İ. Şahin

This manuscript originated as an undergraduate research project and is intended as an exploratory formulation of the proposed connection between compact spatial topology and early-universe configurational capacity. A substantially more developed treatment, particularly of the statistical-mechanical interpretation, spectral comparison, and the scope of the connection to the Past Hypothesis would be required to place the proposal on a more complete theoretical footing; such an extension is presently under consideration. We present a topological perspective on the so-called Past Hypothesis, understood as a proposed explanation for the low-entropy character of the early universe, by analyzing the accessible phase space of quantum fields in a closed Friedmann-Lemaître-RobertsonWalker (FLRW) universe. On spatial slices with S³ topology, field configurations must satisfy global boundary conditions, leading to a discrete mode spectrum and an infrared (IR) cutoff set by the curvature radius, R(t). By comparing the resulting compact spectrum with the volume-matched flat-space continuum at fixed physical ultraviolet (UV) cutoff, we show that compact spatial topology possesses a lower configurational entropy capacity for accessible momentum configurations than its non-compact counterpart. This restriction is strongest at early times, when R(t) is small, and is progressively relaxed as cosmic expansion densifies the allowed spectrum. Our result identifies compact spatial topology as a geometric regulator of IR phase space and suggests a kinematical mechanism by which closed topology may contribute to the low entropy assumed in the Past Hypothesis.

As product diversity continues to expand in today’s market, there is an increasing demand from customers for unique and varied items. Meeting these demands necessitates the transfer of different sub-product components to the production line, even within the same manufacturing process. Lean manufacturing has addressed these challenges through the development of kitting systems that streamline the handling of diverse components. However, to ensure that these systems contribute to sustainable practices, it is crucial to design and implement them with environmental considerations in mind. The optimization of warehouse layouts and kitting preparation areas is essential for achieving sustainable and efficient logistics. To this end, we propose a comprehensive study aimed at developing the optimal layout, that is, creating warehouse layouts and kitting preparation zones that minimize waste, reduce energy consumption, and improve the flow of materials. The problem of warehouse location assignment is classified as NP-hard, and the complexity increases significantly when both storage and kitting layouts are considered simultaneously. This study aims to address this challenge by employing the genetic algorithm (GA) and Ant Colony Optimization (ACO) methods to design a system that minimizes energy consumption. Through the implementation of genetic algorithms (GAs), a 24% improvement was observed. This enhancement was achieved by simultaneously optimizing both the warehouse layout and the kitting area, demonstrating the effectiveness of integrated operational strategies. This substantial reduction not only contributes to lower operational costs but also aligns with sustainability goals, highlighting the importance of efficient material handling practices in modern logistics operations. This article provides a significant contribution to the field of sustainable logistics by addressing the vital role of kitting systems within green supply chain management practices. By aligning logistics operations with sustainability goals, this study not only offers practical insights but also advances the broader conversation around environmentally conscious supply chain practices.

Recently, an argument has been presented against the Pusey–Barrett–Rudolph (PBR) theorem [Found Phys 53, 64 (2023)]. This claim has sparked a debate, with some authors defending the PBR theorem [Found Phys 54, 36 (2024)], but the proponent of the claim has insisted on his argument [Found Phys 54, 69 (2024); Found Phys 54, 48 (2024)]. Moreover, the author claimed to have proved that the PBR theorem is incorrect by a generic counterexample [Found Phys 54, 48 (2024)]. In this paper, we contribute to the discussion with some new arguments. We demonstrate that the PBR theorem contains no errors and remains intact.

Objective: The objective of this study is to distribute the operations time between the operators according to the production mix for a new product type to be added to the relevant module in the existing production line, and to integrate the new product with high workload time into the existing production line without adding a new operator. Theoretical Framework: Assembly line balancing is an important performance enhancement method that is frequently used to improve the performance of manufacturing systems. Method: Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques useful for developing, improving, and optimizing processes. A discrete event simulation model can be defined as a model where the state variable changes only at these discrete points in time when events occur. Results and Discussion: As a result of the research, the production line can produce 386 products under the condition of not having two consecutive units of X202, with a product mix ratio of 85%-15%. Research Implications: The practicality of use and the consistency of the results obtained make the integration of response surface experimental design with simulation a method that can be applied in both academic and industrial settings. Originality/Value: This study contributes to the literature by developing to balance the assembly line under constraints, the simulation model of the line model is established and the optimization of the model parameters with the response surface has not been discussed in the literature.

In this paper we consider time as a property of a preparation or a quantum system and investigate whether it is an epistemic property according to Harrigan and Spekkens’ criterion [Found Phys 40, 125–157 (2010)]. To be precise, using a tabletop setup where the predictions of quantum mechanics and general relativity can be combined, we prepare a quantum state such that it is a quantum superposition of the different durations ticked by a quantum mechanical clock moving in spacetime. Such a preparation provides a quantum system that includes time as an intrinsic property. Indeed, changing the weights in the superposition changes the time as an expectation value as well as the possible different clock readings and histories. With a proof similar to the Pusey-Barrett-Rudolph (PBR) theorem [Nature Phys 8, 475–478 (2012)], it is shown that time in quantum theory is not an epistemic notion, but has ontological reality. In fact, the PBR theorem implies an ontic notion of time. Indeed, by proving that the quantum state is a physical property, the PBR theorem requires quantum state reduction to be a physical process. In this case, quantum probabilities are intrinsic probabilities without epistemic origin, and they generate a genuinely new sequence of events. This novelty introduced by quantum probabilities can be interpreted as time. However, although the PBR theorem implies this result, it does not prove it. First, an additional assumption is required to ensure the intrinsic character of quantum probabilities. Second, the PBR theorem is not constructed to prove that time is ontic, but to prove that ψ is ontic. All these issues are discussed in detail in the paper.

Objective: This paper is an examination of the use of Markov analysis in market share prediction, and the use of Visual Basic for Applications (VBA) to increase the speed and precision of the computation and model. Theoretical Framework: The Markov analysis, used to predict future states from current transitions, sheds light on the consumer behavior and brand loyalty dynamics With VBA, the data processing can be automated, and transition matrices built that are dynamic, so that forecasts can be updated in real time. Method: Markov analysis is based on transition matrices, which define the probabilities of transitioning from one state to another. These matrices are then used to predict long term probabilities by raising the transition matrix to some power that is equal to the number of future steps being studied. Results and Discussion: By utilizing Visual Basic for Applications (VBA), the study facilitates the swift creation of transition matrices and enables real-time modifications in response to new data. This implies that with the use of VBA and Markov analysis, not only is the forecasting process made easier, but also more accurate predictions are made, and for that reason VBA should be one of the tools that any firm looking to maximize its market share strategies should employ. Research Implications: This capability results in a more flexible and responsive forecasting instrument, enhancing the overall adaptability of market share predictions. Originality/Value: The originality of this research is found in its innovative methodological framework that builds upon conventional Markov analysis.

Objective: The objective of this study is to implement the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. Theoretical Framework: Implementing the results of the changes we desire on a real-life system through trial and error can lead to costly and risky outcomes. Simulation allows us to develop a computer model of an existing system, enabling us to understand the system's behavior. Method: Monte Carlo Simulation, a widely used simulation technique, utilizes random sampling to estimate possible outcomes of a process or experiment. Monte Carlo Simulation utilizes random sampling to estimate the outcomes of an experiment. Results and Discussion: The purpose of this study is to implement the Monte Carlo Simulation method using MS Excel Visual Basic Application. When the user interacts with the Excel interface by clicking the "Calculate" button, the system generates random numbers based on predefined probability distributions. Subsequently, statistical measures such as the mean, variance, standard deviation, median, mode, range, and extreme values (maximum and minimum) are calculated using VBA. Research Implications: Thanks to the developed application, Monte Carlo Simulation applications can be calculated easily. Originality/Value: This study contributes to the literature by implementing the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. These statistics provide insights into the underlying randomness and variability of the system, which aids in decision-making processes.

In this paper, inspired by quantum field theory, or more specifically QED, we propose a dynamical model for relativity. By adopting the approach provided by this dynamical model, we provide a dynamical explanation for relativistic phenomena such as length contraction, time dilation and clock desynchronization. The result of the dynamical approach for clock desynchronization is especially interesting. Our dynamical approach exhausts the freedom for different clock synchronizations on frames accelerated from an initial reference frame, which is allowed by Einstein’s principle approach. Furthermore, our dynamical approach arrives at the interesting conclusion that Einstein’s equivalence principle and gravitational redshift are necessary to explain special relativistic clock desynchronization. This result shows that special relativity by itself is an incomplete theory. If our dynamical model assumptions are assumed to be valid at a fundamental level, it implies some modifications to quantum field theory and general relativity via the equivalence principle. These modifications have been discussed.