bluuebunny/crossref_metadata_2025_split huggingface dataset:
On The QSPR Analysis of Degree Based Topological Indices of Drugs used in Peripheral Neuropathy
Authors: S. Nagarajan  
Published: January 2025

A numerical invariant that describes the topology of a graph G is known a topological index. The topological indices are ideal tools that can be used for analysing the structure of molecular graphs as it reflects the chemical properties of the molecules. In this article, the drugs used in the treatment of peripheral neuropathy are analysed with the help of topological indices and a QSPR analysis is done. The degree based topological indices are used for this structural analysis of chemical compounds of these drugs. The graph structure of a chemical component is derived from a chemical component by considering the toms as vertices and the bonds connecting two atoms as edges, the edge-partitions are noted and computed the topological indices. The correlation among the chemical properties of the drugs and the topological indices are computed. A detailed diagrammatic illustration of these correlation analyses is done. These comprehensive analyses may help scientists and chemist working in the drug design of peripheral neuropathy.
An Ensemble Model for Cyber Attack and Threat Detection in Applications Network Using Random Forest, Lightgbm and Xgboost
Authors: Nidhi  
Published: January 2025

Introduction: In the modern digital age, the increasing sophistication of Cyber-attacks and threats jeopardize the integrity and security of networks, systems, and sensitive data. Traditional methods of cyber threat detection, primarily based on predefined signatures, struggle to identify novel or evolving attacks, making organizations vulnerable to breaches. This research proposes a machine learning-based approach to enhance cyber-attack detection by leveraging network traffic analysis. The system utilizes Random Forest, XGBoost, and LightGBM algorithms to categorize network behaviors as either benign or harmful by analyzing traffic patterns. By analyzing correlations between multiple network variables, the proposed solution aims to detect cyber threats on web applications in real-time, thus improving the accuracy and efficiency of cybersecurity measures. To address the challenge of unavailable network standards in datasets this paper explores the UNSW-NB15 dataset which integrates real-world normal network traffic with simulated contemporary attack activities. In the paper, evaluation of the different machine learning algorithms LightGBM, Random Forest, and XGBoost for detecting cyber-attacks and threats in applications from real network traffic datasets and the result performance of all three algorithms are compared which will help improve didgital security.
Objectives: To analyze the effectiveness of Random Forest, XGBoost and LightGBM algorithms in identifying various types of attacks and enhance the security of digital networks by identifying correlations between network features and attack patterns
Methods: The modal analyses the network and checks whether the traffic is harmful to the user by using XGBoost, Random Forest, and LightGBM algorithms for categorization.
Results: The conclusion of the research shows that the accuracy of Random Forest, XGBoost, and LightGBM algorithms are 0.98096, 0.98045, and 0.98023 respectively.
Conclusions: The Random Forest algorithm outperformed the other models. The modal classification of cyber-attacks like DOS and Fuzzers are giving the best results, this possible with the help of Machine Learning Algorithms and their combinations.
Quantifying the Performance and Trade- offs of Sustainable Investment: Triple Bottom Line Analysis using DEA Metrics
Authors: Baisakhi Mukherjee  
Published: January 2025

In this era of global warming and climate change, sustainability is the key to combat the future challenges in almost every sector of the global economy. In this article we dig deep into analyzing the efforts of the benchmarked industries in the fields of ecological, humanity and ethical (GREEN) and their impacts on the economic aspects of the representing financial instruments. These days the investors are have developed interest to know about the contribution of the industries towards the sustainability of the environment, contribution towards society and how the company is monitoring their activities towards it. This research articles attempts to analyses the efficiency of the funds by applying data envelopment analysis and concluding policy recommendations on the basis of duality programing. The articles also analyze the tradeoffs of the funds on the basis of the triple bottom line (economical, environmental and social) factors.
In conclusion, this study intends to quantify the accomplishment of the sustainable funds by applying DEA techniques and analyzing the efficiency of the funds on the triple bottom line parameters. The findings hold significance for the funds house, investors, policy makers and corporate entities that are evolving in the sustainable finance in the increasingly interconnected and socially conscious world.
Evaluation of Hardware Trojans in the Adder circuits
Authors: Allagadda Seetharamaraju  
Published: January 2025

