
It is widely agreed that properly establishing a sustainable supply chain strategy to yield competitive advantages is essential for business enterprises, and a number of research papers on sustainable supply chains have been produced over the last two decades. However, many past studies on sustainable supply chain strategies emphasized either classification schemes or various coordination mechanisms, and few of them have focused on an integrated framework for sustainable supply chains. Therefore, the objective of this study is to develop a strategic framework for the sustainable supply chain management. The study is based on the abductive reasoning process through literature review to establish a strategic framework which is ranked through grey relational analysis (GRA). The weighted data of various strategies collected from the elite interview prove to be comprehensive and evaluable, so it can create values for supply chain members in practice. The results further suggest that each sustainable supply chain in different fields can select the best combination of strategies through GRA to constantly facilitate performance of sustainability. The main contribution is the submission of a strategic framework which makes up the insufficiency of past research papers lacking an integrated strategic framework. At the same time, the proposed strategic framework has also been illustrated through a case study.
Citation: Hsin-Yao Hsu, Ming-Hon Hwang, Yuan-Shyi Peter Chiu. Development of a strategic framework for sustainable supply chain management[J]. AIMS Environmental Science, 2021, 8(6): 532-552. doi: 10.3934/environsci.2021034
[1] | Chiara De Santi, Sucharitha Gadi, Agnieszka Swiatecka-Urban, Catherine M. Greene . Identification of a novel functional miR-143-5p recognition element in the Cystic Fibrosis Transmembrane Conductance Regulator 3’UTR. AIMS Genetics, 2018, 5(1): 53-62. doi: 10.3934/genet.2018.1.53 |
[2] | Mohammad Hashemi, Fatemeh Bizhani, Hiva Danesh, Behzad Narouie, Mehdi Sotoudeh, Mohammad Hadi Radfar, Mehdi Honarkar Ramezani, Gholamreza Bahari, Mohsen Taheri, Saeid Ghavami . MiR-608 rs4919510 C>G polymorphism increased the risk of bladder cancer in an Iranian population. AIMS Genetics, 2016, 3(4): 212-218. doi: 10.3934/genet.2016.4.212 |
[3] | Tahereh Karamzadeh, Hamzeh Alipour, Marziae Shahriari-Namadi, Abbasali Raz, Kourosh Azizi, Masoumeh Bagheri, Mohammad D. Moemenbellah-Fard . Molecular characterization of the netrin-1 UNC-5 receptor in Lucilia sericata larvae. AIMS Genetics, 2019, 6(3): 46-54. doi: 10.3934/genet.2019.3.46 |
[4] | Huong Thi Thu Phung, Hoa Luong Hieu Nguyen, Dung Hoang Nguyen . The possible function of Flp1 in homologous recombination repair in Saccharomyces cerevisiae. AIMS Genetics, 2018, 5(2): 161-176. doi: 10.3934/genet.2018.2.161 |
[5] | Michael T. Fasullo, Mingzeng Sun . Both RAD5-dependent and independent pathways are involved in DNA damage-associated sister chromatid exchange in budding yeast. AIMS Genetics, 2017, 4(2): 84-102. doi: 10.3934/genet.2017.2.84 |
[6] | Jeffrey M. Marcus . Our love-hate relationship with DNA barcodes, the Y2K problem, and the search for next generation barcodes. AIMS Genetics, 2018, 5(1): 1-23. doi: 10.3934/genet.2018.1.1 |
[7] | Noel Pabalan, Neetu Singh, Eloisa Singian, Caio Parente Barbosa, Bianca Bianco, Hamdi Jarjanazi . Associations of CYP1A1 gene polymorphisms and risk of breast cancer in Indian women: a meta-analysis. AIMS Genetics, 2015, 2(4): 250-262. doi: 10.3934/genet.2015.4.250 |
[8] | Xiaojuan Wang, Jianghong Wu, Zhongren Yang, Fenglan Zhang, Hailian Sun, Xiao Qiu, Fengyan Yi, Ding Yang, Fengling Shi . Physiological responses and transcriptome analysis of the Kochia prostrata (L.) Schrad. to seedling drought stress. AIMS Genetics, 2019, 6(2): 17-35. doi: 10.3934/genet.2019.2.17 |
[9] | Jue Er Amanda Lee, Linda May Parsons, Leonie M. Quinn . MYC function and regulation in flies: how Drosophila has enlightened MYC cancer biology. AIMS Genetics, 2014, 1(1): 81-98. doi: 10.3934/genet.2014.1.81 |
[10] | John E. La Marca, Wayne Gregory Somers . The Drosophila gonads: models for stem cell proliferation, self-renewal, and differentiation. AIMS Genetics, 2014, 1(1): 55-80. doi: 10.3934/genet.2014.1.55 |
