Single Value Research Articles

Which pooling method is better: Max, Avg, or Concat (Max, Avg)

Pooling is a non-linear operation that aggregates the results of a given region to a single value. This method effectively removes extraneous details in feature maps while keeping the overall information. As a result, the size of feature maps is reduced, which decreases computing costs and prevents overfitting by eliminating irrelevant data. In CNN models, the max pooling and average pooling methods are commonly utilized. The max pooling selects the highest value within the pooling area and aids in preserving essential features of the image. However, it ignores the other values inside the pooling region, resulting in a significant loss of information. The average pooling computes the average values within the pooling area, which reduces data loss. However, by failing to emphasize critical pixels in the image, it may result in the loss of significant features. To examine the performance of pooling methods, this study comprised the experimental analysis of multiple models, i.e. shallow and deep, datasets, i.e. Cifar10, Cifar100, and SVHN, and pool sizes, e.g. $2x2$, $3x3$, $10x10$. Furthermore, the study investigated the effectiveness of combining two approaches, namely Concat (Max, Avg), to minimize information loss. The findings of this work provide an important guideline for selecting pooling methods in the design of CNNs. The experimental results demonstrate that pooling methods have a considerable impact on model performance. Moreover, there are variances based on the model and pool size.

Abstract 412: Discovery and preclinical characterization of ISM9342A, a novel, potent, and orally bioavailable WRN inhibitor that suppresses MSI-H tumor growth

Abstract Loss of function mutations in DNA mismatch repair genes can result in microsatellite instabilities (MSI) and high levels of MSI (MSI-H) contributes to tumorigenesis and is frequently observed in endometrial carcinoma, gastric cancer, and colorectal cancer. Although immune checkpoint inhibitors have been approved for the treatment of MSI-H tumors, there is still a large proportion of patients with MSI-H tumors who fail to respond to these therapies. Recently, two independent genome-wide loss of function screens have identified Werner syndrome helicase (WRN) as the top dependency for MSI-H cancer cell viability. WRN plays important roles in DNA repair and in the maintenance of genome integrity. Given that WRN deficiency selectively impairs the viability of MSI-H but not microsatellite stable cancer cells, WRN is a synthetic lethal target for MSI-H tumors. ISM9342A is a novel and potent WRN inhibitor exhibiting single digit nanomolar IC50 values in both ATPase and helicase assays. ISM9342A suppressed the proliferation and increased double strand break markers in MSI-H cancer cells. Moreover, ISM9342A demonstrated promising in vivo anti-tumor efficacy in an MSI-H xenograft model along with tumor regression. ISM9342A demonstrated good drug-like properties, including excellent in vitro ADMET profiles and good in vivo exposure, including low clearance and optimal oral bioavailability across multiple preclinical species. Collectively, these findings highlight the potential of ISM9342A as a highly potent WRN inhibitor for the treatment of MSI-H tumors Citation Format: Pei Zhao, Wei Zhu, Xiaoyu Ding, Jianfei Wan, Xiaojing Chen, Man Zhang, Xiao Ding, Supriya Bavadekar, Sujata Rao, Feng Ren, Alex Zhavoronkov. Discovery and preclinical characterization of ISM9342A, a novel, potent, and orally bioavailable WRN inhibitor that suppresses MSI-H tumor growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 412.

Can Short-Term Online-Monitoring Improve the Current WFD Water Quality Assessment Regime? Systematic Resampling of High-Resolution Data from Four Saxon Catchments

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of integrated guidance on surface water chemical monitoring (e.g., WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires monthly sampling to assess the chemical quality status of a body of surface water. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data are investigated under different sampling scenarios and compared with current, monthly grab sampling data. About 23 million data points were analyzed for this study. Three chemical parameters (dissolved oxygen, nitrate-nitrogen, and chloride concentration) and discharge data were selected from four catchments of different sizes, ranging from 51,391 km2 to 84 km2 (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper, we propose short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general, we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as a proxy for analyzing event-mobilized pollutants, and (III) investigate the related uncertainties and costs of both sampling methods. Results show that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law, as the probability of catching a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, such as dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, such as nitrogen-nitrate or chloride. The analysis of discharge events as a proxy parameter for event-mobilized pollutants proves that the probability of capturing samples during events is significantly increased by STOM.

