Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying breast cancer from unregistered multi-view mammograms. This motivates us to leverage the architecture of Vision Transformers to capture long-range relationships of multiple mammograms from the same patient. For this purpose, we employ local Transformer blocks to separately learn patch relationships within a specific view (CC/MLO) of one side (right/left) of mammogram. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of CC and MLO view mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our two-view Transformer-based model achieves lesion classification accuracy of 77.0% and area under ROC curve (AUC = 0.814), outperforming state-of-the-art multi-view CNNs by 3.1% and 3%, respectively. Meanwhile, the new two-view model improves mammographic case classification accuracies of two single-view models by 7.4% (CC) and 4.5% (MLO), respectively. The promising results unveil the great potential of using Transformers to develop high-performing computer-aided diagnosis (CADx) schemes of mammography.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

Concerns associated with global warming and the depleting reserves of fossil fuels have highlighted the importance of high-performance energy storage systems (ESSs) for efficient energy usage. ESSs such as supercapacitors can contribute to improved power quality of an energy generation system, which is characterized by a slow load response. Composite materials are primarily used as supercapacitor electrodes because they can compensate for the disadvantages of carbon or metal oxide electrode materials. In this study, a composite of oxide nanoparticles loaded on a carbon nanofiber support was used as an electrode material for a hybrid supercapacitor. The addition of a small amount of hydrophobic Fe- and N-doped graphene nanoplates modified the surface properties of carbon nanofibers prepared by electrospinning. Accordingly, the effects of the hydrophobic/hydrophilic surface properties of the nanofiber support on the morphology of Co3O4 nanoparticles loaded on the nanofiber, as well as the performance of the supercapacitor, were systematically investigated.

The analytical method for the calculation of the properties of a bolted connection established by the structural Eurocodes proposes the T-stub as a component for the characterization of the tension and compression zones in moment joints. In this article, a review of the state of the art on the T-stub component is developed, where the works developed since it was initially defined, and from the perspectives of formulation, experimentation and numerical simulation are summarized and discussed. Additionally, possible future lines of work are proposed.

The liquid droplet cooling technique for fast scanning chip calorimetry (FSC) is introduced, increasing the cooling rate for large samples on a given sensor. Reaching higher cooling rates and using a gas as the cooling medium, the common standard for ultra-fast temperature control in cooling, requires reducing the lateral dimensions of the sample and sensor. The maximum cooling rate is limited by the heat capacity of the sample and the heat exchange between the gas and the sample. The enhanced cooling performance of the new liquid droplet cooling technique is demonstrated for both metals and polymers, on examples of solidification of large samples of indium, high-density polyethylene (HDPE), and poly (butylene 2,6-naphthalate) (PBN). It was found that the maximum cooling rate can be increased up to 5 MK/s in room temperature environment, that is, by 2 orders of magnitude, compared to standard gas cooling. Furthermore, modifying the droplet size and using coolants at different temperatures provide options to adjust the cooling rate in the temperature ranges of interest.

The implementation of gold-hydrogel core-shell nanomaterials in novel light-driven technologies requires the development of well-controlled and scalable synthesis protocols with precisely tunable properties. Herein, new insights are presented concerning the importance of using the concentration of gold cores as a control parameter in the seeded precipitation polymerization process to modulate – regardless of core size – relevant fabrication parameters such as encapsulation yield, particle size and shrinkage capacity. Controlling the number of nucleation points results in the facile tuning of the encapsulation process, with yields reaching 99% of gold cores even when using different core sizes at a given particle concentration. This demonstration is extended to the encapsulation of bimodal gold core mixtures with equally precise control on the encapsulation yield, suggesting that this principle could be extended to encapsulating cores composed of other materials. These findings could have significant impact on the development of stimuli-responsive smart materials.

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. A cruicial part of the analysis is to avoid unnecessary data retention during that process, in order to prevent subsequent abuse, theft or public exposure of collected datasets and thus, protect the privacy of social media users. There are a number of technical approaches out to face the problem. One of them is using a cardinality estimation algorithm called HyperLogLog to store data in a privacy-aware structure, that can not be used for purposes other than the originally intended. In this case study, we developed and conducted a focus group discussion with teams of social media analysts, in which we identified challenges and opportunities of working with such a privacy-enhanced social media data structure in place of conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisiton process will not distract the data analysis process. Instead, it will improve working with huge datasets due to the improved characteristics of the resulting data structure.

The synthesis of metal nanoparticles using plant extracts is a very promising method in green synthesis. The medicinal value of Moringa oleifera leaves and the anti-microbial activity of metallic copper were combined in the present study to synthesize copper nanoparticles having a desirable added-value inorganic material. The use of a hydroalcoholic extract of M. oleifera leaves for the green synthesis of copper nanoparticles is an attractive method as it leads to the production of harmless chemicals and reduces waste. The total phenolic content in the M. oleifera leaves extract was 23.0 ± 0.3 mg gallic acid equivalent/g of dried M. oleifera leaves powder. The M. oleifera leaves extract was treated with a copper sulphate solution. A color change from brown to black indicates the formation of copper nanoparticles. Characterization of the synthesized copper nanoparticles was performed using UV-Vis spectrophotometer, FT-IR spectrometer, TEM, SEM, and XRD. The synthesized copper nanoparticles have an amorphous nature and particle size of 35.8-49.2 nm. We demonstrate that the M. oleifera leaves extract and the synthesized copper nanoparticles display considerable antioxidant activity. Moreover, the M. oleifera leaves extract and the synthesized copper nanoparticles exert potent anti-bacterial activity against Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Enterococcus faecalis (MIC values for the extract: 500, 250, 250, and 250 μg/mL; MIC values for the cooper nanoparticles: 500, 500, 500, and 250 μg/mL, respectively). Similarly, the M. oleifera leaves extract and the synthesized copper nanoparticles exert relatively more potent anti-fungal activity against Aspergillus niger, Aspergillus flavus, Candida albicans, and Candida glabrata (MIC values for the extract: 62.5, 62.5, 125, and 250 μg/mL; MIC values for the cooper nanoparticles: 125, 125, 62.5, and 31.2 μg/mL, respectively). Our study reveals that the green synthesis of copper nanoparticles using a hydroalcoholic extract of M. oleifera leaves was successful. In addition, the synthesized copper nanoparticles can be potentially employed in the treatment of various microbial infections due to their potent antioxidant, anti-bacterial, and anti-fungal activities.

The recovery of precious metals is a project with both economic and environmental significance. In this paper, it presents how to use bacterial mineralization to selectively recover gold from multi-ionic aqueous systems. The Bacillus licheniformis FZUL-63, separated from a landscape lake in FuZhou University, was shown to selectively mineralize and precipitate gold from coexisting ions in aqueous solution. The removal of Au(III) was almost happened in first hour, and FTIR data show that the amino, carboxyl and phosphate groups on the surface of the bacteria are related to the adsorption of gold ions. XPS results implied that Au(III) ions are reduced to monovalent, and then the Au(I) was adsorbed on the bacterial surface at the beginning stage(first hour). XRD results showed the gold biomineralization began about 10 hours after the interaction between Au(III) ions and bacteria. The Au(III) mineralization has been rarely influenced by other co-existing metal ions. TEM analysis shows the gold nanoparticles are polyhedral structure with a particle size of ~20 nm. The Bacillus licheniformis FZUL-63 could selectively mineralize and recover 478 mg/g(dry biomass) gold from aqua regia-based metal wastewater through four cycles. It could be of great potential in the practical application.

Background: Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets fre- quently encountered in life science domains gives rise to contextual dependence and numerical irregularities in the behavior of model selection criteria (such as MDL, Minimum Description Length) used in BN reconstruction. This renders model features, first and foremost dependency strengths, incomparable and diffi- cult to interpret. In this study, we derive and evaluate a model selection principle that addresses these problems. Results: The objective of the study is attained by (i) approaching model eval- uation as a classification problem, (ii) estimating the effect that sampling error has on the satisfiability of conditional independence criterion, as reflected by Mutual Information, and (iii) utilizing this error estimate to penalize uncertainty with the novel Minimum Uncertainty (MU) model selection principle. We vali- date our findings numerically and demonstrate the performance advantages of the MU criterion. Finally, we illustrate the advantages of the new model evaluation framework on real data examples. Conclusions: The new BN model selection principle successfully overcomes per- formance irregularities observed with MDL, offers a superior average convergence rate in BN reconstruction, and improves the interpretability and universality of resulting BNs, thus enabling direct inter-BN comparisons and evaluations.

For each measurement, at least three objects are needed: a solid, neutral object as a measuring instrument and two objects for comparison. This system of three spatial dimensions per object has a common time. All measurements are based on orbits relative to the firmament and coincidences of the spatial coordinates of the objects after rotations with π and provide the energy of the system. These results in the most suitable coordinate system with dimensions of powers of π in which the radius is curved, as are the longitude and latitude. Polynomials P(2π) correspond to neutral objects, in addition to P(π) for charge, spin, isospin. In addition, it is assumed that the cosmos consists of a single type of particle with a speed of light c. Normalizing to the rest mass of the electron, the neutrinos are ντ=π, νμ=1, and νe=1/π. The energy of an electron is: Ee=gfreq(π)+1−1/π. An algorithm is derived from a Christoffel symbol and similar to a lattice gauge calculation. It provides exact residual masses for neutrons, protons, muons, tauons, quarks u, d, and pions. The theory can be applied to the inner planetary system and the cosmos and explains the hierarchy problem.

The social, environmental and scientific significance of the urban climate has shaped a research stream at Universitat Jaume I’s Climate Lab. Previous research has found higher temperature trends at urban observatories. This study examines in depth the features of the urban environment and the thermal behaviour of land use and land cover and the changes which have taken place. The CORINE Land Cover database was used to delimit the primary Land Use Land Cover (LULC) and its changes between 1990 and 2018. Once this had been established, land surface temperatures (LSTs) between 1985 and 2023 were retrieved from the Landsat database available on the Climate Engine website. Analysis of the primary land cover showed the greatest thermal increase in artificial surfaces, especially in industrial, commercial and transport units which are common on their outskirts. These areas also have the highest temperatures on a monthly basis with the biggest differences occurring in the summer. The results are less clear for urban fabrics and agricultural areas due to their diversity and complexity. The study concludes that urban growth and its commercial and industrial environment has an undeniable effect on the temperatures recorded by urban observatories and enables better decision-making when setting up weather stations for a more detailed time study of the urban heat island (UHI).

When earthquake damage occurs, it is extremely important to quickly identify the presence or absence of such damage. In this study, we developed and evaluated a new remote sensing index to automatically determine "damage" by image analysis when damage greater than partial destruction of a house by an earthquake or tsunami occurs. The accuracy of the houses estimated as "damaged" was high and sufficient for the initial assessment of damage. The results of this research are expected to be utilized for more rapid damage assessment after a disaster.

