This paper combines the Good Regulator Theorem with the Law of Requisite Variety and seven other requisites that are necessary and sufficient for a cybernetic regulator to be effective and ethical. The resulting Ethical Regulator Theorem provides a basis for systematically evaluating and improving the adequacy of existing or proposed designs for systems that make decisions that can have ethical consequences; regardless of whether the regulators are human, machines, cyberanthropic hybrids, organizations, corporations, or government institutions. The theorem is then used to define an ethical design process that has potentially far-reaching implications for society. A six-level framework is proposed for classifying cybernetic and superintelligent systems, which highlights the existence of a possibility-space bifurcation in our future time-line. The implementation of “super-ethical” systems is identified as an urgent imperative for humanity to avoid the danger that superintelligent machines might lead to a technological dystopia. Third-order cybernetics is defined as the cybernetics of ethical systems. Concrete actions, a grand challenge, and a vision of a super-ethical society are proposed to help steer the future of the human race and our wonderful planet towards a realistically achievable minimum viable cyberanthropic utopia.

Since 1974, when Heinz von Foerster made the distinction between “the cybernetics of observed systems” as first-order cybernetics (1oC) and “the cybernetics of observing systems” as second-order cybernetics (2oC), cybernetics has been dominated by this observer-centric paradigm that he claimed cannot be extended meaningfully to a third-order. Rather than attempting to extend his paradigm, we derive an alternative, model-centric paradigm from first principles of regulation that naturally extends to three orders, where the third order is ethical regulation. We thus consider a type of regulator that requires a third model and a third observer: If the third model is a model of acceptable (ethical) situations, then a third observer is a necessary element of the system’s “conscience” that prevents or detects any violations of the model of ethical situations. In this paradigm, the cybernetics of systems that are designed to exhibit ethical behaviour can be characterized as third-order cybernetics (3oC). By being able to extend the paradigm to include ethical systems, the model-centric paradigm brings clarity and utility that is not possible using the philosopher-friendly observer-centric paradigm and its under-specified (abstract) observers. Finally new definitions for cybernetics are proposed that clearly differentiate between the science and the philosophy, of cybernetics.

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight GAN-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.

Virtual assistants (VAs) have gained widespread popularity across a wide range of applications, and the integration of Large Language Models (LLMs) such as ChatGPT has opened up new possibilities for developing even more sophisticated VAs. However, this integration poses new ethical issues and challenges that must be carefully considered, particularly as these systems are increasingly used in public services: transfer of personal data, decision-making transparency, potential biases, and privacy risks. This paper, an extension of the work presented at IberSPEECH 2022, analyzes the current regulatory framework for AI-based VAs in Europe and delves into ethical issues in depth, examining potential benefits and drawbacks of integrating LLMs with VAs. Based on the analysis, this paper argues that the development and use of VAs powered by LLMs should be guided by a set of ethical principles that prioritize transparency, fairness, and harm prevention. The paper presents specific guidelines for the ethical use and development of this technology, including recommendations for data privacy, bias mitigation, and user control. By implementing these guidelines, the potential benefits of VAs powered by LLMs can be fully realized while minimizing the risks of harm and ensuring that ethical considerations are at the forefront of the development process.

Many ethical issues arise when robots are introduced into elder-care settings. When ethically charged situations occur, robots ought to be able to handle them appropriately. Some experimental approaches use (top-down) moral generalist approaches, like Deontology and Utilitarianism, to implement ethical decision-making. Others have advocated the use of bottom-up approaches, such as learning algorithms, to learn ethical patterns from human behaviour. Both approaches have their shortcomings when it comes to real-world implementations. Human beings have been observed to use a hybrid form of ethical reasoning called Pro-Social Rule Bending, where top-down rules and constraints broadly apply, but in particular situations, certain rules are temporarily bent. This paper reports on implementing such a hybrid ethical reasoning approach in elder-care robots. We show through simulation studies that it leads to better upholding of human values such as autonomy, whilst not sacrificing beneficence.

AbstractThe escalating impact of artificial intelligence (AI) has birthed the fascinating technology of deepfakes, capable of meticulously crafting hyper-realistic audio-visual manipulations. Within the realm of healthcare, this technology presents a double-edged sword, brimming with potential for revolutionizing various facets of care delivery while simultaneously harboring significant ethical and practical challenges. This research delves into the complex narrative surrounding AI deepfakes in healthcare, meticulously analyzing both the exciting opportunities they offer, and the inherent risks associated with their implementation.Background:Traditional healthcare practices face limitations in patient engagement, accessibility, and training efficacy. Deepfakes, with their ability to simulate real-world scenarios and interactions, emerge as a potential solution, offering personalized patient education, immersive mental health interventions, remote consultations with virtual avatars, and realistic training simulations for medical professionals.Objective:This research aims to comprehensively understand the multifaceted impact of AI deepfakes in healthcare systems. It seeks to: Identify and analyze the potential benefits of deepfakes in specific healthcare applications. Assess the ethical and practical challenges associated with deepfake implementation in healthcare settings. Propose recommendations for safe, responsible, and ethical integration of deepfakes within the healthcare system.Results/Conclusion:The research findings illuminate a landscape replete with opportunities and challenges. Deepfakes offer the potential to enhance patient education, personalize mental health interventions, facilitate remote consultations, and revolutionize medical training. However, concerns regarding misinformation, ethical breaches, data privacy, and technical limitations pose significant challenges.The conclusion emphasizes the need for a balanced approach that capitalizes on the innovative potential of deepfakes while mitigating potential harm. Collaborative efforts involving healthcare professionals, technologists, and policymakers are crucial for establishing ethical frameworks, ensuring responsible development, and harnessing the positive power of deepfakes to build a future where healthcare benefits from cutting-edge technology while prioritizing patient well-being and ethical principles.

Italy is the leading European producer of rice. The transition to organic farming could represent a solution for environmental protection, as well as for the economic sustainability of farms, consumer safety and as a measure of climate mitigation, but it currently displays several weaknesses in the control and certification system. The objective of the current study is to propose advice for improving the control and certification scheme in the organic rice sector. To achieve this aim, we adopted a qualitative methodology based on participant observation at stakeholders’ meetings, focus groups, community-led workshops and deep interviews with relevant local actors. Findings show that there are some solutions to mitigate the weakness of the Italian certification scheme. The study also produces managerial implications to improve the Italian organic rice system.

Building an ethical culture is challenging and a basic requirement of the companies to embed transparency in their systems, creating a positive image; serving the internal and external communities. This paper based on detailed interviews of 12 family owned businesses over a period of 16 months explores how these companies build the ethical culture, identifying the ethical culture sustainability triggers, challenges and role of religion in such practices. The values, culture, community and social norms are identified as major ingredients of a sustainable ethical culture development and implementation of the ethical policies and procedures require institutional and structural mechanisms for effectiveness in family owned businesses. The findings at numerous occasions are in contrast to the literature, whereas, in other instances are similar. The religion, society, family image, the entrepreneurs themselves and their family members play a vital role. Non-interference in private matters of the employees, whistle blowing, code of ethics, training and awareness creation and a number of other factors play a leading role in ethical culture development in family businesses.

The Capon algorithm can be applied to reprocess SAR images, resulting in super-resolution super-resolution reconstructed scenes with lower sidelobe levels. Base on this idea, we have proposed a processing chain of Super-Resolution Persistent Scatterer Interferometry (SR-PSI), to increase PS density. In this paper, we review the main aspect of SR-PSI. We also propose a revised robust Capon algorithm. The result of real-life Sentinel-1 (S-1) data is shown.

Business school curriculums are designed to improve business skills and a student’s eventual workplace performance. In addition to these business skill sets the emerging business environment demands softer skills associated with ethical decision-making and sustainable business practices. Understanding the key influencers of ethical orientation and attitudes towards the environment is the first critical step for curriculum planning designed to develop both ethical decision-making and environmental sensibilities of students in business schools. Using a bivariate regression analysis (OLS) that compared the established New Ecological Paradigm (NEP) scale and the newly introduced Ethical Orientation Scale (EOS), this study assesses environmental eco-consciousness and ethical orientation over time and across varying socio-demographic variables. The study shows first, that in addition to socio-cultural variables, situational factors influence ethical decision-making. Secondly, it illuminates that ethical orientations as measured by the EOS predicts beliefs about the environment as measured by the NEP scale. It further provides evidence of the ethical underpinnings of the New Ecological Paradigm as well as provides initial validation for the new EOS. These outcomes provide additional levers to assist business educators in the creation of high impact teaching strategies to measure and encourage ethical decision-making and sustainable business practices that protect the environment.

Background: The potential of Artificial intelligent (AI) models to process and interpret large health datasets at scale could revolutionize public health and epidemiology, providing a foundation for public health. Ethics has been recognized as a priority concern in the development and deployment of AI. Because AI technology can jeopardize patient safety, privacy, and posing a new set of ethical problems that must be addressed. Objectives: We aim to provide a holistic view on what are the different ethical and legal principles that was addressed in the included studies regarding the use of AI in public health and what are the ethical challenges that can arise.Methods: Following PRISMA guideline, five bibliographic databases were used in our search: PubMed, Scopus, JSTOR, IEEE Xplore, and Google Scholar from 2015 to February 2022. Four reviewers carried out study selection and data extraction, and the data extracted was synthesized by a narrative approach. Results: This review included 23 unique publications out of a total of 1123 items that were initially identified. Different ethical principles regarding the uses of AI in public health and community health were identified and discussed distinctly in the current review. The common ethical and legal themes that this review focused on are equity, bias, privacy and security, safety, transparency, confidentiality, accountability, social justice, and autonomy. In addition, five ethical challenges were mentioned. Conclusion: Research regarding ethical and legal principles and challenges about using AI in public health specifically consider a new filed, because all previous themes are concerning the physical and patients’ area where it focuses only on the clinical settings.

This paper presents a study on the development of the cross-curricular learning outcome (CCLO) "Ethical, environmental and professional responsibility" by the students of different Bache-lor’sDegrees taught at Universitat Politècnica de València. The work and development of this learning outcome entails great complexity, given the double dimension of responsibility that it involves. At the end of their training at the university, students are expected to show ethical, en-vironmental, and professional responsibility towards themselves and others. Interviews have been conducted with lecturers who work and assess this outcome in their subjects, most/all of them related to science and engineering. The objective was to identify the learning approach used at the different subjects to guarantee the acquisition of this CCLO by the students. A focus group has also been carried out with students to determine the importance they give to this learning outcome, and to know their degree of satisfaction with the training received. The methodology used to obtain the data from lecturers and students and to process the information to get a precise diagnosis is fully described in the paper. Results are satisfactory to some extent: most of the lecturers carry out appropriate activities and most of students achieve the expected proficiency level. Finally, recommendations are given to improve the development of this cross-curricular learning outcome.

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super-Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as data augmentation better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly from a personal laptop.

This study explores the elements that influence consumer vegetarian diet choices in parts of South Korean provinces. The study examines these explanatory variables: ethical-animal-welfare, health, environment, social issues , mood-emotion, and religious-beliefs with vegetarianism serving as the dependent variable. The Likert scale data from 264 respondents were analyzed using logistic regression analysis. Results show that health, ethical-animal-welfare ideals as well as environmental concerns emerge as significant variables influencing the decision to embrace a vegetarian diet. This shows that consumers (international students) in Korea are becoming more aware of and concerned about health, ethical and sustainable dietary choices. Secondly, health plays a crucial role in the vegetarian diet selection, indicating that health-conscious people are more inclined to choose a vegetarian lifestyle. Religious belief and mood have various degrees of influence on the vegetarian diet decision. The findings of this study have consequences for consumer behavior, marketing managers, and decision-makers. Businesses in the food industry, in particular, can gain from catering to the needs and tastes of consumers that prioritize ethical-animal welfare ideals, as well as those that value health and environmental sustainability. Creating and advertising vegetarian and vegan choices in local restaurants can effectively attract and serve this specific target demographic.

