bluuebunny/crossref_metadata_2025 huggingface dataset:
LIPOSOMALNA CYTARABINA – TO JUŻ 25 LAT!
Authors: Pentak Danuta
Published: January 2025

The first research on liposomes took place in the mid-19th century. It was then that lecithin was isolated from egg yolk for the first time. The phenomenon of swelling of lipids in aqueous solution was then observed. This was done by Maurice Gobley. In the early 1960s, English biophysicist Alec Bangham noticed that lipids had a natural tendency to spontaneously transform into a closed, double bilayer vesicle. Bangham called the discovered structures "spherulites". In subsequent years, this name evolved through the term "banghosomes", giving today's name - liposomes. Liposomes can be carriers of various drugs, including anticancer ones
ZASTOSOWANIE METOD CHEMII KWANTOWEJ DO INTERPRETACJI EFEKTU PODSTAWNIKOWEGO W WYBRANYCH UKŁADACH CYKLICZNYCH ZAWIERAJĄCYCH GRUPĘ NITROWĄ
Authors: Jezuita Anna , Szatyłowicz Halina , Krygowski Tadeusz , Ejsmont Krzysztof
Published: January 2025

This study presents an investigation of substituent effect (SE) in physical term, using quantum chemistry modeling. Substituent effect in series of meta(1,3-) and para(1,4)-X- substituted nitro derivatives of cyclic systems: benzene (BEN), cyclohexa-1,3-diene (CHD) and para-bicyclo[2,2,2]octane (BCO), with 16 different substituents (X = NMe2, NH2, OH, OMe, CH3, H, F, Cl, CF3, CN, CHO, COMe, CONH2 , COOH, NO2, NO), was analyzed. To describe the SE, quantum chemistry models: cSAR (charge of the substituent active region), SESE (substituent effect stabilization energy) and π/  Electron Donor/Acceptor indices (pEDA/sEDA) were used. Delocalization of πelectron in the transmitting moiety (BEN, butadiene fragment of the CHD) was described by index HOMA and pEDA parameter for transmitter [pEDA(R)]. The novelty of the analysis presented here is its description of SE by the population of electrons in sigma and pi orbitals for both the nitro group and the ring for derivatives of para- and meta-substituted nitrobenzene and para-type cyclohexadi-1,3-ene systems. Application of quantum chemical modeling made it possible to compare the substituent effects observed in systems differ the nature of transmitter. It was observed that strength of substituent effect decrease in a sequence: olefinic CHD, aromatic BEN and aliphatic BCO. The obtained results clearly document different nature of intramolecular interactions between NO2 group and X in BEN and CHD systems (coexistence of resonance and inductive effects) than in BCO ones (only the inductive effect). Additionally, it was shown that the difference in SESE values for aromatic (BEN) and aliphatic (BCO) series, ΔSESE, may be considered as a measure of the resonance effect of substituent X. An important aspect of this research is showing that descriptors of SE may also be applied to characterize the strength of impact of substituent in water (by PCM model - Polarizable Continuum Model). The obtained data measured by means of SESE and cSAR approaches exhibit a good agreement with traditionally Hammett constants and commonly accepted empirical concepts. The advantage of using these modeling is that they can be applied to any kind of molecules studies in the field of organic and bioorganic chemistry. The results contain the influence of substituent X on electronic properties of the NO2 group (classical substituent effect) and the impact of the NO2 group on electronic properties of substituent X (reverse substituent effect), inductive and resonance effects of nitro groups, changes in the π and  electron structure of transmitter (R) and NO2 groups and solvent effect on the strength of these interactions.
NAPRĘŻENIE KĄTOWE W MOLEKULE BENZENU?
Authors: Stolarczyk Leszek , Krygowski Tadeusz , Cyrański Michał
Published: January 2025

The stabilization energies (often called the aromatic stabilization energies) of π-electron molecules play important role in discussing the properties of these systems. The so-called homodesmotic reactions (involving the π-electron molecules of interest, and a set of reference molecules), treated with reliable methods of computational chemistry, B3LYP/6-31G(d) or B3LYP/6-311+G(2d,p) level of theory, provide a convenient general theoretical framework for calculating the stabilization energies. The present paper is a case study of the benzene molecule, as created from a set of nonstrained linear polyenes. We calculate the corresponding reaction energies, and show how the limit corresponding to the infinite polyene can be obtained. Moreover, in this limit the benzene molecule may be seen as ,,budded off'' from an infinite cyclic polyene (a cyclic model of polyacetylene). A similar treatment can be applied to some analogs of benzene - small cyclic polyenes, thus providing a platform for comparing the corresponding stabilization energies. At this point one faces the problem of strain in (small) cyclic polyenes - in a planar molecule the C-CH-C valence angles are different from 120o. The corresponding angular strain contributes to the calculated reaction energy - and we argue that the strain energy should be somehow ,,filtered out'', in order to arrive at the ,,true'' stabilization energy. Seemingly the benzene molecule is free from the angular strain. But our calculations for linear polyenes indicate that the corresponding C-CH-C valence angles are greater than 124o. Therefore, by going from the reference molecule (a cyclic model of polyacetylene) to the benzene molecule, some energy must be pumped in. Finally, we find that the stabilization energy for benzene equals: - (the reaction energy, including the angular strain energy) + (the estimated angular strain energy) = 17,25 + 3,24 = 20,49 kcal/mol. Thus, the strain energy correction contributes 19% to the final benzene stabilization energy.
Internationalization of SMEs in a Developing Economy: A Triangulation Approach
Authors: Lakshman Duleepa , Kumarasinghe Janak , Weerasinghe Rukmal
Published: January 2025