ACL injuries present significant challenges in sports medicine, particularly in restoring knee joint function and preventing further complications like subsequent injuries or post-traumatic osteoarthritis. Traditional rehabilitation techniques, which often rely on pre-planned exercises and standardized assessments, may not adequately prepare athletes for the unpredictable nature of sports. This paper argues for a comprehensive approach that recognizes ACL injuries as failures of complex nonlinear systems rather than merely biomechanical deficiencies. The complexity of ACL injuries involves a dynamic interplay of individual skills, team dynamics, and external conditions. Traditional rehabilitation approaches that focus solely on biomechanical factors overlook the critical role of interpersonal dynamics and sensorimotor control. The brain’s integration of sensory information to form a comprehensive environmental representation is vital for motor actions. Disruptions in this sensorimotor feedback loop can impair motor control, increasing the risk of ACL injuries. Effective rehabilitation should enhance sensory integration and feedback mechanisms, improving motor control and reducing re-injury risk. Addressing proprioceptive deficits and arthrogenic muscle inhibition (AMI) is crucial for successful rehabilitation. Neurocognitive training, focusing on enhancing decision-making and motor responses, is essential for sports performance and injury prevention. Techniques like neurofeedback training can optimize brain activity patterns, leading to better motor responses and adaptation to dynamic environments. Psychological factors such as motivation, anxiety, and fear significantly influence recovery. Integrating psychological support into rehabilitation programs can address these factors, leading to more comprehensive and effective recovery strategies. Innovative rehabilitation approaches, including EMG biofeedback, motor imagery, and virtual reality-based training, can enhance reactive motor planning and adaptability, preparing athletes for the demands of their sport. The future of ACL rehabilitation lies in embracing the complexity of the sensorimotor system and developing multidisciplinary interventions that promote beneficial neuroplasticity. Advances in neuroscience and technology, such as artificial intelligence, can lead to more personalized and effective rehabilitation programs. Recognizing and addressing the multifaceted nature of ACL injuries can significantly improve rehabilitation outcomes, ensuring athletes can safely and effectively return to their sports.

An atomic matter stores its motion information which is contained in velocity-induced shifts in the atomic energy levels. For redshifts less than or of the order of $\frac{1}{137}$, the velocity-dependent shifts of the energy levels of hydrogen atoms are calculated analytically. These velocity-dependent energy shifts lead to the changes of radiative transition wavelengths and doublet splittings of the quasar spectral lines. These spectral variations can be described by the effective value of the fine structure constant depended on the radiative source velocities. In the expanding Universe, these velocities are variable in time and space. It leads to the change of the effective value of the fine structure constant with the cosmological epochs, whereas the fundamental value of the fine structure constant does not.

This review focuses on social play, a complex behavior that is often difficult to categorize. Although play has been typically associated with positive emotions, emerging research indicates that it relates to various emotional systems, ranging from attachment to conflict. Play oscillates between compe-tition and cooperation, and includes a spectrum in-between, thus quantitatively identifying and demonstrating the emotional nature of play remains challenging. We examined some examples from human and non-human animal studies and explored the emotional and neuro-hormonal systems involved in play. We considered ethological data possibly indicating the emotional states underlying play and we focused on the cooperative and competitive elements of play. We investigated the relationship between play and affiliative/aggressive behaviours, the communicative meaning of play signals (especially primate play faces), and the motor and possibly emotional contagion func-tion of rapid motor mimicry during play. Of all the literature on play, this review selects and com-bines the studies in an innovative way, to present the methods, tools, and evidence indicative of the emotional nature underlying play, which is much more complex than previously thought.

This study investigates the impact of AI regulations and adoption on labor markets and employment in the USA. In order to achieve the study's objective, a robust econometric approach was employed using Principal Components Analysis (PCA) to address multicollinearity among the variables. The regression analysis incorporated principal components representing overall AI adoption and education (PC1), AI innovation and academic output (PC2), economic growth (PC3), and unemployment and AI strategies (PC4). The analysis utilized heteroskedasticity-robust standard errors to ensure reliable coefficient estimates and tested for stationarity to confirm the stability of the time series data. Data were collected from 2010 to 2022, encompassing key AI-related and economic indicators. The results reveal that higher AI adoption and education levels initially lead to job displacement, negatively impacting labor market metrics. Similarly, AI innovation and economic growth driven by AI do not immediately translate into job creation, reflecting transitional challenges. However, the strategic implementation of AI significantly mitigates these adverse effects and enhances employment conditions. The study finds that comprehensive AI strategies, robust regulatory frameworks, and effective workforce retraining and upskilling programs are essential for supporting labor market stability and promoting employment growth. Based on the findings, the study recommended that there is the need to develop and implement comprehensive AI strategies that include robust regulatory frameworks to support workforce transitions. Policies should focus on retraining and upskilling programs to help displaced workers adapt to new AI-driven roles.

This paper presents Double Database Decomposition Framework, which integrates vertical and horizontal table decompositions to minimize data leakage due to intrusions. Vertical decompositions segregate sensitive attributes, while horizontal ones partition data based on user access patterns. In this way, the framework ensures that users access only the data necessary for their operations, adhering to the principle of least privilege. This double decomposition approach improves the robustness of the original database against impersonation attacks and limits the blast radius of potential intrusions. Furthermore, the framework significantly mitigates the risks associated with data breaches by confining unauthorized access to specific data subsets and restricting the exposure of sensitive information. Performance analysis highlights the trade-offs between robustness and overhead offered by distinct allocation strategies of the output of the decompositions to, among other, physical machines, virtual machines, and containers, to balance security and resource efficiency. We present a case study in a healthcare environment that confirms both the effectiveness of the framework and its applicability in complex systems where data security is paramount. By integrating advanced security measures and optimising data access, the framework results in a scalable and adaptable solution for enhancing database security and performance in various domains.

This paper presents the results of theoretical and applied research that employs methodological experimentation in a project for the protection and integrated transformation of the downtown area of the city of Sharjah in the United Arab Emirates. The particular nature of this case relates to two factors: a) strategies and policies to protect and enhance the cultural heritage as a driver for tourism and the cultural development of the entire Emirate, from archaeological goods to the urban fabrics in historical centres (Sharjah and Korfakkhan), in contrast to the surrounding emirates; and b) the client’s request to define guidelines to manage the urban cultural heritage during a time of transition while awaiting the results of UNESCO candidacy. This unique fact denotes a sensitivity and long-term policies regarding the cultural heritage which view the recovery of the urban historical heritage, assuming the cultural component as the fourth dimension of sustainability. The methodological approach, the results of the research, and design are organized on the dual urban and building scale to understand the structure and elements of the historical centre. The actions and interventions are differentiated with respect to urban fabric, building, archaeological good, and landscape and translate the strategies of the guidelines into short-/mid-/long-term design actions.

The art of dyeing textiles has a long history, as natural dyes have been used since prehis-toric times. With the development of synthetic dyes in the 19th century, the focus shifted from natural to synthetic dyes due to their superior properties. Recently, however, interest in natural dyes has increased again due to environmental and health concerns. Among industrial dyes, heterocyclic dyes, especially azo dyes, are of great importance due to their color brilliance and fastness. This review examines the synthesis, application and analysis of azo dyes, especially heterocyclic dyes. It deals with monoazo, diazo and polyazo dyes and highlights their structures, synthesis methods and fastness properties. In addition, the ecological impact of azo dyes and practical solutions for their synthesis and application are discussed.

This paper provides a retrospective analysis of published COVID-19 data and focuses on Costa Rica as an example of data analysis and validation in developing countries. The COVID-19 outbreak has highlighted the importance of robust health systems and adequate resources to test and manage diseases. However, due to limited resources and excessive burdens, developing countries face unique challenges in this regard. This makes it difficult to conduct widespread testing. The lack of testing kits, laboratory facilities, trained personnel, and the necessary infrastructure is a significant barrier. Additionally, logistical issues such as transportation and distribution of testing kits in remote areas further complicate the situation. On the other hand, universal healthcare in developing countries, despite the lack of extensive testing, can still provide reliable data on the spread and impact of the disease. Universal healthcare can lead to better records keeping and data collection, as all individuals, regardless of their economic status, have access to medical services. This inclusivity ensures a more comprehensive and representative data set. Therefore, while testing is an important tool in managing the pandemic, universal healthcare systems can still provide valuable insights in its absence. This description can be applied to the Costa Rica COVID-19 scenario: lack of resources for an adequate testing methodology but a universal healthcare system that ensures inclusivity. In this work, we show that the number of active cases of COVID-19 for Costa Rica does not provide reliable information that can be used to adjust the parameters of a mathematical model, so the effectiveness of the contention measures cannot be precisely stated. However, the number of hospitalizations is more reliable in the centralized and universal healthcare system, but quantifying the effectiveness of such measures requires a different set of tools. Traditionally, most pandemic data analysis is based on the estimation of the Rt number based on the number of active cases. We propose a methodology based on digital signal processing principles and elementary differential calculus. To our knowledge, these kinds of methodologies have not been of widespread use in pandemic data analysis, and, as we show, they may provide a valuable descriptive analysis of pandemic tendencies and they are an accessible, fast and reliable decision-making tool.

Divulsuperbac (DSB) is a Service Learning project targeted at the educational community. It started in 2019 and has engaged Biology degree students in a Microbiology-focused initiative in the Valencian Community (Spain), for four academic years. The DSB project comprises various outreach activities intending to raise awareness of the superbugs' threat to pre-university students. We created an exhibition that contained 14 infographics on the topic of antimicrobial resistance which was presented every week in high schools. The infographics were presented by university students, who explained the problem to the pre-university students and subsequently assessed their comprehension of the topic. The surveys conducted yielded satisfactory results, which motivated us to extend this project to other regions of the world.

Human epidermal growth factor receptor 2 (HER2), a targetable transmembrane glycoprotein receptor of the epidermal growth factor receptor (EGFR) family, plays a crucial role in cell proliferation, survival, and differentiation. Aberrant HER2 signaling is implicated in various cancers, particularly breast and Gastric cancer, where HER2 overexpression or amplification correlates with aggressive tumor behavior and poor prognosis. Her2 activating mutations contribute to accelerated tumorigenesis and metastasis. This review provides an overview of HER2 biology, signaling pathways, mechanisms of dysregulation, diagnostic approaches, and therapeutic strategies targeting HER2 in cancer. Understanding the intricate details of HER2 regulation is essential for developing effective targeted therapies and improving patient outcomes.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

Developing countries, including Small Island Developing States (SIDS) and Least Developed Countries (LDCs), are exceptionally vulnerable to climate change due to their distinct geographical and environmental characteristics. The impacts of climate change extend across their ecosystems, water resources, and vital economic sectors, such as food systems, health, education, and energy. Escalating sea levels and heightened salinity levels imperil freshwater reserves, while warmer ocean temperatures and acidification disrupt water demand, tourism, health services, and fisheries. Furthermore, developing countries bear the brunt of water shortages, flooding, and deteriorating water quality. Alarmingly, nearly two-thirds of the global population experience severe water scarcity for at least one month annually, with the majority residing in developing countries. By 2025, half of the world's population could inhabit areas grappling with water scarcity, predominantly concentrated in developing nations. In this context, innovative adaptation strategies, notably transformative wastewater projects, are imperative for fostering sustainability and resilience in these regions. However, significant barriers impede progress, creating a stark disparity between current financing capabilities and the imperative to fund water security initiatives. Compounding these challenges, developing countries find themselves ensnared in a burgeoning debt crisis, marked by escalating interest rates and inflation, which threatens to impede developmental progress and curtail investments in adaptation and mitigation endeavors. Consequently, there arises a critical necessity to harness innovative financial mechanisms to transform these debts into assets that facilitate climate action. This paper meticulously explores the potential of debt-for-climate swaps as a catalyst for advancing transformative wastewater projects, with a focused lens on their strategic deployment to underpin critical initiatives. Through rigorous examination of case studies and empirical evidence, the paper elucidates how debt-for-climate swaps can effectively bolster sustainable wastewater management systems in developing countries. Furthermore, it delineates best practices for leveraging these mechanisms to fortify environmental sustainability and resilience. Additionally, the paper delineates the roles and responsibilities of key stakeholders, including governments, policymakers, the private sector, communities, and climate financial institutions. By amalgamating theoretical insights with tangible examples, this paper furnishes a comprehensive framework for harnessing debt-for-climate swaps to enhance water security and resilience in developing countries. It endeavors to furnish actionable strategies for policymakers, practitioners, and stakeholders to navigate the complex terrain of climate change and engender sustainable development.