Exporting the design and fabrication of Integrated Circuits (ICs) presents a significant risk to our vital infrastructure since an attacker may exploit them by circumventing security measures by triggering a hardware Trojan. These malicious design changes implemented at an untrusted manufacturing site have the potential to leak nearly any confidential information from a protected system to an attacker. One of the most serious dangers to hardware security has been the hardware Trojan. We present a summary of recent advances in Trojan detection approaches, grouped according to their relevance to various Trojan kinds. Adder has indeed been implemented effectively in integrated circuits like arithmetic circuits as well as arithmetic accelerators. However, recent research indicates that adder circuits contain security flaws as well. Nevertheless, there has been relatively little study of hardware Trojans within adder circuits. The Trojans are based on the attributes of adder circuits. The efficacy of hardware Trojans is often evaluated using adder circuit assessment methodologies. The performance characteristics of adder like power and delay are evaluated with and without hardware Trojans.
(α,β)-Metric on Cartan Space
Authors: Robin Kumar  
Published: January 2025

In Finsler geometry, a Cartan space or Cartan manifold refers to a special type of geometrical structure where there is a preferred connection or curvature, often associated with a homogeneous space that carries additional symmetries. In the present research paper, we have deduced necessary and sufficient conditions under which an (α,β)-metric, K(x,ω)=α(x,ω)+ϵβ(x,ω)+2k (β^2 (x,ω))/(α(x,ω))-k^2/3 (β^4 (x,ω))/(α^3 (x,ω)), on a Cartan space admitting h-metrical d-connection becomes a locally Minkowski and conformally flat space.
Local Linearization Method in Studying stationarity of SechDAR Model
Authors: Omar Saber Mustafa  
Published: January 2025

This research proposes a new model for nonlinear time series called the Secant Hyperbolic Double AR model of order k, SechDAR(k), which is based on the double autoregressive model with an augmented secant Hyperbolic function. In 2007, Ling introduced this family of double autoregressive models, with the prototype model named DAR(P), where p is the order of the model. This approach addresses data volatility that leads to heteroscedasticity. Using dynamic principles and local linear methods, this research analyzes the stability conditions of the model. First, we approximate the previously mentioned model to a linear difference equation using a local linear strategy. Secondly, the roots of the characteristic equation are used to assess stability. In the end, the stability conditions for the above model are applied using data showing the average monthly closing price of heating oil in US dollars from 1990 to 2024. To evaluate the accuracy and quality of the models' fit on the actual data, we use the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC). In the application section, we use the stability condition to evaluate the stability for each order of SechDAR(k) from 1 to 6.
Automated Phishing Detection Through URL Analysis and Machine Learning
Authors: Vijaykumar  
Published: January 2025

Phishing attacks are categorized as one of the greatest threats to cybersecurity. Threat, which is misinformation to make the user provide important and personal information via fake websites or emails. This paper also realizes the notion of a machine phishing detection-based learning tool aimed at classifying URLs they designated as ”phishing”, ”suspicious,” or ”safe.” Utilizing a Random Forest classifier, the system examines URL-based characteristics inclusive of URL. length, special symbols, and the usage of HTTPs to distinguish between real URLs and fake ones (or phishing URLs) with high accuracy. The model was trained and validated on a given dataset of labeled URLs, achieving 95.2% accuracy of classification higher compared to other results. For the sake of usability, the detection tool is implemented as a web application for real-time classification and the results of the classification. user-friendly interface. This is because the performance and metrics such as accuracy and speed depend on them. accuracy by using measures such as precision, recall, and F1-score. effectiveness. This paper will help to improve the level of online security. in an endeavor to provide an automated approach that deprecates dependence on minimizing human judgment and can efficiently detect cases of phishing threats.
Artificial Intelligence Based Public Air Purifying System
Authors: Paresha M. Dudhedia  
Published: January 2025

