High Correlation Values Research Articles

Experimental and artificial neural network (ANN) study on the impact of three different metal oxide nanoparticle combustion enhancers enriched on mahua biodiesel-diesel fuelled CI engine

Abstract The prediction of CI engine parameters has acquired significant attention and is regarded as a crucial tool for engine research and diagnosis studies. This contribution compares two different approaches for diesel engine viz. experimental and artificial neural networks (ANNs) predictions of performance and emission outputs. The base fuel M30 blend consist of mahua biodiesel 30 (% v) and 70 (% v) of diesel. The M30 blend and 50 ppm concentration of NPs (CeO2, CuO, and TiO2) were chosen for basic experimentation. The findings indicate an increase in BTE by 9.1%, peak CP by 11.3%, and HRR by 10.2%, with a decrease in BSFC by 13.7%, CO by 30.4%, HC by 30.1%, smoke by 34.7%, and NOx by 7.1%, resulting from the addition of 50 ppm CeO2 NP to D70M30 blend. The widely used backpropagation technique for ANN is implemented in multilayered feedforward design. To forecast the engine characteristics of a CI engine, a network structures including two inputs and one output is used. The given ANN model examined D100, M30, M30CeNP50, M30CuNP50, and M30TNP50 blends, using engine load and biodiesel with nanoparticle blend as the two input factors. A data-driven ANN model was created to forecast the optimised engine characteristics. The lowest and highest value of correlation coefficient (R2) and mean square errors (MSE), mean relative error (MRE) were found to for peak CP, HRR, BTE, BSFC, CO, CO2, HC, NOx and smoke. Using ANN one can choose right blend ratio among the variety of fuels blends for an appropriate requirement without much experimentation.

Non-contact respiratory monitoring during sleep: comparison of the touchless flow signal with RIPflow signal to assess respiratory events.

A non-intrusive sleep monitoring technology based on the data from a commercially available depth sensing camera has been utilized for respiratory monitoring and shown to have good performance in assessing respiratory rates across a range of rates. This non-contact, or touchless, technology allows continuous respiratory monitoring without attaching probes to the patient. We have noticed a strikingly similar morphology between the touchless flow signal (NCMflow) and the flow signal derived using the chest and abdomen respiratory inductance plethysmography (RIPflow) bands. Here we report on a quantitative assessment of the morphological similarity between the two signals for a cohort of patients undergoing polysomnography (PSG). We collected depth data from 25 sleep patients undergoing attended PSG. Correlation and mutual trending were assessed between the NCMflow and RIPflow signals in subjects undergoing diagnostic and split night PSG using Pearson correlation and concordance between the NCMflow and RIP flow signals. Strong correlation was observed across all patients between the NCMflow and RIPflow signals (range: 0.78 - 0.98, mean: 0.89±0.06). Similarly, high values of concordance were achieved between the NCMflow and RIPflow signals (range: 0.85 - 1.0, mean: 0.96±0.04). The high values of correlation and concordances confirm that the NCMflow signal can potentially be used as a surrogate for RIPflow signal during sleep. Our findings strongly support the potential for non-contact, continuous monitoring of respiratory disturbances during sleep.

Rapid Estimation of Soil Moisture Using Spectroscopic Sensor

ABSTRACT Soil moisture plays a significant role in the global hydrological cycle in the field of agriculture, environment science, urban planning and construction, forestry and many more. Soil moisture can be determined using various approaches, however some of the limitations in these approaches are longer time duration, labor intensive, require installation and maintenance, expensive, lower resolution, etc. This research work proposes a soil moisture detection method which is portable, economic and time efficient. Spectroscopic sensor enables rapid soil moisture estimation using photodiodes of varying sensitivities and wavelengths across a broad spectrum of light, allowing precise identification and analysis of various samples based on their unique spectral fingerprints. In this study, a relation between sensor output data and soil moisture percentage value obtained from gravimetric method has been established. Using various regression methods, the highest value of correlation coefficient was found in case of polynomial function of order 3. Residual values of all wavelengths were then calculated by comparing the calculated with the expected moisture values obtained using the polynomial function. Result showed that 705 nanometers (nm) wavelength has the least residual value, thus signifying that the polynomial equation of order 3 at 705 nm wavelength perfectly demonstrates the relation between soil moisture and sensor reading. Furthermore, the results were verified using optimization method which showed that 705 nm wavelength has the highest closeness coefficient, thus verifying this model and observations. This model can be helpful in determining the soil moisture instantaneously instead of hours as seen in traditional gravimetric methods.

