The effect of budget deficit on current account deficit: Evidence from Iran

The main objective of this paper is to present the theoretical argument of twin deficit hypothesis. In this study we evaluate the effect of budget deficit on current account deficit in Iran in the period of 1981-2012. For this purpose, we using generalize method of movement (GMM) approach. In this paper we use Keynesian and Ricardian Theory about budget deficit. We find that the coefficient of budget deficit, equal 0.09 which shows that a unit of increase in budget deficit leads to 0.09 unit decrease in current account balance, indeed one unit increase in budget deficit leads to increase in current account deficit. Also, the results show that there is positive and significant relationship between the oil revenue and current account balance. But the results show that real exchange rate dose not significant effect on current account balance. Keywords: Budget deficit, Current account deficit, Keynesian approach, GMM model

Effects of the COVID-19 pandemic on academic preparation and performance: a complex picture of equity

IntroductionMany experts have predicted a drop in students’ academic performance due to an extended period of remote instruction and other harmful effects of the pandemic.MethodsAs university instructors and education researchers, we sought to investigate the effects of the pandemic on students’ preparation for college-level coursework and their performance in early college using mixed effects regression models. Data were collected from STEM students at a public research university in the southeastern United States.ResultsWe found that demographic gaps in high school preparation (as measured by ACT scores) between men and women, as well as underrepresented minority and majority students, remained relatively consistent after the start of the pandemic. These gaps were approximately 1 point (out of 36) and 3 points, respectively. However, the gap between first generation and continuing generation students increased from prior to 2020, to after 2020, going from approximately 1 point to 2 points. This gap in preparation was not accompanied by a corresponding shift in the demographics of the student population and there was no corresponding increase in the demographic gaps in students’ first term grades.DiscussionThe data seem to suggest that first-generation students in STEM suffered more from the changes to secondary instruction during the pandemic, but that college instructors were able to mitigate some of these effects on first-semester grades. However, these effects were only mitigated to the extent that they preserved the status quo of pre-pandemic inequities in undergraduate STEM education

Examining the Potential and Pitfalls of ChatGPT in Science and Engineering Problem-Solving

The study explores the capabilities of OpenAI's ChatGPT in solving different types of physics problems. ChatGPT (with GPT-4) was queried to solve a total of 40 problems from a college-level engineering physics course. These problems ranged from well-specified problems, where all data required for solving the problem was provided, to under-specified, real-world problems where not all necessary data were given. Our findings show that ChatGPT could successfully solve 62.5% of the well-specified problems, but its accuracy drops to 8.3% for under-specified problems. Analysis of the model's incorrect solutions revealed three distinct failure modes: 1) failure to construct accurate models of the physical world, 2) failure to make reasonable assumptions about missing data, and 3) calculation errors. The study offers implications for how to leverage LLM-augmented instructional materials to enhance STEM education. The insights also contribute to the broader discourse on AI's strengths and limitations, serving both educators aiming to leverage the technology and researchers investigating human-AI collaboration frameworks for problem-solving and decision-making.Comment: 12 pages, 2 figure

Novel Synthetic Derivatives of Dichloroimidazole Targeting NorA Efflux Pump against Methicillin-Resistant Staphylococcus aureus

Introduction: Antibiotic resistance has been a major health problem in recent years, which has led to a failure in the treatment of infectious diseases. Therefore, research to synthesize compounds that have antibiotic activity is very valuable. In present study four novel compounds (6a-d), derivatives of dichloroimidazole conjugated with triazole, were synthesized in order to obtain new bacterial efflux pump inhibitors (EPIs). Methods and Results: The derivatives were evaluated for their effects on the minimum inhibitory concentration (MIC) of ciprofloxacin against a methicillin and ciprofloxacin resistant Staphylococcus aureus (MCRSA) clinical isolate. Based on broth microdilution method assay, four derivatives at a minimum effective concentration (MEC) fortified the antibacterial efficacy of ciprofloxacin against MCRSA. MIC of ciprofloxacin decreased in the presence of novel compounds compared to ciprofloxacin alone between 2 to 64 fold. These compounds were then evaluated for their potency as efflux pump inhibitors using a fluorometric assay. Results indicated an increase in accumulation of ethidium bromide (a known fluorescent substrate for the NorA pump) in the presence of each compound, like verapamil (a typical inhibitor of efflux pump), thus these compounds acted as inhibitors of the NorA pump. Moreover, the MTT assay confirmed that novel compounds did not demonstrate any cytotoxic effect against three cancer cell lines, HT-29, MCF-7 and Caco-2, and a normal mouse fibroblastic cell line, NIH-3T3. Conclusion: Collectively, our results propose these derivatives as therapeutic options in combination therapies to tackle antibiotic resistance. Grants: This research has been supported by Grant Number 94-02-33-29506 from Deputy of Research, Tehran University of Medical Science

Development of Multi-Objective Supply Chain Model with Stochastic Demand: An Optimization Approach Based on Simulation and Scenario Development

