Chemical solvents are commonly used to prevent microbial growth; dangerous to human health and have limited antibacterial properties. On the other hand, Nanoparticles made of metallic elements (such as copper, silver, and gold) have several uses in the field of biotechnology. Silver nanoparticles are more efficient in their antimicrobial, antibacterial, anti-inflammatory and anti-cancer properties. The current study aimed to determine the green synthesis of silver nanoparticles and their antibacterial activity from the aqueous extracts of leaves of Couroupita guianensis, Punica granatum, Vitex negundo, Cirtrus maxima. AgNPs of plant extracts were prepared using silver nitrate with the respective plant extract. Then they were characterized by FTIR analysis. The respective functional groups in the synthesized silver nanoparticles were confirmed with FTIR Spectra. The antibacterial activities of the synthesized nanoparticle extract were observed by zone of inhibition. From the results, the nanoparticles synthesized from the plants extract could pave a way to formulate a drug to treat microbial infection.

The purpose of the study was to examine Carl Rogers' humanistic approach to supporting women in leadership positions to make a formed decision on how to manage the challenges they face. A qualitative approach was adopted for the study. The population of the study included fifteen (15) headmistresses and housemistresses in the Senior High Schools in the New Juaben Municipality in the Eastern Region. The purposive sampling technique was used to select schools, headmistresses and housemistresses in the schools. The schools selected were Oyoko Methodist Senior High School (OMESS), SDA Senior High School (SEDASS), Ghana Senior High School (GHANASS), Koforidua Technical Institute (KOTECH), Nana Kwaku Boateng Senior High School (OBOSS) and New Juaben Senior High School (NJUASCO). The main instruments used for data collection were a semi-structured interview guide and Reflective dialogue. Data was analysed through the use of the thematic approach. Within-case and across-case analytical technique was used to analyse the qualitative data. This was done through the identification of themes, categories and sub-categories the analytical tool for the qualitative data through interviews and reflective dialogue (RD). Themes that were extracted from the interview corresponding to Carl Rogers' humanistic person-centred) the approach were; inherent potentialities, support, motivation, power relation, INSET, cultural dimension, and guidance and counselling. The study revealed that women face challenges using inadequate school facilities they do their best possible to manage their challenges with the few facilities available, the introduction of Carl Rogers' intervention, and women became more aware of their potential in managing the challenges they face at work in their leadership positions. It is recommended that guidance programmes should be conducted often to inform both teachers and students about the potential of women. It has also emerged that women leaders do not know who they are and therefore they should undergo counselling in order to be self-actualised.

In this Research work, Silver Nanoparticles were green synthesized from Psidium guajava leaves and different Characterization techniques including UV-Visible, FT-IR, SEM and XRD were all employed to ascertain the absorption peaks, functional group, surface morphology and crystalline size of the nanoparticles respectively. These nanoparticles green synthesized were applied against four different pathogens namely, S. aureus (gram- positive bacteria), E. coli (gram- negative bacteria), C. albicans (gram- positive fungus) and Aspergillus niger and the investigation showed that the Silver nanoparticles synthesized were potent against the selected pathogens. From the UV-Vis spectral analysis, it was observed that highest absorption peaks appeared at 400nm and 500nm reflecting the surface plasmon resonance of silver Nanoparticles from guava leaves which is characteristic of Silver Nanoparticles. From the FT-IR spectrum of the sample under studied, the peaks 3416.85 cm^-1, 2923.51 cm^-1, 1618.95 cm^-1, 1384.49 cm^-1 and 1033.63 cm^-1 were observed where the absorption band at 3416.85 cm^-1 corresponds to the stretching due to N-H, while the band at 2923.51 cm^-1 is associated with C-H stretch of alkane and O-H stretching. The peak at 1618.95 cm^-1 shows C=C stretching, 1384.49 cm^-1 reveals the existence of C-H bending and 1033.63 cm^-1 depicted C-O stretching. The SEM analysis revealed the shape of the nanoparticles as being spherical while XRD result admits that the average size of the green synthesized Ag NPs was 45.5 nm using the Scherer’s formula. Augmentin was used as control at concentration of 300μg/L throughout antimicrobial studies. Different concentrations of 100, 200, 300, 400 and 500μg/L of Silver Nanoparticles were tested against each pathogen. It was discovered that with increase in concentrations of Silver Nanoparticles of all the pathogens, there generally appeared to be increase in inhibition zone. At higher concentration of 500μg/L, the zones of inhibition were in the following order; 22.50 mm, 17.00mm, 15.44mm, and 13.23mm for E. Coli, S. aureus, C. albicans and Aspergillus niger respectively. For each concentration investigated, E. coli, demonstrated higher zone of inhibition as opposed to all other pathogens investigated in this research.

