A new method in which completely eliminated the negative value of the scattering power is proposed in this paper. Primarily, here are presented the Freeman-Durden decomposition of the scattering powers, which are computed by using the coherency matrix elements with rotation and without rotation. Secondly, this paper investigates the reasons of the occurrence of negative values of the scattering powers. Then using the modification like in algorithm proposed by Yamaguchi for G4U, it was applied for the algorithm of Freeman-Durden decomposition to eliminate negative values. In this research, Radarsat-2 radar remote sensing data were used, which are acquired for study area Yushu County, Qinghai province, China. At the end, the comparison results of Yamaguchi G4U and a modified Freeman-Durden decompositions were presented.

The failure to acknowledge and account for environmental externalities or spillovers in climate change adaptation policy, advocacy and programming spaces exercabates the risk of ecological degradation, more so, degradation of land. In particular use of unsuitable water sources for irrigation may increase salinisation risks. However, little if any policy assessments and research effort has been directed at investigating how farmer perceptions mediate spillovers from the ubiquitous irrigation adaptation strategy. In this study cognitive failure and/or bias construct is examined and proposed as an analytical lens in research, policy and learning and the convergence of disaster risk reduction and climate change adaptation discourses. The findings from small-scale farmers, Machakos and Kakamega counties, Kenya, suggest multifaceted biases and failures about the existence and importance of externalities in adaptation planning discourses. Among other dimensions, cognitive failure which encompasses fragmented approaches among institutions for use and management of resources, inadequate policy and information support, as well as, poor integration of actors in adaptation planning accounts for adaptation failure. The failures in such Human-Environment system interactions have the potential to exercabate existing vulnerability of farmer production systems in the long run. The findings further suggest that in absence of risk message information dissemination, education level, farming experience and information accumulation, as integral elements to human capital, do not seem to have significant effect on behaviour about mitigation of environmental spillovers. Implicitly, reversing the inherent adaptation failures calls for system approaches that enhance coordinated adaptation planning, prioritises proactive mitigation of slow onset disaster risks and broadens decision support systems, such as, risk information dissemination integration into the existing adaptation policy discourses and practice.

This paper presents a physical and mathematical model that has been developed in the framework of the approach used in the computational mechanics of materials. The model is designed to enable the study of the patterns of deformation and fracture of ceramic composites with a transformation-hardened matrix that are obtained by additive technologies at the mesoscopic and macroscopic levels under intense dynamic loading. The influence of the loading rate on the formation of the fracture and energy dissipation fronts for composite materials, based on the Al2O3 20%ZrO2 system, is shown. Nonlinear effects under intense dynamic loading in the considered composites are associated with the processes of self-organization of structural fragments at the mesoscopic level, as well as the occurrence of martensitic phase transformations in matrix volumes adjacent to the strengthening particles.

The management of a cultural change in higher education is becoming necessary and unavoidable: changes in teaching, changes in research and changes in governance. Digital transformation is an essential part of many countries’ modernisation agendas. The aim of this non-experimental, descriptive, survey-based study was to explore the perceptions of university students toward the digital transformation that took place in university teaching as a consequence of COVID-19. The specific objectives proposed were to: a) analyse the perceptions of students toward digital transformation in university teaching; b) determine the valuation of students about the digital transformation that occurred in university teaching as a result of COVID-19; and c) explore the resources (hardware-software), professional collaboration, digital pedagogy and student empowerment (motivation) with respect to digital education and the recent changes in university teaching due to the pandemic. The results show that a large number of items are positively correlated. In conclusion, further research should delve into motivation, collaboration, reflective experience sharing, self-learning and initiatives that promote the development of competences in future teachers.

The urgency to address the adverse impacts of climate change on livelihoods and ecosystems has seen an increase in global driven initiatives. However, shifting vulnerabilities associated with land use resource based adaptation and maladaptive feedback loops they create have been given low attention. Policy discourses that frame adaptation as a local responsibility and bias towards reducing industrial Greenhouse gas (GHG) emissions at the expense of Agricultural emissions across scale are thought to account for the undesirable situation. This calls for a reflective policy framework and climate policy innovation. We provide counter arguments using Drivers, Pressure, State, Impact, Response (DPSIR) model and telecoupling principles to suggest use of resilience as an integrative lens in visualising the proposal. Using a case study on resource constrained smallholder dairy production systems, western Kenya, we analyse the critical issues in the context of decision making and environmental externalities. The effect of price risks on dairy cattle feeding strategies and ultimately carbon footprints and ecoefficiencies were examined through methane simulation and gross margin analysis (GM). The lowest ecoefficiency was associated with exclusively local coping strategies i.e. Maize Stover (Ms), while the highest ecoefficiency was observed in feeding strategies that utilise external resources and/or legume fodders. We conclude that management of externalities need to capture institutional, economic processes and incentive systems, as well as organizational and policy coherence to shape the interests and behaviour of individual land user. In particular, policy innovation should focus on price and market risks as critical factors that mediate actor decision making at implementation level as they impact GHG emissions which transcend individual decision boundaries.

The human papillomavirus (HPV) type 16 E7 oncogene is critical to carcinogenesis and highly conserved. Previous studies identified a preponderance of non-synonymous E7 variants amongst HPV16-positive cancer-free controls compared to those with cervical cancer. To investigate the function of E7 variants, we constructed full-length HPV16 E7 genes and tested variants at positions H9R, D21N, N29S, E33K, T56I, D62N, S63F, S63P, T64M, E80K, D81N, P92L, and P92S (found only in controls); D14E, N29H (CIN2), and P6L, H51N, R77S (CIN3). We determined the steady-state level of cytoplasmic and nuclear HPV16 E7 protein. All variants from the controls showed a reduced level of steady-state E7 protein, with 7/13 variants having deficient protein levels. In contrast, 2/3 variants from the CIN3 precancer group had near-normal E7 levels. We assayed the activity of representative variants in stably transfected NIH3T3 cells. The H9R, E33K, P92L, and P92S variants found in control subjects had lower transforming activity than D14E and N29H variants (CIN2); and the R77S (CIN3) had activity only slightly reduced from wildtype E7. In addition, R77S and WT E7 caused increased migration of NIH3T3 cells in a wound-healing assay as compared with H9R, E33K, P92L, and P92S (controls) and D14E (CIN2). These data provide evidence that the E7 variants found in HPV16-positive cancer-free women are partially defective for transformation and cell migration further demonstrating the importance of fully active E7 in clinical cancer development.

This paper considers the principles of building a circuit model of film resistor cutting. The conductive resistive medium is defined with the component equations and the topology of the circuit model. A method of estimating the electric parameters of a resistor operating in the system with a measuring voltage source is shown. An equation system for the node voltages is defined, and the resistive layer parameters are analyzed as the circuit model structure changes during the cutting process.

Sensory evaluation has been widely applied in assessing the quality of many consumer products that directly serve human needs such as food, beverages, clothing, etc. This paper first examined the inherent deficiencies in this approach, due mainly to the essentially subjective nature of human sensory preference. It then argued that instinctively designating certain materials attributes as sensory perceptions is unnecessary; considering every scientific concept is in essence the processed results of our sensory organs/brains, i.e., all quantities were initially human perceptions. Scientific advances have inevitably generated gradual transformation of such human perceptions as warmth and heaviness into objective parameters measurable in physical quantities like temperature and kilograms. Then using existing successful examples, it demonstrated firmly how the sensory attributes can be assessed by more reliable instrumental methods, and envisioned the key steps to turning a perception into scientifically measurable parameter(s).

This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients were determined at a Reynolds number of 10^5. Considering the limited availability of data on low Reynolds number airfoils, this dataset is invaluable for a wide range of applications, including unmanned aerial vehicles (UAVs) and wind turbines. Additionally, the study offers a method for automating CFD simulations that could be applied to obtain aerodynamic coefficients at higher Reynolds numbers. The breadth of this dataset also supports the enhancement and creation of machine learning (ML) models, further advancing research into the aerodynamics of airfoils and lifting surfaces.

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel Electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer number of sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: Up, Down, Left, and Right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration has been implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based Brain-Computer Interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, respectively.

In order to counter the impending shortage of skilled professionals in the aging societies of our time in many western countries such as Germany, solutions for business and society are urgently needed. Here, artificial intelligence (AI) can play an important role in mitigating the problem with the help of diverse applications. At the same time, it is important to consider both the needs of the respective employee1 and the company to ensure that the use of AI has a positive impact on the organization and finds social acceptance. In this article, we describe the newly developed OSQE model (Optimize, Secure, Qualify, Expand), which for the first time outlines an AI cycle against the shortage of skilled professionals in a holistic approach that focuses equally on people and companies. This can serve organizations as a guide for strategy development, decision-making for and implementation of AI-supported measures in an entire cycle of an employee's affiliation with a company. The model takes three driving forces into account: companies, professionals, and AI applications. In the model, the measures to be implemented are prioritized with ascending numbering based on what would be most urgent for a company to implement. All measures relate to areas of action that place people at the center and can be assigned to the classic cycle of belonging of an employee in the company. In this regard, the opportunities that AI offers to professionals and companies are highlighted.

Through a brief analysis of the basic principle of relativity, it is found that there is a contradiction in principle between general relativity and special relativity in solving the specific problem of the clock moving around the earth, and one of them must be wrong. Through comparative analysis of five physical phenomena and experiments, it is proved that there are serious problems in the space concept of relativity theory and classical physic. At the same time, the different concept of absolute airspace is further expounded and demonstrated, and the related space-time physical phenomena and experiments are perfectly explained by this concept. According to this idea, a new time transformation relation is deduced and its correctness is demonstrated. It is proved that the principle of special relativity and special relativity are wrong, and a series of physical laws and physical explanations generated by special relativity may also be wrong, and a series of amendments or new interpretations need to be made.

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It provides an image of the subsurface using the same principles as ultrasound medical imaging. It is based on emitting a sound (pressure) wave through the subsurface and recording the reflected echoes using hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors). Contrary to medical imaging, which is done in real time, subsurface seismic imaging is an offline process that involves a huge volume of data and needs considerable computing power. The raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = underfiltering, noise remaining in the data; optimal = good filtering; harsh = overfiltering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km2) to QC the results of the swell noise attenuation process in marine seismic data. The results are encouraging and helped identify localized issues that were difficult for a human to spot.