The potential of Small and Medium Enterprises (SMEs) to expand internationally is still largely untapped, especially in developing economies. The purpose of this study is to determine the different internationalization antecedents and their impact on the internationalization of SMEs in Sri Lanka. This study was conducted following a triangulation approach, where the quantitative findings are triangulated using qualitative techniques. The results of the study ascertained that marketing capabilities and networking capabilities significantly and positively affect the internationalization of Sri Lankan SMEs. Further, the study discovered that firm size moderates the relationship between networking capabilities and the internationalization of Sri Lankan SMEs. The study’s findings provide SMEs’ managers and owners with greater knowledge about how to take advantage of crucial elements that make internationalization successful. The results of this study assist policymakers in developing more effective measures and policies to assist SMEs in diversifying their global operations. Moreover, this study contributes to the existing theory by revealing how firm-level resources and capabilities affect the internationalization of SMEs from a resource-based point of view and a dynamic capability standpoint.
A Novel Approach Analysis of Heart and Eye Disease Coherence Detection using Deep Learning Techniques
Authors: S Nancy Lima Christy , S Nithyakalyani
Published: January 2025

One of the major factors contributing to the rising death rate is cardiovascular disease. Analyzing clinical data has made it harder to predict cardiovascular disease. To solve the aforementioned problems, an improved DenseNet model is presented in this study. The proposed approach forecasts Central Retinal Artery Occlusion (CRAO) and Coronary Artery Disease (CAD) simultaneously by using the patient's data from eye and cardiac examinations. Then, the coherence relationship is calculated with the help of Pearson’s correlation coefficient for both diseases. As far as we are aware, this is the first study to use DL techniques to predict the coherence between CRAO and CAD. While predicting the CAD, Improved DenseNet 97.5% accuracy when compared with benchmarked DL models like ResNet 50 and VGG16.
An Efficient Consensus Protocol for Blockchain Technology in Smart Grid Contracts
Authors: Pathan Mahamoodkhan , J Rameshkumar , V Suresh Chintalapudi
Published: January 2025

The decentralized operation of the power system, which is built entirely on the consensus notion, is one of the most important contemporary subjects in the energy industry. Without the need for a “neutral arbiter, all significant market participants can come to an understanding. Peer-to-peer (P2P) architecture, interface communication, and network security are all discussed in this paper as they pertain to the decentralized nature of the energy market and the paper’s proposed solution: a P2P-based platform. For this reason, it is critical to protect the market player’s communication interfaces from harmful assaults. In this case, a new blockchain platform coinciding with the P2P energy market ensures that the necessary consensus may be reached safely. An efficient algorithm based on the Relaxed Consensus-Innovation (RCI) protocol controls the energy market, with the goal of facilitating power/price trading between participants in a decentralized, peer-to-peer (P2P) setting. Market participants in the proposed model include a microgrid and a smart grid, both of which are assumed to act in their own self-interest while negotiating with one another in a safe setting. Microgrids use wind turbines, solar panels, tidal turbines, and battery storage units, whereas the smart grid uses distributed generators (DGs) and transmission lines modelled after the IEEE 24-bus test system. In the peer-to-peer energy market, a stochastic framework that is based on unscented transform (UT) has been developed to deal with the uncertainty effects caused by the circumstance. For gauging and validating the fault-tolerant system’s resistance to cyber-attack, we model and apply the fault data injection attack (FDIA) on the blockchain-based P2P energy market”. Simulation results validate the paper’s ideas.
Decentralised Secure and Privacy Establishment in VANET Using Key Encrypted Block Chain Scheme
Authors: Almas Shaik Mulla , K Kavitha , Alla Kalavathi
Published: January 2025

Using blockchain technology and smart transportation gadgets, this paper proposes the next-generation VANET system. While VANET has many advantages, it must first be improved in areas such as security and privacy if it is to be widely adopted. Nearby vehicles periodically exchange events providing their unique identifiers, locations, speeds, and statuses. Using key cryptography, it must verify the legitimacy of each car in the network before allowing it to participate, and it must take the blame for any malicious activity that occurs on the road. Due to the fast speeds of vehicles, limited communication capacity, and delay sensitive applications, traditional centralised security solutions are not applicable in VANET. The purpose of this study is to imagine a new blockchain protocol for secure event transactions in a virtual autonomous network (VANET). Blockchain is a distributed ledger system that facilitates resource tracking and administration without the need for a central authority. Therefore, a blockchain-based solution that offers transparency, tamper resistance, and immutability is preferable in a VANET scenario.
HDLHDGAI: Hybridization of Deep Learning Model for Heart Disease Prediction using Generative Artificial Intelligence
Authors: Choudhary Shailee Lohmor , Aggarwal Ritu , Dixit Rinku Sharma , Duraisamy Baskar , V S Divya Sundar , Mane Sulakshana Bhausaheb
Published: January 2025

