Net-zero, resilience, and agile closed-loop supply chain network design considering robustness and renewable energy.

The Net-zero, Resilience, and Agile Closed-Loop Supply Chain Network (NZRACLSCND) concept integrates net-zero, resiliency, and agility in a circular economy. Regarding net-zero, this research embeds renewable energy like solar energy and hybrid trucks to supply energy for facilities and transportation of goods and products between components. Applying redundancy, multi-source, and flexible capacity as resiliency strategies is suggested to cope with the demand disruption. Satisfaction demand level is utilized for the agile approach. This research proposes Robust Stochastic Optimization (RSO), including the weighted expected value and maximum CO2 for NZRACLSCND. This study locates and determines the flow of CLSC in the home appliance industry by considering NZRA, robustness, and risk against demand disruption. CO2 emission using the NZRA concept is 233.33% less than without considering NZRA concepts. In addition, the conservative coefficient, agile coefficient, decreased CO2 coefficient, and the model scale are analyzed. The results show that when the conservative coefficient increases, the risks of CO2 emission increase. In addition, when the agile coefficient increases, as a result, CO2 emission increases. Finally, when the decreased CO2 coefficient and the model scale increase, we can see that CO2 emission and cost are increased.

Antifragile, sustainable, and agile supply chain network design by considering resiliency, robustness, risk, and environmental requirements.

This research suggests an Antifragile, Sustainable and Agile Supply Chain Network Design (ASASCND) as a new network design that integrates these concepts considering resiliency, robustness, risk, and environmental requirements. The cost function combines a novel method with robust stochastic optimization and Entropic Value at Risk (EVaR). This model combines expected value, maximum and EVaR of cost as an objective function. This research adds antifragility by the effect of learning on variable parameters, sustainability by considering the environmental and social issues, resiliency and agility by flexible capacity, and multi-resource and demand satisfaction constraints to the model. The case study is in the automotive industry. This model compares the main problem by considering antifragility without thinking about antifragility. The ASASCND cost is - 0.3% less than without considering antifragility. In addition, when the conservatism coefficient grows, the cost function increase. In addition, the antifragility coefficient and the confidence level affect positively, and the agility coefficient negatively affects the cost function. Expanding the model scale changes the cost function and time computation because the antifragility coefficient changes variable cost. Finally, managerial insights and practical implications are explained.

A robust, resilience multi-criteria decision-making with risk approach: a case study for renewable energy location.

Regarding hard situations like war, the increasing cost of extraction and exploration of fossil fuels make governments move toward green and clear renewable energy (RE). As a result, we propose a novel multi-criteria decision-making (MCDM) method for RE location (REL) for the first time. This model suggests a Robust, Resilience MCDM with Risk approach (RRMCDMR) for REL. We propose a risk approach by adding a risk function in MCDM. A robust convex approach is used to tackle the uncertainty of the model for the real world. We compare the RRMCDMR problem in a wind farm location in Iran with different risk coefficient functions. As defined, Khaf, Nehbandan, and Esfarayan are in locations one to three in all modes. We changed the normalized risk function and suggested two other risk functions that can help risk-averse and risk-neutral decision-makers. We varied the robust convex coefficient and considered that by increasing the robust convex coefficient, the alternative score increased.

Viable supply chain with vendor-managed inventory approach by considering blockchain, risk and robustness.

This research shows a Viable Supply Chain with Vendor Managed Inventory approach by considering Blockchain, Risk, and Robustness. We embedded Blockchain Technology (BCT) to improve SC agility. To tackle risk and robustness, we suggest a new objective function with the weighted expected value, worst case, and Entropic Value at Risk for considering risk and robustness under different scenarios. This model is Mixed-Integer Linear Programming, and GAMS-Bonmin is utilized to solve it. The case study of this research is on the Pharmacy industry. We compare problems in the situation with BCT and without BCT. The results show that the cost function of the main problem without BCT is greater than the same problem with BCT, and its gaps are 0.61%. Finally, the cost function increase by increasing the conservative coefficient and decreasing the resiliency coefficient. The cost function reduces by increasing the confidence level. We change the scale of the problem and define problems. By increasing the scale of the problem, the solution time increased.