This study investigates the multifaceted aspects of 21st century teaching and identifies gaps in current educational practices. The researcher employed a qualitative approach, utilizing interviews, observations, and document analysis. Grounded in a theoretical framework emphasizing student-centered pedagogy, technology integration, and innovative instructional strategies, the study involved a diverse sample of 30 participants, including teachers, students, and administrators. Rigorous measures were implemented to ensure validity and reliability, such as researcher triangulation and member checking. The findings revealed significant gaps in areas such as student engagement, digital literacy, and pedagogical innovation. These findings are discussed in relation to existing literature, highlighting the implications for educational practice and the urgent need for educational transformation. The study's limitations include the small sample size and its focus on a specific educational context. Future research should explore the identified gaps using different methodologies and extend the investigation to other educational settings. The study contributes to the growing body of knowledge on 21st century teaching, providing valuable insights for educators, policymakers, and researchers aiming to promote effective and transformative teaching practices in the modern era.

Humanity faces serious social and environmental problems, including climate change and biodiversity loss. Risks are increasing and conditions deteriorating. Increasingly, scientists, global policy experts, and the general public conclude that incremental approaches are insufficient and transformative change is needed across all sectors of society. However, the meaning of transformation is still unsettled in the literature, as is the proper role of science in fostering it. This paper is the first in a three-part series that adds to the discussion by proposing a novel science-driven research-and-development program aimed at societal transformation. More than a proposal, it offers a perspective and conceptual framework from which societal transformation might be approached and understood. While acknowledging the necessity of reform to existing societal systems (e.g., governance, economic, and financial systems), the focus of the series is on transformation understood as systems change or systems migration—the de novo development of and migration to new societal systems. The series provides definitions, aims, reasoning, worldview, and a theory of change, and discusses fitness metrics and design principles for new systems. This first paper proposes a worldview built using ideas from evolutionary biology, complex systems science, cognitive sciences, and information theory that is intended to serve as the foundation for the R&D program.

This article explores the impact of farmers’ vocational training on their income. Survey data concerning agricultural product quality and agricultural product sales were collected by the research team in Yantai City, China. An endogenous transformation regression model (ESR) was then used to analyze the impact of training on farmers’ family income, and the income mechanism was further tested. The results from the empirical analysis suggest that (1) Participating in training can significantly increase farmers’ income; (2) Farmers who pay more attention to the quality of their agricultural products during the production process are more likely to see their incomes increase after participating in the training.

Climate change, biodiversity loss, and other social and environmental problems pose grave risks. Progress so far has been incremental and insufficient, and as a result scientists, global policy experts, and the general public increasingly conclude that bold change is required across all sectors of society. At least two kinds of bold change are conceivable: reform of existing societal systems (e.g., financial, economic, legal, and governance systems), including their institutions, policies, rules, and priorities; and transformation, understood as the de novo development of and migration to new, improved systems. This paper is the second in a series of three that together propose a novel science-driven research and development program aimed at societal transformation. Moreover, the series advances a conceptual framework and formal mechanics by which societal transformation might be approached. Two of the underlying hypotheses are that new societal systems can be developed in a science-driven process to be fit for purpose, and system fitness can be compared across designs. Societies are viewed as superorganisms, and systems are viewed as a societal cognitive architecture. The first paper in the series provides definitions, aims, hypotheses, and a worldview. This paper discusses motivations, the role of science in societal transformation, a theory of change, and fitness metrics. The proposed R&D program and theory of change are sound, viable, and affordable. The local-global-viral strategy invites the global science community to play a unique co-leadership role with local communities in the development, testing, and monitoring of new societal systems. Systems are implemented via a novel civic club model, where participation is voluntary. Clubs grow and replicate based on merit and aided by club networks, whose systems are also viewed as societal cognitive architectures. Benefits of the program and strategy are discussed.

Proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation is attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry’s 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. Research enlights how SMEs face the transition to digital business and reveals the factors that can facilitate the whole process. Results can be valuable for stakeholders enabled to digital transformation process from both business and academic point of view, while there exist aspects that can contribute to policy makers / motivation developers in state’s level as well.

Improvement of agronomic traits in crops through gene editing (GE) relies on efficient trans-formation protocols for delivering the CRISPR/Cas9-coded transgenes. Recently, a few embryo-genesis-related genes have been described, the co-delivery of which significantly increases the transformation efficiency with reduced genotype dependency. Here we characterized the trans-genic and GE events in wheat (cv. Fielder) when transformed with the GROWTH-REGULATING FACTOR 4 (GRF4) and its cofactor GRF-INTERACTING FACTOR 1 (GIF1) chimeric gene. Trans-formation efficiency in our experiments ranged from 22% to 68%, and the editing events were faithfully propagated into the following generation. Both low and high copy number integration events were recovered in the T0 population with the various level of integrity of the left and right T-DNA borders. We also generated a population of wheat plants with ten different gRNAs targeting 30 loci in the genome. Comparison of the epigenetic profiles at the target sites and editing efficiency revealed a significant positive correlation between chromatin accessibility and muta-genesis rate. Overall, preliminary screening of the transgene quality and GE events in the T0 population of plants regenerated through the co-delivery of GRF-GIF can allow for the propagation of the best candidates for further phenotypic analysis.

Oral squamous cell carcinoma often arises from an oral potentially malignant disorder called oral leukoplakia (OL). With this work we aimed to develop a novel data-driven predictive model based on gene expression profiles to distinguish OL patients who underwent malignant transformation from those who did not. We used the Tree Augmented Naïve (TAN) Bayes classifier to predict the posterior probability of having oral cancer given the data. 86 patients were included with a median follow-up of 7.11 years. Fifty-one patients (51/86; 59%) underwent malignant transformation. We found that 16 genes were predictors of oral cancer in patients with OL and these included SLC7A11, SPINK6, SERPINA12, VIT, ATP1B3, CST6, FLRT2, ELMOD1, AZGP1, RNASE13, DIO2, ECM1, CYP4F11, SYTL4, AKR1C1, and AKR1C3. In conclusion, we showed that Bayesian gene networks are a data-driven approach which could be used also in other predictor models in oncology.

Abstract:Shown is the derivation of Lorentz-Einstein k-factor in SRT as an amplitude-term of oscillation-differential equations of second order.This case is shown for classical Lorentz-factor as solution of an equation for undamped oscillation as well as the developed theorem as a second solution for advanced SRT of fourth order with an equation for damped oscillation-states.This advanced term allows a calculation for any velocities by real rest mass

Creating transgenic insects is a key technology in insect genetics and molecular biology. A widely used instrument in insect transgenesis is the piggyBac transposase, resulting in essentially ran-dom genomic integrations. In contrast, site-specific recombinases allow the targeted integration of the transgene construct into a specific genomic target site. Both strategies, however, often face limitations due to low transgenesis rates. We aimed to enhance transgenesis rates by utilizing a capped mRNA as a source of transposase or recombinase instead of a helper plasmid. A system-atic comparison of transgenesis rates in Aedes mosquitoes, as models for hard to transform in-sects, showed that suppling piggyBac transposase as mRNA increased the average transfor-mation efficiency in Aedes aegypti from less than 5% with the plasmid source to about 50% with mRNA. Similar high transformation activity was observed in Ae. albopictus with pBac mRNA. No efficiency differences between plasmid and mRNA were observed in recombination experi-ments. Furthermore, a codon-optimized version of piggyBac transposase delivered as plasmid didn’t improve the transformation efficiency in Ae. aegypti or the agricultural pest D. suzukii. We believe that the use of mRNA has strong potential for enhancing pBac transformation efficiencies in other mosquitoes and important agricultural pests such as tephritids.

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for ecological models and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative ecological response models. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate-ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It~provides an image of the subsurface using the same principles as ultrasound medical imaging. As for any data acquired through hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors), the raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The~QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = under filtering, noise remaining in the data; optimal = good filtering; harsh = over filtering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km²) to QC the results of the swell noise attenuation process in marine seismic data.

Non-rigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex non-rigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple research fields, including registration of topographic data, scene flow estimation, and dynamic shape reconstruction. To provide context, we begin with a general introduction to the topic of point cloud registration, including a categorization of methods. Next, we introduce a general mathematical formulation for point cloud registration and extend it to address non-rigid registration. A detailed discussion and categorization of existing approaches to non-rigid registration follows. We then introduce our own method where the usage of piece-wise tricubic polynomials for modeling non-rigid deformations is proposed. Our method offers several advantages over existing methods. These advantages include easy control of flexibility through a small number of intuitive tuning parameters, a closed-form optimization solution, and an efficient transformation of huge point clouds. We demonstrate our method through multiple examples that cover a broad range of applications, with a focus on remote sensing applications - namely, the registration of Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TLS) point clouds. The implementation of our algorithms is open source and can be found on GitHub.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

Based on electromagnetic heat transfer and metal phase transformation co-simulations, we modeled an AISI 1045 specimen under high-frequency heat treatment. Hardening zone predictions were confirmed through cooling and metal phase transformation simulations after obtaining the results from electromagnetic heat transfer simulations. The cooling process was modeled by applying the cooling coefficient of the cooling water in the same way as the actual heat-treatment process. To obtain the current flowing through the coil during high-frequency induction heating, the voltage was measured and applied using the resistance–inductance–capacitance circuit calculation method. Experimental and simulated results of the heating temperature and curing depth of an AISI 1045 specimen with a carbon content of 0.45% were compared; the comparison indicated good agreement between the two. Using the simulation results, we established a method for obtaining the current flowing through the induction coil for predicting the extent and depth of the hardening zone during high-frequency induction heat treatment.