The integration of supply chain decisions aims to reduce costs and delivery time for customers. However, uncertainty in supply chain parameters, particularly demand, can disrupt this integration. The increased interest in probabilistic planning and simulation models in supply chain modeling is a response to this demand uncertainty. Therefore, the main objective of this study was to develop a multi-level, multi-product, multi-period supply chain network model that considers conflicting objectives such as cost minimization, delivery time minimization, and system-wide reliability maximization. The supply chain network under investigation consisted of four levels or subsystems: suppliers, manufacturers, distributors, and retailers. In this study, it was assumed that demand follows a random probabilistic distribution function. Consequently, simulation techniques were employed to estimate costs, including shipping costs, lost sales costs, and other expenses. After developing the multi-objective model, various scenarios were created based on different perspectives of inventory levels, namely minimum inventory, maximum inventory, and average inventory level. For each scenario, objective-related values were estimated. Ultimately, based on the Pareto optimal solutions obtained for each case of the model, the Vickor decision-making method was used to rank the answers and select the best solution from the proposed model. The results indicated that the second scenario, considering the average inventory level, was identified as the optimal solution for the described model.IntroductionToday, supply chain management (SCM) encompasses the entire production planning process for the supply chain, from raw material suppliers to the final customer. This has become a focal point for numerous researchers. In most supply chain designs, the objective has been to transfer products from one layer to another in order to meet strategic, tactical, and operational demands while minimizing complications arising from interrelationships and uncertainties across the chain. These challenges have posed significant decision-making hurdles in the supply chain domain. Supply chains can be regarded as complex systems wherein various factors interact with each other, resulting in emergent properties. Designing a versatile supply chain to address conflicting and diverse objectives requires considering them simultaneously and striking a balance among different criteria. The dynamic and intricate nature of the supply chain introduces a high level of uncertainty, thereby impacting the decision-making process in supply chain planning and influencing overall network performance. Based on the aforementioned issues, the focus of investigation includes the following: The examined supply chain network comprises four levels or subsystems, namely suppliers, manufacturers, distributors, and retailers. Raw materials are sourced from suppliers and sent to production factories, where each product is manufactured using a specific combination of raw materials. The products are then transported from manufacturers to distribution centers, and subsequently forwarded to retailers. The market is divided into different regions, and customer demands are fulfilled through visits to the retailers. Demand is assumed to be random and follows a probability distribution pattern. Consequently, simulation techniques are employed to estimate costs, including transportation costs, lost sales costs, and other expenses. Scenarios are created based on different perspectives at each level, focusing on inventory levels (minimum, maximum, and average). For each scenario, the values associated with the investigated objectives are estimated.Materials and methods In this research, data collection involved the examination of relevant literature, including articles published in international journals, books, and treatises. Documentary studies were conducted to gather information. To analyze the collected data, simulation and multi-objective programming concepts and methods were employed. Minitab and ED software were utilized for statistical analysis and simulation purposes.ConclusionsConsidering that the model can be solved under different conditions, including the current situation and various scenarios, the answers obtained for each state are Pareto optimal. This means that it is not possible to determine a single best answer for each state of the model. Therefore, before comparing the scenarios with each other, the Pareto optimal answers for each scenario should be ranked to identify the best options. In this research, a model for designing the supply chain network was presented, taking into account demand randomness. To better understand the proposed model and demonstrate its practicality, numerical examples were examined and evaluated using different scenarios and the Lingo software. It is important to note that the developed model in this study is independent of the number of facilities at each level of the supply chain and the parameter values. Therefore, the general form of this model can be applied to any production environment that aligns with the patterns presented in this research. The proposed model initially employed the design of experiments to estimate the mathematical relationship related to the cost objective function. After developing the multi-objective model, the Lingo software was used to solve the sample problem and evaluate the results under different scenarios. Finally, based on the Victor decision-making method, the Pareto optimal solutions for each state of the model were used to rank the answers and determine the best mode for the proposed models. Based on the obtained results, the third option or the second scenario is suggested as the preferred choice for the described model, considering the index values associated with each optio

Examining the potential and pitfalls of ChatGPT in science and engineering problem-solving

The study explores the capabilities of OpenAI's ChatGPT in solving different types of physics problems. ChatGPT (with GPT-4) was queried to solve a total of 40 problems from a college-level engineering physics course. These problems ranged from well-specified problems, where all data required for solving the problem was provided, to under-specified, real-world problems where not all necessary data were given. Our findings show that ChatGPT could successfully solve 62.5% of the well-specified problems, but its accuracy drops to 8.3% for under-specified problems. Analysis of the model's incorrect solutions revealed three distinct failure modes: (1) failure to construct accurate models of the physical world, (2) failure to make reasonable assumptions about missing data, and (3) calculation errors. The study offers implications for how to leverage LLM-augmented instructional materials to enhance STEM education. The insights also contribute to the broader discourse on AI's strengths and limitations, serving both educators aiming to leverage the technology and researchers investigating human-AI collaboration frameworks for problem-solving and decision-making

Rational positioning of 3D-printed voxels to realize high-fidelity multifunctional soft-hard interfaces

Living organisms use functional gradients (FGs) to interface hard and soft materials (e.g., bone and tendon), a strategy with engineering potential. Past attempts involving hard (or soft) phase ratio variation have led to mechanical property inaccuracies because of microscale-material macroscale-property nonlinearity. This study examines 3D-printed voxels from either hard or soft phase to decode this relationship. Combining micro/macroscale experiments and finite element simulations, a power law model emerges, linking voxel arrangement to composite properties. This model guides the creation of voxel-level FG structures, resulting in two biomimetic constructs mimicking specific bone-soft tissue interfaces with superior mechanical properties. Additionally, the model studies the FG influence on murine preosteoblast and human bone marrow-derived mesenchymal stromal cell (hBMSC) morphology and protein expression, driving rational design of soft-hard interfaces in biomedical applications.</p