Miracle-dz/UniFAR-FTUs huggingface dataset:
ATTRACTION AND RETENTION OF BRITISH COLUMBIA TECHNOLOGY TALENT

This paper investigates issues related to attraction and retention of British Columbia (BC) technology workers. Academic research, interviews, and popular literature provide the basis for analysis. Motivational issues for workers and analysis of technology clusters are key research elements. The purpose of the report is to provide recommendations that address the identified recruiting and retention issues. This paper consists of eleven chapters. The first chapter identifies the key research questions. Chapter two summarizes the BC technology clusters. Chapter three describes technology worker motivational factors for geographic relocation. Chapters four, five, and six provide an analysis of BC against the motivational factors. Chapter seven describes recruiting strategies of other geographic areas. Chapter eight compares BC to general characteristics of technology clusters. Chapter nine discusses strategic alternatives. Chapter ten provides recommendations for improving BC's recruiting and retention. Chapter eleven summarizes the key findings. I would like to thank Dr Aidan Vining, Dr Michael Parent, and Dr Ian McCarthy at Simon Fraser University (SFU) for offering continual advice and counsel throughout my project. I also extend a deep thanks to Kirk Hill, Melissa McRae, and Heather Mounteer of the SFU Business Career Management Centre for helping me find this project. My personal thanks goes to Cindy Pearson at the British Columbia Technology Industries Association (BC TIA), who acted as my project sponsor. Her passion for the BC technology community is infectious! I am also thankful to Rob Cruickshank, the President of BC TIA, who helped me understand the reality of many key issues facing BC. I appreciate the support from Brent Holliday of Greenstone Venture Partners, Allison Rutherford of the HR Tech Group, Darcy O'Grady of Creo, and Don Avison of The University Presidents' Council of BC. Together with Rob and Cindy, they formed my project advisory group. Their guidance and suggestions about my approach and recommendations were indispensable to this project. Finally, I would like to thank friends from my days at Veridian Corporation. In particular, I would like to thank David Langstaff (formerly Chief Executive Officer of Veridian) and Dr Mike Hansen (formerly Senior Vice President of Organization Capability at Veridian). Their commitment to the employees of Veridian was extraordinary. My thanks also extend to Jack Walter, my division's Chief Financial Officer. He always found a way to take care of our employees. TABLE OF CONTENTS . Approval 11 . Abstract III Dedication iv Acknowledgements v Table of
Analytical Modeling for Identification of the Machine Code Architecture of Cyberphysical Devices in Smart Homes

Ensuring the security of modern cyberphysical devices is the most important task of the modern world. The reason for this is that such devices can cause not only informational, but also physical damage. One of the approaches to solving the problem is the static analysis of the machine code of the firmware of such devices. The situation becomes more complicated in the case of a Smart Home, since its devices can have different processor architectures (means instruction sets). In the case of cyberphysical devices of the Smart Home, the destruction of machine code due to physical influences is also possible. Therefore, the first step is to correctly identify the processor architecture. In the interests of this, a machine code model is proposed that has a formal notation and takes into account the possibility of code destruction. The article describes the full cycle of research (including experiment) in order to obtain this model. The model is based on byte-frequency machine code signatures. The experiment resulted in obtaining template signatures for the Top-16 processor architectures: Alpha, X32, Amd64, Arm64, Hppa64, I486, I686, Ia64, Mips, Mips64, Ppc, Ppc64, RiscV64, S390, S390x and Sparc64.
Supplementing Soy-Based Diet with Creatine in Rats: Implications for Cardiac Cell Signaling and Response to Doxorubicin

Nutritional habits can have a significant impact on cardiovascular health and disease. This may also apply to cardiotoxicity caused as a frequent side effect of chemotherapeutic drugs, such as doxorubicin (DXR). The aim of this work was to analyze if diet, in particular creatine (Cr) supplementation, can modulate cardiac biochemical (energy status, oxidative damage and antioxidant capacity, DNA integrity, cell signaling) and functional parameters at baseline and upon DXR treatment. Here, male Wistar rats were fed for 4 weeks with either standard rodent diet (NORMAL), soy-based diet (SOY), or Cr-supplemented soy-based diet (SOY + Cr). Hearts were either freeze-clamped in situ or following ex vivo Langendorff perfusion without or with 25 μM DXR and after recording cardiac function. The diets had distinct cardiac effects. Soy-based diet (SOY vs. NORMAL) did not alter cardiac performance but increased phosphorylation of acetyl-CoA carboxylase (ACC), indicating activation of rather pro-catabolic AMP-activated protein kinase (AMPK) signaling, consistent with increased ADP/ATP ratios and lower lipid peroxidation. Creatine addition to the soy-based diet (SOY + Cr vs. SOY) slightly increased left ventricular developed pressure (LVDP) and contractility dp/dt, as measured at baseline in perfused heart, and resulted in activation of the rather pro-anabolic protein kinases Akt and ERK. Challenging perfused heart with DXR, as analyzed across all nutritional regimens, deteriorated most cardiac functional parameters and also altered activation of the AMPK, ERK, and Akt signaling pathways. Despite partial reprogramming of cell signaling and metabolism in the rat heart, diet did not modify the functional response to supraclinical DXR concentrations in the used acute cardiotoxicity model. However, the long-term effect of these diets on cardiac sensitivity to chronic and clinically relevant DXR doses remains to be established.
Modified Ramped Current Stress Technique for Monitoring Thin Dielectrics Reliability and Charge Degradation

