Estimation of Energy Management Strategy Using Neural-Network-Based Surrogate Model for Range Extended Vehicle

In this paper, an energy-management strategy based on fuel economy is presented to achieve a further range increase for range-extended light commercial vehicles. Estimation of the energy-management strategy was carried out using a neural-network-based surrogate model for an range-extended vehicle. Surrogate-based optimization plays an important role in optimization problems, which are based on complex structures with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate-based models in cases of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power-unit applications and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural-network-based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated in different dynamic and static conditions to determine an energy-management strategy for a range-extended vehicle based on fuel economy under various conditions. It was seen that APU parameters and an energy-management strategy significantly affected the fuel consumption of REX. A neural-network-based surrogate-model approach gave high-precision results in predicting the operating strategy according to different loading conditions to reduce fuel consumption. In some cases, it can be required to determine the fuel consumption results in various conditions with the variables, which may be out-of-boundary conditions. It was seen that the proposed neural-network-model also offers higher prediction ability in cases of unexpected results in data sets of various conditions compared to regression analysis. The results show that estimation and optimization of energy management using a neural-network-based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption. This study presents an approach for future new vehicle projects by transforming a prototype light commercial electric vehicle to REX. The proposed approach was developed to design the most efficient range-extended vehicle by changing all variables without costly computations and time-consuming analysis. It is possible to generate variable data sets and to have reference knowledge for future vehicle projects

Distal Rektum Tümörü Nedeni ile Uzun Dönem Neoadjuvan Kemoradyoterapi Sonrası FOLFOX Tedavisi ile Ameliyatsız Takip Edilen Hastaların Erken Dönem Sonuçları

Amaç: Lokal ileri distal rektum tümörü (LİDRT) hastalarında konsolidasyon neoadjuvan kemoterapi (KNKT) sonrası klinik tam yanıt elde edilen hastalarda ameliyatsız takip (non-operative management (NOM)) stratejisi tercih edilen hastaların klinik tam yanıt, lokal nüks ve uzak metastaz açısından erken dönem sonuçlarını araştırmaktır. Gereç-Yöntem: Bu prospektif faz II kohort çalışmasında LİDRT hastalarında total mezorektal eksizyona (TME) uygun evre II veya III LİDRT hastaları, uzun dönem neoadjuvant kemoradyoterapi (nKRT) sonrası elde edilen yanıta bağlı altı kür FOLFOX (KNKT) tedavisine alındı; nKRT veya KNKT sonrası tedaviye yanıt vermeyen hastalara TME uygulandı. NOM hastaları ilk iki yılda üç ayda bir ve daha sonra altı ayda bir takip edildi. Bulgular: Eylül 2016 ve Kasım 2018 arasında, nKRT sonrası TEM ya da NOM stratejisine yönlendirilen 53 hasta belirlendi. 28 hastaya (% 52,8) nKRT sonrası TME uygulandı ve belirgin klinik yanıt elde edilen 25 hastaya (% 47,2) KNKT uygulandı. KNKT sonrası klinik tam yanıt elde edildi. 18 (%72) hastaya NOM uygulandı ve klinik tam yanıt elde edilemeyen 4 hastaya TME önerildi. Üç hastanın tedavisi devam etmektedir. Ortalama takip süresi 21,4 ay ve tümörün dentat çizgisi ile mesafesi 4.0 (0,3-6,0) cm olarak saptandı. NOM uygulanan dört (% 22,2) hastada lokal yeniden tümör büyümesi, rutin takip sürecinde tespit edildi ve kurtarma cerrahisi (TME) uygulandı. Tüm hastalarda kurtarma cerrahi sonrası pelvik kontrol sağlandı. Yeniden lokal tümör büyümelerin %75’i birinci yılda ve tümü rektum duvarında saptandı. Yeniden lokal tümör büyüme saptanan dört hastanın birinde (%5,55) ayrıca sistemik metastaz saptandı. Genel sağkalım %94,4 ve hastalıksız sağkalım NOM grubunda %77,7 olarak saptandı. Sonuç: Seçilmiş LİDRT hastalarında NOM stratejisi ile hem sfinkterin korunabildiği hem de pelvik tümör kontrolünün iyi bir şekilde sağlandığı ortaya konuldu. Fakat, en sık ilk iki yılda ve en sık da bağırsak duvarında saptadığımız lokal yeniden tümör büyüme riski nedeniyle hastaların yakın takibi kurtarma cerrahisi şansını kaçırmamaları için dikkatle yapılmalıdı

Estimation of Energy Management Strategy Using Neural-Network-Based Surrogate Model for Range Extended Vehicle

In this paper, an energy-management strategy based on fuel economy is presented to achieve a further range increase for range-extended light commercial vehicles. Estimation of the energy-management strategy was carried out using a neural-network-based surrogate model for an range-extended vehicle. Surrogate-based optimization plays an important role in optimization problems, which are based on complex structures with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate-based models in cases of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power-unit applications and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural-network-based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated in different dynamic and static conditions to determine an energy-management strategy for a range-extended vehicle based on fuel economy under various conditions. It was seen that APU parameters and an energy-management strategy significantly affected the fuel consumption of REX. A neural-network-based surrogate-model approach gave high-precision results in predicting the operating strategy according to different loading conditions to reduce fuel consumption. In some cases, it can be required to determine the fuel consumption results in various conditions with the variables, which may be out-of-boundary conditions. It was seen that the proposed neural-network-model also offers higher prediction ability in cases of unexpected results in data sets of various conditions compared to regression analysis. The results show that estimation and optimization of energy management using a neural-network-based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption. This study presents an approach for future new vehicle projects by transforming a prototype light commercial electric vehicle to REX. The proposed approach was developed to design the most efficient range-extended vehicle by changing all variables without costly computations and time-consuming analysis. It is possible to generate variable data sets and to have reference knowledge for future vehicle projects

The impact of various suture materials on experimental colorectal carcinogenesis

Local tumor recurrence following restorative surgery for colorectal cancer may occasionally result from the promotion of a neoplastic lesion in a zone of proliferative instability adjacent to the anastomosis. This study was designed to determine the influence of various suture materials on experimental colorectal carcinogenesis. A total of 72 rats were divided into six groups, four of which were subjected to colotomy and repair using catgut, silk, polyglactin (PG), or stainless steel. The fifth group was given a sham procedure and the sixth group served as a control. Methylnitrosourea was administered rectally to all the animals, at a dose of 4 mg/kg/week for 20 weeks. The mean number of tumors per rat was significantly higher in the PG group than in the other groups. The mean tumor size was found to be significantly larger in each of the suture material groups than in the sham group. A tendency for tumor occurrence to develop at the anastomosis rather than at the other colon sites was seen in the PG group. These results indicate that PG has an adverse effect on local tumor occurrence in experimental colorectal carcinogenesis

RADICAL SURGERY IN EARLY STAGE GASTRIC CANCER: SINGLE CENTER EXPERIENCE

Objective: This study was performed to determine the overall early stage gastric cancer (ESGC) prevalence and to evaluate the short and long-term postoperative outcomes of patients with ESGC who underwent radical surgery