It is widely agreed that properly establishing a sustainable supply chain strategy to yield competitive advantages is essential for business enterprises, and a number of research papers on sustainable supply chains have been produced over the last two decades. However, many past studies on sustainable supply chain strategies emphasized either classification schemes or various coordination mechanisms, and few of them have focused on an integrated framework for sustainable supply chains. Therefore, the objective of this study is to develop a strategic framework for the sustainable supply chain management. The study is based on the abductive reasoning process through literature review to establish a strategic framework which is ranked through grey relational analysis (GRA). The weighted data of various strategies collected from the elite interview prove to be comprehensive and evaluable, so it can create values for supply chain members in practice. The results further suggest that each sustainable supply chain in different fields can select the best combination of strategies through GRA to constantly facilitate performance of sustainability. The main contribution is the submission of a strategic framework which makes up the insufficiency of past research papers lacking an integrated strategic framework. At the same time, the proposed strategic framework has also been illustrated through a case study.
The head groups of membrane lipids have either single charge (e.g. tetraether lipids [1], phosphatidic acid (PA), phosphatidylserine (PS), phosphatidylethanolamine (PE), and phosphatidylinositol (PI)) or electric dipole (e.g. phospholipids, such as dimyristoyl-, dipalmitoyl- and distearoylphosphatidyl choline (DMPC, DPPC and DSPC, respectively)).
Between lipids containing head groups with electric dipole there is short range interaction, i.e. where the two-body potential decays algebraically at large distances with a power equal or larger than the spatial dimension [2]. Theoretical models of lipid membranes usually focus on systems where there is short range lateral interactions between nearest neighbor lipids [3],[4] because it is enough to consider only the interactions between nearest-neighbor lipid molecules. It is much more difficult to model a lipid membrane containing single charged head groups [5]. Between lipids with single charged head groups there is long range interaction, i.e. where the two-body potential decays algebraically at large distances with a power smaller than the spatial dimension [2] and thus modeling this system one has to consider the entire system rather than the interactions between the nearest-neighbor lipids. In order to get closer to the solution of this problem recently we developed a generalized version of Newton's Shell Theorem [6],[7] to calculate the electric potential, V around a surface-charged sphere (of radius R1) surrounded by electrolyte at a distance Z from the center of the sphere (see also Eqs 9,10 in ref.7):
where
Using the Screened Poisson Equation (Eq A4) one can calculate the potential energy of an electrolyte that contains also external charges. The external charges are embedded into the electrolyte (like the charges of the surface-charged sphere) but not part of the electrolyte itself. For the solution one has to know the charge density of the external charges (see Eq 4 in ref.7 or Eq A5 in Appendix 1), i.e. distribution of the charges on the surface-charged sphere and not the distribution of the ions in the electrolyte. In our case it is assumed that the charges on the surface of the sphere are homogeneously distributed and in this case Eqs 1,2 is the exact solution of the Screened Poisson Equation.
Note that recently by using Eqs 1,2 electric energies have been calculated [10], such as the electric potential energy needed to build up a surface-charged sphere, and the field and polarization energy of the electrolyte inside and around the surface-charged sphere.
In this paper the density of electric field energy is calculated around two surface-charged spheres where the smaller sphere is located inside the larger one and the entire system is embedded in neutral electrolyte. This system is close to a charged vesicle [1] or to a cell [11] where charged lipids are located both on the outer and inner leaflet of the membrane, i.e. two concentric surface-charged spheres. It also models an eukaryote [12] where neutral phospholipids such as sphingomyelin and zwitterionic phosphatidylcholine are located primarily in the outer leaflet of the plasma membrane, and most anionic phospholipids, such as phosphatidic acid (PA), phosphatidylserine (PS), phosphatidylethanolamine (PE), and phosphatidylinositol (PI) are located in the inner leaflet of the plasma membrane (represented by the large surface-charged sphere of our model). Eukaryotes also have a single nucleus enveloped by double layer of lipid membranes which may contain charged lipids too (representing the smaller surface-charged sphere of our model). Note that these two charged spheres of an eukaryote are not necessarily concentric. Finally, our model is generalized for the case when the large surface-charged sphere contains several smaller surface-charged spheres. This system may also model osteoclast cells [12] containing many nuclei.
