Why Just Boogie? Translating Between Intermediate Verification Languages

The verification systems Boogie and Why3 use their respective intermediate languages to generate verification conditions from high-level programs. Since the two systems support different back-end provers (such as Z3 and Alt-Ergo) and are used to encode different high-level languages (such as C# and Java), being able to translate between their intermediate languages would provide a way to reuse one system's features to verify programs meant for the other. This paper describes a translation of Boogie into WhyML (Why3's intermediate language) that preserves semantics, verifiability, and program structure to a large degree. We implemented the translation as a tool and applied it to 194 Boogie-verified programs of various sources and sizes; Why3 verified 83% of the translated programs with the same outcome as Boogie. These results indicate that the translation is often effective and practically applicable

CyBERT: Cybersecurity Claim Classification by Fine-Tuning the BERT Language Model

We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS). To train CyBERT, we created a corpus of labeled sequences from ICS device documentation collected across a wide range of vendors and devices. This corpus provides the foundation for fine-tuning BERT’s language model, including a prediction-guided relabeling process. We propose an approach to obtain optimal hyperparameters, including the learning rate, the number of dense layers, and their configuration, to increase the accuracy of our classifier. Fine-tuning all hyperparameters of the resulting model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original architecture to 94.4% obtained with CyBERT. Furthermore, we evaluated CyBERT for the impact of randomness in the initialization, training, and data-sampling phases. CyBERT demonstrated a standard deviation of ±0.6% during validation across 100 random seed values. Finally, we also compared the performance of CyBERT to other well-established language models including GPT2, ULMFiT, and ELMo, as well as neural network models such as CNN, LSTM, and BiLSTM. The results showed that CyBERT outperforms these models on the validation accuracy and the F1 score, validating CyBERT’s robustness and accuracy as a cybersecurity feature claims classifier

Organizational culture, leadership style and effectiveness: A case study of middle eastern construction clients

During the last few decades, organizational effectiveness has received a great deal of attention in many industrial sectors. As a result, a variety of models have been formulated which measure organizational performance. In the construction industry, two factors have subsequently captured the imagination and interest of researchers and practitioners alike: the culture of the organization and the leadership style of project managers. This focus places a requirement upon construction organizations to recognize and understand their organizational culture, and equally, to clearly communicate it to their employees as part of their capitalist drive of constantly improving performance, productivity and profit. Traditional ways of conducting construction business require a sound understanding of the technical and managerial demands of executing projects, which in turn, places an increased emphasis upon the management and leadership competencies of individual project managers. The purpose of the research is to explore the relationship between organizational culture, authentic leadership style and effectiveness within the context of a case study investigation centred on Middle Eastern construction clients and their project managers. The outcomes of the investigation, which include the presentation of an explanatory model, indicate that organizational culture is directly and positively related to performance and effectiveness, while project managers' leadership style has an indirect relationship to effectiveness. A strong organizational culture is therefore deemed critical to organizational performance

Automated robot‐assisted surgical skill evaluation: Predictive analytics approach

BackgroundSurgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot‐assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.MethodsEight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise – novice and expert. Three classification methods – k‐nearest neighbours, logistic regression and support vector machines – are applied.ResultsThe result shows that the proposed framework can classify surgeons’ expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.ConclusionThis study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/1/rcs1850.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/2/rcs1850_am.pd

Nuclear localization of the mitochondrial factor HIGD1A during metabolic stress.

Cellular stress responses are frequently governed by the subcellular localization of critical effector proteins. Apoptosis-inducing Factor (AIF) or Glyceraldehyde 3-Phosphate Dehydrogenase (GAPDH), for example, can translocate from mitochondria to the nucleus, where they modulate apoptotic death pathways. Hypoxia-inducible gene domain 1A (HIGD1A) is a mitochondrial protein regulated by Hypoxia-inducible Factor-1α (HIF1α). Here we show that while HIGD1A resides in mitochondria during physiological hypoxia, severe metabolic stress, such as glucose starvation coupled with hypoxia, in addition to DNA damage induced by etoposide, triggers its nuclear accumulation. We show that nuclear localization of HIGD1A overlaps with that of AIF, and is dependent on the presence of BAX and BAK. Furthermore, we show that AIF and HIGD1A physically interact. Additionally, we demonstrate that nuclear HIGD1A is a potential marker of metabolic stress in vivo, frequently observed in diverse pathological states such as myocardial infarction, hypoxic-ischemic encephalopathy (HIE), and different types of cancer. In summary, we demonstrate a novel nuclear localization of HIGD1A that is commonly observed in human disease processes in vivo

Favorable Mixing Thermodynamics in Ternary Polymer Blends for Realizing High Efficiency Plastic Solar Cells

