bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Differentiation by Integrated Series Expansion (NDBISE) in the Context of Ordinary Differential Equation Estimation Problems
Authors: Strebel Oliver 
Published: November 2024

Abstract

Parameter or model estimation of ordinary differential equations (ODE) nowadays frequently involves the numerical calculation of derivatives from noisy data. This study presents a novel differentiation method (NDBISE) for such calculations. The method was benchmarked against 57 differential equations and compared to five numerical differentiation methods: central finite differences, numerical derivatives using the Friedrichs mollifier, weak differentiation using this mollifier, first order polynomial approximation and spectral derivatives. For the latter method, a novel criterion is presented that allows the determination of the low pass filter parameter. The hyperparameters of all these methods are optimized in order to get a reasonable comparison. The resilience against larger noise or fewer data points per time interval is examined. It turns out that the novel method is overall superior to the other methods. The derivative for the 42 real world data points of the Hudson bay lynx hare data (years 1900-1920) is also calculated. The results match the derivative of a curve fit to the data points astonishingly close. Using a Savitsky-Golay filter, the method can be leveraged, to calculate second and third order derivatives, so that the results are close to the theoretically expected outcome. The paper references a GitHub project that contains code and an application manual to reproduce all figures of the result section from ODE state data.
Dynamic Neural Pathway Synthesis for Large Language Model Processing Using Modular Semantic Encoding
Authors: Rachwan John , Greystoke Pascal , Lewis Matthew , Hollingberry Oswin 
Published: November 2024

Abstract

Recent innovations in computational linguistics have demonstrated the need for adaptive frameworks capable of processing diverse and contextually rich semantic inputs. Addressing this challenge, the introduction of Dynamic Neural Pathway Synthesis (DNPS) offers a groundbreaking approach that dynamically reconfigures neural architectures to align with specific linguistic tasks, overcoming the inherent limitations of static architectures. Through a comprehensive methodological framework, DNPS enables modular semantic encoding and real-time pathway adjustments, facilitating efficient and accurate processing across a wide array of tasks. Empirical evaluations reveal that DNPS-enhanced models exhibit superior adaptability, improved performance metrics, and robust handling of complex and noisy data compared to traditional architectures. These findings highlight the transformative potential of DNPS in advancing the capabilities of large language models, with implications spanning multiple applications, including translation systems, domain-specific data processing, and conversational artificial intelligence. The study establishes DNPS as a significant advancement in computational linguistics, bridging theoretical innovation with practical utility.
Adaptive Graph-Based Neural Signatures for Autonomous Ransomware Detection
Authors: Friehauf Daniel , King Anthony , Oakley Bastian , Wright Robert , Caraway David 
Published: November 2024

Abstract

The increasing sophistication and frequency of cyber threats have exposed critical vulnerabilities in traditional detection mechanisms, particularly against rapidly evolving forms of malicious software. A novel approach is presented, leveraging graph-based neural architectures to dynamically identify complex patterns indicative of malicious activities while adapting seamlessly to emerging threats. The proposed Adaptive Graph-Based Neural Signature (AGNS) framework integrates advanced graph construction techniques with neural processing to achieve precise, real-time ransomware detection. Experimental evaluations demonstrate significant improvements in detection accuracy, precision, and adaptability compared to conventional methods, showing the framework's robustness in diverse and challenging operational scenarios. Additionally, the inclusion of adaptive learning mechanisms enables the system to maintain effectiveness against novel attack vectors, addressing key challenges in scalability and resilience. The results affirm the practicality of AGNS as a transformative solution for automated cybersecurity defenses, advancing the state-of-the-art in adaptive threat detection.
RDW/Albumin ratio (RAR) in premature retinopathy: Evaluation of disease risk and progression
Authors: caliskan busra , yıldırım mustafa 
Published: November 2024

