Articles

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

This work evaluated the bioremediation potential of <i>Jatropha</i><i> </i><i>tanjorensis</i><i> </i>leaf extract at different masses (250g, 500g and 750g) over a 40-day period. To achieve this, crude oil contamination of sandy loam soil was stimulated in twelve plastic reactors containing fixed masses of soil (4kg each) of topsoil homogenized with 500g of Bonny light crude oil. The<i> </i><i>Jatropha</i><i> </i><i>tanjorensis</i><i> </i>leaves were cultivated, rinsed with distilled water, blended, and purified by filtration. The leaf extract was applied at the stated concentrations including a control reactor (without leaf extract). The plastics reactors were kept in an open air shielded away from rainfall. The physicochemical characteristics determined were particle size distribution (PSD), potential of hydrogen (pH), electrical conductivity (EC), organic matter (OM), organic carbon (OC), selected heavy metals (Cr, Cd, Zn, Pb) and sample management were all in line with standard procedure. After 40 days of treatment, results obtained showed that plastic reactor with 750g of leaf extract produced the highest amount of cadmium reduction of 97% (from an initial of and there was significant difference among treatment (P < 0.05). The sequence of reduction among treatment was 750g > 500g > 250g of the leaf extract. Chromium, Lead and zinc followed similar trend. Thus, the <i>Jatropha</i><i> </i><i>tanjorensis</i> leaf extract has the potential to ameliorate crude oil-contaminated soil.

The accurate estimation of Individual Wave Components (IWC) is crucial for automated diagnosis of the human digestive system in a clinical setting. However, this process can be challenging due to signal contamination by other signal sources in the body, such as the lungs and heart, as well as environmental noise. To address this issue, various denoising techniques are commonly employed in bowel sound signal processing. While denoising is important, it can increase computational complexity, making it challenging for portable devices. Therefore, signal processing algorithms often require a trade-off between fidelity and computational complexity. This study aims to evaluate an IWC parameter extraction algorithm that was previously developed and reconstruct the IWC without denoising using synthetic and clinical data. To that end, the role of a reliable model in creating synthetic data is paramount. The rigorous testing of the algorithm is limited by the availability of quality and quantity recorded data. To overcome this challenge, a mathematical model has been proposed to generate synthetic bowel sound data that can be used to test new algorithms. The proposed algorithm’s robust performance is evaluated using both synthetic and clinically recorded data. We perform time-frequency analysis of original and reconstructed bowel sound signals in various digestive system states and characterize the performance using Monte Carlo simulation when denoising is not applied. Overall, our study presents a promising algorithm for accurate IWC estimation that can be useful for predicting anomalies in the digestive system.

Although human oral fluid has become more routine for quantitative drug detection in pain management, detecting a large scope of medications and substances is costly and technically challenging for laboratories. This paper presents a quantitative assay for 64 pain medications, illicit substances, and drug metabolites in human oral fluid. The novelty of this assay is that it was developed on an older model AB SCIEX 4000 instrument and renders obscure the need for more technical and expensive laboratory equipment. This method includes addition of internal standard and a 2-step liquid-liquid extraction and dry-down step to concentrate and clean the samples. The samples were suspended in 50% MeOH in water and separation and detection was accomplished using triple quadrupole mass spectrometry (LC-MS/MS). Separation was achieved using reverse-phase liquid chromatography with detection by LC-MS/MS. A second injection was done in negative mode to determine THC-COOH concentration as an indicator of THC. An aliquot of the (already) extracted samples was analyzed for D- and L- isomers of amphetamine and methamphetamine using a chiral column. The standard curve spanned from 5 to 2000 ng/mL for most of the analytes (1 to 2000 ng/mL for fentanyl and THC-COOH) and up to 1000 ng/mL for 13 analytes. Pregabalin and gabapentin ranged from 25 to 2000 ng/mL. The result is a low-cost method for the sensitive detection of a wide-ranging oral fluid menu for pain management. This assay has a high sensitivity, and good precision and accuracy for all analytes with an older model mass spectrometer.

Methamphetamine and its metabolite amphetamine are frequently abused drugs. Whether obtained legally or from clandestine laboratories it is of relevance to determine the chiral makeup of these drugs for investigative purpose. Although urine and oral fluid matrices are commonly offered, less available to independent laboratories are techniques to verify dextro (D-) or levo (L-) (meth)amphetamine from human K2EDTA plasma. This paper outlines the development and validation of a method that includes the addition of internal standard and a two-step liquid-liquid extraction to remove the analytes from human K2EDTA plasma by triple quadrupole mass spectrometry (LC-MS/MS). The assay was validated according to the United States Food and Drug Administration and College of American Pathologists guidelines, including assessment of the following parameters in plasma validation samples: linear range, limit of detection, lower limit of quantitation, matrix effects, inter- and intra-day assay precision and accuracy, carry over, linearity of dilution, matrix effects and stability. The outcome is a validated and reliable method for the determination of D- and L- isomer concentration of meth(amphetamine) human plasma samples that can be easily adopted by independent clinical laboratories.