In this work the density of the electric field energy inside and outside of two surface-charged spheres are calculated at different locations. The density of the electric field energy at a point can be calculated by the following equation [13]:
where E is the vector of the electric field strength at the considered point, ϵ0 is the absolute vacuum permittivity and ϵr is the relative permittivity of the electrolyte.
Here by using the recently generalized Shell Theorem [7] we calculate the density of electric field energy, uF produced by two surface-charged spheres (see Figure 1) surrounded outside and inside by electrolyte where the smaller sphere is located inside the larger sphere.
Z: the distance between the centers of the spheres (dashed blue line); R1 and RL is the radius of the smaller and larger sphere, respectively; D1 and DL is the distance between point P1 and the center of the smaller and larger sphere, respectively; E1 and EL is the field strength created in point P1 by the smaller and larger surface-charged sphere, respectively.
The origin of the coordinate system (x, y) is attached to the center of the larger sphere and the coordinates of point P1 are xp and yp. The coordinates of the center of the larger and smaller sphere are
In order to calculate the density of electric field energy one has to determine the electric field strength (see Eq 3), i.e. the gradient of the electric potential. The potential produced by the smaller sphere, V1 at a distance D1 from its center can be calculated by Eqs 1,2 (or Eqs 9,10 in ref.[7]). The electric field strength created by the smaller sphere at point P1 (see Figure 1) is:
where
where λD is the Debye length and
Similarly, the electric field strength created by the large sphere at point P1 (i.e. at a distance DL from its center; see Figure 1) is:
where one can construct
Here by using
Inside a large surface-charged sphere of radius
Dotted red line:
The connection point between the large sphere and the small sphere (represented by orange circle in
and
where x1 is the x coordinate of the center of the small sphere.
Dotted red line:
Dotted red line:
Note in
It is also important to note that
In this work the solution of the screened Poisson equation ([7] and Eq A5 in Appendix 1) is used to calculate the field energy density around two surface-charged spheres where the small sphere is located inside the large sphere. This solution is not restricted to small potentials (<< 25 mV) like in the case of the Debye-Hückel approximation of the Poisson-Boltzmann equation [9] where the superposition principle is not applicable either. This is an important advantage because the measured absolute value of the Zeta potentials of the cells are usually higher than 25 mV (e.g. –57.89 ± 22.63 mV on ARO cells, –40.41 ± 5.10 mV on C32TG cells, −46.99 ± 18.71 mV on RT4 cells, –40.13 ± 9.28 mV on TK cells, and −43.03 ± 5.52 mV on UM-UC-14 cells [14].
The considered two spheres (with homogeneously charged surfaces) electrically interact. If the lateral movement of the charges on the spheres would not be restricted the interaction of the smaller sphere (located inside the larger sphere) with the larger sphere would result in inhomogeneous distribution of the surface charges on both spheres. However, the free lateral diffusion of proteins and lipids are usually restricted in biological membranes not only by direct collisions with structures where immobile proteins are crowded, but also by electrostatic deflection, hydrophobic mismatches, and other mechanisms [15].
The density of the electric field energy depends on the electric field strength (Eq 3), i.e. the gradient of the electric potential (Eqs 4,6). In the case of a single surface-charged sphere surrounded by electrolyte with low ion concentration the potential inside the sphere is close to constant (see red curve in Figure 3A in ref. [7]) and thus the absolute value of the electric field strength is close to zero. On the other hand, outside the sphere the absolute value of the potential and also the electric field strength decrease with increasing distance from the surface of the sphere (see red curve in Figure 3A in ref. [7]). At higher electrolyte ion concentration, because of the increased screening effect, the absolute value of the potential and also the electric field strength decrease faster with increasing distance from the surface of the sphere. In this case inside the sphere toward its center the absolute value of the potential and the electric field strength also decrease (see curves in Figure 3A,B in ref. [7]).
