Logarithmic Transformation Research Articles

A Comparison of Home Purchase and Liquid Lazy Portfolio Returns in Turkey

This study presents a comprehensive comparison between the effectiveness of home purchase and liquid Lazy portfolio strategies for generating historical returns in the Turkish market. The primary aim is to develop a liquid Lazy portfolio that outperforms residential property investments. The study covers the period from January 2010 to June 2023, focusing on high-liquidity assets including the Borsa Istanbul 100 Index, currency exchange rates, gold prices, real estate investment trusts, and major stock market indices. Methodologically, the research involves data pre-processing, logarithmic transformations, and cumulative time series creation. Portfolio optimization centers around maximizing the Sharpe ratio with three test portfolios developed. Findings indicate that all portfolios consistently outperform the benchmark Residential Property Price Index, displaying higher returns and Sharpe ratios despite increased volatility. These insights are relevant for both academic researchers and investors seeking practical applications for portfolio management. It's important to note that historical performance offers insights but not future guarantees. In conclusion, the study highlights the potential benefits of constructing scalable, liquid, and high-return Lazy portfolios as alternatives to traditional home purchases

Methods you can count on: A simulation experiment showing the advantages generalized linear modeling (GLM) over the linear regression of log-transformed count data

Count data are the oldest and most basic form of data in the field of archaeology. Count data, however, have long been known to defy the assumptions of many commonly used parametric statistical methods, such as linear regression, in generally failing to follow a normal distribution. Most archaeological analyses have dealt with this problem through the logarithmic transformation of count data for the purposes of normalization. Recently, other scientific fields have recognized major shortcomings associated with log transformation and they have adopted nonparametric alternatives, such as generalized linear modeling (GLM). With a few exceptions, the field of archaeology has not followed suit. This paper presents the results of a simulation experiment designed to compare the performance of linear regression of log-transformed data and GLM under some commonly occurring conditions for count data in the field of archaeology. The results of this simulation experiment confirm the striking superiority of GLM compared with the linear regression of log-transformed data, showing that the latter is particularly prone to false-negative results (i.e. Type II error). We argue that archaeologists, therefore, should join other scientific fields in more widely adopting GLM and its nonparametric counterparts in analyzing count data.

Study on the influence factors of fatigue properties of large-stone asphalt mixtures based on semi-circular bending tests

In order to alleviate the fatigue cracking problem of asphalt pavements. The development of large-stone asphalt mixtures (LSAM) is a worthwhile research direction. In this paper, different loading frequencies, test temperatures and stress ratios are designed to investigate the influence of semicircular bending (SCB) test conditions for LSAM fatigue properties. Comparative tests were also conducted with AC-13 fine-grained asphalt mixtures. The fatigue property of LSAM was analyzed by applying Paris' law, measuring fatigue crack length and using fatigue rupture energy theory. The test results indicated that fatigue life increased approximately 12 times with increasing loading frequency, 5 times with lower test temperatures, and 4 times with reduced stress ratio. Respectively, the magnitude of the single fatigue rupture energy and the total fatigue rupture energy also increased. Compared to AC-13 fine-grained asphalt mixtures, LSAM has a lower rate of rise in the displacement curve and smaller fracture displacement. After logarithmic transformation, the fatigue rupture energy and fatigue life of LSAM show an obvious linear relationship, specifically Wf=0.000806Nf1.12975. In summary, based on the SCB test conditions, LSAM exhibited superior fatigue performance compared to AC-13 fine-grained asphalt mixture. Additionally, LSAM demonstrated better resistance to fatigue loading-induced deformation.