Ternary blends with broad spectral absorption have the potential to increase charge generation in organic solar cells but feature additional complexity due to limited intermixing and electronic mismatch. Here, a model system comprising the polymers poly[5,5-bis(2-butyloctyl)-(2,2-bithiophene)-4,4-dicarboxylate-alt-5,5-2,2-bithiophene] (PDCBT) and PTB7-Th and PC70BM as an electron accepting unit is presented. The power conversion efficiency (PCE) of the ternary system clearly surpasses the performance of either of the binary systems. The photophysics is governed by a fast energy transfer process from PDCBT to PTB7-Th, followed by electron transfer at the PTB7-Th:fullerene interface. The morphological motif in the ternary blend is characterized by polymer fibers. Based on a combination of photophysical analysis, GIWAXS measurements and calculation of the intermolecular parameter, the latter indicating a very favorable molecular affinity between PDCBT and PTB7-Th, it is proposed that an efficient charge generation mechanism is possible because PTB7-Th predominantly orients around PDCBT filaments, allowing energy to be effectively relayed from PDCBT to PTB7-Th. Fullerene can be replaced by a nonfullerene acceptor without sacrifices in charge generation, achieving a PCE above 11%. These results support the idea that thermodynamic mixing and energetics of the polymer-polymer interface are critical design parameter for realizing highly efficient ternary solar cells with variable electron acceptors

Interface Molecular engineering for laminated monolithic perovskite/silicon tandem solar cells with 80.4% fill factor

A multipurpose interconnection layer based on poly(3,4‐ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS), and d‐sorbitol for monolithic perovskite/silicon tandem solar cells is introduced. The interconnection of independently processed silicon and perovskite subcells is a simple add‐on lamination step, alleviating common fabrication complexities of tandem devices. It is demonstrated experimentally and theoretically that PEDOT:PSS is an ideal building block for manipulating the mechanical and electrical functionality of the charge recombination layer by controlling the microstructure on the nano‐ and mesoscale. It is elucidated that the optimal functionality of the recombination layer relies on a gradient in the d‐sorbitol dopant distribution that modulates the orientation of PEDOT across the PEDOT:PSS film. Using this modified PEDOT:PSS composite, a monolithic two‐terminal perovskite/silicon tandem solar cell with a steady‐state efficiency of 21.0%, a fill factor of 80.4%, and negligible open circuit voltage losses compared to single‐junction devices is shown. The versatility of this approach is further validated by presenting a laminated two‐terminal monolithic perovskite/organic tandem solar cell with 11.7% power conversion efficiency. It is envisioned that this lamination concept can be applied for the pairing of multiple photovoltaic and other thin film technologies, creating a universal platform that facilitates mass production of tandem devices with high efficiency

Dengue Virus Infection of Aedes aegypti Requires a Putative Cysteine Rich Venom Protein

Citation: Londono-Renteria, B., Troupin, A., Conway, M. J., Vesely, D., Ledizet, M., Roundy, C. M., . . . Colpitts, T. M. (2015). Dengue Virus Infection of Aedes aegypti Requires a Putative Cysteine Rich Venom Protein. Plos Pathogens, 11(10), 23. doi:10.1371/journal.ppat.1005202Dengue virus (DENV) is a mosquito-borne flavivirus that causes serious human disease and mortality worldwide. There is no specific antiviral therapy or vaccine for DENV infection. Alterations in gene expression during DENV infection of the mosquito and the impact of these changes on virus infection are important events to investigate in hopes of creating new treatments and vaccines. We previously identified 203 genes that were >= 5-fold differentially upregulated during flavivirus infection of the mosquito. Here, we examined the impact of silencing 100 of the most highly upregulated gene targets on DENV infection in its mosquito vector. We identified 20 genes that reduced DENV infection by at least 60% when silenced. We focused on one gene, a putative cysteine rich venom protein (SeqID AAEL000379; CRVP379), whose silencing significantly reduced DENV infection in Aedes aegypti cells. Here, we examine the requirement for CRVP379 during DENV infection of the mosquito and investigate the mechanisms surrounding this phenomenon. We also show that blocking CRVP379 protein with either RNAi or specific antisera inhibits DENV infection in Aedes aegypti. This work identifies a novel mosquito gene target for controlling DENV infection in mosquitoes that may also be used to develop broad preventative and therapeutic measures for multiple flaviviruses

Roadmap on Photovoltaic Absorber Materials for Sustainable Energy Conversion

Photovoltaics (PVs) are a critical technology for curbing growing levels of anthropogenic greenhouse gas emissions, and meeting increases in future demand for low-carbon electricity. In order to fulfil ambitions for net-zero carbon dioxide equivalent (CO2eq) emissions worldwide, the global cumulative capacity of solar PVs must increase by an order of magnitude from 0.9 TWp in 2021 to 8.5 TWp by 2050 according to the International Renewable Energy Agency, which is considered to be a highly conservative estimate. In 2020, the Henry Royce Institute brought together the UK PV community to discuss the critical technological and infrastructure challenges that need to be overcome to address the vast challenges in accelerating PV deployment. Herein, we examine the key developments in the global community, especially the progress made in the field since this earlier roadmap, bringing together experts primarily from the UK across the breadth of the photovoltaics community. The focus is both on the challenges in improving the efficiency, stability and levelized cost of electricity of current technologies for utility-scale PVs, as well as the fundamental questions in novel technologies that can have a significant impact on emerging markets, such as indoor PVs, space PVs, and agrivoltaics. We discuss challenges in advanced metrology and computational tools, as well as the growing synergies between PVs and solar fuels, and offer a perspective on the environmental sustainability of the PV industry. Through this roadmap, we emphasize promising pathways forward in both the short- and long-term, and for communities working on technologies across a range of maturity levels to learn from each other.Comment: 160 pages, 21 figure