The occurrence and intensity of the detonation phenomenon at spark-ignition (SI) engine conditions is investigated, with the objective to successfully predict super-knock and to elucidate the effect of kinetics and transport at the ignition front. The computational singular perturbation (CSP) framework is employed in order to investigate the chemical and transport mechanisms of deflagration and detonation cases in the context of 2D high-fidelity numerical simulations. The analysis revealed that the detonation development is characterized by: (i) stronger explosive dynamics and (ii) enhanced role of convection. The role of chemistry was also found to be pivotal to the detonation development which explained the stronger explosive character of the system, the latter being an indication of the system's reactivity. The role of convection was found to be enhanced at the edge of the detonating front, thereby suggesting that it is the result and not the cause of the detonation onset. Moreover, the increased contribution of convection was found to be related mainly to heat convection. Remarkably, the detonation front was mainly characterized by dissipative and not explosive dynamics. Finally, diffusion was found to have negligible role to both examined cases.

Super-Resolution Microscopy enables non-invasive, molecule-specific imaging of the internal structure and dynamics of cells with sub-diffraction limit spatial resolution. One of its major limitations is the requirement for high-intensity illumination, generating considerable cellular phototoxicity. This factor considerably limits the capacity for live-cell observations, particularly for extended periods of time. Here, we overview new developments in hardware, software and probe chemistry aiming to reduce phototoxicity. Additionally, we discuss how the choice of biological model and sample environment impacts the capacity for live-cell observations.

Multicomponent training is recommended for people with dementia living in long-term care homes. Nevertheless, evidence is limited and people with severe dementia are often excluded from trials. Hence, the aim of this study was to investigate (1) the feasibility and (2) the requirements regarding a multicomponent training for people with moderate to severe dementia. The study was conducted as an uncontrolled single arm pilot study with a mixed methods approach. 15 nursing home residents with a mean age of 82 years (range: 75-90 years; female: 64%) with moderate to severe dementia received 16 weeks of multicomponent training. Feasibility and requirements of the training were assessed by a standardized observation protocol. Eleven participants regularly attended the intervention. The highest active participation was observed during gait exercises (64%), the lowest during strength exercises (33%). It was supportive if exercises were task-specific or related to everyday life. This study confirms that a multicomponent training for the target group is (1) feasible and well accepted. To enhance active participation (2) individual instructions and the implementation of exercises related to everyday life is required. The effectiveness of the adapted training should be tested in future randomized controlled trials.

The unicellular eukaryote S. cerevisiae is an invaluable resource for the study of basic eukaryotic cellular and molecular processes. However, due to its small size compared to other eukaryotic organisms the study of subcellular structures is challenging. Expansion microscopy (ExM) holds great potential to study the intracellular architecture of yeast, especially when paired with pan-labelling techniques visualising the full protein content inside cells. ExM allows to increase imaging resolution by physically enlarging a fixed sample that is embedded and cross- linked to a swellable gel followed by isotropic expansion in water. The cell wall present in fungi – including yeast – and Gram-positive bacteria is a resilient structure that resists denaturation and conventional digestion processes usually used in ExM protocols, resulting in uneven expansion. Thus, the digestion of the cell wall while maintaining the structure of the resulting protoplasts are crucial steps to ensure isotropic expansion. For this reason, specific experimental strategies are needed, and only a few protocols are currently available. We have developed a modified ExM protocol for S. cerevisiae, with 4x expansion factor, which allows the visualisation of the ultrastructure of the cells. Here, we describe the experimental procedure in detail, focusing on the most critical steps required to achieve isotropic expansion for ExM of S. cerevisiae.

Sentinel-2 can provide multi-spectral optical remote sensing images in RGBN bands with a spatial resolution of 10m, but the spatial details provided are not enough for many applications. WorldView can provide HR multi-spectral images less than 2m, but it is a commercial paid resource with relatively high usage costs. In this paper, without any available reference images, Sentinel-2 images at 10m resolution are improved to a resolution of 2.5m through super-resolution (SR) based on deep learning technology. Our model, named DKN-SR-GAN, uses degradation kernel estimation and noise injection to construct a dataset of near-natural low-high-resolution (LHR) image pairs, with only low-resolution (LR) images and no high-resolution (HR) prior information. DKN-SR-GAN uses the Generative Adversarial Networks (GAN) combined of ESRGAN-type generator, PatchGAN-type discriminator and the VGG-19-type feature extractor, using perceptual loss to optimize the network, so as to obtain SR images with clearer details and better perceptual effects. Experiments demonstrate that in the quantitative comparison of the non-reference image quality assessment (NR-IQA) metrics like NIQE, BRISQUE and PIQE, as well as the intuitive visual effects of the generated images, compared with state-of-the-art models such as EDSR8-RGB, RCAN and RS-ESRGAN, our proposed model has obvious advantages.

Malaria remains a primary public health challenge in Africa, and there is growing interest in leveraging artificial intelligence (AI) to raise malaria interventions. This research examines the possible influence, challenges, and recommendations for implementing AI-based personalized malaria interventions in Africa. AI offers several opportunities in malaria management, including early detection and prediction of outbreaks, improved diagnosis, personalized interventions, optimal treatment recommendations, surveillance and response, resource optimization, and research innovation. However, the implementation of AI in malaria interventions faces various challenges. These include data availability and quality, infrastructure and resource constraints, contextual relevance and generalizability, ethical and privacy considerations, integration into healthcare workflows, and the need to build trust and acceptance among stakeholders. To address these challenges, it is recommended to strengthen data infrastructure, build local capacity in AI technologies, contextualize AI models to local settings, address ethical considerations, establish monitoring and evaluation frameworks, promote collaboration and knowledge sharing, and secure sustainable funding and long-term commitment. By considering these recommendations, stakeholders can work towards implementing AI-based personalized malaria interventions in Africa that can contribute to improving malaria control outcomes, reducing the weight of the disease, and advancing public health in the region.

Geoscientists developed geoethics, an intra-disciplinary field of applied philosophical studies, during the last decade. Reaching beyond the sphere of professional geosciences, it led to professional, cultural, and philosophical approaches to handle the social-ecological structures of our planet ‘wherever human activities interact with the Earth system’. Against the backdrop of the COVID-19 and considering geoscientists’ experiences dealing with disasters (related to hazards like tsunamis, floods, climate changes.), this essay (1) explores the geoethical approach, (2) re-casts geoethics within western philosophical systems, such as the Kantian imperatives, Kohlberg scale of moral adequacy, Jonas’ imperative of responsibility, and (3) advances a ‘geoethical thesis’. The latter takes the form of a hypothesis of a much broader scope of geoethics than initially envisioned. That hypothesis appears by suspecting a relationship between the relative successes in the COVID-19 battle with the positioning of agents (individual, collective, institutional) into ethical frameworks. The turmoil caused by the COVID-19 pandemic, calls for the transfer of experiences between different disciplinary domains to further sustainable governance, hence generalising the geoethical approach. It is emphasized that only when behaving as responsible and knowledgeable citizens, then people of any trade (including [geo-]scientists) can transgress the boundaries of ordinary governance practices with legitimacy.

The mitochondrial network (MN) is a dynamic structure undergoing constant remodeling in the cell. It is assumed that the violation of MN may be associated with various pathologies, including Parkinson’s disease (PD). Using automatic image analysis and super-resolution microscopy, we have assessed the MN parameters in fibroblasts from patients with established hereditary PD mutations (associated with PINK1, LLRK2, α-synuclein, PINK1 and Parkin simultaneously proteins) under normal conditions and after hydrogen peroxide-induced stress. Fibroblasts with Pink1/Parkin mutation are the most different in morphology from fibroblasts obtained from conditionally healthy donors: MN is larger, contains longer mitochondria and accumulated individual mitochondria. In addition to MN, we evaluated other cellular parameters, such as: cytosolic and mitochondrial ROS production and mitochondrial membrane potential. It has been shown that mitochondria of fibroblasts with mutations in genes encoding PINK1, α-synuclein and Pink/Parkin tends to hyperpolarization and cytosolic ROS overproduction, while mitochondrial ROS production was higher only in fibroblasts with PINK1 and α-synuclein mutation.

As a high-quality forestry product with excellent performance, particleboard is widely used in many fields and has great market potential. At present, it is a research difficulty to accurately identify small, fine and different shapes of defects on the whole particleboard through machine vision technology. The application of image super-resolution reconstruction technology on particleboard can improve the surface image quality of particleboard, which is conducive to the subsequent improvement of defect detection accuracy. In this study, super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct dense block, and the dense blocks were constructed by densely skip connection. Then, the corresponding 52400 image blocks with high resolution and low resolution were trained, verified and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the two indexes of PSNR and SSIM. It was found that compared with BICUBIC and SRGAN, PSNR index of SRDAGAN increased by 4.88dB and 3.25dB respectively, and SSIM increased by 0.0507 and 0.1122 respectively. The reconstructed images not only had clearer texture, but also had more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed on the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of particleboard images with high quality. In the future, it can also be further combined with defect detection for actual production to improve the quality of forestry products and increase economic benefits.

Current effort with light field displays is mainly concentrated on the widest possible viewing angle, while a single viewer only needs to view the display in a specific viewing direction. To make light field display a practical practice, a super multi-view light field display is proposed to compress the information in the viewing zone of a single user by reducing the redundant viewpoints. A quasi-directional backlight is proposed and a lenticular lens array is applied to achieve the restricted viewing zone. Eye-tracking technique is applied to extend the viewing area. Experimental results show that the proposed scheme can present vivid 3D scene with smooth motion parallax. Only 16.7% conventional light field display data are required to achieve 3D display. Furthermore, an illumination power of 3.5 watt is sufficient to lighten a 31.5-inch light field display，which takes up 1.5% of the illumination power required for planar display of similar configuration.

Deep learning technologies for skin cancer detection have been dramatically advanced based on high resolution dermoscopic images. The low-cost approach based on clinical skin images of low resolution is promising but remains technically challenging due to their undermined image quality. In this paper, we propose a coarse-to-fine efficient super resolution transformer (CF-ESRT) network to reconstruct the dermoscopy-level high resolution skin image from a low resolution clinical image. By connecting the refinement network to the original super resolution transformers and applying perceptual and gradient losses, our framework noticeably improves the finer texture details of skin lesions in the super resolution (SR) images, and is effective to elevate the perceptual quality of the SR images. Quantitative and qualitative evaluations show that our method outperforms ESRT the basis model as well as the other state-of-the-art SR models.

The super-weak interaction includes three simple extensions of the standard model: gauge extension, fermionic extension, and scalar extension. All of these extensions are strongly influenced by their complex phenomenology. They can explain a number of unresolved questions in particle physics and cosmology, including the genesis of dark matter, cosmic inflation, asymmetry of matter and antimatter, neutrino masses, and vacuum stability, if combined into a single structure. This is an extension of the gauge group of the standard model $G_{\rm SM}$ by $G_{\rm SM}\otimes U(1)_Z$ without any anomalies. We investigate the implications of the development of a general Majorana mass renormalization group for neutrinos with masses in the range of 0.03 $eV$ and 0.1 $eV$, which fall within the recently published range as well as the range to be explored in future planned experiments.

