A3 thinking approach to support knowledge-driven design

Problem solving is a crucial skill in product development. Any lack of effective decision making at an early design stage will affect productivity and increase costs and the lead time for the other stages of the product development life cycle. This could be improved by the use of a simple and informative approach which allows the designers and engineers to make decisions in product design by providing useful knowledge. This paper presents a novel A3 thinking approach to problem solving in product design, and provides a new A3 template which is structured from a combination of customised elements (e.g. the 8 Disciplines approach) and reflection practice. This approach was validated using a case study in the Electromagnetic Compatibility (EMC) design issue for an automotive electrical sub-assembly product. The main advantage of the developed approach is to create and capture the useful knowledge in a simple manner. Moreover, the approach provides a reflection section allowing the designers to turn their experience of design problem solving into proper learning and to represent their understanding of the design solution. These will be systematically structured (e.g. as a design checklist) to be circulated and shared as a reference for future design projects. Thus, the recurrence of similar design problems will be prevented and will aid the designers in adopting the expected EMC test results

Quantum information processing using Josephson junctions coupled through cavities

Josephson junctions have been shown to be a promising solid-state system for implementation of quantum computation. The significant two-qubit gates are generally realized by the capacitive coupling between the nearest neighbour qubits. We propose an effective Hamiltonian to describe charge qubits coupled through the cavity. We find that nontrivial two-qubit gates may be achieved by this coupling. The ability to interconvert localized charge qubits and flying qubits in the proposed scheme implies that quantum network can be constructed using this large scalable solid-state system.Comment: 5 pages, to appear in Phys Rev A; typos corrected, solutions in last eqs. correcte

Proteomics of adjacent-to-tumor samples uncovers clinically relevant biological events in hepatocellular carcinoma

Normal adjacent tissues (NATs) of hepatocellular carcinoma (HCC) differ from healthy liver tissues and their heterogeneity may contain biological information associated with disease occurrence and clinical outcome that has yet to be fully evaluated at the proteomic level. This study provides a detailed description of the heterogeneity of NATs and the differences between NATs and healthy livers and revealed that molecular features of tumor subgroups in HCC were partially reflected in their respective NATs. Proteomic data classified HCC NATs into two subtypes (Subtypes 1 and 2), and Subtype 2 was associated with poor prognosis and high-risk recurrence. The pathway and immune features of these two subtypes were characterized. Proteomic differences between the two NAT subtypes and healthy liver tissues were further investigated using data-independent acquisition mass spectrometry, revealing the early molecular alterations associated with the progression from healthy livers to NATs. This study provides a high-quality resource for HCC researchers and clinicians and may significantly expand the knowledge of tumor NATs to eventually benefit clinical practice

Global and local economic impacts of climate change in Syria and options for adaptation:

There is broad consensus among scientists that climate change is altering weather patterns around the world. However, economists are only beginning to develop tools that allow for the quantification of such weather changes on countries' economies and people. This paper presents a modeling suite that links the downscaling of global climate models, crop modeling, global economic modeling, and subnational-level computable equilibrium modeling. Important to note is that this approach allows for decomposing the potential global and local economic effects on countries, including various economic sectors and different household groups. We apply this modeling suite to Syria, a relevant case study given the country's location in a region that is consistently projected to be among those hit hardest by climate change. Despite a certain degree of endogenous adaptation, local impacts of climate change (through declining yields) are likely to affect Syria beyond the agricultural sector and farmers and also reduce economy-wide growth and incomes of urban households in the long term. The overall effects of global climate change (through higher food prices) are also negative, but some farmers can reap the benefit of higher prices. Combining local and global climate change scenarios shows welfare losses across all rural and urban household groups of between 1.6 – 2.8 percent annually, whereas the poorest household groups are the hardest hit. Finally, while there is some evidence that droughts may become more frequent in the future, it is clear that even without an increase in frequency, drought impacts will continue to put a significant burden on Syria's economy and people. Action to mitigate the negative effects of climate change and variability should to be taken on the global and local level. A global action plan for improving food security and better integration of climate change in national development strategies, agricultural and rural policies, and disaster risk management and social protection policies will be keys for improving the resilience of countries and people to climate change.Climate change, Development, drought, Growth, Poverty,

Symptomatic CNS Radiation Necrosis Requiring Neurosurgical Resection During Treatment with Lorlatinib in ALK-Rearranged NSCLC: A Report of Two Cases.

Central nervous system (CNS) metastasis carries a significant morbidity and mortality in anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC). Next-generation ALK tyrosine kinase inhibitors (TKIs) are highly CNS-penetrant and have demonstrated remarkable intracranial activity across clinical studies, and yet radiation remains the mainstay of treatment modality against CNS metastasis. We have previously reported alectinib can induce CNS radiation necrosis even after a remote history of radiation (7 years post-radiation). Lorlatinib is another potent next-generation ALK TKI that can overcome many ALK resistance mutations and has been shown to have excellent activity in patients with baseline CNS metastasis. Here we report two ALK-rearranged NSCLC patients who developed radiation necrosis shortly after initiating lorlatinib following progression on the sequential treatment of crizotinib, alectinib, and brigatinib. In both cases, radiation necrosis is evidenced by serial MRI images and histological examination of the resected CNS metastasis that had previously been radiated. Our cases highlight the importance of recognizing CNS radiation necrosis that may mimic disease progression in ALK-rearranged NSCLC treated with and potentially precipated by next-generation ALK TKIs

A more accurate analog voltage-based photovoltaic maximum power point tracking technique

© 2017 IEEE. In this paper, an analog voltage based maximum power point tracking (MPPT) algorithm for individual photovoltaic (PV) panel is proposed. The fixed voltage reference method is the simplest method for tracking, but it does not give good MPPT efficiency because the MPP voltage point changes at different solar insolation levels. A roughly linear slope is formed when connecting the MPP points measured from the highest isolation level to the lowest. Utilizing this characteristic, a bipolar junction transistor BJT is used to implement a variable voltage reference that improves the accuracy of the maximum power point voltage when the insolation changes. The proposed circuit is simple and easy to implement and it can easily track the maximum power point without the need of a digital controller or PID controller, so the cost and circuit complexity is reduced

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Picosecond ionization dynamics in femtosecond filaments at high pressures

We investigate the plasma dynamics inside a femtosecond-pulse-induced filament generated in an argon gas for a wide range of pressures up to 60 bar. At higher pressures, we observe ionization immediately following a pulse, with up to a threefold increase in the electron density within 30 ps after the filamentary propagation of a femtosecond pulse. Our study suggests that this picosecond evolution can be attributed to collisional ionization including Penning and associative ionizations and electron-impact ionization of excited atoms generated during the pulse. The dominance of excited atoms over ionized atoms at the end of the pulse also indicates an intrapulse inhibition of avalanche ionization. This delayed ionization dynamics provides evidence for diagnosing atomic and molecular excitation and ionization in intense laser interaction with high-pressure gases