Creating Shared Value and Strategic Corporate Social Responsibility through Outsourcing within Supply Chain Management

One way to develop local clusters is to strengthen those clusters by using outsourcing to conduct strategic social responsibility, or in other words, to create shared value, which is a win-win strategy for the buyer, supplier, and society and the best and most viable alternative to traditional corporate social responsibilities. In the leading research, a model for decision-making within the supply chain has been developed for purchasing based on shared value creation, long-term relationship management, and purchasing strategies. The research consists of two strategic mathematical models, using goal programming, and then is solved by a meta-heuristic algorithm. Potential outsourcing companies are assessed and then clustered according to their geographic locations in the decision-making process. One (or several) cluster(s) was selected among clusters based on knowledge and relationship criteria. Besides, in the primary mathematical model, the orders in different periods and the selection of suppliers are determined. In this model, in addition to optimizing the cost, the dispersion of purchases from suppliers is maximized to increase relationships and strengthen all members of the cluster. Maximizing the distribution by converting a secondary objective function to goal-programming variables transforms the multi-objective model into a single-objective model. In addition to economic benefits for buyers and suppliers, this purchasing plan concentrates on strengthening the local industrial cluster, fostering employment and ease of recruitment for human resources, accessing more infrastructures and technical support facilities, developing an education system in the region, and assisting knowledge-based enterprises with development

Integration of pricing and inventory decision in a supply chain under vendor-managed inventory with defective items and inspection errors: a game-theoretic approach

In this paper, the production-inventory-marketing model for a two-stage manufacturer-retailer supply chain under VMI policy with a price-sensitive demand is studied. An imperfect production at the manufacturer and inspection process involving with Type I and II errors at the retailer are considered. We assume that the manufacturer gives products to the retailer in a number of equal-sized shipments. This model is formulated as a Stackelberg game in which the retailer retains a certain degree of autonomy by reserving the right to choose the retail price and the manufacturer determines replenishment frequency, replenishment quantity and wholesale price. The critical supply chain decision factors including the manufacturer’s wholesale price, the retailer’s price, shipment frequencies and number of shipments are determined maximizing the total profit of each member of the supply chain. A solution procedure is proposed to find the Stackelberg game equilibrium. The performance of the model is assessed by a numerical example. The numerical shows that it is more beneficial for both the manufacturer and the retailer when the demand is less price sensitive

The Role of Organic Matter in the Formation of High-Grade Al Deposits of the Dopolan Karst Type Bauxite, Iran: Mineralogy, Geochemistry, and Sulfur Isotope Data

Mineralogical and geochemical analyses of the Dopolan karstic bauxite ore were performed to identify the characteristics of four bauxite horizons, which comprise from top to bottom, bauxitic kaolinite, diaspore-rich bauxite, clay-rich bauxite, and pyrite-rich bauxite. Diaspore, kaolinite, and pyrite are the main minerals; böhmite, muscovite, rutile, and anatase are the accessory minerals. The main minerals of the Dopolan bauxite deposit indicate slightly acidic to alkaline reducing conditions during bauxitization. Immobile elements (Nb, Ta, Zr, Hf, and rare earth elements) are enriched in the diaspore-rich horizon, which also has the highest alumina content, whereas redox sensitive elements (e.g., Cr, Cu, Ni, Pb, Zn, Ag, U, and V) are enriched in the lowest horizon of pyrite-rich bauxite. The presence of a high content of organic matter was identified in different horizons of bauxitic ore from wet chemistry. The presence of organic matter favored Fe bioleaching, which resulted in Al enrichment and the formation of diaspore-rich bauxite. The leached Fe2+ reacted with the hydrogen sulfur that was produced due to bacterial metabolism, resulting in the formation of the pyrite-rich horizon towards the bottom of the Dopolan bauxite horizons. Biogeochemical activity in the Dopolan bauxitic ore was deduced from the reducing environment of bauxitization, and the deposition of framboidal and cubic or cubic/octahedral pyrite crystals, with large negative values of δ34S of pyrite (−10‰ to −34‰) and preserved fossil cells of microorganisms

The Use of Univariate and Multivariate Analyses in the Geochemical Exploration, Ravanj Lead Mine, Delijan, Iran

A geochemical exploration program was applied to recognize the anomalous geochemical haloes at the Ravanj lead mine, Delijan, Iran. Sampling of unweathered rocks were undertaken across rock exposures on a 10 × 10 meter grid (n = 302) as well as the accessible parts of underground mine A (n = 42). First, the threshold values of all elements were determined using the cut-off values used in the exploratory data analysis (EDA) method. Then, for further studies, elements with lognormal distributions (Pb, Zn, Ag, As, Cd, Co, Cu, Sb, S, Sr, Th, Ba, Bi, Fe, Ni and Mn) were selected. Robustness against outliers is achieved by application of central log ratio transformation to address the closure problems with compositional data prior to principle components analysis (PCA). Results of these analyses show that, in the Ravanj deposit, Pb mineralization is characterized by a Pb-Ba-Ag-Sb ± Zn ± Cd association. The supra-mineralization haloes are characterized by barite and tetrahedrite in a Ba- Th- Ag- Cu- Sb- As- Sr association and sub-mineralization haloes are comprised of pyrite and tetrahedrite, probably reflecting a Fe-Cu-As-Bi-Ni-Co-Mo-Mn association. Using univariate and multivariate geostatistical analyses (e.g., EDA and robust PCA), four anomalies were detected and mapped in Block A of the Ravanj deposit. Anomalies 1 and 2 are around the ancient orebodies. Anomaly 3 is located in a thin bedded limestone-shale intercalation unit that does not show significant mineralization. Drilling of the fourth anomaly suggested a low grade, non-economic Pb mineralization