Abstract

Purpose: To determine the role of the RDW-Albumin ratio (RAR), an integrative biomarker of the multidimensional dysfunctional state associated with inflammation, oxidative stress and nutrition in retinopathy of prematurity (ROP), in assessment of the risk of disease development and progression.
Methods: The medical records of 105 preterm infants were retrospectively analysed. All demographic, clinical and laboratory data were recorded. The population was divided into three subgroups as ROP-tx (treated), ROP-reg (regressed) and no-ROP. RDW and albumin measurements were recorded for all groups at specific time points: day 1 after birth (M1), postnatal 1 month (M2), and postnatal 2 months or before treatment (M3). RDW/Albumin ratios were calculated and risk analyses were performed between subgroups.
Results: In all measurements at the specified times, mean RDW values were highest in the ROP-Tx group and lowest in the NO-ROP group, with a gradual decreasing trend. In contrast, albumin levels were lowest in the ROP-Tx group and showed a gradual increase in the other groups.
When the mean RAR values were analysed in all measurements, the highest values were observed in the ROP-Tx group. (Mean RAR values of the ROP-Tx group: M1-RAR: 6.99, M2-RAR: 7.25, M3-RAR: 5.71. In the ROP-reg group: M1-RAR: 6.22, M2-RAR: 5.83, M3-RAR: 5.20. The lowest values were observed in the No-ROP group: M1-RAR: 5.62, M2-RAR: 5.48, M3-RAR: 4.86 ).
When the groups were evaluated independently, the time course of the mean RAR values was analysed. In the ROP-Tx group, a peak was observed at postnatal month 1, followed by a decline. In the ROP-reg and No-ROP groups, a gradually decreasing RAR curve was observed. All these results were statistically significant (p<0.05)
Conclusion: High levels of RAR have been shown to be significantly associated with the risk of development and progression of ROP in preterm infants. RAR has been proposed as an important biomarker in retinopathy of prematurity, reflecting a complex situation related to inflammation, oxidative stress and nutrition.
Exploring the Relationship between NDT and DT Techniques in Concrete: Linear and Non-linear Models
Authors: Patel Arun Kumar 
Published: November 2024

Abstract

The present study involves the development of a correlation of Non-Destructive Testing (NDT) and Destructive Testing (DT) techniques for evaluation of concrete strength. The models selected are Polynomial model (1 to 8 order),Exponential(1 to 2 order), Gaussian( 1 to 2 order), Sum of Sine( 1 to 8 order) and Sigmoidal(Logistic and Gompertz) were the present study. Concrete with grade from M10 to M40 were used for investigation, First the compressive strength is determine by NDT by measuring surface hardness using a rebound hammer. After that same sample were tested by DT using compressive testing machine. There is allot of variation in compressive strength measurement by DT and NDT methods due to various reasons such as mode, environmental, instrument use etc. Correlation is done in order overcome to determine predicated(NDT) and actual value(DT). The main conclusion can be drawn by the analysis showed good correlations in polynomial models, where SSE, the coefficients of determination (R²) and RMSE. The ranged of SSE, R2 and RMSE various from 0.51 to 0.466, 0.96 to 0.973 and 0.05 to 0.0451 respectively. From the various models it is clear that only polynomial Model higher order percentage difference of error had shown decreases in SSE and RSME by 86% to 92% respectively. From the variation it is clear as once increases the order of curve its accuracy increases therefore complex behavior between DT and NDT, Actual V/S Predation required high level of precaution is required. This can be used to prove that the NDT, when calibrated on DT data, allows for accurate estimation of concrete strength with very low intrusion and time investment. The study highlights how advanced mathematical models can facilitate more accurate predictions for concrete strength—which may decisively determine the safety and durability of larger engineering works.
Exploring the effects of extreme events on cereal cropping systems in the Ganga River basin, Haryana
Authors: Dubey Swatantra Kumar , Veettil Prakashan Chellattan , Patil Vikram , Deb Proloy , Kumar Virender 
Published: November 2024

