Search Results (11)

Discovered in late 2019, the SARS-CoV-2 coronavirus has caused the largest pandemic of the 21st century, claiming more than seven million lives. In most cases, the COVID-19 disease caused by the SARS-CoV-2 virus is relatively mild and affects only the upper respiratory tract; it most often manifests itself with fever, chills, cough, and sore throat, but also has less-common mild symptoms. In most cases, patients do not require hospitalization, and fully recover. However, in some cases, infection with the SARS-CoV-2 virus leads to the development of a severe form of COVID-19, which is characterized by the development of life-threatening complications affecting not only the lungs, but also other organs and systems. In particular, various forms of thrombotic complications are common among patients with a severe form of COVID-19. The mechanisms for the development of thrombotic complications in COVID-19 remain unclear. Accumulated data indicate that the pathogenesis of severe COVID-19 is based on disruptions in the functioning of various innate immune systems. The key role in the primary response to a viral infection is assigned to two systems. These are the pattern recognition receptors, primarily members of the toll-like receptor (TLR) family, and the complement system. Both systems are the first to engage in the fight against the virus and launch a whole range of mechanisms aimed at its rapid elimination. Normally, their joint activity leads to the destruction of the pathogen and recovery. However, disruptions in the functioning of these innate immune systems in COVID-19 can cause the development of an excessive inflammatory response that is dangerous for the body. In turn, excessive inflammation entails activation of and damage to the vascular endothelium, as well as the development of the hypercoagulable state observed in patients seriously ill with COVID-19. Activation of the endothelium and hypercoagulation lead to the development of thrombosis and, as a result, damage to organs and tissues. Immune-mediated thrombotic complications are termed “immunothrombosis”. In this review, we discuss in detail the features of immunothrombosis associated with SARS-CoV-2 infection and its potential underlying mechanisms. Full article

Therapeutic advances in oncology in the 21st century have contributed to significant declines in cancer mortality. Notably, targeted therapies comprised the largest proportion of oncology drugs approved by the United States (US) Food and Drug Administration (FDA) over the past 25 years and have become the standard of care for the treatment of many cancers. However, despite the metamorphosis of the therapeutic landscape, some aspects of cancer drug development have remained essentially unchanged. In particular, the dose-finding methodology originally developed for cytotoxic chemotherapy drugs continues to be implemented, even though this approach no longer represents the most appropriate strategy for modern cancer therapies. In recognition of the need to reconsider assumptions, adapt the dose selection process for newer drugs, and design alternative strategies, the FDA has undertaken several initiatives in recent years to address these concerns. These actions include the launch of Project Optimus in 2021 and the issuance of draft guidance for industry on dose optimization of oncology drugs in 2023. Amid this evolving regulatory environment, the present manuscript reviews case studies for six different targeted cancer therapies, highlighting how dose-finding challenges have been managed to date by oncologists, sponsors, and regulators. Full article

When launching a heavy-duty vehicle, torque and position control during automatic clutch engagement is critical, and the driver’s intention to launch and changes in the vehicle’s launching resistance make clutch control more complex. This paper analyses the automatic engagement process of automated mechanical transmission (AMT) clutches and proposes an optimal control of the clutch torque for launching heavy-duty vehicles. Firstly, a fuzzy neural network (FNN)-based vehicle launching states recognition (LSR) system is designed for distinguishing the driver’s launching intention and the vehicle’s launching equivalent moment of resistance. Secondly, jerk, friction work, and launching reserve power are taken as the performance indexes for clutch torque optimization, the weight coefficients of each performance index are adjusted according to the LSR results, and the optimal clutch torque is solved by using the minimum value principle based on the shooting method. Finally, simulations and tests are conducted to validate the strategy of optimizing clutch torque, and the impact of torque optimization on the position change during the engagement process is analyzed. The results indicate that under different driver’s intentions, vehicle masses, and road gradient conditions, the jerk, friction work, and slipping time of heavy vehicles during the launching process are improved by applying the optimization strategy. Full article