Herein, a novel technique to monitor the quality and reliability of thin nano‐scale dielectric films is suggested. The method is based on the modification of the current stress technique (J‐Ramp) described in the corresponding JEDEC standard. The proposed technique, in addition to the J‐ramp tests, allows monitoring a change of charge state of the gate dielectric during all the tests. To implement this, in the suggested method before each transition to a higher current level, the current level is switched to the injection mode by a measurement current level and the change of voltage across the metal–insulator–semiconductor (MIS) structure is measured. A density of the measurement current level is chosen based on the condition that under this current level, a considerable charge degradation of the dielectric should not be observed, and the switches to the measurement current level should not considerably influence the studied sample. In addition to the monitoring of the charge injected until the dielectric breakdown, the proposed method allows to monitor the value of the injected charge at which the unacceptable degradation of MIS device characteristics takes place and, accordingly, to estimate the reliability of the dielectric film based on statistical measurements of these values.
The Concurrent Learned Indexes for Multicore Data Storage

We present XIndex, which is a concurrent index library and designed for fast queries. It includes a concurrent ordered index (XIndex-R) and a concurrent hash index (XIndex-H). Similar to a recent proposal of the learned index, the indexes in XIndex use learned models to optimize index efficiency. Compared with the learned index, for the ordered index, XIndex-R is able to handle concurrent writes effectively and adapts its structure according to runtime workload characteristics. For the hash index, XIndex-H is able to avoid the resize operation blocking concurrent writes. Furthermore, the indexes in XIndex can index string keys much more efficiently than the learned index. We demonstrate the advantages of XIndex with YCSB, TPC-C (KV), which is a TPC-C-inspired benchmark for key-value stores, and micro-benchmarks. Compared with ordered indexes of Masstree and Wormhole, XIndex-R achieves up to 3.2× and 4.4× performance improvement on a 24-core machine. Compared with hash indexes of Intel TBB HashMap, XIndex-H achieves up to 3.1× speedup. The performance further improves by 91% after adding the optimizations on indexing string keys. The library is open-sourced.1
Theory and Experiment of Cooperative Control at Multi-Intersections in Intelligent Connected Vehicle Environment: Review and Perspectives

A heterogeneous traffic flow consists of regular vehicles, and intelligent connected vehicles having interactive functions is updating the composition of the current urban-road network traffic flow. It has been a growing trend and will continue to be so. Because of the urgent demand, the research focused on three main parts of cooperative control methods under intelligent connected vehicles environment, typical traffic control application scenarios and experimental validation in intelligent connected vehicles conditions, and intersection-oriented hybrid traffic control mechanism for urban road. For heterogeneous interrupted traffic flow of intelligent connected vehicles, to analyze the characteristics and information extraction method of heterogeneous traffic flow of intelligent connected vehicles under different conditions, the research examined driving modes of regular vehicles and intelligent connected vehicles, including car following and lane changing. This study summarized control modes of traffic-signal control, active control of intelligent connected vehicles, and indirect control of regular vehicles through intelligent vehicles to study the active control mechanism and multi-intersection coordinated control strategy for intelligent connected vehicle heterogeneous traffic flow. With the combination of coordinated control theory, this work overviewed integrated experiment of information interaction and coordinated control under intelligent-connected-vehicle heterogeneous traffic-flow environments.
Aggregating Features From Dual Paths for Remote Sensing Image Scene Classification

