Exploring the Factors Promoting Team Effectiveness in the Process of Creating International Technology Standards A Case Study of ISO/SAE-joint Standard (21434) for Road Vehicle Cybersecurity

International Technology Standards play an essential role in supporting technology adoption and implementation. Emerging technologies are reshaping global commerce. New technologies have been shown to be an essential factor in boosting the economy as they offer great prospects for growth. It is a complicated journey from promising emerging technology to full industrialization and commercialization. However, the process of creating International Technology Standards itself is dynamic and complicated, consists of many underlying dimensions, and is influenced by political, economic, socio-demographic, and technological changes during the development process. Few theoretical frameworks exist to help in understanding the process of creating technology standards as well as to provide practical guidelines. This dissertation bridges this gap by conducting an in-depth case study analysis that aims to extend our knowledge and gain a deeper understanding of the process of creating International Technology Standards. The case I selected is the first ever joint International Technology Standard by the International Organization for Standardization (ISO, a Europe-based standards development organization), and SAE International (SAE, a U.S.-based standards development organization). The purpose of the standard in question is to setup global policies and guidelines for automotive 4 cybersecurity, in the era during which cars will become fully connected and will soon be autonomous. In this case study, data were collected through documents, observations, interviews, and questionnaires. A total of 18 semi-structured individual and group interviews, including 24 participants, were conducted within four months, and 25 completed questionnaires were collected. Coding was adopted as the data analysis method. The results reveal a set of Input-Process- Outcome (IPO) factors and components that impact team effectiveness in the process of creating International Technology Standards. To be specific, team structure is the most critical IPO factor that influences the team effectiveness. Additionally, an Input-Choice-Outcome (ICO) conceptual framework and several lessons-learned are offered for future International Technology Standards creation projects. The study contributes to literature and practice by providing theoretical and practitioner insights into the process of International Technology Standards creation

The Use of Emoji in Electronic User Experience Questionnaire: An Exploratory Case Study

Emojis are a growing phenomenon that have gained popularity in the digital era. However, little research exists on their use; a notable absent topic is the adoption of emojis in user experience questionnaires (UEQs). This study draws on semi- structured interview data from 31 participants to understand how emojis affect the experience of filling out UEQs. The data analysis reveals two key categories regarding participants’ experiences: advantages and challenges. This research contributes to the existing literature by presenting a detailed exploratory case study of user reactions to emojis in the context of UEQs

Chapter Spectral Efficiency Analysis of Filter Bank Multi‐Carrier (FBMC)‐ Based 5G Networks with Estimated Channel State Information (CSI)

The heterogeneous cellular network (HCN) is most significant as a key technology for future fifth-generation (5G) wireless networks. The heterogeneous network consists of randomly macrocell base stations (MBSs) overlaid with femtocell base stations (FBSs). Stochastic geometry has been shown to be a very powerful tool to model, analyze, and design networks with random topologies such as wireless ad hoc, sensor networks, and multi-tier cellular networks. HCNs can be energy-efficiently designed by deploying various BSs belonging to different networks, which has drawn significant attention to one of the technologies for future 5G wireless networks. In this chapter, we propose switching off/on systems enabling the BSs in the cellular networks to efficiently consume the power by introducing active/sleep modes, which is able to reduce the interference and power consumption in the MBSs and FBSs on an individual basis as well as improve the energy efficiency of the cellular networks. We formulate the minimization of the power consumption for the MBSs and FBSs as well as an optimization problem to maximize the energy efficiency subject to throughput outage constraints, which can be solved by the Karush-Kuhn-Tucker (KKT) conditions according to the femto tier BS density. We also formulate and compare the coverage probability and the energy efficiency in HCN scenarios with and without coordinated multi-point (CoMP) to avoid coverage holes

Spectral Efficiency Analysis of Filter Bank Multi‐Carrier (FBMC)‐ Based 5G Networks with Estimated Channel State Information (CSI)

