Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

Giving Food a Fiber Boost: Adding High Beta-Glucan Ingredient from Barley to Everyday Foods

Lack of dietary fiber contributes to many health issues, particularly chronic vascular diseases. Mixed linkage β-1.3 - 1.4 beta-glucan (beta-glucan, in this paper) is a confirmed beneficial ingredient for the human diet through reduction of cholesterol and the glycemic index. Barley contains the highest beta-glucan content of all the grains, and in this study, a percentage of flour from two high beta glucan lines was, each, added to an array of wheat-based food products to measure how it impacted total dietary fiber. Results showed that beta-glucan content was higher in all the products containing the added high beta-glucan flour, along with increased total dietary fiber content. Protein content in the food products is also increased with the higher protein in the barley flours added. Beta-glucan content in the barley-added products increased to 1.2% - 4.0% versus 0.2% - 0.5% in the pure wheat products, while the dietary fibers increased to 3.5% - 24.4% versus 2.1% - 9.1% in pure wheat product controls. This research provided experimental evidence that using a high beta-glucan barley ingredient in food can increase dietary fiber to benefit health.

Role of intravascular ultrasound and optical coherence tomography in intracoronary imaging for coronary artery disease:a systematic review

Coronary angiography has long been the standard for coronary imaging,but it has limitations in assessing vessel wall anatomy and guiding percutaneous coronary intervention(PCI).Intracoronary imaging techniques like intravascular ultrasound(IVUS)and optical coherence tomography(OCT)can overcome these limitations.IVUS uses ultrasound and OCT uses near-infrared light to visualize coronary pathology in unique ways due to differences in temporal and spatial resolution.These techniques have evolved to offer clinical utility in plaque characterization and vessel assessment during PCI.Meta-analyses and adjusted observational studies suggest that both IVUS and OCT-guided PCI correlate with reduced cardiovascular risks compared to angiographic guidance alone.While IVUS demonstrates consistent clinical outcome benefits,OCT evidence is less robust.IVUS has progressed from early motion detection to high-resolution systems,with smaller compatible catheters.OCT utilizes near infrared light to achieve unparalleled resolutions,but requires temporary blood clearance for optimal imaging.Enhanced visualization and guidance make IVUS and OCT well-suited for higher risk PCI in patients with diabetes and chronic kidney disease by allowing detailed visualization of complex lesions and ensuring optimal stent deployment and positioning in PCI for patients with type 2 diabetes and chronic kidney disease,improving outcomes.IVUS and recent advancements in zero-and low-contrast OCT techniques can reduce nephrotoxic contrast exposure,thus helping to minimize PCI complications in these high-risk patient groups.IVUS and OCT provide valuable insights into coronary pathophysiology and guide interventions precisely compared to angiography alone.Both have comparable clinical outcomes,emphasizing the need for tailored imaging choices based on clinical scenarios.Continued refinement and integration of intravascular imaging will likely play a pivotal role in optimizing coronary interventions and outcomes.This systematic review aims to delve into the nuances of IVUS and OCT,highlighting their strengths and limitations as PCI adjuncts.

Evaluation of a Wireless Solar Powered Personal Weather Station

We are evaluating dryland cotton production in Martin County, Texas, measuring cotton lint yield per unit of rainfall. Our goal is to collect rainfall data per 250 - 400 ha. Upon selection of a rainfall gauge, we realized that the cost of using, for example, a tipping bucket-type rain gauge would be too expensive and thus searched for an alternative method. We selected an all-in-one commercially available weather station;hereafter, referred to as a Personal Weather Station (PWS) that is both wireless and solar powered. Our objective was to evaluate average measurements of rainfall obtained with the PWS and to compare these to measurements obtained with an automatic weather station (AWS). For this purpose, we installed four PWS deployed within 20 m of the Plant Stress and Water Conservation Meteorological Tower that was used as our AWS, located at USDA-ARS Cropping Systems Research Laboratory, Lubbock, TX. In addition, we measured and compared hourly average values of short-wave irradiance (Rg), air temperature (Tair) and relative humidity (RH), and wind speed (WS), and calculated values of dewpoint temperature (Tdew). This comparison was done over a 242-day period (1 October 2022-31 May 2023) and results indicated that there was no statistical difference in measurements of rainfall between the PWS and AWS. Hourly average values of Rg measured with the PWS and AWS agreed on clear days, but PWS measurements were higher on cloudy days. There was no statistical difference between PWS and AWS hourly average measurements of Tair, RH, and calculated Tdew. Hourly average measurements of Rg and WS were more variable. We concluded that the PWS we selected will provide adequate values of rainfall and other weather variables to meet our goal of evaluating dryland cotton lint yield per unit rainfall.