ABSTRACT
Recent studies have highlighted the profound impact of global warming on climate patterns worldwide, but few have specifically addressed its consequences for crop yields. This study aims to bridge that gap by examining the trends of extreme events and their effects on agriculture in adjacent Ganga River Basin Haryana districts from 1981 to 2020, focusing on the Expert Team on Climate Change Detection, Monitoring and Indices. The study area experienced increasing mean maximum and minimum temperatures, raising drought concerns, especially in Sonipat and Panipat districts. Drought indices showed prolonged events in these areas, contrasting with shifting wet-dry patterns in Yamuna Nagar and fluctuating conditions in Karnal. An analysis from 1998 to 2020 revealed intricate relationships between climate factors and rice, wheat, and pearl millet production, with rising temperatures significantly impacting crop yields. Notably, both mean maximum and minimum temperatures have increased, with a significant daytime and nighttime warming trend. Extreme maximum temperature and diurnal temperature range indices were found to negatively impact crop yields, whereas precipitation extremes demonstrated positive correlations with yield outcomes. Collaborative efforts between policymakers and farmers to integrate climate-resilient practices and continuous monitoring are crucial for ensuring food security and sustainable farming amidst climate variability.
Fluoride and nitrogen contamination and potential health risks in the groundwater of a typical agricultural region
Authors: Yang Junzhu , Wang Jiangxia , Xu Han , Xu Zibo , Zhang Yun , Chen Jie 
Published: November 2024

ABSTRACT

Fluoride and nitrogen contamination is a global concern and has been a serious problem in agricultural areas. This study aims to identify the source of fluoride and nitrogen in the groundwater and assess groundwater quality and human health risks in the Guanzhong Plain, northwest China. The results showed that the concentrations were 0.15–4.74 mg/L for F−, 0.02–89.89 mg/L for NO3−-N, and BDL-2.40 mg/L for NH4+-N in groundwater. Distinct area-dependent distributions of fluoride and nitrogen were observed in the study region. Higher F− and NO3−-N concentrations in groundwater were detected in the northern part, and higher NH4+-N levels were observed in the southern part. Water–rock interaction and agricultural activities were the controlling factors for fluoride and nitrogen distribution in groundwater. About 80% of samples are considered to have good water quality with WQI &lt; 100. Exposure to fluoride and nitrogen through drinking should require more attention. The total non-carcinogenic risks through oral ingestion of groundwater were 0.22–3.19 for adults and 0.51–7.44 for children, respectively. The order of pollutants in the groundwater in terms of their hazard to residents was F− &gt; NH4+-N &gt; NO3−-N &gt; NO2−-N. The findings of this study could provide more insights into groundwater management.
Application of UPLC-MS/MS to Study Cellular Pharmacokinetics of Seven Active Components of Cnidii Fructus Extracts
Authors: Bai Yu , Ouyang Huizi , Liu Yang , Zuo Fanjiao , Li Caixia , Zhou Shuting , Chang Yanxu , He Jun 
Published: November 2024

Background:
Cnidii Fructus (CF) is a herbal medicine with pharmacological activities such as antitumor,
antiviral, antiallergic, antipruritic effects, and so on.

Objective:
In this study, an ultra-high performance liquid chromatography/tandem mass spectrometry (UPLC-
MS/MS) method was prepared and verified to measure the concentrations of seven analytes (bergapten, xanthotoxol,
xanthotoxin, imperatorin, osthole, isopimpinellin, isoimperatorin) in HepG2 cells.

Methods:
The separation of seven analytes was performed on an ACQUITY UPLC® BEH C18 column
(2.1×100 mm, 1.7 μm) with a gradient mobile phase system of 0.1% formic acid/water and acetonitrile.

Results:
The CV of analytes was within 7.77%, and the bias was in the range of -5.43%-3.84%. The matrix effects
of analytes ranged from 92.95% to 104.58%, and the extraction recoveries ranged from 76.45% to
104.69%. The relative standard deviation of stability results was less than 8.21%, indicating that seven analytes
were stable.