Local genetic correlation via knockoffs reduces confounding due to cross-trait assortative mating

Local genetic correlation analysis is an important tool for identifying genetic loci with shared biology across traits. Recently, Border etal. have shown that the results of these analyses are confounded by cross-trait assortative mating (xAM), leading to many false-positive findings. Here, we describe LAVA-Knock, a local genetic correlation method that builds off an existing genetic correlation method, LAVA, and augments it by generating synthetic data in a way that preserves local and long-range linkage disequilibrium (LD), allowing us to reduce the confounding induced by xAM. We show in simulations based on a realistic xAM model and in genome-wide association study (GWAS) applications for 630 trait pairs that LAVA-Knock can greatly reduce the bias due to xAM relative to LAVA. Furthermore, we show a significant positive correlation between the reduction in local genetic correlations and estimates in the literature of cross-mate phenotype correlations; in particular, pairs of traits that are known to have high cross-mate phenotype correlation values have a significantly higher reduction in the number of local genetic correlations compared with other trait pairs. A few representative examples include education and intelligence, education and alcohol consumption, and attention-deficit hyperactivity disorder and depression. These results suggest that LAVA-Knock can reduce confounding due to both short-range LD and long-range LD induced by xAM.

Removal, mechanistic and kinetic studies of Cr(VI), Cd(II), and Pb(II) cations using Fe3O4 functionalized Schiff base chelating ligands.

The Schiff base chelating ligands; (E)-2-(3,3-dimethoxy-2-oxa-7,10-diaza-3-silaundec-10-en-11-yl)phenol (L1), (E)-N-(2-((pyridine-2ylmethylene)amino)ethyl)-3-(trimethoxysilyl)propan-1-amine (L2) and (E)-N-(2-((thiophen-2-ylmethylene)amino)ethyl)-3-(trimethoxysilyl)propan-1-amine (L3) were immobilized on Fe3O4 magnetic nanoparticles (MNPs) and utilized in the extraction of Cr(VI), Cd(II) and Pb(II) metal cations from aqueous solutions. The compounds synthesized, denoted as L1@ Fe3O4, L2@Fe3O4, and L3@Fe3O4, were characterized using FT-IR spectroscopy, TEM-SEM, VSM, and BET/BHJ techniques for analysis of functional groups, surface morphology, magnetic properties, and degree of porosity of the adsorbents, respectively. BET/BHJ technique confirmed the mesoporous nature of the compounds as their pore diameters ranged between 15 and 17nm. The initial optimization conditions of pH, adsorbent dosage, initial metal concentration, and contact time on adsorption were studied using L1@ Fe3O4. The optimum efficiencies recorded were 68% and 46% for Cr(VI) and Cd(II), respectively, obtained at pH 3, and a metal concentration of 20ppm while an efficiency of 99% was recorded for Pb(II) cations at pH 7 and a metal concentration of 100ppm. Compounds L2@Fe3O4 and L3@ Fe3O4 were also used in the extraction of metal cations from aqueous solution and gave efficiencies of 22%, 56%, and 78% for L2@ Fe3O4 and 19%, 90%, and 59% using L3@ Fe3O4 for Cr(VI), Cd(II), and Pb(II), respectively. The maximum adsorption capacities of L1@ Fe3O4 for Cr(VI), Cd(II), and Pb(II) cations were obtained from the Langmuir isotherm as 32.84, 41.77, and 450.45mg/g, respectively. The experimental data was analyzed using pseudo-first-order, pseudo-second-order, intraparticle diffusion, and Elovich kinetic models. Both linear and non-linear forms of kinetic isotherms; Langmuir, Freundlich, Redlich-Peterson, and Temkin were utilized to investigate the nature of adsorption on L1@Fe3O4. The mechanistic studies deduced that the Langmuir isotherm and pseudo-second-order kinetic model better described the adsorption process both yielding high correlation coefficient values (R2 > 0.98).