Effect of Staggered Contour Trenches and Hillside Ditches on surface runoff and soil loss on steep slopes in shaded coffee (Coffea arabica), Costa Rica

Soil erosion is one of the main causes of soil degradation and an important environmental problem, often accelerated by human activities. Different types of soil and water conservation measures (SWCM) have been implemented in worldwide to control soil loss, such as Staggered Contour Trenches (SCT) and Hillside ditches (HD) which are recommended in topographies up to 40 % slope. This study measured the effects of STC and HD, on surface runoff, soil loss, exposed of organic matter and soil moisture content, in a shaded coffee crop with steep slopes in an Entisol soil. Nine runoff plots were installed in Llano Brenes, Costa Rica, in the upper basin of the Jesús María River in a study area with slopes between 50 and 71 ± 7.92 %. We conclude that this study could make an important contribution to research on the use of SWCM on tropical soils with slopes greater than 50 %. Due to the non-compliance of assumptions, a logarithmic transformation of the data was used, and they were evaluated using the Kruskal-Wallis one-way nonparametric test. Spearman correlation and linear regressions were used to assess the effect of the precipitation and intensity in the soil loss and runoff in each treatment. The amount of organic matter exposed by soil removal was not statistically different between HD and SCT, but both were different to the control (R2 = 0.81, P < 0.05). Spearman correlation analysis showed the rainfall depth contributed more to runoff depth (HD R = 0.62; SCT R = 0.62; Control R = 0.60; P < 0.001) and soil loss (HD R = 0.50; STC R = 0.53; Control R = 0.50; P < 0.001) than other variables. Regression analysis showed that runoff and soil loss were significant (P < 0.05) and positively correlated with rainfall depth for each treatment. In this study, HD was shown to be efficient in erosion control on steeper slopes. HD helped reduce soil loss compared to the control, by 88 % in 2018 and 83 % in 2019. HD also reduced runoff generation by 55 % in 2018 and 60 % in 2019 compared to the control. While SCT was not statistically different in the generation of surface runoff and soil loss compared to the control.

Species-Abundance Models for the Early Postfire Succession of Subalpine Shrub Grassland

Fire is one of the principal factors influencing subalpine ecosystem succession. Species numbers and plant compositions are used to determine postfire disturbance, vegetation, structural change, and succession. Ecologists also integrate species diversity and mathematical models to enable researchers to obtain increasingly detailed insights into habitats during post-disturbance restoration processes. This study employed five species-abundance models, namely the niche preemption model, the broken-stick model, the log-normal model, the Zipf model, and the Zipf–Mandelbrot model, to perform fitting analysis on the abundance data of postfire species coverage in shrub grasslands near 369 Hut at Xue Mountain in Shei-Pa National Park, Taiwan. We performed the logarithmic transformation on plant-coverage areas for each period of postfire shrub-grassland succession, and then, based on histograms drawn for species–coverage distribution modes, the test results consistently showed normal distributions (p &lt; 0.05). Species-coverage histograms measuring various periods showed that there were comparatively higher numbers of common species during postfire succession and that the numbers of rare species progressively increased. The fitting results of the five species-abundance models showed that although the most suitable abundance models for each period of postfire succession varied, the majority of these periods demonstrated decent fitting with respect to the Zipf–Mandelbrot model. These findings showed that fuel consumption provided nutrients in a manner that facilitated postfire regeneration. Moreover, dominant species, such as Yushania niitakayamensis, and Miscanthus transmorrisonensis, did not fully occupy growing spaces and resource availabilities; consequently, seeded species were able to grow.

Exploring the relationship between malnutrition and the systemic immune-inflammation index in older inpatients: a study based on comprehensive geriatric assessment

BackgroundMalnutrition is a prevalent and major challenge among senior citizens, possibly due to the continual low-grade inflammatory state of the body. A novel inflammatory parameter, the systemic immune-inflammation index (SII), is highly valuable in evaluating and predicting the prognosis of a wide range of diseases. This study aims to explore the significance of the SII in assessing malnutrition in older inpatients.MethodsThis retrospective study included 500 senior hospitalized patients who met the inclusion and exclusion criteria from the Comprehensive Geriatric Assessment database of the First Hospital of Jilin University. The Mini-Nutritional Assessment (MNA) questionnaire was used to evaluate the nutritional status of patients. The SII was calculated using complete blood counts, and we performed natural logarithm transformation of the SII [ln(SII)]. Multivariable logistic regression analysis was used to identify the association between ln(SII) and malnutrition. To ensure the stability of the findings, a sensitivity analysis was conducted.ResultsThe 500 patients had a mean age of 77.29 ± 9.85 years, and 68.6% were male. In accordance with the MNA, 30.4% of the patients were malnourished or at risk of malnutrition, and patients in this group had considerably greater levels of ln(SII) than patients with adequate nutrition (P < 0.001). The optimum ln(SII) cutoff value for patients with malnutrition or at risk of malnutrition was 6.46 (SII = 635.87) with 46.7% sensitivity and 80.2% specificity [95% CI: 0.613–0.721, AUC: 0.667, P < 0.001]. Multivariable logistic regression demonstrated that ln(SII) was an independent risk factor for the risk of malnutrition or malnutrition in older individuals (OR 3.984, 95% CI: 2.426–6.543, P < 0.001). Other metrics from the geriatric comprehensive assessment, including body mass index, calf circumference, fat ratio, activities of daily living and instrumental activities of daily living, and geriatric depression scale scores, were also independently correlated with nutritional status.ConclusionsAccording to our research, a high SII is an independent predictor of older inpatient malnutrition, and the SII aids in screening for malnutrition and may be a potential target for intervention. Comprehensive geriatric assessment parameters such as BMI, calf circumference, fat ratio, activities of daily living and depression were also linked to malnutrition.

