IMPROVING XIAOMI’S PRESENCE IN THE SWEDISH MARKET BY UNDERSTANDING YOUNG CONSUMERS

The main purpose of this study is to define how Xiaomi can improve its market share in Sweden by understanding young consumers’ preferences for smartphone brands. The purpose is to let the case company know more about the Swedish market. This study uses the method of online questionnaire and quantitative method is being used for creating development suggestions for the case company. The main data in this study was collected through the responses of the respondents in the Swedish area. The theoretical part of this study consists of global marketing strategies, such as SWOT, PEST analysis, and combines with the customer buying decision strategy in order to analyze purchasing behavior and interests of consumers. In addition, internal and external analysis tools are introduced to support the theoretical results. The thesis presents steps on how the case company can enter the Swedish market. Moreover, the thesis presents how the case company can make its smartphone products more attractive to young Swedish customers

VegaProf: Profiling Vega Visualizations

Vega is a popular domain-specific language (DSL) for visualization specification. At runtime, Vega's DSL is first transformed into a dataflow graph and then functions to render visualization primitives. While the Vega abstraction of implementation details simplifies visualization creation, it also makes Vega visualizations challenging to debug and profile without adequate tools. Our formative interviews with three practitioners at Sigma Computing showed that existing developer tools are not suited for visualization profiling as they are disconnected from the semantics of the Vega DSL specification and its resulting dataflow graph. We introduce VegaProf, the first performance profiler for Vega visualizations. VegaProf effectively instruments the Vega library by associating the declarative specification with its compilation and execution. Using interactive visualizations, VegaProf enables visualization engineers to interactively profile visualization performance at three abstraction levels: function, dataflow graph, and visualization specification. Our evaluation through two use cases and feedback from five visualization engineers at Sigma Computing shows that VegaProf makes visualization profiling tractable and actionable.Comment: Submitted to EuroVis'2

Interface magnetic and electrical properties of CoFeB /InAs heterostructures

Amorphous magnetic CoFeB ultrathin films have been synthesized on the narrow band gap semiconductor InAs(100) surface, and the nature of the interface magnetic anisotropy and electrical contact has been studied. Angle-dependent hysteresis loops reveal that the films have an in-plane uniaxial magnetic anisotropy (UMA) with the easy axis along the InAs [0-11] crystal direction. The UMA was found to be dependent on the annealing temperatures of the substrates, which indicates the significant role of the Fe, Co-As bonding at the interface related to the surface condition of the InAs(100). I-V measurements show an ohmic contact interface between the CoFeB films and the InAs substrates, which is not affected by the surface condition of the InAs (100)

OWL: A Large Language Model for IT Operations

With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques of Natural Language Processing (NLP) have shown remarkable capabilities for various tasks, including named entity recognition, machine translation and dialogue systems. Recently, Large Language Models (LLMs) have achieved significant improvements across various NLP downstream tasks. However, there is a lack of specialized LLMs for IT operations. In this paper, we introduce the OWL, a large language model trained on our collected OWL-Instruct dataset with a wide range of IT-related information, where the mixture-of-adapter strategy is proposed to improve the parameter-efficient tuning across different domains or tasks. Furthermore, we evaluate the performance of our OWL on the OWL-Bench established by us and open IT-related benchmarks. OWL demonstrates superior performance results on IT tasks, which outperforms existing models by significant margins. Moreover, we hope that the findings of our work will provide more insights to revolutionize the techniques of IT operations with specialized LLMs.Comment: 31 page

Targeting Radioresistant Breast Cancer Cells by Single Agent CHK1 Inhibitor via Enhancing Replication Stress

Radiotherapy (RT) remains a standard therapeutic modality for breast cancer patients. However, intrinsic or acquired resistance limits the efficacy of RT. Here, we demonstrate that CHK1 inhibitor AZD7762 alone significantly inhibited the growth of radioresistant breast cancer cells (RBCC). Given the critical role of ATR/CHK1 signaling in suppressing oncogene-induced replication stress (RS), we hypothesize that CHK1 inhibition leads to the specific killing for RBCC due to its abrogation in the suppression of RS induced by oncogenes. In agreement, the expression of oncogenes c-Myc/CDC25A/c-Src/H-ras/E2F1 and DNA damage response (DDR) proteins ATR/CHK1/BRCA1/CtIP were elevated in RBCC. AZD7762 exposure led to significantly higher levels of RS in RBCC, compared to the parental cells. The mechanisms by which CHK1 inhibition led to specific increase of RS in RBCC were related to the interruptions in the replication fork dynamics and the homologous recombination (HR). In summary, RBCC activate oncogenic pathways and thus depend upon mechanisms controlled by CHK1 signaling to maintain RS under control for survival. Our study provided the first example where upregulating RS by CHK1 inhibitor contributes to the specific killing of RBCC, and highlight the importance of the CHK1 as a potential target for treatment of radioresistant cancer cells

Targeting Radioresistant Breast Cancer Cells by Single Agent CHK1 Inhibitor via Enhancing Replication Stress