Influenza viruses pose a continuing public health challenge due to their high mutation rate and the need for annual vaccine updates. A recent advancement in nanoparticle-based vaccine technology for developing a universal influenza vaccine. We focused on four key studies investigating the use of lipid nanoparticles (LNPs), nanoparticle immunogens, and multivalent epitope-based nanoparticles. The studies demonstrated promising results. The single-dose mRNA -LNP vaccine targeting five influenza B virus antigens effectively protected mice from a broad range of strain. Additionally, nanoparticle immunogens designed to display multiple influenza virus strains induced broad protective immunity in mice. Furthermore, LNPs enhanced the efficacy of mRNA and protein subunit vaccines by stimulating robust T follicular helper cell and humoral responses. Finally, an intranasal multivalent epitope-based nanoparticle vaccine offered broad protection against diverse influenza viruses. These findings highlight the potential of nanoparticle-based approaches for developing a universal influenza vaccine that offers long-lasting and broad-spectrum protection. Further research is needed to evaluate the safety and efficacy of these novel vaccines in humans.

The transportation industry is rapidly transitioning from Internal Combustion Engine (ICE) based vehicles to Electric Vehicles (EVs) to promote clean energy. However, large-scale adoption of EVs can compromise the reliability of the power grids by introducing large uncertainty in the demand. Demand response with a controlled charge scheduling strategy for EVs can mitigate such issues. In this paper, a deep reinforcement learning- based charge scheduling strategy is developed for individual EVs by considering user’s dynamic driving behavior and charging preferences. The temporal dynamics of user’s anxiety about charging the EV battery is rigorously addressed. A dynamic weight allocation technique is applied to continuously tune user’s priority for charging and cost-saving with respect to charging duration. The sequential charging control problem is formulated as a Markov decision process, and an episodic approach to the deep deterministic policy gradient (DDPG) algorithm with target policy smoothing and delayed policy update techniques is applied to develop the optimal charge scheduling strategy. A real-world dataset that captures user’s driving behavior, such as arrival time, departure time, and charging duration, is utilized in this study. The extensive simulation results reveal the effectiveness of the proposed algorithm in minimizing energy cost while satisfying user’s charging requirements.

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—Artificial Neural Network, Multiple Linear Regression, Support Vector Machine, and Regression Tree—are employed and compared for performance. Evaluation metrics such as Mean Absolute Deviation, Root Mean Square Error, Coefficient of Correlation, and Mean Absolute Percentage Error are used. After preprocessing 1030 samples, the dataset is split into 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

In this paper, a class of semi-linear elliptic equations involving Hardy-Sobolev critical exponents has been investigated. This problem comes from the consideration of standing waves in the anisotropic Schr\"{o}dinger equation, also it is very important in the field of hydrodynamics, glaciology, quantum field theory and statistical mechanics. By a detailed estimation for the extremum function and using Mountain Pass Lemma with $\left( PS\right) _{c}$ conditions, the existence of positive solutions has been obtained. On the other hand, by establishing Pohozaev-type identity and using the properties of Bessel function, the nonexistence of positive solution also has been obtained. These results are extensions of E. Jannelli's research (\cite[Theorem 1.A-1.C]{EJ}).

Keywords: conservation; cultural pluralism; environmental regulation; community-based natural resource management; Indigenous law

This paper discusses how Marsascala, a coastal town in Malta, has been urbanized over the years, with particular attention to the development of the tourist industry. In this regard, Bruce Young’s (1983) “general model of the process of ‘touristization’ and landscape change” is engaged with in conjunction with other conceptualisations of tourism and urban development. This paper contributes to the following sustainable development goals respectively, 3 (Good Health and Wellbeing); 8 (Decent Work and Economic Growth); 11 (Sustainable Cities and Communities); 14 (Life Below Water); and 15 (Life on Land), in particular with respect to quality of life, urbanization and respective social, environmental, and economic impacts, and through respective recommendations for a sustainable tourism in Marsascala and similar localities.

Urban rivers and waterfronts play a vital role in the ecological, social, and economic fabric of cities, providing not only aesthetic value but also serving as important ecosystems that contribute to the overall well-being of urban environments. However, many urban rivers, including the Beirut River in Lebanon, face challenges such as pollution, neglect, and the loss of recreational and ecological value due to urbanization and industrialization. According to the Lebanese MOE (Ministry of Environment), almost 70% of Lebanese rivers are polluted due to untreated waste management systems and which leads to undrinkable water and possible flooding disasters if not treated. This study addresses the problem of abandoned and degraded spaces along the Beirut River riverside through the application of urban acupuncture, a strategic approach that targets specific points in the urban environment to stimulate positive change and regeneration. The aim of the study is to assess the effectiveness of implementing small-scale ‘sensitive urban points’ known as urban acupuncture as the method for regenerating the Beirut River riverside, with the hypothesis that targeted interventions through Blue Urban Acupuncture or Aqua-puncture- can lead to significant improvements in the ecological, social, and economic aspects of the area that re-connect the people of Beirut with its river. The methodology involves a multidisciplinary approach that integrates urban design, and community engagement, and analysing similar examples of different approaches through creating a comparative table. Data collection includes environmental assessments of water quality, biodiversity, and ecosystem health, as well as social surveys to understand community needs and perceptions. By applying urban acupuncture principles to the Beirut River riverside, this study seeks to demonstrate how strategic interventions can lead to the regeneration of abandoned spaces, improve the overall quality of the urban environment, and create sustainable and inclusive riverside landscapes that benefit both the environment and the community.

Light can interfere like a wave in a multi-path experiment, but it also has particle properties when interacting with matter. In a closer look, the interference pattern is created by many individual detections ("clicks"), which do not all occur simultaneously. An established assumption is therefore that a single light particle is capable of interfering with itself by superimposing all possibilities to get from the source to the detector. This is in stark contrast to our experience with macroscopic objects, which always "decide" in favour of only one of the alternative paths.In 2010, Jin et al. (Ref. 15) described an event-based model which allowed the authors to show that an interference pattern consisting of clicks can arise in a multi-path experiment even if photons, like macroscopic objects, only follow one path. To do this, photons must be able to transmit the information about their travelling duration to the detector and the detector must have a basic memory. A threshold function uses the message of several light quanta to decide whether a single detection is initiated, which is not the evidence for a single photon. In this "proof of principle", the authors do not claim that the detector function is a realistic one.In the present paper, this detector is replaced by a material consisting of ionizable molecules whose interaction with light takes place according to a modified version of Einstein's radiation laws. Based on this, a photon pair interaction mediated by matter takes place, which in a multi-path experiment generates an interference pattern of clicks at the detector, even if an arbitrary amount of time elapses between the individual detections. In the present model an interference pattern with minima that have less than half the intensity of the maxima is the result of a peculiar behaviour of the detector: In this case, the entirety of its electrons must be described as a unit (a “field”) with many equidistant energy levels. The results of a simulation are presented which enable the model to be tested in experiments.

This paper examines the sociological impact of religion in society across three dimensions. It defines religion through Durkheim's perspective, explores the socio-psychological effects based on Mehan, Wood, Merton, and Fanoz, and delves into religious symbolism drawing from Geertz and Bellah. Emphasizing moral values in religious beliefs, the paper argues that societies rely on religion for social order. Using the Azande tribe as a case study, it illustrates the subjective nature of reality and the link between religious rituals and community harmony. This paper addresses historical suppression during colonialism, discusses Fanon's insights, explores Geertz's symbolic interactionist perspective, and analyzes Civil Religion, highlighting its potential political misuse in the American context. Ultimately, it recognizes religion's influential role in shaping societal dynamics and human interpretations while acknowledging the importance of moral values and symbols.

Light can interfere like a wave in a multi-path experiment, but it also has particle properties when interacting with matter. In a closer look, the interference pattern is created by many individual detections ("clicks"), which do not all occur simultaneously. An established assumption is therefore that a single light particle is capable of interfering with itself by superimposing all possibilities to get from the source to the detector. This is in stark contrast to our experience with macroscopic objects, which always "decide" in favour of only one of the alternative paths.In 2010, Jin et al. (Ref. 15) described an event-based model which allowed the authors to show that an interference pattern consisting of clicks can arise in a multi-path experiment even if photons, like macroscopic objects, only follow one path. To do this, photons must be able to transmit the information about their travelling duration to the detector and the detector must have a basic memory. A threshold function uses the message of several light quanta to decide whether a single detection is initiated, which is not the evidence for a single photon. In this "proof of principle", the authors do not claim that the detector function is a realistic one.In the present paper, this detector is replaced by a material consisting of ionizable molecules whose interaction with light takes place according to a modified version of Einstein's radiation laws. Based on this, a photon pair interaction mediated by matter takes place, which in a multi-path experiment generates an interference pattern of clicks at the detector, even if an arbitrary amount of time elapses between the individual detections. In the present model an interference pattern with minima that have less than half the intensity of the maxima is the result of a peculiar behaviour of the detector: In this case, the entirety of its electrons must be described as a unit (a “field”) with many equidistant energy levels. The results of a simulation are presented which enable the model to be tested in experiments.

The ferrocell is an optical viewing device that interacts with a magnetic field and one or more light sources to produce a real-time visual light display. Although this device has been extensively studied by several researchers, a proper computer simulation replicating this optical effect has not yet been presented. Using numerical methods and the law of reflection, a computer simulation was developed that shows that the light patterns as seen in the ferrocell are primarily caused by light reflections off the ferrofluid nano-particles that self organize into needle-like filaments and align along magnetic flux lines in the presence of a magnetic field.

In recent years, there has been a surge of proposals for innovative approaches to public planning. It has become apparent that the traditional emphasis on "economic development" as the predominant objective of public policy is no longer adequate to promote a thriving society. Common Agricultural Policy in Europe faces challenges to adopt and implement the dynamic of culture. However, culture as a pillar of sustainable development is not a new topic. This concept has been incorporated into economic studies with increasing tension. The present study aims to measure scientific interest through published scientific studies and papers related to the contribution of culture in the context of European development programs and strategies. A total of (n=902) published documents during the period 1990-2023 from the Scopus database were explored to identify (n=27) appropriate publications for research analysis based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach and flow diagram. Bibliometric analyses using Vos Viewer (Center for Science and Technology Studies, Leiden University, Leiden, The Netherlands) and constructed term co-occurrence network plots based on bibliometric evidence were performed. The results show that there was no scientific interest. The systematic review and bibliometric analysis of the present paper could provide a valuable and seminal reference for the researchers of rural development and the relevant policymakers.

