After UV excitation, gas phase thymine returns to ground state in 5 to 7 ps, showing multiple time constants. There is no consensus on the assignment of these processes, with a dispute between models claiming that thymine is trapped either in the first (S1) or in the second (S2) excited states. In the present study, nonadiabatic dynamics simulation of thymine is performed on the basis of ADC(2) surfaces, to understand the role of dynamic electron correlation on the deactivation pathways. The results show that trapping in S2 is strongly reduced in comparison to previous simulations considering only non-dynamic electron correlation on CASSCF surfaces. The reason for the difference is traced back to the energetic cost for formation of a CO p bond in S2.

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

COVID-19 has been extremely difficult to control. The lack of understanding of key aspects of pandemics has affected virus transmission. On the other hand, there is a demand to incorporate Computational Thinking (CT) in the curricula with applications in STEM. However, there are still no exemplars in the curriculum that apply CT to real-world problems such as controlling a pandemic or other similar global crises. In this paper, we fill this gap by proposing exemplars of CT for modeling the pandemic. We designed exemplars following the three pillars of the APEC InMside framework for CT: algorithmic thinking, computational modeling, and machine learning. For each pillar, we designed a progressive sequence of activities that covers from elementary to high school. In an experimental study with elementary and middle school students from 2 schools of high vulnerability, we found that the computational modeling exemplar can be implemented by teachers and correctly understood by students. We conclude that it is feasible to introduce the exemplars at all grade levels, and that this is a powerful example of STEM integration that helps reflect and tackle real-world and challenging public health problems of great impact for students and their families.

In the power plant industry, the turbine inlet temperature (TIT) plays a key role in the efficiency of the gas turbine and, therefore, the overall—in most cases combined—thermal power cycle efficiency. Gas turbine efficiency increases by increasing TIT. However, an increase of TIT would increase the turbine component temperature which can be critical (e.g., hot gas attack). Thermal barrier coatings (TBCs)—porous media coatings—can avoid this case and protect the surface of the turbine blade. This combination of TBC and film cooling produces a better cooling performance than conventional cooling processes. The effective thermal conductivity of this composite is highly important in design and other thermal/structural assessments. In this article, the effective thermal conductivity of a simplified model of TBC is evaluated. This work details a numerical study on the steady-state thermal response of two-phase porous media in two dimensions using personal finite element analysis (FEA) code. Specifically, the system response quantity (SRQ) under investigation is the dimensionless effective thermal conductivity of the domain. A thermally conductive matrix domain is modeled with a thermally conductive circular pore arranged in a uniform packing configuration. Both the pore size and the pore thermal conductivity are varied over a range of values to investigate the relative effects on the SRQ. In this investigation, an emphasis is placed on using code and solution verification techniques to evaluate the obtained results. The method of manufactured solutions (MMS) was used to perform code verification for the study, showing the FEA code to be second-order accurate. Solution verification was performed using the grid convergence index (GCI) approach with the global deviation uncertainty estimator on a series of five systematically refined meshes for each porosity and thermal conductivity model configuration. A comparison of the SRQs across all domain configurations is made, including uncertainty derived through the GCI analysis.

Dengue (DENV) and Zika (ZIKV) virus continue to pose significant challenges globally due to their widespread prevalence and severe health implications. Given the absence of effective vaccines and specific therapeutics, targeting of the highly conserved NS5 RNA-dependent RNA polymerase (RdRp) domain has emerged as a promising strategy. However, limited efforts have been made to develop inhibitors for this crucial target. In this study, we employed an integrated in silico approach utilizing combinatorial chemistry, docking, molecular dynamics simulations, MM/GBSA, and ADMET studies to target the allosteric N-pocket of DENV3-RdRp and ZIKV-RdRp. Using this methodology, we designed lycorine analogs with natural S-enantiomers (LYCS) and R-enantiomers (LYCR) as potential inhibitors of non-structural protein 5 (NS5) in DENV3 and ZIKV. Notably, 12 lycorine analogs displayed a robust binding free energy (<-9.00 kcal/mol), surpassing that of RdRp-Ribavirin (<-7.00 kcal/mol) along with promising ADMET score predictions (<4.00), of which (LYCR728-210, LYCS728-210, LYCR728-212, LYCS505-214) displayed binding properties to both DENV3 and ZIKV targets. This study highlights the potential of non-nucleoside lycorine-based analogs with different enantiomers that may present different or even completely opposite metabolic, toxicological, and pharmacological profiles as promising candidates for inhibiting NS5-RdRp in ZIKV and DENV3, paving the way for further exploration for the development of effective antiviral agents.

This study investigates how students' well-being relates to sustainable development and education for sustainability at Prince Mohammad Bin Fahd University, Saudi Arabia. The authors endeavor to answer the question "How Does Students' Well-Being Relate to Sustainable Development Via Education for Sustainable Development?" and four sub-questions. Primary data were collected by semi-structured interviews exploring the viewpoints of twenty female and male students (n=26) aged between 18 and 26. The content analysis results have identified additional well-being and education for sustainability factors that influence sustainable education and, therefore, sustainable development. These antecedents include workload, transparency, grading criteria, fairness, belonging, work-life balance, mental health support, missing emotional intelligence factors, factors related to Education for Sustainable Development, such as instructors' role models, institutional commitment, leadership, and collaboration with government. In addition, the study developed a theoretical model for the relationship between antecedents for Education for Sustainable Development, Education for Sustainable Development, and Sustainable Development in the Saudi context. Finally, the study answers four research questions: What factors contribute to education for sustainable development? What are the antecedents of education for sustainable development? How do university educators contribute to sustainable education? How do the antecedents of education for sustainable development, education for sustainable development, and sustainable development relate?

Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida's public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3,026 left turn, 1,210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at a university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% Confidence Interval (95% CI): 0.629 - 0.989) for the validation sample and 0.920 (95% CI: 0.891 - 0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

To harness light-matter interaction at nano-/micro-scale better control tools have to be developed. Here, it is shown that by applying external electric and/or magnetic field, ablation of Si and glass under ultra-short (sub-1~ps) laser pulse irradiation can be controlled via the Lorentz force $\mathbf{F} = e\mathbf{E} + e[\mathbf{v}\times\mathbf{B}]$, where $\mathbf{v}$ is velocity of charge $e$, $\mathbf{E}$ is the applied electrical bias and $\mathbf{B}$ is the magnetic flux density. The external electric E-field was applied during laser ablation using suspended micro-electrodes above glass substrate with an air gap for the incident laser beam. The counter-facing Al-electrodes on Si surface were used to study debris formation patterns on Si. Debris was deposited preferentially towards the negative electrode in the case of glass and Si ablation. Also, an external magnetic field was applied during laser ablation of Si in different geometries and is shown to affect ripple formation. The vectorial nature of the Lorentz force widens application potential of surface modifications and debris formation in external E-/B-fields with potential applications in mass and charge spectroscopes.

Mangroves offer vital ecological advantages including air and water filtration, coastal and estuarine habitat provision, sediment stabilization, and wave energy absorption. Their intricate root systems play a key role in safeguarding shorelines from tsunamis and erosive storms by dissipating wave energy. Moreover, mangroves shield against boat wakes and wind-waves, bolstering shoreline defense. Wave dissipation is a function of forest width, tree diameter, and forest density. Restoration efforts of juvenile mangroves in Florida’s Indian River Lagoon (IRL) aim to reduce wave energy in areas vulnerable to erosion. Physical model testing of wave dissipation through mangroves is limited due to the complexity in representing the mangrove structure, where prop roots are non-uniform in both diameter and location. Previous studies have quantified wave dissipating effects through use of scaled and parameterized mangrove structures. This study measures the dissipation effects of live mangroves in a wave flume, forced by conditions representative of the IRL. These measurements are used to validate a parameterized dowel model. Error between wave attenuation factors for the live mangrove and dowel system was on average 2.5%. Validation of the modularized dowel system allowed for further parameterized testing to understand forest structure effects, such as sediment stabilization and wave attenuation.

Improving photobioreactor performance for microalgae cultivation has attracted many researchers over the past years. One of the primary challenges associated with existing photobioreactors is the light penetration. An effective photobioreactor design should maximize light penetration, ensuring uniform illumination throughout the reactor. This study aims to assess the impact of light intensity on microalgal growth from the perspective of energy efficiency and productivity in two photobioreactors. A novel cactus-like and a cylindrical photobioreactor were designed and fabricated using 3D printing technology. These two photobioreactors were used to cultivate two strains of microalgae. The novel photobioreactor achieved a maximum biomass productivity of 0.99 g/L/d and a maximum energy efficiency of 0.3139 g/d/kWh. The cylindrical photobioreactor reached a maximum biomass productivity of 0.74 g/L/d and 0.2199 g/d/kWh of energy efficiency. The increase in biomass productivity can be linked to enhancements in the photobioreactor's surface-to-volume ratio and better light utilization.

To the best of the author’s knowledge, studies of mature plasmacytoid dendritic cell proliferation associated with T lymphoblastic lymphoma were extremely rare in the literature. Here, we report a patient who underwent both mature plasmacytoid dendritic cell proliferation and T lymphoblastic lymphoma. With the findings of lymph node biopsy taken from the right cervical and inguinal regions, we identified eye-catching mature plasmacytoid dendritic cells that were considered to be responsible for this lesion at the beginning, until the immunostaining of Ki67 and TDT showed a small group of positive cells hiding in these plasmacytoid dendritic cells. Bone marrow biopsy was also performed in this patient. Microscopically, the hematopoietic tissue was almost completely replaced by lymphoblastoid cells with condensed chromatin, inconspicuous nucleoli and scanty cytoplasm, which were basically the same as those seen in the lymph nodes in morphology. However, there was no sign of plasmacytoid dendritic cells or Langerhans cells in the bone marrow biopsy. With the help of bone marrow biopsy, our final diagnosis of the lymph node was T lymphoblastic lymphoma coexisting with mature plasmacytoid dendritic cell proliferation . Although accumulations of plasmacytoid dendritic cells may occur in some infections or reactive lymphadenopathy, the presence of extensive nodules or infiltration of plasmacytoid dendritic cells strongly reminds the pathologist to carefully evaluate the bone marrow or peripheral blood status of the patient to exclude a hidden myeloid or other neoplasm.