Local Productive Systems (hereinafter LPS) based on agro-food industries constitute alternative models of development in peripheral rural areas that are subject to internal and external dynamics and processes. The main objective of the research is to investigate the processes and their consequences on four SPLs based on the Iberian Pig Transformation Industry (hereinafter LPS-IPTI) in SW Spain: Fregenal de la Sierra, Higuera la Real, Cumbres Mayores and Jabugo. Using secondary data, a comparison is made between 2002 and 2020 to establish the changes, causes and consequences on the LPS-IPTI studied. The results obtained indicate (1) business and territorial concentration of LPS-IPTI; (2) productive and territorial specialisation in standardised products and quality products; (3) simplification of industrial processes; (4) loss of employment, especially female; (5) external control of companies in the sector which, accordingly, results in the loss of prominence of local actors in favour of foreign companies, reduced social capital and the progressive loss of ownership of the LPS.

The food system represents a key industry for Europe and particularly Germany. However, it is also the single most significant contributor to climate and environmental change. A food system transformation is necessary to overcome the system's major and constantly increasing challenges in the upcoming decades. One possible facilitator for this transformation are radical and disrup-tive innovations that start-ups develop. There are many challenges for start-ups in general and food start-ups in particular. Various support opportunities and resources are crucial to ensure the success of food start-ups. One aim of this study is to identify how the success of start-ups in the food system can be supported and further strengthened by players in the innovation ecosystem in Germany. There is still room for improvement and collaboration toward a thriving innovation ecosystem. A successful innovation ecosystem is characterised by a well-organised, collaborative, and supportive environment with a vivid exchange between the members in the ecosystem. The interviewees confirmed this, and although the different actors are already cooperating, there is still room for improvement. The most common recommendation for improving cooperation is learning from other countries and bringing the best to Germany.

The search for growth and sustainability in any organization largely depends on its ability to carry out digital transformation and agility processes. These processes are essential to adapt to an increasingly changing and competitive business environment, allowing companies to be more efficient and effective in creating an unbreakable bond with their customers. This manuscript addresses the importance of detecting and eliminating anti-patterns in organizational culture for a successful digital and agile transformation. During the early stages of the transformation process, prejudices and paradigms rooted in the organization can become an obstacle to change. The goal of this writing is to offer a practical guide for leaders and teams in organizations seeking successful cultural transformation. Effective strategies are proposed to overcome the challenges faced by organizations during this early stage. In summary, the key steps to be followed in the initial stages of digital transformation with agility are described, including the importance of establishing strong leadership, defining a clear vision and scope of the transformational process, among others. A set of recommendations will be provided to overcome recurring anti-patterns, such as lack of stakeholder participation and commitment, lack of proper planning, and confusion of roles and responsibilities.

The article is devoted to algebraic modeling of universal rules of stochastic organization of genomic DNA of higher and lower organisms, previously published by the author. The proposed algebraic apparatus, which uses formalisms of quantum mechanics and quantum informatics and which is based on the so-called tensor-unitary transformations of vectors that generate families of interrelated stochastic-deterministic vectors of increased dimensions. The features of the vectors' interconnections in these families model the stochastic-deterministic properties of the named phenomenological rules. There are new approaches to modeling of developing multi-parameter biosystems, whose number of parameters increases in the course of step-by-step development. In the light of the presented materials, the issues of fractal-like organization in genetically inherited biosystems are considered. The development of the theory of stochastic determinism as an antipode of deterministic chaos is discussed.

Cadmium (Cd) is an environmental toxicant of worldwide public health significance. Diet is the main non-workplace Cd exposure source other than smoking. The intestinal absorption of Cd involves the transporters for iron, zinc, copper, and calcium. These metal transporters essentially determine the body burden of Cd because only 0.001-0.005% of Cd accumulated in the body can be excreted in urine each day. The International Agency for Research on Cancer listed Cd as a human lung carcinogen. Current evidence, however, suggests that environmental Cd may increase the prevalence of many types of cancer, notably lung, liver, breast, pancreas, and kidney. A two-year bioassay that mimics a lifelong exposure demonstrates Cd caused neoplasms in multiple tissues of mice. Also, several non-tumorigenic human cell lines underwent malignant cell transformation, when they were exposed to a sublethal dose of Cd for a prolonged time. Cd does not directly damage DNA, but it can profoundly affect gene expression through its interactions with various proteins, and Cd can thus be viewed as a transcription modulator. The present review highlights epidemiological studies that connected an enhanced risk of various neoplastic diseases to chronic exposure to environmental Cd. Special emphasis is on the impact of body iron stores on the absorption rate of Cd, and its implications for breast cancer prevention in highly susceptible subpopulation groups of women. Resistance to cell death and other cancer phenotypes acquired during Cd-induced cancer-cell transformation, under in vitro conditions, are briefly discussed. The potential role for ZnT1 efflux transporter in cellular acquisition of tolerance to Cd cytotoxicity are highlighted.

Novel strategies in coastal protection are needed to cope with climate change-induced sea level rise. They aim at the sustainable development of coastal areas in light of an intensification and land use changes. A promising approach is the design of nature-based solutions (NbS), complementing the safety levels of technical infrastructures. However, NbS lack a widespread and large-scale implementation. To address this deficit, co-design concepts are needed that combine experiences from science and practice. This work presents and discusses the approach of a coast-specific real-world laboratory (RwL) addressing the inclusive design of ecosystem-based coastal protection. Strategies of RwLs are applied for the first time in a coastal context along the North Sea coastline in Germany. We found the concept of RwLs suitable for coastal transdisciplinary research, although adaptions in the spatial reference level or flexibility in location and time of experimentation are necessary. A profound actor analysis is indispensable to specify participatory processes and interaction levels. A criteria-based cooperative selection of RwL sites helps to reveal and solve conflicting interests to achieve trust between science and practice. Addressing site-specific characteristics and practitioners’ needs, our coastal RwL provides a mutual learning space to develop and test NbS to complement technical coastal protection.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.

Digital transformation can become a complex process when workers have insufficient skills, which makes training in the digital field essential. Herein, we intended to relate digital literacy perceived by workers with their training needs for the Portuguese public sector context. Additionally, based in the Human capital theory, we also investigate which professional/demographic characteristics increase training propensity in digital fields. Through an online questionnaire, a dataset with information on 573 workers was obtained. Data analysis was made by using a probabilistic regression model and additional statistical techniques. The results revealed that workers with higher levels of education and higher professional skills have higher probability of participating in training in the digital field. On average, workers reveal low levels of digital knowledge and low participation in training in the digital fields, but the majority present a willingness to participate in future training sessions, especially in the fields of Dataset management, Cybersecurity and Communication systems. This study provides information on training in the digital field of workers in the public sector, which is essential for public organizations to better prepare for digital transformation. Additionally, it contributes to a very recent literature on digital learning, and it can be extended to other contexts.

Bacterial transformation is the essential tool for introducing foreign DNA into bacterial cells in a host of recombinant technology applications useful for biomanufacturing and drug production. Thus far, chemically competent cells remain an important enabling technology for bacterial transformation given its facile transformation approach. However, many parameters affect transformation efficiency, and there is relatively little documentation of their cross-interacting effect in the literature. This work explores the roles played by incubation time on ice, competent cell concentration, and plasmid-to-cell ratio on transformation efficiency in Escherichia coli DH5α. Results revealed that increases in incubation time improve transformation efficiency, but 2 minutes incubation still yield appreciable efficiency. On the other hand, low concentration competent cells (10⁶ CFU/mL) yielded significant improvement in transformation efficiency compared to high concentration competent cells (10⁸ CFU/mL). In terms of plasmid-to-cell ratio, higher ratio increases transformation efficiency. Overall, bacterial transformation is incompletely understood particularly in the area of cross-interacting parameters. Experiments reported here revealed the possibility of short duration transformation where lower competent cell concentration and higher plasmid-to-cell ratio could improve transformation.

The present paper introduces a method of basis transformation of vector fields that is specifically applicable to polynomials space and differential equations with certain polynomials solutions such as Hermite, Laguerre and Legendre polynomials. The method based on separated transformation of vector space basis by a set of operators that are equivalent to the formal basis transformation and connected to it by linear combination with projection operators. Applying the Forbenius covariants yields a general method that incorporates the Rodrigues formula as a special case in polynomial space. Using the Lie algebra modules, specifically , on polynomial space results in isomorphic algebras whose Cartan sub-algebras are Hermite, Laguerre and Legendre differential operators. Commutation relations of these algebras and Baker-Campbell-Hausdorff formula gives new formulas for special polynomials

This study aims to determine the role of Industry 4.0 sensors in digital transformation. Using a systematic literature review with a bibliometric analysis, we examine a sample of 52 studies from the SCOPUS database to identify research activity on this topic until December 2022. We find that 2022 was the year with the highest number of peer-reviewed documents on the subject, with 18 publications. We also examine current themes and find that sensors can connect multiple systems and devices, thus enabling machine-to-machine communication and facilitating great precision during product designing, development, and production, thus enabling customization and improving quality. However, despite these benefits, the current sensor technologies face multiple challenges that undermine their maximum adoption.

Abstract Most Asian countries like South Korea have transformed themselves from poor agrarian nations into one of the fastest-growing industrialized economies in the world. Rural transformation poses great challenges to rural people and areas, but also it also provides great opportunities for sustainable rural development. The evolution of South Korea's rural policies is embedded through the different national institutions with different sectors. Diversification of rural economic opportunities may further extend to the industries for economic development in rural regions alongside efforts to improve living. Examining the rural transformation in South Korea, mainly the rural development with land, agriculture, truism, politic,s and strategy they used that shaped the structure of the rural transformation and its activities over time and Ethiopia takes lessons is the objective of the study. Document analysis is a tool for achieving information. Ethiopia takes lessons from South Korea from the general sector of rural development to the particular agricultural development sector, rural truism development, land reform which is the core asset for the world people, health care reform, and market and trade transformation (indicates that the base for developed country development was trade reform from import to export), rural job employment transformation (which have the multidimensional impact to the development of nation) and rural political transformation are the basic areas of rural transformation in which Ethiopia focused to do. The country can grow with the integrated/ collaborative work of the stakeholders. The government of Ethiopia applying a functional regional strategy to shape the socio-economic and cultural changes of the rural people and working with civil societies and other non-governmental stakeholders must be the primary tasks

Real-world optimization problems are often gov- erned by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Opti- mization Problems (COPs) fueled by advances in computational power. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose a novel hybrid Evolutionary Algorithm based on the Differential Evolution algorithm and Adaptive Parameter Gaining Sharing Knowledge-based algo- rithm to solve global real-world constrained parameter space. The proposed CHAGSKODE algorithm leverages the power of multiple adaptation strategies concerning the control parameters, search mechanisms, as well as uses knowledge sharing between junior and senior phases. We test our method on the benchmark functions taken from the CEC2020 special session & competition on real-world constrained optimization. Experimental results indicate that CHAGSKODE is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

This article examines how digitalisation is impacting the management of IT. Phase 1 of the re-search focuses on the German automotive industry, and adopts a qualitative inductive approach, based on feedback from 19 semi-structured interviews with IT managers and professionals. Five key aspects of digitalisation are identified, and a new model for the management of IT is devel-oped. Phase 2 then reviews the deployment of digital technologies in eight organisations from other industry sectors and assesses the overall impact on the management of IT. The results suggest that the transition to new IT management structures in the German automotive industry may be the forerunner of a change process that will impact more widely in other industries, in particular where digital technologies are now embedded in an organisation’s products or services. The results also provide some new perspectives on the conceptualisation of digitalisation and digital transformation, and represent a contribution to the developing theory around new tech-nology deployment and IT management in the digital era.

