) leads to Cu(I)-containing quench fluids and subsequent native Cu precipitates. Both products are enriched in heavy isotopes by up to 5‰, caused by preferential enrichment of 65Cu at the surface layer during the alloying-induced diffusion transport process. Cuprite oxidative leaching in HCl leads to fluids enriched in Cu(II), and the corresponding Δ65CuCu(II)-Cu(I) increases from 0.52 ± 0.04‰ at 25 °C to 0.89 ± 0.02‰ at 50 °C. Fluids are enriched with light isotopes (63Cu) during cuprite dissolution at 200, 250, and 800 °C, i.e., 0–0.55 ± 0.04‰ lower than the precursor. At 250–300 °C, Cu(I) disproportionation into Cu(II) and Cu(0) dominates the observed isotope fractionation, yielding Δ65Cufluid-Cu2O up to 0.59 ± 0.03‰ and Δ65CuCu(0)-Cu2O up to –0.28 ± 0.02‰.
Rapid cooling (3–25 K s–1) relative to slow cooling (0.014 K s–1) can cause phase separation as well as significant isotope fractionation, particularly if fluids cool from an intermediate high temperature to ambient temperature (e.g., from 200–300 to 25 °C), which highlights the importance of kinetic processes that may potentially alter the isotope composition of natural ore-forming fluids.
Design and application of soft sensors in rural sewage treatment facilities
Authors: Li Bing , Mao Siyuan , Tian Tuo , Bi Huaibin , Tian Yuxin , Ma Xueyan , Qiu Yong 
Published: November 2023

Abstract
Recently, with the growing demand for water quality monitoring, soft measurement sensors have drawn public attention, which can overcome the drawbacks of high cost and long time needed in traditional measurement methods. In this study, a machine learning-based soft monitoring sensor was developed to simultaneously monitor four water quality indicators including COD, NH4+-N, NO3--N, PO43--P. Firstly, specialized experimental equipment and calibration methods were developed to generate a matching dataset that collected over 94,000 data points. Secondly, five models including Multiple Linear Regression, Ridge Regression, AdaBoost, Decision Tree Regression, and Bagging Regression were constructed and compared. The learning accuracy of the models ranged from 0.8860 to 0.9999, among which the predicted value of Bagging Regression is highly fit to the true value. Subsequently, the fuzzy grade method was adopted to reduce the prediction error and strike a balance between efficiency and accuracy. Finally, the designed soft sensor was used for real-time monitoring at three monitoring points in Changzhou, China from September to October 2020, and the results proved the feasibility of the soft sensor in practical application. This study provided a fast and accurate method for water quality measurement, which is of great significance for the management of rural sewage treatment facilities.
Reservoir capacity loss and sedimentation assessment of Dholbaha dam located in Punjab, India using remote sensing and bathymetric survey techniques
Authors: Singh Mahesh Chand , Prashar Avishek , Singh Jaswinder , Kumar Sukhdeep 
Published: November 2023

Abstract

A study was undertaken to assess the live storage capacity of the Dholbaha reservoir located in Punjab, India using remote sensing and bathymetric survey techniques. The primary objectives included comparing the estimated capacity with findings from a bathymetric survey, refining the elevation-area-capacity curve, and determining the rate of capacity loss due to sediments deposition. This analysis utilized water elevation data spanning from 1987 to 2022 and satellite imageries from Landsat 7, 8 & 9. The satellite data was processed using software tools such as Erdas Imagine and ArcGIS. The water extent of the reservoir was calculated using Modified Normalized Difference Water Index (MNDWI). Over the course of 34 years, the reservoir experienced reductions in its dead, active, and total storage capacities by 81.5%, 19.7%, and 28.9%, respectively. These changes correspond to annual depletion rates of 2.40%, 0.58%, and 0.85%, respectively. The sediment yield from the surrounding catchment area was determined to be approximately 1203.56 m3/km2/year. The findings demonstrate substantial declines in Dholbaha reservoir storage capacities over a 34-year period (1987-2022) attributable to sedimentation. This underscores the critical need for sustainable water management and provides key insights for monitoring and strategic planning. The study advocates immediate, targeted interventions within the catchment area to mitigate sedimentation in the Dholbaha reservoir, highlighting the importance of ongoing sedimentation rate monitoring and collaboration with stakeholders for informed reservoir management and sustainable solutions to maintain water capacity for the region.
Laboratory study of the effect of vegetation and gravel on runoff parameters under variable rainfall intensities
Authors: Ghazvinian Hamidreza , Karami Hojat 
Published: November 2023