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

Background: The study investigates patients' use of eHealth services, awareness of the availability of these services, and their intention to use them in primary care. It also examines patient characteristics and factors that influence their use. Methods: A cross-sectional design using questionnaire were conducted. Based on the unified theory of acceptance and use of technology (UTAUT), participants rated the two most common services. Descriptive analyses and linear correlation analyses were performed. Simple linear regression was conducted identify factors influencing participants' intention to use eHealth services. Results: 1203 participants were surveyed and their’ ages averaged 43.7 years. Participants usage rates varied, with the lowest at 2.4% for measuring vital signs and the highest at 47.4% for booking appointments. Intentions to use services ranged from 22.5% for video consultations to 46.6% for prescription refill requests. Approximately 20% of respondents were unaware of each service's availability. Positive associations were found between all constructs and the intention to use online services, with younger age being the most significant factor. Conclusions: Use of and intentions to use eHealth services varied greatly. Participants were often unaware of the availability of services. Promoting the availability and benefits of eHealth services could enhance patient engagement in primary care settings.

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper to build such a surrogate model, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9,000 test samples used for building the surrogate model, and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.

Satellite Image Time Series (SITS) is a data set which includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two scenes or three scenes in different time phases). However, these methods are not suitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, this study proposed a Nonlinear Radiometric Normalization Model (named NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algroithm (GA) . In this method, GA was used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoided the selection of a single reference image. ANN was used to obtain the optimal solution of error function, which minimized the radiometric distortion between different images in SITS. SITS composed of 21 Landsat-8 images in Tianjin City from October 2017 to January 2019 were selected to test the method. We compared NMAG with other two contrasting method (refered as CM1 and CM2), and found that the average of root mean square error $(\mu_{RMSE})$ of NMAG $(497.22)$ was significantly smaller than those of CM1 $(641.39)$ and CM2 $(543.47)$, and the accuracy of normalized SITS obtained by using NMAG has increased by 22.4\% and 8.5\% toward CM1 and CM2, respectively. These experimental results confirmed the effectiveness of NMAG in the reduction of radiometric distortion caused by continuously changing noise between images in SITS.

Blockchain technology (BCT) has the potential for sustainable development, enhancing business efficiency, reducing costs, and strengthening data security. Despite its potential, adoption among small and medium-sized enterprises (SMEs) is low due to the need for significant changes in technology infrastructure and the challenge of interoperability. A cross-sectional survey was done with 226 Hong Kong SMEs. The PLS-SEM results highlighted favorable transformation conditions as the most critical factor for SMEs considering the adoption of BCT. These conditions address both internal and external readiness for adopting the disruptive strategy. Other contributing factors to BCT adoption include trust in BCT developers, perceived usefulness and ease of use, government support, and perceived security. The research results contributed to the literature on disruptive technology acceptance, adoption, transformation conditions, technology readiness, and SMEs’ technology adoption. Government-supported policies and initiatives can promote BCT adoption, including educational programs, case studies, and resources for readiness. Financial support and regulatory frameworks can further encourage adoption. BCT developers can demonstrate the value of their systems, effectively communicate potential ROI and cost savings, and provide user-friendly interfaces to increase SMEs’ intention to adopt BCT.

The aim of the present trial was to evaluate the growth performance of buffalo calves fed diets characterized by different forage/concentrate ratio with or without the Saccharomyces Cerevisiae supplementation (CBS 493.94, Yea-Sacc®). Twenty-four male buffalo calves (mean age 145.1 ± 16.1 days; mean weight 108.0 ± 18.7) were assigned randomly to 4 groups, homogeneous for age, fed four different diets: diet 1 F:C ratio 50:50; diet 2 F:C ratio 30:70; diet 3 F:C ratio 50:50 + Yea-Sacc® and diet 4 F:C ratio 30:70 + Yea-Sacc®. Buffalo calves were individually weighted before the start of the experiment and the data were used as a covariate, monthly until the end of the trial. Dry matter intake (DMI), daily weight gain (DWG) and feed conversion ratio (FCR) were calculated. The differences in diets’ composition significantly (p

In mesoscale eddies, chemical properties and biological composition are different from those in surrounding water, due to their unique physical processes. It is unclear how physical-biological coupling occurs in warm-core eddies, particularly since no study has examined the relationship between bacteria and viruses. The purpose of the present study was to examine the influence of anticyclonic warm eddy on the relationship between bacterial and viral abundances as well as viral activity (viral production) in different depths. In the core of warm eddy, bacterial abundance (0.48 to 2.82 ×105 cells mL−1) fluctuated less than that of the outside eddy (1.12 to 7.03×105 cells mL−1). In particular, there was four-folds higher of viral-bacterial abundance ratio (VBR) estimated within the eddy below DCM than the outside eddy. We suggest that prevailing physical forces with downwelling at the center of the anticyclonic warm eddy, viruses can also be transmitted into the deep ocean directly through adsorption on particulate organic matter during sinking. Overall, our findings provide valuable insights into the interaction between bacterial and viral communities and their ecological mechanism within the warm eddy.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

This paper examines the energy efficiency of three variations of the two-degree-of-freedom transverse galloping energy harvester. These variants differ in the number and placement of electromechanical transducers. By utilizing the harmonic balance method, limit cycles of mathematical models of devices were determined. Analytical expressions derived from models were then used to formulate the efficiency of the systems. It was demonstrated that efficiency depends on flow speed and can be comprehensively characterized by four criteria parameters: peak efficiency, denoting the maximum efficiency of the system, and high efficiency bandwidth, which describes the range of flow velocities within which the efficiency remains at no less than 90% of peak efficiency. The values of these parameters are heavily reliant on the speed at which the system achieves peak efficiency—referred to as the nominal speed nominal speed, which in turn is related to the critical speed of the system. Comparative analysis revealed that only the two-degree-of-freedom device equipped with two electromechanical transducers can potentially outperform a simple one-degree-of-freedom system in terms of efficiency.

The development of ballistic protection vests (BPVs) has become increasingly significant, particularly in the design of soft panels, which are critical to the overall size and configuration of these vests. Despite their importance, a standardized design process for soft panels is lacking in the literature. This research addresses this gap by creating soft panels tailored to the body types of regional soldiers while adhering to relevant standards. We propose a comprehensive design algorithm and evaluate the applicability of the National Institute of Justice (NIJ) standard 0101.06 test templates for configuring these panels. This study aims to design soft ballistic panels for BPVs that cater to the body type requirements of regional soldiers and comply with NIJ 0101.06 standards. We have developed an algorithm for designing soft panels and analyzed the feasibility of using NIJ test templates for their configuration. The research encompasses a detailed literature review, analysis of existing BPV soft panels, and comparison with NIJ test templates. The panels were designed and scaled using computer-aided design (CAD) systems and evaluated through by physical fitting on mannequins and virtual fitting using the Clo3D program. Our findings indicate that the NIJ standard test templates are only partially applicable to the design of soft panels. The developed methodology includes an algorithm and size specifications tailored to regional covers. A coefficient (K) was identified to calculate the soft panel surface area prior to design, thereby saving time and resources.

Aluminum alloy Al 2024-T3 were successfully joined using friction stir spot jwelding joining (FSSW). Satisfactory joint strengths were obtained at different welding parameters (tool rotational speed, tool plunge depth, dwell time) and tool pin profile (straight cylindrical, triangular and tapered cylindrical). Resulting joints were welded with welded zone. The different tools significantly influenced the evolution on the stir zone in the welds. Lap-shear tests were carried out to find the weld strength. Weld cross section appearance observations were also done. The macrostructure shows that the welding parameters have a determinant effect on the weld strength (x: the nugget thickness, y: the thickness of the upper sheet and SZ: stir zone). The main fracture mode was pull out fracture modes in the tensile shear test of joints. The results of tensile shear tests showed that the tensile-shear load increased with increasing rotational speed in the shoulder penetration depth of 0.2 mm. Failure joints were obrerved in the weld high shoulder penetration depth and insufficient tool rotation.

This paper investigates the possible failure modes of the product development process in production companies that are active in the B2C markets with a focus on household products. Since these cases require short lead times and are difficult to differentiate, in many instances the end result will not be the desired one and could affect profitability for a season or for good. A model of these possibilities is created and an approach to plan contingencies for their solutioning is proposed in the article. The main guideline is to switch from failure probability determination to accepting failure as inevitable and using digital solutions to reinforce the development process in order to offset its impact. For this goal, an Industry 5.0 Abatement Factor (abbreviated IFAF) is introduced in the contingency planning approach, that factors in the evaluation the low cost of digital instruments and the proper mix of Technology, Humans and AI (abbreviated THAI). The new working procedure based on these concepts and their interlinkages is discussed based on specific examples.

In Model-Driven Development (MDD), the models, their generation, and imposing changes on them (model transformation) are used for the development of software. Models provide a framework to start from the imagination and abstraction to create and accomplish the final system. Models create a slow and steady transition from whatness to howness, i.e. from the natural path of the generation of software. For supporting this path, the Logic and Functionality of software must be changeable during its evolution. Here we provide a brief introduction to the concept of Model Driven Development.

