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Bis-argentivorous molecules (La and Lb), which have phenyl and 4,4'-biphenyl groups as linkers, have been prepared. The structures of Ag+ complexes with the new ligands (La and Lb) were investigated in solution and the solid state. The CSI-MS and 1H NMR titration of La and Lb with Ag+ show 1:1 and 1:2 complexes depending on the [Ag+]:[L] ratios. In the solid-state structures, single crystals of La and Lb with 2 equiv of Ag+ were prepared. X-ray crystallography of the silver(I) complexes with La and Lb showed that an intramolecular racemic structure (Δ(δδδδ)Λ(λλλλ) form) and a racemic mixture of Δ(δδδδ)Δ(δδδδ) and Λ(λλλλ)Λ(λλλλ) forms were formed, respectively. The dynamic 1H NMR studies suggest the following: (i) the activation entropies (ΔS⧧) of the side arm rotations in the Ag+ complex with La were all negative, indicating restricted rotation of the side arms due to their shortness, and (ii) the ΔS⧧ values of the Ag+ complexes with Lb were negative only when the side arms of both cyclens rotated simultaneously, and the ΔS⧧ values for the 1:1 and 1:2 complexes were positive when one cyclen side arm was rotated. These values of ΔS⧧ indicate that the biphenyl side arms between the two cyclens are not long enough to rotate the ring freely.

In recent decades, there has been a gradual penetration of plant factories achieving semiautomated crop cultivation. However, efficient energy utilization, as well as quality control of crops, are very important factors with regard to sustainable operation. Operating parameters, such as room temperature, affect not only the quality of crops but also the electric power required to realize the target operation while being influenced by the environment outside the plant. Therefore, a methodology is needed to analyze and interpret the relationships among these manipulated variables, exogenous variables, crop quality, and the amount of required electric power. Constructing a directed acyclic graph composed of regression models is an attractive approach for such analysis; however, the relationships can possibly be nonlinear, so the direct application of existing analytic approaches will not be appropriate. In this paper, we propose a methodology for relationship analysis among variables based on the directed acyclic graphs while identifying the linearity/nonlinearity in their relationships. In general, the construction of such a graphical model has computational issues, especially when the number of variables is large, and the risk of overfitting. The proposed method utilizes the idea of sparse regularization, which has been actively discussed in the field of machine learning, for realizing the automatic identification of linearity/nonlinearity between variables and screening redundant candidate structures; this approach relaxes the computational complexity issue and controls the risk of overfitting. As a case study, the proposed method is applied to a dataset collected from a real-world cultivation system in a plant factory to discuss its usefulness.

Recent changes in power system, e.g. large-scale penetration of renewable energy source, have led to change the behavior of the electricity demand and market price drastically; this behavior is also influenced by various factors such as weather and economic conditions. The factors that affect such demand and price are complex and the analysis of these factors enables stakeholders to construct an electricity business strategy. Therefore, a framework is needed to analyze and interpret the relationships among these demand, price, and factors. Construction of a graphical model is an attractive approach to describe the statistical relationships among these factors; the directed graphical model flexibly visualizes the direct/indirect factors affecting the demand and price. In this study, the authors applied a graphical modeling scheme using the sparse partially linear additive models for factor analysis of electricity demand and price. The scheme is applied to a real-world dataset and discussed from the viewpoint of interpretability.

In recent years, electricity power system structures have changed to accommodate large-scale penetration of renewable energy sources and energy-saving trends. Because of these system changes, the behavior of electricity net-loads, i.e. the difference between total demand and aggregate supply of distributed variable renewable energy sources, has changed drastically. This behavior is also influenced by various other factors such as weather, economic conditions, and consumer lifestyle. Analyzing the factors that affect the dynamic characteristics of the electricity net-load enables stakeholders to construct an electricity business strategy. Conventionally, such demand analysis has been conducted by targeting a limited number of explanatory variables that have been screened according to the prior knowledge of experts. The identification of essential explanatory variables through data-centric analysis, with a focus on variables that co-occur with demand, has long been recognized as important; however, discussion has been limited because it is difficult to describe plausible statistical relationships among the many possible explanatory variables with only a limited number of historical data samples available. This study focuses on the dynamics in hourly electricity demand and approach to identify annually important variables by constructing situation-dependent models. These models are based on the dataset consisting of demand and multiple explanatory variables that co-occur in the target time slices. The powerful concept of sparse modeling is applied to handle the large number of possible explanatory variables used in the situation-dependent modeling process. In particular, this study discusses inconsistency of the selected variables when the statistical models are constructed focusing on different data subsets; when the model is trained based on a dataset focusing on a specific time period, the selected variables may be significantly different from those resulting from a dataset focusing on another time period. The authors propose to derive a limited number of annually dominant variables by enumerating suboptimal models for each situation, and by selecting, as much as possible, essential variables that are commonly and consistently used for all situations. The proposed scheme was applied to a real-world demand dataset and discussed in the context of representation errors and interpretability. The results show that the proposed method is an effective approach for representing the situation-dependent impact of variables on demand.

Plastidial phosphorylase (Pho1) accounts for ∼96% of the total phosphorylase activity in developing rice (Oryza sativa) seeds. From mutant stocks induced by N-methyl-N-nitrosourea treatment, we identified plants with mutations in the Pho1 gene that are deficient in Pho1. Strikingly, the size of mature seeds and the starch content in these mutants showed considerable variation, ranging from shrunken to pseudonormal. The loss of Pho1 caused smaller starch granules to accumulate and modified the amylopectin structure. Variation in the morphological and biochemical phenotype of individual seeds was common to all 15 pho1-independent homozygous mutant lines studied, indicating that this phenotype was caused solely by the genetic defect. The phenotype of the pho1 mutation was temperature dependent. While the mutant plants grown at 30°C produced mainly plump seeds at maturity, most of the seeds from plants grown at 20°C were shrunken, with a significant proportion showing severe reduction in starch accumulation. These results strongly suggest that Pho1 plays a crucial role in starch biosynthesis in rice endosperm at low temperatures and that one or more other factors can complement the function of Pho1 at high temperatures.