A Personalized and Smart Flowerpot Enabled by 3D Printing and Cloud Technology for Ornamental Horticulture.

This paper presents a personalized and smart flowerpot for ornamental horticulture, integrating 3D printing and cloud technology to address existing design limitations and enable real-time monitoring of environmental parameters in plant cultivation. While 3D printing and cloud technology have seen widespread adoption across industries, their combined application in agriculture, particularly in ornamental horticulture, remains relatively unexplored. To bridge this gap, we developed a flowerpot that maximizes space utilization, simplicity, personalization, and aesthetic appeal. The shell was fabricated using fused deposition modeling (FDM) in 3D printing, and an Arduino-based control framework with sensors was implemented to monitor critical growth factors such as soil moisture, temperature, humidity, and light intensity. Real-time data are transmitted to the Bamfa Cloud through Wi-Fi, and a mobile application provides users with instant access to data and control over watering and lighting adjustments. Our results demonstrate the effectiveness of the smart flowerpot in enabling automated monitoring of plant growth and environmental control. This innovation holds significant promise for advancing smart device development in ornamental horticulture and other related fields, enhancing efficiency, plant health, and overall user experience. Future research in this area has the potential to revolutionize horticultural practices and contribute to the advancement of smart agriculture.

Safety and efficacy of enhanced recovery after surgery (ERAS) programs in patients undergoing hepatectomy: A prospective randomized controlled trial.

BACKGROUND AND AIM: Enhanced recovery after surgery (ERAS) programs, following a variety of perioperative treatments with evidence-based medical evidence, has indicated its validity to accelerate rehabilitation in a wide variety of surgical procedures. This randomized controlled trial (RCT) study was implemented to verify the safety and efficacy of the perioperative effects in patients undergoing hepatectomy with ERAS or with conventional surgery (CS). METHODS: From August 2016 to November 2017, according to the inclusion criteria, 160 patients with liver diseases were suitable for participating in this experiment. Patients before liver resection were randomized into ERAS group (n = 80) and CS group (n = 80), and then the outcome measures were compared between the two groups. RESULTS: Enhanced recovery after surgery group had significantly less complications than CS group (P = .009). Compared with CS group, patients in ERAS group had low peak of WBCs in postoperative day (POD1), ALT in POD1 and POD3 (P < .05), high value of ALB in POD3 and POD5 (P < .05), less pain and higher patient satisfaction (P < .001), earlier exhaust, oral feeding, ambulation and extubation time (P < .05),and also had less hospital stay and cost (P < .001). There were no significant differences in readmission rate (<30 days) between two groups (P = .772). CONCLUSIONS: Enhanced recovery after surgery programs applied to patients undergoing hepatectomy can safely and effectively relieve stress response, reduce the incidence of complications, improve patient satisfaction, accelerate patient recovery, reduce financial burden, and bring economic benefits.

Research on roller monitoring technology based on distributed fiber optic sensing system.

As one of the key components of the belt conveyor, the roller bears the task of supporting and rolling the conveyor belt, and monitoring its condition is very important. The traditional monitoring of the conveyor roller group adopts worker inspection, which has strong subjectivity. Monitoring using sensors necessitates the use of numerous sensors, which can pose wiring challenges. The use of inspection robots for monitoring results can be discontinuous, and their performance may be limited. This study proposes a fault diagnosis method for rollers based on a distributed fiber optic sensing system. By improving the traditional Isolation Forest (IForest), a framework called Incremental Majority Voting Isolation Forest (IMV-IForest) is proposed. By analyzing the optical signal, we extracted the variation patterns of roller faults over time and space, and analyzed the abnormal score distribution between fault data and normal data. Using the dataset collected on-site, we compared and analyzed IMV-IForest with the traditional IForest and the Extended Isolation Forest (E-iForest). The results indicate that the variation of the fault of the faulty roller with time and space can be used for early prediction of roller faults; determine an anomaly score threshold of 0.6; improved IForest have faster computation time and higher accuracy. Finally, to verify the effectiveness of the proposed scheme, a 3-month experiment was conducted on a 600 m long belt conveyor in a certain mine, and on-site monitoring results were obtained. By comparing with manual detection results, it was shown that the proposed method has high recognition rate for faulty idlers, with an accuracy rate of 97.92%, and can effectively diagnose faulty idlers.

Efficient high-solids enzymatic hydrolysis of corncobs by an acidic pretreatment and a fed-batch feeding mode.

Corncob is an abundant and renewable resource that could be enzymatically hydrolyzed to fermentable sugar. A major impediment in corncob utilization is the low hydrolysis efficiency at high-solids content. This study attempted different pretreatment methods and fed-batch modes to achieve a 25% solids content hydrolysis with high yields. Natural corncobs were compared with acid-treated and acid-alkali-treated corncobs in terms of kinetics parameters, conversion rate and glucose titer. By feeding in batches, a "low amount and high frequency" mode (10%-3%-3%-3%-3%-3%, every 5 h) was confirmed to be optimal for a 25% high-solids hydrolysis system with a cellulase loading of 12 mg/g (7.3 FPU/g), resulted with an 84.4% glucose yield at 96 h. Our results demonstrated that combination of both optimized pretreatment method and fed-batch mode were a favored process model for high-solids hydrolysis of lignocellulose, boosting cellulose hydrolysis efficiency and sugar yields on an industrial scale.

Low expression of GNAI3 predicts poor prognosis in patients with HCC.

PURPOSES: This study was performed with an aim to explain the underlying role of GNAI3 on the prognosis of patients with HCC. METHODS: The expression of GNAI3 at protein level was detected with the utilization of Immunohistochemistry (IHC). Chi-square test was conducted to assay the relationship between GNAI3 expression and clinical parameters of HCC patients. The correlation between expression level of GNAI3 and survival time after surgeries of HCC patients was evaluated by Kaplan-Meier method. Finally, the Cox regression was established to evaluate the relationship between GNAI3 expression and the prognosis of patients with HCC. RESULTS: In this study, the negative rate of GNAI3 expression in HCC samples was about 76.6%, which was significantly higher than that in paired normal specimens (12.5%). Result showed that there was no correlation between GNAI3 expression and age, gender, liver cirrhosis and vein invasion (P>0.05), but tight relationship between GNAI3 expression and TNM stage and tumor size was found (P<0.05). The following Kaplan-Meier analysis result illustrated that negative expression of GNAI3 induced high mortality of HCC patients. Cox regression result revealed that GNAI3 might be a biomarker for prognosis of patients with HCC (HR: 0.218, P=0.016, 95% CI 0.063-0.750). CONCLUSION: Generally, results of this study demonstrated that expression of GNAI3 shared a tight relationship with the prognosis of patients with HCC. Therefore, GNAI3 could be considered as a novel index for prognosis of patients with HCC.