The maker movement has reached the optics labs, empowering researchers to actively create and modify microscope designs and imaging accessories. 3D printing has especially had a disruptive impact on the field, as it entails an accessible new approach in fabrication technologies, namely additive manufacturing, making prototyping in the lab available at low cost. Examples of this trend are taking advantage of the easy availability of 3D printing technology. For example, inexpensive microscopes for education have been designed, such as the FlyPi. Also, the highly complex robotic microscope OpenFlexure represents a clear desire for the democratisation of this technology. 3D printing facilitates new and powerful approaches to science and promotes collaboration between researchers, as 3D designs are easily shared. This holds the unique possibility to extend the open-access concept from knowledge to technology, allowing researchers from everywhere to use and extend model structures. Here we present a review of additive manufacturing applications in microscopy, guiding the user through this new and exciting technology and providing a starting point to anyone willing to employ this versatile and powerful new tool.

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved super-resolution model which involves the self-attention augmented WGAN is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the HR results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we therefore design a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features, this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.

SARS-CoV2 (corona virus) has spread globally at an unprecedented rate; so far, increasing SARS-CoV2-infected individuals have been identified. Although the situation in China is improving and is currently under control, the outbreak in other countries and its pandemic management is only beginning to develop. Based on 154 SARS-CoV2 genome sequence analyses, we used receptor–ligand docking to identify one potential point mutation (V354F) on the spike structure which enhances spike binding to ACE2 receptors underlying potential super infection. Importantly, the V354F site on spike S1 had been identified in 5/10 infected French patients living in Paris, who sharing 100% identical SARS-CoV2 genomes. With Covid-19 cases increasing rapidly in France that could lead to a new explosion, we suggest that the French government should identify all potential super spreaders and treat them accordingly. In summary, our study provides on of the measures to avoid the potential second worldwide explosion of SARS-CoV2.

Direct laryngoscope and videolaryngoscope are the dominant endotracheal intubation tools. Styletubation technique (using a video-assisted intubating stylet) has shown its advantages regarding in short intubation time, high success rate, less stimulation, and operator’s satisfaction. The learning curve can be steep but easy to overcome if the technical pitfalls could be avoided. Conditions make styletubation challenging include secretions/blood, short/stiff neck, restricted mouth opening and cervical spine mobility, anatomical abnormalities over head and neck regions, and obesity, etc. In this clinical report, we present the effectiveness and efficiency of routine use of the styletubation for tracheal intubation in a super-super obese patient (BMI 103 kg/m2) undergoing bariatric surgery with laparoscopic sleeve gastrectomy.

Multispectral image (MSI) and hyperspectral image (HSI) fusion (MHIF) aims to address the challenge of acquiring high-resolution (HR) HSI images. This field combines a low-resolution (LR) HSI with an HR-MSI to reconstruct HR-HSI. Existing methods directly utilize transformers to perform feature extraction and fusion. Despite the demonstrated success, there exist two limitations: 1) Employing the entire transformer model for feature extraction and fusion fails to fully harness the transformer’s potential in integrating the spectral of the HSI and spatial information of the MSI. 2) HSI has a strong spectral correlation and exhibits sparsity in the spatial domain. Existing transformer-based models do not optimize this physical property, which makes their methods prone to spectral distortion. To accomplish these issues, this paper introduces a novel framework for MHIF called Sparse Mix-Attention Transformer (SAMformer). Specifically, to fully harness the advantages of the Transformer architecture, we propose a Spectral Mix Attention Block (SMAB), which concatenates the keys and values extracted from LR-HSI and HR-MSI to create a new multi-head attention module. This design facilitates the extraction of detailed long-range information across spatial and spectral dimensions. Besides, to address the spatial sparsity inherent in HSI, we incorporated a sparse mechanism within the core of SMAB called Sparse Spectral Mix Attention Block (SSMAB). In the SSMAB, we compute attention maps from queries and keys and select the K highly correlated values as the sparse attention map. This approach enables us to achieve a sparse representation of spatial information while eliminating spatially disruptive noise. Extensive experiments conducted on three benchmark datasets, namely Cave, Harvard, and Pavia Center, demonstrate the SMAformer method outperforms state-of-the-art methods.

Super-high (x400) magnification dermoscopy (D400) is a new non-invasive imaging technique that has been shown to add information for the differential diagnosis of melanocytic lesions in a pilot study. Our study aimed to confirm if D400 can add details for the discrimination of clinically atypical nevus and melanoma. This is a retrospective observational, multicentric study enrolling patients who received an x20 (D20) and x400 (D400) magnification dermoscopic examination of pigmented skin lesions. Dermoscopic images were retrospectively evaluated by three observers for the presence/absence of nine D20 and twenty D400 dermoscopic features defined in the previous pilot study. Univariate and multivariate analyses were carried out to find predictors of benign and malignant behaviors. At D20 atypical pigment network, blue-whitish veil, atypical vascular pattern, regression, and homogenous pattern were more frequent in melanoma than in nevi (p<0.001). At D400 melanoma showed more frequently than benign lesions melanocytes with an irregular arrangement and irregular in shape and size (p<0.001). Network with edged papillae was more frequent in benign lesions than melanomas (p<0.001). Our study confirms that D400 can identify melanocytes with an irregular arrangement and irregular in shape and size in melanomas, adding information to the conventional D20 examination.

Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest in the Remote Sensing community, who sees it as an oportunity to compensate for satellite's ever-limited spatial resolution with respect to end users needs. While there has been a great amount of work on network architures in the latest years, deep learning based SISR in remote sensing is still limited by the availability of the large training sets it requires. The lack of publicly available large datasets with the required variability in terms of landscapes and seasons pushes researchers to simulate their own dataset by means of downsampling. This may impair the applicability of the trained model on real world data at the target input resolution. In this paper, we propose an open-data licenced dataset composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meter resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations on earth with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring the spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g. 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms 8 other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20% and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction. Our code is available at https://github.com/xian1234/SRBuildSeg.

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

This study investigates the relationship between Corporate Social Responsibility (CSR) dimensions and employees' satisfaction and retention for sustainability in banks. Carroll (1991) proposed a theoretical approach that consists of four components (economic, legal, ethical, and philanthropic) to analyze CSR activities and their effects on employee’s satisfaction and retention in the company. Simple random sampling was used to get the information from 221 participants. The entire form of the dataset is utilized to execute regression and correlation analysis using SPSS. In order to find out the relationship between economic, legal, ethical, and philanthropic factors and employee's satisfaction and retention, regression beta coefficient and correlation coefficients were used to analyze. This study also examines the relationship between job satisfaction and intentions to retain with an organization. The findings demonstrate that the CSR aspects of ethical and philanthropic have a considerable and favorable influence on employee's satisfaction. The outcome also demonstrates a good and prominent influence of legal CSR on the satisfaction of employee's to retain with the firm. Moreover, this study demonstrates that economic aspect of CSR has no significant impact on employee's retention and satisfaction. Correlation analysis depicts that economic CSR is positively and significantly connected with employee's retention and satisfaction. This research came to the conclusion that enhancing employees view regarding CSR activities such as economic, legal, ethical, and philanthropic will increase employee's satisfaction. Therefore, executives and managers in the banks should take steps to influence how employees see CSR areas in order to raise employee's satisfaction and retention in the banks for sustainability.

As ethically motivated vegans (EMV) frequently feel responsible for suffering caused by the consumption of animal products, feelings of guilt are therefore an important issue for many of them. To date, studies on the experience and handling of guilt in vegans have rarely been identified. Therefore, we investigated this topic using a qualitative design. N=64 interviews with N=57 EMV were analyzed using content analysis. Key results are that they perceive guilt as a psychological and sometimes even as a physical influencing factor. They describe how feelings of guilt arise in combination with anger, revulsion and pity thereby affecting their thinking and behavior. Some experience even corporal strain, a physical heaviness. Others helped the transition to a vegan diet to get rid of guilt and to feel emotional and partially also physical relieve. In contrast, others were faced with new problems: no dissociation from other people’s guilt, heightened awareness of one’s own perception, more compassion, and a stronger sense of responsibility. Some even registered an increased tendency to depression and self-destructive thoughts. Beyond its limitations, our study is one of the first illustrating the possible impact of feelings of guilt on mental and physical well-being EMV may sense and suffer from.

Using a variety of theoretical foundations, this paper examines the association between corporate social responsibility (CSR) and employees’ knowledge-sharing behavior, investigating the sequential mediation role of perceived organizational support and organizational commitment as well as the moderating role of ethical climate. Hypotheses were formulated to address both the sequential mediation effect of perceived organizational support and organizational commitment on the link between CSR and knowledge-sharing and the moderation effect of ethical climate on the relationship between CSR and perceived organizational support. In order to mitigate the problems of potential common method bias, data were gathered from a sample of 204 South Korean employees at three distinct time points. The results of the study offer compelling evidence that CSR activities exert a positive influence on employees’ knowledge-sharing behavior, an effect that is sequentially mediated by both perceived organizational support and organizational commitment. Furthermore, the research uncovers the positive moderating role played by ethical climate in the relationship between CSR and perceived organizational support. By integrating multiple theoretical frameworks, this study not only advances the extant literature but also offers invaluable insights for organizations striving to bolster knowledge-sharing through the strategic implementation of CSR initiatives.

From 2008 to 2024, the Newborn Screening Translational Research Network (NBSTRN), part of the National Institute of Child Health and Human Development (NICHD) Hunter Kelly Newborn Screening Program, served as a robust infrastructure to facilitate groundbreaking research in newborn screening (NBS), rare disease, and genomics. Over fifteen years, NBSTRN developed into a significant international network, supporting innovative research on novel technologies to screen, diagnose, treat, manage, and understand the natural history of more than 280 rare diseases. A key component of these efforts has been the development of resources and expertise for the newborn screening community in ethical, legal, and social issues (ELSI). This paper presents findings from a survey conducted by the NBSTRN team with input from one of theNBSTRN's expert workgroups, the Bioethics and Legal Workgroup. With responses from 88 members of the NBS community including researchers and state NBS programs, the survey found that individuals rely on internal and collegial ELSI resources and highlighted a substantial demand for structured, accessible training and resources. The survey’s insights emphasize the importance of privacy and informed consent in NBS research, and suggested a need for improved collaborative tools and educational programs focused on ELSI for the NBS community. The survey results may inform future efforts in ELSI and NBS research including the development of expanded ELSI tools that leverage digital platforms and consultative services.

The objective of this research was to create a Spanish-adapted version of the 30-item Multidimensional Ethics Scale (MES-30), analyze its structural validity, and create a short, valid, and culturally-adapted version. A total of 594 employees from organizations operating in different industries, primarily located in Ecuador, participated in this study. The comparison of different models using Confirmatory Factor Analysis (CFA) showed that the bifactor model proved better fit in all scenarios. The high common variance explained by the general factor (> 80%) and the high values of ωh (> .90) led to the conclusion that the explanatory model of the adapted MES-30 is essentially one-dimensional. In order to obtain a more valid and reliable measurement of the substantive common factor, a short version (Spanish MES-15) has been proposed, which contains items from all philosophical approaches and three non-redundant scenarios. The one-dimensional model for the Spanish MES-15 showed excellent goodness of fit in all scenarios. Regarding the metric equivalence between the original instrument (adapted MES-30) and the short one, the correlation between both measurements was greater than .90, the correlation of the short version with the social desirability scale was lower, and both instruments similarly predicted the intention of executing the actions described in the scenarios, the probability of executing the actions attributed to peers and supervisors, and the general ethical evaluation of those actions.