Conclusion:
The method was successfully applied to the determination of the content of seven analytes of CF
extracts by UPLC-MS/MS, and the results will provide a reference for the cellular pharmacokinetics of CF.
Design and Comparative Characterization of Water Level IDE Sensors at Resonant Frequencies
Authors: Ranjan Prem , Chowdhury Atanu 
Published: November 2024

Background:
This paper reports the design and characterization of three Interdigital
electrode (IDE) water-level sensors at resonant frequencies. The geometries of the proposed IDE
sensors are comb type, circular type, and Archimedean spiral type. These IDE sensors have been
fabricated by the printed circuit board technology. The sensor’s performance has been evaluated
on both tap and distilled water.

Methods:
The multiple resonant frequencies are investigated for the frequency span of 40 Hz to
110 MHz using a 4294A impedance analyzer. The peak of the projected admittance graph appeared
at the first resonant frequency. This first resonant frequency is chosen here for the characterization
of the proposed sensors.

Results:
The study asses that the variations in resonant frequency are caused by both the sensor's
geometry and the water under test. The resonant frequency subtly states that sensors can be presented
as lumped element equivalent series RLC circuits. In this work, an attempt has been made to
show that the change in capacitance plays a pivotal role in estimating the resonant frequency. It is
found that the sensor’s sensitivity decreases with water elevation and always be negative.

Conclusion:
The circular and Archimedean spiral sensors have comparable sensitivity performance,
while the comb IDE sensor is found to be the most sensitive. The IDE sensor features the
highest sensitivity at 1 cm of water elevation. The circular and spiral IDE sensor more closely follows
the reference resonant frequency fr α 1/(√C) when compared with the comb IDE sensor.
Real-Time Energy Management Method for Data Center Considering Shiftable Workload and Renewable Energy
Authors: Li Feng , Zhang Yinya , Xi Jianghui , Liu Yize , Yan Mingyu 
Published: November 2024

Aim:
This study aimed to improve the electricity economy of data centers.

Background:
Data centers have been rapidly developed over the last decade to balance increasing
computing workloads. Such a rapid development has been found to result in a significant increase
in higher electricity expenses for data centers.

Objective:
The objective of this study was to improve the operation efficiency of a data center by
developing a data center energy management model considering shiftable data workloads, energy
storage, and renewable energy.

Method:
First, a data center workload model has been established to describe its temporal shifting
characteristics. Then, a data center power consumption model has been developed to describe
the energy usage of IT equipment and cooling systems. Finally, the energy management model
for the data center has been developed to reduce the electrical energy costs of the data center.

Results:
Case studies have been performed on a trial system to illustrate the validity of the proposed
model, which has been found to meet the requirements.

Conclusion:
Rationally re-scheduling the delay-tolerant workloads and making full use of energy
storage can enhance the flexibility of the power system and reduce electricity costs by 19.06%.
mHealth-Augmented Care for Reducing Depression Symptom Severity Among Patients With Chronic Pain: Exploratory, Retrospective Cohort Study
Authors: Holley Dan , Brooks Amanda , Hartz Matthew , Rao Sudhir , Zaubler Thomas 
Published: November 2024

Abstract

Background
Depression and chronic pain are commonly comorbid, mutually reinforcing, and debilitating. Emerging approaches to mobile behavioral health care (mHealth) promise to improve outcomes for patients with comorbid depression and chronic pain by integrating with existing care models to bolster support and continuity between clinical visits; however, the evidence base supporting the use of mHealth to augment care for this patient population is limited.

Objective
To develop an evidence base that sets the stage for future research, we aimed to explore the associations between changes in depression severity and various integrated care models, with and without mHealth augmentation, among patients with comorbid depression and nonmalignant chronic pain.

