Assessing the yield difference of double-cropping rice in South China driven by radiation use efficiency

Double-cropping rice in South China continues to break the total yield record,but the yield potential of singlecropping rice is not being realized.Radiation use efficiency(RUE)has been singled out as an important determinant of grain yield in many cereal species.However,there is no information on whether the yield gaps in doublecropping rice involve differences in RUE.Field experiments were performed over two years to evaluate the effects of intercepted radiation(IP)and RUE on the above-ground biomass production,crop growth rate(CGR),and harvest index(HI),in four representative rice varieties,i.e.,Xiangyaxiangzhan(XYXZ),Meixiangzhan 2(MXZ2),Nanjingxiangzhan(NJXZ),and Ruanhuayoujinsi(RHYJS),during the early and late seasons of rice cultivation in South China.The results revealed that grain yield in the early season was 8.2%higher than in the late season.The yield advantage in the early season was primarily due to higher spikelets per panicle and above-ground biomass resulting from a higher RUE.The spikelets per panicle in the early season were 6.5,8.3,6.9,and 8.5%higher in XYXZ,MXZ2,NJXZ,and RHYJS,respectively,than in the late season.The higher early season grain yield was more closely related to RUE in the middle tillering stage(R^(2)=0.34),panicle initiation(R^(2)=0.16),and maturation stage(R^(2)=0.28),and the intercepted photosynthetically active radiation(IPAR)in the maturation stage(R^(2)=0.28),while the late season grain yield was more dependent on IPAR in the middle tillering stage(R^(2)=0.31)and IPAR at panicle initiation(R^(2)=0.23).The results of this study conclusively show that higher RUE contributes to the yield progress of early season rice,while the yield improvement of late season rice is attributed to higher radiation during the early reproductive stage.Rationally allocating the RUE of double-cropping rice with high RUE varieties or adjustments of the sowing period merits further study.

Robot Vision over CosGANs to Enhance Performance with Source-Free Domain Adaptation Using Advanced Loss Function

Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions.Domain shift will reduce accuracy in results.To prevent this,domain adaptation is done,which adapts the pre-trained model to the target domain.In real scenarios,the availability of labels for target data is rare thus resulting in unsupervised domain adaptation.Herein,we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks(GANs)are integrated to improve the performance of computer vision or robotic vision-based systems in our study.Cosine Generative Adversarial Network(CosGAN)is developed as a GAN that uses cosine embedding loss to handle issues associated with unsupervised source-relax domain adaptations.For less complex architecture,the CosGAN training process has two steps that produce results almost comparable to other state-of-the-art techniques.The efficiency of CosGAN was compared by conducting experiments using benchmarked datasets.The approach was evaluated on different datasets and experimental results show superiority over existing state-of-the-art methods in terms of accuracy as well as generalization ability.This technique has numerous applications including wheeled robots,autonomous vehicles,warehouse automation,and all image-processing-based automation tasks so it can reshape the field of robotic vision with its ability to make robots adapt to new tasks and environments efficiently without requiring additional labeled data.It lays the groundwork for future expansions in robotic vision and applications.Although GAN provides a variety of outstanding features,it also increases the risk of instability and over-fitting of the training data thus making the data difficult to converge.

Cellulase Production from Species of Fungi and Bacteria from Agricultural Wastes and Its Utilization in Industry: A Review

In energy deficient world, cellulases play a major role for the production of alternative energy resources utilizing lignocellulosic waste materials for bioethanol and biogas production. This study highlights fungal and bacterial strains for the production of cellulases and its industrial applications. Solid State Fermentation (SSF) is more suitable process for cellulase production as compared to submerge fermentation techniques. Fungal cellulosomes system for the production of cellulases is more desirable and resistant to harsh environmental conditions. Trichoderma species are considered as most suitable candidate for cellulase production and utilization in industry as compared to Aspergillus and Humicola species. However, genetically modified strains of Aspergillus have capability to produce cellulase in relatively higher amount. Bacterial cellulase are more resistant to alkaline and thermophile conditions and good candidate in laundries. Cellulases are used in variety of industries such as textile, detergents and laundries, food industry, paper and pulp industry and biofuel production. Thermally stable modified strains of fungi and bacteria are good future prospect for cellulase production.