On the Mathematical Relationship Between Contextual Probability and N400 Amplitude.

Accounts of human language comprehension propose different mathematical relationships between the contextual probability of a word and how difficult it is to process, including linear, logarithmic, and super-logarithmic ones. However, the empirical evidence favoring any of these over the others is mixed, appearing to vary depending on the index of processing difficulty used and the approach taken to calculate contextual probability. To help disentangle these results, we focus on the mathematical relationship between corpus-derived contextual probability and the N400, a neural index of processing difficulty. Specifically, we use 37 contemporary transformer language models to calculate the contextual probability of stimuli from 6 experimental studies of the N400, and test whether N400 amplitude is best predicted by a linear, logarithmic, super-logarithmic, or sub-logarithmic transformation of the probabilities calculated using these language models, as well as combinations of these transformed metrics. We replicate the finding that on some datasets, a combination of linearly and logarithmically-transformed probability can predict N400 amplitude better than either metric alone. In addition, we find that overall, the best single predictor of N400 amplitude is sub-logarithmically-transformed probability, which for almost all language models and datasets explains all the variance in N400 amplitude otherwise explained by the linear and logarithmic transformations. This is a novel finding that is not predicted by any current theoretical accounts, and thus one that we argue is likely to play an important role in increasing our understanding of how the statistical regularities of language impact language comprehension.

Biocidal Verification Program for Healthcare Facilities: Evaluation of the Efficacy and Microbial Tolerance of Disinfectants

Context: The disinfection of healthcare facilities is of utmost importance to prevent the spread of infectious diseases. In this regard, biocidal activity testing is used to evaluate the efficacy of disinfectants in hospitals. Aim: Establishing a disinfectant efficacy test aimed at combating infections and cross-contamination. Settings and Design: The experiment was carried out in two treatment groups for each of the four microorganisms on ten surface coupons, with a viability control group determined as the baseline inoculum. Methods and Materials: The study used two disinfectants—hydroxyethane (ethylene hydrate) and 2-hydroxypropane (sec-propyl alcohol)—to evaluate their efficacy in reducing bacterial contamination on inanimate surfaces. Statistical analysis used: The data obtained was analyzed using Minitab software, which involved a normal probability plot, a homogeneity of variances test, and a test for equal variances. The statistical significance level was set at α = 0.05. Results: The normal probability plot of microorganism-disinfectant groups with a 95% confidence interval showed improvement in normalization after the logarithmic transformation of datasets. The homogeneity of variances test did not reveal any statistically significant differences between the groups, and all the comparison intervals overlapped for the logarithmically transformed datasets in contrast to the raw datasets. The bacterial reduction after exposure to disinfectants for five minutes compared with the control group in the surface challenge biocidal test expressed as absolute and logarithmic reduction showed a significant reduction in bacterial growth, with Burkholderia cepacia showing greater persistence on the examined sample of the surfaces. Conclusions: The current study showed that the effect of the microorganism tolerance contributed to the variation in responses rather than the disinfectant or the surface types, suggesting the risk of resistant bacteria on the contaminated surface in the dissemination of infections. Thus, monitoring and routine disinfection validation programs for the control of microbial burden in healthcare are crucial for infection and contamination mitigation