A computational model for analysing the tooth deflection of polymer gears is presented in this paper. Because polymer gears have less stiffness compared to metal gears, the proposed approach considers a comprehensive analysis to determine the most suitable numerical model, i.e., the number of teeth that should be modelled. The developed computational model has been evaluated on the spur gear pair, where the pinion made of POM has meshed with a support gear made of steel. Material properties were assigned with linear elastic characteristics for the gear, while the pinion was characterised by hyperelastic properties using POM material. Furthermore, a nonlubricated frictional contact between the gear and pinion was considered in the numerical computations. The computational results that were obtained were compared to the empirical results according to VDI 2736 guidelines. Here, the computational approach showed more accurate results due to the hyperelastic material characteristics of POM. However, VDI 2736 calculation showed the comparability with computational results with a slightly larger deviation at lower loads.

This experimental study probes the dynamic behaviour of a 3mm ABS sheet during positive mould vacuum-assisted thermoforming. In this process, the sheet undergoes large and fast deformations, caused by the applied vacuum and mechanical stretching by the mould. The objective is to elucidate the complexities of these large, rapid and non-isothermal deformations. The non-isothermal conditions are caused by the radiative heating of the sheet, convective heat loss to the surrounding air and conductive heat transfer from the sheet to the mould. Utilizing stereo Digital Image Correlation (DIC) in tandem with thermal imaging, the present study accurately maps the occurring displacement, strain and strain rate field in relation to real-time temperature variation of the material. The study progresses to observe the ABS material from the moment it contacts the mould until it conforms to a positive 250mm diameter semi-sphere cast aluminium mould. The DIC methods are validated by comparing thickness values derived from DIC’s principal strain directions to ultrasonic thickness gauge readings. This knowledge not only broadens the understanding of the thermo-mechanical behaviour of the material but also aids in optimizing process parameters for improved thickness uniformity in thermoformed products.

To identify the exchange coefficients of a nonlinear polynomial tricompartmental general system of α+β , we follow the procedure; firstly, the recommended solution is to introduce an adequate time t*>0 in a determinable manner. That is, after injecting quantity a into the main compartment, wait a moment for the exchange in the polynomial α+β order nonlinear general system to settle, and then compare this compartment to compartment 2 at t*. Secondly, applying the Taylor formula will linearize the system and identify the exchange coefficients. Finally, we will prove that the linearization method is stable.

Photoacoustic imaging (PAI) is a cutting-edge biomedical imaging modality, providing detailed anatomical and functional information about the area beneath the skin surface. Its light energy deposition is such that PAI typically provides clear images of the skin with high signal-to-noise ratios. Specifically, the rich optical contrast of PAI allows biological information related to lesion growth, malignancy, treatment response, and prognosis to be seen. Given its significant advantages and emerging role in imaging skin lesions, we summarize and comment on representative studies of skin PAI, such as the guidance of skin cancer biopsies and surgical excisions, and the accurate diagnosis of psoriasis. We conclude with our insights about the clinical significance of skin PAI, showing how its use to identify biological characteristics in lesion microenvironments allows early diagnosis and prognosis of disease.

In CNS drug discovery, estimation of brain exposure of lead compounds is critical for their optimization. Compounds need to cross the blood-brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active mechanisms of transport and efflux transporters such as P-glycoproteins (P-gp) and breast cancer resistance protein (BCRP), which play an essential role in CNS penetration of small molecules. Several in vivo, in vitro and in silico methods are available to estimate human brain penetration. Preclinical species are used as in vivo models to understand the unbound brain exposure by deriving the Kp,uu parameter, the brain/plasma ratio of exposure corrected with the plasma and brain free fraction. MDCK-mdr1 (Madin Darby canine kidney cells transfected with the MDR1 gene encoding for the human P-gp) assay is the most commonly used in vitro assay to estimate compound permeability and human efflux. The in silico methods to predict brain exposure, such as CNS MPO, CNS BBB scores and various Machine Learning models, help save costs and speed up compound discovery and optimization at all stages. These methods enable the screening of virtual compounds and building of a CNS penetrable compounds library as well as optimization of lead molecules for CNS penetration. Therefore, it is crucial to understand these methods' reliability and ability to predict CNS penetration. We review the in silico, in vitro and in vivo data and their correlation with each other and assess published in silico approaches to predict the BBB penetrability of compounds.

Literature review indicates that the basic microstructure of Ti-6Al-4V is bimodal which consist of two phases: α + β, and it occurs after the fabrication in conventional methods such as casting, plastic forming or machining processes. After the additive manufacturing methods, there could be significant changes in the microstructure. Due to the rapid heat exchange during heat-treatment process, bimodal microstructure transforms into lamellar microstructure which consists of two phases α′ + β. Despite applying optimum printing parameters, 3D-printed products exhibit typical surface defects and discontinuities. Recently, two of the primary technologies for the improvement of surface layer properties of titanium alloys are shot peening and electropolishing. Literature review signalizes that shot peening and electropolishing processes have positive impact on the corrosion behaviour, mechanical properties and the condition of the surface layer of titanium alloy. Furthermore, there is a lack of studies about combining shot peening and electropolishing in one hybrid process on titanium alloys, which could synthesize the benefits of both processes. The scope of future investigation is also included in this review.

The Woka-Cuona rift zone, situated in the easternmost portion of southern Tibet, displays complex geological conditions. It consists of three distinct grabens or half grabens, spanning the Himalayan and Gangdese terranes from south to north. Examining the distribution patterns and genetic mechanism of the geothermal system in the Woka-Cuona rift zone holds significant guidance for studying the genetic mechanisms of geothermal systems and the production and utilization of ge-othermal resources in the rift zones of southern Tibet. Based on the geological conditions of the study area, the mechanism of rift formation and the distribution characteristics of geothermal water in the three distinct grabens were analyzed through data collection, ground surveys, geochemical analysis, and isotope analysis. Alongside topographic, drainage, and structural features, the Woka-Cuona rift zone was subdivided into four zones from north to south - Woka, Qiongduojiang, northern Cuona, and southern Cuona grabens. Additionally, the study investigated the geochemical char-acteristics, recharge sources, and water-rock interactions of geothermal water in different zones, providing a scientific foundation for subsequent geothermal energy production.

The purpose of the research is to investigate the unique solvability of a nonlocal boundary value problem for a loaded equation of parabolic-hyperbolic type in a special domain. Methods: Using representations of a general regular solution, the existence and uniqueness of the problem posed is proved. Results: A new method for proving the unique solvability of nonlocal boundary value problems for one loaded equation of mixed type in a special domain has been formed and developed. Conclusions: The obtained results make it possible to solve some problems of genetics, immunology, transonic gas dynamics and thermal physics, which are associated with displacement.

This research explores the transformation of learning methodologies from traditional to modern approaches, focusing on the development of a gamified educational application with an Artificial Intelligence (AI) mentor for secondary school graduates preparing for Croatian state graduation exams. Traditionally, education has been marked by a passive student role and teacher-centric transmission of knowledge. In contrast, modern learning tools, such as gamified applications, introduce interactive and engaging elements. The purpose of this study is to identify user-centric learning approaches through a comprehensive survey of graduating students, assessing their readiness for exams, learning styles, and preferences. The research explores issues such as financial burdens on parents for preparatory courses and copyright concerns with external educational materials. In summary, the learning application should prioritise sustained knowledge retention, motivation, and stress reduction, offering an alternative to external preparatory courses. Conclusions underscore the importance of fostering an engaging learning environment, incorporating teacher-like elements, and aligning with user expectations. As digital technology positively impacts the learning experience, understanding user preferences is crucial in designing educational apps that cater to learners' needs. The study provides insights for developing a gamified learning application that not only prepares students for exams but also enhances long-term knowledge acquisition and engagement.

Query reformulation is a fundamental task in information retrieval systems aimed at improving the accuracy and relevance of search results. This abstract presents a query reformulation approach that utilizes a domain-specific ontology to enhance the effectiveness of query reformulation. The use of ontology enables a more precise understanding of the domain and its associated concepts, leading to more accurate query interpretation and reformulation. The proposed approach consists of two main steps: ontology-based query interpretation and ontology-guided query expansion. The proposed approach use Power thesaurus tool that perform sentiment analysis for suggestion of the synonyms of the query words to improve the accuracy of the retrieved results. The proposed methodology gives high accuracy as compared to the previous approaches that used Wordnet for the suggestions of synonyms. The proposed approach demonstrates the potential of utilizing ontologies in query reformulation, offering a more accurate and context-aware search experience in domain-specific information retrieval systems.

The study explores the value of indigenous knowledge in mitigating and adapting to the effects of climate change in rural communities of Uganda. It provides an overview of how rural farmers use traditional and indigenous knowledge to predict weather changes to inform their adaptation and mitigation plans. The study intended to investigate how climate change effects were managed by the rural communities of Bakiga using indigenous knowledge and practices. Specifically, it sought to establish how local farmers’ innovations and practices were used to mitigate the effects of climate change and enhance food security. A case study research design with triangulation of data sources was employed. The study was conducted in Rukiga District, southwestern Uganda, where rural farmers were interviewed to explore their practices for reducing the effects of climate change to enhance food security. Thematic data analysis was used to get participants’ experiences and views regarding indigenous knowledge and effects of climate change mitigation. The findings indicate that rural farmers have indigenous practices of mitigating the effects climate change and contributing to the Sustainable Development Goals (SDGs).

The construction business environment is becoming competitive and intense, and Business Models (BMs) are receiving considerable attention as potential sources of sustainable survival and growth. Converse to the linear economy model that industry follows, the Circular Economy (CE) has the potential to create a sustainable construction industry, while creating a sustainable business environment in construction organisations. Nevertheless, in traditional BMs, value is often concentrated on the financial value for the stakeholders; in Circular Economy Business Models (CEBMs), value is seen more broadly by considering a comprehensive range of value chain partners, the environment, and the society. Hence, implementing CE in the construction industry requires reforms in each value chain. Thus, the aim of this paper is to develop a conceptual model for construction organisations' adoption of CEBMs for their organisations. To accomplish the aim, the objectives of this paper are twofold: (i) to define the concept of CEBMs, and (ii) to map the Conceptual CEBMC (Circular Economy Business Model Canvas) as applicable in the construction organisational context. This research adopted a systematic document review, including a content analysis of selected papers. The study contributes significantly by proposing a CEBMC, in which construction organisations could successfully transform their businesses from linear to circular environments.

Cytokinesis, as the last stage of the cell division cycle, is a tightly controlled process amongst all eukaryotes, and defective division can lead to severe cellular consequences, and is implicated in serious human diseases and conditions such as cancer. Both mammalian cells and the fission yeast Schizosaccharomyces pombe use binary fission to divide into two equal sized daughter cells. Similar to mammalian cells, in S. pombe such cytokinetic division is driven by the assembly of an actin-myosin contractile ring (ACR) at the cell equator between the two cell tips. The ACR is composed of a complex network of membrane scaffold proteins, actin filaments, myosin motors, and other cytokinesis regulators. Contraction of the ACR leads to the formation of a cleavage furrow which is severed by the endosomal sorting complex required for transport (ESCRT) proteins leading to the final cell separation during the last stage of cytokinesis, abscission. This review describes recent findings defining the three phases to establish cytokinesis in S. pombe: ACR assembly, ACR constriction, and its coordination with septation. Collectively, we provide an overview of current understanding of the mechanisms regulating the ACR-mediated cytokinesis in S. pombe with a potential role of ESCRT proteins in this process.

