Developing an Automated Monitoring System for Cable Yarding Systems

Cable yarders are often the preferred harvesting system when extracting trees on steep terrain. While the practice of cable logging is well established, productivity is dependent on many stand and terrain variables. Being able to continuously monitor a cable yarder operation would provide the opportunity not only to manage and improve the system, but also to study the effect on operations in different conditions. This paper presents the results of an automated monitoring system that was developed and tested on a series of cable yarder operations. The system is based on the installation of a Geographical Navigation Satellite System (GNSS) onto the carriage, coupled with a data-logging unit and a data analysis program. The analysis program includes a set of algorithms able to transform the raw carriage movement data into detailed timing elements. Outputs include basic aspects such as average extraction distance, average inhaul and outhaul carriage speed, but is also able to distinguish number of cycles, cycle time, as well as break the cycles into its distinct elements of outhaul, hook, inhaul and unhook. The system was tested in eight locations; four in thinning operations in Italy and four clear-cut operations in New Zealand, using three different rigging configuration of motorized slackpulling, motorized grapple and North Bend. At all locations, a manual time and motion study was completed for comparison to the data produced by the newly developed automated system. Results showed that the system was able to identify 98% of the 369 cycles measured. The 8 cycles not detected were directly attributed to the loss of GNSS signal at two Italian sites with tree cover. For the remaining 361 cycles, the difference in gross cycle time was less than 1% and the overall accuracy for the separate elements of the cycle was less than 3% when considered at the rigging system level. The study showed that the data analyses system developed can readily convert GNSS data of the carriage movement into information useful for monitoring and studying cable yarding operations

Blooming charge assessment in apple orchards for automatic thinning activities

Summary This work aims to develop an automatic system capable of providing objective information about the bloom charge in an apple orchard in order to manage flower-thinning activities. The article presents and discusses the use of a mobile lab (ByeLab) equipped with several optical sensors to carry out a site-specific bloom charge assessment in apple trees. The data collected by the sensors were processed by a specific algorithm implemented in MatLab®. Investigations of the flower reflectance signature indicated that the Normalized Difference Vegetation Index (NDVI) is the most suitable parameter to distinguish leaves from flowers. Pure flowers produce NDVI values slightly negative or at least very near to 0. Despite the homogeneous behavior of the NDVI flower response, OptRx™ sensors, which provide an average assessment of an area, were not able to highlight a significant correlation between the number of flowers and the NDVI values. In the future, further studies will be conducted to assess if other techniques based on image analyses can provide better and more sensitive results regarding the bloom charge assessment. Such results could then be used as a reference in automating machines for thinning operations according to a site-specific approach

Solutions for the automation of operational monitoring activities for agricultural and forestry tasks

Summary An innovative approach for the automation of operational monitoring activities in agricultural and forestry tasks is described and discussed in this article. This approach can be considered as a solution for Precision Agriculture and Precision Forestry applications and can be used as an information and communication technology (ICT) tool for the management aims by a variety of agricultural and forestry companies. The aim of the proposed concept is to develop a system, composed of both hardware and software units, with the ability to collect and manage operative raw data and then to translate them into operational information that will be used in decision-making processes. All the procedures will be carried out automatically, in order to ensure an objective compilation of the field activity register. Thus, the entrepreneur will have all the operative information automatically updated in a dedicated database system. All the obtained documents can then be used for certification and traceability processes, if required by the procedural guideline, as well as to satisfy any other management tasks, including the estimation of the actual operative costs of the farm

Modelling battery packs of real-world electric vehicles from data sheet information

Lithium-ion batteries have emerged as the leading enabling technology in developing Electric Vehicles (EVs), But, large-scale publicly available EV data are extremely difficult to find. So it becomes difficult to research and disseminate new methods for monitoring the battery pack of an EV. In this work, we propose a Simulink-based approach to define a virtual-EV model that simulates EV battery pack signals starting from input driving sessions. The battery pack module within the virtual-EV has been fine-tuned using data gathered from real-world EV data sheets. Moreover, the battery pack module includes thermal and aging models, impacting on the output signals, considering the temperature of the surrounding environment and the initial State of Health (SOH) of the battery pack. The virtual-EV generates time series of vehicle's speed, and battery pack's current, State of Charge (SOC), voltage, and average internal temperature according to the input driving cycle. We defined two Simulink EV models emulating two distinct real-world-EVs. Then, we assessed the performances of the simulators comparing the simulated data and real EV data signals collected by the same real-world-EV models, and we obtain, for both simulated EV models, R2 values higher than 0.70 and an RMSE of at most 7V and 8% for the voltage and SOC of the battery pack, respectively

An Electric Vehicle Simulator for Realistic Battery Signals Generation from Data-sheet and Real-world Data