Enhancing Biogas Production Through the Co-Digestion of Fish Waste (FW) and Water Hyacinth (WH) Using Cow Dung as an Inoculum: Effect of FW/WH Ratio

The current investigation explores biogas production from water hyacinth (WH) and fish waste (FW) with cow dung (CD) as an inoculum source in two scenarios. In the first scenario, the optimization of mono-digestion was performed where the effect of WH/FW (substrates) with CD (inoculum) in varied ratios of 1:1, 1:2, 2:1, and 3:1 was observed to enhance the biogas production. In the second scenario, the optimization of co-digestion using both FW and WH as substrates in different ratios (1:1, 1:2, and 2:1) with a fixed amount of inoculum was studied. The experiments were conducted in 500 mL digesters in duplicate under mesophilic conditions. Under mono-digestion conditions for FW, the digester operating with FW/CD in a 1:2 ratio demonstrated the highest biogas yield of 970 ± 14.1 mL/g VS, containing 610 CH4 mL/g VS, while in WH, the WH/CD ratio of 1:1 exhibited the highest biogas yield of 925 ± 49.4 mL/g VS, with a methane content of 440 CH4 mL/g VS. The co-digestion of the WH/FW ratio (1:1) showcased the highest biogas production of 1655 ± 91.92 mL/g VS, accompanied by 890 ± 70.7 CH4 mL/g VS. This was followed by the 1:2 and 2:1 ratio, yielding 1400 ± 56.5 and 1140 ± 169.7 mL/g VS. of biogas and 775 and 585 CH4 mL/g VS, respectively. The CD and WH mixture at a 1:1 ratio demonstrated the most significant decrease in chemical oxygen demand (COD), reaching 91.68%. COD reductions over 80% in all combinations were observed in all instances. Anaerobic digestion (AD) simulations were validated using the Gompertz model, with high correlation coefficient values (R-squared) above 0.99 for all of the studied ratios, depicting a significant correlation between experimental data and model predictions. The propionic to acetic acid ratio did not cross the threshold level, indicating no inhibition of methane production. ANOVA analysis of biogas production between the co-digestion and mono-digestion of substrates showed non-significant results (p > 0.310 and p > 0.824, respectively), while overall digestion was significant (p < 0.024), indicating efficiency variations among substrates. Paired sample t-tests revealed substantial differences between co-digestion ratios, which were also significant.

Long-term rainfall and water table influence on groundwater nutrient dynamics from an oil palm plantation

ABSTRACT Conservation of groundwater quality is of paramount importance for sustainable agricultural management. Hydrological factors such as rainfall patterns and water table (WT) management, including drainage practices, play a crucial role in groundwater recharge. This in turn has a significant impact on WT fluctuations, nutrient losses in the soil, and the leaching of fertilizers into groundwater which leads to groundwater pollution. Consequently, this study evaluates the long-term influence of seasonal rainfall and WT fluctuations on groundwater nutrient dynamics in tropical peatlands from an oil palm plantation (OPP). Linear regression analyses and Pearson correlation matrices were adopted to evaluate the relationships between seasonal rainfall, WT, and groundwater chemical parameters. The results showed that there was a significant increase in mean pH, NO3 −, Na+, Ca2+, and PO4 3- values during the wet season compared to the dry season which could be attributed to the leaching of nutrients into groundwater due to rainfall, nutrient runoff from drainage systems and increased nitrification rate. A significant positive correlation (p < 0.01) was found between groundwater pH and NO3 − during the wet season, suggesting that increased groundwater pH due to heavy rainfall directly affects the nitrification process. It was also observed that low WT promotes denitrification in shallow groundwater, with this effect being more significant (p < 0.05) during the dry season. This was reflected in the higher correlation values between the WT fluctuations and the NH4 + concentrations in the groundwater. This research represents the first study to explore the long-term impacts of rainfall and WT fluctuations on groundwater quality in tropical peatlands. The insights gained from this study offer valuable guidance for WT management strategies aimed at conserving groundwater quality in such environments, ultimately contributing to sustainable agricultural practices.