Kaybarkuh (Mount Kaybar) consists of intrusive igneous bodies with two age periods, located in North of Dasht-e-Bayaz left-lateral fault terminal. The spatial and structural analysis of fractures and dike networks may allow for the accurate identification of mineralization zones in the area. This study aims to characterize lineament network in the study area by automatic method using multispectral satellite images from Landsat 8 Operational Land Imager (OLI), visual extraction of lineaments from Landsat-8 and SENTINEL-2 images, and extraction of drainage network as lineament based on digital elevation models (DEMs) and their validation, compared with fault network of the area. The results showed that there is a significant relationship between the trend of studied lines in the region by the three methods mentioned and the overall trend is about N330⁰. This can indicate a tensile regime with a trend perpendicular to the mentioned orientation, which results from the activity of the Dasht-e-Bayaz fault. Finding more evidences requires further studies.

Background: From centuries of evolution, knowledge and technological progress for mankind to one day rediscover nature. Currently, the control of bacterial infections is becoming complex due to the concern of antibiotic resistance, which has been a significant global health problem. The aim was to determine and compare phytochemical constituents and in the in vitro evaluation of antimicrobial and antioxidant activities of aqueous, methanol, acetate, dichloromethane extracts from Cola acuminata nuts grown in the Nord Ubangi Province, DRC. Methods: The nuts of Cola acuminata were harvested in April 2016 at Yakoma city, Nord-Ubangi, DRC. The microscopic features of this species were performed in order to identify specific histological structures. Three bacterial strains notably Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 8739 and Pseudomonas aeroginosa ATCC 9027 were used for the assessment of the antibacterial activity. The qualitative and quantitative phytochemical screening were used for compound identification using different fractions and fractions which presented a good extraction yield was used for further analysis. The antioxidant activity was evaluated using ABTS and DPPH scavenging tests while the antibacterial activity was performed using the diffusion method. Findings: The micrography of C. acuminata revealed the presence of following histological elements of which: fibers, spiral vessels, trichomes, ovoid starch grains, sclerenchyma and the fragments of endosperm. Only the methanol and aqueous extracts presented a good extraction yield. The phytochemistry shows the presence of flavonoids, anthocyanins, terpenes, iridoids and tannins. All fractions showed IC₅₀ values lower than 10 µg/mL in the ABTS test and lower than 100 µg/mL in the DPPH test. The antibacterial activity of this plant was low against the three strains used. Conclusion: Seeing the potency of C. acuminata and different biological activities displayed, further analysis are required in order to identify and purify the active ingredients, to study the toxicity of cell lines in vitro, to perform the in vivo experiments and to test for other activities such as the anti-hypoglycemic and anti-inflammatory.

In this study, oil was extracted from Moringa seed using mechanical and solvent methods. To transesterify the oil into biodiesel, factorial design of experiment of 2⁴ was used to obtain different combination factors at different level of reaction temperature, catalyst amount, reaction time and alcohol to oil ratio, giving rise to 48 experimental runs. The oil sample was transesterified in 48 experimental runs, in each case the biodiesel yield was recorded in percentage. The biodiesel was then characterized according to ASTM test protocol. Factorial design model was developed using Design Expert 7.0, the model generated R of 0.987 and Mean Square Error (MSE) of 5.0453 and was used to predict and optimize biodiesel yield. Artificial Neural Network (ANN) model from MATLAB R2016a was developed using 4 input variables and 30 runs, the remaining 18 runs were tested with the ANN model to predict and compare the biodiesel yield with the experimental biodiesel yield, the model generated R value of 0.99687 and MSE of 3.50804. It was found that solvent method yielded more oil than mechanical method, the biodiesel has good thermo-physical property, optimum biodiesel yield of 91.45 % was obtained at 5:1 alcohol/ oil molar ratio, 18.89 wt% catalyst amounts, 45 minutes reaction time and at 45 reaction temperature. The experimental validation yielded 88.33 % biodiesel. The ANN model adequately predicted the remaining 18 runs with R² value of 0.99649 and MSE of 4.914243. Both models proved adequate enough to predict biodiesel yield but ANN model proved more adequate.