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, Significantly avoid power waste. As a result, time series forecasting in the field of power can bring significant benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous reserches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model’s ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments.

The key to building a strong bridge country lies in talent, and the foundation lies in education. The cultivation of bridge engineering talents is related to the future and destiny of building a strong bridge country. This course adopts the educational philosophy of "holographic teaching method" centered on the comprehensive development of students, cultivating "engineering thinking" guided by engineering education certification, enhancing "abilities and qualities" through professional knowledge, reconstructing the course knowledge content system through engineering philosophy thinking, and implementing the ideological and political construction of the course in a multi-level unified manner of "explicit + implicit". Based on restructuring the course content system, we will strengthen the construction of diversified course resources, integrate theory with practice throughout the entire process of the course, practice the concept of "bridge architects + bridge engineers" parallel development, and cultivate cross-border and compound talents with comprehensive qualities of "engineering + art + humanities".

The classification and summarization of online reviews using machine learning and deep learningbased approaches are the subject of this systematic literature review. Consumers, companies, and researchers can now find vital information from online reviews. Automatically categorizing and summarizing these reviews can offer useful information and facilitate decision-making. The purpose of this study is to offer a thorough overview of the current methodology, approaches, and developments in this area. The review concentrates on machine learning-based methods for categorization and summarizing tasks, highlighting their advantages, drawbacks, and new developments. This systematic review provides insights into the most recent methodologies and indicates prospective directions for further investigation by synthesizing the data from a wide range of pertinent research publications

Due to the advancement and availability of internet in present age, device tend to become wireless as well. Mobile ad-hoc network is a wireless environment that consist of advanced agility of nodes and does not contain on any central administrative authority. As it does not contain any central administrative authority so being wireless environment, the network is vulnerable to security threats and suffers in performance as well. Security of MANET is the core issue in this era, meanwhile the data is sent over untrustworthy network. Several techniques were proposed to secure data over Mobile Adhoc Network though cryptography that is one of the most reliable techniques to protect data. In our proposed methodology a Secure Cryptographic technique for Mobile Adhoc Networks (SCTMA) we proposed A Collusion attack prevention (CAP) scheme with ElGamal key generator along with DNA encryption Algorithm is used to make it more secure. The proposed scheme protects the network against collusion attacks and improves the performance as well. Performance analysis will be measured on the basis of key generation time, response time, encryption time and decryption time. The proposed scheme also protects the network against collusion attacks.

One of the most popular developments today that potentially address security issues with cloud computing is the use of blockchain. Blockchain is a decentralized data management platform that offers data integrity, security, and anonymity without the involvement of a third party. This work presents a comprehensive study of blockchain-based data storage system and their security concerns with their solutions to address these concerns. We provide a thorough overview of how blockchain is used in the cloud computing model to provide security services, and we examine the research trends of blockchain-related technologies in the current cloud computing models. As we examine, we also quickly look into how cloud computing may impact blockchain, particularly the performance enhancements that cloud computing may offer. This paper performs a review of the cutting-edge investigations of the blockchain-based system with a selfish-mining attack and the primary focus on IPFS. We also note their challenges, open issues, and future directions. The findings demonstrate that blockchain offers a productive platform in this area. However, one of the most significant problems that still need more research is security-related issues. The article offers a platform for future research and action as well.

Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios

Over the last few decades, there is a remarkable increase in air traffic worldwide; however, despite of rapid technological development and improved airport infrastructure, there is still an increasing number of aviation accidents and fatalities. In order to meet safety demands of Air Traffic Control (ATC), Machine Learning (ML) based aircraft conflict detection techniques are focused to predict events before they come to reality. This research presents a systematic literature review (SLR) about the applications of ML techniques in safety prediction of ATC system. Initially, the existing aircraft conflict prediction models are classified into three broad categories; logical, geometric and probabilistic. A formal process of SLR is then conducted which is initiated with preprocessing and effective searching. A series of filtering called title, abstract and objective-based are then applied to extract the most relevant articles. Detailed and critical reviews of selected articles are performed to identify challenges which are segregated into dataset, model and evaluation techniques, furthermore, the optimal solutions for each are proposed. Finally the matrices to map the identified challenged with their proposed solutions are constructed. This research serves as a foundation for future research and towards integration of ML based safety prediction techniques into modern ATC system.

WBAN is a blessing to mankind because it can monitor people's health and activities regardless of place or time. It brings feasibility in patients’ treatment. The main problems with WBAN, however, are related to the security and privacy of health data. To prevent data misuse, health information should be safeguarded. On the other hand, the medical professional needs to receive the patient's health information promptly. As security of health data impact human life greatly so secure an efficient method is required. Efficient cryptographic technique for data protection of wireless body area network is proposed. This research aims to provide data protection in WBAN to increase data security and efficient transmission. The suggested approach offers effective security with quick key generation, quick encryption, and secure data transmission across the network. The proposed methodology is considered to be secure and will consume less time. The proposed method further uses a less complex encryption algorithm. The less complex the approach is the higher performance it will provide in wireless network. The proposed methodology will prevent many attacks, which makes the proposed approach highly suitable for wireless body area network’s security.

Atomic force microscopes are used, beside their principal function as surface imaging tools, in surface manipulation and measurement of interfacial properties. In particular, they can be modified to measure lateral friction forces that occur during the sliding of the tip against the underlying substrate. However, the shape, size, and deformation of the tips profoundly affect the measurements in a manner that is difficult to predict. In this work, we investigate the contribution of these effect to the magnitude of the lateral forces during sliding. The surface substrate is chosen to be few-layer AB-stacked graphene surface whereas the tip is initially constructed from face-centered cubic gold. In order to separate the effect of deformation from shape, rigid tips of three different shapes were considered first, namely a cone, a pyramid and a hemisphere. The shape was seen to dictate all aspects of the interface during sliding, from temperature dependence to stick-slip behavior. Deformation was investigated next by comparing a rigid hemispherical tip to one of identical shape and size but with all but the top three layers of atoms free to move. The deformation, as also verified by an indentation analysis, occurs by means of the lower layers collapsing on the upper ones, thereby increasing the contact area. This collapse mitigates the friction force and decreases it with respect to the rigid tip for the same vertical distance. Finally, the size effect is studied by means of calculating the friction forces for a much larger hemispherical tip whose atoms are free to move. In this case, the deformation is found to be much smaller but the stick-slip behavior is much more clearly seen.

Machine learning models facilitate the search for non-linear relationships when modeling hydrological processes, but they are equally effective for automation at the data preparation stage. The tasks whose automation was analyzed consisted in estimating changes in the roughness of a mountain streambed and the extent of floods from images. The Segment Anything Model (SAM) developed this year by Meta was used for this purpose. Images from many years from the Wielka Puszcza mountain stream located in the Polish Carpathians were used as the only input data. The model was not additionally trained for the described tasks. The model can be run in several modes, but the two most appropriate were used in this study. The first one is available in the form of a web application, while the second one is available in the form of a Jupyter notebook run in the Google Colab environment. Both methods do not require specialized knowledge and can be used by virtually any hydrologist. In the roughness estimation task, the average Intersection over Union (IoU) ranges from 0.55 for grass to 0.82 for shrubs/trees. Ultimately, it was possible to estimate the roughness coefficient of the mountain streambed between 0.027 and 0.059 based solely on image data. In the task of estimation of the flood extent, when selecting appropriate images, one can expect IoU at the level of at least 0.94, which seems to be an excellent result considering that SAM is a general-purpose segmentation model. It can therefore be concluded that SAM model can be a useful tool for a hydrologist.

In this work, quiescence is added to the Susceptible-Infectious-Recovered (SIR) model with demography. In order to investigate consequences of quiescence in the infection process in more depth, we use stochastic simulations on the stochastic version of model that we built. This method provides a more accurate picture of the dynamics of infectious diseases by taking into consideration the inherent randomness in the disease processes. We examine the effects of quiescence on the number of infected people using simulations. The results, presented in histograms depicting the distribution of infected individuals, reveal a notable trend: the mean number of infected individuals is higher when quiescence is incorporated into the dynamics. These finding emphasizes the dynamic influence of quiescence on infectious disease spread. The higher mean number of infections during periods of quiescence highlights the need for public health strategies that are flexible enough to focused interventions during these times to reduce the possibility of an increase in infections.

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty for the sector are expected to increase in the future, increasing the need for improved price modeling. With the emergence of machine learning (ML) algorithms, novel tools are now available to enhance the modeling of agricultural commodity prices. This research uses both univariate and multivariate ML techniques to perform probabilistic price prediction modelling for the Canadian beef industry, taking into account beef production, commodity market, and international trade features to enhance accuracy. We apply multivariate algorithms by using support vector regression (SVR), random forest (RF), and adaboost (AB) and univariate algorithms by using autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX). We apply these models to monthly fed steer price data between January 2005 and September 2023 and compare predicted prices with observed prices using several validation metrics. The outcomes indicate that both random forest (RF) and adaboost (AB) in multivariate modeling show superior performance in accurately predicting Alberta fed steer prices in comparison to other algorithms. To better account for the variance of the best model performance, we subsequently adopted a probabilistic approach by considering uncertainty in our best-selected ML model. The beef industry can use these improved price models to minimize resource waste and inefficiency in the sector and improve the long-term sustainability prospects for beef producers in Canada.

Degenerative mitral valve disease (DMVD) consists of progressive degeneration of the mitral valve that can occur either in isolation or in association with the tricuspid valve, leading to incomplete coaptation of its leaflets and subsequent valve regurgitation. It's the most common cardiac condition in small animals, accounting for about 75% of cases of heart disease in dogs, mainly affecting elderly small-breed dogs. Breeds with a genetic predisposition such as Cavalier King Charles Spaniels and Bull Terriers tend to develop the disease early. It can be classified into 4 stages: A, B, C, and D, with B having subdivisions (B1 and B2). Animals in stages A, B1, and B2 are asymptomatic, showing clinical signs only from stage C onwards. The report details the case of a 13-year-old Fox Terrier diagnosed at stage B2 of the disease, progressing to stage C within two years. A cardiologist conducted annual monitoring.

Bambara groundnut (BG) (Vigna subterranean L. verdc) is a highly nutritious and economically important leguminous crop of African origin that forms symbiotic relationships nitrogen fixing rhizobia, but it has a low yield. Currently, there is paucity of information on effective rhizobium inoculant, (to improve the yield), for BG which are cheaper and safer alternatives to chemical fertilizers. Indigenous rhizobia strains were trapped from 54 farm soils collected at three Nigerian states (Niger, Kaduna and Kano). Six selected rhizobium strains (Bradhyrhizobium spp) were identified using 16S rDNA sequencing, applied as inoculant in field experiment using completely randomized block design and compared with USDA110 strain (Bradhyrhizobim japonicum), urea fertilizer and un-inoculated plants on 2 selected BG varieties (TVSU1248 and TVSU 631). Nutrient utilization and nitrogen fixation in plants were determined at 6 weeks while leaf chlorophyll was determined forth-nightly. Seed yield was determined at maturity. The strains increased the %nitrogen (1.33±0.10 - ≥1.37±0.05), and %phosphorous (0.48±0.02 - ≥0.51±0.02), %nitrogen fixed (21.07±2.38 - ≥61.30±6.13), nodulation (72.50±21.075 - 154.00±23.79) and leaf chlorophyll were ≥49.74±1.47mg/L, ≥51.82±1.86mg/L and ≥49.65±1.56mg/L at 2nd, 4th and 6th week after planting respectively and the yield of BG (from average of 398.6 Kgha-1 to 1454.725 Kgha-1). Seed yield was highest (1869.85±273.68 Kgha-1) in BG variety TVSU1248 using BN5 strain. Indigenous Bambara-symbiotic strains significantly increased the seed yields, and plant nutrient, showing their potential as use for inoculant to improve the yield of Bambara groundnut.