The study was conducted to assess the antibacterial activity of <i>Phyllanthus</i><i> </i><i>amarus</i><i> (</i>Schum and Thonn) extract against <i>Salmonella </i><i>typhi</i> causative agent of typhoid fever at the laboratories of the Departments of Chemistry and Theoretical and Applied Biology of the College of Science, Kwame Nkrumah University of Science and Technology, Kumasi. The objectives were to determine the highest yield of crude extract of <i>P. </i><i>amarus</i><i> </i>using different proportions of water to ethanol and to determine the sensitivity of <i>Salmonella </i><i>typhi</i> to these. Three different extraction procedures were carried out. In the first procedure, seven extraction setups each containing different proportions of the two extract (water and ethanol) were used with 10g of the plant sample. In the second procedure, eight setups were used for the two solvents. Ten grams of both fresh and dry plant sample were extracted in two different 200ml of water and in another two different 200ml of water; 20g of both fresh and dry plant sample were again extracted. The same procedure was repeated using ethanol as the solvent. In the third procedure, 10g each of fresh plant sample were boiled in 100ml and 200ml of water for 30 minutes. A sensitivity test to determine the zones of inhibition for the various plant extracts was done on <i>Salmonella </i><i>typhi</i> isolated from human. Results from the crude yield of <i>P. </i><i>amarus</i> using water only had the highest crude yield of 2.57g, followed by ethanol only which was 2.52g. The sensitivity studies conducted on the fresh <i>P. </i><i>amarus</i><i> </i>indicated that aqueous extract of <i>P. </i><i>amarus</i><i> </i>inhibited <i>S. </i><i>typhi</i> to a zone of 5.00mm in 10g/200ml and 7.17mm in 20g/200ml. Ethanol extract also recorded an inhibition zone of 2.67mm and 5.33mm in 10g/200ml and 20g/200ml respectively. Again, sensitivity studies using dry <i>P. </i><i>amarus</i><i> </i>samples showed that the aqueous extracts recorded a zone of inhibition of 7.33mm in 10g/200ml and 13.50mm in 20g/200ml. Also ethanol extracts also recorded an inhibition zone of 6.83mm in 10g/200ml and 10.50mm in 20g/200ml. Significant differences were observed among the extracts and the control in both 10g/200ml and 20g/200ml concentrations (P<0.05). Aqueous and ethanol extracts of <i>P. </i><i>amarus</i><i> </i>proved inhibitory to <i>S. </i><i>typhi</i>.

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.

<b>Objective</b><b>:</b> The study investigated the impacts of third molar (M3) extraction on periodontal status of adjacent second molar (M2) and oral health-related Quality of life (QoL). <b>Methods:</b> 272 cases with M3 were randomly divided into treatment group and control group, each of 136 cases. Questionnaire survey evaluated demographic features of the subjects. Gingival index (GI), plaque index (PLI) and conscious symptoms of adjacent teeth of the M3 were detected in both groups at baseline (T0), 1 week (T1), 1 month (T2) and 6 months (T3) after treatment. The impacts of QoL were evaluated using OHIP-14 to measure total scores and various items of OHIP-14 at different observation point. <b>Results: </b>The percentage of swelling and toothache in the treatment group was significantly lower than that in the control group at T1, T2 and T3. Significant differences could be seen in GI and PLI between the treatment and control groups at T2 and T3. The total score of OHIP-14 in T2 and T3 was significantly lower than that in T0 and T1 in the treatment group. The 14 items scores and the percentage of positive reaction in the treatment group were significantly lower than those in the control group at T2 and T3. <b>Conclusion</b>: M3 extraction had significant impacts on the periodontal status of the second molar and quality of life. This study would provide an important basis for the prophylactic removal of M3 in clinical work.

In this Research work, Silver Nanoparticles were green synthesized from Psidium guajava leaves<b> </b>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^-1corresponds 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^-1reveals 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.

During the past few decades, many of the synthetic chemicals are able to produce nanoparticles and nanoclusters, although these chemicals primarily act as reducing and capping agents, they are very toxic and hazardous and make the nanoparticles biologically incompatible. Thus there is need for green chemistry that includes a clean, non-toxic and environmental friendly method of nanoparticles synthesis. Cobalt, iron and copper nanoparticles were synthesized using the stem-bark extract of khayasenegalensis (mahogany) where cobalt chloride (CoCl₂ 6H₂O), ferric chloride (FeCl₂), and copper sulphate (CuSO₄ H₂O) were used as the metal precursor respectively. The change in color from light brown to dark brown indicates the formation of cobalt nanoparticles, from light brown to dark green indicates the formation of copper nanoparticles and also the change in color from light brown to a dark color indicates formation of iron nanoparticles. The nanoparticles were further characterized using UV visible spectroscopy, FTIR, and SEM. The UV result for CoNPs showed the highest peak at 500nm and both FeNPs and CuNPs showed the highest peak at 300nm. The FTIR results for all the nanoparticles showed the presence of Alkaloids and triterpenes. Also the SEM result showed spherical granular, partially dispersed and monodispersed morphology for CoNPs, FeNPs and CuNPs respectively. Moreover, the antibacterial activity of the synthesized NPs when tested against two gram positive bacteria and two gram negative bacteria was evaluated and good results were obtained. The antifungal activity when tested against two fungi showed a very good result.

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.

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.

<b>Background</b>: 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 <i>in</i><i> </i><i>vitro</i> evaluation of antimicrobial and antioxidant activities of aqueous, methanol, acetate, dichloromethane extracts from <i>Cola</i><i> </i><i>acuminata</i> nuts grown in the Nord Ubangi Province, DRC. <b>Methods</b>: The nuts of <i>Cola</i><i> </i><i>acuminata</i><i> </i>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 <i>Staphylococcus</i><i> </i><i>aureus</i> ATCC 25923, <i>Escherichia</i><i> </i><i>coli</i> ATCC 8739 and <i>Pseudomonas</i><i> </i><i>aeroginosa</i> 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. <b>Findings</b>: The micrography of <i>C.</i><i> </i><i>acuminata</i><i> </i>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 IC<SUB>50</SUB> 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. <b>Conclusion</b>: Seeing the potency of <i>C.</i><i> </i><i>acuminata</i> 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 <i>in</i><i> </i><i>vitro</i>, to perform the in vivo experiments and to test for other activities such as the anti-hypoglycemic and anti-inflammatory.