In a world where supply chains are increasingly complex and unpredictable,finding the optimal way to move goods through transshipment networks is more important and challenging than ever.In addition to addressing the ...In a world where supply chains are increasingly complex and unpredictable,finding the optimal way to move goods through transshipment networks is more important and challenging than ever.In addition to addressing the complexity of transportation costs and demand,this study presents a novel method that offers flexible routing alternatives to manage these complexities.When real-world variables such as fluctuating costs,variable capacity,and unpredictable demand are considered,traditional transshipment models often prove inadequate.To overcome these challenges,we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers.This framework allows for more adaptable and flexible decision-making in multi-objective transshipment situations by effectively capturing uncertain parameters.To overcome these challenges,we develop an innovative,fully fuzzy-based framework using LR flat fuzzy numbers to effectively capture uncertainty in key parameters,offering more flexible and adaptive decision-making in multi-objective transshipment problems.The proposed model also presents alternative route options,giving decisionmakers a range of choices to satisfy multiple requirements,including reducing costs,improving service quality,and expediting delivery.Through extensive numerical experiments,we demonstrate that the model can achieve greater adaptability,efficiency,and flexibility than standard approaches.This multi-path structure provides additional flexibility to adapt to dynamic network conditions.Using ranking strategies,we compared our multi-objective transshipment model with existing methods.The results indicate that,while traditional methods such as goal and fuzzy programming generate results close to the anti-ideal value,thus reducing their efficiency,our model produces solutions close to the ideal value,thereby facilitating better decision making.By combining dynamic routing alternatives with a fully fuzzybased approach,this study offers an effective tool to improve decision-making and optimize complex networks under real-world conditions in practical settings.In this paper,we utilize LINGO 18 software to solve the provided numerical example,demonstrating the effectiveness of the proposed method.展开更多
目的探讨长链非编码RNA(lncRNA)-N1LR在脑缺血再灌注损伤后血脑屏障的作用机制。方法原代小鼠脑微血管内皮细胞常规培养,经氧糖剥夺/复糖复氧(OGD/R)处理模拟脑缺血再灌注损伤,实验分对照组、OGD组、lncRNA-N1LR过表达组(OGD处理后转染...目的探讨长链非编码RNA(lncRNA)-N1LR在脑缺血再灌注损伤后血脑屏障的作用机制。方法原代小鼠脑微血管内皮细胞常规培养,经氧糖剥夺/复糖复氧(OGD/R)处理模拟脑缺血再灌注损伤,实验分对照组、OGD组、lncRNA-N1LR过表达组(OGD处理后转染质粒lncRNA-N1LR过表达)、lncRNA-N1LR沉默组(OGD处理后转染质粒lncRNA-N1LR沉默)。采用逆转录聚合酶链反应检测各组中lncRNA-N1LR mRNA、紧密连接蛋白5(claudin-5)及闭合蛋白(occludin)mRNA表达水平;异硫氰酸荧光素-葡聚糖(FITC-dextran)渗透法检测血脑屏障通透性;免疫蛋白印迹法检测claudin-5、occludin蛋白表达。结果与对照组比较,OGD组lncRNA-N1LR mRNA、occludin、claudin-5 mRNA表达水平下降(0.31±0.01 vs 1.00±0.10,0.42±0.03 vs 1.01±0.13,0.38±0.03 vs 1.00±0.15,P<0.05),血脑屏障FITC-dextran通透性明显升高(58.79±3.04 vs 8.87±0.63,P<0.01)。与OGD组比较,lncRNA-N1LR过表达组lncRNA-N1LR mRNA、occludin、claudin-5 mRNA表达水平升高(0.67±0.07 vs 0.31±0.01,0.92±0.02 vs 0.42±0.03,0.70±0.08 vs 0.38±0.03,P<0.05),血脑屏障FITC-dextran通透性降低(41.57±2.43 vs 58.79±3.04,P<0.05)。与OGD组比较,lncRNA-N1LR沉默组lncRNA-N1LR mRNA、occludin、claudin-5 mRNA表达水平降低(0.21±0.02 vs 0.31±0.01,0.31±0.03 vs 0.42±0.03,0.22±0.02 vs 0.38±0.03,P<0.05),血脑屏障FITC-dextran通透性升高(72.34±1.43 vs 58.79±3.04,P<0.05)。结论LncRNA-N1LR上调可能通过降低血脑屏障通透性发挥神经保护作用。展开更多
基金the financial support of the European Union under the REFRESH-Research Excellence for Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition and has been done in connection with project Students Grant Competition SP2025/062"specific research on progressive and sustainable production technologies"and SP2025/063"specific research on innovative and progressive manufacturing technologies"financed by the Ministry of Education,Youth and Sports and Faculty of Mechanical Engineering VSB-TUOThe authors would like to extend their sincere appreciation to Researchers Supporting Project number(RSP2025R472)King Saud University,Riyadh,Saudi Arabia.