As the world faces unprecedented environmental challenges and the rapid advancements of artificial intelligence (AI), it is crucial for universities to adapt their chemistry education to remain relevant and contribute to sustainability and the United Nations' Sustainable Development Goals (SDGs). This report offers critical insights into the development of university chemistry education over the next 10-20 years, forecasts its development over the next 10-20 years, and offers recommendations for the Ministry of Education and universities to ensure the continued relevance of chemistry programs in the face of AI.

This article aims to investigate the potential of artificial intelligence (AI) in revolutionizing the education sector and further advancing the vision of Education 6.0. This study explores how AI can foster personalized learning experiences and adaptive pedagogy. By analyzing recent developments and real-world applications of AI in education, this article offers insights into how AI can support educators in tailoring instruction to individual students' needs, promoting engagement, and optimizing learning outcomes. The article also discusses ethical considerations and challenges associated with the integration of AI in education, emphasizing the importance of responsible implementation and human supervision.

Brachycephalic dogs are not only affected by brachycephalic obstructive airway syndrome (BOAS), but also comprise up to 30% of canine patients seen by veterinary dermatologists, with English Bulldogs and Pugs particularly overrepresented. Some skin diseases are associated with the abnormal anatomic conformation of brachycephalic dogs, while for others there is a underlying genetic basis or a general predisposition. Anatomic alterations associated with brachycephaly, leading to fold formation of the skin and stenosis of the ear canal, together with primary immunodeficiencies described in some breeds, favor the development of pyoderma, Malassezia dermatitis and otitis externa/media. Frequently neglected but often lifelong dermatological problems of brachycephalic dogs are an important consideration when discussing genetic and medical conditions affecting the welfare of those dogs. Here we review the current state of knowledge concerning dermatological problems of brachycephalic dogs, and combine it with clinical experience in the management of these challenging disorders.

Cause-Related Marketing (CRM) has emerged as a new concept that links CSR activities to marketing, enabling corporations to pursue both building company-consumer partnership and gaining sustainable mutual benefits that are elicited from the partnership. Among various cause-related marketing activities, on-line cause-related advertising is the most commonly used one. The purpose of this study is to examine the effects of on-line cause-related advertising and moderating role of ethical consumption propensity. In particular, this study intends to contribute to successful development of on-line cause-related advertising strategy by focusing on the relationship between the four factors: customers' perception of on-line cause-related advertising, brand-consumer relationship, brand attachment and loyalty. The results of this study are as follows: First, customers' perception of on-line cause-related advertising was related positively to consumer-brand- relationship. Second, brand-consumer relationship was related positively to brand attachment and customer loyalty. Third, brand attachment was related positively to customer loyalty. Furthermore, from the consumer’s psychological viewpoint, ethical consumption propensity had a significant moderating effect to between customers' perception of cause-related advertising and brand-consumer relationship as well as brand attachment. The results of this study may contribute to the existing literature as an new empirical attempt to examine the effect of the on-line cause-related advertising.

Super-resolution technology is important not only in bio-related fields but also in nanotechnology, particularly in the semiconductor industry, where fine patterning is required and super-resolution is essential. However, observing microstructures beyond the diffraction limit proposed by Abbe and Rayleigh is considered impossible because of diffraction in traditional optical microscopy ob-servation techniques. However, in recent years, it has been possible to observe microstructures beyond the Rayleigh criterion by analysing the phase distribution of light. This study investigated the physical reasons why phase analysis makes this new analysis technique possible using simula-tions. The results confirmed that the phase component of the zero-order diffracted light reflected from the microstructure and able to pass through the lens system contained the phase information related to the shape of the measured object. Analysis of this information demonstrates the possi-bility of realising super-resolution based on speckle interferometry.

In the context of economic development and international economic integration, the logistics industry in Vietnam is developing to meet the market demand for the transportation of goods; thus, many logistics enterprises have been formulated and grown up in recent years. This research aims to measure the efficiency of logistics enterprises and recommend a feasible solution to improve their future performance by integrating the Super Slacked-based Measure model (Super-SBM) in Data Envelopment Analysis and Fuzzy Analytic Hierarchy Processes (Fuzzy AHP) in multi-criteria decision-making. The super-SBM model was utilized to conduct the efficiency scores of logistics enterprises from 2016 to 2022 based on calculating the ratio between input and output variables; the empirical result determined each enterprise's effectiveness and ineffectiveness. Next, the Fuzzy AHP method evaluated and ranked criteria that directly impacted the operational process of logistics enterprises based on experts' opinions; the examined result suggested a feasible direction to improve their business efficiency in the future. The proposed hybrid models are a helpful solution for efficiency determination and development direction for logistics enterprises. An overall picture of the logistics enterprises was also drawn to describe their operational business process.

In this study, a new methodology based on the circuit approach is employed to obtain renewable electric energy. Although a voltage source and a current source generate voltage and current, respectively, they work only when they receive electric power, which is the product of voltage and current. However, this paper proposes a circuit with a voltage source, a current source and a super-condenser connected in series, in which the voltage source provides voltage for the current source and the current source provides current for the voltage source. Resultantly, electric power is generated both at the voltage and current sources, and the energy (i.e., the electric power) from the current source is converted to the charging energy in the super-condenser. Herein, we describe the method for charging the super-condenser using a voltage source and a current source. To confirm the abovementioned concept, we conducted experiments, and the energy was successfully produced, with high reproducibility. Finally, the brief mechanism for the energy conversion, a schematic method for reducing the capacitances of stored batteries, and the significance of this study are described.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Gwosch et al. (2020) and Balzarotti et al. (2017) purport MINFLUX as the next revolutionary fluorescence microscopy technique claiming a spatial resolution in the range of 1-3 nm in fixed and living cells. Though the claim of molecular resolution is attractive, I am concerned whether true 1 nm resolution has been attained. Here, I compare the performance with other super-resolution methods focussing particularly on spatial resolution claims, atypical image rendering, visualisation enhancement, subjective filtering of localizations, detection vs labelling efficiency and the possible limitations when imaging biological samples containing densely labelled structures. I hope the analysis and evaluation parameters presented here are not only useful for future research directions but also microscope users, developers and core facility managers when deciding on an investment for the next 'state-of-the-art' instrument.

In order to obtain stable super-hydrophobicity, suitable hydrophobic treatment agent should be selected according to different materials. In this paper, cotton and poly (-ethylene terephthalate) (PET) fabric was respectively coated by dodecyl methacrylate (LMA) via argon combined capacitively coupled plasma (CCP), and the surface hydrophobicity and durability of treated cotton and polyester fabrics were also discussed. An interesting phenomenon was happened that LMA coated cotton fabric (Cotton-g-LMA) had better water repellency and mechanical durability than LMA coated PET fabric (PET-g-LMA), and LMA coated hydroxyl grafted PET fabrics (PET fabrics were successively coated with polyethylene glycol (PEG) and LMA, PET-g-PEG&LMA) had similar performance to those of cotton fabrics. The water contact angle (WCA) of Cotton-g-LMA, PET-g-LMA and PET-g-PEG&LMA was 156 °, 153 ° and 155 °, respectively, and after 45 washing cycles or 1000 rubbing cycles, the corresponding WCA was decreased to 145 °, 88 °, 134 °and 146 °, 127 °, 143 °, respectively. Also, thermoplastic polyurethane (TPU) and polyamides-6 (PA6) fabrics were all exhibited the same properties to PET fabric. Therefore, the grafting of hydroxyl can improve the hydrophobic effect of LMA coating and the binding property between LMA and fabrics effectively without changing the wearing comfort..

In this paper, the effect of multi-rotational speeds super-gravity field on the grain refinement and tensile properties of as-cast H13 steel were investigated systematically. The experimental results revealed that the as-cast grains of H13 steel can be significantly refined in multi-rotational speeds supergravity field. In conventional supergravity field, with the decrease of rotational radius, the secondary dendrite average spacing (SDAS) and the austenite grain average size (AGAS) increase, and the maximum values of SDAS and AGAS are 90 µm and 55 µm, respectively. while in multi-speeds supergravity fields, at the range of increasing rotational speeds, SDAS and AGAS decrease as the rotational radius decreases. In three-rotational speeds supergravity field, the maximum values of SDAS and AGAS are 80 µm and 50 µm. In five-rotational speeds supergravity field, the maximum values of SDAS and AGAS are reduced to 58 µm and 34 µm. Accordingly, both the tensile strength and the plasticity are enhanced with the increasing the number of rotational speeds in supergravity field, especially for the inner position of supergravity sample. The ultimate tensile strengths at outer, middle, and inner positions of H13 steel solidified in conventional supergravity field are 1445 MPa, 1378 MPa, and 1023 MPa, corresponding elongations of 2%, 1.5%, and 0.5%, while in the five-rotational speeds supergravity field, they are 1408 MPa, 1443 MPa, and 1453 MPa, corresponding elongations of 1.8%, 3.9%, and 2.2%. The mechanism for the grain refinement is that multi-speeds super-gravity can reduce the critical nucleation work of austenite, and the tangential force produced by changing the rotational speeds breaks dendrites at the solidification front, refining solidification structure.

The stage of a super-early scale-invariant Universe is considered on the basis of the Poincaré–Weyl gauge theory of gravity in a Cartan–Weyl space-time. An approximate solution has been found that demonstrates an inflationary behavior of the scale factor and, at the same time, a sharp exponential decrease in the effective cosmological constant from a huge value at the beginning of the Big Bang to an extremely small (but not zero) value in the modern era, which solves the well-known “cosmological constant problem”.

Silicates are among the most abundant and important inorganic materials, not only in the Earth’s crust but also in the interstellar medium in the form of micro-/nano-particles or embedded in the matrices of comets, meteorites and other asteroidal bodies. Although the crystalline phases of silicates are indeed present in Nature, amorphous forms are also highly abundant. Here we report a theoretical investigation of the structural, dielectric and vibrational properties of the amorphous bulk for forsterite (Mg₂SiO₄) as a silicate test case by a combined approach of classical molecular dynamics (MD) simulations for structure evolution, and periodic quantum mechanical DFT calculations for electronic structure analysis. Using classical MD based on an empirical partial charge rigid ionic model within a melt-quenching scheme at different temperatures performed with the GULP 4.0 code amorphous bulk structures for Mg₂SiO₄ were generated using as initial guess the crystalline phase. This has been done for bulks with three different unit cell sizes adopting a super-cell approach; i.e., 1×1×2, 2×1×2 and 2×2×2. The radial distribution functions indicated a good degree of amorphisation of the structures. Periodic B3LYP-geometry optimizations performed with the CRYSTAL14 code on the generated amorphous systems were used to analyze their structure, to calculate their high frequency dielectric constants (ε_∞), and to simulate the IR, Raman and reflectance spectra, which were compared with the experimental and theoretical crystalline Mg₂SiO₄. The most significant changes of the physico-chemical properties of the amorphous systems compared to the crystalline ones are presented and discussed (e.g., larger deviations in the bond distances and angles, broadening of the IR bands, etc.), which are consistent with their disordered nature. It is also shown that by increasing the unit cell size the bulk structures present larger degree of amorphisation.

This paper examines the transformative journey of Artificial Intelligence (AI) agents from copilots to integral coworkers within diverse sectors. Beginning with a foundational overview, we delve into the historical evolution of AI agents, highlighting key technological milestones and the shift from simple automated tools to sophisticated decision-making systems capable of learning and adapting over time. We explore the capabilities of AI agents, including problem-solving, predictive analytics, and automation, and their applications across healthcare, business, and education, showcasing their role in diagnostics, customer service, and personalized learning, among other areas. We also address the integration of AI agents in the workplace, presenting strategies for collaboration between humans and AI, and outline the challenges and ethical considerations that accompany the development and deployment of AI technologies. A comprehensive tutorial provides insight into the development process of AI agents, emphasizing goal definition, data handling, algorithm selection, and ethical guidelines. We also consider the pathway towards Artificial General Intelligence (AGI) and the societal and ethical implications of widespread AI integration, proposing strategies for equitable and ethical AI use.