Climate-change habitat shifts for the vulnerable endemic oak species(Quercus arkansana Sarg.)

Quercus arkansana(Arkansas oak)is at risk of becoming endangered,as the total known population size is represented by a few isolated populations.The potential impact of climate change on this species in the near future is high,yet knowledge of its predicted effects is limited.Our study utilized the biomod2 R package to develop habi-tat suitability ensemble models based on bioclimatic and topographic environmental variables and the known loca-tions of current distribution of Q.arkansana.We predicted suitable habitats across three climate change scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5)for 2050,2070,and 2090.Our findings reveal that the current suitable habitat for Q.arkansana is approximately 127,881 km^(2) across seven states(Texas,Arkansas,Alabama,Louisiana,Mississippi,Georgia,and Florida);approximately 9.5%is encompassed within state and federally managed protected areas.Our models predict that all current suitable habitats will disap-pear by 2050 due to climate change,resulting in a northward shift into new regions such as Tennessee and Kentucky.The large extent of suitable habitat outside protected areas sug-gests that a species-specific action plan incorporating pro-tected areas and other areas may be crucial for its conserva-tion.Moreover,protection of Q.arkansana habitat against climate change may require locally and regionally focused conservation policies,adaptive management strategies,and educational outreach among local people.

Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique

Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.

Correlation between Intestinal Health and Coccidiosis Prevalence in Broilers during Different Seasons of the Year in Brazil from 2012 to 2018

Coccidiosis is a disease caused by intracellular protozoan parasites, specifically belonging to the genus Eimeria. These parasites target the gastrointestinal tract in different types of hosts, causing sores in the intestinal lining. The presence of these lesions reduces the animal’s ability to digest and absorb nutrients, significantly impacting their overall performance. The current study aimed to explore the potential correlation between seasonal variations and the incidence of Eimeria spp-induced lesions in broiler chickens’ gastrointestinal tracts in Brazil from 2012 to 2018. A total of 8,607 broiler chickens, aged 14 to 42 days, were sampled from 103 poultry integrated companies in Brazil to conduct intestinal health examinations. The sampling process involved selecting 3 to 6 chickens from each poultry house for examination. The assessment included various abnormalities such as shedding of intestinal cells and excessive fluid and mucus presence, thickening and tension of the intestines, food movement, roughened mucosal surface resembling a Turkish towel in the small intestine, tissue death, duodenal inflammation, intestinal inflammation, gizzard erosion, presence of worms and bedding material, and ingestion of mealworms. During the seasons, winter exhibited the highest average occurrence of Eimeria maxima microorganisms at 52.83%, with E. acervulina following closely at 26.42% in second place. In spring, E. maxima had an occurrence of 11.31%, while in fall, E. tenella had the lowest occurrence at 6.74%. When analyzing the seasonal occurrence of Eimeria, it was observed that E. maxima micro was more common during winter compared to summer (P = 0.0491). However, no discernible variation was observed in the occurrence of the remaining species across different seasons. Research findings suggest that subclinical coccidiosis is most prevalent during the winter season in Brazil. Likewise, clinical disease caused by E. acervulina is also prevalent during this time. In contrast, E. maxima is more likely to cause clinical disease in the spring, whereas E. tenella is more commonly associated with clinical disease in the fall. Lesions induced by Eimeria spp. are associated with factors influencing the overall health of broiler intestines. These findings allow for the utilization of seasonal metrics in disease management, thereby reducing economic losses associated with the condition.

Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.