The paper presents the findings of research on companies in the early stage of development based on the concept of ensuring their sustainability. The starting point is attributes shaping a sustainable model of start-ups. A solution has been proposed and examined that identifies the factors dynamising a business model, which give the company the ability to make fast changes in the configuration of the business model, and that applies stabilizing factors based on the concept of the Triple Bottom Line. The research problem is a response to the phenomena of the bankruptcy of many start-ups that cannot find a way to survive in the long term. The business models of companies in the early stage of development should be designed taking into account the possibility of their dynamic and iterative changes when the earlier assumptions do not work when implemented in practice. A cognitive gap has been observed as the relevant literature does not propose a systemic solution to the problem of instability of the startups viewed from the perspective of the business model concept. The research problem, therefore, is to identify potential new methods to ensure the sustainability of young companies trying to combine aspects dynamising changes in business models, taking into account economic, environmental and social aspects. The findings of research on companies in the early stage of development show that when the methods of increasing the flexibility of the business model are applied and attention is drawn to environmental and social activities and a strong emphasis is put on financial performance, reflecting the expectations of shareholders, the concept of ensuring the sustainability of young companies from the point of view of sustainable business model attributes emerges.

Advanced persistent threat (APT) attacks are sophisticated and organized attacks commonly motivated by political, financial, and strategic objectives. In order to comprehend their tactics, techniques, and procedures (TTP) and indicators, APT reports are valuable sources. While blue teams typically rely on server logs, firewall rules and user authorizations managed in database tables, attackers have a graph-based mindset. In this work, we propose a framework for discovering and evaluating APTs using graph-based algorithms. Cyber threat intelligence (CTI) was extracted from 40,358 pages of APT reports and transformed into a graph. Centrality, community, and similarity analyses were executed on the graph. As a result, critical and influential APT groups and indicators of compromise (IoC) were discovered. Similar attacks and APT groups were revealed. Analysis results were interpreted to create new strategic CTI that can be utilized in future security operations.

Integrated crop-livestock production (ILP) is an interesting alternative for more sustainable soil use. However, more studies are needed to analyze soil pore properties under ILP at the micrometer scale. Thus, this study proposes a detailed analysis of soil pore architecture at the micrometer scale in three dimensions. For this purpose, samples of an Oxisol under ILP subjected to minimum tillage (MT) and no-tillage (NT) with ryegrass as cover crop (C) and silage (S) were investigated. The soil micromorphological properties of the soil were analyzed using X-ray microtomography. The MT(C) system showed the highest values of porosity (c. 20.4%), connectivity (c. 32.8×103), volume (c. 26%), and number of pores (c. 32%) in a rod-like shape. On the other hand, the MT(S), NT(C), and NT(S) systems showed higher tortuosities (c. 2.2, c. 2.0, and c. 2.1) and lower pore connectivities (c. 8.3×103, c. 6.9×103, and c. 6.2×103), especially in S use. Ellipsoidal and rod-shaped pores predominated over spheroidal and disc-shaped pores in all treatments. The results of this study show that the use of ryegrass as a cover crop improves soil physical properties, especially in MT. For S use, the type of soil management (MT or NT) did not show any differences.

Viral hepatitis is currently acknowledged as a significant public health issue that necessitates a prompt and immediate response. Hepatitis is a significant worldwide health problem that causes the death of approximately 1.5 million individuals annually. The objective of this study was to ascertain the occurrence rate of hepatitis A, E, B, and C infections in the Hajjah governorate of Yemen from 2020 to 2023.A retrospective study was conducted in the Hajjah Governorate of Yemen for four years, 2020– 2023. About 41162 cases were collected from the clinical records of epidemiological surveillance in the Public Health Office of the Hajjah Governorate. All cases were diagnosed in different laboratories based on direct observation of viral hepatitis in the blood sample. The required data were obtained by using the designed questionnaire. The findings of the present investigation were summarized as follows: The current findings indicate that the prevalence rate of Hepatitis A and E was 761 (1.85%), while the prevalence rate of Hepatitis B and C was 971 (2.36%) among the participants of the study. The current findings indicate that the incidence of Hepatitis A and E was greater in 2022 (2.3%), while Hepatitis B and C had a higher rate in 2020 (2.9%). The study revealed a higher prevalence of hepatitis A and E among individuals originating from Al Miftah district 116, with a rate of 7.5%. Furthermore, the prevalence of hepatitis B and C in Hajjah city was significantly higher, reaching 579 cases (22.9%). The month with the highest incidence of hepatitis A and E viruses was September, with a rate of 85 cases (2.6%), while the lowest incidence was observed in April, with a rate of 1.3%. The prevalence of hepatitis B and C viruses was highest in October 1997, with a rate of 2.8%, followed by November 1997 with the same rate. The lowest observed incidence of 1.6% was recorded in April. In Autumn, a significant prevalence of hepatitis A and B viruses, as well as hepatitis B and C viruses, was identified at rates of 2.2% and 2.6%, respectively.The study findings indicate that the occurrence of HAV, HEV, HBV, and HCV in the study area is consistent with global trends. Although the study found a low prevalence of hepatitis viruses, these types of viruses will continue to pose a significant risk to public health. Based on this study, it is imperative for authorities to actively enhance public awareness in order to enable prompt planning and essential arrangements for an effective response to outbreaks or epidemics. This will ultimately lead to a reduction in illness, death, and economic burden.

Motivated by the fact that the restrictive conditions for a Turing instability are relaxed in sub- diffusive regime, we investigate the effects of subdiffu- sion in the predator−prey model with toxins under the homogeneous Neumann boundary condition. First, the stability analysis of the corresponding ordinary differ- ential equation is carried out. From this analysis, it fol- lows that stability is closely related to the coefficient of toxicity. In addition, the temporal fractional derivative does not systematically widen the range of parameters to maintain a point in the stability domain. Further- more, we derive the condition which links the Turing instability to the coefficient of toxicity in the subdif- fusive regime. System parameters are varied in order to test our mathematical predictions while comparing them to ecological literature. It turns out that the mem- ory effects, linked to the transport process can, depend- ing on the parameters, either stabilize an ecosystem or make a completely different configuration.

Since the first case of 2019 novel coronavirus disease (COVID-19) detected on Jan 30, 2020, in India, the number of cases rapidly increased to 3819 cases including 106 deaths as of 5 April 2020. Taking this into account, in the present work, we are studying a Bats-Hosts-Reservoir-People transmission fractional-order COVID-19 model for simulating the potential transmission with the thought of individual social response and control measures by the government. The real data available about infectious cases from $14^{th}$ March to $26^{th}$ March 2020 is analysed and accordingly various parameters of the model are estimated or fitted. The Picard successive approximation technique and Banach's fixed point theory have been used for verification of the existence and stability criteria of the model. Numerical computations are done utilizing the iterative Laplace transform method. In the end, we illustrate the obtained results graphically. The purpose of this study is to estimate the effectiveness of preventive measures, predicting future outbreaks and potential control strategies using the mathematical model.

This paper is focused on typical rural settlements in the Stara Planina Mountain area in Serbia. They are distinguished by their authenticity, identity and character, and they have still not been destroyed by inappropriate development as is the case in some other mountain areas in Serbia, where the effects of urbanization, globalization and homogenization are obvious. Consequently, there is a need to prevent these effects in mountain areas where there is a threat of disbalance between development and the inherited natural and cultural values. The paper starts from several important concepts that relate contemporary approaches to cultural heritage preservation, place morphology and planning future development. The concept definitions from recent documents from related fields are used. These concepts are then applied to the area of Stara Planina Mountain and its rural settlements. The necessity for an integrative approach to planning this area with significant natural and anthropogenic potential is pointed out, including its integration into international programs of sustainable development.

Decades ago, when computational was expensive and limited, the structural design was mostly performed by hand calculations using simple mathematical models. For example, it was a common practice to design a structure as complex as the wing of an aircraft by simple beam analysis. However, ever since the classic paper by Turner et al., due to a rapid increase in computational, more complex mathematical models are being used to simulate the physical behavior of complex structural components. To solve intractable problems unsolvable by hand calculations, numerical techniques like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Finite Difference Method etc. are being employed. In fact, the availability of these methods has led to the development of an entirely new area of research known as Multidisciplinary Design Optimization (MDO) where various disciplines are considered in an optimization problem. The most important question while using a mathematical model to represent practical industrial problems is to what extent these models represent the real-life situation. Computational models are always built on upon assumptions. Simply at looking at the simulation outcomes i.e. the graphical and numerical results, it is often very difficult to ensure if the underlying assumption holds and that the results are reliable. This has led to the development of another field of research known as Verification and Validation (called V&V in short).

This study aims to investigate the influence of various economic, environmental, and social factors on sustainable development, with a specific focus on the impact of green technology and climate change adaptation. A panel dataset comprising 38 OECD member countries from 1990 to 2020 is employed, and a series of dynamic panel data models are estimated using the system generalized method of moments (GMM) approach. Numerous covariates, such as globalization, socio-economic conditions, economic and political values, climate and technological progress, and environmental determinants, are examined. The empirical findings highlight the significant role played by macroeconomic, institutional, social, and government policy-related factors in sustainable development. More importantly, our results provide novel and robust evidence that the diffusion of green technology and climate change adaptation positively affect green growth. The study's empirical outcomes are demonstrated to be robust to misspecification errors and statistically significant at traditional significance levels. These findings carry substantial policy implications for the development and implementation of strategies that promote climate change adaptation and green innovation. Consequently, policymakers should prioritize the integration of green technology and adaptive measures in their sustainable development agendas to foster a greener, more resilient future.