As in heart disease patients in biomarkers such as heart rate, ECG (electrocardiogram), pulse rate slow due to blood pressure is essential to get to know about heart disease. Deep learning model for HD diagnosis as wearable sensors collecting and applied as a inputs for measureable. Data gathering and in balancing are observing by the model accuracy. In this current study DL framework such as CNN, BiLSTM, Bi,LRU model used with GAI hybridization technique. In this current study computed for the results on using the different machine learning techniques for also drug recovery in heart disease through deep learning. BiLSTM is a bidirectional model which is used to generate the better results through long short-term memory. BILSTM-GAI & BILRU-GAI model hybridization technique to evaluate the framework by generative model. The deep learning model gives the better accuracy as in terms of prediction of heart disease. The generative artificial intelligence is computing on the patient attributes. Heart disease is a major disease at an early stage and it is very difficult to detect and diagnose by physicians. This model is train and test to diagnose the HD. The Cleveland dataset has taken for detects and diagnoses heart disease.
PSO-Optimized Watermarking Using Lifting Wavelet Transform and SVD for Enhanced Image Security
Authors: Babu Praveenkumar , G Supraja , Kasinathan Gopi , K Kavitha Devi , J Yogapriya
Published: January 2025

A significant data protection technique for a number of problems, the most prominent being identity authentication and copyright protection, is digital image watermarking. The rapid digital transformation of the world has given rise to a variety of vision modification techniques, which have significant consequences for picture data security. As a result, maintaining the validity and integrity of digital images is crucial, which is why researchers are focusing on creating effective watermarking techniques. This study proposes an optimized robust watermarking technique based on lifting wavelet transform (LWT) with singular value decomposition (SVD) using the particle swarm optimization (PSO) algorithm for achieving multiple scaling factors (MSF). To increase security and durability, cover images are exposed to numerous attacks. The evaluation criteria, which encompass normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR), were used to compare our outcomes with those of leading watermarking methods. The comparison reveals that our proposed strategy surpasses existing methods in terms of both robustness and imperceptibility. The results suggest that this technique is suitable for tamper detection in various domains, including cryptography, medical imaging and multimedia transmission.
Advanced Vessel Detection and Classification in SAR Imagery through Integrated Deep Learning Framework Utilizing Multi Architecture Neural Synthesis
Authors: S Devika Priyadharshini , K Vadivazhagan
Published: January 2025

Maritime surveillance remains a critical component of national security and environmental monitoring thereby necessitating the continuous advancement of vessel detection technologies. Traditional methods often struggle with the challenges posed by Synthetic Aperture Radar (SAR) imagery, particularly in detecting small or partially obscured vessels within complex marine environments. This paper introduces a novel approach that significantly enhances the accuracy and efficiency of maritime vessel detection by utilizing advanced deep learning techniques. Utilizing the High-Resolution SAR Images Dataset (HRSID), the proposed method incorporates a sophisticated preprocessing phase that combines Median Filtering for noise reduction and Adaptive Histogram Equalization for contrast enhancement. The novelty in proposed work methodology is a state-of-the-art segmentation process using Mask-RCNN which is well-known for its efficiency in distinguishing objects from cluttered backgrounds, which is quite crucial in marine settings. This is further complemented by the innovative use of DenseNet101 for robust feature extraction, capturing complex vessel characteristics often missed by conventional models. A Convolutional Recurrent Neural Network (CRNN) is then employed for the classification of vessels, integrating spatial and temporal data to enhance detection accuracy. The proposed approach not only fills the existing gap in real-time and reliable small vessel detection but also sets new benchmarks in computational efficiency which is a critical factor for real-time applications. Experimental results demonstrate significant improvements over existing methods in both accuracy and processing speed, promising a substantial impact on the operational capabilities of maritime surveillance systems.
Prediction of Electricity Consumption in Residential Areas using Temporal Fusion Transformer and Convolutional Neural Network
Authors: B N Shwetha , K S Harish Kumar
Published: January 2025