Cell division that is not controlled leads to cancer, an incurable condition. An early diagnosis has the potential to lower death rates from breast cancer, the most frequent disease in women worldwide. Imaging studies of the breast may help doctors find the disease and diagnose it. This study explores an effectiveness of DL and ML models in a classification of mammography images for breast cancer detection, utilizing the publicly available CBIS-DDSM dataset, which comprises 5,000 images evenly divided between benign and malignant cases. To improve diagnostic accuracy, models such as Gaussian Naïve Bayes (GNB), CNNs, KNN, and MobileNetV2 were assessed employing performance measures including F1-score, recall, accuracy, and precision. The methodology involved data preprocessing techniques, including transfer learning and feature extraction, followed by data splitting for robust model training and evaluation. Findings indicate that MobileNetV2 achieved a highest accuracy99.4%, significantly outperforming GNB (87.2%), CNN (96.7%), and KNN (91.2%). The outstanding capacity of MobileNetV2 to identify between benign and malignant instances was shown by the investigation, which also made use of confusion matrices and ROC curves to evaluate model performance.

The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.

Traffic congestion across the globe is a multimodal problem, intertwining vehicular, pedestrian, and bicycle traffic. The relationship between the multimodal traffic flow is a key factor in understanding urban traffic dynamics. The impact of excessive congestion extends to the excessive cost spent on traffic maintenance, as well as the inherent transportation inefficiency and delayed travel times. From an urban transportation standpoint, an immediate consideration on one hand is monitoring traffic conditions and demand cycles, while on the other hand inducing flow modifications that benefit the traffic network and mitigate congestion. Embedded and centralized control systems that characterize modern traffic management systems extract traffic conditions specific to their regions but lack communication between networks. Moreover, innovative methods are required to provide more accurate up-to-date traffic forecasts that characterize real-world traffic dynamics and facilitate optimal traffic management decisions. In this chapter, we briefly outline the main difficulties and complexities in modeling, managing, and forecasting traffic dynamics. We also compare various conventional and modern Intelligent Transportation Strategies in terms of accuracy and applicability, their performance, and potential opportunities for optimization of multimodal traffic flow and congestion reduction. This chapter introduces various proposed data-driven models and tools employed for traffic flow prediction and management, investigating specific strategies' strengths, weaknesses, and benefits in addressing various real-world traffic management problems. We describe that the design phase of dependable Intelligent Transportation Systems bears unique requirements in terms of the robustness, safety, and response times of their components and the encompassing system model. Furthermore, this architectural blueprint shares similarities with distributed coordinate searching and collective adaptive systems. Town size-independent models induce systemic performance improvements through reconfigurable embedded functionality. These AI techniques feature elaborate anytime planner-engagers ensuring near-optimal performances in an unbiased behavior when the model complexity is varied. Sustainable models minimize congestion during peaks, flooding, and emergency occurrences as they adhere to area-specific regulations. Security-aware and fail-safe traffic management systems relinquish reasonable assurances of persistent operation under various environmental settings, to acknowledge metropolis and complex traffic junctions. The chapter concludes by outlining challenges, research questions, and future research paths in the field of transportation management.

The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is done through a bag of words method, and 80-20 data is split using training and testing. The experimental findings demonstrate that the LSTM model outperforms the conventional models, such as SVM and Naïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of 99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, it performs well the model consumes heavy computational resources and longer training time. In summary, the results show that deep learning performs well in sentiment analysis and can be used to social media monitoring, customer feedback evaluation, market sentiment analysis, etc.

Crime is a serious and widespread problem in their society, thus preventing it is essential. Assignment. A significant number of crimes are committed every day. One tool for dealing with model crime is data mining. Crimes are costly to society in many ways, and they are also a major source of frustration for its members. A major area of machine learning research is crime detection. This paper analyzes crime prediction and classification using data mining techniques on a crime dataset spanning 2006 to 2016. This approach begins with cleaning and extracting features from raw data for data preparation. Then, machine learning and deep learning models, including RNN-LSTM, ARIMA, and Linear Regression, are applied. The performance of these models is evaluated using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RNN-LSTM model achieved the lowest RMSE of 18.42, demonstrating superior predictive accuracy among the evaluated models. Data visualization techniques further unveiled crime patterns, offering actionable insights to prevent crime.