Associations of Kidney Tubular Biomarkers With Incident Macroalbuminuria and Sustained Low eGFR in DCCT/EDIC.

Tubulointerstitial injury contributes to diabetic kidney disease (DKD) progression. We tested tubular biomarker associations with DKD development in type 1 diabetes (T1D). We performed a case-cohort study examining associations of tubular biomarkers, measured across seven time points spanning ∼30 years, with incident macroalbuminuria ("severely elevated albuminuria," urinary albumin excretion rate [AER] ≥300 mg/day) and sustained low estimated glomerular filtration rate (eGFR) (persistent eGFR <60 mL/min/1.73 m2) in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study. Biomarkers included KIM-1 and sTNFR1 in serum/plasma, MCP-1 and EGF in urine, and a composite tubular secretion score reflecting secreted solute clearance. We assessed biomarkers using single values, as mean values from consecutive time points, and as change over consecutive time points, each as time-updated exposures. At baseline, mean diabetes duration was 5.9 years, with mean HbA1c 8.9%, eGFR 125 mL/min/1.73 m2, and AER 16 mg/day. There were 4.8 and 3.5 cases per 1,000 person-years of macroalbuminuria and low eGFR, respectively. Assessed according to single biomarker values, KIM-1 was associated with risk of subsequent macroalbuminuria and low eGFR (hazard ratio [HR] per 20% higher biomarker 1.11 [95% CI 1.06, 1.16] and 1.12 [1.04, 1.21], respectively) and sTNFR1 was associated with subsequent macroalbuminuria (1.14 [1.03, 1.25]). Mean KIM-1 and EGF-to-MCP-1 ratio were associated with subsequent low eGFR. In slope analyses, increases in KIM-1 and sTNFR1 were associated with subsequent macroalbuminuria (per 20% biomarker increase, HR 1.81 [1.40, 2.34] and 1.95 [1.18, 3.21]) and low eGFR (2.26 [1.65, 3.09] and 2.94 [1.39, 6.23]). Serial KIM-1 and sTNFR1 are associated with incident macroalbuminuria and sustained low eGFR in T1D.

Full-Size Experimental Measurement of Combustion and Destruction Efficiency in Upstream Flares and the Implications for Control of Methane Emissions from Oil and Gas Production

Accurately measuring the combustion and destruction removal efficiency of flaring is important when accounting for methane emissions from oil and gas production. Despite this, the amount of experimental data from full-size flares is limited, especially for flares built without air or steam assistance. The use of a single destruction value of 98% is commonly applied. In this paper, we present new empirical measurements of flare efficiency using three common flare designs employed in upstream applications. Combustion products were analyzed using an extractive sampling method. The results demonstrate that whilst destruction efficiencies in excess of 98% are achievable, if the gas composition falls below a critical heating value of ~300 BTU/scf, the efficiency deteriorates leading to elevated methane emissions. This is further complicated by accurately measuring the flow of combustible gas and the impact of crosswinds. In an operational setting, continuous tracking of flare conditions is therefore a key resource in reducing methane emissions but further work is required to standardize how continuous performance tracking is evaluated if such measurements are to attain full traceability.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

How P. aeruginosa cells with diverse stator composition collectively swarm.

Swarming is a macroscopic phenomenon in which surface bacteria organize into a motile population. The flagellar motor that drives swarming in Pseudomonas aeruginosa is powered by stators MotAB and MotCD. Deletion of the MotCD stator eliminates swarming, whereas deletion of the MotAB stator enhances swarming. Interestingly, we measured a strongly asymmetric stator availability in the wild-type (WT) strain, with MotAB stators produced at an approximately 40-fold higher level than MotCD stators. However, utilization of MotCD stators in free swimming cells requires higher liquid viscosities, while MotAB stators are readily utilized at low viscosities. Importantly, we find that cells with MotCD stators are ~10× more likely to have an active motor compared to cells uses the MotAB stators. The spectrum of motility intermittency can either cooperatively shut down or promote flagellum motility in WT populations. In P. aeruginosa, transition from a static solid-like biofilm to a dynamic liquid-like swarm is not achieved at a single critical value of flagellum torque or stator fraction but is collectively controlled by diverse combinations of flagellum activities and motor intermittencies via dynamic stator utilization. Experimental and computational results indicate that the initiation or arrest of flagellum-driven swarming motility does not occur from individual fitness or motility performance but rather related to concepts from the "jamming transition" in active granular matter.IMPORTANCEIt is now known that there exist multifactorial influences on swarming motility for P. aeruginosa, but it is not clear precisely why stator selection in the flagellum motor is so important. We show differential production and utilization of the stators. Moreover, we find the unanticipated result that the two motor configurations have significantly different motor intermittencies: the fraction of flagellum-active cells in a population on average with MotCD is active ~10× more often than with MotAB. What emerges from this complex landscape of stator utilization and resultant motor output is an intrinsically heterogeneous population of motile cells. We show how consequences of stator recruitment led to swarming motility and how the stators potentially relate to surface sensing circuitry.