Background: Cardiac development is a complex developmental process. The early cardiac straight tube is composed of an external myocardial layer and an internal endocardial lining. Soon after rightward looping, the embryonic heart becomes externally covered by a new epithelial lining, the embryonic epicardium. A subset of these embryonic epicardial cells migrate and colonize the embryonic myocardium, contributing to distinct cell types. In recent years, our understanding of the molecular mechanisms that govern proepicardium and embryonic epicardium formation has greatly increased. We have recently witnessed a novel layer of complexity governing gene regulation with the discovery of non-coding RNAs. Our laboratory recently identified three distinct lncRNAs, adjacent to the Wt1, Bmp4 and Fgf8 chicken gene loci, with enhanced expression in the proepicardium that are distinctly regulated by Bmp, Fgf and thymosin 4, providing support of their plausible implication in epicardial formation. Methods: Expression of lncRNAs was analyzed in different chicken and mouse tissues as well as their subcellular distribution in chicken proepicardial, epicardial, ventricle explants and in different murine cardiac cell types. lncRNA transcriptional regulation was analyzed by using siRNAs and expression vectors of different transcription factors in chicken and mouse. Antisense oligonucleotides were used to inhibit Gm14014 expression. Cardiac injury was induced ex vivo in mouse ventricle explants by cryoinjury. Furthermore, RT-qPCR, immunocytochemistry, RNA pulldown, Western blot, viability and cell migration assays were conducted to investigate the biological function of Wt1_76127 and Gm14014. Results: We demonstrated that Wt1_76127 in chicken and its evolutionarily conserved homologue Gm14014 in mice are widely distributed in different embryonic and adult tissues and distinctly regulated by cardiac enriched transcription factors, particularly Mef2c and Nkx2.5. Gm14014 is distinctly regulated in mouse ventricular cryoinjury ex vivo models, displaying a negative correlation with epicardial and epithelial to mesenchymal transition markers. Furthermore, silencing assays demonstrated that mouse Gm14014, but not chicken Wt1_76127, is essential for epicardial, but not endocardial or myocardial cell migration. Such process is governed by partnering with Myl9, promoting cytoskeletal remodeling. Conclusion: Our data evidence that Gm14014 plays a pivotal role in epicardial cell migration essential for heart regeneration.

In the last decades, it has been established that astrocytes play key roles in the regulation of neuronal morphology. However, the contribution of astrocyte-derived small extracellular vesicles (sEVs) to morphological differentiation of neurons has only recently been addressed. Here, we showed that cultured astrocytes expressing a GFP tagged version of the stress-regulated astrocytic enzyme Aldolase C (Aldo C-GFP) release small extracellular vesicles (sEVs) which are transferred into cultured hippocampal neurons. Surprisingly, Aldo C-GFP-containing sEVs (Aldo C-GFP sEVs) displayed an exacerbated capacity to reduce the dendritic complexity in developing hippocampal neurons compared to sEVs derived from control (i.e. GFP-expressing) astrocytes. Using bioinformatics and biochemical tools, we found that the total content of overexpressed Aldo C-GFP correlates with an increased content of endogenous miRNA-26a-5p in both total astrocyte homogenates and sEVs. Notably, neurons magnetofected with a nucleotide sequence that mimics endogenous miRNA-26a-5p (mimic 26a-5p) not only decreased the levels of neuronal proteins associated to morphogenesis regulation and also reproduced morphological changes induced by Aldo-C-GFP sEVs. Furthermore, neurons magnetofected with a sequence targeting miRNA-26a-5p (antago 26a-5p) were largely resistant to Aldo C-GFP sEVs. Our results support a novel and complex level of astrocyte-to-neuron communication mediated by astrocyte-derived sEVs and the activity of their miRNA content.

Candidiasis is considered an emerging public health concern because of the occurrence of drug-resistant Candida strains and the lack of available structurally diverse antifungal drug armamentariums. The indole alkaloid globospiramine from the anticandidal Philippine medicinal plant Voacanga globosa exhibits a variety of biological activities; however, its antifungal properties remain to be explored. In this study, we report the in vitro antifungal activities of globospiramine against Candida albicans and Candida tropicalis and explore its possible target proteins using in silico methods. The colony-forming unit (CFU) viability assay revealed time- and concentration-dependent anticandidal effects of the alkaloid by decreasing almost 50% of the viable CFUs 60 min after treatment. Results of the MIC and MFC assays indicated inhibitory and fungicidal effects of globospiramine against C. albicans (MIC = 8 µg/mL; MFC = 4 µg/mL) and potential fungistatic effects against C. tropicalis at lower concentrations (MIC = 4 µg/mL; MFC > 64 µg/mL). The FAM-FLICA poly-caspase assay showed metacaspase activation in C. albicans cells at concentrations of 16 and 8 µg/mL, which agreed with the MIC and MFC values. Molecular docking and molecular dynamics simulation experiments indicated stable, strong binding of globospiramine with the target proteins 1,3-β-glucan synthase and Als3 adhesin enzymes, which are indirectly involved in apoptotic-driven candidal inhibition.

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

In this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the prediction differential model (PDM) using the global approximation capability of genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS), i.e., MWNN-GAIPAS. The famous PDM is known as a variant of functional differential system that works as an opposite of the historical delay differential models. A fitness function is optimized using the mean square error by applying the GA-IPAS for solving the PDM. Three PDM examples have been presented numerically to check the authenticity of the MWNN-GAIPAS. For the perfection of the designed MWNN-GAIPAS, the comparability of the obtained results and exact results is performed. Moreover, the neuron analysis is performed by taking the 3, 10 and 20 number of neurons. The statistical observations have been performed to authenticate the reliability of the MWNN-GAIPAS for solving the PDM.

Effectively avoiding gas accident is vital to the security of mineral manufacture, and the coal mine gas accident is often caused by gas concentration overrun. The prediction accuracy of gas emission quantity in coal mine is the key to solve this problem. To maintain concentration of gas in a secure range，grey theory and neural network model increasingly diffusely used in forecasting gas emission quantity in coal mine critically. Nevertheless, the limitation of the grey neural network model is that researchers merely bonded the conventional neural network and grey theory. To enhance accuracy of prediction, a modified grey GM(1,1) and RBF neural network model is proposed combined amended grey GM(1,1) model and RBF neural network model. Then the proposed model was put into simulation experiment which is built based on Matlab software. Ultimately, conclusion of the simulation experiment verified that the modified grey GM(1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This showed that the modified grey GM(1,1) and RBF neural network model achieves effectiveness in precision of prediction much better than grey GM(1,1) model and RBF neural network model.

Over the years, various models have been developed in the stages of the mining process that have allowed predicting and enhancing results, but it is the breakage the variable that connects all the activities of the mining process from the point of view of costs (drilling, blasting, loading, hauling, crushing and grinding). To improve this process, we come up with an idea to develop a breakage prediction model; on the basis of the main variables involved in the drilling and blasting process. For this purpose, we design a computer model based on an Artificial Neuronal Networks (ANN), built by using the most representative variables that come into play with drilling and blasting, such as: the properties of the explosives, the geomechanical parameters of the rock mass, and the design parameters of drilling-blasting. For its experimentation and validation, we have taken the data from a copper mine as reference located in the north of Chile, because of we have the dataset of that ore deposit, which is valid and reliable to evaluate prediction problems based on ANN applied to copper mines. The ANN architecture was of the supervised type, feedforward, with 3 layers and 13 neurons in the only hidden layer, trained with the input data using a dataset with the previously mentioned variables, which then were compared with the breakage results. The model was feed backed in its learning process until it becomes perfected, and is a prediction option that can be used in future blasting of ore deposits with similar characteristics using the same representative variables. Therefore, this is a valid alternative for predicting rock breakage, given that it has been experimentally validated, and has achieved moderately reliable results, providing higher correlation coefficients than traditional models, and with the additional advantage that an ANN model provides, due to its ability to learn and recognize compiled dataset patterns. In this way, using this computer model we can obtain satisfactory results that allow us to predict breakage, providing an alternative for evaluating the costs that this entails.

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.

Neurodegenerative diseases (NDs) are a set of progressive, chronic, and incurable diseases characterized by the gradual loss of neurons, culminating in the decline of cognitive and/or motor functions. Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common NDs and represent an enormous burden both in terms of human suffering and economic cost. The available therapies for AD and PD only provide symptomatic and palliative relief for a limited period and are unable to modify the diseases’ progression. Over the last decades, research efforts have been focused on developing new pharmacological treatments for these NDs. However, to date, no breakthrough treatment has been discovered. Hence, the development of disease-modifying drugs able to halt or reverse the progression of NDs remains an unmet clinical need. This review summarizes the major hallmarks of AD and PD and the drugs available for pharmacological treatment. It also sheds light on potential directions that can be pursued to develop of new, disease-modifying drugs to treat AD and PD, thereby decreasing the social and economic burdens linked to these NDs.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