In this work two surface-charged spheres (with the same surface charge density) are considered where the smaller sphere is located inside the larger sphere. The above mentioned electric properties of a single surface-charged sphere remain the same for the smaller sphere (located inside a larger sphere) if the surfaces of the spheres are far enough from each other (farther than 4 λD), i.e. the absolute value of the potential decreases close to zero between the surfaces of the two spheres. However, when part of the surfaces of the two spheres are close enough to each other one sphere contributes to the potential and electric field strength around the other sphere. The electric field energy density is particularly high at the place where the surfaces of the two spheres touch each other. This maximal electric field energy density is very close to the outer surface of the larger sphere. Thus one can detect at the outer surface of the erythrocyte when the nucleus is getting close.
The electric field energy density has maximum when the horizontal line crosses the circles in
This maximal electric field energy density is very close to the outer surface of the larger sphere (
In general the first maximum of uF (see the left maximum in Figures 3–5) is getting smaller when the center of the small sphere approaches the center of the large sphere. This is the case because the interaction between the spheres is reducing when the average distance between the surfaces of the two spheres is increasing.
In the case of horizontal lines where
When the location of the center of the small and large sphere is identical (i.e.
When
In the case of
Cross # | xP [m] | uF [J] | E1x [V/m] | ELx [V/m] | E1y [V/m] | ELy [V/m] |
Z = 0.8 RL | ||||||
1 | −9.9·10−7 | 7.4·107 | 1.8·108 | 2.47·108 | −1.42·108 | −3.74·107 |
2 | −9.4·10−7 | 2.2·107 | 2.29·108 | −1.16·108 | −2.45·108 | 1.86·107 |
3 | −6.6·10−7 | 4.65·107 | −2.29·108 | −5.45·107 | −2.45·108 | 1.24·107 |
4 | 9.9·10−7 | 2.15·107 | −9.7·104 | −2.47·108 | −8.2·103 | −3.74·107 |
Z = 0.5 RL | ||||||
1 | −9.9·10−7 | 2.73·107 | 3·107 | 2.47·108 | −9.22·106 | −3.74·107 |
2 | −6.4·10−7 | 2.97·107 | 2.29·108 | −5.16·107 | −2.45·108 | 1.2·107 |
3 | −3.6·10−7 | 4.1·107 | −2.29·108 | −2.2·107 | −2.45·108 | 9.16·106 |
4 | 9.9·10−7 | 2.15·107 | −3.2·105 | −2.47·108 | −3.22·104 | −3.74·107 |
Z = 0.0 RL | ||||||
1 | −9.9·10−7 | 2.2·107 | 2.6·106 | 2.47·108 | −3.97·105 | −3.74·107 |
2 | −1.4·10−7 | 3.63·107 | 2.29·108 | −7.6·106 | −2.45·108 | 8.17·106 |
3 | 1.4·10−7 | 3.63·107 | −2.29·108 | 7.6·106 | −2.45·108 | 8.17·106 |
4 | 9.9·10−7 | 2.2·107 | −2.6·106 | −2.47·108 | −3.97·105 | −3.74·107 |
For example in the case of
As an other example in the case of
Finally, the analytical equation, Eq 7, for the calculation of the electric field energy density of two surface-charged spheres (the smaller sphere located inside the larger sphere), can be generalized for the case when N small surface-charged spheres are located inside the large sphere (see Appendix 2). Also when the radius of the smaller sphere approaches zero the total surface charge of the smaller sphere, Q1 approaches zero too and consequently the electric field strength of the smaller sphere, E1 approaches zero. Thus, based on Eq 7 one can calculate the field energy density around a single charged sphere by:
Based on the generalized version of Newton's Shell Theorem [7] the electric field energy density, uF around two surface-charged spheres surrounded by electrolyte where the smaller sphere is inside the larger one is analytically calculated. According to the calculations when the surfaces of the spheres are farther from each other than four times of the Debye length the field energy density around and inside the smaller sphere is basically independent from the presence of the larger sphere. The electric field energy density is maximal when the smaller sphere touches the inner surface of the larger sphere and the maximum of uF is located at the touching point on the outer surface of the larger sphere.