Wronskian rational solutions to the generalized (2 + 1)-dimensional Date–Jimbo–Kashiwara–Miwa equation in fluid dynamics

The main topic of the paper is to investigate the generalized (2 + 1)-dimensional Date–Jimbo–Kashiwara–Miwa (DJKM) and Korteweg–de Vries (KdV) equations, which are widely used in many physical areas, especially in fluids. A new Wronskian formulation is presented for these two equations associated with the bilinear Bäcklund transformation. Based on Wronskian identities of the bilinear Kadomtsev–Petviashvili (KP) hierarchy, the Wronskian determinant solution is verified by a direct and concise calculation. The newly introduced Wronskian formulation provides a comprehensive way for building rational solutions. A few rational Wronskian solutions of lower order are computed for the generalized (2 + 1)-dimensional DJKM equation. Our work can show that the extended (2 + 1)-dimensional KdV equation possesses the similar rational Wronskian solutions through the corresponding logarithmic transformation.

Shape Constrained Kernel PDF and PMF Estimation

We consider shape constrained kernel-based probability density function (PDF) and probability mass function (PMF) estimation. Our approach is of widespread potential applicability and includes, separately or simultaneously, constraints on the PDF (PMF) function itself, its integral (sum), and derivatives (finite-differences) of any order. We also allow for pointwise upper and lower bounds (i.e., inequality constraints) on the PDF and PMF in addition to more popular equality constraints, and the approach handles a range of transformations of the PDF and PMF including, for example, logarithmic transformations (which allows for the imposition of log-concave or log-convex constraints that are popular with practitioners). Theoretical underpinnings for the procedures are provided. A simulation-based comparison of our proposed approach with those obtained using Grenander-type methods is favourable to our approach when the DGP is itself smooth. As far as we know, ours is also the only smooth framework that handles PDFs and PMFs in the presence of inequality bounds, equality constraints, and other popular constraints such as those mentioned above. An implementation in R exists that incorporates constraints such as monotonicity (both increasing and decreasing), convexity and concavity, and log-convexity and log-concavity, among others, while respecting finite-support boundaries via explicit use of boundary kernel functions.

Predicting epithelial ovarian cancer prognosis: correlation of posttreatment 18F-fluorodeoxyglucose positron emission tomography-computed tomography metabolic parameters, serum carbohydrate antigen, and human epididymis protein levels with overall survival.

Identifying reliable prognostic indicators can aid in improving patient care. The aim of this study was to establish the association of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) whole-body metabolic parameters, serum carbohydrate antigen 125 (CA125), and human epididymis protein 4 (HE4) with overall survival (OS) in patients with epithelial ovarian cancer (EOC) after surgery combined with platinum-based chemotherapy. From May 2014 to May 2019, a total of 79 patients with EOC who underwent posttreatment 18F-FDG PET/CT in the First Affiliated Hospital of Chongqing Medical University were included. Clinical data and laboratory indicators were obtained. The whole-body maximum standardized uptake value (WBSUVmax), whole-body metabolic tumor volume (WBMTV), and whole-body total lesion glycolysis (WBTLG) were measured and calculated on 18F-FDG PET/CT. The follow-up was conducted until February 2023, and the endpoint was death from any cause. Pearson correlation analysis, Kaplan-Meier, and Cox proportional regression were used in this study. The PET-positive (PET-P) patients had significantly decreased OS based on either Kaplan-Meier survival analysis (P<0.001) or univariate Cox regression analysis [hazard ratio (HR) =40.177, 95% confidence interval (CI): 2.690-600.134; P=0.007]. "Ln" is a logarithmic transformation with a base of "e" (natural logarithm). LnWBMTV, lnWBTLG, and therapy after PET were independent predictors of OS in a cohort of 63 PET-P patients. The difference in OS between groups sorted by the median WBMTV (4.16; P<0.001) and WBTLG (14.71; P<0.001) was statistically significant. There were statistically significant differences in CA125 and HE4 levels between patients in the PET-P and PET-negative (PET-N) groups (P<0.001). In the PET-P patient cohort, serum HE4 levels were substantially correlated with WBMTV and WBTLG. Kaplan-Meier survival analysis suggested a reduction in OS after treatment in patients with EOC positive for CA125, HE4, and PET (P<0.001). Post-PET/CT treatment strategy, WBMTV, and WBTLG demonstrated significant prognostic utility in predicting posttreatment OS in patients with EOC. Patients who tested positive for both tumor markers CA125 and HE4 and had a positive PET scan demonstrated a significantly poorer prognosis in terms of posttreatment OS.