Laboratory modeling of heat transfer system of radiology tube using multi-walled carbon nanofluid and evaluation of heat transfer coefficient

X-ray tube is the main part of the radiology device to produce X-ray. One of the main problems with this equipment is the too much heat generated at the anode of the tube as a result of clashing the high-energy electron beam on its surface. Lack of proper transfer of heat generated at the anode, further destruction and reduces the life time of the X-ray tube. Considering the important role of oil around the X-ray tube as a transferor of generating heat, in this article, this oil is replaced by different weight percentages of nanofluids and heat transfer coefficients have been investigated in different conditions for the first time in the world. However experimental models of X-ray tube heating system was built with heating element and the same amount of heat generated at the anode was produced by applying the electrical power to the element. In addition, the nanofluid has been built with the combination of multi-walled carbon nanotubes and transformer oil in different weight concentration and thermal behavior of the tube has been compared using conventional oil and nanofluids. The obtained experimental results indicate that using improves the heat transfer significantly. Using this new technology in radiology systems and similar equipment, in addition to eliminating the complicated process of X-ray tubes, the efficiency of this type of equipment will increase considerably

The interpolation methods and neural network to estimate the spatial variability of soil organic matter affected by land use type

The use of geostatistical methods and artificial neural network (ANN), which can predict the spatial variability of SOM is of outmost significance. The present research investigated the effects of different land use types at different altitudes on the spatial variability of SOM using the above-mentioned methods. A total of 249 combined samples from the depth of 0–15 cm on the basis of land use type and topography were collected from different parts of the research area (9545 km2). Using cross validation, the methods were compared and the best fitted one was selected on the basis of mean error (ME) and root mean square error (RMSE). The best-fitted semivariogram models for SOM (R2= 0.894) and SOC (R2= 0.761) were spherical. The cross-validation method indicated ANN as the most accurate method for the prediction of SOM and SOC with the ME and RMSE of 0.37 and 0.36 for SOM, and 0.37 and 0.35 for SOC, respectively

Development and validation of a questionnaire to evaluate the state of Iranian hospital nutrition support

Background: Recently, nutrition support was implemented as a part of clinical services in hospitals. The implementation of nutrition support needs to be assessed for its improvement. Objective: This study aimed to develop and validate a questionnaire to assess the state of nutrition support in Iranian hospitals. Methods: A mixed method approach was used in this study. This study was performed in training hospitals of Iran in 2016. In the development stage, pre-determined keywords were searched on international electronic databases. Additionally, semi-structural interviews were performed with 13 key informants based on purposive sampling. Themes were extracted from articles and interviews by thematic analysis. A primary questionnaire was generated based on extracted themes. In the validating stage, the content validity ratio (CVR) and content validity index (CVI) were used. The reliability of the questionnaire was also computed through a pilot study using Cronbach’s alpha test. SPSS version 16.0 was used for data analysis. Results: Based on 16 items elicited from the content analysis, 110 questions were generated, out of which, 65 questions were selected. Then, 55 questions showing acceptable CVI and CVR were chosen for the pilot study. The Cronbach’s alpha coefficient of the questionnaire was found to be 0.80. This value remained stable for each item, even after an item was deleted. Conclusion: For the first time, a validated questionnaire for the assessment of the state of nutrition support in hospitals was developed in a methodological approach process with high validity and reliability indexes which intended to be comprehensive based on the mixed method approach

Designing an Intelligent, Non-Contact System for Controlling Neonate’s Body Temperature in Incubators

Background and Objective: Regulating body temperature of premature infants and preparing them a desirable physical condition for completing physical and mental development is essential. Using a system that guarantees neonate’s health in incubator at first birth hours is cardinal. Materials and Methods: In this method, a set of non-contact infrared sensors and microcontrollers regulate neonate’s body temperature without skin sensor. Temperatures were converted to digital data after mean evaluation. They were sent to digital microcontroller for user's settings and functional orders of internal conversion system of incubator.  Results: In this study, an intelligent system is presented for controlling body temperature of premature infants in incubators without skin sensors. Conclusion: This technique causes incubator’s proper performance and safety. It prevents damages to brain and other organs of premature neonates and reduces neurological disorders of these infants

Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet++

Abstract Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1

Assessing the risk factors before pregnancy of preterm births in Iran: a population-based case-control study

Abstract Background Preterm birth is a major cause of prenatal and postnatal mortality particularly in developing countries. This study investigated the maternal risk factors associated with the risk of preterm birth. Methods A population-based case-control study was conducted in several provinces of Iran on 2463 mothers referred to health care centers. Appropriate descriptive and analytical statistical methods were used to evaluate the association between maternal risk factors and the risk of preterm birth. All tests were two-sided, and P values < 0.05 were considered to be statistically significant. Results The mean gestational age was 31.5 ± 4.03 vs. 38.8 ± 1.06 weeks in the case and control groups, respectively. Multivariate regression analysis showed a statistically significant association between preterm birth and mother’s age and ethnicity. Women of Balooch ethnicity and age ≥ 35 years were significantly more likely to develop preterm birth (OR: 1.64; 95% CI: 1.01–-2.44 and OR: 9.72; 95% CI: 3.07–30.78, respectively). However, no statistically significant association was observed between preterm birth and mother’s place of residence, level of education, past history of cesarean section, and BMI. Conclusion Despite technological advances in the health care system, preterm birth still remains a major concern for health officials. Providing appropriate perinatal health care services as well as raising the awareness of pregnant women, especially for high-risk groups, can reduce the proportion of preventable preterm births