Filter bank multi‐carrier (FBMC) modulation, as a potential candidate for physical data communication in the fifth generation (5G) wireless networks, has been widely investigated. This chapter focuses on the spectral efficiency analysis of FBMC‐based cognitive radio (CR) systems, and spectral efficiency comparison is conducted with another three types of multi‐carrier modulations: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), and universal‐filtered multi‐carrier (UFMC). In order to well evaluate and compare the spectral efficiency, we propose two resource allocation (RA) algorithms for single‐cell and two‐cell CR systems, respectively. In the single‐cell system, the RA algorithm is divided into two sequential steps, which incorporate subcarrier assignment and power allocation. In the two‐cell system, a noncooperative game is formulated and the multiple access channel (MAC) technique assists to solve the RA problem. The channel state information (CSI) between CR users and licensed users cannot be precisely known in practice, and thus, an estimated CSI is considered by defining a prescribed outage probability of licensed systems. Numerical results show that FBMC can achieve the highest channel capacity compared with another three waveforms

Boosting Adversarial Attacks on Neural Networks with Better Optimizer

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam Iterative Fast Gradient Method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models

Transgenic mice over-expressing carbonic anhydrase I showed aggravated joint inflammation and tissue destruction

BACKGROUND: Studies have demonstrated that carbonic anhydrase I (CA1) stimulates calcium salt precipitation and cell calcification, which is an essential step in new bone formation. Our study had reported that CA1 encoding gene has a strong association with rheumatoid arthritis (RA) and ankylosing spondylitis (AS), two rheumatic diseases with abnormal new bone formation and bone resorption in joints. This study investigated the effect of CA1 on joint inflammation and tissue destruction in transgenic mice that over-express CA1 (CA1-Tg). METHODS: CA1-Tg was generated with C57BL/6J mice by conventional methods. CA1-Tg was treated with collagen-II to induce arthritis (CIA). Wild-type mice, CA1-Tg treated with bovine serum albumin (BSA) and transgenic mice over-expressing PADI4 (PADI4-Tg), a gene known to be involved in rheumatoid arthritis, were used as controls. Histochemistry and X-ray radiographic assay were used to examine joint destruction. Western blotting and real time-PCR were used to examine CA1 expression. RESULTS: CIA was observed in 60% of CA1-Tg, 20% of PADI4-Tg and 20% of wild-type mice after collagen injections. No CIA was found in CA1-Tg mice that received injections of BSA. The arthritic score was 5.5 ± 0.84 in the CA1-Tgs but the score was less than 2 in the injected wild-type mice and the PADI4-Tgs. The thickness of the hind paws in the CA1-Tgs was 3.46 ± 0.11 mm, which was thicker than that of PADI4-Tgs (2.23 ± 0.08 mm), wild-type mice (2.08 ± 0.06 mm) and BSA-treated CA1-Tgs (2.04 ± 0.07 mm). Histochemistry showed obvious inflammation, synovial hyperplasia and bone destruction in the joints of CA1-Tg that was not detected in PADI4-Tgs or wild-type mice. X-ray assays showed bone fusion in the paws and spines of CA1-Tg mice. CONCLUSION: Over-expression of CA1 may aggravate joint inflammation and tissue destruction in the transgenic mice

Multi-Omics Study on the Molecular Mechanisms of Tetraodon Nigroviridis Resistance to Exogenous Vibrio Parahaemolyticus Infection

Vibrio parahaemolyticus is an important marine pathogen that causes inflammation and even death in teleost fishes. It has brought significant economic losses to the aquaculture industry as well as high risks to the sustainable development of marine fisheries. In the present study, the fish Tetraodon nigroviridis and the bacterial pathogen Vibrio parahaemolyticus were used to explore the molecular mechanisms underlying the immune response of T. nigroviridis to V. parahaemolyticus exogenous infection. The microRNA (miRNA)–mRNA–protein omics and corresponding experimental validation, followed by comparative analysis, revealed several differentially expressed genes involved in various components of the immune system, including the following: complement system, chemokines, lysosomes, phagocytes, B-cell receptor signaling pathway, T-cell receptor signaling pathway, Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway, and phospholipid metabolism, among others. Especially, the complements component 3 (C3) gene and protein expression levels were significantly higher after V. parahaemolyticus infection, and miRNAs targeting C3, including mir-6089-y, mir-460-y, and mir-1584-x, were significantly down-regulated. The gene and protein expression levels of complement 1 subunit qA (C1qA) were significantly down-regulated, while mir-203 targeting C1qA was significantly up-regulated. Overall, four complement genes (C1qA, IG, C3, and C5), which are key genes in the classical pathway of complement system activation for inflammatory response, were identified. Evolutionary analysis suggested that T. nigroviridis, acquired an increased ability to recognize pathogens by evolving a more complex complement system than terrestrial vertebrates. In addition, quantitative real-time polymerase chain reaction showed high consistency with the obtained multi-omics results, indicating the reliability of the sequencing data generated in the present study. In summary, our findings can serve as a fundamental basis for further in-depth multi-omics studies on the inflammatory processes of aquatic pathogens hindering fish sustainable production

