In order to optimize the environmental factors for cucumber growth, a fertilizer and water control system was designed based on the Internet of Things (IoT) system. The IoT system monitors environmental factors such as temperature, light and soil Ec value, and uses image processing to obtain four growth indicators such as cucumber stem height, stem diameter size, number of leaves and number of fruit set to establish a single growth indicator model for temperature, light, soil Ec value and growth stage, and the four growth indicators were fused to obtain the comprehensive growth indicator I_c for cucumber, and calculates its deviation to determine the cucumber growth status. Based on the integrated growth index I_c of cucumber, a soil Ec control model was established to provide the optimal environment and fertilizer ration for cucumber at different growth stages to achieve stable and high yield of cucumber.

One of the biggest environmental problems that has affected the planet is global warming, due to high concentrations of carbon (CO₂), which has led to crops such as coffee being affected by climate change caused by greenhouse gases (GHG), especially by the increase in the incidence of pests and diseases. However, carbon sequestration contributes to the mitigation of GHG emissions. The objective of this work was to evaluate the carbon stored in above and below ground biomass in four six-year-old castle coffee production systems. In a trial established under a Randomized Complete Block Design (RCBD) with the treatments Coffee at free exposure (T1), Coffee-Lemon (T2), Coffee-Guamo (T3) and Coffee-Carbonero (T4), at three altitudes: below 1,550 masl, between 1,550 and 2,000 masl and above 2,000 masl. Data were collected corresponding to the stem diameters of coffee seedlings and shade trees with which allometric equations were applied to obtain the carbon variables in the aerial biomass and root and the carbon variables in leaf litter and soil obtained from their dry matter. Highly significant differences were obtained in the four treatments evaluated, with T4 being the one that obtained the highest carbon concentration both in soil biomass with 100.14 t ha^-1 and in aerial biomass with 190.42 t ha^-1.

This paper reviews and comments on the evaluation methods of the recreational value of natural landscape resources, pointing out that the current popular TCM method and CVM method both rely too much on the market prediction conclusion and cannot truly reflect the recreational value, and putting forward the idea, specific operation steps and calculation methods of evaluating the recreational value of natural landscape resources with tourism environmental capacity.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.