Introduction: Air pollution poses a significant threat to public health and well-being, necessitating innovative solutions to mitigate its adverse effects. This research presents an AI-based Public Air Purifying System (PAPS) designed to enhance outdoor air quality in urban environments, densely polluted locations and other similar locations. The proposed system leverages artificial intelligence algorithms to monitor, analyze, and respond dynamically to pollution levels in real-time.
Objectives: A system for real-time surveillance of air quality metrics, including particulate matter and contaminants, using IoT-enabled sensors. Develop a system that autonomously initiates and controls air purifiers in response to pollution metrics, guaranteeing energy efficiency and peak performance. Utilize AI algorithms to enhance airflow distribution for optimal pollutant elimination in various regions.
Methods: The proposed technology integrates AI-driven optimization with a sophisticated multi-stage filtration and purification process. The prototype has a modular design with an AI system that adaptively modifies purification levels according to real-time air quality data, guaranteeing efficient and precise pollutant elimination in various settings.
Results: The show results in the air pollution ppm values before and after using the purifier. The results show the data according to the current date and time. A steady decrease in PPM values indicates continuous improvement in air quality over time. PPM values fluctuate, initially rising and then stabilizing, indicating dynamic changes in air quality following the process.
Conclusions: The AI-Based Public Air Purifying System demonstrates a unique and significant method for addressing air pollution. Its cognitive talents facilitate real-time modifications, although enhancement is required to guarantee dependability and efficacy. With further developments, this technology may significantly improve urban air quality, mitigate health hazards, and foster sustainable living in highly populated regions
Innovativeness and Firm Performance in Apparel Firms in Sri Lanka: The Moderating Role of Family Continuity
Authors: GS Nanayakkara  
Published: January 2025

Purpose: Empirical evidence relating to the link between innovativeness and performance is largely unequivocal with innovativeness significantly influencing firm performance. However, the influence of the family dynamic on this relationship is not well established and few studies have been carried out that explores the influence of family context on the innovativeness –performance relationship. The objective of this article is to examine the influence of family continuity and its relationship with innovativeness and firm performance in an under-examined industry and country context using the resource based view.
Method: The study methodology was survey based and took place in the apparel sector in Sri Lanka. The sample frame was 234 registered apparel firms with the Export Development Board (EDB) of Sri Lanka.
Findings: Family continuity is moderating the relationship between innovation and firm performance when the moderator is strong as well as weak. In situations that family continuity is low, the influence of innovation on performance is greater and significant compared to high family continuity’ circumstances. Higher family continuity weakens the impact of innovation on firm performance. Family continuity has a large effect on the endogenous variable firm performance and contributes in a very significant way to the endogenous construct, firm performance. Therefore, there is a large negative moderation effect in the model as family continuity weakens the relationship between innovation and performance
Research Limitations: The generalizability of the findings are limited by the focus on a single country setting. The study is also confined to the apparel sector and applicability of the findings to other sectors is not easily generalizable due to differences in organizational practices.
Novelty – Whilst the relationship between innovativeness and firm performance has been assessed, this is among the first theoretically sensitized studies linking resource based view, family continuity, innovativeness and firm performance in a developing country context. Prior family business empirical research has focused on Western European or US contexts and studies from developing family business country contexts are limited.
Design of a High speed Low Low-Power Latched Comparator for Medical Implants
Authors: Sharmila Vallem  
Published: January 2025

The latched dynamic comparator is a fundamental component in all ADC architectures. Thermal effects, kickback, and offset voltage influence it. The kickback noise of the latched comparator in medical implant ADCs can impact the resolution, precision, and settling period. The current study examines a latching comparator that aims to reduce kickback noise. This research presents a low-power latched comparator for medical implants functioning at 1 V. This investigation implements a comparator utilising the sampling switching approach. This method successfully minimises kick-back noise and clocking feed-through by minimising unwanted charge injections in the comparator. The comparator decreases power usage while maintaining noise levels. The comparator uses 45-nanometre CMOS technology. The proposed comparator demonstrates superior performance compared to leading comparators regarding kickback noise, power consumption, delay. The input voltage has an inverse effect on the comparator's delay. Simulations indicate that the comparator uses 31 nW at 1 V. The comparator consumes 70% less power compared with different systems. The suggested sample modification decreases kickback noise by a minimum of 18%.
Towards 6G: Next Generation Wireless Systems
Authors: Haresh L. Judal  
Published: January 2025