The consumption of energy in the residential area causes adverse impacts on the environment. The mitigation or maintenance of power consumption can be the main step to preserve electricity for the future and proper supply. In context with this, the work focuses on predicting the consumption of energy with the novel hybrid tactic. The hybrid tactic is the integration of Temporal Fusion Transformer (TFT) and Convolutional Neural Network (CNN) (HTFT-CNN). This work is developed to predict the usage of energy across varying time frames with the grip of a multivariate time series of the power consumption of individual residential areas. The proposed HTFT-CNN is implemented to combine both the feature and temporal-based data and can be utilized to observe intricate consumption patterns. The Attention mechanism (AM) is implemented for the fusion of features that are obtained using the proposed HTFT-CNN tactic. The multi-step (k=24) for input sequences and k=24 is the length of input sequence at 24 hours. Simulations are conducted to analyze the robustness and forecasting accuracy of the designed model with the parameters such as Root Mean Square error (RMSE), and Mean Absolute Percentage Error (MAPE). The analyzed performance depicts that the proposed design can be used for planning and energy management in the residential area with minimized RMSE and MAPE values.
Intrusion Detection Systems for IoT Based on Machine Learning Under the Learning Environment
Authors: Abed Qusay Abdullah , Al-Yaseen Wathiq Laftah
Published: January 2025

In an ever-evolving global landscape, concerns regarding network security continue to grow. Integrating information technologies into daily life has made safeguarding computer security imperative. The rise of internet connectivity and innovations like the Internet of Things (IoT) have introduced new challenges in breaching computer systems. Organizations are dedicating resources to research methods for enhancing cyber-attack discovery, opting for intelligent approaches to achieve the highest accuracy rates. The combination of IoT and ML is changing how services and applications work. In the classical ML approaches, data are collected and centrally processed. Nevertheless, this approach is challenging to implement in modern IoT networks because they deal with a significant amount of data, and privacy is often an issue. In contrast, federated learning (FL) has been reported as a possible approach to address such limitations. FL enables ML methods to perform collaborative training through model parameter sharing rather than client data. This study comprehensively reviews cutting-edge literature on enhancing computer network security with ML in the FL environment and IoT. This work further explores various methods and applications in intrusion detection (ID) mechanisms within computer networks through a contemporary and thorough examination.
A Study on the Security Weakness Detection of Solidity Smart Contracts using Graph Neural Networks on Blockchain Platforms
Authors: Kim Sunghyun , Jung Seunggi , Son Yunsik , Lee Yangsun
Published: January 2025

Blockchain is a distributed ledger technology that allows users to record and share information safely and transparently. A smart contract is a contract decided based on a blockchain and is a program that automatically executes or executes contract terms. Smart contracts improve the transparency and reliability of transactions by utilizing the tampering prevention function of blockchain technology. Software security vulnerability refers to the fundamental cause of vulnerabilities caused by logical errors, bugs, and mistakes that can be defective in software development. To prevent software security accidents, security weaknesses must be analyzed before the program is distributed. Smart contract codes that operate on ethereum, a blockchain-based framework, can have security vulnerabilities inside the code. When the contract is completed and the block is created, the chaincode cannot be arbitrarily modified, so the security weakness must be analyzed before execution. In this paper, we used deep learning's graph neural network (GNN) to detect security vulnerabilities in solidity codes. To analyze security vulnerabilities in solidity code, we defined eight types of security weakness items, converted the solidity code into graph data. In order to represent both the structural elements of the program, the control flow, and the data flow, the solidity code was converted into an abstract syntax tree (AST) and the graph information required for GNN learning was extracted from AST to convert the solidity code into a graph. Next, after generating several datasets for training GNN models by integrating graph data and their properties with labels, it is possible to detect whether security vulnerabilities exist in the solidity code through GNN learning. This method performs security weakness detection more effectively than conventional rule-based methods.
Depression Classification using Bert Embedding Model on Social Media Posts
Authors: R Nareshkumar , K Nimala
Published: January 2025

Developing sophisticated methods to precisely detect health-related concerns on social media, including identifying sadness and anxiety, has become essential due to the expansion of the Internet. These systems focus on using machine learning techniques to determine the meaning and structure of writings shared by users on social media. Social media users’ data is confusing and inconsistent. Novel methods using deep learning and social networking platforms data to detect health issues. Provide just a little bit of information and understanding on the various texts individuals provide. This investigation introduces an innovative approach utilizing BERT to accurately and specifically detect posts related to sadness and anxiety. This approach preserves the contextual and semantic importance of words across the collection. The researcher employed word2vec, fasttext, BERT and Enhanced Grey Wolf Optimizer (E-GWO) and Deep Learning technologies to promptly analyze and detect indications of anxiety and melancholy in social media messages. Our solution surpasses previous advanced methods and incorporates the knowledge distillation methodology to achieve an accuracy of 95.9%.
A conceptual model for network security automation: Leveraging ai-driven frameworks to enhance multi-vendor infrastructure resilience
Authors: Afees Olanrewaju Akinade  , Peter Adeyemo Adepoju  , Adebimpe Bolatito Ige  , Adeoye Idowu Afolabi  , Olukunle Oladipupo Amoo
Published: January 2025