Pleural Fluid Alkaline Phosphate Levels to Differentiate between Tuberculosis and Malignant Pleural Effusion a Tertiary Care Experience

Introduction: Pleural effusion (PF) is a common clinical presentation in several diseases. Tuberculosis is one of the most frequent causes of exudative pleural effusions in immunocompetent patients. Tuberculosis is the leading cause of morbidity and mortality from an infectious disease in developing countries. Pakistan is ranked fifth in the world in terms of tuberculosis high-burden countries. Various pleural fluid parameters have been used to identify the cause of pleural effusion. It has been discovered that tuberculous pleural effusions had a greater alkaline phosphatase (ALP) concentration than transudative effusions. This study used pleural fluid alkaline phosphatase levels to distinguish between tuberculous pleural effusion and malignant pleural effusion because there is little information from tuberculosis-high burden nations like Pakistan. Study Design: A descriptive cross-sectional study conducted at the Jinnah Postgraduate Medical Center in Karachi between October 2016 and October 2017. Material and Methods: The study comprised all patients who were admitted to the department of chest medicine at Jinnah post graduate medical centre (JPMC) of either gender between the ages of 18 and 70 who had exudative lymphocytic pleural effusions lasting two weeks or more included in the study. Non probability consecutive sampling was used to collect data. Patients who have tonsillitis, pharyngitis, pneumonia, asthma, Chronic obstructive pulmonary disease (COPD), or a history of hemoptysis, Bleeding disorders like, platelet function disorder, thrombocytopenia, Liver cirrhosis and Pregnant women were excluded. Parents’ informed consent was obtained after being informed of the study’s protocol, hazards, and advantages. Each patient had their level of pleural fluid alkaline phosphate (PALP) assessed. In order to evaluate the patient’s pleural effusion, a pre-made questionnaire was used. All the collected data were entered into the SPSS 20. An independent sample t-test was used to recognize alkaline phosphate levels association with pleural fluid secondary to tuberculosis or malignancy. Results: In this Descriptive Cross-Sectional Study, the total of 156 patients with age Mean ± SD of was 41.96 ± 17.05 years. The majority of patients 110 (70.5%) were male and 46 (29.5%) were female. Advanced age was associated with raised pleural fluid alkaline phosphatase. The difference of pleural fluid alkaline phosphate level between tuberculous v/s malignant group was found to be (38.03 ± 45.97) v/s (82.77 ± 61.80) respectively with P-value (P = 0.0001). Conclusion: Malignant pleural effusions had elevated PALP when compared to tuberculous pleural effusions in exudative lymphocytic pleural effusions;better differences are seen in older ages and shorter disease durations.

Hepatitis B virus therapy:What's the future holding for us?

Hepatitis B is one of the leading causes of liver cancer worldwide and unfortunately the number of people affected with hepatitis B virus(HBV) infection is still on the rise. Although the HBV has been known to cause fatal illness since decades but the population effected by this lethal virus have still only a few options for its management. The major treatment strategies include interferons and nucleos(t)ide analogues. These agents have so far produced unsatisfactory results in terms of complete virus eradication. Interferons cannot be used for long term therapy because of their potential side effects. Prolong treatment with nucleos(t)ide analogues has also been reported to cause serious side effects besides the increasing resistance by the virus. The need for new innovative solutions for treatment of HBV has been realized by global research institutes and pharmaceutical industry. Present review focuses in detail on the new ideas that are being transformed into therapeutic tools for use as future therapies in HBV infection. Modern drug designing and screening methods have made the drug discovery process shorter and more reliable. HBV therapeutics will take a new turn in coming years owing to these intelligent drug designing and screening methods. Future therapy of HBV is aiming to include the use of vaccines(both prophylactic and therapeutic), immunomodulators such as antibodies, non-nucleoside antivirals such as RNAi and inhibitors of viral life cycle.