Decoding of auditory surprise in adult magnetoencephalography data using Bayesian models

BackgroundThe Bayesian brain framework has been proposed to explain how the brain processes and interprets sensory information. Magnetoencephalography (MEG) and electroencephalography (EEG) are two neuroimaging techniques commonly used with decoding models to study neural responses to auditory, visual and somatosensory stimuli. Our study aims to investigate neural responses to auditory stimuli using MEG data and to determine which temporal components in MEG data are sufficient for decoding surprise based on Bayesian models. MethodMEG data acquired from 18 subjects during an auditory binary oddball task was used. The data were pre-processed, and features were selected from different time windows. Five Bayesian learning models were applied to the experimental task stimuli, and each single trial's surprise value was calculated. The relationship between the extracted features in MEG data and the surprise regressors was investigated using linear regression and 5-fold cross-validation. ResultsThe results showed that the middle and late components of the MEG evoked potentials were significantly more informative than the early components. The results indicated that the Dirichlet-Categorical model outperformed the other model's decoding performance as demonstrated by higher R-squared values and lower MSE and BIC values. ConclusionsThe findings of this study provide evidence for the existence of a neural network that generates surprise in the human brain and highlight the importance of the middle and late components of the MEG evoked potentials for decoding the surprise value of auditory stimuli.

Imperfectly conducting eigenflows in a vertical fluid layer

The stability of buoyant flow in an infinite vertical fluid layer bounded by imperfectly conducting rigid walls, called imperfectly conducting eigenflows, is discussed. The third kind boundary conditions describing heat transfer to the external environment are applied to perturbations in temperature. The linear stability analysis is carried out numerically by employing the Chebyshev collocation method. Instability arises when the Grashof number G exceeds its critical value, which depends on the Prandtl number Pr and the Biot number Bi. It is found that the onset of instability changes dramatically depending on the magnitude of Prandtl and Biot numbers particularly when the instability is through the traveling-wave mode. The numerical results show that the Biot number plays a pivotal role in determining the transition Prandtl number PrT at which the instability switches over from one mode to another mode. The novel outcomes suggest the presence of a single PrT for Bi&amp;lt;2.1739 while three distinct values of PrT for Bi≥2.1739. The departure from the conventional single value typically observed at isothermal boundaries signifies the complexity of the instability mechanism.

An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of phenobarbital in human serum and plasma.

Phenobarbital serves as an antiepileptic drug (AED) and finds application in the treatment of epilepsy either as monotherapy or adjunctive therapy. This drug exhibits various pharmacodynamic properties that account for its beneficial effects as well as potential side effects. Accurate measurement of its concentration is critical for optimizing AED therapy through appropriate dose adjustments. Therefore, our objective was to develop and validate a new reference measurement procedure (RMP) for the accurate quantification of phenobarbital levels in human serum and plasma. A sample preparation protocol based on protein precipitation followed by a high dilution step was established in combination with a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method using a C8 column to separate target analytes from known and unknown interferences. Assay validation and determination of measurement uncertainty were performed based on current guidelines. Selectivity and Specificity were assessed using spiked serum and plasma samples; to investigate possible matrix effects (MEs) a post-column infusion experiment and a comparison of standard line slopes was performed. Precision and accuracy were determined within a multiday precision experiment. The RMP was shown to be highly selective and specific, with no evidence of matrix interferences. It can be used to quantify phenobarbital in the range of 1.92 to 72.0 μg/mL. Intermediate precision was less than 3.2 %, and repeatability coefficient of variation (CV) ranged from 1.3 to 2.0 % across all concentration levels. The relative mean bias ranged from -3.0 to -0.7 % for native serum levels, and from -2.8 to 0.8 % for Li-heparin plasma levels. The measurement uncertainties (k=1) for single measurements and target value assignment were 1.9 to 3.3 % and 0.9 to 1.6 %, respectively. A novel LC-MS/MS-based candidate RMP for the quantification of phenobarbital in human serum and plasma is presented which can be used for the standardization of routine assays and the evaluation of clinically relevant samples.