The present paper brings to the fore issues relating to the meaning and construction of ethics in online team communication by exploring the discursive strategies that contribute to the construction of a team’s sense of duty and individual virtuousness. The study relies on a complex toolkit which includes ethnolinguistics, sociolinguistics, discourse and conversation analysis. Data consist in a one-day interaction unit as part of a larger set of real communication exchanges (ca. 34,000) over a time period of six months, observation notes, as well as unstructured interviews. Our empirical analysis has revealed that individual virtuousness and sense of duty are actually interrelated. A virtuous team climate leads team members to share positive perceptions about the team, which in turn increases team commitment. Furthermore, we argue that the blurring of private and professional life not only allows for the enactment of ethic-driven discourse strategies that result in enhanced cooperation and improved team performance but also for high levels of interconnectivity and improved social interaction. The results of the analysis supplement organisational literature based on ethics-centred observations on the effectiveness of virtual work, and show how a discourse-driven approach can provide tools for further theorisations about the practices and the ecology of digital communication.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

Background: Little is known about the decisional background of choosing an appropriate disease model. Therefore, this paper aims, (1) to identify a spectrum of reasons for choosing between animal and non-animal disease models and (2) provides an ethical analysis of these reasons. Methods: 13 researchers from basic and preclinical research were interviewed, the interviews were analyzed qualitatively and the results were categorized. The ethical analysis was based on the principlism approach and a value judgement model. Results: This paper presents 66 reasons why researchers use animal (41 %) or alternative, non-animal disease models (59 %). Most of the reasons were be assigned to the work environment (29 reasons) and scientific standards (22 reasons). Other reasons were assigned to personal attitudes (11 reasons) and animal welfare (4 reasons). Qualitative relevant normative differences are presented in the ethical analysis. Even if few reasons can be rejected outright from an ethical point of view, there are good reasons to give some more weight than others. Conclusions: The spectrum of reasons and its ethical assessment provide a framework for reflection for researchers who may have to choose between animal models and (investing in) alternatives. This can help to reflect on and justify decisions.

Cobalt oxide nanoparticles are promising candidates for various kinds of practical application. The interest of researchers and scientists in these nanoparticles is due to their unique physical and chemical properties such as density, melting point, magnetic characteristics and band gap. The need to develop high performance and low-cost technologies for obtaining Co3O4 nanoparticles is a major reason in research in this area. The widespread commercial use of Co3O4 nanoparticles will open doors for numerous novel cost effective technologies, which are mostly dependent on the type of synthesis method and raw materials. A possible solution to this problem is to use Cobalt nitrates as a cost-effective precursor using the solution combustion method. Solution combustion synthesis method is one of the best candidate methods that have the potential for high performance and is also cost-effective. This review examines recent progress in the synthesis of Co3O4 nanoparticles by solution combustion method and underscores their significance across various fields, highlighting the need for further research and exploration of their multifaceted applications. In addition, the most recent advancements in the applications of Co3O4 nanoparticles in sensitive sensing, catalysis, supercapacitors/batteries and water treatment/pollutants removal are reviewed and described, focusing on current trends, challenges, and future outlooks.

The level of development of the innovation factor plays a crucial role in supporting the high-quality development of China's economy. To advance the development of regional innovation factor allocation efficiency along the Belt and Road. This article introduces the super-efficiency SBM-DEA-Malmquist model for the static and dynamic analysis of innovation factor allocation efficiency in 17 provinces along the Belt and Road from 2012 to 2021. The Moran index model analyzes the spatial correlation. The results show that: (1) The overall innovation factor allocation efficiency along the Belt and Road is not high, and there are obvious differences among different regions. The eastern region is the highest compared to other regions. (2) According to the efficiency decomposition results, pure technical efficiency is the main reason for the low innovation factor allocation efficiency. (3) Through the Malmquist index and decomposition index, it is found that pure technical efficiency and scale efficiency are key factors in improving technical efficiency. (4) The analysis of spatial correlation reveals a strong spatial agglomeration feature among the provinces along the Belt and Road. The innovation factor allocation efficiency is mainly manifested in the L-L type. Finally, according to the analysis results, suggestions and policies are put forward to improve the innovation factor allocation efficiency in the regions along the Belt and Road.

Wool quality and yield are important economic traits of livestock. In this study, the skin tissues of the upper scapula of Super Merino sheep (SM) and Small-Tailed Han sheep (STH) during the growing period were studied. Differentially expressed (DE) long non-coding RNAs (lncRNAs) and messenger RNAs (mRNA) were identified via histopathological observation and high-throughput RNA sequencing. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze differentially expressed genes. Reverse transcription quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify nine selected DE lncRNAs and five DE mRNAs. Significant differences were found in the expression levels of LOC114113396 (KRTAP15-1), CRABP1, KRT10, KRTAP1-3, KRTAP4.3, KRT2, FGF1, IGF-1, and KIT in SM and STH skin tissues. These results indicate that these mRNAs may play an important role in the growth and development of hair follicles and the regulation of hair fiber traits. Differentially expressed lncRNA target genes and mRNAs were mainly enriched in biological processes and pathways related to skin development and fiber traits, which is consistent with previous studies. LncRNA MSTRG.9225.1, MSTRG.124320.1, MSTRG.96951.1, MSTRG.91685.1, MSTRG.122229.1, MSTRG.89255.1, MSTRG.98769.1, and MSTRG.84658.1 may indirectly participate in the growth and development of sheep hair follicles and the regulation of hair fiber diameter by regulating the expression of their respective target genes. These candidate lncRNAs and mRNAs may provide a basis for the study of sheep hair follicle growth and development and hair fiber traits.

Dawn of the omics revolution in biological sciences meant that we seek to understand more and in greater detail the molecular constituents of cells and biological systems. While we have gained significant insights from conventional omics tools, we now seek to understand the spatial dimensions of the data where subcellular localisation may impact on cellular physiology and phenotype. This review paper seeks to address current questions in the new field of image-based spatial proteomics as well as outline future challenges of the field. At first glance, spatial proteomics offers enormous potential to expand our understanding of different cell types in different disease and cell states. But limitations in types of fluorophores and issues with spectral overlap significantly hampers the practical implementation of the technique. On the other hand, while we have super-resolution microscopy techniques such as STED, PALM and STORM able to achieve 10 to 20 nm spatial resolution in single molecule localisation, problems with slow image acquisition limits high temporal resolution tracking of multiple protein targets in live cell imaging. Hence, the field of spatial proteomics is a mix of promises and challenges where we could image, in multi-colour, upwards of 10 well-chosen proteins that could inform on the molecular mechanisms of selected biological processes, but, at present, the method could not tackle larger scale questions. In essence, current implementation of image-based spatial proteomics is useful, but it is unable to fulfil the mission of large-scale projects such as the Human Protein Atlas or Human Cell Atlas. Future challenges in the field includes the development of more fluorophores (especially photoswitchable and photoactivable ones) for single molecule localisation microscopy, as well as seeking to improve temporal resolutions to the sub-millisecond range.

Existing methods enhance mid-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.

This research was specifically aimed at assessing the influence of sulfur in triple-super phosphate (TSP) on wheat yield. From the results, wheat showed response to sulfur (S) from gypsum (in 67%); and nitrogen (N) from urea in about 100% (of 24 sites). Based on this N was found to be the most limiting element to wheat production followed by sulfur, and then by phosphorus. TSP is tested to contain agronomically up to 2-6% by weight of S. However, wheat didn’t show response to S impurity supplied in the form of TSP. Though, not statistically significant, it is observed that there have always been yield increments by certain percent due to S from TSP in 8 out of 10 target sites, which is depicted in the increasing trends of yield response curves. From this it is learnt that, the benefits of the accidental/incidental application of such high analysis fertilizers can be many-folds in the quality attributes of wheat, if the soils of such investigation at the same time would contain significant amount of organic matter (OM). Indeed, such analysis would be vital in varietal specific nutrient requirement studies in precision-farming and/or in categorizing soils into fertility gradients and fertilizer recommendation domains.

Within the framework of multiblock data analysis, a unified approach of supervised methods is discussed. It encompasses multiblock redundancy analysis (MB-RA) and multiblock partial least squares (MB-PLS) regression. Moreover, we develop new supervised strategies of multiblock data analysis, which can be seen as variants of one or the other of these two methods. They are respectively refered to as multiblock weighted redundancy analysis (MB-WRA) and multiblock weighted covariate analysis (MB-WCov). The four methods are based on the determination of latent variables associated with the various blocks of variables. They are derived from clear optimization criteria whose aim is to maximize either the sum of the covariances or the sum of squared covariances between the latent variable associated with the response block of variables and the block latent variables associated with the various explanatory blocks of variables. We also propose indices to help better interpreting the outcomes of the analyses. The methods are illustrated and compared based on simulated and real datasets.

