Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation

Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world software project databases. We analyze the predictive performance after using the k-NN missing data imputation technique to see if it is better to tolerate missing data or to try to impute missing values and then apply the C4.5 algorithm. For the investigation, we simulated three missingness mechanisms, three missing data patterns, and five missing data percentages. We found that the k-NN imputation can improve the prediction accuracy of C4.5. At the same time, both C4.5 and k-NN are little affected by the missingness mechanism, but that the missing data pattern and the missing data percentage have a strong negative impact upon prediction (or imputation) accuracy particularly if the missing data percentage exceeds 40%

Variations in Nitrogen Metabolism are Closely Linked with Nitrogen Uptake and Utilization Efficiency in Cotton Genotypes under Various Nitrogen Supplies

Cotton production is highly sensitive to nitrogen (N) fertilization, whose excessive use is responsible for human and environmental problems. Lowering N supply together with the selection of N-efficient genotypes, more able to uptake, utilize, and remobilize the available N, could be a challenge to maintain high cotton production sustainably. The current study aimed to explore the intraspecific variation among four cotton genotypes in response to various N supplies, in order to identify the most distinct N-efficient genotypes and their nitrogen use efficiency (NUE)-related traits in hydroponic culture. On the basis of shoot dry matter, CCRI-69 and XLZ-30 were identified as N-efficient and N-inefficient genotypes, respectively, and these results were confirmed by their contrasting N metabolism, uptake (NUpE), and utilization efficiency (NUtE). Overall, our results indicated the key role of shoot glutamine synthetase (GS) and root total soluble protein in NUtE. Conversely, tissue N concentration and N-metabolizing enzymes were considered as the key traits in conferring high NUpE. The remobilization of N from the shoot to roots by high shoot GS activity may be a strategy to enhance root total soluble protein, which improves root growth for N uptake and NUE. In future, multi-omics studies will be employed to focus on the key genes and pathways involved in N metabolism and their role in improving NUE

Genotypic Variation in Cotton Genotypes for Phosphorus-Use Efficiency

Low phosphorus (P) availability is a major constraint for cotton production. Consequently, P-efficient genotypes can improve productivity under conditions where the higher application of P is not economical. This study was conducted to characterize cotton genotypes for P-use efficiency under various P concentrations (0, 10, 20, 40, 80, and 500 μM KH2PO4). The results showed large genotypic variation in five selected traits, such as root dry weight, shoot dry weight, photosynthetic activity, P-utilization efficiency, and P-uptake efficiency. Based on these five selected traits, the genotypes were grouped into three main classes as efficient, moderate efficient, and inefficient genotypes as proposed by different researchers. Most of the genotypes behaved in a similar pattern under different P concentrations. Among the genotypes, Xinluzao-49 and Xinluzao-48 were considered as P efficient while CCRI-64 and Yumian-21 as inefficient genotypes. However, the rest of the genotypes were considered as moderately P efficient. The results prove that a large genetic potential exists in cotton genotypes for P-use efficiency, and the use of P-efficient genotypes for cultivation will reduce the application of phosphatic fertilizers. Furthermore, the use of P-efficient genotypes will improve cotton breeding activities and help in improving the environmental sustainability of cotton production

Influence of Growth Velocity on the Separation of Primary Silicon in Solidified Al-Si Hypereutectic Alloy Driven by a Pulsed Electric Current

Investigating the separation of the primary silicon phase in Al-Si hypereutectic alloys is of high importance for the production of solar grade silicon. The present paper focuses on the effect of growth velocity on the electric current pulse (ECP)-induced separation of primary silicon in a directionally solidified Al-20.5 wt % Si hypereutectic alloy. Experimental results show that lower growth velocity promotes the enrichment tendency of primary silicon at the bottom region of the sample. The maximum measured area percentage of segregated primary silicon in the sample solidified at the growth velocity of 4 μm/s is as high as 82.6%, whereas the corresponding value is only 59% in the sample solidified at the growth velocity of 24 μm/s. This is attributed to the fact that the stronger forced flow is generated to promote the precipitation of primary silicon accompanied by a higher concentration of electric current in the mushy zone under the application of a slower growth velocity

Efficient Bias-Free Degradation of Sulfamethazine by TiO₂ Nanoneedle Arrays Photoanode and Co₃O₄ Photocathode System under LED-Light Irradiation

Solving high electrical-energy input for pollutants degradation is one of the core requirements for the practical application of photoelectrocatalytic (PEC) technology. Herein, we developed a self-driven dual-photoelectrode PEC system (TiO2 NNs-Co3O4) composed of a TiO2 nanoneedle arrays (TiO2 NNs) photoanode and Co3O4 photocathode for the first time. Under light-emitting-diode (LED) illumination, the bias-free TiO2 NNs-Co3O4 PEC system exhibited excellent PEC performance, with an internal bias as high as 0.19 V, achieving near complete degradation (99.62%) of sulfamethazine (SMT) with a pseudo-first-order rate constant of 0.042 min−1. The influences of solution pH, typical inorganic anions, natural organic matter, and initial SMT concentration on the PEC performance were investigated. Moreover, the main reactive oxygen species (h+, •OH, •O2−) in the dual-photoelectrode PEC system for SMT decomposition were elaborated. The practical application feasibility for efficient water purification of this unbiased PEC system was evaluated. It was proved that the TiO2 NNs photoanode provided a negative bias while the Co3O4 photocathode provided a positive bias for the photoanode, which made this system operate without external bias. This work elucidated the cooperative mechanism of photoelectrodes, providing guidance to develop a sustainable, efficient, and energy-saving PEC system for wastewater treatment

Comparative Study on the Grain Refinement of Al-Si Alloy Solidified under the Impact of Pulsed Electric Current and Travelling Magnetic Field

It is high of commercial importance to generate the grain refinement in alloys during solidification by means of electromagnetic fields. Two typical patterns of electromagnetic fields, pulsed electric currents (ECP) and traveling magnetic field (TMF), are frequently employed to produce the finer equiaxed grains in solidifying alloys. Various mechanisms were proposed to understand the grain refinement in alloys caused by ECP and TMF. In this paper, a comparative study is carried out in the same solidification regime to investigate the grain refinement of Al-7 wt. %Si alloy driven by ECP and TMF. Experimental results show that the application of ECP or TMF can cause the same grain refinement occurrence period, during which the refinement of primary Al continuously occurs. In addition, the related grain refinement mechanisms are reviewed and discussed, which shows the most likely one caused by ECP and TMF is the promoted dendrite fragmentation as the result of the ECP-induced or TMF-induced forced flow. It suggests that the same grain refinement process in alloys is provoked when ECP and TMF are applied in the same solidification regime, respectively