Edge Computing Driven Data Sensing Strategy in the Entire Crop Lifecycle for Smart Agriculture.

In the context of smart agriculture, high-value data sensing in the entire crop lifecycle is fundamental for realizing crop cultivation control. However, the existing data sensing methods are deficient regarding the sensing data value, poor data correlation, and high data collection cost. The main problem for data sensing over the entire crop lifecycle is how to sense high-value data according to crop growth stage at a low cost. To solve this problem, a data sensing framework was developed by combining edge computing with the Internet of Things, and a novel data sensing strategy for the entire crop lifecycle is proposed in this paper. The proposed strategy includes four phases. In the first phase, the crop growth stage is divided by Gath-Geva (GG) fuzzy clustering, and the key growth parameters corresponding to the growth stage are extracted. In the second phase, based on the current crop growth information, a prediction method of the current crop growth stage is constructed by using a Tkagi-Sugneo (T-S) fuzzy neural network. In the third phase, based on Deng's grey relational analysis method, the environmental sensing parameters of the corresponding crop growth stage are optimized. In the fourth phase, an adaptive sensing method of sensing nodes with effective sensing area constraints is established. Finally, based on the actual crop growth history data, the whole crop life cycle dataset is established to test the performance and prediction accuracy of the proposed method for crop growth stage division. Based on the historical data, the simulation data sensing environment is established. Then, the proposed algorithm is tested and compared with the traditional algorithms. The comparison results show that the proposed strategy can divide and predict a crop growth cycle with high accuracy. The proposed strategy can significantly reduce the sensing and data collection times and energy consumption and significantly improve the value of sensing data.

Durability of Prestressed Piles in a Leachate Environment.

Prestressed pipe piles are common concrete components characterized by dense concrete structures and favorable mechanical properties, and thus, extensively used as coastal soft soil foundations. However, their durability in harsh environments has not been fully clarified. In this study, leachate from an actual landfill site was collected from the east coast of China as the corrosive medium, and the corrosion process was accelerated by electrifying prestressed pipe piles. The results demonstrated that the concentration of chloride ions in the concrete of the prestressed pile increased with the increase in corrosion time. Moreover, the experimental corrosion of these prestressed piles in the drying-wetting cycle proved to be the most severe. However, a protective layer of epoxy resin coating can effectively inhibit the diffusion of chloride ions into the interior of the piles. The final theoretical corrosion amounts of the piles were 1.55 kg, 1.20 kg, and 1.64 kg under immersion, epoxy resin protection, and a drying-wetting cycle environment. The application of epoxy resin reduced chloride penetration by 22.6%, and the drying-wetting cycle increased chloride penetration by 5.8%, respectively, with corresponding corrosion potentials following similar patterns. The actual corrosion depth of the welding seam was 3.20 mm, and there was a large corrosion allowance compared with the requirement (6.53 mm) for the ultimate bending moment. In summary, these prestressed piles exhibited good durability in a leachate environment.

Novel staging for gastric neuroendocrine neoplasms by incorporating the WHO grading into the TNM staging system.

BACKGROUND: The 8th tumor-node-metastasis (TNM) classification of the American Joint Committee on Cancer (AJCC) can be used to estimate the prognosis of gastric neuroendocrine tumor (gNET) and gastric neuroendocrine carcinoma (gNEC) patients but not gastric neuroendocrine neoplasms (gNENs). METHODS: First, in the SEER (training) dataset, a TNMG system was built by combining the WHO G grade (G1-4; NEC grouped into G4) with the 8th AJCC T (T1-4), N (N0-1), and M (M0-1) stage, which was then validated in a Chinese (validation) cohort. RESULTS: In all, 2245 gNENs cases from the training dataset and 280 cases from the validation dataset were eligible. The T stage, M stage, and G grade were independent prognostic factors for OS in both datasets (all p < 0.05). The TNMG staging system demonstrated better C-index for predicting OS than the 8th AJCC TNM staging system in both the training (0.87, 95%CI: 0.86-0.88 vs. 0.79, 95%CI: 0.77-0.81) and validation (0.77, 95%CI: 0.73-0.80 vs. 0.75, 95%CI: 0.71-0.79) datasets. The AUC of the 3-year OS for the TNMG staging system was 0.936 and 0.817 in the SEER and validation dataset, respectively; higher than those of the 8th AJCC system (vs. 0.843 and 0.779, respectively). DCA revealed that compared with the 8th AJCC TNM staging system, the TNMG staging system demonstrated superior net prognostic benefit in both the training and validation datasets. CONCLUSIONS: The proposed TNMG staging system could more accurately predict the 3- and 5-year OS rate of gNENs patients than the 8th AJCC TNM staging system.

Standardizing the classification of gastric cancer patients with limited and adequate number of retrieved lymph nodes: an externally validated approach using real-world data.

BACKGROUND: Currently, there is no formal consensus regarding a standard classification for gastric cancer (GC) patients with < 16 retrieved lymph nodes (rLNs). Here, this study aimed to validate a practical lymph node (LN) staging strategy to homogenize the nodal classification of GC cohorts comprising of both < 16 (Limited set) and ≥ 16 (Adequate set) rLNs. METHODS: All patients in this study underwent R0 gastrectomy. The overall survival (OS) difference between the Limited and Adequate set from a large Chinese multicenter dataset was analyzed. Using the 8th American Joint Committee on Cancer (AJCC) pathological nodal classification (pN) for GC as base, a modified nodal classification (N') resembling similar analogy as the 8th AJCC pN classification was developed. The performance of the proposed and 8th AJCC GC subgroups was compared and validated using the Surveillance, Epidemiology, and End Results (SEER) dataset comprising of 10,208 multi-ethnic GC cases. RESULTS: Significant difference in OS between the Limited and Adequate set (corresponding N0-N3a) using the 8th AJCC system was observed but the OS of N0limited vs. N1adequate, N1limited vs. N2adequate, N2limited vs. N3aadequate, and N3alimited vs. N3badequate subgroups was almost similar in the Chinese dataset. Therefore, we formulated an N' classification whereby only the nodal subgroups of the Limited set, except for pT1N0M0 cases as they underwent less extensive surgeries (D1 or D1 + gastrectomy), were re-classified to one higher nodal subgroup, while those of the Adequate set remained unchanged (N'0 = N0adequate + pT1N0M0limited, N'1 = N1adequate + N0limited (excluding pT1N0M0limited), N'2 = N2adequate + N1limited, N'3a = N3aadequate + N2limited, and N'3b = N3badequate + N3alimited). This N' classification demonstrated less heterogeneity in OS between the Limited and Adequate subgroups. Further analyses demonstrated superior statistical performance of the pTN'M system over the 8th AJCC edition and was successfully validated using the SEER dataset. CONCLUSION: The proposed nodal staging strategy was successfully validated in large multi-ethnic GC datasets and represents a practical approach for homogenizing the classification of GC cohorts comprising of patients with < 16 and ≥ 16 rLNs.