文摘In a world where supply chains are increasingly complex and unpredictable,finding the optimal way to move goods through transshipment networks is more important and challenging than ever.In addition to addressing the complexity of transportation costs and demand,this study presents a novel method that offers flexible routing alternatives to manage these complexities.When real-world variables such as fluctuating costs,variable capacity,and unpredictable demand are considered,traditional transshipment models often prove inadequate.To overcome these challenges,we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers.This framework allows for more adaptable and flexible decision-making in multi-objective transshipment situations by effectively capturing uncertain parameters.To overcome these challenges,we develop an innovative,fully fuzzy-based framework using LR flat fuzzy numbers to effectively capture uncertainty in key parameters,offering more flexible and adaptive decision-making in multi-objective transshipment problems.The proposed model also presents alternative route options,giving decisionmakers a range of choices to satisfy multiple requirements,including reducing costs,improving service quality,and expediting delivery.Through extensive numerical experiments,we demonstrate that the model can achieve greater adaptability,efficiency,and flexibility than standard approaches.This multi-path structure provides additional flexibility to adapt to dynamic network conditions.Using ranking strategies,we compared our multi-objective transshipment model with existing methods.The results indicate that,while traditional methods such as goal and fuzzy programming generate results close to the anti-ideal value,thus reducing their efficiency,our model produces solutions close to the ideal value,thereby facilitating better decision making.By combining dynamic routing alternatives with a fully fuzzybased approach,this study offers an effective tool to improve decision-making and optimize complex networks under real-world conditions in practical settings.In this paper,we utilize LINGO 18 software to solve the provided numerical example,demonstrating the effectiveness of the proposed method.
文摘目的探讨长链非编码RNA(lncRNA)-N1LR在脑缺血再灌注损伤后血脑屏障的作用机制。方法原代小鼠脑微血管内皮细胞常规培养,经氧糖剥夺/复糖复氧(OGD/R)处理模拟脑缺血再灌注损伤,实验分对照组、OGD组、lncRNA-N1LR过表达组(OGD处理后转染质粒lncRNA-N1LR过表达)、lncRNA-N1LR沉默组(OGD处理后转染质粒lncRNA-N1LR沉默)。采用逆转录聚合酶链反应检测各组中lncRNA-N1LR mRNA、紧密连接蛋白5(claudin-5)及闭合蛋白(occludin)mRNA表达水平;异硫氰酸荧光素-葡聚糖(FITC-dextran)渗透法检测血脑屏障通透性;免疫蛋白印迹法检测claudin-5、occludin蛋白表达。结果与对照组比较,OGD组lncRNA-N1LR mRNA、occludin、claudin-5 mRNA表达水平下降(0.31±0.01 vs 1.00±0.10,0.42±0.03 vs 1.01±0.13,0.38±0.03 vs 1.00±0.15,P<0.05),血脑屏障FITC-dextran通透性明显升高(58.79±3.04 vs 8.87±0.63,P<0.01)。与OGD组比较,lncRNA-N1LR过表达组lncRNA-N1LR mRNA、occludin、claudin-5 mRNA表达水平升高(0.67±0.07 vs 0.31±0.01,0.92±0.02 vs 0.42±0.03,0.70±0.08 vs 0.38±0.03,P<0.05),血脑屏障FITC-dextran通透性降低(41.57±2.43 vs 58.79±3.04,P<0.05)。与OGD组比较,lncRNA-N1LR沉默组lncRNA-N1LR mRNA、occludin、claudin-5 mRNA表达水平降低(0.21±0.02 vs 0.31±0.01,0.31±0.03 vs 0.42±0.03,0.22±0.02 vs 0.38±0.03,P<0.05),血脑屏障FITC-dextran通透性升高(72.34±1.43 vs 58.79±3.04,P<0.05)。结论LncRNA-N1LR上调可能通过降低血脑屏障通透性发挥神经保护作用。