[1] | Cetinkaya B (2010). Developing a sustainable supply chain strategy. In: Sustainable supply chain management. Springer, Berlin, Heidelberg, 17-55 |
[2] |
Stoughton AM, Ludema J (2012). The driving forces of sustainability. J Organ Change Manag 25: 501-517. doi: 10.1108/09534811211239191
![]() |
[3] | Zheng XX, Li DF, Liu Z, et al. (2021). Willingness-to-cede behaviour in sustainable supply chain coordination. Int J Prod Econ 108207. |
[4] |
Wang J, Dai J (2018). Sustainable supply chain management practices and performance. Ind Manage Data Syst 118: 2-21. doi: 10.1108/IMDS-12-2016-0540
![]() |
[5] |
Brandenburg M, Gruchmann T, Oelze N (2019). Sustainable supply chain management—A conceptual framework and future research perspectives. Sustainability 11: 7239. doi: 10.3390/su11247239
![]() |
[6] | Heizer J, Render B, Munson C (2017). Operations management: sustainability and supply chain management 12th Edition (232-248). Pearson Education. |
[7] | Koberg E, Longoni A (2019). A systematic review of sustainable supply chain management in global supply chains. J Clean Prod 207: 1084-1098. |
[8] | Beske-Janssen P, Johnson MP, Schaltegger S (2015). 20 years of performance measurement in sustainable supply chain management-what has been achieved? Supply Chain Manag 20: 664-680. |
[9] |
Hassini E, Surti C, Searcy C (2012). A literature review and a case study of sustainable supply chains with a focus on metrics. Int J Prod Econ 140: 69-82. doi: 10.1016/j.ijpe.2012.01.042
![]() |
[10] | Hwang MH, Huang YF (2011). Development of an approach to coordinate the objectives for members of a supply chain. Afr J Bus Manage 5: 1001-1013. |
[11] |
Hwang MH (2010). Establishment of a comprehensive framework for strategic supply chain management. Hum Syst Manage 29: 127-137. doi: 10.3233/HSM-2010-0721
![]() |
[12] |
Kanda A, Deshmukh SG (2008). Supply chain coordination: perspectives, empirical studies and research directions. Int J Prod Econ 115: 316-335. doi: 10.1016/j.ijpe.2008.05.011
![]() |
[13] |
Sauer PC, Seuring S (2017). Sustainable supply chain management for minerals. J Clean Prod 151: 235-249. doi: 10.1016/j.jclepro.2017.03.049
![]() |
[14] |
Govindan K, Rajendran S, Sarkis J, et al. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. J Clean Prod 98: 66-83. doi: 10.1016/j.jclepro.2013.06.046
![]() |
[15] |
Govindan K, Popiuc MN, Diabat A (2013). Overview of coordination contracts within forward and reverse supply chains. J Clean Prod 47: 319-334. doi: 10.1016/j.jclepro.2013.02.001
![]() |
[16] |
Purvis L, Gosling J, Naim MM (2014). The development of a lean, agile and leagile supply network taxonomy based on differing types of flexibility. Int J Prod Econ 151: 100-111. doi: 10.1016/j.ijpe.2014.02.002
![]() |
[17] | Folger R, Stein C (2017). Abduction 101: Reasoning processes to aid discovery. Hum Syst Manage Rev 27: 306-315. |
[18] |
Carter CR, Rogers DS (2008). A framework of sustainable supply chain management: moving toward new theory. Int J Phys Distrib Log Manage 38: 360-387. doi: 10.1108/09600030810882816
![]() |
[19] |
Dong A, Lovallo D, Mounarath R (2015). The effect of abductive reasoning on concept selection decisions. Des Stud 37: 37-58. doi: 10.1016/j.destud.2014.12.004