To solve a series of major problems, such as the singularity before the Big Bang, the inflation of the early universe and the accelerating expansion of the present universe, this series of studies start with super gravity, dark energy and dark matter, go beyond the surface of a lot of observation data, and systematically reveal the arche-internal mechanism of all things, to establish the arche-unity of physics, and give a natural and reasonable explanation. At present, there are at least three basic pathways that are leading our research field to repulsion against gravitation, quantum repulsion against quantum gravitation and high-dimensional superrepulsion against high-dimensional supergravity, that is, the research on the accelerating expansion mechanism of the current universe, the research on the dynamic mechanism of the early cosmic inflation, and the research on the rebound mechanism near the singularity before the Big Bang. In this series of studies, the arche problem of everything is always placed under the extreme environmental conditions of the initial universe, the starting point of scientific logic is closely combined with the starting point of the universe history, the pseudo vacuum energy considered by the inflationary universe model, the Higgs field predicted by the standard physical model and the dark energy speculated by the observational cosmology are reduced to the positive energy system with strong negative pressure, and they are reduced to the repulsion (all the resistance to gravity) that forms a conjugate relation with gravity. In our opinion, dark energy and dark matter are essentially at the level of quantum gravity and quantum repulsion. Therefore, we not only consider the conventional quantum effect and non commutative quantum effect, but also further consider the arche-conjugate effect and the cluster resonance-at-cofrenquence effect. For noncommutative space-time, we put forth the ultimate supersymmetry, the arche-conjugation and the cluster resonance-at-cofrenquence. A relatively complete natural mechanism forms such an important inference: the closer they get to Planck scale, the stronger the conventional quantum effect, non commutative quantum effect, arche-conjugate effect and co-frequency resonance effect are. In the high-dimensional space-time (the 11 dimensional hyperspace compactly reduced to M 4 × N r ), corresponding to the introduction of supergravity with the supersymmetry between the graviton and the gravitino, we put forth the superrepulsion with the supersymmetry between the repulsion and the repulsitino, and establish the arche-conjugate relationship between the high-dimensional supergravity and the high-dimensional superrepulsion. Therefrom, we propose a basic inference that can be promoted to the principle of hyperholography of high-dimensional universe (or multiuniverse): in a parallel universe formed by mutual coupling and holographic correspondence between the P universe dominated by negative pressure (high-dimensional superrepulsion and quantum repulsion of accumulating positive energy, mainly composed of dark energy) and the N universe dominated by positive pressure (high-dimensional supergravity and quantum gravity of accumulating negative energy, mainly composed of dark matter), there is a correspondence between the small-scale physics (ultraviolet cutoff) of the P universe and the large-scale physics (infrared cutoff) of the N universe, while there is a correspondence between the large-scale physics (ultraviolet cutoff) of the P universe and the small-scale physics (infrared cutoff) of the N universe. In the new research paradigms set in this series, the arche-conjugation between the high-dimensional supergravity and the high-dimensional super repulsion, as well as between the quantum gravity and the quantum repulsion, is higher than the supersymmetry, so it becomes the core concept of the new theory. The arhce-unity of physics is a super unity that transcends the grand unification of four basic interaction, which is formed between high-dimensional supergravity (related to dark matter) and high-dimensional superrepulsion (related to dark energy), between quantum gravity (related to dark matter) and quantum repulsion (related to dark energy). From the basic component point of view, before or after the big bang, the gravitons and repulsons are coupled together, the negative energy contraction dominated by the positive pressure and the positive energy expansion dominated by the negative pressure work alternately, forming the arche-conjugate pulsaton of neither the point particle nor the linear superstring. Under the big unified framework of quantum gravitational field and quantum repulsive field, this series of work comprehensively expands the general relativity and loop quantum ring gravitation theory, sets up the quantum repulsive field equation corresponding to the quantum gravitational field equation, and then establishes the quantum hedge-unified field equation describing the interaction of quantum gravitational field and quantum repulsive field; On this basis, the quantum repulsive universe equation corresponding to the quantum gravitational universe equation is established, thus a set of new equations describing the expansive universe with quantum hedge-unified field are established. On the one hand, scalar disturbance is considered as the exhibition of repulsive effect, on the other hand, tensor disturbance is considered as the exhibition of gravitational effect. In the initial stage of the big ripping, due to the mutual restraint of quantum repulsion and quantum gravity, the scalar and tensor perturbations are not obvious. However, at the end of the big ripping, the scalar and tensor perturbations are very significant, and there is a large power spectral exponential run. Between particle physics and cosmology, we improve and expand the path integral of quantum gravity, create the path integral of quantum hedge-unified field, and set up the basic equation of quantum cosmic-synergetic dynamics, and then establish the basic equation of quantum-cosmic paradigm dynamics. Thus we put up the operator distribution function and its dynamic equation of the quantum arche- pulsaton, and finally use the Wigner-Ville distribution as a non-linear time-frequency distribution to establish the Wigner joint-distribution of quantum-conjuagted pulsation function, so as to analyze the arche of all things with three evolutionary forms: quantum chaotic-pulsaton, quantum quasi-spherical pulsaton and quantum spherical-pulsaton. In this series of work, the mechanism to avoid the singularity of the universe is put forward fundamentally, and the dark energy and dark matter are explained through new mechanisms and effects at the level of quantum gravity. Between particle physics and cosmology, we improve and expand the path integral of quantum gravity, establish the path integral of quantum hedge-unified field, thus establish the basic equation of quantum cosmic-synergy dynamics, and then establish the basic equation of quantum cosmic-paradigm dynamics. Thus the covariant gravitational mass and the covariant inertial mass formed by the ultra-synergy between particles and the universe are presented, and the equivalence hypothesis on the covariant non-inertial system which can be elevated to the basic principle, is presented. Based on this hypothesis, a systematic calculation and analysis of binary stars composed of neutron stars and black holes are made. The master equation, variance and Fokker-Planck equation for the birth-death and migration of a large number of particles are established between binary stars composed of neutron stars and black holes.

The Ni-Co-Cr-W-Mo system is critical for the design of nickel-based super-alloys. This system stabilizes different topologically close packed (TCP) phases in many of the commercially super-alloys with high W and Mo contents. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermodynamic calculations are applied to investigate the thermodynamics of the precipitates in two different W content Ni-Co-Cr-W-Mo super-alloys. Computational thermodynamics verified experimental observation of the f u phase formation as a function of temperature and alloy chemistry, but the kinetics for the precipitation of M6C phase do not agree with the experimental findings. The major precipitates of alloy 1 at temperatures 700 and 750 °C during long time exposure are M23C6, γ′ phase, and MC, and alloy 2 are M23C6, γ′ phase, MC, M6C and u phase. The W addition is found to promote the precipitation of M6C and u phase during exposure. The M6C has higher W and lower Ni content than that of u phase, meanwhile, M6C is an unstable phase would transform into M12C after 5000 h exposure at 750 °C. A great quantity of needle-like u phases precipitated after exposure at 750 °C for 5000h, which have no effect on the impact property of alloy 2.

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research is the use of innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. The study introduces new types of adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing the current robustness and security of AI models. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against an array of adversarial challenges. Importantly, the study also delves into the ethical and societal implications associated with employing advanced 'jailbreak' techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance the reliability and ethical soundness of AI technologies, paving the way for safer and more secure AI applications.

Accounting studies neglected to link corporate social responsibility to accounting information, the theoretical background revealed the study gap and the paper referred to it in the question: Do the corporate social responsibility CSR dimensions affect the accounting information quality? as the study aimed to reveal the effect of corporate social responsibility dimensions (economic, ethical, legal, and philanthropic) on accounting information quality (relevance and reliability) in banks in the Riyadh Governorate in Saudi Arabia. The researcher used a questionnaire to collect data from the study sample, and the data analysis was done using SPSS and smart partial least squares (pls), and its reliability and validity have been confirmed. Result revealed a positive effect of CSR (economic, ethical) dimension on the relevance of accounting information, a negative effect of CSR Legal dimension on the relevance of accounting information, and no effect of CSR philanthropic dimension on the relevance of accounting information, a positive effect of CSR (ethical, philanthropic) dimension on the reliability of accounting information and no effect of CSR economic and legal dimension on the reliability of accounting information.

This review article is aimed at exploring the possibility of developing an integrated approach in the management of the different needs of endometrial cancer (EC) patients seeking to become pregnant. Life preservation of the woman, health preservation of the baby, a precocious and – as much as possible – minimally invasive characterization of the health and fertility parameters of the patient, together with the concerns regarding the obstetric, neonatal, and adult health risks of the children conceived via assisted reproduction techniques (ART) are all essential aspects of the problem to be taken into consideration, yet the possibility to harmonize such needs through a concerted integrated approach is still highly challenging. This article aims to illustrate the main features of EC and how it affects the normal physiology of pre-menopausal women. Then, we focus on the prospect for a miR-based, molecular evaluation of patient health status, including both EC early diagnosis and staging and, similarly, the receptivity of the woman, discussing the possible evaluation of both aspects using a single specific panel of circulating miR in the patient, thus allowing a relatively fast, non-invasive way to perform such testing with a significantly reduced margin of error. Finally, the ethical and legal/regulatory complexities arising from such innovative techniques require not only a risk-benefit analysis; respect for patient autonomy and equitable health care access allocation are fundamental issues as well.

Barry Turner’s 1978 Man-made Disasters and Charles Perrow’s 1984 Normal Accidents were seminal books but a detailed comparison has yet to be undertaken. Doing so is important to establish content and priority of key ideas underpinning contemporary safety science. Turner’s research found socio-technical and systemic patterns that meant that major organisational disasters could be foreseen and were preventable. Perrow’s macro-structuralist industry focus was on technologically deterministic but unpredictable and unpreventable ‘system’ accidents, particularly rare catastrophes. Andrew Hopkins and Nick Pidgeon respectively suggested that some prominent writers who wrote after Turner may not have been aware of, or didn’t properly acknowledge, Turner’s work. Normal Accidents didn’t cite Turner in 1984 or 1999. Using a methodology involving systematic reading and historical, biographical and thematic theory analysis, a detailed review of Turner’s and Perrow’s backgrounds and publications sheds new light on Turner’s priority and accomplishment, highlighting substantial similarities as well as clear differences. In a previously unpublished letter, Perrow confirmed reading Man-made Disasters while writing Normal Accidents. Turner became better known after a 1997 second edition but under-acknowledgment issues continued. Ethical citation and potential reasons for under-acknowledgment are discussed. It is concluded that Turner’s foundational importance for safety science should be better recognised.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

LiFePO4 (LFP) has undergone extensive research and is a promising cathode material for Li-ion batteries. The high interest is due to its low raw material cost, good electrochemical stability, and high-capacity retention. However, poor electronic conductivity and a low Li+ diffusion rate decrease its electrochemical reactivity, especially at fast charge/discharge rates. In this work, the volumetric energy density of lithium-ion batteries is successfully increased by using different amounts of conductive carbon (Super P) in the active material content. The particle size and morphology of the electrode material samples are studied using field emission scanning electron microscopy and dynamic light scattering. Two-point-probe DC measurements and adhesive force tests are used to determine the conductivity and evaluate adhesion for the positive electrode. Cyclic voltammetry, electrochemical impedance spectroscopy (EIS), and charge/discharge tests are used to analyze the electrochemical properties of the battery. The samples containing 88% LFP, 5.5% Super P and 6.5% PVDF perform best, with discharge capacities reaching 169.8 mAhg-1 at 0.1C, and they can also manage charging/discharging of 5C. EIS indicates that this combination produces the lowest charge-transfer impedance (67 Ω) and the highest Li+ ion diffusion coefficient (5.76 x 10-14 cm2s-1).

There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the RGB value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images. The project page is available at https://rishavbb.github.io/ow-slr/index.html

Groundwater (GW) flooding mechanisms differ from river flooding both spatially and temporally, and preventative methods against groundwater flooding must take this into account. Although groundwater flooding caused by river water rise occurs seldom, it can occasionally become severe and last for a long time if the river is significantly flooded. In the southwest portion of the research domain, Friedrichshafen with a few urban communities, the level of the groundwater table was discovered to be roughly 1 m below the surface. It was discovered that the urban settlement area only has one-story buildings. In the study region, it is typical for the single-story building's foundation bottom level to extend up to a depth of about 1.5 meters. Therefore, flood mitigation methods are taken into account for the southwest portion of the study region. The installation of a pumping well, drainage, and a barrier in the affected area are three different flood control strategies that are taken into consideration for the study area. From a technical and cost-benefit perspective, installing a pumping well that withdraws water and lowers the groundwater table was found to be the most effective flood control measure locally in a small region (e.g., 1km x 1km). By contrast, removing groundwater by building drainage and barriers was also shown to be ineffective to lower the groundwater table over an extended region and was significantly more expensive than the installation of wells. Additionally, when river flooding is taken into account compared to the default scenario where no intake of water from the river is included along the western border of the study area, it is discovered that the spread of pollution is significantly greater.