Advanced glycation end product signaling and metabolic complications:Dietary approach

Advanced glycation end products(AGEs)are a heterogeneous collection of compounds formed during industrial processing and home cooking through a sequence of nonenzymatic glycation reactions.The modern western diet is full of heat-treated foods that contribute to AGE intake.Foods high in AGEs in the contemporary diet include processed cereal products.Due to industrialization and marketing strategies,restaurant meals are modified rather than being traditionally or conventionally cooked.Fried,grilled,baked,and boiled foods have the greatest AGE levels.Higher AGE-content foods include dry nuts,roasted walnuts,sunflower seeds,fried chicken,bacon,and beef.Animal proteins and processed plant foods contain furosine,acrylamide,heterocyclic amines,and 5-hydroxymethylfurfural.Furosine(2-furoil-methyl-lysine)is an amino acid found in cooked meat products and other processed foods.High concentrations of carboxymethyl-lysine,carboxyethyl-lysine,and methylglyoxal-O are found in heat-treated nonvegetarian foods,peanut butter,and cereal items.Increased plasma levels of AGEs,which are harmful chemicals that lead to age-related diseases and physiological aging,diabetes,and autoimmune/inflammatory rheumatic diseases such as systemic lupus erythematosus and rheumatoid arthritis.AGEs in the pathophysiology of metabolic diseases have been linked to individuals with diabetes mellitus who have peripheral nerves with high amounts of AGEs and diabetes has been linked to increased myelin glycation.Insulin resistance and hyperglycemia can impact numerous human tissues and organs,leading to long-term difficulties in a number of systems and organs,including the cardiovascular system.Plasma AGE levels are linked to all-cause mortality in individuals with diabetes who have fatal or nonfatal coronary artery disease,such as ventricular dysfunction.High levels of tissue AGEs are independently associated with cardiac systolic dysfunction in diabetic patients with heart failure compared with diabetic patients without heart failure.It is widely recognized that AGEs and oxidative stress play a key role in the cardiovascular complications of diabetes because they both influence and are impacted by oxidative stress.All chronic illnesses involve protein,lipid,or nucleic acid modifications including crosslinked and nondegradable aggregates known as AGEs.Endogenous AGE formation or dietary AGE uptake can result in additional protein modifications and stimulation of several inflammatory signaling pathways.Many of these systems,however,require additional explanation because they are not entirely obvious.This review summarizes the current evidence regarding dietary sources of AGEs and metabolism-related complications associated with AGEs.

An Evaluation of Manure Management Strategies, Phosphorus Surface Runoff Potential and Water Usage at an Arkansas Discovery Dairy Farm

Manure management is an essential component of dairy production. Nutrient-laden, field-applied dairy manure often serves as a fertilizer source, but can also pose environmental threats if not properly managed. The Haak dairy farm, located in Decatur, Arkansas, was granted a permit by the Arkansas Department of Environmental Quality (ADEQ) to employ a unique method in treating and storing cattle manure generated during the milking process. This method includes minimizing water use in wash water, dry scraping solids to combine with sawdust for composting and pumping effluent underground into a sloped concrete basin that serves as secondary solid separator before transporting the manure effluent into an interception trench and an adjacent grassed field to facilitate manure nutrient uptake and retention. The Arkansas Discovery Farm program (ADF) is conducting research to evaluate the environmental performance of the dairy’s milk center wash water treatment system (MCWW) by statistical analysis, characterization of phosphorus (P) migration in soil downslope from the inception trench, temperature measurements, and nutrient analysis of a stored dry stack manure/sawdust mixture. Goals included determining possible composting effectiveness along with comparisons to untreated dairy manure and quantifying the use of on-farm water. Results from this research demonstrated that: 1) The MCWW was effective at retaining manure-derived nutrients and reducing field nutrient migration as the MCWW interception trench had significantly higher total nitrogen (TN) (804.2 to 4.1), total phosphorus (TP) (135.6 to 1.5), and water extractable phosphorus (WEP) (55.0 to 1.0) concentrations in milligrams per liter (mg⋅L-1) than the downhill freshwater pond respectively;2) temperature readings of the manure dry stack indicated heightened levels of microbial and thermal activity, but did not reach a standard composting temperature of 54°C;3) manure dry stack nutrient content was typically higher than untreated dairy manure when measured on a “dry basis” in ppm, but was lower on an “as is basis” in ppm and kg/metric ton;and 4) water meter readings showed that the greatest use of on-farm water was for farm-wide cattle drinking (18.77), followed by water used in the milking center (3.45) and then followed by human usage (0.02) measured in cubic meters per day (m3⋅d-1). These results demonstrate that practical innovations in agricultural engineering and environmental science, such as the Haak dairy’s manure treatment system, can effectively reduce environmental hazards that accompany the management of manure at this dairy operation.