![]() |
[20] |
Velázquez-Quesada FR, Soler-Toscano F, Nepomuceno-Fernández Á (2013). An epistemic and dynamic approach to abductive reasoning: Abductive problem and abductive solution. J Appl Log 11: 505-522. doi: 10.1016/j.jal.2013.07.002
![]() |
[21] |
Prajapati H, Kant R, Shankar R (2019). Bequeath life to death: State-of-art review on reverse logistics. J Clean Prod 211: 503-520. doi: 10.1016/j.jclepro.2018.11.187
![]() |
[22] |
Alfaro-Saiz JJ, Bas MC, Giner-Bosch V, et al. (2020). An evaluation of the environmental factors for supply chain strategy decisions using grey systems and composite indicators. Applied Mathematical Modelling 79: 490-505. doi: 10.1016/j.apm.2019.10.048
![]() |
[23] | Chang S, Huang F, Zhao JJ, et al. (2018). Identifying Influential Climate Factors of Land Surface Phenology Changes in Songnen Plain of China Using Grid-based Grey Relational Analysis. J Grey Syst 30: 18-33. |
[24] |
Zakeri S, Yang Y, Hashemi M (2018). Grey strategies interaction model. J Strat Manage 12: 30-60. doi: 10.1108/JSMA-06-2018-0055
![]() |
[25] | Yu W (2019). Research and Evaluation of Chinese Internet Enterprise Supply Chain Strategy. Int Core J Engin 5: 227-233. |
[26] | You ML, Wang CW, Yeh CK. (2006). The development of completed grey relational analysis toolbox via Matlab. J Grey Syst 9: 57-64. |
[27] | Lee PTW, Lin CW, Shin SH (2012). A comparative study on financial positions of shipping companies in Taiwan and Korea using entropy and grey relation analysis. Expert Syst Appl 39: 5649-5657. |
[28] | Schneider P, Schmitt A, Liebe S, et al. (2016). AFTERCARE OPTIMISATION TOOLS FOR CLOSED LANDFILL SITES-A CASE STUDY FROM GO CAT LANDFILL IN HO CHI MINH CITY, VIETNAM, 1281-1297 |
[29] | Schniederjans MJ, Schniederjans DG, Cao R Q, et al. (2018) Topics in lean supply chain management. World Scientific. |
[30] |
Chong AYL, Ooi KB (2008). Collaborative commerce in supply chain management: A study of adoption status in Malaysian electrical and electronic industry. J Appl Sci 8: 3836-3844. doi: 10.3923/jas.2008.3836.3844
![]() |
[31] |
Allaoui H, Guo Y, Sarkis J (2019). Decision support for collaboration planning in sustainable supply chains. J Clean Prod 229: 761-774. doi: 10.1016/j.jclepro.2019.04.367
![]() |
[32] |
Kausar K, Garg D, Luthra S (2017). Key enablers to implement sustainable supply chain management practices: An Indian insight. Uncertain Supply Chain Managt 5: 89-104. doi: 10.5267/j.uscm.2016.10.005
![]() |
[33] | Accorsi R, Manzini R (2019). Sustainable Food Supply Chains: Planning, Design, and Control Through Interdisciplinary Methodologies. Academic Press. |
[34] | Ross DF (2015). Distribution Planning and control: managing in the era of supply chain management. Springer. |
[35] | Barney JB (2014). Gaining and sustaining competitive advantage 4th. Pearson new international edition. |
[36] | Taylor DA (2010). Supply chains: A manager's guide. Addison Wesley Professional. |
[37] |
Bai C, Sarkis J (2014). Determining and applying sustainable supplier key performance indicators. Supply Chain Manag 19: 275-291. doi: 10.1108/SCM-12-2013-0441
![]() |
[38] | Chopra S, Meindl P (2016), Supply Chain Management: strategy, planning, and operation 6th. Person Education, Chapter 3: 56-59. |