5G will only be able to meet some of the demands of the coming technological advances in 2030 and beyond. Compared to 5G networks, sixth-generation (6G) networks are expected to introduce novel use cases and performance metrics, such as global coverage, cost efficiency, increased radio spectrum, energy intelligence, and safety. The growing global demand for ultra-high spectral efficiencies, data rates, speeds, and bandwidths in next-generation wireless networks motivates researchers to investigate the peak capabilities of massive MIMO (multiple input multiple outputs) and new technique filter bank multi-carrier (FBMC). Lower out-of-band emissions are observed in the FBMC technique compared to orthogonal frequency division multiplexing (OFDM), an essential requirement of upcoming next-generation wireless systems. This paper compares the spectral efficiency for Massive MIMO uplink in a single-cell scenario using linear detectors at the BS with perfect CSI. Arbitrarily larger SNR values are obtained with a higher number of BS antennas in Massive MIMO, which helps to increase the data rate. This paper also demonstrates how linear detectors can help to reduce the symbol error rate (SER) in a Massive MIMO. This paper demonstrates that with the same number of BS antennas and user combinations ZF detector outperforms the MRC detector.
OPTIMIZATION OF COLD PLASMA TREATMENT FOR MICROBIAL DECONTAMINATION IN LICORICE (GLYCYRRHIZA GLABRA L.) AS A MEDICINAL PLANT
Authors: Yari Peyman , Khanahmadi Masoumeh , Tajehmiri Ahmad , Hosseini Saman , Hosseininezhadian koushki Elnaz , Yari Kheirollah 
Published: January 2025

The cold plasma treatment is a novel, non-thermal, and environmentally safe technique to reduce the microbial population of pathogenic microorganisms in various food packaging materials and agriculture products. This research investigated the effect of cold plasma treatment on the microbial population of licorice (Glycyrrhiza glabra L.). Samples of licorice root were treated with cold plasma in different conditions. The effect of main variables such as energy generator, power, exposure time, pressure, temperature, injected gas, frequency, and the amount of injected gas was optimized. Also, the effect of cold plasma on the glycyrrhizic acid in samples at the highest cold plasma level was studied. The results illustrated that cold plasma technology reduced the undesirable biological contamination in the treated samples. In the current study, the lowest microbial population in treated samples was shown in conditions with cold-vacuumed plasma systems, pressure at 10-2 TORR, injected gas with oxygen and argon (60 L/min), radio frequency energy (RF) with a frequency of 13.56 MHz, exposure time of 3 min, the temperature at 30oC, and power of 100w. This treatment had high efficiency in microbial population reduction and growth inhibition, with 69.6% and 97.38%, respectively. Also, the results of HPLC methods demonstrated that there were not any significant differences between the glycyrrhizic acid of the control sample and cold-plasma-treated samples. The results of this study indicate that cold plasma technology can be used as an important antibacterial method for medicinal plants to reduce microbial population and minimize food quality losses.
INTEGRATIVE INVESTIGATION OF MANILKARA KAUKI PHYTOEXTRACT: ELUCIDATING MULTIFACETED ANTINEOPLASTIC MECHANISMS FROM CELLULAR VIABILITY MODULATION TO IN VIVO TUMOR REGRESSION
Authors: Hegde Medha Mohan , K. Lakshman  , Mukund Handral  
Published: January 2025