Four-Order Superconvergent Weak Galerkin Methods for the Biharmonic Equation on Triangular Meshes

A stabilizer-free weak Galerkin(SFWG)finite element method was introduced and analyzed in Ye and Zhang(SIAM J.Numer.Anal.58:2572–2588,2020)for the biharmonic equation,which has an ultra simple finite element formulation.This work is a continuation of our investigation of the SFWG method for the biharmonic equation.The new SFWG method is highly accurate with a convergence rate of four orders higher than the optimal order of convergence in both the energy norm and the L^(2)norm on triangular grids.This new method also keeps the formulation that is symmetric,positive definite,and stabilizer-free.Four-order superconvergence error estimates are proved for the corresponding SFWG finite element solutions in a discrete H^(2)norm.Superconvergence of four orders in the L^(2)norm is also derived for k≥3,where k is the degree of the approximation polynomial.The postprocessing is proved to lift a P_(k)SFWG solution to a P_(k+4)solution elementwise which converges at the optimal order.Numerical examples are tested to verify the theor ies.

Sepsis during short bowel syndrome hospitalizations:Identifying trends,disparities,and clinical outcomes in the United States

BACKGROUND Short bowel syndrome(SBS)hospitalizations are often complicated with sepsis.There is a significant paucity of data on adult SBS hospitalizations in the United States and across the globe.AIM To assess trends and outcomes of SBS hospitalizations complicated by sepsis in the United States.METHODS The National Inpatient Sample was utilized to identify all adult SBS hospitalizations between 2005-2014.The study cohort was further divided based on the presence or absence of sepsis.Trends were identified,and hospitalization characteristics and clinical outcomes were compared.Predictors of mortality for SBS hospitalizations complicated with sepsis were assessed.RESULTS Of 247097 SBS hospitalizations,21.7%were complicated by sepsis.Septic SBS hospitalizations had a rising trend of hospitalizations from 20.8%in 2005 to 23.5%in 2014(P trend<0.0001).Compared to non-septic SBS hospitalizations,septic SBS hospitalizations had a higher proportion of males(32.8%vs 29.3%,P<0.0001),patients in the 35-49(45.9%vs 42.5%,P<0.0001)and 50-64(32.1%vs 31.1%,P<0.0001)age groups,and ethnic minorities,i.e.,Blacks(12.4%vs 11.3%,P<0.0001)and Hispanics(6.7%vs 5.5%,P<0.0001).Furthermore,septic SBS hospitalizations had a higher proportion of patients with intestinal transplantation(0.33%vs 0.22%,P<0.0001),inpatient mortality(8.5%vs 1.4%,P<0.0001),and mean length of stay(16.1 d vs 7.7 d,P<0.0001)compared to the non-sepsis cohort.A younger age,female gender,White race,and presence of comorbidities such as anemia and depression were identified to be independent predictors of inpatient mortality for septic SBS hospitalizations.CONCLUSION Septic SBS hospitalizations had a rising trend between 2005-2014 and were associated with higher inpatient mortality compared to non-septic SBS hospitalizations.

Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization problems.Hence novel optimization methodologies are still needed.The Al-Biruni earth radius(BER)search optimization algorithm is proposed in this paper.The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals.The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems.The results show that BER can both explore and avoid local optima.BER has also been tested on an engineering design optimization problem.The results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

Skim resequencing finely maps the downy mildew resistance loci RPF2 and RPF3 in spinach cultivars whale and Lazio