[39] |
Kull AJ, Mena JA, Korschun D (2016). A resource-based view of stakeholder marketing. J Bus Res 69: 5553-5560. doi: 10.1016/j.jbusres.2016.03.063
![]() |
[40] | Strategy execution software (2021). "VRIO Analysis Framework for Strategic Planning" URL: https: //bscdesigner.com/vrio-analysis.htm |
[41] | Liu S, Forrest J, Yang Y (2013, November). A summary of the progress in grey system research. In Proceedings of 2013 IEEE international conference on grey systems and intelligent services (GSIS) (1-10). |
[42] | Pagell M, Shevchenko A (2014). Why research in sustainable supply chain management should have no future. J Supply Chain Manag 50: 44-55. |
[43] |
Hald KS, Mouritsen J (2018). The evolution of performance measurement systems in a supply chain: A longitudinal case study on the role of inter-organizational factors. Int J Prod Econ 205: 256-271, doi: 10.1016/j.ijpe.2018.09.021
![]() |
[44] | ANDLIFE Inc. http://www.andlife.com.tw/, Taiwan |
[45] | Kizone Information Inc. http://www.kizone.com/, Taiwan |
[46] | The Global Logistic & Commerce Council of Taiwan, http://www.glct.org.tw/, Taiwan |
[47] | SOLE- The International Society of Logistics Taiwan (Taipei) Chapter, http://www.sole.org.tw/, Taiwan |
![]() |
![]() |
1. | Abdelali Hannoufa, Craig Matthews, Biruk A. Feyissa, Margaret Y. Gruber, Muhammad Arshad, 2018, Chapter 25, 978-3-030-36326-0, 41, 10.1007/124_2018_25 | |
2. | Sagar Prasad Nayak, Priti Prasad, Vinayak Singh, Abhinandan Mani Tripathi, Sumit Kumar Bag, Chandra Sekhar Mohanty, Role of miRNAs in the regulation of proanthocyanidin biosynthesis in the legume Psophocarpus tetragonolobus (L.) DC., 2023, 0167-6903, 10.1007/s10725-023-00971-9 | |
3. | Habibullah Khan Achakzai, Muhammad Younas Khan Barozai, Muhammad Din, Iftekhar Ahmed Baloch, Abdul Kabir Khan Achakzai, Allah Bakhsh, Identification and annotation of newly conserved microRNAs and their targets in wheat (Triticum aestivum L.), 2018, 13, 1932-6203, e0200033, 10.1371/journal.pone.0200033 | |
4. | Sevgi Marakli, Identification and functional analyses of new sesame miRNAs (Sesamum indicum L.) and their targets, 2018, 45, 0301-4851, 2145, 10.1007/s11033-018-4373-7 | |
5. | Mohandas Snigdha, Duraisamy Prasath, Transcriptomic analysis to reveal the differentially expressed miRNA targets and their miRNAs in response to Ralstonia solanacearum in ginger species, 2021, 21, 1471-2229, 10.1186/s12870-021-03108-0 | |
6. | Lan Li, Guangling Chen, Mingzhu Yuan, Shirong Guo, Yu Wang, Jin Sun, CsbZIP2-miR9748-CsNPF4.4 Module Mediates High Temperature Tolerance of Cucumber Through Jasmonic Acid Pathway, 2022, 13, 1664-462X, 10.3389/fpls.2022.883876 | |
7. | Thiago F. Martins, Pedro F. N. Souza, Murilo S. Alves, Fredy Davi A. Silva, Mariana R. Arantes, Ilka M. Vasconcelos, Jose T. A. Oliveira, Identification, characterization, and expression analysis of cowpea (Vigna unguiculata [L.] Walp.) miRNAs in response to cowpea severe mosaic virus (CPSMV) challenge, 2020, 39, 0721-7714, 1061, 10.1007/s00299-020-02548-6 | |