Under the big unified framework of quantum gravitational field and quantum repulsive field, the earlier in this series of work has comprehensively expanded the general relativity and loop quantum ring gravitation theory, set up the quantum repulsive field equation corresponding to the quantum gravitational field equation, and then established the quantum hedge-unified field equation describing the interaction of quantum gravitational field and quantum repulsive field; On this basis, the quantum repulsive universe equation corresponding to the quantum gravitational universe equation has be established, thus a set of new equations describing the expansive universe model under the condition of fully considering the hedge effect of quantum gravitational field and quantum repulsion field, which can be called the expansive universe equation of quantum hedge-unified field theory, has be established. Now, let's go deep into the arche-unified physics behind the creation of the universe. Although all kinds of inflationary universe models can solve cosmological problems to a certain extent, there is no natural and reasonable analysis and explanation for all cosmological and physical consequences. For example, it is impossible to predict a large tensor-scale ratio and a relatively large spectral index run at the same time. At present, string theory, membrane theory, loop quantum gravity theory, super-gravitational holography principle and M theory, which are the theoretical basis of cosmology, are basically established by simplification in the case of breaking away from the extreme background conditions of the universe. While cosmology, which is deficient in nature, cannot provide strong support for particle physics to further advance to a wider field and a deeper level. Due to the lack of new ideas, the construction of existing theoretical models is difficult. Therefore, the existing research can only be further promoted by new astronomical observation. Unlike existing studies, we concentrate on the emergence of the big rip-rebound clusters of quantum chaotic-reticulate distribution between the outwards-push of the universe as a whole (related to pseudo-vacuum energy, Higgs field, dark energy, etc.) and the inwards-pull of the universe in local scale (related to dark matter, quantum gravity and initial gravity separated from high-dimensional supergravity, and the initial strong force separated from the grand unified force subsequently). On the one hand, we find a great impetus to make the inflation of the universe into firstly-imposed suppression, then-imposed acceleration and finally-imposed deceleration, which really explains why the universe expands at a critical rate and has the same temperature in different regions, on the other hand, we find a fundamental unit of matter and its emerging clusters evolving in the big bang, inflation and expansion of the universe accompanied by the great rip-rebound clusters of quantum chaos-reticulate distribution, which can be reduced to an arche-pulsator of neither punctiform particle nor linear string. From these two aspects, it can been seen that the evolution of the early universe is much more complicated than people originally thought. The complexity of the super inflation of the universe is mainly reflected in the big rip-rebound cluster emerging behind the gravitational disturbance (the pressure and tensor disturbance related to dark matter), the repulsive disturbance (the pressure and scalar disturbance related to dark energy), the cosmological disturbance (related to the interaction between high-dimensional supergravity and high-dimensional superrepulsion). This is a system of nonlinear stochastic differential dynamics, or a quantum statistical physical process of nonlinear nonequilibrium state. In the new research paradigms established in this series, the arche-conjugation between the high-dimensional supergravity and the high-dimensional superrepulsion, as well as between the quantum gravity and the quantum repulsion, is higher than the supersymmetry, and then becomes the core concept of the new theory. In this series, cluster-inflating configuration, spin network space and cosmic paradigm vectors are proposed, so the model of the super-inflated universe with quantum chaos-reticulate distribution emerged under the great impetus. The new model reveals that the unified inflation of the universe as a whole and the local inflation of the universe emerge together in the great impetus of the big rip-rebound cluster of quantum chaos-reticulate distribution. As a spin network model variable, the spatiotemporal mode variable is a vector composed of a series of variables, which variables include: dimension, curvature, range, expansion speed, dynamics, quantization, chaotic-reticulate distribution (inhomogeneity, concentration, fluctuation), synergy, and so on. Between particle physics and cosmology, this series of papers proposes the cosmic quantum evolution mode, the physical-sphere distributing configuration and the interaction situation variables, organizes the concepts, variables and models of these three levels, and establishes a systematic super-synergy paradigm. In this paradigm, the general Langevan equation and the general Fokker-Planck equation are established. For the quantum cosmic-sphere, a series of trend parameters are introduced to establish the dynamics of quantum gravitational mode and its master equation, and then to establish the operator distribution function and its motion equation of the action mode. For the Tachyon of upheaval in the rip-rebound-inflating of the universe, the hypothesis on the bifurcate-chaos wave of quantum is given, the quantum theory of the early photons in upheaval is set up, and the analysis on the particles of the ultrahigh energy cosmic ray from the γ ray burst is made. Finally, using Wigner-Ville distribution as a nonlinear time-frequency distribution, we establish the ultra-synergistic field equation of quantum-classic unifying action-sphere.

Artificial Intelligence based on Deep Learning is opening new horizons in Biomedical research and promises to revolutionize the Microscopy field. Slowly, it now transitions from the hands of experts in Computer Sciences to researchers in Cell Biology. Here, we introduce recent developments in Deep Learning applied to Microscopy, in a manner accessible to non-experts. We overview its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how Deep Learning shows an outstanding potential to push the limits of Microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are carefully discussed, as well as the future directions expected in this field.

Single-particle tracking with quantum dots (QDs) constitutes a powerful tool to track the nanoscopic dynamics of individual cell membrane components unveiling their membrane diffusion characteristics. Here we tested the possibility of extracting from the nano-resolved (16 ms and 30 nm) population dynamics of several quantum dots, time-binned at the second time-scale, the rapid structural changes of the cell membrane surface. We used for this proof-of-concept study bright, small and stable biofunctional QD nanoconstructs recognizing the neuronal cannabinoid type 1 (CB1) receptor and a commercial point-localization microscope to reconstruct in 3D the dynamics of the plasma membrane surface of cultured cells with a spatial resolution of tens of nanometers. CB1 receptor was chosen because it’s a highly expressed and fast diffusing membrane protein. Therefore, rapid QD diffusion on the axonal plasma membrane of cultured hippocampal neurons allowed highly precise reconstruction of the membrane surface in less than one minute. QD nanoconstructs diffused into the membrane of synaptic clefts allowing the entire topological reconstruction of the presynaptic component. In addition, we demonstrated successful reconstruction of the remarkably high dynamics of membrane surface topology at the second time-scale both in HEK-293 cell filopodia and axons. Our results show that this novel technique, which we named nanoPaint, is a powerful precision tool for the study of the structural plasticity of cell membrane surfaces.

We analyse the light curve in the R-band gathered during the 2015/16 observing season of OJ287. We did a search for QPOs using several methods in wide time domain. No statistically significant periods were found in the high frequency domain both in the ground data and also in K2 observations. In the longer periods domain the Lomb-Scargle periodogram revealed several peaks above the 99\% significance level. The longest one, about 95-day, corresponds to the ISCO period of the more massive SMBH. The 43-day period could be an alias or can be attributed to accretion in the form of two armed spiral wave.

AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

This article purposed to examine the impact of integration between Lean accounting (LA) tools & Just in Time (JIT) technique on Cost Reduction (CR) in modern manufacturing environment in Saudi establishments, these two variable moderated by Ethical Standards (ESs). A quantitative methodology was used for realizing the study objectives and answering study questions, the main tool exercised for producing the incipient data was a fully-structured questionnaire with closed ended questions, which was designed and passed around online among a selected sample of (109) managers and accountants in Saudi manufacturing firms. Descriptive and deductive statistics adopted to derive the findings, wherefore the compiled responses analyzed by SPSS & AMOS and the results showed that there is direct and indirect effect of LA tools & JIT technique on CR in Saudi industrial companies, especially when using ESs. These findings could provide decision makers in Saudi manufacturing companies with the acuity of adopting both methods in a combined way to maximize the profits of these firms. The results of this study also suggest that the application of LA tools & JIT technique has a positive effect on the promotion of sustainable manufacture and consequent achievement of high sustainable performance.

The purpose of this paper is to explore the effect of corporate social responsibility (CSR) on brand image in the apparel industry in Jordan, using the moderating variable of customer consciousness. The current research uses a quantitative, descriptive, cause-effect, and cross-sectional approach, and also it uses a convenience sampling method. Data was collected from 440 respondents. Results indicate that Jordanian Apparel organizations have medium to highly implementing of all items and constructs of CSR, brand image items, and consciousness items. Moreover, there is a strong relationship between CSR dimensions, a medium relationship be-tween each CSR dimension and brand image, a medium relationship between each CSR dimension and consciousness, a medium relationship between total CSR and brand image, a medium relationship between total CSR and consciousness, and the medium relationship between brand image and consciousness. Results of the first model show that total CSR affects the brand image. Where social responsibility has the highest positive effect, followed by ethical responsibility, then economic responsibility, however, environmental responsibility negatively affects the brand image. The second model shows consciousness moderates the effect of CSR on the brand image. The study recommends longitudinal research on the Jordanian Apparel industry, especially to test the effect of environmental responsibility on the brand image. Moreover, the re-search uses customers’ consciousness as a moderating variable, which improves the effect of CSR on brand image. The Apparel industry organizations in Jordan should revise their daily practices particularly environmental responsibility and/or increase their customers’ awareness of their practices. Finally, the current research contributes to existing studies by using consciousness as a moderating variable.

Purpose: The goal of the present study was to investigate chief executives’ intention and potential to create a family-supportive culture in the Brazilian context, further assessing the role of their aspirations in their employees’ perceptions. Methodology: Two researchers conducted 60 minutes of online semi-structured interviews with CEOs of seven companies of different sizes (measured by the number of employees), economic sectors, and capital structure. To complement the data gathered from CEOs, we also conducted private and individual 30-minute online interviews with three employees from each company. Findings: A total of four categories and 11 sub-categories emerged from the analysis of CEO interviews, and four categories and six sub-categories emerged from the analysis of employee interviews. Originality: The results suggest that family-supportive culture is promoted through behaviors that are consistent with the organization’s core values, as well as through commitment of the agenda and resources of the company’s leadership team.

Aiming at the difficult problem of broadband oscillation localization in power systems, the intel-ligent localization method of oscillation source based on the digital twin is proposed and the os-cillation source localization system based on the digital twin is constructed. Firstly, a digital twin-based oscillation source localization method and system architecture are proposed. Fur-thermore, an intelligent positioning method of oscillation source based on data-driven and mechanism fusion is proposed. It includes three steps: oscillation signal preprocessing, oscillation modal analysis and oscillation source localization. For the oscillation signal preprocessing, the generative adversarial imputation network is used to repair the missing samples, and the su-per-resolution technique is used to realize the super-resolution measurement of broadband os-cillation. In the oscillation modal analysis, the spectrum of the oscillation signal is extracted using the fast Fourier transform. To accurately locate the oscillation source, the branch potential energy is used as input to the data-driven model, such as LSTM and CNN. Finally, an oscillation source localization system is developed based on the digital twin workshop CloudPSS-XStudio, which can locate the oscillation source quickly and accurately.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

Spatial biology is a rapidly growing research field which focuses on the transcriptomic or proteomic profiling of single cells within tissues with preserved spatial information. Imaging-based spatial transcriptomics uses epifluorescence microscopy, which has shown remarkable results for identification of multiple targets in situ. Nonetheless, the number of genes that can be reliably visualized is limited by the diffraction of light. Here, we investigate the effect of structured illumination (SIM), a super-resolution microscopy approach, over the performance of single gene transcript detection in spatial transcriptomics experiments. We performed direct mRNA-targeted hybridization in situ sequencing for multiple genes in mouse coronal brain tissue sections. We evaluated spot detection performance in widefield and confocal images versus those with SIM in combination with 20X, 25X and 60X objectives. In general, SIM increases the detection efficiency of gene transcripts spots compared to widefield and confocal modes. For each case, the specific fold increase in localizations is dependent on gene transcripts density and the numerical aperture of the objective used, which showed to play an important role especially for densely clustered spots. Taken together, our results suggest that SIM has the capacity to improve spot detection and overall data quality in spatial transcriptomics.

With the development of deep learning technology, various structures and research methods for super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial network. Super-resolution restoration is ill-posed problem because of some complex restraints such as a lot of high-resolution images being restored for the same low-resolution image and also difficulty in restoring noises like edges, light smudging, and blurring. In this study, we utilized the spatially adaptive de-normalization (SPADE) structure for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. Consequently, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Also, the proposed method’s PSNR and SSIM scores are geting 8% and 15% higher, respectively, compared to the previous methods.