The increasing complexity of multi-vendor network infrastructures presents significant challenges in maintaining robust security. Traditional network security approaches are often insufficient to address the dynamic and sophisticated nature of modern cyber threats. This study proposes a conceptual model for network security automation, leveraging Artificial Intelligence (AI)-driven frameworks to enhance resilience across multi-vendor environments. The model integrates advanced AI techniques, including machine learning, predictive analytics, and natural language processing, to automate threat detection, response, and prevention. A central feature of the proposed framework is its ability to harmonize security protocols and policies across diverse vendor systems, enabling seamless interoperability and real-time threat intelligence sharing. The model incorporates automated anomaly detection to identify irregular network behaviors and a risk-based decision-making engine to prioritize and mitigate threats proactively. By employing AI, the model ensures adaptive learning, allowing the system to evolve with emerging threats and changes in network architecture. Key components of the framework include a centralized security orchestration layer, vendor-agnostic APIs, and a unified dashboard for real-time monitoring and analytics. This approach enhances operational efficiency by reducing manual intervention, accelerating incident response times, and minimizing false positives. Furthermore, the model emphasizes compliance with industry standards and regulatory frameworks, providing organizations with a robust foundation for secure multi-vendor network management. Preliminary findings suggest that adopting this AI-driven security automation model significantly improves threat resilience, operational scalability, and resource optimization in complex network environments. The study concludes by highlighting the potential of such frameworks to redefine network security practices, offering a transformative approach to managing risks in increasingly interconnected and heterogeneous infrastructures.
Optimizing risk management frameworks in banking: Strategies to enhance compliance and profitability amid regulatory challenges
Authors: Lawrence Damilare Oyeniyi  , Abbey Ngochindo Igwe  , Onyeka Chrisanctus Ofodile  , Chikezie Paul-Mikki Ewim  , Titilayo Deborah Olorunyomi
Published: January 2025

This paper explores the imperative of optimizing risk management frameworks in the banking sector to enhance compliance and profitability amidst evolving regulatory challenges. The discussion begins with an overview of the regulatory landscape, highlighting key compliance challenges and the impact of regulatory changes on risk management practices. It then delves into strategies for enhancing compliance, emphasizing the implementation of robust risk assessment tools, the development of comprehensive internal policies and procedures, the leveraging of advanced technologies for regulatory reporting and monitoring, and the importance of staff training and awareness programs. The paper also examines how banks can balance profitability with effective risk management by aligning risk management strategies with business objectives, implementing risk-based pricing and decision-making models, enhancing capital allocation efficiency, and defining a clear risk appetite. The conclusion summarizes key points and offers recommendations for banks to optimize their risk management frameworks, ensuring long-term stability and success in a complex regulatory environment.
Mitigating credit risk during macroeconomic volatility: Strategies for resilience in emerging and developed markets
Authors: Lawrence Damilare Oyeniyi  , Abbey Ngochindo Igwe  , Olajumoke Bolatito Ajani  , Chikezie Paul-Mikki Ewim  , Titilope Tosin Adewale
Published: January 2025

Macroeconomic volatility poses significant challenges to financial systems, amplifying credit risk and exposing vulnerabilities in both emerging and developed markets. This paper explores the dynamics of credit risk during economic instability, emphasizing the distinct challenges faced by diverse market categories. It highlights critical strategies for mitigating risk, including robust credit assessments, regulatory interventions, and technology-driven solutions for real-time monitoring. The analysis underscores the importance of building resilience through enhanced institutional capabilities, diversification, and contingency planning. Drawing on cross-market insights, the paper provides actionable recommendations to strengthen financial systems, foster global collaboration, and ensure long-term stability. By integrating innovative practices and leveraging international cooperation, stakeholders can navigate complex risk landscapes and contribute to sustainable economic growth.
The future of energy and technology management: Innovations, data-driven insights, and smart solutions development
Authors: Rita Uchenna Attah  , Baalah Matthew Patrick Garba  , Olakojo Yusuff Ogunsola
Published: January 2025

The future of energy and technology management is being shaped by innovations, data-driven insights, and the development of smart solutions that aim to address global energy challenges while promoting sustainability. As the demand for energy continues to rise, there is a growing need for advanced technologies that optimize energy production, distribution, and consumption. Key innovations, such as renewable energy technologies, smart grids, and energy storage systems, are paving the way for more efficient and sustainable energy management. The integration of data analytics and Internet of Things (IoT) devices allows for real-time monitoring and predictive analytics, enabling better decision-making in energy systems. These technologies are driving the shift towards decentralized energy models, where consumers can generate, store, and manage their energy consumption autonomously. Data-driven insights are crucial in optimizing energy usage and enhancing system reliability. Machine learning and artificial intelligence (AI) are being utilized to predict energy demands, identify inefficiencies, and optimize operational processes in real-time. By leveraging big data, energy managers can gain a deeper understanding of consumption patterns, enabling the creation of tailored energy solutions that reduce waste and lower costs. Furthermore, the development of smart cities and smart homes is transforming how energy is consumed, with interconnected systems that adjust energy use based on real-time conditions. As energy management becomes more sophisticated, the role of technology in developing smart solutions for energy efficiency and sustainability will continue to expand. The convergence of AI, IoT, and renewable energy will play a critical role in building a resilient and low-carbon energy infrastructure. The future of energy and technology management is not only about meeting the growing energy demand but also about achieving environmental sustainability and operational efficiency. Embracing these innovations will be key to unlocking the full potential of energy systems in the years ahead.
МЕТОДИ ОЦІНКИ КРЕДИТОСПРОМОЖНОСТІ ПІДПРИЄМСТВА
Authors: СИТНИК Олександр Олексійович
Published: January 2025