Reducing the levels of toxic chemicals that cause adverse health and environmental impacts is a challenge for the international community. There is a need of a global strategy. Indeed, too often the problems associated to the exposure of chemical substances is recognized a posteriori, in the presence of consequences already observed. The prediction of the likely effects of chemical exposure on human health is based on classical tests with animals, which are time and money consuming, may deviate from an accurate prediction towards humans, and arises ethical concerns. Regulations are now considering the adoption of in silico (or computational) methods, which can be used for prioritizing substances according to the probability to be toxic for the biosphere. Several initiatives have prioritized chemicals, typically based according to some criteria, such as chemicals which may be endocrine disruptors, or persistent, bioaccumulative, or toxic (PBT). However, these initiatives focused on a certain range of adverse properties, and covered a certain number of substances only. We applied a set of largely validated and widely used predictive methods to large collections of chemicals: (i) to about 340,000 with a defined function, and (ii) to about 6 millions, which have been synthetized. The aim of this study is to quantify the putative impact of existing and future chemicals on towards human health, ecological and environment properties. The impact on the environment is the cause of major concern. This is the case of pesticides and hazardous, which is quite expected; however, also pharmacologically active candidate compounds of natural origin may have a high level of ecotoxicity. Pesticides and hazardous are also the categories of higher concern for humans, followed by pharmaceuticals. The pesticides and the hazardous are the categories of higher concern also on the environmental point of view. The results of our analysis could be the basis for the identification of new safety rules.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Growing food autonomously on Mars is challenging due to the Martian soil's low nutrient content and high salinity. Understanding how plants adapt and evaluating their nutritional attributes are pivotal for sustained Mars missions. This research delved into the regeneration, stress tolerance, and dietary metrics of sweet potato (Ipomoea batatas) across different Mars Global Simulant (MGS-1) concentrations (0, 25, 50, and 75%). In our greenhouse experiment, 75% MGS-1 concentration most significantly inhibited sweet potato growth, storage root biomass, and chlorophyll content. This concentration also elevated plant tissues' H2O2, proline, and ascorbic acid levels. Higher MGS-1 exposures (50 and 75%) notably boosted vital amino acids and sugar groups in the plant's storage roots. Yet, increased MGS-1 concentrations notably diminished the total C: N ratio and elemental composition in both vines and storage roots. In summary, sweet potato exhibited optimal growth, antioxidant properties, yield, and nutrient profiles at 25% MGS-1 exposure, compared to higher concentrations. This study underscores the need for future interventions, like nutrient enhancement and controlled metal accessibility, to render it a suitable plant for space-based studies.

In this research, a new conceptual framework introduced to analysis of water security. Using the exponential, logistic growth model, and employing the historical population of Tabriz city from 1956 to 2016, the future period (2022-2100) population predicted. Pearson Correlation, Spearman's Rho and Kendall's rank coefficients were used for analyzing correlation between population and water consumption. In addition, the amount of city water demand in the future period predicted using the ordinary least square model. Then, water stress and water scarcity indices calculated for Tabriz city in the future period. Results indicated that the mathematical population growth models are very suitable for prediction of city population. There was a significant positive correlation between the city population and water demand. In addition, the amount of water demand will proportionally increase with rising number of population. Finally, linear and quadratic equations presented for city water demand based on the city population number. It can be concluded that Tabriz city will be faced with severe water stress in the future period. The proposed new method in this study can be used for other different cities of the world. Also, Results will be helpful for decision makers in optimum management of urban water resources.

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To answer this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In the present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32 % improvement over the docked structures in terms of the change of root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements was also discussed to help their implementation in future methods and applications.

China’s greenhouse industry has thousands of years of development history, although the development of modern greenhouses has come late. After decades of development of China, its greenhouse industry is in the forefront of the world. China’s greenhouse industry is experiencing rapid development, transformation and upgrading. China ranks first in the world in terms of greenhouse area, used mainly for vegetable cultivation and plays an important role in the utilization of agricultural resources and food production. In 2023, the cumulative area of new greenhouses nationwide was 1835.87 million ha. In this paper we present the latest developments in the greenhouse sector. More specifically, we present a greenhouse market development overview, the greenhouse production, the greenhouse industry, and technology development. Current problems are underpinned, development opportunities and prospects are analyzed, and measures and specific proposals are detailed for the technological development of the greenhouse sector both for increasing product quantity and quality.

This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

Rapid screening of botanical extracts for the discovery of bioactive natural products was performed using a fractionation approach in conjunction with flow-injection high-resolution mass spectrometry for obtaining chemical fingerprints of each fraction, enabling the correlation of the relative abundance of molecular features (representing individual phytochemicals) with the read-outs of bioassays. We applied this strategy for discovering and identifying constituents of Centella asiatica (C. asiatica) that protect against Aβ cytotoxicity in vitro. C. asiatica has been associated with improving mental health and cognitive function, with potential use in Alzheimer’s disease. Human neuroblastoma MC65 cells were exposed to subfractions of an aqueous extract of C. asiatica to evaluate the protective benefit derived from these subfractions against amyloid β-cytotoxicity. The % viability score of the cells exposed to each subfraction was used in conjunction with the intensity of the molecular features in two computational models, namely Elastic Net and selectivity ratio, to determine the relationship of the peak intensity of molecular features and % viability. Finally, the correlation of mass spectral features with MC65 protection and their abundance in different sub-fractions were visualized using GNPS molecular networking. Both computational methods unequivocally identified dicaffeoylquinic acids as providing strong protection against Aβ-toxicity in MC65 cells, in agreement with the protective effects observed for these compounds in previous preclinical model studies.

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

Dendritic cells (DCs) are the most specialized antigen-presenting cells and lymph nodes (LNs) play an important role in the DC-mediated T-cell response. We evaluated the infiltration of CD1a-positive DCs (CD1a-DCs) i.e. immature DCs, and S100-positive dendritic cells (S100-DCs), a mixture of immature- and mature DCs, in 73 cases of laryngeal cancer and its regional LNs. Among them, 31 patients underwent radiotherapy (RT) or chemoradiotherapy (CRT) prior to surgery. No significant difference was found for CD1a-DC infiltration in the primary tumor, metastatic LNs and non-metastatic LNs, while S100-DCs were significantly fewer in number in the primary tumor and metastatic LNs compared to non-metastatic LNs. Cases which showed a high infiltration of S100-DCs in metastatic LNs appeared to show a favorable prognosis although statistical significance was not reached. In the RT/CRT group, infiltration of CD1a-DCs and S100-DCs were fewer in the primary tumor and metastatic LNs compared to the treatment naive group. Conversely, the RT/CRT group showed higher CD1a-DCs and S100-DCs numbers in non-metastatic LNs compared to the treatment naïve group. Thus, DC maturation in metastatic LNs plays an important role in tumor immunity in laryngeal cancer, and infiltration of DCs into the primary tumor and metastatic LNs is impaired by RT/CRT.

Computational fluid dynamics (CFD) modeling has emerged as a valuable tool for investigating complex processes like microencapsulation. This paper aims to validate the ability of CFD simula-tions to predict particle size distribution in a polymer microencapsulation process. The CFD mod-eling approach employed an Eulerian multiphase framework, incorporating a discrete population balance model to track the evolution of the droplet population. A realizable k-ε turbulence model and a multiple reference frame strategy were utilized to capture the system’s flow dynamics. The results reveal that while the CFD simulations align well with experimental data at higher agitation speeds (>10000 rpm), discrepancies arise at lower speeds (

Gastric cancer is the fifth most common and fourth deadliest cancer worldwide, with a bleak 5-year survival rate of about 20%. Despite significant research into its pathobiology, prognostic predictability remains insufficient due to pathologists’ heavy workloads and the potential for diagnostic errors. Consequently, there is a pressing need for automated and precise histopathological diagnostic tools. This study leverages Machine Learning and Deep Learning techniques to classify histopathological images into healthy and cancerous categories. By utilizing both handcrafted and deep features and shallow learning classifiers on the GasHisSDB dataset, we conduct a comparative analysis to identify the most effective combinations of features and classifiers for differentiating normal from abnormal histopathological images without employing fine-tuning strategies. Our methodology achieves an accuracy of 95% with the SVM classifier, underscoring the effectiveness of feature fusion strategies. Additionally, cross-magnification experiments produced promising results with accuracies close to 80% and 90% when testing the models on unseen testing images with different resolutions.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

We present a general process for the halting problem, valid regardless of the time and space computational complexity of the decision problem. It can be interpreted as the maximization of entropy for the utility function of a given Shannon-Kolmogorov-Bernoulli process. Applications to non-polynomials problems are given. The new interpretation of information rate proposed in this work is a method that models the solution space boundaries of any decision problem (and non polynomial problems in general) as a communication channel by means of Information Theory. We described a sort method that order objects using the intrinsic information content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. The limits of the search space are defined by the Kolmogorov-Chaitin complexity of the sequences encoded as Shannon-Bernoulli strings. We conclude with a discussion about the implications for general decision problems in Turing machines.

This study aims to categorize the morphological changes during cabbage (B. oleracea ssp. capitata) development: seedling, rosette, folding, and heading and to elucidate the cellular mechanisms of leaf curvature, essential for the formation of the leafy head. We followed the growth of two cabbage morphotypes with distinct head shapes (round and pointed); we phenotyped the size and volume of the whole-plant as well as the size, shape, and curvature of the leaves during growth. By integrating these phenotypic data, we determined the four vegetative stages for both cabbages. Cytological phenotypes of microtome sections from five distinct leaf positions of the rosette, folding, and heading leaves at two timepoints during leaf growth were quantified and revealed variations in cellular parameters among leaf types, between leaf positions, and between the adaxial and abaxial sides. We identified two synergistic cellular mechanisms contributing to the curvature of heading leaves: differential growth across the leaf blade, with increased growth at the leaf's centre relative to the margins; and increased expansion of the spongy parenchyma layer compared to the palisade parenchyma layer, resulting in the direction of the curvature, which is inwards. These two processes together contribute to the typical leafy heads of cabbages.