Hepatitis C virus NS5A promotes insulin resistance through IRS-1 serine phosphorylation and increased gluconeogenesis

AIM: To investigate the mechanisms of insulin resistance in human hepatoma cells expressing hepatitis C virus(HCV) nonstructural protein 5A(NS5A).METHODS: The human hepatoma cell lines,Huh7 and Huh7.5,were infected with HCV or transientlytransfected with a vector expressing HCV NS5 A. The effect of HCV NS5 A on the status of the critical players involved in insulin signaling was analyzed using realtime quantitative polymerase chain reaction and Western blot assays. Data were analyzed using Graph Pad Prism version 5.0.RESULTS: To investigate the effect of insulin treatment on the players involved in insulin signaling pathway,we analyzed the status of insulin receptor substrate-1(IRS-1) phosphorylation in HCV infected cells or Huh7.5 cells transfected with an HCV NS5 A expression vector. Our results indicated that there was an increased phosphorylation of IRS-1(Ser307) in HCV infected or NS5 A transfected Huh7.5 cells compared to their respective controls. Furthermore,an increased phosphorylation of Akt(Ser473) was observed in HCV infected and NS5 A transfected cells compared to their mock infected cells. In contrast,we observed decreased phosphorylation of Akt Thr308 phosphorylation in HCV NS5 A transfected cells. These results suggest that Huh7.5 cells either infected with HCV or ectopically expressing HCV NS5 A alone have the potential to induce insulin resistance by the phosphorylation of IRS-1 at serine residue(Ser307) followed by decreased phosphorylation of Akt Thr308,Fox01 Ser256 and GSK3β Ser9,the downstream players of the insulin signalingpathway. Furthermore,increased expression of PECK and glucose-6-phosphatase,the molecules involved in gluconeogenesis,in HCV NS5 A transfected cells was observed.CONCLUSION: Taken together,our results suggest the role of HCV NS5 A in the induction of insulin resistance by modulating various cellular targets involved in the insulin signaling pathway.

Blockchain-enabled resource management and sharing for 6G communications

The sixth-generation(6G)network must provide better performance than previous generations to meet the requirements of emerging services and applications,such as multi-gigabit transmission rate,higher reliability,and sub-1 ms latency and ubiquitous connection for the Internet of Everything(IoE).However,with the scarcity of spectrum resources,efficient resource management and sharing are crucial to achieving all these ambitious requirements.One possible technology to achieve all this is the blockchain.Because of its inherent properties,the blockchain has recently gained an important position,which is of great significance to the 6G network and other networks.In particular,the integration of the blockchain in 6G will enable the network to monitor and manage resource utilization and sharing efficiently.Hence,in this paper,we discuss the potentials of the blockchain for resource management and sharing in 6G using multiple application scenarios,namely,Internet of things,deviceto-device communications,network slicing,and inter-domain blockchain ecosystems.

Dynamic characteristics of nanoindentation in Ni:A molecular dynamics simulation study

In this work, three-dimensional molecular dynamics simulation is carried out to elucidate the nanoindentation behaviour of single crystal Ni. The substrate indenter system is modelled using hybrid interatomic potentials including the manybody potential （embedded atom method） and two-body Morse potential. The spherical indenter is chosen, and the simulation is performed for different loading rates from 10 m/s to 200 m/s. Results show that the maximum indentation load and hardness of the system increase with the increase of velocity. The effect of indenter size on the nanoindentation response is also analysed. It is found that the maximum indentation load is higher for the large indenter whereas the hardness is higher for the smaller indenter. Dynamic nanoindentation is carried out to investigate the behaviour of Ni substrate to multiple loading-unloading cycles. It is observed from the results that the increase in the number of loading unloading cycles reduces the maximum load and hardness of the Ni substrate. This is attributed to the decrease in recovery force due to defects and dislocations produced after each indentation cycle.

Multi-objective design optimization of composite submerged cylindrical pressure hull for minimum buoyancy and maximum buckling load capacity

This paper presents the design optimization of composite submersible cylindrical pressure hull subjected to 3 MPa hydrostatic pressure.The design optimization study is conducted for cross-ply layups[0_(s)/90_(t)/0_(u)],[0_(s)/90_(t)/0_(u)]s,[0_(s)/90_(t)]s and[90_(s)/0_(t)]s considering three uni-directional composites,i.e.Carbon/Epoxy,Glass/Epoxy,and Boron/Epoxy.The optimization study is performed by coupling a Multi-Objective Genetic Algorithm(MOGA)and Analytical Analysis.Minimizing the buoyancy factor and maximizing the buckling load factor are considered as the objectives of the optimization study.The objectives of the optimization are achieved under constraints on the Tsai-Wu,Tsai-Hill and Maximum Stress composite failure criteria and on buckling load factor.To verify the optimization approach,optimization of one particular layup configuration is also conducted in ANSYS with the same objectives and constraints.