Scene classification is an important and challenging task employed toward understanding remote sensing images. Convolutional neural networks have been widely applied in remote sensing scene classification in recent years, boosting classification accuracy. However, with improvements in resolution, the categories of remote sensing images have become ever more fine-grained. The high intraclass diversity and interclass similarity are the main characteristics that differentiate remote scene image classification from natural image classification. To extract discriminative representation from images, we propose an end-to-end feature fusion method that aggregates features from dual paths (AFDP). First, lightweight convolutional neural networks with fewer parameters and calculations are used to construct a feature extractor with dual branches. Then, in the feature fusion stage, a novel feature fusion method that integrates the concepts of bilinear pooling and feature connection is adopted to learn discriminative features from images. The AFDP method was evaluated on three public remote sensing image benchmarks. The experimental results indicate that the AFDP method outperforms current state-of-the-art methods, with advantages of simple form, strong versatility, fewer parameters, and less calculation.
Habitat Suitability for Small Mammals in Mediterranean Landscapes: How and Why Shrubs Matter

Fires are usually seen as a threat for biodiversity conservation in the Mediterranean, but natural afforestation after abandonment of traditional land uses is leading to the disappearance of open spaces that benefit many species of conservation interest. Fires create open habitats in which small mammals can live under more favourable conditions, such as lower predation, interspecific competition, and higher food availability. We analysed the role of changes in shrub cover and shrub preference by small mammals along the Mediterranean post-fire succession. We used data (period 2008–2018) from 17 plots woodlands and post-fire shrublands present in the study area (Barcelona’s Natural Parks, Catalonia, NE Spain), and vegetation structure was assessed by LiDAR technology for modelling ground-dwelling small mammal preferences. The diversity, abundance, and stability of Mediterranean small mammal communities negatively responded to vegetation structural complexity, which resulted from the combined effects of land abandonment and recovery after wildfires. We suggest that biotic factors such as vegetation profiles (providing food and shelter) and their interaction with predators and competitors could be responsible for the observed patterns. Considering the keystone role of small mammals in the sustainability of Mediterranean forest, our results could be useful for management under the current global change conditions.
Tropism of Highly Pathogenic Avian Influenza H5 Viruses from the 2020/2021 Epizootic in Wild Ducks and Geese

Highly pathogenic avian influenza (HPAI) outbreaks have become increasingly frequent in wild bird populations and have caused mass mortality in many wild bird species. The 2020/2021 epizootic was the largest and most deadly ever reported in Europe, and many new bird species tested positive for HPAI virus for the first time. This study investigated the tropism of HPAI virus in wild birds. We tested the pattern of virus attachment of 2020 H5N8 virus to intestinal and respiratory tissues of key bird species; and characterized pathology of naturally infected Eurasian wigeons (Mareca penelope) and barnacle geese (Branta leucopsis). This study determined that 2020 H5N8 virus had a high level of attachment to the intestinal epithelium (enterotropism) of dabbling ducks and geese and retained attachment to airway epithelium (respirotropism). Natural HPAI 2020 H5 virus infection in Eurasian wigeons and barnacle geese also showed a high level of neurotropism, as both species presented with brain lesions that co-localized with virus antigen expression. We concluded that the combination of respirotropism, neurotropism, and possibly enterotropism, contributed to the successful adaptation of 2020/2021 HPAI H5 viruses to wild waterbird populations.
Approach to the Characterization of Monolithic Catalysts Based on La Perovskite-like Oxides and Their Application for VOC Oxidation under Simulated Indoor Environment Conditions

Catalysts are very important in controlling the pollutant emissions and are used for hundreds of chemical processes. Currently, noble metal-based catalysts are being replaced for other kinds of materials. In this study, three lanthanum-based perovskite-like oxides were synthesized (LaCo, LaCoMn, and LaMn) by the glycine-combustion method. The powder catalysts obtained were supported onto cordierite ceramic monoliths using an optimized washcoating methodology to obtain the subsequent monolithic catalysts (LaCo-S, LaCoMn-S, and LaMn-S). Sample characterization confirmed the formation of the perovskite-like phase in the powder materials as well as the presence of the perovskite phase after supporting it onto the monolithic structure. The XPS analysis showed a general decrease in lattice oxygen species for monolithic catalysts, mainly caused by the colloidal silica used as a binder agent during the washcoating process. Additionally, some variations in the oxidation state distribution for elements in Co-containing systems suggest a stronger interaction between cordierite and such catalysts. The catalytic activity results indicated that powder and monolithic catalysts were active for single-component VOC oxidation in the following order: 2-propanol > n-hexane ≅ mixture > toluene, and there was no evidence of loss of catalytic activity after supporting the catalysts. However, LaMn-S had a better catalytic performance for all VOC tested under dry conditions, achieving oxidation temperatures between 230–420 °C. The oxidation efficiency for the VOC mixture was strongly affected by the presence of moisture linking the oxidation efficiency at wet conditions to the VOC chemical nature. Additionally, for higher VOC concentrations, the catalyst efficiency decreased due to the limited number of active sites.
Carbon Nanomaterials (CNMs) and Enzymes: From Nanozymes to CNM-Enzyme Conjugates and Biodegradation