Enhancing the Visual Effectiveness of Overexposed and Underexposed Images in Power Marketing Field Operations Using Gray Scale Logarithmic Transformation and Histogram Equalization

Abstract In this paper, we propose an adaptive gamma transform that adjusts the local values of bright and dark parts to enhance the effect of low-illumination images, thereby improving the light component. We then apply diff texture enhancement to enhance the contrast of images processed by the Retinex algorithm, thereby optimizing the perception of overexposed and underexposed imagery. Analyze the effect of image brightness enhancement based on a nonlinear transformation combined with the LOL dataset. Use PSNR and SSIM image quality evaluation criteria to analyze the visual effect of improving low-illumination images based on Retinex theory. Create a dataset of power marketing field operation inspection images and examine the effects of overexposure and underexposure image processing on four types of images: high-voltage towers, transmission lines, high-voltage fixtures, and high-voltage wireframes, using the low-light image texture fusion algorithm based on Retinex theory. Overall, this paper’s algorithm and the three DeblurGAN and DMCNN models achieve the effect of deblurring overexposed and underexposed power marketing field operation inspection images. From the local details, the model in this paper has a better effect on the de-exposure of the image, which can provide effective help for the electric power staff to understand the situation of the electric power marketing operation site and has strong practicality.

ILLUMINATION ENHANCEMENT OF NIGHTTIME IMAGES USING A REGULATED SINGLE SCALE RETINEX ALGORITHM

People are active during the nighttime and capture many photos. Due to the low-light nature of the environment, captured images tend to appear dimmed with imbalanced illumination, low contrast, noise, and a lack of vibrant colors. For this purpose, this paper presents a direct and practical approach to improving the lighting of night images based on the single-scale retinex model and using image processing methods and other statistical approaches. The proposed algorithm begins by converting the image from the RGB to the HSI model. Then, it enhances only the I channel while preserving the H and S channels. Thus, processing on the I channel begins with estimating the illumination version of the image and calculating the logarithms of both the illuminated and original image, like the SSR model. Then, it subtracts these two images using a subtraction of logarithmic image processing. Subsequently, the cumulative distribution function of the Gumble probability distribution is applied, and the resulting output is further treated using a logarithmic transformation method. That produces the processed I channel, which combines with the conserved H and S channels to give the HSI image. Ultimately, it converts the image to RGB format. The proposed algorithm is applied to two datasets of nighttime images, compares their performance to seven different contemporary algorithms, and evaluates the results of the compares using three specific metrics of quality evaluation based on the results generated, deducing that the suggested algorithm enhanced the brightness of night images and surpassed other algorithms in terms of execution time, and image assessment methods. Additionally, it exceeded them in all metrics used.

The association of systemic immune-inflammation index with lung function, risk of COPD and COPD severity: A population-based study.

The relationship between the levels of Systemic Immune-inflammation Index (SII) and chronic obstructive pulmonary disease (COPD), lung function, and COPD severity were not fully understood. We conducted this cross-sectional, population-based study to investigate the complex association between SII and COPD, lung function, and COPD severity among the US adults. Overall, 18,349 participants were included in the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018. The exposure variable was SII, calculated from platelet counts, neutrophil counts, and lymphocyte counts. Weighted univariable and multivariable logistic regression, subgroup analysis, and restricted cubic spline (RCS) regression were performed to assess the relationship between COPD, lung function, COPD severity and SII. Last, we used a propensity score matching (PSM) analysis to reduce selective bias and validate these relationships. Approximately 1,094 (5.96%) of the participants were diagnosed as COPD. The multivariable-adjusted odds ratio (OR) (95% confidence interval, CI) for the Q2 group (Log-SII > 2.740) was 1.39 (1.16 to 1.68). Before and after matching, multivariable logistic regression models revealed that increased Log-SII levels (SII Logarithmic transformation) associated positively with the risk of COPD. The subgroup analysis showed no interaction between Log-SII and a variety of variables (P for interaction > 0.05). RCS showed a reversed L-shaped relationship between Log-SII with COPD (P for nonlinear = 0.001) in individuals. In addition, we observed negative significant correlations between forced expiratory volume in one second (FEV1) / forced vital capacity (FVC) %, FEV1/FVC% predicted and SII, and reversed U-shaped curve relationships between FEV1, FEV1% predicted and SII. High SII level is associated with severity of COPD, especially at Global Initiative on Obstructive Lung Disease (GOLD) 1 and GOLD 3. In summary, the Log-SII level is associated with COPD risk, lung function, and COPD severity.