Laser shock peening as an innovative surface treatment technology can effectively improve the fatigue life, sur-face hardness, corrosion resistance, and residual compressive stress. Compared with the laser shock peening, the warm laser shock peening (WLSP) is a new surface treatment technology to improve materials’ surface performances, which takes advantage of thermal mechanical effects on stress strengthening and micro-structure strengthening, results in more stable distribution of the residual compressive stress under heating and cyclic loading process. In this paper, the microstructure of GH4169 nickel super-alloy processed by WLSP technology with differ-ent laser parameters were investigated. The proliferation and tangling of dislocations in GH4169 were observed and the dislocation density increased after WLSP treatment. The influences of different treatment by LSP and WLSP on the microhardness distribution of the surface and along cross-sectional depth were investi-gated. The microstructure evolution of the GH4169 alloy being shocked with WLSP were studied by TEM. The effect of temperature on the stability of high temperature microstructure and properties of GH4169 alloy WLP was investigated.

With our long experience in the field of unification of gravity and quantum mechanics, we understood that, when mass of any elementary is extremely small/negligible compared to macroscopic bodies, highly curved microscopic space-time can be addressed with large gravitational constants and magnitude of elementary gravitational constant seems to increase with decreasing mass and increasing interaction range. In our earlier publications, we proposed that, 1) There exist three atomic gravitational constants associated with electroweak, strong and electromagnetic interactions; 2) There exists a strong interaction elementary charge in such a way that, it's squared ratio with normal elementary charge is close to inverse of the strong coupling constant; and 3) Considering a fermion-boson mass ratio of 2.27, quarks can be split into quark fermions and quark bosons. Further, we noticed that, electroweak field seems to be operated by a primordial massive fermion of rest energy 584.725 GeV and hadron masses seem to be generated by a new hadronic fermion of rest energy 103.4 GeV. In this context, starting from lepton rest masses to stellar masses, we have developed many interesting and workable relations. With further study, a workable model of final unification can be developed.

A phenomenological constitutive model is developed to describe the uniaxial transformation ratcheting behaviors of super–elastic shape memory alloy (SMA) by employing a cosine–type phase transformation equation with the initial martensite evolution coefficient that can capture the feature of the predictive residual martensite accumulation evolution and the nonlinear hysteresis loop on a finite element (FE) analysis framework. The effect of the applied loading level on transformation ratcheting are considered in the proposed model. The evolutions of transformation ratcheting and transformation stresses are constructed as the function of the accumulated residual martensite volume fraction. The FE implementation of the proposed model is carried out for the numerical analysis of transformation ratcheting of the SMA bar element. The integration algorithm and the expression of consistent tangent modulus are deduced in a new form for the forward and reverse transformation. The numerical results are compared with those of existing model and the experimental results to show the validity of the proposed model and its FE implementation in transformation ratcheting. Finally, a FE modeling is established for a repeated preload analysis of SMA bolted joint

To develop green super rice varieties with high and stable yields under the rainfed conditions and improved nutrient use efficiency (NuUE), a modified backcross (BC) breeding approach was adopted using a high yielding and widely adaptable Xian variety, WTR1, as the recipient and a Geng variety, HAN, as the donor. Starting from the BC1F2 generation, the BC population had gone through one generation of selection under the IG, LI and RF conditions, followed by consecutive four generations of screening and selection for high GY under six different nutrient conditions, leading to the development of 230 BC1F6 introgression lines (ILs). The final evaluation of the 230 ILs under the six nutrient conditions identified many ILs with improved yields under various combinations of nutrient deficient conditions, including 12 promising lines that had significantly improved NuUE under two or more nutrient deficiency conditions. Our results demonstrated an efficient inter-subspecific BC breeding procedure with first round selection under the rainfed-drought condition followed by four generations of progeny testing for yield performances under six different nutrient conditions. The promising ILs were studied under replicated yield trials under 75N and -NPK conditions for developing high yield rice varieties with improved NuUE. Our results indicated that NuUE in rice was controlled by complex genetic and physiological mechanisms and the developed ILs provided useful materials for genetic and molecular dissection of NuUE in rice.

According to the International Agency for Research on Cancer (IARC) of the World Health Organization (data of year 2020), Breast Cancer (BC) is one of the most common cancer types worldwide, with large geographical variations occurring between countries and world regions and highest incidence rates in countries that have undergone economic transition. The risk factors for BC include women ageing, genetic mutations, reproductive history, dense breast tissues, personal history of BC or specific non-cancerous breast diseases, family history of breast or ovarian cancer, previous treatment using radiation therapy, and exposure to hormone-like drugs such as diethylstilbestrol (DES). Additional risk factors include being overweight or having obesity after menopause, and taking hormones. MicroRNAs (miRNAs or miRs) are small non-coding RNA molecules that have an essential role in gene expression and are significantly linked to the tumor development and angiogenesis process in different types of cancer, including BC. In the last years, the knowledge about miRNAs role in BC has significantly improved, and complex interactions among coding and non-coding RNA has been elucidated. In this context, an increasing number of papers had been published regarding the role of miR-125 in BC. In this review, we summarize the state-of-the-art about this research topic in addition to elaborating on the need to set novel ethical and legal standards for the governance of such innovations in healthcare.

The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, socio-economic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities, we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.

With reference to Planck scale Hubble parameter, super luminal expansion speeds, super luminal rotation speeds, Mach’s principle and Holographic principle, we review the current cosmological observations with eight simple assumptions. By understanding Yuri N. Obukhov and V.A. Korotky proposed cosmic rotational effects of polarization of radiation due to massive bodies, to some extent cosmic rotation can be deep-rooted in an observational approach and the ratio of current angular velocity and Hubble parameter can be estimated. It is possible to show that, at H₀ =70 km/sec/Mpc, current cosmic temperature, age, radius, mass, mass density are 2.721 K, 4.41x10¹⁷ sec, 90 billion light years, 1.14654x10⁵⁴ kg, 0.0482 times the current critical density respectively. Clearly speaking, current universe seems to constitute 267 Hubble spheres. Important point to be noted is that, current rotational kinetic energy is 0.6667 times the current critical energy. Based on the estimated current mass density and current rotational kinetic energy, current cosmic dark matter density can be shown to be 0.2851 times the current critical density. These numerical coincidences cast serious doubt on the on the real existence of currently believed ‘dark energy’. Initial and current expansion speeds are 3x10⁸ m/sec and 3.56x10⁹ m/sec respectively. With increasing cosmic age and increasing cosmic expansion speed, current universe is expanding with a speed of 11.885c. By knowing the time to time future cosmic temperatures, future Hubble parameters and corresponding future cosmic expansion speeds can be estimated and thus future expansion speed can be understood. Starting from ‘speed of light’, our model assumes a continuous increase in expansion speed and attains a current radius of 90 billion light years (without inflationary concepts) and casts a serious doubt on the actuality of currently believed ‘inflation’.

As an important support for power system with high penetration of renewable energy, energy storage system (ESS) has changed the traditional model of simultaneous implementation of electricity production and consumption, and its installed capacity under the source-grid-load scenario is rising year by year, but it faces the problems of insufficient utilization and benefits. This study analyzes the functional combination of ESS under the source-grid-load scenarios, and proposes a comprehensive benefit evaluation method of energy storage projects based on fuzzy decision-making trial and evaluation laboratory (DEMATEL) and super efficiency data envelopment analysis (DEA) model, which considers the perspective of economic and social environment. Firstly, the functional requirements of energy storage on the source-grid-load scenarios are explored respectively, and the characteristics of various functions are analyzed to form functional combination schemes. Secondly, index modeling is carried out from three aspects: the whole life cycle cost, functional combination benefit and social environmental benefit, and the comprehensive benefit evaluation index system of energy storage project is proposed, which includes both economic and social environment. Then, the intuitionistic trapezoidal fuzzy number is combined with DEMATEL to form an effective analysis method for the input-output relationship of the indexes, and the comprehensive evaluation is realized based on the super efficiency DEA model. Finally, the effectiveness of the proposed evaluation method and the rationality of the functional combination are verified under source-grid-load scenarios respectively.

Imaging of the ocular vasculature can provide new insights into the pathophysiology of ocular diseases. This study proposes a novel high-frequency super-resolution ultrasound localization microscopy (SRULM) technique and evaluates its ability to measure in vivo perfusion changes in the rat eye at elevated intraocular pressure (IOP). A 38.4 MHz center frequency linear array transducer on a VisualSonics Vevo F2 imaging platform was used to collect high frame rate (1 kHz) radiofrequency data of the posterior rat eye following systemic microbubble contrast injection. Following clutter and spatiotemporal non-local means filtering, individual microbubbles were localized and tracked. The microbubble tracks were accumulated over 10,000 frames to generate vascular images quantifying perfusion velocity and direction. Experiments were performed using physiologic relevant controlled flow states for algorithm validation and subsequently performed in vivo on the rat eye at 10 mm Hg IOP increments from 10 to 60 mm Hg. The posterior vasculature of the rat eye, including the ophthalmic artery, long posterior ciliary arteries and their branches, central retinal artery and retinal arterioles and venules were successfully visualized, and velocities quantified at each IOP level. Significant reductions in arterial flow were measured as IOP was elevated. High-frequency SRULM can be used to visualize and quantify perfusion velocity of the rat eye in both the retrobulbar and intraocular vasculature simultaneously. The ability to detect ocular perfusion changes throughout the depth of the eye may help elucidate the role ischemia has in the pathophysiology of ocular diseases such as glaucoma.

This paper demonstrates an on-off keying (OOK) super-regenerative quenching transmitter operating in 402- 405MHz MICs band applications. To reduce power consumption, the transmitter is controlled by a novel digital quenching signal controller that generates a digital control signal to start transmitter operation when a baseband signal is input to the transmitter. The digital signal controller consists of an envelope detector, a comparator, and a quench timer designed using a state machine to synchronize the operation between the digital controller and the input baseband signal. The transmitter consists of a Colpitts oscillator operating in double operating frequency followed by a frequency divider by 2, this configuration reduces system area and improves phase noise and signal spectrum. The proposed transmitter is implemented using UMC 130nm CMOS technology, and a 1.2V supply. Simulation shows that the proposed transmitter can meet MICS band mask specifications with data rates up to 1Mbps and total power dissipation of 537uW.

The dynamic effects observed in collisions represent a specific area of high-energy interaction located at the boundary of mechanics, hydrodynamics, shock wave physics, and alternating high-pressure regions. The paper shows that in the volume of a solid metal body, as a result of dynamic alloying by a high-speed stream of powder particles in the super-deep penetration mode (SDP), fibre structures of altering material arise, forming the framework of the composite material. The stream of powder particles in the metal obstacle following the path of least resistance and the impact of shock waves on particles results in a volumetric framework from the products of interaction between the injected and matrix materials. When using SDP, defective structural elements (channelled) - germs of reinforcing fibres arise. At the subsequent heat treatment, there is an intensive diffusion. The growth process of reinforcing fibres shifts to higher temperatures (as compared to the standard mode), leading to an increase in the bending strength of the fibre material up to 13 times for high-speed tool W6Mo5Cr4V2 steel. As a result of the completion of the growth of reinforcing fibres in the volume of the W6Mo5Cr4V2 steel, the material's bending strength in 1.2 times is realised. Simultaneously, it provides an increase of wear resistance 1.7-1.8 times.

Corona viruses cause extensive SARS epidemics via super spread events (SSE). Due to variation in infection risk and heterogeneity of reproduction numbers specific distinction between SSE’s and typical case events is essential. SARS transmissions unveil a complex scenario in which SSE’s are shaped by multiple factors. Specific screening strategies for infection emergence within potential super spreading groups will help to efficiently control the SARS-2 pandemic and alleviate the partially effective general restriction measures.