Carbon nanomaterials (CNMs) and enzymes differ significantly in terms of their physico-chemical properties—their handling and characterization require very different specialized skills. Therefore, their combination is not trivial. Numerous studies exist at the interface between these two components—especially in the area of sensing—but also involving biofuel cells, biocatalysis, and even biomedical applications including innovative therapeutic approaches and theranostics. Finally, enzymes that are capable of biodegrading CNMs have been identified, and they may play an important role in controlling the environmental fate of these structures after their use. CNMs’ widespread use has created more and more opportunities for their entry into the environment, and thus it becomes increasingly important to understand how to biodegrade them. In this concise review, we will cover the progress made in the last five years on this exciting topic, focusing on the applications, and concluding with future perspectives on research combining carbon nanomaterials and enzymes.
ERA-MIN: A Decade since the Inception of the EU Led Effort to Support the International Raw Materials Research Community

ERA-NET Cofund on Raw Materials (ERA-MIN) is a global, innovative and flexible pan-European network of research funding organisations, supported by EU Horizon 2020, that counts now with its third edition. ERA-MIN3 (2020–2025) builds on the experience of the FP7 ERA-NET ERA-MIN (2011–2015) and the still running H2020 ERA-MIN 2 (2016–2022). ERA-MIN aims to support the European Innovation Partnership on Raw Materials (EIP RM), the EU Raw Materials Initiative, the Circular Economy Action Plan and further develop the raw materials (RM) sector in Europe through funding of transnational research and innovation (R&I) activities. This is achieved through calls designed and developed specifically for the non-fuel, non-food raw materials sector.
E-Novation Deployment: Creating New “Spaces” and Distribution Using E-Novation

Space is the final frontier for e-marketing. Advances in storage space, digital data transmission, and infrastructure development have created a near limitless marketspace that exists over the contemporary physical marketplaces, and as an independent market of ideas, data, experience, and content. This chapter overviews a series of key issues in the use of the new “space” for e-novation with attention given to the rise of user generated content through prosumer activity. This chapter is based on exploring how companies and individuals are currently co-creating value in the dynamic marketplace of the new collaborative platforms, and how these new concepts such as the “home shopping channel”, digital rights management, and user generated distribution channels can factor in the future success on and offline for marketing. DOI: 10.4018/978-1-4666-1598-4.ch011
Coordinating Search in Modular Systems: The Value of (Temporary) Integration

It is a central tenet in design theory that decomposing complex systems into modules allows the module-level units to search independently for new alternatives. Yet as complex systems are at best near-decomposable, modular decompositions contain inter-modular interdependencies, some of which may be unknown, ignored, or incompletely understood. As decisions made in one module may thus affect the value of decisions made in other parts of the system, decentralized search in modular systems requires coordination. Using an agent-based simulation model, this paper explores how integration efforts - aimed at assessing the fit of the modules and providing performance feedback to the module-level units - help to address these coordination requirements. In particular, the paper examines the role and interdependence of four contingency factors in shaping the value of integration: the structural properties of modular decompositions, the degree of module-level exploration, the time horizon, and the coordination frequency. For instance, I show that the value of integration is particularly distinctive when systems consist of many modules, when the modules are strongly coupled internally or among each other, or when the module-level units search broadly for new alternatives. I also find that constant integration efforts denote the best short-term strategy. In the long term, however, temporary integration - repeated changes between module-level autonomy and coordinative intervention - yields the highest overall performance, suggesting that decentralized organizations may benefit from temporarily neglecting their need for coordination.
Wafer Scratch Pattern Reconstruction for High Diagnosis Accuracy and Yield Optimization

Spatial failure patterns in wafer defect maps are very useful for root cause analysis, which is essential for yield optimization in the manufacturing process. Especially, the scratch defect type is the most challenging pattern to recognize because the position, shape, size and curvature vary widely from one scratch to another. Discontinuity points within scratches also contribute to the low recognition rate, and such points are often hidden defective dies that become reliability threat. Previous studies on defect pattern identification show that the recognition rate for scratches is among the lowest in all patterns even if the overall accuracy is high. In this paper, we propose a novel scratch pattern recognition method. In this paper, image processing techniques are applied to recognize scratch patterns in wafer maps. The Hough transform is first employed to identify line segments, from which scratches can be reconstructed. In contrast to previous machine-learning based methods, there is no need to train a complicated prediction model so that the computation time is small. The method is validated by using wafer maps from six real products and public data. Experimental results show that the proposed method achieves high recall, precision and accuracy in all cases.