A single Cancer immunotherapy has ushered in a transformative era in oncology, bringing forth unprecedented promise and opportunities. However, even with its significant breakthroughs, the domain faces the continual issue of treatment resistance. Such resistance not only restricts the broad effectiveness of these pioneering treatments but also highlights the imperative need for further research. Delving into the complex world of cancer immunotherapy resistance, we identify a range of mechanisms, from primary and secondary resistance to the notable effects of genetic and epigenetic variables, and the pivotal importance of the tumor microenvironment (TME). Additionally, we emphasize the need for innovative strategies to mitigate this resistance, including combination treatments, customized immune checkpoints, and instantaneous monitoring. By advocating for these cutting-edge methods, we foresee a direction that incorporates personalized healthcare, enhanced treatment options, and a steadfast dedication to the welfare of patients. Through a comprehensive and diverse approach, we aim to address the hurdles posed by resistance, elevating cancer immunotherapy as a symbol of hope for patients worldwide.

Antimicrobial resistance is one of the most important global health issues identified in recent decades. Different approaches have been used to establish the presence and abundance of antimicrobial resistance genes (ARGs) in the environment. In this study, we analyzed soil samples from Fildes Peninsula (King George Island, maritime Antarctica), exposed to different types of impact. The objective of the work was to identify ARGs in the samples, and evaluate if these genes were located in plasmids, using two different strategies. A metagenomic analysis was used to identify ARGs and if they were associated to plasmid sequences. The analysis showed that the site highly exposed to anthropogenic activity had a higher number of ARGs compared to other sites. A high percentage of those ARGs (19.4%), were located in plasmidic contigs. We also assessed replicon mobilization, using microbial communities from these soil samples as donors through an exogenous plasmid isolation method. In this case, we could recover plasmids with ARGs in a Tcr transconjugant clone. Although they could not be fully assembled, we could detect broad host range IncP1 and IncQ plasmid sequences. Our results indicate that sewage impacted soils could be a hotspot for ARGs spread into the Antarctic environment

The initiation of micro-cracks in concrete is primarily due to its brittleness, which causes severe impacts on the overall behavior of the concrete. However, incorporating fibers in concrete could significantly improve its mechanical properties. The current study demonstrates the influence of two types of fibers on the mechanical properties of concrete. Nylon Fiber Threads (NFT) and Nylon Fiber Strands (NFS) at a fiber content of 2.5 kg/m³ were incorporated in the concrete mix. Concrete with a compressive strength of 30 MPa and a water-cement ratio of 0.4 was produced to investigate the fresh and mechanical properties of conventional and nylon-fiber reinforced concrete (NFRC). The concrete prims and cylinders were tested for compressive, splitting, and flexural strength using ASTM standards (ASTM C-39, C-496, and D-790, respectively). It was found that NFT-reinforced samples achieved higher mechanical properties of concrete (13.05%) compared to NFS-reinforced samples. Test results showed that incorporation of nylon fibers (NF) in concrete decreased the compressive strength of cylindrical and cubical specimens by 0–10% and 5–15%, respectively. However, it increased the splitting tensile and flexural strengths by 0-15%, and 5-15% respectively. Furthermore, the incorporation of NF in concrete significantly improved the ductility and delayed the crack initiation in concrete.

Magnesium alloy stents have been extensively studied in the field of biodegradable metal stents due to their exceptional biocompatibility, biodegradability and excellent biomechanical properties. Nevertheless, the specific in vivo service environment causes magnesium alloy stents to degrade rapidly and fail to provide sufficient support for certain time. This paper presents an overview of the development history and protection strategies for magnesium alloy stents. Alloying, optimizing stent structure and preparing protective coating have improved the corrosion resistance of magnesium alloy stents. Based on the corrosion mechanism of magnesium alloy stents, as well as their deformation during use and environmental characteristics, we present some novel solution strategies aiming at reducing the corrosion rate of magnesium alloys and enhancing the comprehensive performance of magnesium alloy stents. These strategies include adapting protective coatings for the deformation of the stents, preparing rapid endothelialization coatings to enhance the service environment of the stent, and constructing coatings with self-healing functions. It is hoped that this review can help to understand the development of magnesium alloy cardiovascular stents and solve the problems related to magnesium alloy stents in clinical applications at the early implantation stage.

A study on the dependence of the thermal conductivity of the percolation network of carbon nanotubes (or CNTs) on the type and degree of functionalization has been carried out. The study was focused on the influence of the more commonly used -COOH, -OH and -СОNH2 groups. A non-linear dependence of the conductivity on the number of functional groups has been revealed. A small number of functional groups can improve conductivity, and a large one can impair it. We presuppose the existence of competing processes that increase thermal conductivity (changes in the CNT geometry, improvement of the contact between them) and enhance photon scattering (emergence of defects and scattering centers). The data can be used for controlling the thermophysical properties of the CNTs as well as determining the optimal degree of functionalization during the development of composites and nanodevices.

Due to an urgent need for reducing the fast progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of heat demand coverage of buildings at district and city level must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of heat demand, city planners, energy agencies seek a tool, which will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path-Analysis (LCPA) and Closest Facility (CF) allow to find the shortest and “cheapest” way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA and CF based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only few open-source data input, like street networks and building footprints are needed, the methods can be implemented in all communities of the country of Poland. In this paper one example of one community of Wroclaw county, called Siechnice and surroundings is presented.

The surprisingly high catalytic activity of gold has been known to the heterogeneous catalysis community since the mid-1980s. Significant effort has been directed towards improving the reactivity of these surfaces towards important industrial reactions. One such strategy is the introduction of small amounts of other metals to create Au-based surface alloys. In this work, we investigate the synergistic effect of Pt doping of the Au(111) surface towards decreasing the activation barrier of the methanol dehydrogenation elementary step, within first-principles density functional theory. To this aim we construct several models of \ce{Pt}-doped Au(111) surfaces, including a full \ce{Pt} overlayer and monolayer. The effect of \ce{Pt} surface doping is then investigated via the computation of adsorption energies of the various chemical species involved in the catalytic step, and the estimation of activation barriers of methanol dehydrogenation. Both electronic and strain effects induced by \ce{Pt} surface doping concur in substantially lowering the activation energy barrier of this important reaction elementary step. Morever, in the presence of preadsorbed atomic oxygen, \ce{Pt} surface doping can be used to reduce the activation energy for methanol dehydrogenation to as low as 0.1 eV.

Recent research has unveiled intriguing insights suggesting that the body's immune system may be implicated in Parkinson's disease (PD) development. Studies have observed disparities in pro-inflammatory and anti-inflammatory markers between PD patients and healthy individuals. This finding underscores the potential influence of immune system dysfunction in the genesis of this condition. A dysfunctional immune system can serve as a primary catalyst for systemic in-flammation in the body, which may contribute to the emergence of various brain disorders. The identification of several genes associated with PD, as well as their connection to neuroinflamma-tion, raises the likelihood of disease susceptibility. Moreover, advancing age and mitochondrial dysfunction can weaken the immune system, potentially implicating them in the onset of the dis-ease, particularly among older individuals. Compromised integrity of the blood-brain barrier could facilitate the immune system's access to brain tissue. This exposure may lead to encounters with native antigens or infections, potentially triggering an autoimmune response. Furthermore, there is mounting evidence supporting the notion that gut dysbiosis might represent an initial trigger for brain inflammation, ultimately promoting neurodegeneration. In this comprehensive review, we will delve into the numerous hypotheses surrounding the role of both innate and adaptive immunity in PD.

In air-breathing proton exchange membrane fuel cells (Air PEM FCs), high rate of water evaporation from the cathode might influence the resistance of the membrane electrode assembly (MEA), which is highly dependent on the water content of the Nafion membrane. We propose a dead-end hydrogen anode as a means of intermediate storage of water/humidity for self-humidification of the membrane. Such inflatable bag integrated with the PEM FC has a potential in blimp applications for anode self-humidification. A dynamic numerical water balance model, validated by experimental measurements, is derived to predict the effect of MEA configuration, membrane’s hydration state, and water transfer rate at the anode on MEA resistance and performance. The experimental setup included humidity measurements, polarization and electrochemical impedance spectroscopy tests to quantify the effect of membrane hydration on its resistance in a lightweight MEA integrated with an inflatable dead-end hydrogen storage bag. Varying current density (20 mA, 40 mA and 60 mA) and cathode humidity (20, 50 and 80%) were examined and compared with the numerical results. The validated model predicts that the hydration state of the membrane and water transfer rate at the anode can be increased by using a thin membrane and thicker gas diffusion layer.

The main objective of this paper is to assess pro-environmental behavior, climate change perception, anxiety, hope and despair in different political orientations. Specific aims included to validate all the instruments; to assess whether the factor structure of the scales were valid across political orientations; to evaluate their reliability; to assess differences concerning age, gender and political orientation; to know the variables that explain pro-environment behavior; and to evaluate the moderating role of climate change perception, despair and hope in the relationship between climate change anxiety and pro-environmental behavior. Confirmatory factor analysis (CFAs), multi-group CFAs (to measurement invariance), multiple linear regressions and moderations were performed. Results showed that pro-environment behavior and climate change hope achieved the four levels of invariance across different political orientations; climate change anxiety achieved the three first levels of invariance; climate change perception and climate change despair achieved configural invariance. Climate change anxiety personal experience, climate change perception total, reality and consequences present higher values in left political orientation that in right or center. Climate change anxiety variables contribute most to explaining pro-environmental behaviors. Hope, despair and climate change perception consequences moderate de relationship between climate change anxiety and pro-environment behavior. These results open up new avenues of investigation, namely, to understand why high levels of anxiety lead to more pro-environment al behaviors.

Facing the charging difficulties of free-floating shared electric vehicle and the high cost of mobile charging for single demand, this article proposes a collaborative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the minimization model of system charging total cost, seeking the optimal charging scheme and the number of mobile charging vehicles. The lower model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% compared with the pure fixed charging scheme and 14.9% compared with the pure mobile charging scheme

We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We design the generator of TOLGAN as a series of decoding layers where the map is applied between the layers. The generated TOL image is further enhanced through an AdaIN architecture. The discriminator of TOLGAN processes a generated image and its groundtruth TOL artwork image. TOLGAN is trained through a dataset composed of paired TOL artwork images and their TOL maps. We present a tool through which users can produce a TOL map by specifying and organizing the elements of TOL artworks. TOLGAN successfully generates a series of TOL images from the TOL map. We evaluate our approach using a quantitative way by estimating FID and ArtFID scores and a qualitative way by executing two user studies. Through these studies, we prove the excellence of our approach by comparing our results with those from several important existing works.