Radiotherapy (RT) remains a standard therapeutic modality for breast cancer patients. However, intrinsic or acquired resistance limits the efficacy of RT. Here, we demonstrate that CHK1 inhibitor AZD7762 alone significantly inhibited the growth of radioresistant breast cancer cells (RBCC). Given the critical role of ATR/CHK1 signaling in suppressing oncogene-induced replication stress (RS), we hypothesize that CHK1 inhibition leads to the specific killing for RBCC due to its abrogation in the suppression of RS induced by oncogenes. In agreement, the expression of oncogenes c-Myc/CDC25A/c-Src/H-ras/E2F1 and DNA damage response (DDR) proteins ATR/CHK1/BRCA1/CtIP were elevated in RBCC. AZD7762 exposure led to significantly higher levels of RS in RBCC, compared to the parental cells. The mechanisms by which CHK1 inhibition led to specific increase of RS in RBCC were related to the interruptions in the replication fork dynamics and the homologous recombination (HR). In summary, RBCC activate oncogenic pathways and thus depend upon mechanisms controlled by CHK1 signaling to maintain RS under control for survival. Our study provided the first example where upregulating RS by CHK1 inhibitor contributes to the specific killing of RBCC, and highlight the importance of the CHK1 as a potential target for treatment of radioresistant cancer cells

Thermal induced spin-polarized current protected by spin-momentum locking in nanowires

Spin-momentum locking arising from strong spin-orbit coupling is one of the key natures of topological materials. Since charge can induce a spin polarization due to spin-momentum locking, the search for materials that exhibit this feature has become one of the top priorities in the field of spintronics. In this paper, we report the electrical detection of the spin-transport properties of nanowires, using a nonlocal geometry measurement. A clear hysteresis voltage signal, which depends on the relative orientations between the magnetization of the ferromagnetic electrodes and the carrier spin polarization, has been observed. The hysteresis voltage states can be reversed by altering the electron movement direction, providing direct evidence of the spin-momentum locking feature of nanowires and revealing its topological nature. Furthermore, the current-dependent measurement suggests that the charge (spin) current is induced by thermal effect, which utilizes the thermoelectric properties of . Using the thermal effect to control the spin-polarized current protected by spin-momentum locking offers possibilities for small-sized devices based on the topological materials

Recent Development of Nano-Materials Used in DNA Biosensors

As knowledge of the structure and function of nucleic acid molecules has increased, sequence-specific DNA detection has gained increased importance. DNA biosensors based on nucleic acid hybridization have been actively developed because of their specificity, speed, portability, and low cost. Recently, there has been considerable interest in using nano-materials for DNA biosensors. Because of their high surface-to-volume ratios and excellent biological compatibilities, nano-materials could be used to increase the amount of DNA immobilization; moreover, DNA bound to nano-materials can maintain its biological activity. Alternatively, signal amplification by labeling a targeted analyte with nano-materials has also been reported for DNA biosensors in many papers. This review summarizes the applications of various nano-materials for DNA biosensors during past five years. We found that nano-materials of small sizes were advantageous as substrates for DNA attachment or as labels for signal amplification; and use of two or more types of nano-materials in the biosensors could improve their overall quality and to overcome the deficiencies of the individual nano-components. Most current DNA biosensors require the use of polymerase chain reaction (PCR) in their protocols. However, further development of nano-materials with smaller size and/or with improved biological and chemical properties would substantially enhance the accuracy, selectivity and sensitivity of DNA biosensors. Thus, DNA biosensors without PCR amplification may become a reality in the foreseeable future

Multimodal MRI-based classification of migraine: using deep learning convolutional neural network

Abstract Background Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characterized by unilateral, throbbing and pulsating headaches. Unfortunately, a large number of migraineurs do not receive the accurate diagnosis when using traditional diagnostic criteria based on the guidelines of the International Headache Society. As such, there is substantial interest in developing automated methods to assist in the diagnosis of migraine. Methods To the best of our knowledge, no studies have evaluated the potential of deep learning technologies in assisting with the classification of migraine patients. Here, we used deep learning methods in combination with three functional measures (the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength) based on rs-fMRI data to distinguish not only between migraineurs and healthy controls, but also between the two subtypes of migraine. We employed 21 migraine patients without aura, 15 migraineurs with aura, and 28 healthy controls. Results Compared with the traditional support vector machine classifier, which has an accuracy of 83.67%, our Inception module-based convolutional neural network approach showed a significant improvement in classification output (over 86.18%). Our data also indicate that the Inception module-based CNN performs better than the AlexNet-based CNN (Inception module-based CNN reached an accuracy of 99.25%). Finally, we also found that regional functional correlation strength (RFCS) could be regarded as the optimum input out of the three indices (ALFF, ReHo, RFCS). Conclusions Overall, our study shows that combining the three functional measures of rs-fMRI with deep learning classification is a powerful method to distinguish between migraineurs and healthy individuals. Our data also highlight that deep learning-based frameworks could be used to develop more complicated models or systems to aid in clinical decision making in the future