Abstract

The present study aims to evaluate the performance of the impervious surface as a control (O), sandy loam substrate (SL), gravel (G), gravel with geocell layer (GGE), rosemary (R), rosemary with geocell layer (RRE), turf (T), and turf with geocell layer (TGE) in the reduction of runoff volume, time-to-start runoff (TR), runoff coefficient (C), time-to end runoff (TER), peak flow rate (PF), time to peak (TP), and time base (TB) in the laboratory dimension under three different scenarios of rainfall intensity and two different slopes using a rainfall simulator. The results revealed a significant difference between the data at the level of 5% in all cases. Generally, three rainfall scenarios for all hydrological parameters TR, TER, TP, TB, C, and PF were classified into different groups. In all cases, GGE treatment performed better than that of the rest of the test groups in reducing runoff and cumulative volume. Further, treatments O and GGE experienced the highest and lowest flow rates, respectively. For a specific scenario of rainfall intensity and slope, the value of C is the lowest for GGE treatment. Finally, the implementation of geocell in the pavements was able to delay the time to start runoff.
Overview of Natural Supplements for the Management of Diabetes and
Obesity
Authors: Singh Sonia , Shukla Arpit , Sharma Shiwangi 
Published: November 2023

Abstract:
Bioactive compounds found in various natural sources, such as fruits, vegetables, and
herbs, have been studied for their potential benefits in managing obesity and diabetes. These
compounds include polyphenols, flavonoids, other antioxidants, fiber, and certain fatty acids.
Studies have found that these compounds may improve insulin sensitivity, regulate blood sugar
levels, and promote weight loss. However, the effects of these compounds can vary depending on
the type and amount consumed, as well as individual factors, such as genetics and lifestyle.
Nutraceutical substances have multifaceted therapeutic advantages, and they have been reported
to have disease-prevention and health-promoting properties. Several clinically used nutraceuticals have been shown to target the pathogenesis of diabetes mellitus, obesity, and metabolic syndrome and their complications and modulate various clinical outcomes favorably. This review
aims to highlight and comment on some of the most prominent natural components used as antidiabetics and in managing obesity.
Energy Management System for Distributed Energy Resources using Blockchain Technology
Authors: Kavin R. , Jayakumar J. 
Published: November 2023

:
Power generation in today’s world is of utmost importance, due to which blockchain is
used for the categorization and formation of decentralized structures. This paper has proposed decentralized energy generation using a nester, i.e., energy sharing without third-party intervention.
Decentralized blockchain technology is applied to ensure power sharing between buyer and seller,
and also to achieve efficient power transmission between prosumer and consumer. Energy management is associated with controlling and reducing energy consumption. Blockchain technology
plays a major role in distributed power generation, for example, power-sharing (solar and wind
energy), price fixation, energy transaction monitoring, and peer-to-peer power-sharing. These are
operations performed by blockchain in renewable power generation. Solar power generation using
blockchain technology can obtain an impact resting upon the power generation system. Distributed ledger is the key area of blockchain technology for recording and tracking each transaction in
the distribution system to improve the efficiency of the overall transmission system. A smart contract is another important tool in the blockchain technology, which is issued to confirm an assent
between buyer and seller before starting any energy transaction without external intervention and
also to avoid time delay. Maximum power point tracking is conducted in PV cells using blockchain technology. Blockchain influences energy management systems to improve the utilization
of energy, optimize energy usage, and also to reduce the cost.
Investigation of Development of Phonology using Artificial Intelligence: A
Preliminary Study
Authors: Kumar Vijay , Jain Anjani , Saini Jitendra Kumar , Bhardwaj Leena 
Published: November 2023

Background:
Investigation of the development of acquisition of phonological processes is a very important phenomenon to predict language development in
childhood. Phonological development refers to the gradual acquisition of an adult-like
speech system used to convey meaning in a language. Adequate phonological development can lead to higher speech intelligibility, essential for perception and speech
production. The test battery approach for phonological assessment includes formal as
well as informal testing modalities. Speech intelligibility is a vital parameter for the
assessment of phonological development. This is done via scoring collected audio
speech samples, followed by IPA transcription and scoring and analysis of data manually. The primary objective of the study was to investigate the speech intelligibility
score across real-timed stimuli (rhyming, storytelling, open-ended questions, and picture description tasks) across two age categories, i.e., 3-4 and 4-5 years among 40
Hindi-speaking children.

Methods:
A total of 40 children (15 girls and 25 boys) in the age range of 3 to 5 years
were randomly selected from a junior high school in New Delhi, India. Groupwise
speech intelligibility score was calculated for rhyming, storytelling, open-ended questions, and picture description tasks. Results: It was observed that the speech intelligibility score for open-ended questions was significantly higher in the 4-5 years group
(94.09 ± 3.7) than the 3-4 years (89.83 ± 8.2), t (1, 38) =2.12, p =0.04). However, no
significant improvement in rhyming task [t (1, 38) =1.14, p =0.18), storytelling [t (1,
38) =1.81, p =0.07), and picture description [t (1, 38) =1.89, p =0.06) was observed.

Conclusion:
Chances of error in conventional method of phonology assessment still
persists which may be controlled using Artificial Intelligence (AI). The target phonetic stimulus can be customized based on the linguistic experience and environment.