Molecular dynamics study of the mechanical characteristics of Ni/Cu bilayer using nanoindentation

In the present work, a three-dimensional molecular dynamics simulation is carried out to perform the nanoindentation experiment on Ni single crystal. The substrate indenter system is modeled using hybrid interatomic potentials including the many-body potential embedded atom method （EAM）, and two-body morse potential. To simulate the in- dentation process, a spherical indenter （diameter = 80A, 1A=0.1 nm） is chosen. The results show that the mechanical behaviour of a monolithic Ni is not affected by crystalline orientation. To elucidate the effect of a heterogeneous interface, three bilayer interface systems are constructed, namely Ni（100）/Cu（111）, Ni（110）/Cu（111）, and Ni（111）/Cu（111）. The simulations along these systems clearly describe that mechanical behaviour directly depends on the lattice mismatch. The interface with the smaller mismatch between the specified crystal planes is proved to be harder and vice versa. To describe the relationship between film thickness and interface effect, we choose various values of film thickness ranging from 20 A to 50 A to perform the nanoindentation experiment. It is observed that the interface is significant only for the relatively small thickness of film and the separation between interface and the indenter tip. It is shown that with the increase in film thickness, the mechanical behaviour of the film shifts more toward that of monolithic material.

Structural, electronic, and optical properties of ZnO_(1-x)Se_x alloys using first-principles calculations

The structural, electronic, and optical properties of binary ZnO, ZnSe compounds, and their ternary ZnOl_xSex alloys are computed using the accurate full potential linearized augmented plane wave plus local orbital （FP-LAPW ＋ lo） method in the rocksalt （B 1） and zincblende （B3） crystallographic phases. The electronic band structures, fundamental energy band gaps, and densities of states for ZnO1_xSex are evaluated in the range 0 〈 x 〈 1 using Wu-Cohen （WC） generalized gradient approximation （GGA） for the exchange-correlation potential. Our calculated results of lattice parameters and bulk modulus reveal a nonlinear variation for pseudo-binary and their ternary alloys in both phases and show a considerable deviation from Vegard＇s law. It is observed that the predicted lattice parameter and bulk modulus are in good agreement with the available experimental and theoretical data. We establish that the composition dependence of band gap is semi-metallic in B1 phase, while a direct band gap is observed in B3 phase. The calculated density of states is described by taking into account the contribution of Zn 3d, O 2p, and Se 4s, and the optical properties are studied in terms of dielectric functions, refractive index, reflectivity, and energy loss function for the B3 phase and are compared with the available experimental data.

Machine learning in vehicular networking:An overview

As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning(ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover management and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.

Design optimization of composite egg-shaped submersible pressure hull for minimum buoyancy factor

This paper describes a design optimization study of the composite egg-shaped submersible pressure hull employing optimization and finite element analysis(FEA)tools as a first attempt to provide an optimized design of the composite egg-shaped pressure hull for manufacturing or further investigations.A total of 15 optimal designs for the composite egg-shaped pressure hull under hydrostatic pressure are obtained in terms of fibers’angles and the number of layers for 5 lay-up arrangements and 3 unidirectional(UD)composite materials.The optimization process is performed utilizing a genetic algorithm and FEA in ANSYS.The minimization of the buoyancy factor eB:FT is selected as the objective for the optimization under constraints on both material failure and buckling strength.Nonlinear buckling analysis is conducted for one optimal design considering both geometric nonlinearity and imperfections.A sensitivity study is also conducted to further investigate the influence of the design variables on the optimal design of the egg-shaped pressure hull.

眼血流的变化对青光眼病情进展的影响（英文）

青光眼是由多种因素引起的神经退行性疾病,眼压过高会损害视神经而导致永久性视力丧失。虽然青光眼的基本病理生理机制尚未确定,但眼组织如视神经,视网膜,脉络膜以及虹膜的血流改变是青光眼发病的重要危险因素。由于不同因素所引发的视神经损害的有限认知,测量方法和治疗方面缺乏,人们对青光眼的理解存在障碍。尽管研究人员在不断地积累证据,力证眼血流的变化在青光眼发病机制中起着重要的作用,但大部分情况下,对于眼血流的变化和青光眼的患病风险之间的关系,他们都持有多样甚至矛盾的结论。本文中,我们回顾了青光眼的不同方面以及眼血流在疾病发展中的影响。

Biochemical association between the prevalence of genetic polymorphism and myocardial infarction