Кредитоспроможність як одна з ключових характеристик та предумов формування фінансової гнучкості підприємства потребує актуальних методик оцінювання. Систематизація наявних методик дозволила виявити найбільш перспективні, з точки зору потенціалу застосування в сучасних умовах: комплексні, на основі поєднання кількісних та якісних показників, – як для підприємств, так і для банків та класифікаційні, на основі нечітких множин та машинного навчання, – для банків. Основним недоліком наявних методик є низька прогностична цінність на середньострокову перспективу, що є особливо актуальним для довгострокового кредитування в умовах високого рівня невизначеності.
Запропоновано комплексний підхід до оцінювання кредитоспроможності на підставі оцінювання зовнішнього та внутрішнього середовища, поєднання кількісних та якісних показників і прогнозування фінансового стану підприємства на основі методології сценарного планування. Методика дозволяє інтегрувати процес аналізу кредитоспроможності підприємства в систему фінансового контролінгу та моніторингу підприємства, будувати на його засадах механізми управління кредитоспроможністю на підприємстві.
Manufacture of kachasu, a traditional distilled liquor in Lusaka, Zambia
Authors:   University of Zambia, Takayindisa Shungu Rudo , Moonga Himoonga Bernard ,   The University of Zambia, Shindano John ,   The University of Zambia
Published: January 2025

This study examines the manufacturing processes and safety concerns of kachasu, a traditional distilled liquor produced in Zambia and other African countries. Despite being illegal in Zambia, the production and sale of kachasu continue, raising public health concerns due to unsanitary manufacturing conditions and high alcohol content. The research involved key informant interviews with 14 processors from high-density compounds in Lusaka, revealing the use of raw materials such as maize malt, sugar, water, and starter cultures from previous batches. Seven distinct production methods were identified, each involving spontaneous fermentation and distillation. The alcohol content of kachasu ranged between 20% and 70%, with three different grades collected during distillation. The unsanitary conditions of production, including the use of makeshift distillation equipment and contaminated packaging materials, pose significant health risks. This study highlights the need for regulatory oversight to improve the safety of kachasu and mitigate its potential public health impacts.
A review on Bioinformatics in Animal Breeding and Research on Disease Resistance
Authors:   Federal University Oye-Ekiti, Tella Adetunmbi , Adebayo Francis Bosede ,   Federal University Oye-Ekiti, Oluwadele Joshua Femi ,   Federal University Oye-Ekiti, Osunkey Olumuyiwa Jacob ,   Federal University Oye-Ekiti, Dandara Gazali Bala ,   Federal University Oye-Ekiti
Published: January 2025

Integrating bioinformatics in animal breeding has transformed how genetic information is utilized to improve disease resistance and overall productivity in livestock. This review examines the roles of bioinformatics in animal breeding, with a particular focus on research related to disease resistance. The article explores the latest advances in genome sequencing, genomic selection, and the identification of quantitative trait loci (QTLs) that contribute to disease resistance. By leveraging recent literature, it highlights the potential of bioinformatics tools and techniques to accelerate breeding programs, improve animal health, and enhance the efficiency of livestock production. It also discusses the challenges associated with bioinformatics in animal breeding, such as data management and the need for interdisciplinary collaboration.
Keywords: Bioinformatics, Animal Breeding, Disease Resistance, Genomic Selection, Quantitative Trait Loci (QTLs)
Factors Associated with Occurrence of Antibiotic Resistance in Broiler Chickens in Selected districts, Zambia
Authors:   University of Zambia, Mirisho Robert , Mukuma Mercy ,   The University of Zambia, Mainda Geoffrey ,   The University of Zambia, Bumbangi Flavien Nsoni ,   Eden University, Mudenda Steward ,   The University of Zambia, Silwamba Isaac ,   The University of Zambia, Phiri Nelson ,   The University of Zambia, Muligisa Elizabeth ,   The University of Zambia, Mwansa Mwaba ,   The University of Zambia, Munyeme Musso ,   The University of Zambia, M'kandawire Ethel ,   The University of Zambia, Muma John Bwalya ,   The University of Zambia
Published: January 2025