A robust and resilience machine learning for forecasting agri-food production.

This research proposes a new framework for agri-food capacity production by considering resiliency and robustness and paying attention to disruption and risk for the first time. It is applied robust stochastic optimization by adding robustness to the constraint's objective function and resiliency situation. This research minimizes the mean absolute deviation and coefficient of standard deviation errors by linear function in the agri-food capacity production. This study suggests agri-food managers and decision-makers use this mathematical method to forecast and improve production management. The results of this research lead to better decision-making and are compared with other sine functions. The main model's Robust and Resiliency Mean Absolute Deviation (RRMAD) value is 1.28% lower than other sine-type functions. The conservativity coefficient, confidence level, weight factor, resiliency coefficient, and probability of the scenario vary. The main model's RRMAD value is 1.28% lower than other sine-type functions. Growing the weight factor will result in an increase in RRMAD and a smooth decline in R-squared. Additionally, as the resilience coefficient rises, the RRMAD function increases while the R-squared declines. By altering the probability of the scenario, the RRMAD function drops, and the R-squared goes up.

Sustainable Earthquake Resilience with the Versatile Shape Memory Alloy (SMA)-Based Superelasticity-Assisted Slider.

Earthquakes threaten humanity globally in complex ways that mainly include various socioeconomic consequences of life and property losses. Resilience against seismic risks is of high importance in the modern world and needs to be sustainable. Sustainable earthquake resilience (SER) from the perspective of structural engineering means equipping the built environment with appropriate aseismic systems. Shape memory alloys (SMAs) are a class of advanced materials well suited for fulfilling the SER demand of the built environment. This article explores how this capability can be realized by the innovative SMA-based superelasticity-assisted slider (SSS), recently proposed for next-generation seismic protection of structures. The versatility of SSS is first discussed as a critical advantage for an effective SER. Alternative configurations and implementation styles of the system are presented, and other advantageous features of this high-tech isolation system (IS) are studied. Results of shaking table experiments, focused on investigating the expected usefulness of SSS for seismic protection in hospitals and conducted at the structural earthquake engineering laboratory of the University of Bonab, are then reported. SSS is compared with currently used ISs, and it is shown that SSS provides the required SER for the built environments and outperforms other ISs by benefitting from the pioneered utilization of SMAs in a novel approach.

Viable closed-loop supply chain network by considering robustness and risk as a circular economy.

The viable closed-loop supply chain network (VCLSCND) is a new concept that integrates sustainability, resiliency, and agility into a circular economy. We suggest a hybrid robust stochastic optimization by minimizing the weighted expected, maximum, and entropic value at risk (EVaR) of the cost function for this problem. This form considers robustness against demand disruption. Finally, CLSC components are located, and quantity flows are determined in the automotive industry. The results show that the VCLSCND cost is less than not considering viability and has a - 0.44% gap. We analyze essential parameters. By increasing the conservative coefficient, confidence level, and the scale of the main model, decreasing the allowed maximum energy, the cost function, time solution, and energy consumption grow. We suggested applying the Fix-and-Optimize algorithm for producing an upper bound for large-scale. As can be seen, the gap between this algorithm and the main problem for cost, energy, and time solution is approximately 6.10%, - 8.28%, and 75.01%.

Resource-constrained time-cost-quality-energy-environment tradeoff problem by considering blockchain technology, risk and robustness: a case study of healthcare project.

Blockchain Technology (BCT) is expanding day by day and is used in all pillars of life and projects. In this research, we survey applicable BCT in project management for the first time. We presented a Resource-Constrained Time-Cost-Quality-Energy-Environment Tradeoff Problem by considering BCT, Risk and Robustness (RCTCQEETPBCTRR) in project scheduling. We utilize hybrid robust stochastic programming, worst case, and Conditional Value at Risk (CVaR) to cope with uncertainty and risks. This type of robustification and risk-averse is presented in this research. A real case study is presented in a healthcare project. We utilize GAMS-CPLEX to solve the model. Finally, we analyze finish time, conservative coefficient, the confidence level of CVaR, and the number of scenarios. The most important research result is that applying BCT decreases cost, energy, and pollution and increases quality. Moreover, the total gap between RCTCQEETPBCTRR and without BCT is approximately 2.6%. When compacting finish time happens or if the conservative coefficient increases to 100%, costs, energy, and pollution environment increase, but quality decreases. If the confidence level of CVaR increases, the cost, energy, and environment function functions grow up, and quality is approximately not changed.