Mitigating stellar activity in radial velocity measurements through the spectral tracking of starspots

Extreme-precision radial velocity observations used to search for low-mass extrasolar planets are hampered by astrophysical noise originating from stellar photospheres. Starspots are a particular nuisance when observing young and active stars. New algorithms are needed to overcome the stellar noise barrier in radial velocity measurements. Using simulations of stellar spectra, we aim to test a technique, which we call GUSTS, that directly measures the contribution from starspots by using spectral features that are distinct from the rest of the stellar photosphere. Their contributions are expected to be anti-correlated with the starspot-induced radial velocity jitter of the star. This is reminiscent of high-dispersion observations of a transiting planet, which causes a Rossiter-McLaughlin effect but also leaves an atmospheric transmission signature that, in the case of spin-orbital alignment, is anti-correlated in radial velocity with the Rossiter-McLaughlin effect. We simulated rotating stars with a single starspot to test the method. Synthetic spectral time series were averaged to obtain a virtual spot-free star spectrum. The individual spectra were subsequently convolved with a kernel, using single value decomposition, to match the average spectrum as closely as possible, after which this average spectrum was removed from the individual spectra. The residual spectra were subsequently searched for spot signatures using the template spot spectrum used to make the synthetic stars. We tested this method on a variety of spectra with different signal-to-noise ratios and investigated what data quality is needed to use this technique in practice. We demonstrate that the new GUSTS technique can work to reduce radial velocity jitter from starspots given a highly sampled, high S/N dataset. Though this alone cannot take radial velocity jitter down to the level to see Earth-like planets, it can be combined with other methods and can be used on starspot-dominated stars to detect smaller and farther planets. This technique could be useful for the future Terra Hunting Experiment, which will provide high S/N data with large samples.

Comparison of three recent discrete stochastic inversion methods and influence of the prior choice

Groundwater flow depends on subsurface heterogeneity, which often calls for categorical fields to represent different geological facies. The knowledge about subsurface is however limited and often provided indirectly by state variables, such as hydraulic heads of contaminant concentrations. In such cases, solving a categorical inverse problem is an important step in subsurface modeling. In this work, we present and compare three recent inverse frameworks: Posterior Population Expansion (PoPEx), Ensemble Smoother with Multiple Data Assimilation (ESMDA), and DREAM-ZS (a Markov chain Monte Carlo sampler). PoPEx and ESDMA are used with Multiple-point statistics (MPS) as geostatistical engines, and DREAM-ZS is used with a Wasserstein generative adversarial network (WGAN). The three inversion methods are tested on a synthetic example of a pumping test in a fluvial channelized aquifer. Moreover, the inverse problem is solved three times with each method, each time using a different training image to check the performance of the methods with different geological priors. To assess the quality of the results, we propose a framework based on continuous ranked probability score (CRPS), which compares single true values with predictive distributions. All methods performed well when using the training image used to create the reference, but their performances were degraded with the alternative training images. PoPEx produced the least geological artifacts but presented a rather slow convergence. ESMDA showed initially a very fast convergence which reaches a plateau, contrary to the remaining methods. DREAM-ZS was overly confident in placing some incorrect geological features but outperformed the other methods in terms of convergence.

Exploring protein derivative profiles in cheese whey through native Candida tropicalis fermentation