Blind Image Despeckling Using a Multiscale Attention-Guided Neural Network

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This non-linear property makes despeckling of imaging data an intractable problem. To enhance despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multi-scale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network (FPN), is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of MSANN, a multi-scale feature enhancement attention module (FEAM) is incorporated into MSANN to extract multi-scale features for improving imaging quality. A multi-scale mixed loss function is further presented to increase network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of MSANN. Experimental results have shown that MSANN has the capacity of effectively removing speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures.

Analysis and Forecasting the Price of the S&amp;P 500 Index Using the Arima Model

The results of the research allowed to determine that the chosen S&amp;P 500 index can serve as a reflection of the state and forecasts of economic development of the United States. A successful forecast of the index can serve not only as a key point in building an individual investment strategy, but also as an indicator of the general state of the economy. The mathematical model for predicting the dynamics of the index was built. Through exploratory data analysis, a better understanding of the time series and its characteristics was obtained. The application of various statistical methods, such as moving statistics and stationarity tests, made it possible to identify trends and seasonality in the data. Seasonal decomposition and logarithmic transformation helped to better understand the contribution of each component to the overall index dynamics, and special attention was paid to the stationary ADF test, where was considered not only the code but also the significant formulas. The optimal selection of parameters was done automatically. ARIMA model showed good results – the evaluation of the model accuracy included the comparison of the predicted values with the actual values of the SP500 index, both visually and using several metrics – MAE, MSE, RMSE, MAPE. The result of the work is a model for predicting the dynamics of the S &amp; P 500 index, implemented using the Python programming language with a MAPE of about 1.9%, the accuracy of the model is 98.1%. and such good results indicate the possibility of using this tool by market participants in real conditions.

Length-Weight Relationship of Fishes in Some Rivers of Morang, Nepal

The length-weight relationship is a crucial aspect of fishery assessments, providing valuable biological information. The length-weight relationship is expressed by the equation W = aLb, where W represents the total weight of the fish and L denotes the standard length. The LWR equation was transformed into a linear form using logarithmic transformation: LogW = a + bLogL. The biometric data obtained from this study includes information on species, sample numbers (N), standard length range, total weight range, and the length-weight relationship constants (a and b) for each species. In this study, we estimated the length-weight relationship of 16 freshwater fish species from some rivers of Morang district, Eastern Nepal. A total of 1,600 fish specimens, representing 16 different species from 5 families were studied from September 2022 to February 2023. The fishes were captured from the river and preserved in 20% formalin for subsequent biometric measurements in the laboratory. Biometric measurements were taken as follows: for each fish, length measurements were listed as standard length or fork length (SL in cm) from mouth to where the caudal fin is lobate. Length measurements were taken with a regular ruler validated against a Vernier Calliper, with an accuracy of 0.1 cm. Weight measurements were obtained using an electronic digital balance with a precision of 0.01 g. While listing weight, a fish consistency was maintained concerning the degree of wetness of the body to ensure accuracy. Among the species listed in the Morang district, Puntius terio has the smallest range of standard length (2.7 cm - 7.4 cm) and total weight (1.07 g - 7.06 g) while Garra gotyla gotyla has the largest range of standard length (4.3 cm - 18.6 cm) and total weight (4.5 g - 15.6 g). These variations indicate the natural size differences among the species. The analysis of the length-weight relationships revealed that all the studied fish species exhibited a negative allometric growth pattern. This means that as the length of the fish increases, its weight increases at a slower rate. The findings of this study contribute to our understanding of the growth patterns of freshwater fish species in the study area.