Este artigo apresenta o processo de implementação da Hiperautomação no setor público brasileiro, especialmente no âmbito da Procuradoria Geral do Estado de Pernambuco. Devido à atual economia competitiva, a demanda por soluções inteligentes e confiáveis está aumentando. Para atender a essa necessidade, tecnologias inovadoras que abordem a complexidade inerente à transformação digital são desejáveis. Assim, as organizações públicas têm buscado aplicar a automação de processos para atender às necessidades dos cidadãos por um serviço mais eficiente e eficaz. No entanto, podemos observar que a automação pura e direta é apenas parte do processo de transformação. Algumas das razões que explicam a necessidade de ir além da automação de processos e alcançar a hiperautomação é a abundância de dados de várias fontes e tipos. Tudo deve estar dentro dos padrões legais, ressaltando que toda tecnologia deve estar alinhada com transparência e auditabilidade para ser incorporada com segurança. Para a experimentação, foi utilizado o método de pesquisa-ação com a aplicação da Hiperautomação, visando aumentar a arrecadação e a economia do Estado na Procuradoria Geral do Estado de Pernambuco.

Construction 4.0 is witnessing exponential growth in Digital Twin (DT) technology developments and applications, revolutionizing the adoption of Building Information Modelling (BIM) and other emerging technologies used throughout the lifecycle of the built environment. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, DT enhances this with real-time data for cyber-physical integration, enabling live asset monitoring and better decision-making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study presents a holistic review of the existing research focusing on the critical components responsible for developing DT applications in the construction industry. The study identifies five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, nontechnical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices.

The cubic B2 to monoclinic B19’ martensitic transformation in NiTi shape memory alloys proceeds by the nucleation and propagation of habit plane interfaces that remain undistorted and unrotated by the transformation. Due to incompatibility of austenite and martensite lattices in NiTi, the interfaces form between austenite and twinned martensite as described by Phenomenological Theory of Martensite Crystallography (PTMC). This have been assumed also for stress-induced martensitic transformation where, however, interfaces with single martensite variant have been frequently reported. On this account, we propose a different solution of strain compatibile habit planes by considering the effect of elastic deformation of both lattices due to external stress. Using modified PTMC theory, we evaluate the magnitudes and orientations of the critical uniaxial stress in tension and compression for which strain compatible habit plane interfaces with a single variant of martensite can form. The calculated stress is too high, but considering the pre-transformation softening of the C’ elastic constant for the {110}A⟨110⟩A austenite shear mode, the critical stress decreases down to 500 MPa. For such situation, we analyse orientation dependence and tension-compression asymmetry of required C’ softening, and the orientations of strain compatible habit planes that are compared with available experimental results. All calculations, including source code and interactive graphs, are available for download.

With the advancement of the economy and society, policymakers acknowledge the significance of improving rural households' well-being as a crucial factor in promoting sustainable economic and social development. Based on the matching data of China Household Finance Survey and Digital Rural County Index, this study empirically examines the effect and mechanisms of digital village construction on subjective well-being of rural households. The results revealed that digital village construction has a significant improvement in their subjective well-being. The impact of digital village construction is particularly significant in rural areas located in the western regions of China, as well as among households possessing higher levels of human capital. The process through which digital village construction enhances the subjective well-being of rural households involves the augmentation of household income, the promotion of non-farm employment opportunities, and the enhancement of rural governance. This study provides robust micro-empirical evidence highlighting the positive welfare effects of digital rural construction. This study provides insights into the digital village construction within the happiness economy framework, offering a new perspective. The imperative to strengthen rural digital infrastructure and improve farmers' proficiency in modern information skills is a strategic policy aimed at enhancing well-being of rural households.

We consider mixture experiments in which the proportions of the components must be non-negative and their sum must equal one. Thus, the experimental region for a mixture of components is a simplex. Li and Zhang (2017) made the conjecture that the pseudo component transformation of the lattice points in the simplex has a special property. In this paper, we show that this conjecture is not true in general. Furthermore, we refine this conjecture and prove the refined conjecture.

Digital federated platforms and data cooperatives for secure, trusted and sovereign data exchange will play a central role in the construction industry of the future. With the help of platforms, cooperatives and their novel value creation, the digital transformation and the degree of organization of the construction value chain can be taken to a new level of collaboration. The goal of this research project was to develop an experimental prototype for a federated innovation data platform along with a suitable exemplary use case. The prototype is to serve the construction industry as a demonstrator for further developments and form the basis for an innovation platform. It exemplifies how an overall concept is concretely implemented along one or more use cases that address high-priority industry pain points. This concept will create a blueprint and a framework for further developments, which will then be further established in the market. The research project illuminates the perspective of various governance innovations to increase industry collaboration, productivity and capital project performance and transparency as well as the overall potential of possible platform business models. However, a comprehensive expert survey revealed that there are considerable obstacles to trust-based data exchange between the key stakeholders in the industry value network. The obstacles to cooperation are predominantly not of a technical nature, but of a competitive, predominantly trust-related nature. To overcome these obstacles and create a pre-competitive space of trust, the authors therefore propose the governance structure of a data cooperative model, which is discussed in detail in this paper.

At present, countries around the world adopt digital strategy, so that enterprises apply digital technology, digital supply chain, blockchain, and a series of digital measures to enhance their competitiveness. By analyzing the relationship between digital transformation of manufacturing export enterprises, and their business performance and export performance, this paper focused on the current situation and existing problems of digital transformation of manufacturing export enterprises in China. Based on the Resource Based View theory and Trade theory, the impact of digital transformation of enterprises performance, proposing hypotheses, and building regression models were explored. With the samples of 1007 listed companies in Shanghai and Shenzhen Stock markets from 2012 to 2019, the regression analysis, intermediate effect test, robustness test, stage lag, and heterogeneity analysis were conducted. The results shew that: (1) Listed manufacturing export enterprises carried out digital transformation, which had a significantly positive effect on enterprise performance; (2) It was found that digital transformation could improve enterprise performance by reducing costs, improving R&D intensity and improving human resources etc. (3) According to the fractal analysis, non-state-owned enterprises had more positive effects on enterprise performance, and digital transformation of manufacturing export enterprises in developed areas had a more significant impact on enterprise performance. Finally, according to the empirical results, relevant suggestions for digital transformation were provided.

Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

The present-day financial system is being influenced by the rapid development of Fintech (financial technologies), which are technologies created to improve and automate traditional forms of finance for businesses and consumers. The topic of Fintech as a financial disruptor is gaining popularity in line with the swift spread of digitalization across the banking industry, whereby this paper contributes to the field by presenting a novel bibliometric analysis of academic literature related to Fintech as a financial disruptor. The analysis is based on metadata extracted from the Scopus database through the VOSviewer and Biblioshiny software. The bibliometric analysis of 363 documents identified the most impactful sources of publication, keywords, authors, and most cited documents on the topic of Fintech as a financial disruptor. As our analysis demonstrates the number of publications on the given topic increases, defining both the interest among academia and potential for future research.

Digital transformation of companies is often referred to as a future track and a strategic path towards ensuring the survival and sustainability of companies. In this paper, we aim to outline this transformation process in Tunisian companies and identify its driving factors and finally explain its key success factors. To this end, we examine a sample of 70 companies operating in all economic sectors. The results indicate that such a digital strategy has a significant and a positive effect on the success of digital transformation of companies. Leadership has an effect at a low level of digital maturity. On the other hand, corporate culture does not have a significant effect on digital transformation. These results contribute to explaining this emerging phenomenon by focusing on the driving factors as well as the factors that contribute to its success.

In the dynamic interplay between people and their physical environments, the Global South stands as a mosaic undergoing a multitude of transformative influences in architecture and urbanism where examining social sustainability becomes imperative. While prevailing attention remains on environmental and economic sustainability, this study addresses a persistent gap in the urban literature by focusing on the dynamic and manifold nature of social sustainability. Positioning itself within the context of sustainable development, the study links the pursuit of social aspects of sustainability with selected unique urban contexts from the Global South. Five cases that include Alexandria (Egypt), Tripoli (Libya), Basra (Iraq), Lilongwe (Malawi), and Accra (Ghana) are discussed through multi-layered investigations that involve attitude surveys, interviews, focus groups, participatory systematic observations, and behavioral mapping, engaging directly with inhabitants and stakeholders. Uncovering people-place narratives in the identified contexts, the cases are developed into five knowledge typologies that serve as practical tools for planning and design decision-making, policy formulation, and academic discourse. Discussions are conceived to demonstrate the transformative role people-place narratives play in fostering a more sustainable and equitable urban future. Conclusions are drawn to offer practical insights for stakeholders involved in various capacities in shaping the urban landscape of the Global South.

First came the digital banks without offices, and then the “fintech” companies that combined technology and finance to achieve greater efficiency and new business models. Later was the turn of transport and logistics, understood as the sector whose economic activities derived from the transport of people and goods, while on the one hand, it has had a new spring thanks to factors such as online sales. Then, perhaps catalyzed by the pandemic scenario, the turn has reached education, where years ago was only a complement of traditional education, now is a central part with multiple modern methodologies. Numerous studies exist about using or implementing these e-learning procedures, but technical analysis is barely found in references. This work aims to analyze the digital transformation of the main sectors of our society, including finances, transport, and education, together with several examples of how technology was helping to fight the pandemic scenario.