Implementing the Circular Economy (CE) is largely a mirage. There are some political decisions translated into penalties and/or incentives to try to adjust the “optimal” level of circularity. The consumer’s desire to purchase circular products, given the increase in price associated with them, and the sum of associated fines and penalties, generates complex financial equations, which become unfavorable to the transition to the CE. CE-friendly solutions in use are associated with situations in which circularity contributes to lower production costs. The authors are committed to altering the course of events. They believe that the success of this transition will have the will of the consumer as its main vector. To this end, they launched an investigation that leads to clues on how, by identifying barriers, facilitators, and motivations, proposals for solutions that are focused on the consumer are designed. The research project started by surveying and systematically analyzing the existing published information, in order to reach the State of the Art. The path taken involved a systematic review of the literature and the consequent bibliometric analysis, fulfilling a methodology whose steps are not innovative, but whose relationship/sequencing of the same is insufficiently treated in the literature. As the most relevant results of the application of the proposed methodology to the subject under analysis, in addition to the recognition of a set of significant and guiding texts, explored as graphically as possible, the identification of relevant sub-themes stands out, as well as the framing of opportunities for future investigations. With this investigation, we conclude that the consumer is not the trigger for the transition from the linear economy to the CE. Full article

Proper hazard recognition is fundamental to effective safety management in construction workplaces. Nevertheless, poor hazard recognition levels are a widespread and persistent problem in the construction industry. For example, recent investigations have demonstrated that a significant number of workplace hazards often remain unrecognized in construction workplaces. These unrecognized workplace hazards often remain unmanaged and can potentially translate into devastating and unexpected safety incidents. Therefore, interventions targeted at improving hazard recognition levels are foundational to enhancing safety management in construction workplaces. The main objective of the current investigation was to examine if ChatGPT, a language model recently launched by OpenAI, can aid hazard recognition when integrated into the curriculum of students pursuing a career in the construction industry. The investigation was carried out as an experimental effort with 42 students enrolled in the construction program at a major state university in the United States. First, prior to the introduction of ChatGPT as an intervention, the pre-intervention hazard recognition ability of the students was measured. Next, ChatGPT and its capabilities were introduced to the students in a classroom setting. Guidance was also offered on how the students could leverage ChatGPT to aid hazard recognition efforts. Finally, the post-intervention hazard recognition ability of the students was measured and compared against their earlier performance. The result suggests that ChatGPT can be leveraged to improve hazard recognition levels. Accordingly, integrating ChatGPT as part of safety education and training can yield benefits and prepare the next generation of construction professionals for industry success. Full article

Advancements in optical satellite hardware and lowered costs for satellite launches raised the high demand for geospatial intelligence. The object recognition problem in multi-spectral satellite imagery carries dataset properties unique to this problem. Perspective distortion, resolution variability, data spectrality, and other features make it difficult for a specific human-invented neural network to perform well on a dispersed type of scenery, ranging data quality, and different objects. UNET, MACU, and other manually designed network architectures deliver high-performance results for accuracy and prediction speed in large objects. However, once trained on different datasets, the performance drops and requires manual recalibration or further configuration testing to adjust the neural network architecture. To solve these issues, AutoML-based techniques can be employed. In this paper, we focus on Neural Architecture Search that is capable of obtaining a well-performing network configuration without human manual intervention. Firstly, we conducted detailed testing on the top four performing neural networks for object recognition in satellite imagery to compare their performance: FastFCN, DeepLabv3, UNET, and MACU. Then we applied and further developed a Neural Architecture Search technique for the best-performing manually designed MACU by optimizing a search space at the artificial neuron cellular level of the network. Several NAS-MACU versions were explored and evaluated. Our developed AutoML process generated a NAS-MACU neural network that produced better performance compared with MACU, especially in a low-information intensity environment. The experimental investigation was performed on our annotated and updated publicly available satellite imagery dataset. We can state that the application of the Neural Architecture Search procedure has the capability to be applied across various datasets and object recognition problems within the remote sensing research field. Full article

The chemokine receptor CCR7, together with its ligands, is responsible for the migration and positioning of adaptive immune cells, and hence critical for launching adaptive immune responses. CCR7 is also induced on certain cancer cells and contributes to metastasis formation. Thus, CCR7 expression and signalling must be tightly regulated for proper function. CCR7, like many other members of the G-protein coupled receptor superfamily, can form homodimers and oligomers. Notably, danger signals associated with pathogen encounter promote oligomerisation of CCR7 and is considered as one layer of regulating its function. Here, we assessed the dimerisation of human CCR7 and several single point mutations using split-luciferase complementation assays. We demonstrate that dimerisation-defective CCR7 mutants can be transported to the cell surface and elicit normal chemokine-driven G-protein activation. By contrast, we discovered that CCR7 mutants whose expression are shifted towards monomers significantly augment their capacities to bind and internalise fluorescently labelled CCL19. Modeling of the receptor suggests that dimerisation-defective CCR7 mutants render the extracellular loops more flexible and less structured, such that the chemokine recognition site located in the binding pocket might become more accessible to its ligand. Overall, we provide new insights into how the dimerisation state of CCR7 affects CCL19 binding and receptor trafficking. Full article