Synthesis of Silver Nanoparticles and Gold Nanoparticles Used as Biosensors for the Detection of Human Serum Albumin-Diagnosed Kidney Disease.

Background/Objectives: This study aims to develop a screen-printed carbon electrode (SPCE) modified with silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) for the detection of human serum albumin (HSA). The objectives include utilizing green synthesis methods for nanoparticle production and evaluating the electrochemical performance of the modified electrodes. Methods: AgNPs and AuNPs were synthesized using Phulae pineapple peel extract (PPA) as a reducing agent. The nanoparticles were characterized using UV-visible spectrophotometry (UV-vis), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The electrochemical performance of AgNP/SPCE and AuNP/SPCE was assessed by cyclic voltammetry (CV) studies, and the electrodes were functionalized with anti-HSA antibodies for HSA detection. Results: Characterization revealed spherical nanoparticles ranging from 10 to 30 nm. Both AgNP/SPCE and AuNP/SPCE demonstrated improved electrochemical performance compared to bare SPCEs. The modified sensors could detect serum albumin concentrations from 10 to 400 μg/mL, with high correlation values of 0.97 and 0.99 for AgNPs and AuNPs, respectively. Conclusions: This research demonstrates the potential of using agricultural waste for green synthesis of nanoparticles and highlights the application of AgNPs and AuNPs in developing sensitive biosensing platforms for the detection of human serum albumin.

Enhanced SAR Compression through Multi-Look Doppler Compensation and Auto-Focusing Technique.

This paper presents a simple and streamlined compensation technique for improving the quality of synthetic aperture radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in the space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve the optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing applications.

Insurance loss model vs. meteorological loss index – how comparable are their loss estimates for European windstorms?

Abstract. Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity.

Evaluation of geodiversity in the Brazilian Amazon using different quantification methodologies

When one considers geodiversity as the variability of abiotic elements distributed on the earth's surface in an unequal and complex manner, and that the configuration of these elements plays an important role in ecosystem dynamics, it becomes necessary and important to evaluate the different forms by which its spatial organization can be quantified and mapped. A recent academic effort in different countries, scales, and territories aims to evaluate and quantify geodiversity and its spatial variation. Thus, this work aims to apply and compare the spatial distribution of the geodiversity indices derived from four methodologies in the Brazilian Amazon. From this analysis, we seek to understand the distribution of the geodiversity indices in the Brazilian Amazon and organize a cartographic base for future correlations with spatial data representative of biodiversity and human appropriation of the territory. When one considers the tendencies of spatial similarity for each methodology, regardless of the size and shape of the quantification cells, the indices generated with the second methodology have a higher correlation with the basic index. In general, since they consider the same diversity measure (richness), the tendencies of direct spatial similarity are highlighted by the basic index and the products of the second methodology. The Shannon's index in the 250 × 250 km hexagonal grids, besides the high value of positive correlation, is also equivalent to the basic index, regaining the spatial proportion of each class, above all other indices. The indices generated from Shannon's and Simpson's indices tend to differ from the other indices because, besides class richness, they also measure evenness. The comparison between the quantitative indices shows some tendencies in the spatial patterns of geodiversity of the Brazilian Amazon but also presents indications of the limitations imposed by the different measurement criteria of the abiotic variables used.