Based on a literature review, a framework for digital organizational culture with a focus on innovation is proposed. This digital organizational culture becomes relevant today since digital transformation requires an appetite for risk and a willingness to undertake, aspects that are strengthened by an environment and atmosphere that motivate individuals to generate new ideas. The proposal begins with a diagnosis of the existing organizational culture, which allows identifying the differences between the components of the current organizational culture and the innovation-oriented digital organizational culture. Through this diagnosis, the gaps that need to be addressed to achieve a digital organizational culture with innovative characteristics are identified. In addition, specific recommendations are proposed to carry out this transformation. In the process, the assumptions underlying the current typology of the company's organizational culture are highlighted. The importance of integrating creativity, risk readiness, and entrepreneurial willingness into the digital organizational culture aimed at innovation is emphasized. These elements are essential to create an environment

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

With the continuous development of digital transformation and upgrading of Chinese construction enterprises, it is becoming increasingly important to measure their digital level, find the problems in the enterprise transformation process, and identify the key factors of enterprise digital capacity enhancement. This paper constructs a construction enterprise digital transformation maturity evaluation model from six first-level indicators and 20 second-level indicators, including digital strategy, digital business application, digital technology capability, data capability, digital organization capability, and change management. Digital maturity is divided into five levels: business management, process operation, intelligent construction, intelligent scene application, and industrial ecological collaboration. A detailed process of digital maturity evaluation based on the method of Analytic Hierarchy Process (AHP)-Decision Testing and Evaluation Laboratory (DEMATEL) is then developed. A questionnaire survey of 25 experts is used to weight the various parameters in the model, which is then demonstrated with an example construction enterprise. The model comprehensively reflects digital levels under the background of the digital economy. Its application will help understand the advantages and disadvantages enterprises face in their digital transformation to enable targeted measures to improve their digital transformation capabilities and efficiency, enhance their core competitiveness of enterprises, and promote the development of digital transformation in the construction industry.

As a service business models have been ranked by management consultants the management innovation of 2020, and yet are scarcely theorized. Therein, value stipulations in customer contracts lead to the shift from product to service. However, there is no available research about the nature, formula and decisional process about these value stipulations. The motive for this research is to fill in this gap. The goal is to provide first insight about value stipulations in as a service business models: their formula and decisional methodology. The article is a multiple theory building case study on leading platform as a service providers, which aims to contruct the paradigm of the value decision methodology to invest in this technology by purchasing it from dedicated vendors. The methodology works to identify the management instruments involved in the decisional activities to invest in digital technology, where each management instrument is assumed to match an activity at seller and buyer; the sequence of the activities using the Critical Path Method gives the decisional process. Dependent variables are the elements of the as a service business contracts: the customer offering (product-service solution) and the value created, captured and delivered by the solution. Independent variables are given by empirical data analysis. Findings show a relationship between dependent variable value in the as a service business model contracts and independent variables governance objective, strategy, as a service business model type, activity management techniques (roadmap, program, project, operations, lean start-up), capital allocation techniques (equity, debt, options, venture capital typology). If innovation is exploitative, then the customer offering is known and value is determinable via roadmap – project management techniques (program, project, an operations) and capital allocation techniques for precictable environment, If innovation is radical, then the customer offering is not known and value is a target used to architect the solution via lean start-up innovation techniques funded by venture capital. The contribution is a new theoretical construct and deep insight into the practice of digital transformation considering all inherent issues.

This comprehensive study delves into the implementation and effectiveness of the flipped classroom model in engineering EMI (English as a Medium of Instruction) courses in Taiwan. Situated within the strategic framework of Taiwan's Bilingual Nation 2023 policy, this research aims to enhance the global competitiveness of Taiwan's semiconducting manufacturing engineering sector. The study’s primary focus is on understanding the perceptions and learning outcomes of EFL (English as a Foreign Language) students in engineering courses. Through a methodical approach using descriptive statistics and chi-square tests, the study analyzes data from 33 junior engineering students specializing in mechanical and mechatronic fields. The findings highlight a significant positive influence of the flipped classroom model on various aspects of student learning, including increased engagement, heightened creativity, and enhanced problem-solving skills. Additionally, the study emphasizes the critical role of timely L1 support in facilitating effective learning in EMI settings. These outcomes not only demonstrate the flipped classroom's viability as a sustainable educational approach but also provide crucial insights for educators and policymakers. By aligning cutting-edge teaching methods with the evolving needs of engineering education, this study contributes to the discourse on educational innovation. It ultimately guides the development of future engineering programs that cater to diverse student needs and fosters the cultivation of skilled engineering professionals.

Elastic properties of materials and their changes with temperature are important for their applications in engineering. A study on the influence of phase composition of AISI 4130 alloy on Young's modulus (Ed), shear modulus (Gd), and damping (Q-1) was carried out by Impulse Excitation Technique (IET). The material characterization was carried out using confocal microscopy, XRD, MET, HV, and dilatometry. A stable structure, composed of ferrite (BCC) and pearlite (α-Fe+Fe3C), was obtained by annealing. Metastable structure of martensite (BCT) was obtained by quenching. The Ed, Gd, and Q-1 were measured, varying the temperature from RT to 900 °C. The values of Ed and Gd, at RT, were determined as 201.5 and 79.2 GPa (annealed), and 190.13 and 76.5 GPa (quenched), respectively. In the annealed steel, the values Ed and Gd decrease linearly on heating up to 650 °C, with the thermal expansion. In the quenched steel, weak changes in the dilatometric curve, Ed, Gd, and Q-1, in the range of 350-450 °C, indicated decompositions of the martensitic phase. Sharp decrease in the moduli and high peak of Q-1, was observed for both samples around 650-900 °C, revealing low lattice elastic stability of the phases during transformations α(BCC)+Fe3C↔γ(FCC).

In an era characterized by rapid technological advancements, economic fluctuations, and global competition, adaptability and resilience have become critical success factors for businesses navigating uncertainty and complexity. This article explores the role of enterprise agility in today’s business landscape at Latam branch of Tata Consultancy Services (TCS), where organizations face complex and diverse operations. We aim to examine how companies can become more agile in the face of emerging challenges and seize opportunities swiftly to drive growth and deliver value. Since 2014, the division has embarked on an agile transformation journey to drive growth, deliver value, foster innovation, and build resilience in an increasingly dynamic environment. We scrutinize an approach to measuring and enhancing enterprise agility, employing statistical analysis and continuous improvement methodologies to tackle real-world challenges while offering valuable insights and recommendations for organizations aiming to implement similar systems. The results of an agile transformation in a certain company’s Latam branch serve as a compelling case study, demonstrating how the implementation of targeted measures and continuous improvement can significantly bolster enterprise agility. Methodologically, our work applies a novel sequence of parametric statistical tests which, to the best of our knowledge, have not been used in the industry to validate the results of business agility metrics. In future work, we aim to create a new workflow considering non-parametric tests to address data with other statistical distributions. We conclude our work by proposing a sequence of steps for organizations to implement business agility metrics.

Digital transformation has become the top priority for 80% of sports companies worldwide, but statistics show that between 70% and 95% of all digital transformation projects fail due to signif-icant and varied challenges that sports companies face during the process of digital transfor-mation. This is because strategy, not digital technology, drives digital transformation, and suc-cess is unlikely without a mature digital transformation model. Moreover, the digital transfor-mation model of developing and emerging markets is not the same and cannot be adopted as a blueprint. Therefore, the aim of this research was to propose a framework for the digital trans-formation maturity of esports businesses in developing and emerging markets. Semi-structured interviews were conducted with a sample of 15 stakeholders of esports businesses in Iran. Thematic analysis was used to analyze the interviews, and three main perspectives were identified: (1) enablers, (2) digital resilience and capabilities, and (3) digital transformation ma-turity stages. The enablers were formed by six sub-themes, including governance and leader-ship, strategy, culture and skills, innovation, technology, and data. Digital resilience and capa-bilities were formed by four sub-themes, including business model and ecosystem, digital cus-tomer experience, digital employee experience, and digital processes. Additionally, it was found that digital transformation maturity stages are based on four stages of digital beginners, digital followers, digital conservatives, and digital leaders. The findings indicate that moving towards digital transformation and achieving digital resilience in esports businesses will be impossible without synergy between enablers and digital resilience and capabilities. This framework can be useful for esports businesses to evaluate their current digital status and effectively guide them towards a desirable digital status. Furthermore, esports businesses can prioritize their efforts and resources for digital transformation by focusing on targeted capabilities and enablers and ensure more effective and efficient allocation of resources towards digital transformation.

The studies of laminar unsteady boundary layer flows is crucial for understanding laminar-turbulence transition and origins of turbulence. However, the task of finding its solutions poses a significant challenge. In this paper, we propose a novel approach by introducing a similar transformation to convert the 2D unsteady laminar boundary layer equations into a single partial differential equation. By applying this transformation, we are able to obtain the "exact" similarity solutions for the 2D unsteady laminar boundary layer equations, specifically for the case of flat plate boundary flow. Notably, this is the first time that such an exact solution has been obtained. Application of this exact solution is being used to solve Stokes' first problem in 2D boundary layer. Perspectives on the transition fron Rayleigh solution to Blasius solution is provided.

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

The COVID-19 led to changes modifying the role of digital transformation and unleashing unparalleled opportunities to improve business production processes in all three dimensions of sustainability - economic, social, and environmental through data-driven innovative service implications that significantly impact restaurant business, resulting in an increased competitive advantage. This paper identifies the innovative service implications and summarizes sustainable social systems measures in the context of strategic management in the restaurant industry triggered by COVID-19. Based on in-depth interviews with sixteen key experts in the restaurant industry, the paper clarifies and concretizes the rapidly transforming problem of identifying the main changes in the restaurant business, using three dimensions: consumer demand, corporate strategy optimization, and the use of the company's innovative potential. The findings reveal how companies need to expand their digital strategies and adapt their management approach to all stakeholders bringing sustainable business development in the post-pandemic period into focus. This paper serves as a framework for understanding the digital transformation role in resilient business management within restaurant social systems, with friction occurring along the workflow from kitchen to customer.