An extended robust mathematical model to project the course of COVID-19 epidemic in Iran.

This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models.

Viable medical waste chain network design by considering risk and robustness.

Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by conditional value at risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem makes to increase the cost function.

First Case Report of Adnexal Fetus in Fetu.

BACKGROUND: There is an ambiguous and controversial line between fetiform teratoma and fetus in fetu (FIF) as differential diagnoses of a fetiform mass. Classically, the presence of vertebral column often with a relatively proper arrangement of other organs around the central axis favor the diagnosis of FIF over teratoma. CASE: Based on previously proposed criteria and the presence of vertebral organization in the radiological and histopathological assessment of the fetiform mass, we present an extremely rare case of adnexal FIF in a 10-year-old girl presenting with acute abdominal pain. SUMMARY AND CONCLUSION: Whether FIF and fetiform teratoma are one entity or two, clinical discrimination for the choice of treatment seems to be unnecessary and the patient should be clinically followed for the probable malignant potential.

Antimicrobial Activity of the Essential Oil Obtained from the Seed and Oleo-Gum-Resin of Ferula Assa-Foetida against Oral Pathogens.

OBJECTIVES: The objective of this study was to evaluate the antimicrobial activity of the essential oil obtained from oleo-gum-resin and seeds of Ferula assa-foetida. MATERIALS AND METHODS: Ferula assa-foetida plants were collected from Tabas, Yazd Province, Iran, during summer 2017. Then, essential oils were obtained from its seeds and oleo-gum-resin using hydrodistillation. Gas chromatography-mass spectrometry (GC-MS) test was performed to determine the contents of the essential oils. Four different concentrations of each oil were prepared (2.5, 5, 10, and 20 µg/ml), and the antimicrobial activity of each dose against four oral bacteria (Streptococcus mutans, Streptococcus sobrinus, Streptococcus sanguis, Streptococcus salivarius, and Lactobacillus rhamnosus) was evaluated using the disk diffusion method. The data were analyzed using analysis of variance (ANOVA) and Kruskal-Wallis test in SPSS 17 software. RESULTS: The GC-MS findings exhibited that the main compounds found in essential oils yielded from the seeds and oleo gum resin were (Z) -1-propenyl sec-butyl disulfide and (E) -1-propenyl sec-butyl disulfide. Ferula assa-foetida plant showed a significant antimicrobial effect (P<0.05). The essential oil from Ferula assa-foetida oleo-gum-resin had significantly stronger antibacterial properties compared to the essential oil from Ferula assa-foetida seeds (P<0.001). Both essential oils showed antibacterial properties similar to that of Chlorhexidine. The growth inhibition zone was significantly dependent on the essential oil concentration for all bacteria (P<0.05). CONCLUSION: Our study revealed that essential oils from seeds and oleo-gum-resin of Ferula assa-foetida have antimicrobial properties. More laboratory studies are required to reach a definitive conclusion.

Correlates of the "No-Reflow" or "Slow-Flow" Phenomenon in Patients Undergoing Primary Percutaneous Coronary Intervention.