Blending gauge, multi-satellite and atmospheric reanalysis precipitation products to facilitate drought monitoring

Long-term, continuous, and highly accurate precipitation estimation is crucial for reliable drought monitoring. Precipitation estimation based on gauge measurements, satellite retrieval, and reanalysis datasets exhibits heterogeneous uncertainties across different regions of mainland China. This study introduces two modifiable weighting schemes (Cheng-Kling-Gupta Efficiency (CKGE) weighted-ensemble model (CWEM) and Bayesian Model Averaging (BMA)), utilizing posterior probabilities generated by BMA and the performance of CKGE, to merge 7 monthly precipitation datasets into new weighted precipitation (BMA Ensemble Precipitation (BMAEP) and CKGE Weighted-Ensemble Precipitation (CWEP)) using various quantity combination schemes. Subsequently, the precision and drought monitoring utility of the weighted precipitation are evaluated and compared with the representative fused precipitation product Multi-Source Weighted-Ensemble Precipitation (MSWEP) in mainland China using gauged data. The amalgamated results demonstrate that, compared to individual datasets, certain weighted precipitation schemes exhibit superiority over MSWEP. In particular, BMAEP-2P demonstrates superior performance in the composite index CKGE, achieving a value of 0.828. CWEP-4P exhibits a higher correlation coefficient (CC), reaching 0.905. CWEP-2P excels in terms of relative bias (BIAS) and root mean square error (RMSE), with values of 0.579 % and 20.755 mm, respectively. Furthermore, BMAEP or CWEP performs optimally in drought monitoring applications across all sub-regions of mainland China at different time scales (1, 3, 6, 12 and 24 months), with the average of the highest values of CC and probability of detection (POD) reached 0.919 and 0.844, respectively. Further contribution analysis reveals CPC as the dominant factor contributing to the greatest improvement in the performance (excluding CKGE, CC_SPEI1, CC_SPEI24 and POD_SPEI24) of the fusion models, among which it boosted MERRA2′s performance on POD_SPEI6 by 9.41 %. In conclusion, the merging schemes based on CWEM and BMA methods effectively generate a new precipitation dataset, integrating information from multiple products into drought monitoring applications.

Susceptibility and Metallics Elements in Soil Struck by Lightning in West Kotawaringin Regency, Central Kalimantan

Abstract Magnetic susceptibility is a magnetic parameter that assesses a magnetic material’s susceptibility to external magnetic fields. Magnetic susceptibility values can determine the properties of magnetic minerals related to the elements they contain. This research aims to determine the value of magnetic susceptibility and metal element content in soil struck by lightning in West Kotawaringin Regency. Sample measurements were performed using the Bartington MS2B instrument to determine the magnetic susceptibility value and XRF (X-ray fluorescence) to determine the metal element content in the soil samples. The magnetic susceptibility value obtained for sample A with low frequency (&lt;10 strikes per month) was 26.1 x 10−8 m 3/kg; for sample B with medium frequency (11–200 strikes per month), it was 538.4 x 10−8 m 3/kg; and for sample C with high frequency (&gt;20 strikes per month), it was 24.4 x 10−8 m 3/kg. Fe (388,000 mg/kg) was the most prevalent metal element in samples B and C, while element V (300 mg/kg) was the least prevalent in sample A. The sample with the highest metal element content was Fe (388,0 mg/kg), while sample A had the lowest metal element content, V (300 mg/kg). The highest correlation value between magnetic susceptibility and metals is 0.9 (K); 0.96 (Cr); 0.98 (Fe); and 1.00 (Ca, Ni, Zn, and Re).

Metric Stability of One Month Handgrip Maximal and Explosive Isometric Strength Measured by Classic and Impulse Contractions

Abstract The aim of this study is to determine the metric stability of the one-month handgrip test (HGT) in order to define the contractile characteristics of the biological variation of maximal isometric strength (F max) and maximal isometric rate of force development (RFD max) of the handgrip in two different testing regimes (classic and impulse). The study was conducted with a total of 16 participants (11 men and 5 women). Testing was performed using an isometric handgrip probe with a standardized test protocol and equipment sports medical solutions (SMS). The results of F max showed a low relative standard error of the mean (RS = 1.33 %), a high value of inter-class correlation (ICC = 0.996), and no statistically significant change in trend (p &gt; 0.05) during the testing period. Therefore, can conclude that the HGT procedure in classic mode can be used as a stable parameter in a human subject sample. However, the RFD max results showed a low RS (2.13 %) and a high ICC value (0.996), but a statistically significant change of trend (p &lt; 0.05) during the measurement period. The regression constant (RCO) trend was 42.629 N/s, which can be attributed to learning or to the adaptive effects of the test procedure, which triggered similar adaptation processes as the training. In general, it can be concluded that the handgrip can be used to sensitively measure the effects of different long-term health improvements, or the effects of different medical/health exercises, rehabilitation programs, effects of medication applications, or dietary supplements for F max. However, further research should be conducted for the RFD max considering the metric stability parameters.