Purpose: The rapid development of technological innovations is causing major transformations in organisations. These changes are facilitating the transition of organisations from traditional to digital cultures and are influencing the positioning and even the survival of many of them. Higher Education Institutions are no exception. This article aims to understand the main factors that facilitate and drive this transformation from the point of view of their external stakeholders. Design/methodology/approach - Due to the complexity of the study, the case study method has been used as an appropriate methodology. Findings - As the results of the study show, the use of new agile methodologies, the development of continuous improvement processes, the automation of tasks with the use of new technologies, data-driven decision making, and the management of new digital talent are enablers. Research limitations/implications - Further research could conduct similar analyses with other stakeholders, to achieve a complete management model. Practical implications - The case study allows researchers to learn from practitioners and business leaders, while providing a theoretical model that can help other managers in the process of transforming their organisations. Originality/value - The main contribution of this study has been to apply the study to a specific Higher Education Institution.

High-resolution wave measurements at intermediate water depth are required to improve coastal impact modeling. Specifically, such data sets are desired to calibrate and validate models, and broaden the insight on the boundary conditions that force models. Here, we present a wave data set collected in the North Sea at three stations in intermediate water depth (6-14~m) during the 2021/2022 storm season as part of the RealDune/REFLEX experiments. Continuous measurements of synchronized surface elevation, velocity and pressure were recorded at 2-4~Hz by Acoustic Doppler Profilers and an Acoustic Doppler Velocimeter for a 5~month duration. Time series were quality-controlled, directional-frequency energy spectra were calculated, and common bulk parameters were derived. Measured wave conditions vary from calm to energetic with 0.1-5.0~m sea-swell wave height, 5-16~s mean wave period and W-NNW direction. Nine storms, i.e. wave height beyond 2.5~m for at least six hours, were recorded including the triple storms Dudley, Eunice and Franklin. This unique data set can be used to investigate wave transformation, wave nonlinearity and wave directionality for higher and lower frequencies (e.g. sea-swell and infragravity waves) to compare with theoretical and empirical descriptions. Furthermore, the data can serve to force, calibrate and validate models during storm conditions.

In recent years, Chinese manufacturing enterprises compete to chase the wave of "digital revolution", digital empowerment has become an important strategic path of technological reform for many manufacturing enterprises. Based on the micro data of listed companies, this paper investigates the impact of digital transformation of Chinese listed companies on the quality of their export products. It is found that digital transformation can significantly improve the quality of enterprises' export products. After a series of robustness tests, this conclusion is still valid, and there are two ways to improve innovation performance and total factor productivity. The export product quality enhancement effect of enterprise digital transformation also has the heterogeneity of ownership, region and industry. In the further study, this paper also examines the impact of digital transformation on the internal salary gap of enterprises, and finds that digital transformation may increase the internal salary gap of enterprises, and form a "masking effect" on the quality of export products. To a certain extent, this paper deepens the understanding of the study of enterprise digital transformation on the quality of export products and its differences, and provides certain guidance for enterprises to implement the strategy of digital transformation.

The discovery of solitonlike coherent structure (SCS) in boundary layer flows is crucial for understanding turbulence origins and in particular for laminar-turbulence transition. However, the task of finding solutions for solitonlike coherent structure from the Navier-Stokes equations poses a significant challenge. In this paper, based on the author's previous work [B.H. Sun, Exact similarity solutions of unsteady laminar boundary layer flows, Preprints 2023, 2023092117], we are able to study convergent flow boundary layers, whose solution encompasses both shock wave and solitary wave solutions, and their superposition gives rise to solitary-like waves, namely solitonlike coherent structure. It is found that the solitonlike coherent structure can only be obtained by combining the Navier-Stokes equations and mass conservation, since the combined equations will have the third order derivatives respect to coordinate $x$, in other words, without the mass conservation condition, the Navier-Stokes equations does not contain solitonlike coherent structure by itself. Finally proved solitonlike coherent structure is a universal motion form of viscous fluid.

In this paper, a short survey of the existed results concerning the Jacobian Conjecture is first given. Then the 3-fold linear type polynomial map will be analyzed in detail. The expansion of the Jacobian condition is deduced to obtain its equivalent algebraic equations, and the Jacobian condition will be analyzed to derive two coordinate transformations that can maintain the invariance of the Jacobian condition. Finally, it is proved by mathematical induction method that one general chain expression presented in this paper is just the inverse polynomial map of 3-fold linear type polynomial map, i.e. LJC(n,[3]) holds such that the Jacobian Conjecture holds.

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and, based on the in-depth case study, a model is put forward to aid the manageability of chatbot implementation. The presented model can serve as an exemplar guide for researchers and practitioners working on the design, development and implementation of virtual assistants. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience of chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

Oxide additive-free α- ⇀ β-Si3N4 phase transformation of a high-purity commercial α-Si3N4 powder was investigated at 1600 to 1900 ℃ under nitrogen pressure of 980 kPa. The XRD analysis revealed that the α- ⇀ β-Si3N4 phase transformation proceeded mainly at 1900 ℃, and was completed by the extensive 1900 ℃-heat treatment for 20 h. This phase transformation temperature was 33 ℃ lower than the theoretical α-Si3N4 dissociation temperature and confirmed as completely different from that often discussed for the liquid phase sintering of α-Si3N4 powder by direct comparison with the phase transformation behavior of a reference powder, α-Si3N4 powder doped with 1 mol% Y2O3. The unique α- ⇀ β-Si3N4 phase transformation was further studied by a set of characterization techniques including elemental analysis, HAADF-STEM and STEM-EDS analyses. The results strongly suggested that the oxide additive-free α- ⇀ β-Si3N4 phase transformation was governed by the for-mation of metastable solid solution between α-Si3N4 and impurity oxygen of approximately 0.6 wt%, which promoted the dissociation below the theoretical α-Si3N4 dissociation temperature to afford thermodynamically favorable β-Si3N4. Along with the β-Si3N4 formation, the impurity oxygen was concentrated at the Si3N4 crystal grain boundaries and subsequently released from the sample via the grain boundary diffusion.

This paper examines the use of business maturity models as a tool for achieving business maturity and improving organizational performance in a rapidly changing business environment. Specifi-cally, the paper evaluates existing maturity models and methodologies, analyzes their effective-ness, and develops a novel and enhanced model tailored to the oil and gas sector. The authors highlight the importance of considering fundamental factors that underlie the implementation of maturity models and the need to evaluate their fit with organizational goals and values. The pa-per concludes by emphasizing the ongoing nature of business maturity and the effectiveness of theproposed Business Maturity Framework (BMF) model in enhancing operational excellence, competitiveness, and sustainable growth in the oil and gas industry. The paper makes a notable contribution to the specialized literature precisely through the differ-ent approaches to the business maturity process and through the proposed business maturity model itself as a base of strategy formulation, which includes a new dimension. This dimension analyzes whether the market and customers are ready for the level of digitization that the com-pany wants to achieve in a future state.

Mesenchymal stromal cells (MSCs) are a subset of heterogeneous non-hematopoietic fibro-blast-like cells which play important roles in tissue repair, inflammation, and immune modula-tion. MSCs residing in the bone marrow microenvironment (BMME) functionally interact with hematopoietic stem progenitor cells regulating hematopoiesis. However, MSCs have also emerged in the last years as key regulator of tumor microenvironment. Indeed, they are now con-sidered as active players in the pathophysiology of hematologic malignancies rather than passive by-standers in hematopoietic microenvironment. Once the malignant event occurs, the BMME acquires cellular, molecular, and epigenetic abnormalities affecting tumor growth and progres-sion. In this context, MSC behavior is affected by signals coming from cancer cells. Furthermore, it has been showed that stromal cells themselves play a major role in several hematological malig-nancies’ pathogenesis. This bidirectional crosstalk creates a functional tumor niche unit wherein tumor cells acquire a selective advantage over their normal counterpart and are protected from drug treatment. It is therefore of critical importance to unveil the underlying mechanisms which activate a protumor phenotype of MSCs for defining unmasked vulnerabilities of hematological cancer cells which could be pharmacologically exploited to disrupt the tumor/MSCs coupling. The present review will focus on the current knowledge about MSC dysfunction mechanisms in the BMME of hematological cancers, sustaining tumor growth, immune escape, and cancer pro-gression.

It has been proposed that tumourigenicity was an intrinsic feature of embryonic/germ cell developmental axis as well as embryonic/germ cell-related genes play a crucial role in tumourigenicity. Our previous studies indicated that primordial germ cell (PGC)-like potential could be reactivated in tumourigenesis. In this study, 4T1, 168FARN and 67NR cells which originated from the same mouse breast cancer were studied and the results indicated that the acquisition of embryonic/germ cell-like state is essential for tumourigenicity. We further demonstrated that somatic to PGC-like transformation (SPLT) was activated in 4T1 cells and that inhibition of PGC-like cell formation by depleting pluripotency and/or PGC specification-related genes markedly repressed SPLT and the tumourigenicity. Collectively, our findings reveal that tumourigenicity is linked to the acquisition of PGC-like state through SPLT in 4T1 cells, provide new insight into deeper understanding the biological nature of tumours and novel therapeutical strategies for cancer targeting.

Nuclear Magnetic Resonance (NMR) and its various forms are extensively utilized in both research and clinical settings to analyse molecules. NMR data pre-processing, while essential to NMR data analysis, can be uniquely complex. Despite the availability of software tools, understanding these processes can be challenging, complicating the selection of appropriate pre-processing steps. In this review, we elucidate pre-processing steps in the time domain from a mathematical and statistical perspective, explaining direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, and domain transformation using plain language and fundamental mathematical formulas. Our objective is to clarify these processes in simple terms and provide general guidance.