Background: Despite recent advances in interventional equipment and techniques, the angiographic no-reflow phenomenon occurs in a considerable number of patients undergoing primary percutaneous coronary intervention (PCI). We investigated the clinical, angiographic, preprocedural, and procedural characteristics associated with the no-reflow phenomenon among patients undergoing primary PCI. Methods: Between March 2008 and April 2013, 530 patients (78.5% male, mean age=58.11±12.39 y) with ST-segment-elevation myocardial-infarction who underwent primary PCI were categorized in 2 groups according to their postprocedural thrombolysis-in-myocardial infarction (TIMI) flow grades: those with a maximum score of 2 (the no-reflow or slow-flow group) and the ones with a score of 3 (the reflow group). A multivariable logistic regression model was used to find the multiple correlates of the no-reflow phenomenon after PCI. Results: There were 166 (31.3%) patients in the no-reflow group and 364 (68.7%) in the reflow group. The no-reflow patients were older and had significantly longer target lesion lengths, higher SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery (SYNTAX) scores, higher infarct-related artery SYNTAX scores, more thrombus burden, and a higher frequency of initial TIMI flow grades of 2 or lower. Our multivariable logistic regression analysis demonstrated that older age, higher numbers of Q waves, not using statin, longer target lesion lengths, higher thrombus grades, and higher infarct-related artery SYNTAX scores remained the independent correlates of increased no-reflow rates after primary PCI (area under the ROC curve=0.784, 95% CI: 0.742-0.826; P<0.001). Conclusion: Clinical, angiographic, and procedural features of patients undergoing primary PCI may be correlated with the occurrence of the no-reflow phenomenon. The thrombus grade and the infarct-related artery SYNTAX score could be among these factors.

A level-crossing based QRS-detection algorithm for wearable ECG sensors.

In this paper, an asynchronous analog-to-information conversion system is introduced for measuring the RR intervals of the electrocardiogram (ECG) signals. The system contains a modified level-crossing analog-to-digital converter and a novel algorithm for detecting the R-peaks from the level-crossing sampled data in a compressed volume of data. Simulated with MIT-BIH Arrhythmia Database, the proposed system delivers an average detection accuracy of 98.3%, a sensitivity of 98.89%, and a positive prediction of 99.4%. Synthesized in 0.13 µm CMOS technology with a 1.2 V supply voltage, the overall system consumes 622 nW with core area of 0.136 mm (2), which make it suitable for wearable wireless ECG sensors in body-sensor networks.

Nonlinear Signal-Specific ADC for Efficient Neural Recording in Brain-Machine Interfaces.

A nonlinear ADC dedicated to the digitization of neural signals in implantable brain-machine interfaces is presented. Benefitting from an exponential quantization function, effective resolution of the proposed ADC in the digitization of action potentials is almost 2 bits more than its physical number of bits. Hence, it is shown in this paper that the choice of a proper nonlinear quantization function helps reduce the outgoing bit rate carrying the recorded neural data. Another major benefit of digitizing neural signals using the proposed signal-specific ADC is the considerable reduction in the background noise of the neural signal. The 8-b exponential ADC reported in this paper digitizes large action potentials with maximum resolution of 10.5 bits , while quantizing the small background noise is performed with a resolution of as low as 3 bits. Fully-integrated version of the circuit was designed and fabricated in a 0.18-µm CMOS process, occupying 0.036 mm(2) silicon area. Designed based on a two-step successive-approximation register ADC architecture, the proposed ADC employs a piecewise-linear approximation of the target exponential function for quantization. Operating at a sampling frequency of 25 kS/s (typical for intra-cortical neural recording) and with a supply voltage of 1.8 V, the entire chip, including the ADC and reference circuits, dissipates 87.2 µW. According to the experiments, Noise-Content-Reduction Ratio (NCRR) of the ADC is 41.1 dB.

Cavitary pulmonary lesions in systemic lupus erythematosus: an unusual manifestation.

Systemic lupus erythematosus (SLE) is an autoimmune disease of unknown pathogenesis. The frequency of SLE with cavitary lesion manifestation is very rare and is thought to be due to infection or pulmonary embolism. A 19-year-old female diagnosed with SLE complicated by lupus nephritis and cavitary pulmonary lesion is presented in this case report. Other diseases that can lead to such lesions were ruled out in the patient. The patient improved briefly after the initiation of immunosuppressive therapy, but was unresponsive to supportive treatment due to pneumothorax. Pneumothorax is caused by cavitary lesions and possibly bronchopleural fistulas - these later caused respiratory distress and death. The patient did not show any improvement in the lesions after the initiation of immunosuppressive therapy. This case report suggests that the differential diagnosis of cavitary lung lesions should include SLE.