BiFeO3/La0.7Sr0.3MnO3 (BFO/LSMO) epitaxial heterostructures were successfully synthesized by pulsed laser deposition on (001)-oriented SrTiO3 single crystal substrates with Au top electrodes. Stable bipolar resistive switching characteristics regulated by ferroelectric polarization reversal was observed in the Au/BFO/LSMO heterostructures. The conduction mechanism was revealed to follow the Schottky emission model, and the Schottky barriers in high resistance and low resistance states were estimated based on temperature-dependent current–voltage curves. Further, the observed memristive behavior was interpreted via the modulation effect on the depletion region width and the Schottky barrier height caused by ferroelectric polarization reversal, combining with the oxygen vacancies migration near the BFO/LSMO interface.

Bacillus Calmette-Guérin (BCG) is the only widely used prophylactic tuberculosis (TB) vaccine that can prevent severe TB in infants. However, it provides poor protection in adults, and therefore, there in ongoing research on new TB vaccines and immunization strategies with more durable immune effects. The recombinant BCG and BCG prime-protein booster are two important vaccine strategies that have recently been developed based on BCG and could improve immune responses. In this study, three immune strategies based on four protective antigens, namely, ESAT-6, CFP-10, nPPE18, and nPstS1, were applied to construct recombinant rBCG-EPCP009, EPCP009 subunit protein, and BCG prime-EPCP009 booster vaccine candidates. The short- and long-term immune effects after vaccination in Balb/c mice were evaluated based on humoral immunity, cellular immunity, and the ability of spleen cells to inhibit in vitro mycobacterial growth. At 8 and 12 weeks after the initial immunization, splenocytes from mice inoculated with the BCG prime-EPCP009 protein booster secreted higher levels of PPD- and EPCP009-specific IFN-γ, IL-2, TNF-α, IL-17, GM-CSF, and IL-12 and had a higher IFN-γ⁺CD4⁺ TEM:IL-2⁺CD8⁺ TCM cell ratio than splenocytes from mice inoculated with the rBCG-EPCP009 and EPCP009 proteins. In addition, the EPCPE009-specific IgG2a/IgG1 ratio was slightly higher in the BCG prime-EPCP009 protein booster group than in the other two groups. The in vitro mycobacterial inhibition assay showed that the splenocytes of mice from the BCG prime-EPCP009 protein booster group exhibited stronger inhibition of Mycobacterium tuberculosis （M. tuberculosis）growth than the splenocytes of mice from the other two groups. These results indicate that the BCG prime-EPCP009 protein booster exhibited superior immunogenicity and M. tuberculosis growth inhibition to the parental BCG, rBCG-EPCP009, and EPCP009 proteins under in vitro conditions. Thus, the BCG prime-EPCP009 protein booster may be important for the development of a more effective adult TB vaccine.

Food allergies are a growing public health concern worldwide, especially in children and young adults. Allergen-specific IgE plays a central role in the pathogenesis of food allergies, but their titers poorly correlate with allergy development. Host immune systems yield allergen-specific immunoglobulin (Ig)A, IgE and IgG subclasses with low or high affinities and differential Fc N-glycosylation patterns that can affect the allergic reaction to food in multiple ways. High affinity IgE is required to induce strong mast cell activation eventually leading to allergic anaphylaxis while low affinity IgE can even inhibit the development of clinically relevant allergic symptoms. IgA and IgG antibodies can inhibit IgE-mediated mast cell activation through various mechanisms, thereby protecting IgE-positive individuals from allergy development. The production of IgE and IgG with differential allergenic potential seems to be affected by the signaling strength of individual B cell receptors, and by cytokines from T cells. This review provides an overview of the diversity of the B cell response and the diverse roles of antibodies in food allergy.

Methylglyoxal (MGO) is the major compound belonging to reactive carbonyl species (RCS) responsible for the generation of advanced glycation end products (AGEs). Its upregulation followed by deleterious effects at the cellular and systemic level is associated with metabolic disturbances (hyperglycemia/hyperinsulinemia/insulin resistance/hyperlipidemia/inflammatory processes/carbonyl stress/oxidative stress/hypoxia). Therefore, it is implicated in a variety of disorders including metabolic syndrome, diabetes mellitus and cardiovascular diseases. In this review an interplay between pathways leading to MGO generation and scavenging is addressed, in regard to this system’s impairment in pathology. The issues associated with mechanistic MGO involvement in pathological processes, as well as the discussion on its possible causative role in cardiometabolic diseases are enclosed. Finally, the main strategies aimed at MGO and its AGEs downregulation with respect to cardiometabolic disorders treatment are addressed. Potential glycation inhibitors and MGO scavengers are discussed, as well as the mechanisms of their action.

In Greece, as in most European countries, the heterogeneity of students leads to conflicts resulting in a general inability of the educational process to function. This review study is focused on modern issues affecting youth and school education procedures such as internet addiction, student behaviour expressed as bullying, and adolescent sexuality development. All sides agree that the most essential purposes of education are to emphasize the cultivation of interpersonal relationships, the development of sociability, and self-esteem, and the prevention. Teachers wanting to respond to the spirit of the times to rationalize the pedagogical process introduced the pedagogical contract as a school practice as a valuable tool in taking control of the class and gaining students' attendance. The teacher's positive attitude will create added value to their knowledge and experiences while helping them to integrate into society.

Coronary artery disease is the most prevalent cardiovascular disease, claiming millions of lives annually around the world. The current treatment includes surgically inserting a tubular construct called as a stent inside arteries to restore blood flow. However, due to lack of patient-specific design, the commercial products cannot be used with different vessel anatomies. In this review, we have summarized the drawbacks in existing commercial metal stents which face problems of restenosis and inflammatory responses, owing to the development of neointimal hyperplasia. Further, we have highlighted the fabrication of stents using biodegradable polymers, which can circumvent most of the existing limitations. In this regard, we elaborated on utilization of new fabrication methodologies based on additive manufacturing such as three-dimensional printing to design patient-specific stents. Finally, we have discussed on the functionalization of these stent surfaces with suitable bioactive molecules which can prove to enhance their properties in preventing thrombosis and better healing of injured blood vessel lining.

The objective of this was to evaluate the effects of organic fertilization on biomass production in Urochloa spp. pastures, and the soil biological and physical properties in the Colombian Amazon region. Three pastures crops (Urochloa decumbens, Urochloa humidicola and Urochloa brizantha) with 5 years old were examined in a hill landscape. A randomized complete blocks design covering an area of 5005 m2, with 1 m separation between units to minimize edge effects. Highly significant differences in Urochloa spp. biomass production were observed before and after organic fertilization (i.e., initial time: 992.19±97.66 kg DM ha-1 vs. final time: 1920.94±155.44 kg DM ha-1 vs.), but not between different fertilization treatments. The interaction between the treatment and time was significant for total density (p=0.0300) and richness (p=0.0300) of soil macrofauna. Furthermore, the interaction between treatment and soil depth was significant for bulk density (p=0.0187). The linear mixed effects analysis showed that soil bulk density and penetration resistance were lower in 0 - 10 cm depth than that observed in 10 - 20 depth and 20 - 30 cm depth. Overall, the application of organic fertilizers had a significant and positive effect on Urochloa spp. forage biomass and on the physical and biological properties of soils that had historically been affected by extensive livestock farming in a deforested hill landscape in the Colombian Amazon region.

The purpose of the investigation was to determine the relationship between rotary kiln liner loss and steel structure ovality. As measurement apparatus, a terrestrial laser scanner was used. The interior and exterior of the rotary kiln were measured. The primary focus object was inner-lining loss and the geometric characteristics of cylindrical shells. The research uncovered significant disparities in inner lining loss between sections. A correlation was found between ovality and elimination of inner lining. Due to the hypothesis of constant inner linig loss from the middle of the rotary kiln, the investigation found that the loss of brick lining was less than the value reported from the boreholes. The study offers significant information on maintenance and repair strategies for rotary kilns, which have the potential to increase their efficiency and useful life.

Carbon dots are carbon based nanoparticles, which have the characteristics of simple preparation process, photoluminescence, biocompatibility, adjustable surface function, water solubility, and low toxicity. They are widely used in biological applications such as imaging, biosensing, photocatalysis, and molecular transfer. It has also aroused great interest among researchers in agriculture and has made significant progress in improving crop growth and production. This review presents the physiological functions of carbon dots for crop growth and development, photosynthesis, water and nutrient absorption, and abiotic stress resistance and their applications in improving ecological environment and agriculture as biosensors, and the future application prospects and research directions of carbon dots in agriculture were prospected.

This study presents finite-time synchronization of chaotic fractional order systems with disturbance uncertainty and unknown time delay. First, a PID sliding surface is presented. Then, for the finite-time synchronization of the master and slave systems, a robust sliding-adaptive control method is provided. Update rules have been retrieved to estimate the system parameters by using the Lyapunov function while establishing the stability of the suggested mechanism and assuring the convergence of errors to zero. The proposed approach was used to solve the fractional order system of Gensou-Tsei with time-varying parameters, and the simulation results show that the presented approach performs well.

The phenomenon of ordered self-assembly on solid substrates is a topic of interest in both fundamental surface science research and its applications in nanotechnology. The regulation and control of two-dimensional (2D) self-assembled supra-molecular structures on surfaces have been realized through applying external stimuli. By utilizing scanning tunneling microscopy (STM), researchers can investigate detailed phase transition process about self-assembled monolayers (SAMs), providing insight into the interplay between intermolecular weak interactions and substrate-molecule interactions, which govern the formation of molecular self-assembly. This review will discuss the structural transition of self-assembly probed by STM in response to external stimuli and provide state-of-art methods such as tip induced confinement for the alignment of SAM domains and selective chirality. Finally, we discuss the challenges and opportunities in the field of self-assembly and STM.

Conservation agriculture and conservation tillage have drawn the attention not only of scientists but of politicians and societies worldwide. This paper aims to present a method for selecting re-search studies on conservation agriculture, conservation tillage, and the role of straw residues on soil health and soil stability against climatic hazards. VOSviewer version 1.6.18 free software tool was used in this study to achieve the bibliometric and mapping approach for studies on the effects of climate change in terms of conservation, tillage, soil health, and straw and microbe or straw and climate interaction or other paradigms. Scopus database (accessed July 7, 2023) indexed a total of 752.2018 bibliographic items classified into 15 paradigms e.g., Carbon (AND Straw) or Microbes (AND Straw). From the examination of the selected papers, it was concluded that straw residue has an impact on soil's physical, chemical, and biological properties and the environment in general.

Advanced genomics, transcriptomics and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids, and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research, the principles of genomics, transcriptomics and epigenomics, alongside their most advanced technologies (like single cell omics and spatial omics), which allows to investigate the molecular events that occur at single cell resolution retaining the spatial information.

At the beginning of 2020, with the onset of the pandemic, the traditional learning environment for learners drastically changed globally. Since then, most students/teachers have started and practiced distance and virtual learning/teaching. Thus, a technological breakthrough in virtual learning has followed. In connection with this, many countries worldwide have commenced allocating additional financing and funds for educational institutions' technological improvement and development. The long-term stay in distance learning has revealed and highlighted new problems students face: their knowledge level has decreased, they lack socialization skills, and they face psychological and physical health problems. Due to this negative impact on students, a need to research and evaluate how much the EU countries allocated to solve the distance learning-caused problems and what programs or models they prepared has emerged and encouraged further studies. The research has found that many countries increased their allocations very minimally, e.g., 0.0.1%, but some increased their available budgets to 32%. Notably, most countries did not separate distance learning exit funding from distance learning preparation funding. Based on the problems the countries saw, only a few states identified withdrawal from distance learning as a problem. Considering this, we set ourselves the goal to evaluate exit models from distance learning and allocated funding amounts. The following objectives were planned to achieve the goal: · to evaluate the global practice of exit from distance learning; · to determine the scope of funding for pandemic management; · to evaluate the amounts of funding allocated to manage pandemic-caused consequences and the GDP ratio. Research methods: mathematical-statistical analysis, empirical analysis, and analysis of scientific literature.