The impact of the climate crisis on urban environments and life within them is evident. In the face of challenges like heatwaves, floodings and other extreme events, pollution, and declining quality of life in cities, there is a growing demand for the preservation and expansion of urban green spaces, often driven by citizen-led transformations. This paper examines 100 urban greening projects initiated or supported by citizens globally, categorizing them according to the type of greenery, the stakeholders involved, the mode of implementation and the use of smart technologies incorporated. We notice variations in green endeavors based on the stakeholders spearheading them, however most of entirely citizen-led initiatives aimed at the creation of urban farms and food growing, demonstrating the pressuring needs for food securing and self-determination in communities. More than half of the assessed initiatives that managed to scale up and multiply had public authorities providing a framework or a type of support for their development or an NGO or other organization providing expertise and mobilize citizens at various stages. In terms of technological use, we find mostly websites and social media platforms easing participatory endeavors and knowledge sharing of best practices, accelerating scaling efforts, while there is a low integration of more advanced digital technologies, which if used, could enable real-time monitoring of green spaces, inform evidence-based decision-making, and streamline processes in scaling up green initiatives.

The complex adaptive systems (CAS) research study investigates into the theoretical link of a sustained economic growth as a critical pathway to lift people out of poverty and improve their quality of life. The results show a left-skewed global behavior of an economy-driven human development of 189 countries with 20% or 27 countries obtaining low human development; 80% or 162 countries obtaining medium to very high human development of which 21.16% or 39 countries with medium HDI, 53 or 28.04% or 53 countries with high HDI, and 31.22% or 59 countries with very high HDI. Using the Log (GDP) for visual transformation, closer investigation into the scatterplot diagram reveals that there are also bimodal patterns where countries in the high amplitude and low amplitude groups obtained different human development outcomes despite less or more on economic growth variances mirror on the government policy priorities resulting in four differentiating conditions. Very few countries experience virtuous cycles where both growth and human development are successful; few countries with vicious cycles where both are weak; and many countries with lopsided conditions where the economy is strong but human development is weak, or conversely ones where human development is strong but the economy is weak. The global pattern of economy and human development illustrated by an upward sinusoid or sine wave refutes the conventional linear approach. Uniquely and most importantly, the sinusoidal wave illustrates a decreasing amplitude and decreasing periods as countries manifest apparently complex behaviors that emerge as a result of often nonlinear spatio-temporal interactions among a large number of component systems at different levels of organizations and governments leading to an emergence of a behavior characterizing of a complex adaptive system.

Lasiodiplodia theobromae, as one of the causing agents associated with Chinese hickory trunk cankers, has caused huge economic losses to the Chinese hickory industry due to its extremely strong pathogenicity. Though the biological characteristics of this pathogen and the occurrence pattern of this disease have been reported, few studies have focused on investigating the mechanisms responsible for L. theobromae survival strategies and pathogenicity. The high-quality genome data and the efficient transformation system are the basis for researching above mechanism events. In this study we sequenced and assembled L. theobromae strain LTTK16-3, and established the first protoplasmic preparation method and polyethylene glycol (PEG) -mediated genetic transformation system for L. theobromae. These genetic information and transformation methods established the foundation for the future mechanisms study of L. theobromae and set up the possibility of targeted molecular improvements for Chinese hickory canker control.

Abstract: Establishing carbon neutrality (or Net Zero) in cities appears to be an ambitious challenge, which drives urban transformation efforts around the world to enhance people-centric living. Cities launch corresponding strategies focusing mainly on the United Nations 2030 goals to form their climate-neutral future. This paper provides practical guidance on city digital transformation to achieve carbon net zero. These guidelines illustrate actionable recommendations to reduce their carbon footprint using digital technologies. Following a multi-method research approach, findings from a bibliometric literature and a standards’ gap analysis were combined with practical evidence and circulated with focus group experts to conclude to these guidelines, which are the foundation for further standardization. The outcomes provide concrete guidelines to policymakers, planners, and other stakeholders for city digital transformation to establish carbon neutrality, via emphasizing the need for digital technologies to enhance efficiency in several areas and via leveraging green ICTs for city economic growth while also transforming citizen behavior.

This paper investigates the ion-acoustic wave structures in fluid ions for the Benjamin-Bona-Mahony-Peregrine-Burgers (BBMPB) equation. The various types of wave structures, are extracted including the three-wave hypothesis, breather wave, lump periodic, mixed-type wave, periodic cross-kink, cross-kink rational wave, M-shaped rational wave, M-shaped rational wave solution with one kink wave and M-shaped rational wave with two kink wave solutions. The Hirota bilinear transformation is a powerful tool that allows us to accurately solutions and predict the behavior of these wave structures. Through our analysis, we gain a better understanding of the complex dynamics of ion-acoustic waves and their potential applications in various fields. Moreover, our findings contribute to the ongoing research in plasma physics that utilize ion-acoustic wave phenomena. To show the physical behavior of the solutions, some 3D plots have been performed and their respective contour level, choosing different values of the parameters.

This study examines the unique circumstances surrounding state-owned equity participation in non-state-controlled enterprises in China. Specifically, this study examines the impact of state-owned equity participation on the environmental, social, and governance (ESG) performance of non-state-controlled enterprises. Focusing on A-share listed firms on the Shanghai and Shenzhen Stock Exchanges and using data from 2013 to 2021, empirical testing shows that state-owned equity participation can significantly improve the ESG performance of non-state-controlled enterprises, with this conclusion remaining reliable after a series of robustness tests. Top management incentives is a mediating mechanism for state-owned equity participation in enhancing ESG performance. This study also finds that when state-owned equity participates in large enterprises or companies with a high degree of digital transformation, the effect on ESG performance is greater than that of small to medium-sized enterprises (SMEs) or enterprises with a low level of digital transformation. The findings of this study add to the current body of the existing field of research on the factors influencing corporate ESG performance and the impact of state-owned equity on corporate non-financial performance.

Digital transformation has become a strategic priority for the enterprises, the public organizations, and the cities lately since it addresses the adoption of cutting-edge information and communication technologies (ICT) for optimizing their business processes and for generating new types of value to their end users. Although the Covid-19 pandemic is claimed to be over, it appears that organizations and cities changed their digital transformation strategies that had defined before the outbreak. The aim of this paper is to investigate the type of changes that were caused both to enterprises and cities due to the pandemic. It investigates whether and in which directions it caused business changes and how the smart city strategic objectives were affected due to pandemic, especially in cases where digital transformation strategies were defined before the outbreak. This study uses bibliometric analysis to determine the effects of Covid-19 on businesses and cities. Findings specify strategic changes and important trends, like the migration of the production supply chains to nearby territories. Moreover, this article utilizes the European Intelligent Cities Challenge initiative, in which cities had defined their priorities for their digital transformation before the outbreak. From the analysis of the strategic plans of the involved cities, it is attempted to determine whether, which cities and in which directions they changed their strategies due to the pandemic.

This work investigates how integrated polyimide inlays with applied sensor bodies influence the guided ultrasonic wave propagation in narrow glass fiber-reinforced polymer specimens. Preliminary numerical simulations indicate that in a damping-free specimen, the inlays show reflections for the S0-mode propagation. Hence, an air-coupled ultrasonic technique and a 3D laser Doppler vibrometer measurement are used to measure different parts of the propagating waves’ displacement field after burst excitation at different frequencies. No significant reflections on the inlay can be seen in the experiments. However, it is shown that the reflections from the strip specimen’s narrow width cause periodical reflections that superimpose with the excited wave fronts. A continuous wavelet transformation in the time-frequency domain filters discontinuities from the measurement signal and is used for reconstruction of the time signals. The reconstructed signals are used in a spatial continuous wavelet transformation to identify the occurring wavelengths and hence to prove the assumption of reflections from the narrow edges. Since the amplitude of the reflections identified in the numerical data at the polyimide inlays are an order of magnitude smaller than the excited wave packages, it is concluded that material damping of the epoxy resin matrix extincts possible reflections from the inlays.

Accelerating the green transformation of the economic development mode and promoting high-quality economic development is an international trend and the general direction. This article utilizes data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2021, based on dynamic capability theory, to explore the impact of enterprise digital transformation on green technological innovation. Furthermore, dynamic capabilities are further subdivided into absorptive capacity, innovative capacity, and adaptive capacity to investigate their mediating role. The research findings demonstrate that enterprise digital transformation has a significant positive impact on green technological innovation. Moreover, enterprise digital transformation positively influences the absorptive capacity, innovative capacity, and adaptive capacity, with these three factors playing a mediating role in the impact of enterprise digital transformation on green technological innovation. The results of the bootstrap test confirm this mediating effect. The conclusions of this study are validated through a series of robustness tests. This research not only provides theoretical support for the impact mechanism of digital transformation on green technological innovation but also offers practical insights for enterprises to achieve sustainable development.

Digitalization technologies have been identified as enablers for the adoption of Circular Economy practices. The machinery value chain, addressed in this study, is affected by the introduction of digital technologies that enable real-time monitoring data on product condition and control optimization deploying predictive analytics techniques, as well as the offerings of circular-based services. Machinery lifetime extension can be digitally enabled on both old and new machines. The research objectives were to investigate how digital technologies enable the adoption of circular economy-based business models in manufacturing companies and provide an answer about i) what Life Cycle Extension Strategy is suitable for digital circular business model adoption and ii) how digitalization of machines enables manufacturing companies to innovate their business model. The correlation matrix is the tool developed from the proposed approach; it aims at supporting manufacturers in the very first contact with circular business models. In the context of the European RECLAIM project, two manufacturers apply the approach, proving its validity and wide applicability. The next steps are expected to introduce quantitative indicators to define thresholds for the steps toward circularity, without replacing the qualitative approach, as it guarantees the wide applicability of the approach in context that never considered circularity yet.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

Phialemonium inflatum is a useful fungus known for its ability to mineralise lignin during primary metabolism and decompose polycyclic aromatic hydrocarbons (PAHs). However, no functional genetic analysis techniques have been developed yet for this fungus, specifically in terms of transformation. In this study, we applied an Agrobacterium tumefaciens-mediated transformation (ATMT) system to P. inflatum for functional gene analysis. We generated 3,689 transformants using the binary vector pSK1044, which carried either the hygromycin B phosphotransferase (hph) gene or the enhanced green fluorescent protein (eGFP) gene to label the transformants. Southern blot analysis showed that the probability of a single copy of T-DNA insertion was approximately 50% when the co-cultivation time of fungal spores and Agrobacterium tumefaciens cells was performed at 24–36 h, whereas at 48 h, it was approximately 35.5%. Therefore, when performing gene knockout using ATMT, co-cultivation was reduced to ≤ 36 h. The resulting transformants were mitotically stable, and PCR analysis confirmed gene integration into the transformant genome. Additionally, hph and eGFP gene expressions were confirmed by PCR amplification and fluorescence microscopy. This optimised transformation system will enable functional gene analysis to study genes of interest in P. inflatum.