Electric vehicles (EVs) have been globally recognized as a reliable alternative to fossil fuel vehicles. The core component of an electric vehicle is its rechargeable battery pack. However, there still needs to be large-scale publicly available EV data to investigate and distribute effective solutions to monitor the conditions of the EV’s battery pack. Hence, we propose an EV simulator that generates EV battery pack internal signals starting from the input driving cycle. The simulated data resemble the behavior of a multi-cell EV battery pack undergoing the user’s utilization of the EV. The simulated data include vehicle speed, voltage, current, State of Charge (SOC), and internal temperature of the battery pack. The virtual-EV model simulator, including the battery pack subsystem, has been tuned using real-world EV data-sheet information. The battery pack embeds thermal and aging models for further realism, influencing the output signals given the environmental temperature and the battery’s State of Health (SOH). The data generated by the virtual EV simulator have been validated with real EV data signals sampled by an equivalent real-world EV. The data comparison yields a minimum R2 value of 0.94 and a Root Mean Squared Error not higher than 2.74V for the battery pack’s voltage and SOC, respectively

Efficacy of a low-dose diosmin therapy on improving symptoms and quality of life in patients with chronic venous disease. Randomized, double-blind, placebo-controlled trial

Chronic Venous Disease (CVD) is a common medical condition affecting up to 80% of the general population. Clinical manifestations can range from mild to more severe signs and symptoms that contribute to the impairment of the quality of life (QoL) of affected patients. Among treatment options, venoactive drugs such as diosmin are widely used in the symptomatic treatment in all clinical stages. The aim of this study is to determine the effectiveness of a new formulated diosmin in relieving symptoms and improving QoL in patients suffering from CVD. In this randomized, double-blind, placebo-controlled, multicenter clinical study, CVD patients with a Clinical-Etiology-Anatomy-Pathophysiology (CEAP) classification system between C2 and C4 were randomized to receive a bioavailable diosmin (as μsmin® Plus) 450 mg tablet once daily or a placebo for 8 weeks. Clinical symptoms and QoL were monitored using the measurement of leg circumference, visual analogue scale (VAS) for pain, Global Index Score (GIS) and Venous Clinical Severity Score (VCSS). A total of 72 subjects completed the study. From week 4, leg edema was significantly decreased in the active group (p < 0.001). An improvement in the VAS score was observed in the active group compared to placebo at the end of treatment (p < 0.05). GIS and VCSS scores were significantly improved in the active group at week 8 (p < 0.001). No treatment related-side effects were recorded. The results of this study showed that the administration of low-dose μsmin® Plus was safe and effective in relieving symptoms and improving QoL in subjects with CVD

Diagnostic and prognostic value of Bcl-2 in uterine leiomyosarcoma

Background Uterine leiomyosarcoma (uLMS) may show loss of expression of B-cell lymphoma-2 (Bcl-2) protein. It has been suggested that Bcl-2 loss may both be a diagnostic marker and an unfavorable prognostic marker in uLMS. Objective To define the diagnostic and prognostic value of Bcl-2 loss in uLMS through a systematic review and meta-analysis. Methods Electronic databases were searched from their inception to May 2020 for all studies assessing the diagnostic and prognostic value of Bcl-2 loss of immunohistochemical expression in uLMS. Data were extracted to calculate odds ratio (OR) for the association of Bcl-2 with uLMS vs leiomyoma variants and smooth-muscle tumors of uncertain malignant potential (STUMP), and hazard ratio (HR) for overall survival; a p value < 0.05 was considered significant. Results Eight studies with 388 patients were included. Loss of Bcl-2 expression in uLMS was not significantly associated with a diagnosis of uLMS vs leiomyoma variants and STUMP (OR = 2.981; p = 0.48). Bcl-2 loss was significantly associated with shorter overall survival in uLMS (HR = 3.722; p = 0.006). High statistical heterogeneity was observed in both analyses. Conclusion Loss of Bcl-2 expression appears as a significant prognostic but not diagnostic marker in uLMS. The high heterogeneity observed highlights the need for further research and larger studies

COULD JC VIRUS PROVOKE METASTASIS IN COLON CANCER?

AIM: to evaluate the prevalence of John Cunningham Virus (JC virus) in a small cohort of patients with colon cancer and to assess its presence in hepatic metastasis.METHOD: Neneteen consecutive patients with histologically diagnosed colon cancer were included in our study, together with ten subjects affected by histologically and serologically diagnosed hepatitis C virus infection. in the patients included in the colon cancer group, JC virus was searched for the surgical specimen; in the control group, JC virus was searched in the hepatic biopsy. The difference in the prevalence of JC virus in the hepatic biopsy between the two groups was assessed through the X2 test. RESULTS: Four of 19 patients with colon cancer had a positive polymerase chain reaction (PCR) test for JC virus, and four had liver metastasis. Among the patients with liver metastasis, three out four had a positive PCR test for JC virus in the surgical specimen and in the liver biopsy; the only patient with liver metastasis with a negative test for JC virus also presenrted a negative test for JC virus in the surgical specimen. in the control group of patients with hepatitis C infection, none of the ten patients presented JC virus infection in the hepatic biopsy. The difference between the two groups regarding JC virus infection was statistically significant (X2=9.55, P=0,002)