In recent years, Ethiopia's number of returnee entrepreneurs has increased dramatically, significantly contributing to personal financial achievement and the nation's economic development and transformation. However, their businesses are typically smaller and less profitable than non-migrant entrepreneurs. This study explores factors affecting the success of these returnee migrant entrepreneurs, including individual characteristics, entrepreneurial skills, and experience, financial resources, as well as the mediating role of government policies and institutional support. Data was collected from 390 returnee migrants who started their businesses and analyzed using a robust theoretical foundation and PLS-SEM to understand the impact of success factors. The results show that entrepreneurial skills, experience, and financial resources positively and significantly influence the success of returnee migrant entrepreneurs. Furthermore, social networks, while not directly significantly influencing the entrepreneurial success of returnee migrants are mediated by government policies and institutional support. Professional guidance and counseling provided by experts and mentors of the accelerator program were found to be essential for entrepreneurial success but did not improve the effectiveness of the policy. Financial resources, although crucial, did not significantly impact policy efficacy. These findings underscore the need for tailored policy interventions addressing the challenges faced by returnee migrants, including economic self-sufficiency, socio-psychological support, and effective social networks to boost the success of returnee migrants. Experienced returnee entrepreneurs play a key role in developing a vigorous and supportive entrepreneurial ecosystem through community engagement and policy advocacy. Additionally, personal traits are crucial for entrepreneurial success, while practical experience influences government policies for substantiated policy development in returnee migrant entrepreneurship and its benefits.

This study presents extended Immunity Agent-Based Model (IABM) simulations to evaluate vaccination strategies in controlling the spread of infectious diseases. The application of IABM in the analysis of vaccination configurations is innovative, as a vaccinated individual can be infected depending on how their immune system acts against the invading pathogen, without a pre-established infection rate. Analysis at the microscopic level demonstrates the impact of vaccination on individual immune responses and infection outcomes, providing a more realistic representation of how the humoral response caused by vaccination affects the individual’s immune defense. At the macroscopic level, the effects of different population-wide vaccination strategies are explored, including random vaccination, targeted vaccination of specific demographic groups, and spatially focused vaccination. The results indicate that increased vaccination rates are correlated with decreased infection and mortality rates, highlighting the importance of achieving herd immunity. Furthermore, strategies focused on vulnerable populations or densely populated regions prove to be more effective in reducing disease transmission compared to randomly distributed vaccination. The results presented in this work show that vaccination strategies focused on highly crowded regions are more efficient in controlling epidemics and outbreaks. Results suggest that applying vaccination only in the densest region resulted in the suppression of infection in that region, with less intense viral spread in areas with lower population densities. Strategies focused on specific regions, in addition to being more efficient in reducing the number of infected and dead people, reduce costs related to transportation, storage, and distribution of doses compared to the random vaccination strategy. Considering that, despite scientific efforts to consolidate the use of mass vaccination, the accessibility, affordability, and acceptability of vaccines are problems that persist, investing in the study of strategies that mitigate such issues is crucial in the development and application of government policies that make immunization systems more efficient and robust.

This study developed a system dynamics model to understand the spread of coronavirus disease (COVID-19) in Japan. The model is built on the generic SEIR (Susceptible, Exposed, Infected, and Recovered) framework adopted by Ghaffarzadegan and Rahmandad (2020) to build a system dynamics model for the spread of COVID-19 in Iran. Japan seems to be successful in containing the spread compared with other countries, and its first peak has passed. However, because this also leaves a large population still susceptible to the virus, it could cause a second and potentially higher peak of infection after the state of emergency aimed at reducing contact rate is lifted. As the government has proposed the “New Lifestyle,” it is critical to behave cautiously so as not to be infected. While the model focuses on the SEIR structure, the reflection of other sub-structures such as economy and hospital capacity that have tradeoffs with reducing contact rate should be beneficial. However, before extending the model, it is also critical to conduct estimates using confidence intervals rather than point estimates to better reflect uncertainties.

Background: Outbreak of the Covid-19 is now an ongoing global health emergency. At the end of December 2019, the first infection was reported in Wuhan and the world did not pay attention to this extremely contaminated disease and plucked to react rapidly. The World is in an vulnerable state in disease spreading, facing a great loss of lives and socio-economic aspects also. That is why we have proposed a potential mathematical model with data analysis to predict and control the outcome of this pandemic. Methods: The model presented the epidemic dynamics of multiple compartments. We collected available online data of Spain. In primary step, we estimated the parameters using either the data analysis or reference papers. Then we did the data fitting analysis in comparison with the outcome of our mathematical results. The results of the system depended not only the parameters also on social consciousness. Results: It is found that disease progression in this model is determined by the basic reproductive ratio, $R_0$, the actual epidemic of $R_0$ and effective $R(t)$ of each day. If $R_0>1$, the number of latently infected individuals grows exponentially; endemic solution is stable while infection rate decays if $R_0<1$. The optimal control theory stated that vaccination and treatment strategies are highly effective for reducing both susceptible and infected population and to increase the recover rate high. In Spain, after state of alarm (quarantine) on 14 March 2020, reported cases increasing for 13 days only and from the 14th day, daily reported cases started to decline albeit with small fluctuation. Our proposed model approximates that the disease in Spain could be fully under control by after July 2020. Conclusion: Outbreak will be in control of health care system, reduce the death rate and will ensure social-economic stability.

Metazoans have an elaborate and functionally segmented body. It evolves from a single cell by systematic divisions. Metazoans attain structural complexity with exquisite precision, which is a molecular mystery. The indispensable role of centrioles in cell division and ciliogenesis can shed insight into this riddle. Cell division helps in growth of the body and is a highly regulated and integrated process. Its errors cause malignancies. The cell mass is organized during organogenesis. Prior to it, the centrioles are retrieved from the cell cycle to initiate ciliogenesis. The cilia-modulated developmental signaling pathways elaborate the body plan. The secluded compartment of the cilium reduces noise during signaling and is essential for a precise body plan development. The dysfunctional centrioles and cilia can distort body plan. Thus, centriole has a dual role in growth and cellular organization. This concept review analyses the comprehensive interactome and the key domain features (like C2 domain) of molecules which connect and disarm the centriole from the cell cycle and ciliogenesis by switching on or off the essential regulators of the pathways. The concentration of these signaling pathways at the centriole reinforces the hypothesis that centriole is the molecular workstation to carve out structural design and complexity in metazoans.

Rare Earth Elements (REE), also known as Lanthanides (Ln3+), are a group of 17 elements showing peculiar physical and chemical properties. Unlike technological applications, very little is known about the physiological role and toxicity of Ln3+ on biological systems, in particular on microorganisms (e.g., bacteria), which represent the most abundant domains on our planet. Up to now, very limited studies have been conducted due to Ln3+ precipitation with some anions commonly present in the culture media. Therefore, the development of a minimal medium is essential to allow the study of Ln3+-microbial interactions, limiting considerably the precipitation as insoluble salts. In this regard, a new minimal culture medium capable of solubilizing large amounts of Ln3+, and allowing the growth of different microbial taxa, was successfully developed. Assays have shown that the medium is capable of solubilizing Ln3+ up to 100 times more than other common culture media and allowed the growth of 63 bacteria and 5 fungi. The kinetic growth of one yeast and one gram-positive bacterium has been defined, proving to support superior growth and biomass compared to other commonly used minimal media. Moreover, the sensitivity and uptake/absorption of a Bacillus stratosphericus strain was tested, highlighting its capability to tolerate concentrations up to 10 mM of either Cerium, Gadolinium or Lanthanum and accumulate different quantities of the three.

Microsporidia are intracellular parasites infecting a wide range of vertebrates and invertebrates. In bees, they cause a diarrheal disease called nosematosis, which represents a growing problem contributing to the decline of bee colonies. This parasitic disease infects the gastrointestinal tract of bees, disrupts their immune system, weakens them, and shortens their lifespan. There are two known species of this microsporidial parasite with a global distribution: Vairimorpha apis and Vairimorpha ceranae, with current research indicating that the species V. ceranae is gradually replacing V. apis. In this study, we focused on the detection and occurrence of Vairimorpha spp. in bee colonies kept in different environments, as well as on comparing the impact of hive location on its occurrence. The methods used included sampling from winter bee carcasses, microscopic examination of bee gut contents, and subsequent molecular duplex PCR analysis. Altogether, we examined 82 hives located at 4 different sites and focused on the impact of transportation as an abiotic factor disturbing bees leading to the development of Vairimorpha spp. disease. The results indicate an increased occurrence of this parasite in hives located in urban sites and hives situated in intravillage locations, which may be attributed to transportation and associated vibrations as an abiotic stress factor. Detection revealed that all samples were positive for Vairimorpha ceranae.

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a groundbreaking integrated model, creatively fusing the unique strengths of Graph Convolutional Network (GCN), Residual Neural Network (ResNet), and Long Short-Term Memory Network (LSTM) into a single architecture, which we name GraphResLSTM, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

The Dph1•Dph2 heterodimer from yeast is a radical SAM (RS) enzyme that generates the 3-amino-3-carboxy-propyl (ACP) precursor for diphthamide, a clinically relevant modification on eukaryotic elongation factor 2 (eEF2). ACP formation requires SAM cleavage and atypical Cys-bound Fe-S clusters in each Dph1 and Dph2 subunit. Intriguingly, the first Cys residue in each motif locates next to another, ill-defined cysteine that we show in-here is conserved across eukaryotes. As judged from structural modeling, the orientation of these tandem cysteine motifs (TCM) suggests a candidate Fe-S cluster ligand role. Hence, we generated by site-directed DPH1 and DPH2 mutagenesis, Dph1•Dph2 variants with cysteines from each TCM replaced individually or in combination by serines. Assays diagnostic for diphthamide formation in vivo reveal that while single substitutions in the TCM of Dph2 cause mild defects, double mutations almost entirely inactivate the RS enzyme. Based on enhanced Dph1 and Dph2 subunit instability in response to cycloheximide chases, the variants with Cys substitutions in their cofactor motifs are particularly prone to protein degradation. In sum, we identify a fourth functionally cooperative Cys residue within the Fe-S motif of Dph2 and show that the Cys-based cofactor binding motifs in Dph1 and Dph2 are critical for the structural integrity of the dimeric RS enzyme in vivo.