Methods
Our team leveraged retrospective, real-world data from 3837 patients with comorbid depression and nonmalignant chronic pain who received integrated behavioral health care (IBH) at a subspecialty pain clinic. We analyzed one IBH-only, non-mHealth cohort (n=2765), an mHealth-augmented cohort (n=844), and a collaborative care (CoCM)+mHealth cohort (n=136), which were supported by the NeuroFlow mHealth platform, and a pre-CoCM mHealth cohort (n=92), which was supported by the mHealth platform for 3 months prior to beginning the chronic pain treatment. We evaluated changes in depression severity between treatment cohorts via longitudinal analyses of both clinician- and mHealth-administered Patient Health Questionnaire-9 (PHQ-9) assessments.

Results
mHealth-augmented integrated care led to significantly greater proportions of patients reaching clinical benchmarks for reduction (725/844, 86% vs 2112/2765, 76%), response (689/844, 82% vs 2027/2765, 73%), and remission (629/844, 75% vs 1919/2765, 69%) compared with integrated care alone. Furthermore, hierarchical regression modeling revealed that patients who received mHealth-augmented psychiatric CoCM experienced the greatest sustained reductions in on-average depression severity compared with other cohorts, irrespective of clinical benchmarks. In addition, patients who engaged with an mHealth platform before entering CoCM experienced a 7.2% reduction in average depression severity before starting CoCM treatment.

Conclusions
Our findings suggest that mHealth platforms have the potential to improve treatment outcomes for patients with comorbid chronic pain and depression by providing remote measurement–based care, tailored interventions, and improved continuity between appointments. Moreover, our study set the stage for further research, including randomized controlled trials to evaluate causal relationships between mHealth engagement and treatment outcomes in integrated care settings.
The Effect of Interventions Based on the Information-Motivation-Behavioral Skills Model on the Human Papillomavirus Vaccination Rate Among 11-13-Year-Old Girls in Central and Western China: Protocol for a Randomized Controlled Trial
Authors: Jing Shu , Wu Yijin , Dai Zhenwei , Tang Shenglan , Su Xiaoyou , Qiao Youlin 
Published: November 2024

Background
Persistent infection of high-risk human papillomavirus (HPV) can lead to cervical intraepithelial neoplasia, cervical cancer, and even death. HPV vaccination for girls aged 9-14 years can effectively prevent the occurrence of cervical cancer. Some Chinese provinces and cities have launched free HPV vaccination programs for school-age girls; however, due to the lack of supportive government policies, the high price and supply shortage of HPV vaccines, and vaccine hesitancy, some parents refuse to vaccinate their daughters.

Objective
This protocol reports the design of a randomized controlled trial (RCT) aiming to explore the efficacy of a digital HPV vaccination education intervention based on the information-motivation-behavioral skills (IMB) model in improving the HPV vaccination rate among 11-13-year-old girls in central and western China.

Methods
A multicenter intervention study based on an online applet will be conducted in December 2024, and about 750 eligible parents of 11-13-year-old girls will be assigned in a 1:1 ratio to an intervention group receiving 7-day digital HPV vaccination education based on the IMB model or a control group using non-HPV publicity materials. Free HPV vaccination pilot projects will be carried out among this population by our research team in central and western China (some parents might refuse to vaccinate their daughters). All participants will be asked to complete online questionnaires at baseline; postintervention; and 1 week, 1 month, and 3 months after the intervention.

Results
The primary outcome of this study will be receipt of the first HPV vaccination within 3 months. Data will be analyzed based on an intention-to-treat approach, and Stata 16.0 will be used for statistical analysis.

Conclusions
This study aims to improve the HPV vaccination rate among 11-13-year-old girls and will examine the impact of a digital HPV vaccination education intervention based on the IMB model. The findings of this study may offer promising intervention measures for HPV vaccine hesitancy in low-health-resource areas in the future.

Trial Registration
Chinese Clinical Trial Registry, ChiCTR2300067402; https://tinyurl.com/v5zt4hc9

International Registered Report Identifier (IRRID)
PRR1-10.2196/58873