ABSTRACT Native yeasts produce most peptides and amino acids from cheese whey fermentation. The research aimed to determine the protein, peptides, and amino acid profiles regarding whey fermentation by spontaneous fermentation and re-inoculation by native yeasts as a basis for determining potential yeasts in amino acid production. The selected native yeasts were identified through genome-based analysis on the DNA base sequence of the Internal Transcribed Spacer (ITS) region. The molecular identification shows that isolate CT1 is 95.62% comparable to Candida tropicalis strain VIT-NN02, CT2 is 98.85% same to stain NB5Y, and CT3 is 99.42% similar to GT 6. Whey fermentation was conducted by inoculating 3% each native C. tropicalis and without inoculation for spontaneous fermentation, then incubated for 48 hours at ambient temperature (26 ± 2°C). Unlike the other samples, the spontaneously fermented mozzarella whey was incubated without inoculation after cheese separation. After 48 hours, fermented whey was kept at 4°C for examination. The Kjeldahl method evaluated protein concentration, Folin-Ciocalteau determined peptide levels, and HPLC determined amino acid composition. C.tropicalis was identified as cheese whey native yeasts, and all treatments decreased protein and peptides while increased the total amino acid includes every single amino acid value except histidine during isolate CT1 whey fermentation. Candida tropicalis CT2 shown as potential isolate for the amino acid production from whey, with the protein contents of 0.846 ± 0.151%, protein decreases of 0.384 ± 0.186, peptide levels of 264.75 ± 40.40ppm, peptide decreases of 99.87 ± 37.96 and the highest total amino acid of 6149.56ppm.

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the artificial intelligence (AI) methods and the rationale for this decision is one issue, but the assessment of such a model is a completely different matter. People mostly use mean square error or less often mean absolute error in the absolute or percentage versions. One should remember that an error does not equal an error and a single value does not provide enough knowledge about the causes of some behavior. Proper interpretation of the results is crucial. It leads to further model improvement. It might be challenging, but allows us to obtain better and more robust solutions, which ultimately solve real-life problems. The ML model assessment is the multicriteria task. A single measure delivers only a fraction of the picture. This paper aims at filling that research gap. Commonly used integral measures are compared with alternative measures like factors of the Gaussian and non-Gaussian statistics, robust statistical estimators, tail index and the fractional order. The proposed methodology delivers new single-criteria indexes or the multicriteria approach, which extend the statistical concept of the moment ratio diagram (MRD) into the index ratio diagram (IRD). The proposed approach is validated using real data from the Full Truck Load cost estimation example. It compares 35 different ML regression algorithms applied to that task. The analysis gives an insight into the properties of the selected methods, enables their comparison and homogeneity analysis and ultimately leads towards constructive suggestions for their eventual proper use. The paper proposes new indexes and concludes that correct selection of the residuum analysis methodology makes the assessment and the ML regression credible.

Interval reservoir computing: theory and case studies

The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.

Spatial Distribution, Source Analysis, and Health Risk Assessment of Heavy Metals in the Farmland of Tangwang Village, Huainan City, China

The impacts of heavy metal pollution in arable soil on agricultural production, environmental health, and the wellbeing of urban and rural residents cannot be overlooked. It has become a significant bottleneck in achieving comprehensive rural revitalization. To accurately grasp the characteristics of heavy metal pollution in suburban cultivated soil, Tangwang Village (a suburb of Huainan City) was subjected to scrutiny. The contents of heavy metals (Hg, Cu, Hg, As, Pb, Cr, Cd, and Zn) in the topsoil of cultivated land in this area were detected, and their spatial distribution characteristics were analyzed using inverse distance spatial interpolation. (1) After conducting a comprehensive analysis and thorough examination of the PMF model sources, it was determined that Cu, Cd, and Zn exhibit a direct correlation with agricultural practices, collectively contributing to a cumulative percentage of 21.10%. Meanwhile, Cr is derived from a combination of sources, including both natural parent materials and human activities, accounting for a total proportion of 24.45%. Notably, lead emissions from automobile exhausts constitute a significant source, while arsenic is primarily associated with dispersed factories and their respective operations, contributing to respective proportions of 36.38% and 18.07%. It is evident that agricultural practices, transportation, and industrial activities are the main reasons for heavy metal pollution in arable soil. (2) The evaluation of geological accumulation indicators reveals that the level of soil arsenic accumulation pollution is mild to moderate (1.199). On the other hand, the cumulative pollution level of Cd, Hg, Cr, and Cu was relatively low (0.462→0.186), whereas the levels of Pb and Zn were below the threshold. (3) The assessment of the ecological risk index revealed that the predominant elements posing potential ecological risks in the investigated region were Hg, As, and Cd, with average Ei values of E(Hg) = 86.81, E(As) = 80.67, and E(Cd) = 67.83, respectively. (4) The human health risk assessment revealed significant differences in the single non-carcinogenic risk values of heavy metals generated by different exposure pathways, with oral ingestion &gt; dermal contact &gt; oral nasal inhalation. Children were more susceptible to the toxic effects of heavy metals compared to adults. Both As and Cr caused an increased risk of cancer in both children and adults, which is a matter of great concern. The results of this study contribute to a more accurate description of the sources of heavy metals in farmland soil. This study indicates that the application of PMF for soil source analysis yields clear results that can be further applied. This research also has potential policy significance as it can help to improve the sustainability of ecosystems by coordinating both environmental and human activities.