Wastewater contaminated with antibiotics is a major environmental challenge. We developed here the green synthesis of bio-graphenes by using natural precursors (Xanthan, Chitosan, Boswellia, Tragacanth). The use of these precursors can act as templates to create 3D doped graphene structures with special morphology. Also, this method is a simple method for in-situ synthesis of doped graphenes. The elements present in the natural polymers (N) and other elements in the natural composition (P, S) are easily placed in the graphene structure and improve the catalytic activity due to the structural defects, surface charges, increased electron transfers, and the high absorption. In this mechanism, O2 dissolved in water absorbs onto the positive charged C in doped graphenes to create oxygenated radicals, which enables the degradation of antibiotic molecules. Light irradiation increases the amounts of radicals and rate of antibiotic removal. The results have shown that the hollow cubic Chitosan-derived graphene has shown the best performance due to the doping of N, S, and P. The Boswellia-derived grapheme shows the highest surface area, but lower catalytic performance, which indicates the more effective role of doping in the catalytic activity. The effect of oxygen and light were also studied to accelerate the degradation process.

We investigate numerically the interactions of in-phase Airy beams modulated by a fundamental Gaussian beam and fourth-order diffraction in Kerr nonlinear media. Directly numerical simulations show that normal (anomalous) fourth-order diffractions and in-phase (out-of-phase) Gaussian beam affect the interactions of solitons generated from Airy beams in unique manners. Different from previous results that interactions between in-phase (out-of-phase) conventional beams are always attractive (repulsive), many anomalous interactions of Airy beams are obtained. Stable bound states of breathing Airy soliton pairs can be formed with the help of fourth-order diffraction and fundamental Gaussian beam.

The rapid changes in technology on a global scale, combined with the widespread adoption of business operations in cyberspace, have intensified the need for robust protection against escalating risks posed by cyber threats. This research paper aims to identify fundamental cyber resilience management attributes that enable organizations to manage cybersecurity, sustain, and adapt amidst evolving cyber risks and threats. By integrating resilience theory and security theory, this study establishes the attributes for resilience within cyber domains, making a novel contribution to cyber resilience management in organizations. The study introduces a model featuring seven main variables: Rationale, Reliable, Readiness, Resistance, Robust, Rebound, Reflective, and sub-variables across the Physical, Logical, and Social cyber domains, providing a converged framework for achieving cyber resilience. The findings of the study highlight the significance of fundamental attributes for enhancing cyber resilience management in organizations, such as clarity in purpose, vision, and values for security management, an empowered culture, availability of resources, avoidance of single points of failure, development, and coordination of resources to respond to threats and risks, promotion of continual improvement, and the sharing of information and knowledge. In conclusion, this research paper presents a model for managing cybersecurity in organizations by identifying key attributes for achieving cyber resilience.

The main objective of this article was to evaluate the repercussions of the crises resulting from the COVID-19 pandemic and the war in Ukraine on changes in the European Union's reliance on energy raw material imports, particularly from Russia. Furthermore, several specific objectives were outlined: to ascertain the EU's energy balance and any alterations therein, to identify changes in the concentration of energy raw material imports among EU countries during the crises, to determine the level and fluctuations in energy raw material import dependence within the EU and individual member states throughout the crises, and lastly, to assess changes in the concentration of energy raw material imports from Russia in EU countries during the crises. The research encompassed all 27 EU Member States as of 31st December 2022, and the examination period spanned from 2010 to 2022. The information sources included relevant literature on the subject and data sourced from Eurostat. In conducting the analysis and presenting the data, several methodologies were employed, including descriptive, tabular, and graphical techniques. Additionally, quantitative measures such as the Gini structure concentration coefficient and concentration analysis through the Lorenz curve were utilized. During the COVID-19 pandemic, the EU experienced a slight reduction in dependence on energy raw material imports. However, this reduction was short-lived and mainly attributed to the decrease in energy demand caused by the pandemic. Conversely, the war in Ukraine had a substantial impact, leading to a notable surge in energy raw material imports as a result of stockpiling efforts and the need to safeguard against further disruptions in the energy market. Notably, the EU's reliance on energy imports from Russia was already considerable. Following the outbreak of the conflict in Ukraine, imports of oil, petroleum products, and solid fossil fuels experienced significant decline due to imposed sanctions. Nonetheless, restrictions on natural gas imports were comparatively less severe.

The binding of actinide ions (Am(III) and U(VI)) in aqueous solutions by hybrid silica-hyperbranched poly(ethylene imine) nanoparticles (NP) and xerogels (XG) has been studied by means of batch experiments at different pH values (4, 7 and 9) under ambient atmospheric conditions. Both materials present relatively high removal efficiency at pH 4 and pH 7 (> 70%) for Am(III) and U(VI). The lower removal efficiency for the nanoparticles is basically associated with the compact structure of the nanoparticles and the lower permeability and access to active amine groups compared to xerogels, and the negative charge of the radionuclide species formed under alkaline conditions (e.g. UO2(CO3)34- and Am(CO3)2-). Generally, the adsorption process is relatively slow due to the very low radionuclide concentrations used in the study and is basically governed by the actinide diffusion from the aqueous phase to the solid surface. On the other hand, the adsorption is favored with increasing temperature assuming that the reaction is endothermic and entropy-driven, which is associated with increasing randomness at the solid-liquid interphase upon actinide adsorption. To the best of our knowledge, this is the first study on hybrid silica-hyperbranched poly(ethylene imine) nanoparticle and xerogel materials used as adsorbents for americium and uranium at ultra-trace levels. Compared to other adsorbent materials used for binding americium and uranium ions, both materials show far higher binding efficiency. Xerogels could remove both actinides even from seawater by almost 90%, whereas nanoparticles could remove uranium by 80% and americium by 70%. The above, along with their simple derivatization to increase the selectivity towards a specific radionuclide and their easy processing to be included in separation technologies, could make these materials attractive candidates for the treatment of radionuclide/actinide contaminated water.

The development of sarcopenia in the elderly is associated to many potential factors and/or processes, that impair the renovation and the maintenance of skeletal muscle mass and strength as ageing progresses. Among them, a defect by skeletal muscle to respond to anabolic stimuli is to be considered. Common anabolic stimuli/signals in skeletal muscle are hormones (insulin, growth hormones, IGF-1, androgens, β-agonists such epinephrine), substrates (amino acids as protein precursors on top, but also glucose and fat, as source of energy), metabolites (such as β-agonists and HMB), some cytokines, various biochemical/ intracellular mediators), physical exercise, neurogenic and immune-modulating factors, etc. Each of them may exhibit a reduced effect upon skeletal muscle as ageing progresses. In this review article, we will concisely overview the effects of anabolic signals on muscle metabolism, as well as currently available evidence of a resistance, at skeletal muscle level, to any of the above-mentioned anabolic factors, from both in vitro and in vivo studies.

Prostate cancer is the most common cancer among men in Poland. A significant proportion of prostate cancers can be cured, providing longer survival. Patients have the opportunity to undergo minimally invasive treatment methods like brachytherapy, while maintaining their previous quality of life. The disease itself, as well as treatment complications, primarily affect the functioning of the urinary and digestive systems and impact the sexual sphere. Aim: This study aims to assess the quality of life of patients after prostate brachytherapy. Methods: The research was conducted at the Lower Silesian Oncology Centre in Wroclaw. The study included fifty men aged between 51 and 85 years. The following questionnaires were used: EORTC QLQ-C30, QLQ-PR25. Scales: Katz, Mini-MAC, PSS-10. Moreover, the study used a self-designed survey covering socio-demographical factors. Results: Prostate brachytherapy has a significant impact on the degree of sexual dysfunction. Compared to sexually inactive individuals, sexually active individuals exhibit fewer negative psychological symptoms. Choosing a destructive coping strategy intensifies the occurrence of symptoms, lowers patients' level of independence, and negatively affects their quality of life. Conclusions: Brachytherapy contributes to worsening sexual function and temporarily affects urinary system function, which may lower the overall quality of life in patients.

With the rapid development of the economy and the transformation of social contradictions, the economic development model in China has also changed, and a high-quality economic development model has been proposed. This paper uses the entropy method to measure the level of innovation and high-quality economic development in nine prefecture-level cities in Fujian Province from 2011 to 2020, and finds that the overall level shows an increasing trend year by year, but there are obvious regional differences. By constructing a grey correlation degree model, it is found that the innovation subsystem and the high-quality economic development are strongly correlated, the development level of innovation and the subsystem of high-quality economic development are both strongly correlated, the grey correlation degree from high to low is green, coordinated, shared, and livelihood, indicating that innovation has a significant impact on high-quality economic development. Based on the research results, countermeasures and suggestions are proposed to promote the coordinated development of innovation and high-quality economy.

Helicobacter pylori is a gastric pathogen that infects nearly half of the global population and is recognized as a group 1 carcinogen by the Word Health Organization. The global rise in antibiotic resistance has increased clinical challenges in treating H. pylori infections. Biofilm growth has been proposed to contribute to H. pylori’s chronic colonization of the host stomach, treatment failures, and the eventual development of gastric diseases. Several components of H. pylori have been identified to promote biofilm growth, and several of these may also facilitate antibiotic tolerance, including the extracellular matrix, outer membrane proteins, the coccoid morphology, modulated metabolism, efflux pumps, and virulence factors. Recent developments in therapeutic approaches targeting H. pylori biofilm have shown that synthetic compounds, such as small molecule drugs and plant-derived compounds, are effective at eradicating H. pylori biofilms. These combined topics highlight the necessity for biofilm-based research in H. pylori, to improve current H. pylori targeted therapeutic approaches and alleviate relative public health burden. In this review we discuss recent discoveries that have decoded the life cycle of H. pylori biofilms and current biofilm targeted treatment strategies.

MicroRNAs (miRNAs) within the exosomes are short non-coding RNAs that are associated with many diseases including cancers and are crucial players in regulating gene expression and regula-tion. Owing to their potential role as emerging biomarkers, efficient isolation, and quantification methods of miRNA from the exosomes in the cell’s supernatant are complicated and challenging. The quantitative PCR method is the gold standard for miRNA identification and estimation. Nucleic acid-based assays are expensive, labour-intensive, and require expertise. In this work, we demon-strated an aptamer-based magnetic separation of the exosomes, and quantification of the miRNA using a fluorescence switching assay. The fluorescence assay relies on the fluorescence of the fluorophore labelled cDNA quenched by carbon nanomaterials coated on magnetic beads (OFF state). When the target miRNA210 is introduced to the system, the cDNA detaches from the quenching surface. As a result, the fluorescence intensity increases (ON state) with increasing miRNA concentration within the dynamic range of 0-100 nM. This method can detect miR210 as low as 5 pM. This method does not exhibit any cross-reactivity with other closely related miRNAs. The method was validated by spiking miRNA with the standard RT-PCR method using SYBR green as a fluorescent probe. This method can be used for the minimally invasive detection of cancer biomarkers in breast cancer patients.