8. | Muhammad Younas Khan Barozai, Zhujia Ye, Sasikiran Reddy Sangireddy, Suping Zhou, Bioinformatics profiling and expressional studies of microRNAs in root, stem and leaf of the bioenergy plant switchgrass (Panicum virgatum L.) under drought stress, 2018, 8, 23522151, 1, 10.1016/j.aggene.2018.02.001 | |
9. | Yusuf Ceylan, Yasemin Celik Altunoglu, Erdoğan Horuz, HSF and Hsp Gene Families in sunflower: a comprehensive genome-wide determination survey and expression patterns under abiotic stress conditions, 2023, 0033-183X, 10.1007/s00709-023-01862-6 | |
10. | Abdul Baqi, Wajid Rehman, Iram Bibi, Farid Menaa, Yousaf Khan, Doha A. Albalawi, Abdul Sattar, Identification and Validation of Functional miRNAs and Their Main Targets in Sorghum bicolor, 2023, 1073-6085, 10.1007/s12033-023-00988-5 | |
11. | Caoli Zhu, Yicheng Yan, Yaning Feng, Jiawei Sun, Mingdao Mu, Zhiyuan Yang, Genome-Wide Analysis Reveals Key Genes and MicroRNAs Related to Pathogenic Mechanism in Wuchereria bancrofti, 2024, 13, 2076-0817, 1088, 10.3390/pathogens13121088 | |
12. | Kishan Saha, Onyinye C. Ihearahu, Vanessa E. J. Agbor, Teon Evans, Labode Hospice Stevenson Naitchede, Supriyo Ray, George Ude, In Silico Genome-Wide Profiling of Conserved miRNAs in AAA, AAB, and ABB Groups of Musa spp.: Unveiling MicroRNA-Mediated Drought Response, 2025, 26, 1422-0067, 6385, 10.3390/ijms26136385 |
Cross # | xP [m] | uF [J] | E1x [V/m] | ELx [V/m] | E1y [V/m] | ELy [V/m] |
Z = 0.8 RL | ||||||
1 | −9.9·10−7 | 7.4·107 | 1.8·108 | 2.47·108 | −1.42·108 | −3.74·107 |
2 | −9.4·10−7 | 2.2·107 | 2.29·108 | −1.16·108 | −2.45·108 | 1.86·107 |
3 | −6.6·10−7 | 4.65·107 | −2.29·108 | −5.45·107 | −2.45·108 | 1.24·107 |
4 | 9.9·10−7 | 2.15·107 | −9.7·104 | −2.47·108 | −8.2·103 | −3.74·107 |
Z = 0.5 RL | ||||||
1 | −9.9·10−7 | 2.73·107 | 3·107 | 2.47·108 | −9.22·106 | −3.74·107 |
2 | −6.4·10−7 | 2.97·107 | 2.29·108 | −5.16·107 | −2.45·108 | 1.2·107 |
3 | −3.6·10−7 | 4.1·107 | −2.29·108 | −2.2·107 | −2.45·108 | 9.16·106 |
4 | 9.9·10−7 | 2.15·107 | −3.2·105 | −2.47·108 | −3.22·104 | −3.74·107 |
Z = 0.0 RL | ||||||
1 | −9.9·10−7 | 2.2·107 | 2.6·106 | 2.47·108 | −3.97·105 | −3.74·107 |
2 | −1.4·10−7 | 3.63·107 | 2.29·108 | −7.6·106 | −2.45·108 | 8.17·106 |
3 | 1.4·10−7 | 3.63·107 | −2.29·108 | 7.6·106 | −2.45·108 | 8.17·106 |
4 | 9.9·10−7 | 2.2·107 | −2.6·106 | −2.47·108 | −3.97·105 | −3.74·107 |
Cross # | xP [m] | uF [J] | E1x [V/m] | ELx [V/m] | E1y [V/m] | ELy [V/m] |
Z = 0.8 RL | ||||||
1 | −9.9·10−7 | 7.4·107 | 1.8·108 | 2.47·108 | −1.42·108 | −3.74·107 |
2 | −9.4·10−7 | 2.2·107 | 2.29·108 | −1.16·108 | −2.45·108 | 1.86·107 |
3 | −6.6·10−7 | 4.65·107 | −2.29·108 | −5.45·107 | −2.45·108 | 1.24·107 |
4 | 9.9·10−7 | 2.15·107 | −9.7·104 | −2.47·108 | −8.2·103 | −3.74·107 |
Z = 0.5 RL | ||||||
1 | −9.9·10−7 | 2.73·107 | 3·107 | 2.47·108 | −9.22·106 | −3.74·107 |
2 | −6.4·10−7 | 2.97·107 | 2.29·108 | −5.16·107 | −2.45·108 | 1.2·107 |
3 | −3.6·10−7 | 4.1·107 | −2.29·108 | −2.2·107 | −2.45·108 | 9.16·106 |
4 | 9.9·10−7 | 2.15·107 | −3.2·105 | −2.47·108 | −3.22·104 | −3.74·107 |
Z = 0.0 RL | ||||||
1 | −9.9·10−7 | 2.2·107 | 2.6·106 | 2.47·108 | −3.97·105 | −3.74·107 |
2 | −1.4·10−7 | 3.63·107 | 2.29·108 | −7.6·106 | −2.45·108 | 8.17·106 |
3 | 1.4·10−7 | 3.63·107 | −2.29·108 | 7.6·106 | −2.45·108 | 8.17·106 |
4 | 9.9·10−7 | 2.2·107 | −2.6·106 | −2.47·108 | −3.97·105 | −3.74·107 |