A Novel Technique to Design and Optimize Performances of Custom Load Cells for Sport Gesture Analysis

BackgroundThe assessment of the force exerted during a gesture in human motion analysis can provide direct and indirect information regarding the expended energy, especially during the execution of a sport gesture. In this field, assessment and improvement of the performance can be supported by instrumented devices able to measure and process mechanical quantities. In cycling, strain gauges-based instrumented pedals represent one of the last innovations in the sector, because they can provide data about the power exerted (produced) during training and the pedal efficiency. Optimization of the strain-gauges positioning is thus required to improve accuracy in the exerted force estimation.MethodsA new technique to give a support for evaluating the best compromise between maximum sensitivity and ease of assembly was developed in the present work, based on a Finite Element Model (FEM) and a parametric analysis of the strain field at different sensor placements. Optimal positions were identified as those combining high sensitivity and low dependence from positioning inaccuracies.ResultsParametric strain-load trends obtained from the developed model show a linear behavior of strain gauges pairs and confirm that there is a good sensitivity of the adopted sensors if they are mounted in handy positions of the developed load cell.DiscussionThe conducted analysis enables to calculate the sensitivity of the load cell to the exerted forces, and evaluates its dependence to the positioning of strain gauges, and makes it possible to appropriately choose strain gauges positioning in areas where border effects are minimized. The strain distributions obtained by the FEM analysis in the presented load cells gives useful indications for all the situations where small strain gauges are requested to be mounted on a reduced offered area.     

Evaluation of a countercurrent biosorption system for the removal of lead and copper from aqueous solutions

Abstract: An automated bench-scale countercurrent biosorption system (CBS) has been designed for the removal of metals from aqueous effluents. The system has been tested with activated sludge microorganisms as a biosorbent and lead and copper as model metals. Nearly 5 1 of a lead nitrate solution at 100 mg l⁻¹ of lead have been treated down to a final concentration of 0.1 mg l⁻¹ (99.9% removal) by using 4.8 g of dry biosorbent. Under similar conditions, copper chloride solutions at 100 mg 1⁻¹ of copper were treated down to a final concentration of 35–45 mg l^−l representing 60% removal. The advantage of the CBS is to maximize metal concentration in the biosorbent, from which the metal may thereby be recovered if desired. In addition, the CBS minimizes metal concentration in the treated effluent, which is the first objective of the treatment.     

A novel approach for pilot error detection using Dynamic Bayesian Networks

In the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment’s data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system.     

DCE-MRI药代动力学参数评估患癌胰腺不同区域功能状态的价值研究

目的:比较胰腺癌(pancreatic cancer,pCa)患者癌灶区、近端胰腺区、远端炎性区及正常胰腺的动态对比增强磁共振成像(dynamic contrast-enhanced MRI,DCE-MRI)药代动力学参数K~(trans)和k_(ep),无创量化患癌胰腺不同区域微血管功能状态,评估pCa近端胰腺组织是否为相对正常的胰腺组织。方法:收集病理及临床证实的pCa患者29例腿机选取正常志愿者30例。所有受试者行胰腺DCE-MRI扫描并计算每个观测区的K~(trans)和k_(ep)。根据患癌胰腺不同区域状态的差异将其分为pCa组、癌灶近端胰腺组、癌灶远端炎症组,30例正常志愿者胰腺为正常对照组。采用One-way ANOVA分别比较不同组别间K~(trans)s、k_(ep)的差异。结果源腺癌、近端胰腺区、远端炎症区及正常胰腺的K~(trans)和k_(ep)分别是0.114±0.023、0.066±0.009、0.323±0.026、0.061±0.005和0.341±0.077、0.105±0.017、0.772±0.068、0.107±0.005。pCa的K~(trans)和k_(ep)高于近端胰腺区和正常胰腺,而低于远端炎症区,远端炎症区的K~(trans)和k_(ep)高于pCa、近端胰腺区和正常胰腺,以上P均0.001;而近端胰腺和正常胰腺的K~(trans)和k_(ep)均无统计学差异(P=0.298、0.893)。结论患癌胰腺不同区域微血管功能状态有明显差异同时提示pCa近端≥1 cm的区域为相对正常的胰腺区,以上可为pCa精准治疗、手术切缘选择提供参考。     