Cancer, a complex and profound ailment, needs new treatment ideas to address its pervasive worldwide implications. Within this context, Manilkara kauki, a relatively unexplored tropical botanical plant, emerges as a reservoir of significant potential due to its inherent bioactive properties. Its traditional uses and preliminary investigations suggest it might open up a new path in cancer treatment. The present study investigated the promising antitumor activity of the ethanolic extract of Manilkara kauki (EEMK) by undertaking an integrated investigation via in vitro and in vivo assessments. The cytotoxic effect of EEMK was meticulously evaluated by 2 discrete assays, namely Trypan Blue and MTT at doses of 50,100,200,400 and 800 µg/ml, and apoptosis induction on EAC cells was studied by DAPI staining at dose IC50 value ~265 µg/ml. Further antitumor activity was tested at 200 and 400 mg/kg in Erlich ascites carcinoma-bearing mice. The outcomes from both cytotoxic assays consistently revealed a concentration-dependent decline in cell viability, as discerned from IC50 values ~265 µg/ml and ~276 µg/ml respectively. DAPI staining confirmed the apoptosis induction in EAC cells. Transitioning to an in vivo milieu, EEMK demonstrated pronounced antitumor efficacy in mice bearing experimentally induced tumors by substantial reductions in tumor volume, and tumor weight, alongside a commendable extension in mean survival time and bringing back the hematological parameters to normal. Furthermore, EEMK exhibited hepatoprotective attributes, effectively mitigating SGPT and SGOT levels. These findings serve as a pivotal cornerstone in unraveling the multifaceted mechanisms underlying EEMK's interventions within cancer management.
ENHANCING LACCASE ENZYME PRODUCTION BY CO-CULTURE OF ENDOPHYTIC FUNGI WITH POTENTIAL APPLICATION IN BIOREMEDIATION OF DYES
Authors: Abd El Aty Abeer 
Published: January 2025

In the search for safe and economical methods for laccase enzyme production with high levels, the co-cultivation of two endophytic fungi Alternaria tenuissima MG975633 and Alternaria arborescens MK629314 was examined. In-vitro antagonistic characteristics indicated the ability of both Alternaria spp. to grow together without affecting each other. Maximum laccase production obtained from the co-culture fermentation reached 49.5 U.ml-1 using rice bran medium under shaking conditions, that increased about 2.44-fold than the synthetic medium (20.31 U.ml-1), and increased 1.50 & 1.21-folds compared to Alternaria tenuissima MG975633 and Alternaria arborescens MK629314 monocultures, respectively after incubation for 12 days. The fungal isolates exhibited degradative and decolourization potential against the different dyes tested at varying degree. On solid medium the highest decolorization zones were obtained by A. arborescens MK629314 for Trepan blue (90 mm), followed by Brilliant green (85 mm), Rimazol brilliant blue R and Methyl red (80mm). Application of the crude laccase enzyme exhibited the highest decolorization rate in Rimazol brilliant blue R (15.57%) and Trepan blue (13.71%), followed by Methyl red (11.12) and Congo red (8.83%). The potential performance of A. tenuissima MG975633 and A. arborescens MK629314 in improving laccase production and biodegradation of different textile dyes encourage the use of laccase based production for environmental decontamination in future.
Needs Analysis-Based Teaching Materials Develoment for English Speaking Skill Course in Tertiary Level of Education
Authors: Kaharuddin Kaharuddin 
Published: January 2025

English is a compulsory subject for students in Indonesia. However, the English learning process is often not concordant with the needs and level of students. The development of English learning materials is not based on needs analysis, but based on the instincts of lecturers so that their effectiveness to help students master English is not reliable. This research was conducted to explain the benefits of using needs analysis to develop the materials of English teaching in university. 137 respondents were determined purposively. The research data were obtained by questionnaire distribution. The data analysis technique was descriptive technique. This research was conducted by using the development method with three systematic phases. 1) Input phase: conducting analysis of students’ needs, 2) development phase: formulating English learning goals and objectives which become the basis for determining and developing course content, and 3) output phase: creating draft lesson plans and prototype learning materials. This research produces a methodology for developing English learning materials where a learning plan and prototype for a needs-based English learning unit are obtained. The methodology of this teaching materials development can be used by lecturers in creating teaching materials for other English courses they teach.