With unclaimed property assets reaching record levels, businesses have become, in some cases, overwhelmed and hamstrung by stagnant, unoptimized processes. That sentiment is compounded by ever-evolving regulatory changes, resulting in organizations struggling to hit compliance deadlines while delivering an optimal claimant experience. Often, early systems had periods of short-term success but are on the verge of obsolescence, resulting in stressed workflows and cumbersome integrations. Deploying an integrated IT infrastructure, supported by cloud-enabled AI, represents the quickest path to modernizing unclaimed property management. A fully integrated IT infrastructure is crucial to optimize the management of unclaimed property [1]. When lone solutions exist across an organization, companies miss out on automation opportunities generated through the interconnectedness of systems and data. AI presents organizations with the opportunity to traverse these gaps, enabling a vast library of applications to improve the perturbed workflows of unclaimed property teams. Automated data extraction, document comparison, fraudulent claim detection, and workflow completion analysis are just a few popular applications well suited for the unclaimed property space. In addition to the lagging technology currently deployed by many organizations, the unclaimed property landscape itself is evolving. Compliance issuance, asset availability, rates, the ability to collect fraudulently posted claims, and the claimant experience have all become hot-button items that are now front of mind for regulation agencies and businesses alike. Issuing duplication letters in a compliant manner, accommodating claimant inquiries regarding held assets, and managing, processing, and understanding the operational impact of rate changes are vexing problems many organizations now find themselves playing catch-up to address. The opportunity posed by cloud-enabled AI is furthered by economic, regulatory, and report cycle pressures on unclaimed property teams to do more with the same size or fewer resources. It’s now no longer simply a case of hitting the audit date deadline and checking off a box but an emerging priority for businesses at all sides of the market, from Fortune 500 to mid-market firms. In-house shared service teams are comfortable in areas of monitoring and curating business data; however, unclaimed property is an unknown territory with a learning curve, compliance gaps, and operational holes that, if ignored, stand to scale up exponentially. The combined fallout from regulatory changes and the recent pandemic have only made the situation riskier, with increased volatility in balancing time-sensitive tasks against stringent regulatory deadlines and growing claimant outreach.

While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. To this end, in this paper authors present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders [1]. Large information databases on various public issues exist, but their usage for public policy formulation and impact analysis has been limited so far, as no cloud-based service ecosystem exists to facilitate their efficient exploitation. With the increasing availability and importance of both public big and traditional data, the need to extract, link and utilize such information efficiently has arisen. Current data-driven web technologies and models are not aligned with the needs of this domain, and therefore, potential candidates for big data, cloud-based and service-oriented public policy analysis solutions should be investigated, piloted and demonstrated [2]. This paper presents the conceptual architecture of such an ecosystem based on the capabilities of state-of-the-art cloud and web technologies, as well as the requirements of its users.

Machine Learning (ML) and Artificial Intelligence (AI) are having an increasingly transformative impact on all industries and are already used in many mission-critical use cases in production, bringing considerable value. Data engineering, which combines ETL pipelines with other workflows managing data and machine learning operations, is also significantly impacted. The Intelligent Data Engineering and Automation framework offers the groundwork for intelligent automation processes. However, ML/AI are not the only disruptive forces; new Big Data technologies inspired by Web2.0 companies are also reshaping the Internet. Companies having the largest Big Data footprints not only provide applications with a Big Data operational model but also source their competitive advantage from data in the form of AI services and, consequently, impact the cost/performance equilibrium of ETL pipelines. All these technologies and reasons help explain why the traditional ETL pipeline design should adapt to current and emerging technologies and may be enhanced through artificial intelligence.

Cloud native ETL pipelines support the extract and transform phases of real time claims processing in large scale insurers. The cloud native approach offers dramatic improvements in scalability, reliability, resiliency and agility as well as seamless integration with the diverse set of data sources, destinations and technologies characteristic of large scale insurers. The ETL process extracts data from source systems such as core transaction, fraud, customer and accounting processes, transforms the data to create a usable format for analytics and other applications, and loads the resulting tables into business intelligence or data lake systems for subsequent storage and analysis. By addressing these two phases of the overall ETL process, cloud native ETL pipelines can provide timely, reliable and consistent data to data scientists, actuaries, underwriters and other analysts. Real time processing represents a key priority within the overall claims process: faster, more accurate claim approvals reduce insurer costs, improve customer service and enhance premium pricing. As a result, a variety of claims related use cases are moving from batch to real time.

Data warehousing is a technique for collecting, managing, and presenting data to help people analyze and use that data effectively. It involves a large database designed to support management-level staff by providing all the relevant historical data for analysis. This chapter begins with a definition of data warehousing, followed by an overview of large-scale policy management to highlight the need for data warehousing. Next, an overview of an ETL framework is presented, along with a discussion of advanced ETL techniques. The chapter concludes with an outline of performance optimization techniques for data warehousing. Data warehousing is considered a key enabler for efficient reporting and analysis, with implementation choices ranging from cost-effective desktop systems to large-scale, mission-critical data marts and warehouses containing petabytes of data. Extract, transform, and load (ETL) systems remain one of the largest cost and effort areas within data warehouse development projects, requiring significant planning and resources to build, manage, and monitor the flow of data from source systems into the data warehouse. The technology and techniques used for ETL can greatly influence the success or failure of a data warehouse. Complex business requirements for data cleansing, loading, transformation, and integration have intensified, while operational plans for real-time and near-real-time reporting add additional challenges. Parallel loading mechanisms, incremental data loading, and runtime update and insert strategies not only improve ETL performance but also optimize data warehousing performance, particularly for large-scale policy management.