Multivariable model versus AJCC staging system: cancer-specific survival predictions in adrenocortical carcinoma.

We developed a novel contemporary population-based model for predicting cancer-specific survival (CSS) in adrenocortical carcinoma (ACC) patients and compared it with the established 8th edition of the American Joint Committee on Cancer staging system (AJCC). Within the Surveillance, Epidemiology, and End Results database (2004-2020), we identified 1056 ACC patients. Univariable Cox regression model addressed CSS. Harrell's concordance index (C-index) quantified accuracy after 2000 bootstrap resamples for internal validation. The multivariable Cox regression model included the most informative, statistically significant predictors. Calibration and decision curve analyses (DCAs) tested the multivariable model as well as AJCC in head-to-head comparisons. Age at diagnosis (>60 vs ≤60 years), surgery, T, N, and M stages were included in the multivariable model. Multivariable model C-index for 3-year CSS prediction was 0.795 vs 0.757 for AJCC. Multivariable model outperformed AJCC in DCAs for the majority of possible CSS-predicted values. Both models exhibited similar calibration properties. Finally, the range of the multivariable model CSS predicted probabilities raged 0.02-75.3% versus only four single AJCC values, specifically 73.2% for stage I, 69.7% for stage II, 46.6% for stage III, and 15.5% for stage IV. The greatest benefit of the multivariable model-generated CSS probabilities applied to AJCC stage I and II patients. The multivariable model was more accurate than AJCC staging when CSS predictions represented the endpoint. Additionally, the multivariable model outperformed AJCC in DCAs. Finally, the AJCC appeared to lag behind the multivariable model when discrimination addressed AJCC stage I and II patients.

Probabilistic CDF-based load forecasting model in a power distribution system

The study searches for a probabilistic forecasting model for electric power demand based on the correlation between weather temperature and electric power demand using hourly time series data spanning a year. The model is based on the temperature and power demand data's cumulative probability distribution functions or CDFs. In probabilistic forecasting, a range of potential values and their associated probabilities are predicted in addition to a single value for a given range. The qualities of CDF modeling allow it to cover these uncertainties. For scenario 1, case 2, and case 3, the mean absolute percentage error (MAPE)-based forecasting accuracy for the hourly load demand is 3.93, 4.64, and 2.56, respectively. The Gaussian distribution-based CDFs' capacity to forecast outside of the present data range is an extra advantage. The world's electric power networks are growing increasingly intricate and unpredictable through the integration of several distributed energy resources (DERs) and cutting-edge technology from renewable energy resources (RERs). Other variables, such as variations in weather patterns, shifts in the status of the economy, or adjustments to energy policies, can also add to uncertainty and not be foreseen. A body of literature demonstrates a shortage of research on LTLF and general-purpose forecasting methods. The proposed approach is flexible and might establish a standard for many different forecasting tasks. The proposed method is intended to be a flexible strategy that may be used for different utilities with varying time horizons.

Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.

An Overview of TOD Level Assessment Around Rail Transit Stations

AbstractBy integrating land use and transportation systems, transit-oriented development (TOD) focuses on transit and land development, development potential, and the balance between transportation and land use. The TOD level assessment not only helps optimize existing TODs but also guides TOD planning. Based on previous literature, methods for assessing TOD around rail transit stations can be roughly divided into three categories: TOD index, the node-place model, and the data envelopment analysis (DEA) model. The TOD index aggregates indicators from different dimensions into a single value for evaluation. The node-place model emphasizes transportation, land use, and the balance between them, as well as expansion factors. And the DEA model is suitable for situations without input indicator weights or consideration of different units of measurement. TOD indicators are the basis of the evaluation, and in both the TOD index and node-place models, indicator weights are mostly determined by using subjective methods. Furthermore, a limited number of studies to date have pre-evaluated the implementation of TOD at rail stations under construction or in planning. This paper aims to assess the level of TOD areas at the rail transit station level, which can help in constructing the evaluation index system, selecting TOD stations, and optimizing them.