Carbon (C) losses due to the conversion of natural forests adversely affect the abiotic components of terrestrial ecosystems. In tropical China, rubber cultivation often extends from its traditional range to elevations of up to 1400 m. However, C storage in rubber plantations across elevation gradients is poorly understood. In this study, we investigated biomass and C storage along elevation gradients in two age groups (8- and 12-year-old) of rubber monoculture plantations in Xishuangbanna, Southwest China. We investigated the C distribution across various tree sections, ranging from aboveground biomass (AGB) to belowground biomass (BGB), including litter, big dead branches, and different soil depths. A significant negative correlation was observed between total ecosystem C stocks and elevation in both age groups of rubber plantations. The highest ecosystem C stock of 197.90 Mg C ha–1 was recorded at 900 m in 8-year-old plantations, whereas in 12-year-old rubber plantations, the highest value of 183.12 Mg C ha–1 was found at 700 m. The total ecosystem C stocks decreased to its lowest level at 1000 m in both the 8-year-old and 12-year-old plantations, ranging between 113.05 Mg C ha–1 and 125.75 Mg C ha–1, respectively. We concluded that the total ecosystem C stocks were negatively correlated with elevation and significantly decreased from 8.05% to 51.55% and 11.46% to 42.96% between 700 m and 1100 m in both 8-year-old and 12-year-old plantations, respectively. However, no correlation was observed between the total soil C stocks and elevation in either age group of rubber plantations. Regardless of elevation gradient, the total ecosystem C stock of 12-year-old rubber plantations was 1.98% greater than that of 8-year-old rubber plantations. Biomass was the second largest contributor, while soil accounted for 82% to 90%, and the other factors contributed less than 2% of the total ecosystem C stock in both age groups. These fluctuations in C stocks along elevation gradients in both 8- and 12-year-old plantations suggested that rubber growth, biomass and C storage capacity decreased above 900 m and that age and elevation are key factors for biomass and C storage in monoculture rubber plantations.

Interferon gamma (IFNg) is a pleiotropic cytokine that can potentially reprogramme the tumour microenvironment. However, the antitumour immunomodulatory properties of IFNg still need to be validated due to variable therapeutic outcomes in preclinical and clinical studies. We developed a replication-deficient Semliki Forest virus vector expressing IFNg (SFV/IFNg) and evaluated its immunomodulatory antitumour potential in vitro in a model of 3D spheroids and in vivo in immunocompetent 4T1 mouse breast cancer model. We demonstrated that SFV-derived IFN-g stimulated bone marrow macrophages to acquire the tumoricidal M1 phenotype in 3D nonattached conditions. Coculturing SFV/IFNg-infected 4T1 spheroids with BMDMs inhibited spheroid growth. In the orthotopic 4T1 mouse model, intratumoural administration of SFV/IFNg virus particles alone or in combination with the Pam3CSK4 TLR2/1 ligand led to significant inhibition of tumour growth compared to the administration of the control SFV/Luc virus particles. Analysis of the composition of intra-tumoural lymphoid cells isolated from tumours after SFV/IFNg treatment revealed an increase in CD4+ and CD8+ and a decrease in T-reg (CD4+/CD25+/FoxP3+) cell populations. Furthermore, a significant decrease in the populations of cells bearing myeloid cell markers CD11b, CD38 and CD206 was observed. In conclusion, the SFV/IFNg vector induces a therapeutic antitumour T-cell response and inhibits myeloid cell infiltration in treated tumours.

Patients with heart failure have difficulty in self-care management, as daily monitoring and recognizing symptoms do not readily trigger an action to avoid hospital admissions. The purpose of this study was to understand the impact of a nurse-led complex intervention on symptom recognition and fluid restriction. A latent growth model was designed to estimate self-care management and quality of life changes on patients with heart failure and assessed by a pilot study, for three months, to sixty-three patients (33 control, 30 intervention). Patients in the control group had a higher risk of hospitalisation (IRR 11.36; p<.001) and emergency admission (IRR 4.24; p<.001) at three-months follow-up. Analysis of the time scores demonstrated that the intervention group had a clear improvement in self-care behaviours (βSlope. Assign-ment_group=-.881; p<.001) and in the quality of life (βSlope. Assignment_group=1.739; p<.001). This study supports that a nurse-led program on symptom recognition and fluid restriction can have a positive impact on self-care behaviours and quality of life in patients with heart failure.

In most econometrics literature, the Autoregressive Distributed Lag (ARDL) model is often applied in many economic analyses to study short and long run relationships. This is because ARDL model can deal with economic variables that are integrated of different order (I(0), I(1) or combination of both) and also it is robust where there is single long-run relationship between the underlying variables in a simple sample size. This study applied the ARDL model to examine the contributions of commercial Banks to GDP growth in Nigeria. To achieve this, annual data covering 1981 to 2015 for loans and advances, savings, lending rates and GDP of Financial Institutions were collected from CBN bulletin. The ADF test revealed that the variables are I(1) except for lending rate which was of I(0) order. The ARDL(1,1,1,2) model revealed that loans and advances, and lending rates are significantly positively related to GDP in Nigeria but savings was not significant in the model. The model revealed some evidence of short run relationships while the ecm(-1) was -0.6156 (P-value=0.0038<0.05) which means that the rate of the speed of adjustment to equilibrium is 61.56% annually. The estimated model is free from serial correlation, multicollinearity, heteroscedasticity while the model is stable and the residuals are normally distributed. The study recommends that savings and savings culture should be encouraged in Nigeria since economic theory states that savings and investment are related in any economic development.

Lactoperoxidase (LP), which is a high-value minor whey protein, has recently drawn extensive attention from research scientists and industry due to its multi-function and potential therapeutic applications. In this study, the separation and optimization of two similar-sized proteins, LP and lactoferrin (LF) were investigated using strong cation exchange column chromatography. Optimization was started with central composite design based experiments to characterize the importance of different decision variables. The three variables used in the optimization were flow rate, length of gradient and final salt concentration in the linear elution gradient step. The obtained empiric functional model represented the effect of the significant factors on the yield as the objective function. Afterwards, the calibrated mechanistic model was employed to predict accurate optimal set of variables. The optimal operating points were found and the results were compared with validation experiments. Predictions respecting yield confirmed a very good agreement with experimental results while keeping purity, a product quality characteristic, equal or above to a predefined value.

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited disorder characterized by renal tubular cystic dilatations. The cysts can develop anywhere along the nephron and over time the cystic dilatation lead to kidney enlargement. On the other hand, the cysts begin to reduce the number of functional nephrons as a consequence of cystic expansion that further contributes to the decline in renal function over the years. The pressure exerted by the dilated cysts lead to compensatory mechanisms that further contribute to the decline in renal function. These structural changes are responsible of glomerular hyperfiltration states, albuminuria, proteinuria, and haematuria. However, the presentation of ADPKD varies in children from a completely asymptomatic child with incidental ultrasound detection of cysts to a rapidly progressive disease. There have been reports of early onset of ADPKD in children younger than 2 years that showed a more rapid decline in renal function. ADPKD is caused by mutation in PKD1 and PKD2 genes. Today, the PKD1 gene mutation seems to account for up to 85% of the cases worldwide, and it is associated with worse renal outcomes. Individuals with PKD2 gene mutation seem to present a milder form of the disease, with a more delayed onset of end stage kidney disease. The cardinal sign of ADPKD is the presence of renal cysts during renal ultrasound. The current guidelines provide the clinicians the recommendations for genetic testing in children with a positive family history. Given that the vast majority of children with ADPKD present with normal or supra normal kidney function we explored the glomerular filtration rates dynamics and the renal ultrasound adjusted percentiles. 14 out of 16 patients had kidney percentile over 90%. The gene mutations were equally distributed among our cohort. In addition, we compared the modified Schwartz formula to the quadratic equation after adjusting the serum creatinine measurements. It seems that even though children with ADPKD have enlarged kidneys, the renal function is more likely normal or near normal when the quadratic estimation of glomerular filtration rate is used (qGFR tended to be lower, 111.95 ± 12.43 ml/min/1.73m2 compared to Schwartz eGFR 126.28 ± 33.07ml/min/1.73m2, p=0.14). Also, when the quadratic equation was employed, not even a patient reached the glomerular hyperfiltration thresholds. The quadratic formula showed that glomerular filtration rates are linear or slightly decreasing after 1-year of follow-up (Quadratic ΔeGFR=0.32±5.78ml/min/1.73m2) as opposed to Schwartz formula that can falsely classify children in a hyperfiltration state ΔeGFR=7.51±19.46ml/min/1.73m2), p=0.019.

Aortic aneurysms (AA) occur in 4.8% of people causing 150,000 deaths annually. While endovascular aneurysm repairs reduce surgical morbidity, device-related failures (leak/displacement) are frequent highlighting the need for test models that better represent the mural geometry and compliance changes in human AAs. We aimed to develop and characterise an ex vivo porcine aortic model of AA. The optimal duration of tissue elastase exposure to emulate AA changes in elastin microstructure and content was determined using porcine aortic rings. Elastase-induced changes were quantified morphologically, and mechanical properties assessed via ring tensile testing. Subsequent experiments tested the potential for localised elastase treatment in a 1 cm segment of porcine aorta using a specially designed 3D printed test rig. The effect on pressure-diameter behaviour was investigated via inflation-extension testing. Elastase treatment produced time dependent decreases in elastin, resulting in an increased tensile modulus and circumferential length in the ring samples in the final phase of the J-shaped tissue stress-strain curves. In whole aortic segments, localised elastase-induced luminal degradation was successfully limited to a central region. The degree of elastin degradation achieved was sufficient to cause localised dilation with respect to controls under physiological pressures. Localised elastin degradation in porcine aortic segments is feasible and emulates the changes seen clinically in aortic aneurysms.