New Documented Records of the Mesopelagic Fish Valenciennellus tripunctulatus (Sternoptychidae) in the Strait of Messina and a Review of Its Mediterranean Occurrences

The constellation fish, Valenciennellus tripunctulatus, is a small mesopelagic teleost typically found in equatorial and temperate waters worldwide, although the species has been also rarely recorded in the Mediterranean Sea (less than 20 specimens from the available literature since 1918). The Mediterranean records have been documented for the Strait of Messina, the southern Tyrrhenian Sea, the Ligurian Sea and the Adriatic Sea. The present paper offers an updated review on the occurrence of V. tripunctulatus in the Mediterranean basin and provides data on three new specimens, collected in the Strait of Messina between April 2010 and March 2024. Furthermore, we also report information from two individuals found in March 1992 and February 2017, respectively. Overall, the standard length of these specimens ranged from 24.1 to 28.2 mm, whereas weight varied from 0.18 g to 0.20 g. Morphometric and meristic data were reported, and this analysis revealed few differences between examined individuals in the number of PV (ventral series anterior to pelvic fin base) and AC (from anal fin origin to caudal fin base) photophore series. The relationship between total length and standard length (SL = 0.75 × TL + 2.98), total length and head length (HL = 0.17 × TL + 1.47) and standard length and head length (HL = 0.23 × SL + 0.88) showed high correlation values. Considering the lack of data on this species in the Mediterranean, updating morphometric, meristic, biological, ecological and distribution information is very important for taxonomic purposes and useful in understanding the characteristics of the regional population and eventual differences between Mediterranean stock(s) and those from other oceanic areas.

Improved Brain Tumor Detection MRI Using Advanced Processing Techniques: Enhancement and Convolution Case Studies

Brain tumors present a significant challenge in medical imaging due to their complexity, requiring early detection and precise analysis for effective treatment. This study develops and evaluates advanced image processing workflows aimed at enhancing brain tumor image analysis. The proposed method involves four main steps: enlargement, pre-processing with min-max filters, enhancement, and convolution. The dataset used is from Kaggle, comprising 3,364 images categorized into Glioma (100 images), Meningioma (115 images), No Tumor (105 images), and Pituitary Tumor (74 images). For this study, images from the Glioma, Meningioma, and Pituitary Tumor categories were used, with one image selected from each category for technique evaluation. The results showed significant improvements in image clarity and detail, with high correlation values of 0.9851 for Meningioma and 0.9886 for Pituitary. These findings highlight the effectiveness of the proposed techniques in enhancing image quality and diagnostic accuracy.

PHENOTYPIC CHARACTERISTICS OF SAHELIAN GOAT BREED IN KATSINA STATE

This study was conducted to assess the phenotypic characteristics of the Sahelian goat breed in Katsina State. One hundred and sixty-three (163) physically healthy and non-pregnant Sahelian goats, comprising of male (n = 64) and female (n = 99) between one to four years, were randomly selected for this study. Data measured included; body weight (kg) and five (5) body morphometric traits on individual goat. The results obtained revealed that majority (n = 99, 60.74%) of the goats included inthe study were female. The result also revealed that sex significantly affected body weight and linear body parameters in Sahelian goats, giving credence to the manifestation of sexual dimorphism among the goat population. Chest girth had the highest correlation value with body weight, this shows that chest girth is the best predictor of body weight in the goat population studied.