A cross-sectional study was conducted in five districts of Zambia. The study aimed to investigate the risk factors associated with antimicrobial resistance (AMR) in broiler chickens in Zambia. A total of 109 broiler farms were included in the study, of which 104 were small-scale farms, two were medium-scale, and three were commercial-scale. A semi-structured questionnaire was administered alongside the collection of cloacal swabs and litter samples to determine the risk factors associated with the occurrence of AMR of food pathogen microorganisms on the selected broiler farms. Data collected was analysed using STATA version 12. The broiler farm-level AMR prevalence was 84.4% (n= 109). Gender, marital status, administration of drugs by veterinary personnel or the farmer and the poultry house drainage destination were among the predictors of AMR on broiler chicken farms. The high prevalence of AMR in broiler chicken farms suggests a high dependence on antimicrobials in poultry production in Zambia. In addition, socio-demographics such as gender and age, as well as farm characteristics and management practices, may primarily contribute to the high prevalence of antibiotic resistance. This study has demonstrated the association between the AMR of foodborne pathogens and the risk factors for AMR during poultry production. It is, therefore, necessary for risk management measures such as biosecurity to be put in place to target the identified AMR predictor variables. There is also a need to strengthen AMR surveillance to ensure intervention strategies are appropriate to the existing risk.
Clinico-pathology and Therapeutic Management of a Primary Atypical Extra-genital Ocular Transmissible Venereal Tumour in a dog in Lusaka, Zambia
Authors:   University of Zambia, Matale Mubita , Hankanga Careen ,   The University of Zambia, Phiri Farai ,   The University of Zambia, Chipolo Mercy ,   The University of Zambia, Mwandabantu Mawiini ,   The University of Zambia, Changula Katendi ,   The University of Zambia, Pandey Girja Shanker ,   The University of Zambia
Published: January 2025

Canine transmissible venereal tumor (CTVT) is a contagious cancer commonly found in the genitalia of dogs, with rare extra-genital presentations, such as cutaneous, oral, nasal, and ocular forms. Extra-genital CTVT cases, especially in the ocular region, are infrequently reported, often leading to diagnostic challenges and underdiagnosis in regions with high prevalence.
A 2-year-old male German Shepherd mix was presented to the University of Zambia, School of Veterinary Medicine, with a red, lobulated mass originating from the third eyelid of the right eye, causing partial vision loss and continuous lacrimation. Initial clinical examination suggested ocular squamous cell carcinoma. However, cytology and histopathology findings from a tissue biopsy revealed round and ovoid neoplastic cells with large pleomorphic nuclei, leading to a definitive diagnosis of primary ocular CTVT. The dog exhibited no genital involvement or evidence of tumour spread to other sites.
The dog was treated with vincristine sulphate administered intravenously in six doses over six weeks. Following the second dose, the patient experienced leukopenia, thrombocytopenia, decreased appetite, and weight loss, all common side effects of vincristine. Supportive care, including antibiotics, fluid therapy, and appetite stimulants, was provided, resulting in improved condition. By the end of the treatment, the ocular mass had completely regressed, vision was restored, and the dog remained in remission with no recurrence observed after 12 months.
This case represents the first reported instance of primary ocular CTVT in Zambia without genital involvement. The successful use of vincristine sulphate highlights its efficacy in treating extra-genital CTVT forms. Given CTVT's potential for atypical presentations, it is crucial for veterinarians in endemic areas to consider it in the differential diagnosis of extra-genital ocular masses to prevent underdiagnosis and ensure timely treatment.
Kernel type estimator of the mean for a heavy-tailed distribution under random truncation
Authors: Bendahmane Khanssa , Benatia Fatah , Benchaira Souad , BenDahmane Raouia
Published: January 2025

In this paper, we introduce a kernel estimator designed for estimating the mean of a heavy-tailed distribution under random truncation. The classical estimator for this problem was proposed by (khanssa bendahmane et al , 2021), but it is often limited in its ability to handle the complexities of heavy-tailed distributions. Our proposed kernel estimator addresses these limitations by leveraging kernel smoothing techniques to more accurately estimate the mean under truncation. We derive the asymptotic distributional properties of the new estimator, providing a rigorous mathematical foundation for its performance. In addition, we conduct a simulation study to assess the effectiveness of the estimator. The results of this simulation demonstrate that, compared to the classical estimator, our new approach outperforms it in terms of bias reduction and root mean squared error (RMSE). This highlights the practical advantages of using the kernel estimator, especially in cases involving heavy-tailed distributions where classical methods may be less reliable.
Effect of high temperature on the microstructure and residual strength of green flowable sand concrete produced with granite waste and seashell powder
Authors: Hadjadj Moussa , Guendouz Mohamed , Boukhelkhal Djamila
Published: January 2025

The durability of concrete structures and human safety can be seriously affected by fire. The present investigation was conducted to determine the fire resistance of flowable sand concrete (FSC) prepared using seashell powder (SSP) cement substitute (5, 10, and 15% by weight) combined with granite waste a replacement for natural fine aggregate (10, 20, 30, and 40% by volume). For this purpose, the changes in weight, compressive strength, and microstructure properties of hardened FSCs were measured before and after exposure to different high temperatures of 200 °C, 400 °C, 600 °C, and 800 °C. The results showed that the use of up to 40% GW as a replacement for fine aggregate and 10% SSP as a cement replacement could be positively incorporated in the production of SSP-blended FSC, where failure due to fire is a problem.
Anisotropy's impact on the deformation of an infinite bicrystal caused by an interfacial dislocation
Authors: Kherraf Allaoua , Benbouta Rachid , Brioua Mourad
Published: January 2025

The study of elastic deformations associated with interfacial dislocation networks plays a fundamental role in understanding the physical properties of crystalline materials. These networks, located at the interface between two crystals, significantly influence the mechanical, thermal and electrical behaviors of materials. Despite their importance, these phenomena have received relatively little attention in the scientific literature. This gap is largely explained by the difficulty of analyzing these complex systems under realistic experimental conditions, particularly using advanced techniques such as transmission electron microscopy (TEM) or high-resolution transmission electron microscopy (HRTEM), which take into account the anisotropy of materials. In this context, the present work focuses on the anisotropic elasticity specific to each crystal constituting a bicrystal. The main objective is to model and understand the elastic fields that emerge at the interface of an infinite bicrystal when a unidirectional network of mismatch dislocations is present. To do this, an analytical approach based on Fourier series was adopted. The coefficients of these series are calculated by numerical inversion of a system of twelve linear equations with twelve complex unknowns. The results obtained by this method are illustrated through specific applications. The Al/Al and Cu/Cu single crystals, as well as the Al/Cu bicrystal, serve as examples to demonstrate the effectiveness and relevance of the proposed approach.
Existence and uniqueness theorem of solution for stochastic differential equations driven by G-Brownian motion
Authors: Hacène Chalabi El- , Amar Ouaoua
Published: January 2025

Due to the different applications of the stochstic differential equations in many areas of life, such as physics, mechanical and electrical engineering, as well as economic and finance. Many researchers have been interested in studying these equations, as they have proven in many cases that they accept unique solutions, as well as their stability in some cases according to various conditions, especially Lypschitz conditions and growth conditions with different methods. Many physical phenomena and economic and financial issues have been modeled using these equations. These studies have witnessed rapid developments recently, as some researchers have expanded them to sublinear spaces by replacing Brownian motion with G-Brownian motion and setting more precise conditions to reach very important results. In this paper we prove the existence and the uniqueness of solution for a stochastic differential equation driven by G-Brownian motion where the coefficients of the equation satisfy the weakened Hölder condition and a weakened linear growth condition.
A Quantitative Study on the Impact of Servant Leadership on Employee Engagement in the Fire Service Industry
Authors: Williams Michael , Tucker Jan
Published: January 2025

This quantitative correlational study examines the relationship between servant leadership and employee engagement within the fire service industry in the United States. Declining employee engagement among firefighters adversely affects individual and organizational performance, compromising the department's ability to provide high-quality community services. While previous research has demonstrated a positive correlation between servant leadership and employee engagement in various public service sectors, a gap remains in extending this research to the fire service industry. Using purposive sampling, 609 career firefighters from a metropolitan fire department in Colorado were surveyed with a 16-question instrument
combining the Servant Leadership Survey (SLS-7) and the shortened Utrecht Work Engagement Scale (UWES-9). With a response rate of 30%, 183 participants provided data. Reliability analysis revealed high reliability for the servant leadership variable (α = .90) and the employee engagement variable (α = .986). Normality tests indicated non-normal distributions for both variables. Spearman's rank correlation rho revealed a moderate positive correlation, suggesting a significant positive relationship between servant leadership and employee engagement. These findings support the beneficial impact of servant leadership on firefighter motivation, well-being, and commitment. The study highlights the importance of implementing servant leadership to cultivate a positive work environment that enhances employee engagement. Practice recommendations include developing formal servant leadership programs and integrating education and training with accountability and corporate social responsibility.
A Review of the Influence of Innovation Platforms and School-Enterprise Cooperation on Engineering Innovation in Henan’s Vocational Education
Authors: Xue Yuanbo , Keat Ooi Boon
Published: January 2025

This review explores the role of innovation platforms and school-enterprise cooperations in enhancing the engineering innovation abilities of students in Henan's higher vocational education institutions. Henan has invested in over 20 innovation platforms, providing practical learning opportunities and fostering collaborations between universities, industries and government agencies. These platforms enable students to engage in real-world engineering challenges, enhancing their creativity and problem-solving skills. Additionally, school-enterprise cooperations have surged in Henan, with over 30 significant partnerships formed, offering students industry-relevant curriculum designs and practical experiences. The review also identifies challenges including outdated curricula and a lack of structured communication between educational institutions and industries. Henan’s institutions are responding by integrating technology, interdisciplinary learning and project-based methods into the curriculum. The review highlights the importance of fostering practical innovation abilities, combining theoretical knowledge with real-world application, arguing that cultivating engineering innovation abilities through experiential learning, internships and cross-sector cooperation is essential for aligning vocational education with industry demands, ultimately producing graduates equipped to meet modern market challenges.
The improvement of fuel economy of planes by improving wing airfoils
Authors: Yao Haosheng
Published: January 2025

This paper explains how plane designers improve the fuel economy of planes by improving the wing airfoils. This article describes three stages of wing airfoil designs. They are periods: the period between World War One and World War Two, the 1980s, and 2000s. Every time stage there is a specific kind of airfoil design to reduce the air resistance and improve fuel economy. In the end, it also gives some suggestions about future airfoil designs to improve the future fuel economy of planes.