Genetic polymorphism has a vital role in the pathogenesis and development of myocardial infarction(MI).Single nucleotide polymorphism at any one of the amino acid sequences can result in a diseased state.A single gene can exhibit genetic polymorphism at more than one position giving rise to different variants.Genetic polymorphism of angiotensinogen(AGT)M235T,AGT T174M,and angiotensin-1-converting enzyme(ACE)I/D,endothelial nitric oxide synthase(eNOS),and methylenetetrahydrofolate reductase(MTHFR)can be a risk factor for MI.However,it is important to study the prevalence of genetic polymorphisms of these genes among different populations.MI is influenced by genetic polymorphism of various genes,including AGT,ACE,eNOS,MTHFR,etc.However,the association of genetic polymorphism of these genes varies among different populations,but different ethnic groups could show contradictory results.These genes have shown a positive association with risks of MI in some populations,whereas the results have not been consistent with every ethnic group.In this article,we have summarized the genetic variations in the aforementioned genes and their association with MI.

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection.

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions.However,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance degradation.To overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset.The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad f-measure.Finally,a classifier with the best performance is recommended for the emotion classification.

Effect of parameter setting and spectral normalization approach on study of matrix effect by laser induced breakdown spectroscopy of Ag–Zn binary composites

The complex nature of laser-material interaction causes non-stoichiometric ablation of alloy samples.This is attributed to matrix effect, which reduces analyzing capability. To address this issue, the analytical performance of three different normalization methods, namely normalization with background, internal normalization and three point smoothing techniques at different parameter settings is studied for quantification of Ag and Zn by Laser induced breakdown spectroscopy(LIBS).The LIBS spectra of five known concentration of silver zinc binary composites have been investigated at various laser irradiances(LIs). Calibration curves for both Ag(I) line(4d^(10)5s^2S_(1/2)→4d^(10)5p^2P_(1/2) at 338.28 nm) and Zn(I) line(4s5s^3S_1→4s4p^3P_2 at 481.053 nm) have been determined at LI of 5.86?×?10^(10)W cm^(-2). Slopes of these calibration curves provide the valuation of matrix effect in the Ag–Zn composites. With careful sample preparation and normalization after smoothing at optimum parameter setting(OPS), the minimization of sample matrix effect has been successfully achieved. A good linearity has been obtained in Ag and Zn calibration curve at OPS when normalized the whole area of spectrum after smoothing and the obtained coefficients of determination values were R^2?=?0.995 and 0.998 closer to 1. The results of matrix effect have been further verified by analysis of plasma parameters. Both plasma parameters showed no change with varying concentration at OPS. However, at high concentration of Ag, the observed significant changes in both plasma parameters at common parameter setting PS-1 and PS-2 were the gesture of matrix effect. In our case, the better analytical results were obtained at smoothing function with optimized parameter setting that indicates it is more efficient than normalization with background and internal normalization method.

Theoretical investigations of half-metallic ferromagnetism in new Half-Heusler YCrSb and YMnSb alloys using first-principle calculations

Structural,electronic,and magnetic properties of new predicted half-Heusler YCrSb and YMnSb compounds within the ordered MgAgAs Clb-type structure are investigated by employing first-principal calculations based on density functional theory.Through the calculated total energies of three possible atomic placements,we find the most stable structures regarding YCrSb and YMnSb materials,where Y,Cr(Mn),and Sb atoms occupy the(0.5,0.5,0.5),(0.25,0.25,0.25),and(0,0,0) positions,respectively.Furthermore,structural properties are explored for the non-magnetic and ferromagnetic and anti-ferromagnetic states and it is found that both materials prefer ferromagnetic states.The electronic band structure shows that YCrSb has a direct band gap of 0.78 eV while YMnSb has an indirect band gap of 0.40 eV in the majority spin channel.Our findings show that YCrSb and YMnSb materials exhibit half-metallic characteristics at their optimized lattice constants of 6.67 and 6.56 ,respectively.The half-metallicities associated with YCrSb and YMnSb are found to be robust under large in-plane strains which make them potential contenders for spintronic applications.

Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand.This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its customers.Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection criteria.These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy Process.Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and risk.Subsequently,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection criteria.Three experts with seven and three and ten years of experience have participated in the study.Findings of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred customers.Moreover,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are consistent.Furthermore,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision.Unlike traditional supply chain problems of supplier selection,this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria.