A dark matter solution from the supersymmetric axion model

We study the effect of the late decaying saxino (the scalar superpartner of the axion) and find out that there is a possible dark matter solution from a class of supersymmetric extensions of the invisible axion model. In this class of models, the saxino which decays into two axions acts as the late decaying particle which reconciles the cold dark matter model with high values of the Hubble constant. Recent observations of the Hubble constant are converging to $H_0=70\!-\!80\,{\rm km}\,{\rm sec}^{-1}\,{\rm Mpc}^{-1}$, which would be inconsistent with the standard mixed dark matter model. This class of models provides a plausible framework for the alternative cold dark matter plus late decaying particle model, with the interesting possibility that both cold dark matter and the extra radiation consist of axion.Comment: 11 pages, no figure, REVTEX 3.

Frontal Sinus Lymphoma Presenting As Progressive Multiple Cranial Nerve Palsy

Primary frontal sinus lymphoma is a very uncommon disease. In all the previously reported cases, the presenting symptoms have been due to the tumor mass effect. We present an unusual case report of an immunocompetent patient who presented with facial palsy, and then progressively developed other cranial nerve palsies over several months. He was later diagnosed with diffuse large B cell lymphoma originating from the frontal sinus. The patient underwent chemotherapy, but eventually had to receive autologous peripheral blood stem cell transplantation. He is currently disease-free. The clinical course, diagnostic workup, and therapeutic outcome are described

Optimal portfolio choice of couples with tax-deferred accounts and survival-contingent products

Financial products for retirement planning generally have complex taxation structures and death conditions. In particular, tax-deferred accounts (TDAs) can provide tax-sheltered wealth accumulation by deferring taxes, even with the same financial products. Additionally, various survival-contingent products (SCPs), such as annuity products and life insurance contracts, have different payouts for policyholders. In this study, considering both the TDA and SCPs, we formulate and solve a couple???s lifetime portfolio choice problem using a multistage stochastic programming model. Owing to its high-dimensional state space and lifelong planning periods, stochastic dual dynamic programming (SDDP) was used to solve this problem. We find some interesting results; when both the TDA and SCPs are available, the portfolio is less concentrated in annuity holdings than when the TDA is unavailable. Moreover, the couple ends their contribution to the TA earlier than when SCPs are unavailable

Value Function Gradient Learning for Large-Scale Multistage Stochastic Programming Problems

A stagewise decomposition algorithm called ???value function gradient learning??? (VFGL) is proposed for large-scale multistage stochastic convex programs. VFGL finds the parameter values that best fit the gradient of the value function within a given parametric family. Widely used decomposition algorithms for multistage stochastic programming, such as stochastic dual dynamic programming (SDDP), approximate the value function by adding linear subgradient cuts at each iteration. Although this approach has been successful for linear problems, nonlinear problems may suffer from the increasing size of each subproblem as the iteration proceeds. On the other hand, VFGL has a fixed number of parameters; thus, the size of the subproblems remains constant throughout the iteration. Furthermore, VFGL can learn the parameters by means of stochastic gradient descent, which means that it can be easil0y parallelized and does not require a scenario tree approximation of the underlying uncertainties. VFGL was compared with a deterministic equivalent formulation of the multistage stochastic programming problem and SDDP approaches for three illustrative examples: production planning, hydrothermal generation, and the lifetime financial planning problem. Numerical examples show that VFGL generates high-quality solutions and is computationally efficient

Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems

Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments

Augmented Reality-Based BIM Data Compatibility Verification Method for FAB Digital Twin implementation

With the advancement of state-of-the-art technologies, the semiconductor industry plays a key role as an essential component in the manufacture of various electronic products. Since the manufacturing of a semiconductor goes through very sophisticated and complex processes, efficient and accurate work and management are essential in the design, construction, and operation stages of the semiconductor fabrication (FAB) plant. Recently, the combined application of building information modeling (BIM) and augmented reality (AR) technology has gained increased attention in this semiconductor FAB industry as an advanced way to improve work efficiency and accuracy while eliminating other related problems, such as human errors. Despite the perceived benefits of combined use of BIM and AR, many technical problems still exist when integrating the target test model and the 3D virtual object model using BIM data and existing AR visualization technology, due to the unique characteristics of the FAB sites. To solve these problems, this study proposed an AR-based real-time BIM data compatibility verification method for future FAB digital twin implementation and demonstrated that it could be converted into a system and applied to actual FAB sites. As a result of the development and verification of this system, the proposed AR-based real-time BIM data compatibility verification system enables the accurate fitting of the AR model and actual object through AR tracking and anchoring technology considering the characteristics of FAB sites. After the fitting, the system was able to maintain compatibility, even when the camera moved and the marker moved away from the screen. By expanding the effective distance of compatibility between the AR model and the actual object, it was possible to increase the AR application range between the 3D virtual object model and the test target model and to improve the compatibility

A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments

Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against various situations. In this paper, we propose a semantic navigation framework based on a Triplet Ontological Semantic Model (TOSM) to manage various conditions affecting the execution of tasks. The framework allows robots with different kinematics to perform tasks in indoor and outdoor environments. We define the TOSM-based semantic knowledge and generate a semantic map for the domains. The robots execute tasks according to their characteristics by converting inferred knowledge to Planning Domain Definition Language (PDDL). Additionally, to make the framework sustainable, we determine a policy of maintaining the map and re-planning when in unexpected situations. The various experiments on four different kinds of robots and four scenarios validate the scalability and reliability of the proposed framework

A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments

Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against various situations. In this paper, we propose a semantic navigation framework based on a Triplet Ontological Semantic Model (TOSM) to manage various conditions affecting the execution of tasks. The framework allows robots with different kinematics to perform tasks in indoor and outdoor environments. We define the TOSM-based semantic knowledge and generate a semantic map for the domains. The robots execute tasks according to their characteristics by converting inferred knowledge to Planning Domain Definition Language (PDDL). Additionally, to make the framework sustainable, we determine a policy of maintaining the map and re-planning when in unexpected situations. The various experiments on four different kinds of robots and four scenarios validate the scalability and reliability of the proposed framework

Anticancer Effects of 6-Gingerol through Downregulating Iron Transport and PD-L1 Expression in Non-Small Cell Lung Cancer Cells

Iron homeostasis is considered a key factor in human metabolism, and abrogation in the system could create adverse effects, including cancer. Moreover, 6-gingerol is a widely used bioactive phenolic compound with anticancer activity, and studies on its exact mechanisms on non-small cell lung cancer (NSCLC) cells are still undergoing. This study aimed to find the mechanism of cell death induction by 6-gingerol in NSCLC cells. Western blotting, real-time polymerase chain reaction, and flow cytometry were used for molecular signaling studies, and invasion and tumorsphere formation assay were also used with comet assay for cellular processes. Our results show that 6-gingerol inhibited cancer cell proliferation and induced DNA damage response, cell cycle arrest, and apoptosis in NSCLC cells, and cell death induction was found to be the mitochondrial-dependent intrinsic apoptosis pathway. The role of iron homeostasis in the cell death induction of 6-gingerol was also investigated, and iron metabolism played a vital role in the anticancer ability of 6-gingerol by downregulating EGFR/JAK2/STAT5b signaling or upregulating p53 and downregulating PD-L1 expression. Also, 6-gingerol induced miR-34a and miR-200c expression, which may indicate regulation of PD-L1 expression by 6-gingerol. These results suggest that 6-gingerol could be a candidate drug against NSCLC cells and that 6-gingerol could play a vital role in cancer immunotherapy

Bimodal Light-Harvesting Microfluidic System Using Upconversion Nanocrystals for Enhanced Flow Photocatalysis

Microfluidic systems with large surface-to-volume ratios and superior light transmission are used to efficiently transfer mass and convert energy, and to enhance photocatalytic reactions. Utilizing the entire solar spectrum for promoting photocatalytic reactions is highly desirable and near-infrared (NIR) radiation, in particular, has a high transmission efficiency through common polymers and materials used to construct microfluidic devices. Herein, a reliable microfluidic system using bimodal light-harvesting technique is reported to improve the photocatalytic efficiency of C(sp3)-H functionalization reactions using coumarin dye (C153) and lanthanide-doped upconversion nanocrystals (UCNs). Using two light-harvesting components (C153 and UCNs) in polycarbosilane polymer matrix, a bimodal light-harvesting microfluidic reactor is realized in which the inner surface of the microfluidic channel is reliably coated with a transparent composite of C153/UCNs to simultaneously downshift visible light and upconvert NIR light. A double-stacked microfluidic system that successfully enhanced the photocatalytic conversion efficiency of Rose Bengal-based aza-Henry photocatalytic reactions by twofold (approximate to 93% conversion). The study provides a design principle of next-generation microfluidic reactor for a robust photocatalytic organic synthesis