Commercial production of spinach(Spinacia oleracea L.)is centered in California and Arizona in the US,where downy mildew caused by Peronospora effusa is the most destructive disease.Nineteen typical races of P.effusa have been reported to infect spinach,with 16 identified after 1990.The regular appearance of new pathogen races breaks the resistance gene introgressed in spinach.We attempted to map and delineate the RPF2 locus at a finer resolution,identify linked single nucleotide polymorphism(SNP)markers,and report candidate downy mildew resistance(R)genes.Progeny populations segregating for RPF2 locus derived from resistant differential cultivar Lazio were infected using race 5 of P.effusa and were used to study for genetic transmission and mapping analysis in this study.Association analysis performed with low coverage whole genome resequencing-generated SNP markers mapped the RPF2 locus between 0.47 to 1.46 Mb of chromosome 3 with peak SNP(Chr3_1,221,009)showing a LOD value of 61.6 in the GLM model in TASSEL,which was within 1.08 Kb from Spo12821,a gene that encodes CC-NBS-LRR plant disease resistance protein.In addition,a combined analysis of progeny panels of Lazio and Whale segregating for RPF2 and RPF3 loci delineated the resistance section in chromosome 3 between 1.18–1.23 and 1.75–1.76 Mb.This study provides valuable information on the RPF2 resistance region in the spinach cultivar Lazio compared to RPF3 loci in the cultivar Whale.The RPF2 and RPF3 specific SNP markers,plus the resistant genes reported here,could add value to breeding efforts to develop downy mildew resistant cultivars in the future.

Phosphorus Fertilizer Effects on Near-Surface Soil Aggregation in Furrow-Irrigated Rice on a Silt-Loam Soil

Well-aggregated soil has been shown to improve soil infiltration and reduce runoff and soil erosion, making well-aggregated soil important for productive, sustainable agriculture. One factor that may influence near-surface soil aggregate stability is fertilizer application. Rapid dissolution of fertilizers, which are mostly salts, can potentially disperse clays and destabilize aggregates. The objective of this study was to evaluate the potential effect of various fertilizer-phosphorus (P) and -nitrogen (N) sources [i.e., triple superphosphate (TSP), monoammonium phosphate (MAP), chemically precipitated struvite (CPST), electrochemically precipitated struvite (ECST), environmentally smart nitrogen (ESN)] and soil depth on water-stable aggregates (WSA) in furrow-irrigated rice on a silt-loam soil (Typic Albaqualf). Total WSA (TWSA) concentration was unaffected (P > 0.05) by fertilizer treatment or soil depth, while WSA concentration was numerically largest (P ∙g-1), which did not differ from CPST, ECST, and ESN in the 0 - 5 cm depth or the unamended control in the 0 - 5 and 5 - 10 cm depths, and was at least 1.7 times larger than ESN in the 5 - 10 cm depth (0.03 g∙g-1). Results indicated that WSA concentration among non-struvite fertilizer-P sources was generally similar to that from the struvite fertilizer materials. Principal component analysis determined that 32% of the variation of TWSA was mainly explained by changes in soil bulk density, pH, and electrical conductivity. Long-term, continual annual application of fertilizer-P and N could negatively impact soil aggregate stability, soil structure, and potentially erosion.

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal platform.The traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is created.Then, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.

An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis

With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.

Effectiveness of Combined Biochar and Lignite with Poultry Litter on Soil Carbon Sequestration and Soil Health

Healthy soils are important to ensure satisfactory crop growth and yield. Poultry litter (PL), as an organic fertilizer, has proven to supply the soil with essential macro and micronutrients, enhance soil fertility, and improve crop productivity. Integrating this treatment has the potential to improve soil physical and biological properties by increasing soil carbon, C. However, rapid decomposition and mineralization of PL, particularly in the hot and humid southeastern U.S., resulted in losing C and reduced its effect on soil health. Biochar and lignite have been proposed to stabilize and mitigate C loss through application of fresh manure. However, their combined effects with PL on C sequestration and soil health components are limited. A field experiment was conducted on Leeper silty clay loam soil from 2017 to 2020 to evaluate the combined effect on soil properties when applying biochar and lignite with PL to cotton (Gossypium hirsutum L.). The experimental design was a randomized complete block involving nine treatments replicated three times. Treatments included PL and inorganic nitrogen, N, fertilizer with or without biochar and lignite, and an unfertilized control. Application rates were 6.7 Mgkg⋅ha−1 for PL, 6.7 Mgkg⋅ha−1 for biochar and lignite and 134 kg⋅ha−1 for inorganic N fertilizer. Integration of PL and inorganic fertilizer with biochar and lignite, resulted in greater soil infiltration, aggregate stability, plant available water, reduced bulk density and penetration resistance as compared to the sole applications of PL and inorganic fertilizer.

Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely used.Froma couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial success.However,they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction.In this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition.The proposed architecture consists of five steps.In the first step,data augmentation is performed to increase the numbers of training samples.In the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning.Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm.The best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification results.The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village.The proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,respectively.Acomparison with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.