Environmental Conditions Associated with Long-Track Tornadoes

Abstract Reanalysis proximity vertical profile attributes associated with long-track tornadoes [LTTs; pathlength ≥48 km (30 mi)] and short-track tornadoes [STTs; pathlengths &lt;48 km (30 mi)] for a total of 48 212 tornadoes with pathlengths ≥0.16 km (0.1 mi) from 1979–2022 in the United States were examined. Both longer- and shorter-track tornadoes were associated with vast ranges of mixed-layer convective available potential energy, together with relatively low mixed-layer lifted condensation level heights and minimal convective inhibition. A large range of 500–9000-m wind speeds and bulk wind differences, 500–3000-m streamwise vorticities, storm-relative helicities, and storm-relative wind speeds were found for STTs. In stark contrast, LTTs only occurred when these kinematic attributes were larger in amplitude through the troposphere, supporting previously documented associations between observed longer-track tornado pathlengths and faster-propagating parent storms. A novel parameter, heavily weighted by kinematic parameters and lightly weighted by thermodynamic parameters, outperformed the significant tornado parameter in differentiating environments that were more supportive of both LTTs as well as tornadoes rated &lt;EF5. The high correlation values R2 = 0.79 between tornado pathlength and Bunkers’ approximate tornado duration (pathlength / VBunkers) calls for improved understanding of mesocyclone periodicities, which impact tornado longevity, to improve tornado pathlength diagnoses and forecasts. Pragmatically, diagnosing LTT environments using vertical profile attributes, perhaps, is not so much a problem of determining when there might be higher expectations for LTTs, but rather a problem of when there might be lower expectations for LTTs, e.g., weaker kinematic attributes in the lower troposphere.

New porous adsorbents based on natural polymers for treatment of motor oil contaminated wastewaters

AbstractThis study aims to develop efficient adsorptive materials for retaining used 5w40 motor oil using xanthan (XG), xanthan esterified with acrylic acid (XGAC), Lignoboost lignin (LB), and LB esterified with oleic and stearic acids. The adsorption capacities of these materials were systematically evaluated, revealing a hierarchical efficacy with XG exhibiting the highest performance (60.16 g/g), followed by XGAC/LB (59.92 g/g) and XGAC (55.79 g/g). These capacities were correlated with the materials' surface area and morphology. Additionally, accelerated weathering studies assessed the degradation process of the adsorptive materials over time, revealing that all of them undergo significant structural changes, which suggests potential pathways for environmentally friendly disposal. Kinetic studies indicated that the experimental data fit well with the pseudo‐second‐order (PSO) model. The high correlation coefficient values for the PSO model (0.9318–0.9998) confirmed its suitability for describing the kinetics of oil sorption. FTIR spectra of the used motor oil‐loaded materials confirmed the physical nature of the adsorption process, involving interactions such as hydrogen bonding, van der Waals forces, and π–π interactions. SEM analysis demonstrated that materials with larger pore sizes showed less efficient oil adsorption despite having high porosity, highlighting the importance of optimal pore size distribution for effective adsorption.

ANALISIS HUBUNGAN MARKETING MIX TERHADAP LOYALITAS KONSUMEN PADA KOFFIELOTS COFFEE &amp; EATERY

Marketing mix is one of the marketing strategies to convey information widely, introduce a product of goods and services, stimulate consumers to give and even create personal preferences for the image of a product. It can be seen that Koffielots Coffee &amp; Eatery experienced a significant decline in sales from January to December. In January, sales were recorded at Rp 17,134,500 with a sales percentage reaching 12%. However, in December, sales dropped drastically to Rp 7,814,500 with a sales percentage of only 5%. The type of research data uses a quantitative approach. The data analysis method uses multiple linear regression. The study shows that the variables Product (X1), Price (X2), Promotion (X3), and Location (X4) have a significant positive effect on Consumer Loyalty (Y). This is evidenced by the positive and significant regression coefficient, as well as the high Adjusted R Square value (80.9%). The high correlation value (ranging from 0.804 to 0.868) indicates that an increase in each variable will tend to increase consumer loyalty in one direction