As China put forward its “carbon emissions peak and carbon neutrality” goals, how to achieve carbon reduction had become a key for China’s goal. The manufacturing industry is an important source of carbon dioxide emissions. For a manufacturing country like China, adjustments in various aspects of the industry would have a huge impact on carbon emissions. As an important reform of contemporary production mode, the process of production automation in China will inevitably affect China's carbon emissions. Therefore, the analysis of the impact of production automation on carbon dioxide emissions was an important basis for judging the future carbon reduction in China. Refer to the traditional study of carbon Kuznets curve, this paper analyzed the impact of average wage on production automation and the role of production automation in the carbon Kuznets curve(CKC). This paper proposed that production automation plays a mediating role in the process of carbon emissions, and gives a verification model of the mediating role. By analyzing the relationship between average wage and production automation process, the U-shaped curve relationship between them was verified. By examining the relationship between carbon dioxide emission data and production automation industry in China, we verified that production automation plays a partial mediating role in the change of carbon Kuznets curve. Combined with the analysis of the two parts, this paper believed that with the continuous development of China's intelligent manufacturing industry, China's carbon reduction prospects were more optimistic, and there was a good industrial foundation to achieve the “carbon peaking and carbon neutrality” goals. Finally, this paper proposes policy suggestions as increase research investment in production automation, help promote the application of production automation, encourage the research and application development of low-carbon technology, especially encourage modular design, so as to give full play to the role of production automation in the process of carbon neutrality in China.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

Numerous researchers acknowledge that occupational pension protects employees. However, the total occupational pension is shared by employees and employers in China, which is a cost. This study aimed to explore the occupational pension on corporate social responsibility (CSR) and organizational resilience. We drew upon insights from cost-stickiness and resource-based theories to develop a model that illuminates how occupational pension affected firms’ stance toward CSR in the context of the COVID-19 pandemic and how this, in turn, has result organizational resilience. This study categorized CSR into strategic and responsive activities based on cost stickiness. We selected a total of 34,145 observations of Chinese listed companies over the period 2010-2023 as a sample to investigate whether occupational pension changes CSR strategies. The results showed that the cost pressure to contribute to occupational pension drove firms to reduce their responsive CSR and boost their strategic CSR. Moreover, strategic CSR improved organizational resilience, but responsive CSR did not. This relationship between occupational pension and CSR was significantly and negatively moderated by the minimum wage and population aging. Whereas, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities. This study has implications for decision-making regarding high-quality firm development.

Lamb waves occur in thin-walled structures in two wave modes, the symmetric and antisymmetric mode. Their oscillation on the structures‘ surfaces is either in phase (symmetric) or shifted by a phase angle of π (antisymmetric). In this work, a method is developed to compare the surfaces‘ oscillation phase relation. It is based on the evaluation of time signals regarding the instantaneous phase angle using the continuous wavelet transformation and as a comparative method the short-time Fourier transformation. For this purpose, numerical simulations utilizing the finite element method provide time signals from the top and bottom surface of different thin-walled structures. They differ with respect to their material settings and laminate configurations. The numerically obtained time signals are evaluated by the developed methods. The occurring oscillation phase differences on the top and bottom surface are studied and both methods are compared. Subsequently, the oscillation phase is evaluated experimentally for the wave propagation in a fiber metal laminate. It is shown that the method based on the continuous wavelet transformation is suitable for the evaluation of oscillation phase relations in time signals. Additionally, it is proven that fiber metal laminates show only two phase relations which indicates the occurrence of Lamb waves.

Clarifying the impact mechanism of digital transformation of enterprises on the M&A efficiency of listed companies can provide a factual basis for improving capital market regulatory policies. Taking the mergers and acquisitions of listed companies from 2007 to 2021 as a research sample, the influence mechanism of the digital transformation degree of companies on their M&A efficiency was studied. The research results show that the digital transformation of listed companies will improve their M&A efficiency. Digital transformation will reduce the degree of mispricing of stocks of M&A companies, curb conflicts between managers and agents of M&A companies, and improve their M&A efficiency. Further research finds that the promotion effect of digital transformation on M&A efficiency is more significant in non-state-owned companies, with a higher degree of financing constraint and high analyst attention. In the future, regulatory authorities should actively promote the digital transformation of listed companies, curb mispricing and management agency problems in the capital market with digital governance, and improve the efficiency of mergers and acquisitions in the capital market.

A rapid synthesis of chiral sulfoxide-functionalized meta-terphenyl derivatives by a 2,5-[C4+C2] ring transformation reaction of pyrylium salts with in-situ generated enantio-merically pure .alpha.-sulfinylacetaldehydes is described. This synthetic method demonstrates for the first time the use of .alpha.-sulfinylacetaldehydes in a reaction sequence initiated by nu-cleophilic attack of pyrylium salts by .alpha.-sulfinylcarbanions to generate chiral aromatic systems. The method presented shows broad applicability starting with various methyl sulfoxides and a number of functionalized pyrylium salts, furnishing meta-terphenyls with complex substitution patterns from readily accessible starting compounds.

Brain tumors are abnormal cells growth in the brain tissues that can be cancerous, or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool commonly adopted by neurologists and radiologists to identify and classify any possible anomalies present in the brain anatomy. In the present work, an automatic unsupervised method called Z2-$\gamma$, based on the use of adaptive finite-elements and suitable pre-processing and post-processing techniques, is introduced. In particular, the proposed methodology is able to automatically classify whether a given MR image represents a healthy or a diseased brain and in this latter case, is able to locate the tumor area.

This paper introduces extensions H4,p and X8,p of Horn’s double hypergeometric function H4 and Exton’s triple hypergeometric function X8, taking into account recent extensions of Euler’s beta function, hypergeometric function, and confluent hypergeometric function. Among the numerous extended hypergeometric functions, the primary rationale for choosing H4 and X8 is their comparable extension type. Next, we present various integral representations of Euler and Laplace type, Mellin and inverse Mellin transforms, Laguerre polynomial representation, transformation formulae and a recurrence relation for the extended functions. In particular, we provide a generating function for the X8,p and several bounding inequalities for the H4,p and X8,p.

Image encryption based on chaotic maps is an important way to ensure the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed by employing a compound chaotic map and random cyclic shift. Firstly, an improved Hybrid chaotic system is designed by coupling Logistic, ICMIC, Tent, and Chebyshev maps (HLITC). Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and Kolmogorov entropy illustrate the new hybrid chaotic map has better chaotic performance. Then the proposed HLITC chaotic system is used to develop a new chaotic image encryption scheme with the double cyclic shift. The improved HLITC chaotic system is performed to generate key sequences used in the image scrambling and diffusion stage. The spiral transformation controlled by chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments of statistical analysis, key sensitivity, and keyspace analysis are conducted. Experimental results show the proposed encryption scheme has good robustness against brute force attacks, statistical attacks, and differential attacks, and is more effective than many existing chaotic image encryption algorithms.

Responding to changing value creation processes in the sheet metal working sec-tor, where the complexity and interchangeability of products challenge traditional dif-ferentiation strategies, this empirical analysis explores integrating service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. The research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies re-quire support in their implementation. Fields of action were identified for the industry. These are "Creating awareness and understanding", "Recognizing added value", "In-creasing company maturity" and "Understanding the change process". Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This pa-per identifies challenges in the fields of action that companies must address through a structured approach promoting awareness, recognizing value, improving organiza-tional maturity, and understanding the change process to successfully implement ser-vice-oriented and data-driven business models.

It is universally accepted that Maxwell equations do not remain invariant under the Galilean transformation. This conflicts the principle of relativity which states that the physical law must remain invariant in the mathematical form in all inertial frames of reference. For this reason, the Lorentz transformation is invented, and the Galilean transformation is nowadays superseded by the Lorentz transformation. However, this paper challenges this widely held belief that the Maxwell equations are not invariant under the Galilean transformation. By applying the Galilean transformation to Lienard-Wiechert electromagnetic fields it is mathematically proven that the Maxwell equations indeed remain invariant under the Galilean transformation. In addition, the critical error in Lorentz's proof of Galilean non-invariance of Maxwell equations is pointed out, and it turns out that Lorentz's conclusion that Maxwell equations are not invariant under the Galilean transformation is the result of a mathematical error.

the process of rapidly digitalizing and transforming businesses, known as Industry 4.0, was already underway before COVID-19. However, the imposed restrictions during the pandemic significantly amplified the need and motivation for both businesses and consumers to utilize digital tools across all sectors including the sectors of bioeconomy. The agricultural and food production sectors have a predominant role in the bioeconomy of the European Union (EU), followed by wood production. These sectors make significant contributions not only to national economies but also to rural areas. Consequently, the digitalization of businesses within the bioeconomy sector not only transforms the enterprises and value chains themselves but also benefits the rural communities in which these enterprises are situated. This study aims to assess the barriers of the bioeconomy sector and ways to support digital transformation within this sector. The paper analyses bioeconomy in EU and the state of digitalisation in the EU. The empiric analysis is based on the cluster analysis of the digitalisation and R&D indicators of the EU and the AHP analysis that allows determining the digitalisation scenarios in Latvia, Lithuania and Poland carried out by four stake-holder groups - national government; advisory and extension; research and entrepreneurship. The results of the AHP indicate that experts from Lithuania and Poland preferred the scenario that suggested the self-initiative from the entrepreneurs of the sector, but experts from Latvia – prioritization of support for digital transformation using national and EU funding. The AHP results also indicate that the opinions of the national government, consulting, and research experts are more aligned throughout all three countries, but the opinion of entrepreneurs differs from these groups.