The seabed can be considered as the world’s largest museum, and underwater sites explored and studied so far provide priceless information on human interaction with the sea. In recognition of the importance of this cultural resource, UNESCO, in its 2001 Convention on the Protection of the Underwater Cultural Heritage, determined that objects/sites should be preserved in situ, whilst also advocating for public access and sharing. The implementation of these principles is not without difficulties. Some states have opened up underwater sites to the public—mainly through diving, yet the vast majority of the world’s population does not dive. In Malta, 7000 years of human occupation is reflected in and on the landscape, and recent offshore surveys show that the islands’ long and complex history has also left an indelible mark on the seabed. Besides difficulties related to their protection and management, these sites also present a challenge with regard to sharing and communicating. Recent advances in underwater imaging and processing software have accelerated the development of 3D photogrammetry of submerged sites and the idea for a virtual museum was born. The virtual museum, UnderwaterMalta, was created out of a need to share the plethora of underwater sites located on the seabed of the Maltese Islands. A multitude of digital tools are used to share and communicate these sites, offering visitors a dry dive into submerged sites that would otherwise remain invisible to the vast majority of the public. This paper discusses the basic principle of the sharing of underwater cultural heritage and the difficulties that beset the implementation of such a principle. A detailed explanation and evaluation of the methods used to gather the raw data needed is set in the context of the particular and unique working conditions related to deep water sites. The workings of this paper are based on first-hand experiences garnered through the recording of numerous wrecks over the years and the creation and launch of The Virtual Museum-Underwater Malta—a comprehensive virtual museum specifically built for “displaying” underwater archaeological sites that are otherwise invisible to the general public. Full article

Under the fierce competition and budget constraints, most mobile apps are launched without sufficient tests. Thus, there exists a great demand for automated app testing. Recent developments in various machine learning techniques have made automated app testing a promising alternative to manual testing. This work proposes novel approaches for one of the core functionalities of automated app testing: the detection of changes in usage-phases of a mobile app. Because of the flexibility of app development languages and the lack of standards, each mobile app is very different from other apps. Furthermore, the graphical user interfaces for similar functionalities are rarely consistent or similar. Thus, we propose methods detecting usage-phase changes through object recognition and metrics utilizing graphs and generative models. Contrary to the existing change detection methods requiring learning models, the proposed methods eliminate the burden of training models. This elimination of training is suitable for mobile app testing whose typical usage-phase is composed of less than 10 screenshots. Our experimental results on commercial mobile apps show promising improvement over the state-of-the-practice method based on SIFT (scale-invariant feature transform). Full article

The institutional recognition obtained by family farming in Brazil over recent decades has translated into the launching of a broad and diverse set of public policies specifically aimed towards this sociopolitical category. However, the design of these policies was heavily influenced by the productivist bias derived from the agricultural modernization paradigm, making the sector increasingly dependent on input and capital markets. In this same movement of institutional evolution, policies consistent with the agroecological approach created new margins for maneuvering for development trajectories founded on the use of local resources self-controlled by rural families and communities. Taking as a reference the recent trajectory of rural development in Brazil’s semi-arid region, the article analyses the role of the agroecological perspective in the strategic combination between territorially endogenous rural resources and public resources redistributed by the State. Based on the analysis of the economy of agroecosystems linked to two sociotechnical networks structured by contrasting logics of productive intensification, the study demonstrates agroecology’s potential as a scientific-technological approach for the combined attainment of various Sustainable Development Goals, starting with the economic and political emancipation of the socially most vulnerable portions of the rural population. Full article

Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD) threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA) is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs). Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA) was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA). The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE) and percent root mean square difference (PRD) respectively, and increased 40.36% in signal-to-noise ratio improvement (SNR_imp) compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual acoustic-based diagnosis system for cutting state recognition of the coal mining shearer to demonstrate the practical effect. Full article