With the development of material life, the importance of plants in life has become increasingly prominent, and indoor flowers are also popular. As we all know, plants have purified air, refreshing brainwashing, promote sleep, sterilization and other effects, such as mint, Clivia, aloe and so on. Therefore, the choice of plants corresponding to their own needs is particularly important, while to note that some flowers should not be placed indoors. And different flowers on the water, temperature, light, soil and other requirements are not the same.

Introduction: The selection of genotypes with determinate growth habit in tomato should contemplate adequate selection criteria to increase the efficiency of the breeding program. Objective: The objective of this work was to estimate selection criteria for “chonto” type tomato lines with determined growth habit. Materials and methods: This work was carried out at the Universidad Nacional de Colombia (Palmira Campus), in 2016, with seven lines with determinate growth habit and a control with indeterminate growth. Heritability in a broad sense (h² g), coefficient of environmental variation, coefficient of genetic variation, selection efficiency and genetic gain were determined in parameters of morphological, phonological, fruit quality, fruit shape and production, using the RELM/BLUP procedure of the SELEGEN software. Results: There were three ranges of h² g, the first with values of h² g greater than 0.76, the second between 0.53 and 0.38, and the third with a value less than 0.38. The highest values of h² g were for final plant height with 0.92, plant height at harvest with 0.88, yield per plant with 0.83, days to flowering with 0.83, number of fruits per plant with 0.82, and days to harvest with 0.82. For genetic gain it was found that the control had the highest values for final plant height, plant height at harvest, internode length, days to harvest, harvest duration, soluble solids content, number of fruits per plant, fruit weight and yield per plant; however, in some parameters such as height and phenology for selection by determined growth habit, the lowest values were better. Conclusion: There was evidence of genetic parameters that could be considered as selection criteria for “chonto” type tomato lines with determinate growth habit.

Among major global threats to papaya cultivation, papaya ringspot virus (PRSV) is the most challenging one. In the absence of any PRSV resistant commercial papaya cultivar, PRSV management is restricted to minimizing yield losses. ICAR-Indian Agricultural Research Institute, Regional Station, Pune has developed PRSV tolerant dioecious papaya lines, Pune Selection (PS)-1, PS-2, PS-3 and PS-5. Being dioecious these lines have limited acceptability among farmers. Gynodioecious population from these lines were developed and characterized. They are numbered PS-1-1, PS-2-1, PS-3-1 and PS-5-1. These lines were characterized against prevailing commercial gynodioecious cultivar, Red Lady, for five generations. The average plant height of PS-2-1 and PS-5-1 (183 cm) was more than Red Lady (158 cm), however, stem girth of all lines was lesser than Red Lady. The fruiting height of all lines was less than Red Lady (87 cm). Length of the fruiting column of all lines was more than Red Lady (37 cm), except in PS-1-1. Fruit yield of all lines was more than Red Lady (16 kg/plant). Intensity of PRSV infection in Red Lady (48%) was considerably more than all lines. These lines can be used for developing PRSV tolerant gynodioecious papaya variety.

This study meticulously explores the crucial elements precipitating corporate failures in Taiwan during the decade from 1999 to 2009. It proposes a new methodology, combining ANOVA and tuning the parameters of the classification so that its functional form describes the data best. Our analysis reveals the ten paramount factors, including Return on Capital ROA(C) before interest and depreciation, debt ratio percentage, consistent EPS across the last four seasons, Retained Earnings to Total Assets, Working Capital to Total Assets, dependency on borrowing, ratio of Current Liability to Assets, Net Value Per Share (B), the ratio of Working Capital to Equity, and the Liability-Assets Flag. This dual approach enables a more precise identification of the most instrumental variables in leading Taiwanese firms to bankruptcy based only on financial rather than including corporate governance variable. By employing a classification methodology adept at addressing class imbalance, we substantiate the significant influence these factors had on the incidence of bankruptcy among Taiwanese companies that rely solely on financial parameters. Thus, our methodology streamlines variable selection from 95 to 10 critical factors, improving bankruptcy prediction accuracy and outperforming Liang's 2016 results.

Outsourcing logistics operations is a common trend as businesses prioritize core activities. Establishing a sustainable partnership between businesses and logistics service providers requires a systematic approach. This study is needed to develop a more effective and adaptive framework for logistics service provider selection by integrating diverse criteria and decision-making methodologies, ultimately enhancing the precision and sustainability of procurement processes. This study advocate for leveraging industry-based knowledge in procurement, emphasizing the need to define decision-making elements. The research analyzes nearly 300 logistics procurement projects, using a neural network-based methodology to propose a model that aids businesses in identifying optimal criteria for evaluating logistics service providers based on extensive industry knowledge. The goal of this study is to develop and test a practical model that would support businesses in choosing most suitable criteria for selection of logistics service providers based on cumulative market patterns. The results of this study are as follows. It introduces novel elements by gathering and systematizing unique market data using developed data processing methodology. It innovatively classifies decision-making elements, allocating them into distinct groups for use as features in a neural network. The study further contributes by developing and training a predictive model based on a prepared dataset, addressing pre-defined goals, expectations related to green logistics, and specific requirements in the tendering process for selecting logistics service providers. Study is concluded by summarizing suggestions for future research in area of adopting neural networks for selection of logistics service providers.