Short-duration dynamic FDG PET imaging: Optimization and clinical application

PurposeWe aimed to investigate whether short dynamic PET imaging started at injection, complemented with routine clinical acquisition at 60-min post-injection (static), can achieve reliable kinetic analysis.MethodsDynamic and static 18F-2-fluoro-2-deoxy-D-glucose (FDG) PET data were generated using realistic simulations to assess uncertainties due to statistical noise as well as bias. Following image reconstructions, kinetic parameters obtained from a 2-tissue-compartmental model (2TCM) were estimated, making use of the static image, and the time duration of dynamic PET data were incrementally shortened. We also investigated, in the first 2-min, different frame sampling rates, towards optimized dynamic PET imaging. Kinetic parameters from shortened dynamic datasets were additionally estimated for 9 patients (15 scans) with liver metastases of colorectal cancer, and were compared with those derived from full dynamic imaging using correlation and Passing–Bablok regression analyses.ResultsThe results showed that by reduction of dynamic scan times from 60-min to as short as 5-min, while using static data at 60-min post-injection, bias and variability stayed comparable in estimated kinetic parameters. Early frame samplings of 5, 24 and 30 s yielded highest biases compared to other schemes. An early frame sampling of 10 s generally kept both bias and variability to a minimum. In clinical studies, strong correlation (r ≥ 0.97, P < 0.0001) existed between all kinetic parameters in full vs. shortened scan protocols.ConclusionsShortened 5-min dynamic scan, sampled as 12 × 10 + 6 × 30 s, followed by 3-min static image at 60-min post-injection, enables accurate and robust estimation of 2TCM parameters, while enabling generation of SUV estimates.     

How to increase the recovery rate for waste cooking oil-to-biofuel conversion: A comparison of recycling modes in China and Japan

Improvement of recovery rates is crucial for the supply chain management of waste cooking oil-to-biofuel conversion. This paper constructs a dynamic game model between biofuel enterprises and recyclers to compare the recycling modes and recycling rate decisions of waste cooking oil-to-biofuel conversion in China and Japan. Results demonstrate that: (1) the recovery rate of Chinese mode with recyclers as price leader is not necessarily lower than that of Japanese mode theoretically, indicating that the collection-storage-transportation integrated mode in several cities led by Chinese government may not be the optimal choice. (2) The subsidies for recyclers have insignificant impact on recovery rate, while the subsidies for biofuel enterprises can significantly improve the recovery rate. (3) Decreasing difficulty coefficient and expanding the market size can greatly improve the recovery rate. Therefore, reforming the beneficiary of subsidies, improving the recycling infrastructure, implementing strict technical and administrative control and creative sales policy of biofuel finished products may benefit the recovery rate in China.     

Gene and protein interaction network analysis in the epithelial-mesenchymal transition of Hertwig's Epithelial Root Sheath reveals periodontal regenerative drug targets – An in silico study

Background and aimHertwig’s Εpithelial Root Sheath (HΕRS) has a major function in the developing tooth roots. Earlier research revealed that it undergoes epithelial–mesenchymal transition, a vital process for the morphogenesis and complete development of the tooth and its surrounding periodontium. Few studies have demonstrated the role of HERS in cementogenesis through ΕMΤ. The background of this in-silico system biology approach is to find a hub protein and gene involved in the EMT of HERS that may uncover novel insights in periodontal regenerative drug targets.Materials and methodsThe protein and gene list involved in epithelial–mesenchymal transition were obtained from literature sources. The protein interaction was constructed using STRING software and the protein interaction network was analyzed. Molecular docking simulation checks the binding energy and stability of protein-ligand complex.ResultsResults revealed the hub gene to be DYRK1A(Hepcidin), and the ligand was identified as isoetharine. SΤRIΝG results showed a confidence cutoff of 0.9 in sensitivity analysis with a condensed protein interaction network. Overall, 98 nodes from 163 nodes of expected edges were found with an average node degree of 11.9. Docking results show binding energy of ?4.70, and simulation results show an RMSD value of 5.6 Å at 50 ns.ConclusionIsoetharine could be a potential drug for periodontal regeneration.