Microbial carbon and nitrogen limitation in Larix gmelinii forests along an altitudinal gradient: Evidence from ecoenzymatic stoichiometry and vector analysis

Mountain ecosystems have obvious variations in environmental factors at small spatial scales and thus provide a natural platform for investigating climate change. To investigate the characteristics of extracellular enzymes and microbial metabolic limitations in the hostile environmental conditions of cold temperate mountain ecosystems, soil physicochemical properties and enzyme activities were measured in the 0–10 cm and 10–20 cm soil layers of Larix gmelinii forests at elevations of 830 m, 950 m, 1100 m, and 1300 m. Our results showed that altitude and soil depth had a significant effect on 1,4-β-glucosidase, 1,4-β-N-acetyl-glucosaminidase, leucine aminopeptidase, and acid phosphatase activities. Extracellular enzyme activities decreased with increasing altitude and soil depth. The stoichiometric ratio of soil C-, N-, and P-acquiring enzymes after logarithmic transformation was 1:1.27:0.72 across all altitudes and soil depths. The metabolism of the microorganisms was mainly limited by C and N, and a high C limitation was observed at higher-altitude sites. With increased soil depth, the N limitation gradually weakened. Moreover, soil bulk density and pH were the main factors affecting extracellular enzyme activities and microbial nutrient constraints. Our study revealed that the carbon and nitrogen limitation of soil microbes was induced by the variation in soil factors in a cold temperate mountain forest ecosystem. This finding provides important insights into the mechanism of microbial metabolic limitation in the face of climate change.

Analytical optical solutions to the nonlinear Zakharov system via logarithmic transformation

In the past few years, there has been a growing interest in investigating the search for solitary wave solutions in the realm of nonlinear partial differential equations. This endeavor represents a captivating and challenging area of research. The primary objective of this study is to investigate the nonlinear Zakharov system through a comprehensive analysis that integrates logarithmic transformations and the symbolic structures of exponential functions. The nonlinear Zakharov system holds remarkable importance as a fundamental cornerstone in the field of plasma physics, derived from the profound intellect of esteemed mathematicians and physicists. By examining a diverse range of solution forms including trigonometric, hyperbolic, and rational expressions, this research delves into the complexities of plasma behavior. Notably, the introduction of arbitrarily selected constants imbues these solutions with a multifaceted and dynamic nature. In support of the findings, the article presents several numerical simulations that align with the derived solutions. The utilized methods stand out for being simple, reliable, and capableof creating fresh solutions for nonlinear partial differential equations in the realm of mathematical physics. Other significance of this research lies in its introduction of innovative methodologies previously unexplored in the study of this particular model, thereby broadening the scientific toolkit. Moreover, the versatility of these techniques offers the potential for seamless adaptation in addressing various other nonlinear partial differential equations.

Screening and optimization of interpolation methods for mapping soil-borne polychlorinated biphenyls

There is yet no scientific consensus, and for now, on how to choose the optimal interpolation method and its parameters for mapping soil-borne organic pollutants. Take the polychlorinated biphenyls (PCBs) for instance, we present the comparison of some classic interpolation methods using a high-resolution soil monitoring database. The results showed that empirical Bayesian kriging (EBK) has the highest accuracy for predicting the total PCB concentration, while root mean squared error (RMSE) in inverse distance weighting (IDW) is among the highest in these interpolation methods. The logarithmic transformation of non-normally distributed data contributed to enhance considerably the semivariogram for modeling in kriging interpolation. The increasing of search neighborhood reduced IDW's RMSE, but slightly affected in ordinary kriging (OK), while both of them resulted in over smooth of prediction map. The existence of outliers made the difference between two points increase sharply, and thereby weakening spatial autocorrelation and decreasing the accuracy. As predicted error increased continuously, the prediction accuracy of different interpolation methods reached unanimity gradually. The attempt of the assisted interpolation algorithm did not significantly improve the prediction accuracy of the IDW method. This study constructed a standardized workflow for interpolation, which could reduce human error to reach higher interpolation accuracy for mapping soil-borne PCBs.