Mapping the distribution of invasive tree species using deep one-class classification in the tropical montane landscape of Kenya

Some invasive tree species threaten biodiversity and cause irreversible damage to global ecosystems. The key to controlling and monitoring the propagation of invasive tree species is to detect their occurrence as early as possible. In this regard, one-class classification (OCC) shows potential in forest areas with abundant species richness since it only requires a few positive samples of the invasive tree species to be mapped, instead of all the species. However, the classical OCC method in remote sensing is heavily dependent on manually designed features, which have a limited ability in areas with complex species distributions. Deep learning based tree species classification methods mostly focus on multi-class classification, and there have been few studies of the deep OCC of tree species. In this paper, a deep positive and unlabeled learning based OCC framework—ITreeDet—is proposed for identifying the invasive tree species of Eucalyptus spp. (eucalyptus) and Acacia mearnsii (black wattle) in the Taita Hills of southern Kenya. In the ITreeDet framework, an absNegative risk estimator is designed to train a robust deep OCC model by fully using the massive unlabeled data. Compared with the state-of-the-art OCC methods, ITreeDet represents a great improvement in detection accuracy, and the F1-score was 0.86 and 0.70 for eucalyptus and black wattle, respectively. The study area covers 100 km2 of the Taita Hills, where, according to our findings, the total area of eucalyptus and black wattle is 1.61 km2 and 3.24 km2, respectively, which represent 6.78% and 13.65% of the area covered by trees and forest. In addition, both invasive tree species are located in the higher elevations, and the extensive spread of black wattle around the study area confirms its invasive tendency. The maps generated by the use of the proposed algorithm will help local government to develop management strategies for these two invasive species.Peer reviewe

Apatinib combined with camrelizumab in the treatment of recurrent/metastatic nasopharyngeal carcinoma: a prospective multicenter phase II study

BackgroundPreclinical studies demonstrated that immune checkpoint inhibitors combined with antiangiogenic drugs have a synergistic anti-tumor effect. This present phase II trial aimed to evaluate the efficacy and safety of apatinib combined with camrelizumab in patients with recurrent/metastatic nasopharyngeal carcinoma (RM-NPC).MethodsPatients with RM-NPC were administered with apatinib at 250 mg orally once every day and with camrelizumab at 200 mg via intravenous infusion every 2 weeks until the disease progressed or toxicity became unacceptable. The objective response rate (ORR) was the primary endpoint, assessed using RECIST version 1.1. Progression-free survival (PFS), overall survival (OS), disease control rate (DCR) and safety were the key secondary endpoints. This study was registered with ClinicalTrials.gov, NCT04350190.ResultsThis study enrolled 26 patients with RM-NPC between January 14, 2021 and September 15, 2021. At data cutoff (March 31, 2023), the median duration of follow-up was 16 months (ranging from 1 to 26 months). The ORR was 38.5% (10/26), the disease control rate (DCR) was 61.5% (16/26), and the median PFS was 6 months (IQR 3.0-20.0). The median OS was 14 months (IQR 6.0-21.25). Treatment-related grade 3 or 4 adverse events occurred in seven (26.9%) patients, and comprised anemia (7.7%), stomatitis (3.8%), headache (3.8%), pneumonia (7.7%), and myocarditis (3.8%). There were no serious treatment-related adverse events or treatment-related deaths.ConclusionIn patients with RM-NPC, apatinib plus camrelizumab showed promising antitumor activity and manageable toxicities