Hydrothermal /super critical processes are important process to synthesize materials which are otherwise difficult to form under normal conditions. A mathematical model is developed using standard transport equations to calculate the time for heating of hydrothermal reactor and computer simulation of the model was carried out in SOLIDWORKS® to validate it. The materials used to form reactor vessel were stainless steel (outer body) and TEFLON (PFA) (inner vessel). It was shown that composite wall, its geometry, construction & properties greatly affect the time and pattern of heat transfer. The time calculated and pattern generated were found to be in good agreement with experimental values.

This work aims to investigate the effect of Pt concentration on crystal growth mechanism of Platinum-Palladium (Pt-Pd) binary alloy system during the annealing process starting from amorphous phase until some definite temperatures. The calculations have been performed by using molecular dynamic (MD) simulations. Interatomic interactions are described by on Sutton-Chen type Embedded Atom Potential Energy function. In order to understand the main structural properties at the stable and unstable phases, changes in RDF curves versus time have been analysed for different annealing temperatures. Crystalline type bonded pairs have been determined using MD calculations which is required for the computation of Avrami coefficients and for understanding crystal growth mechanism. The results demonstrate that the increase in concentration of Pt during annealing leads to migration of atoms in the crystal lattice points, elimination of dislocations and formation of perfect crystal structure.

Background: Disparity in adult mortality (AM) with reference to social dynamics and health care has not been sufficiently examined. This study aimed to identify the gap in the understanding of AM in relation to religion, political stability, economic level, and universal health coverage (UHC). Methods: A cross-national study was performed with different sources of data, using the administrative record linkage theory. We created a new data set using data from the 2013 World Bank data catalogue by region, The Economist (Political instability index 2013), Stuckler David et al. (Universal health coverage, 2010), and religious categories of all UN country members. Descriptive statistics, a t-test, an ANOVA followed by a post hoc test, and a linear regression were used where applicable. Result: The average AM rate for males and females was 0.20 ± 0.10 and 0.14 ± 0.10, respectively. AM was significantly higher in economically weak countries, countries with political instability, countries with traditional religion, without achievement of UHC, and Sub-Saharan Africa (p<0.01). There was high disparity of AM between countries with and without UHC (F = male: 61.89, female: 51.85, p<0.001) and between groups with low and high income (F = male: 36.33, female: 42.39, p<0.001). UHC and political stability would significantly reduce AMR by > 0.41 in both sexes and high economic status would reduce male AMR by 0.44, and female AMR by 0.70, in relation to countries without UHC, with political instability, and low economic status. Conclusions: Disparities in AM can be reduced after the achievement of UHC and economically productive activities for those adults affected by conflict and political unrest.

: This study explores the perceptions of Chinese learners in Spain regarding the use of social networks for informal Spanish language learning. The objective is to identify the challenges and benefits of using social networks to address the real needs of students in learning Spanish. A qualitative phenomenological approach was adopted, focusing on participants’ perceptions before and after using social networks. Eight Chinese students were selected for the study. The study was conducted in Valencia and Barcelona, Spain, from 1 September 2023 to 20 March 2024, and three commonly used social media networks were compared. The results indicate positive perceptions towards social media as a tool for learning Spanish, highlighting its usefulness in improving language skills and enhancing cultural awareness. Additionally, Xiaohongshu and Bilibili emerged as the most popular platforms for Spanish language learning among Chinese students. This study concludes that social media effectively meets the authentic needs of Chinese students learning Spanish in Spain, enhancing both language skills and cultural adaptation. This multifaceted approach reflects the complexity of learning Spanish in the digital age, combining personal passion, professional aspirations, and cultural adaptation needs.

This study was performed to evaluate the efficacy of nicotinic acid against Streptococcus pneumoniae biofilm. The experimental results indicate that S. pneumoniae forms biofilms, particularly at the liquid-air interface, as evidenced by the pellicle assay. Nicotinic acid demonstrated significant inhibition of S. pneumoniae biofilm formation at 6 h and 18 h, with higher concentrations leading to lower biofilm biomass, suggesting a concentration-dependent response. However, minimal biofilm inhibition was observed at 12 h. Treatment with nicotinic acid induced FTIR spectral changes in protein biomolecules of S. pneumoniae biofilm at 6 h and 18 h, evidenced by alterations in spectral peaks at 750 cm-1, 906 cm-1, 1540 cm-1, 1548 cm-1, and 1644 cm-1. Notably, no FTIR spectral changes were detected in 12 h S. pneumoniae biofilm following exposure to nicotinic acid. These findings suggest that nicotinic acid inhibits biofilm formation in a time and concentration-dependent manner, possibly by altering the structure of protein biomolecules within the biofilm.

Predators are dependent on the capture of prey to meet their energetic and nutritive requirements, imposing a regime of predation risk. Consequently, prey experience both consumptive and non-consumptive effects. Non-consumptive effects may manifest through altered growth and ontogenetic trajectories of prey species, a dynamic modulated by olfactory or other sensory cues from predators. Bactrocera dorsalis Hendel represents a great invasive threat to global horticulture. While earlier research has primarily centered on the consumptive interactions between B. dorsalis and its natural enemies, the potential consequences of non-consumptive interactions on the development of B. dorsalis have been overlooked. In this study, we investigated the impact of predation risk effects, induced by both visual exposure to the predatory mantis Hierodula patellifera Serville and its associated odor, on the life history traits of B. dorsalis. Female B. dorsalis demonstrated reduced development time in the presence of isolated H. patellifera or H. patellifera odor, yet evidenced significantly increased fecundity. Conversely, males displayed no significant change in development time. Additionally, neither female nor male body weight at death was significantly influenced by predation risk from isolated predators or predator odor. These results indicated adaptive developmental strategies of B. dorsalis when faced with risks from predators and highlight the important role of predator odor, which has the potential to lay the groundwork for improving biological control efforts and innovative approaches within pest control.

In this paper, the transmutation operator method is used to construct an exact solution to the initial boundary value problem for the equation of multidimensional free transverse vibration of a thin elastic plate with a singular Bessel operator acting on geometric variables. We emphasize that multidimensional Erdélyi–Kober operators of fractional order have the property of a transmutation operator, allowing one to transform more complex multidimensional partial differential equations with singular coefficients acting over all variables into simpler ones. If formulas for solutions are known for a simple equation, then we also obtain representations for solutions to the first complex partial differential equation with singular coefficients. In particular, it is successfully applied to the singular differential equations, especially with Bessel-type operators. Using this operator, the considered problem is reduced to a similar problem without the Bessel operator. Based on the solution to the auxiliary problem, an exact solution to the original problem is constructed and analyzed.

Density functional theory (DFT) calculations of hydrogen adsorption on titanium nitride had previously shown that hydrogen may adsorb on both titanium and nitrogen sites with a moderate adsorption energy. Further, the diffusion barrier was also found to be low. These findings may qualify TiN, a versatile multifunctional material with electronic conductivity, as electrode material for the hydrogen evolution reaction (HER). This was the main impetus of this work which aims to experimentally and theoretically investigate the electrocatalytic properties of TiN-layers that were processed on Ti substrate using reactive ion sputtering. The properties are discussed focusing on the role of oxygen defects introduced during the sputtering process on the HER. Based on DFT calculations, it is shown that these oxygen defects alter the electronic environment of the Ti atoms which entails a low hydrogen adsorption energy in the range of -0.1 eV; this leads to HER performances that match those of Pt-NPs in acidic media. When a few nanometr thick layer of Pd-NPs is sputtered on-top of the TiN-layer, the performance is drastically reduced. This is interpreted in terms of oxygen defects being scavenged by the Pd-NPs near the surface which is thought to reduce the hydrogen adsorption sites.

Transformation formula under the action of a general linear fractional transformation for generalized Dedekind eta-function has been a subject of intensive study, Rademacher, Dieter, Meyer et al. However, the (Hecke) modular relation structure has not been recognized until the work of Goldstein-de la Torre, and streamlining the classical proofs in the modular relation will reveal the meaning hidden in those works. Our main aim is to elucidate the works of these researchers in the context of modular relations.

It is often said in human society that it all starts from the mind, and this is true. The answer to the question “Why is it that pets require much fewer years to fully develop than humans?” could be headed to the Theory of Relativity and the perceived time according to the levels of instinct and rationality. It is perhaps no wonder that the French language translates the word “mind” as “l’esprit”, which also means “spirit” in the English language. Given that the Theory of Relativity applies to all physical matter, the theory applies to chemistry, biology and psychology as well. The level of instinct is proportional with the perceived speed of time and implicitly, to the speed of time itself for the whole organism, given that the nervous system coordinates all bodily functions. A pet may experience a few years as humans experience over a decade, making their perceived lifetime to be approximately equal to the human’s. The same applies to the perception of space and its components. This aspect would emphasise upon the fact that all life on Earth shows the same degree of value with regards to uniqueness. Nevertheless, the values represented by the hierarchy of competences among living organisms at the same time differs based on species. Honour of uniqueness and hierarchy of competences represent two scientific concepts that never overcross. As a result, there is no evidence to suggest that scientific facts as such constitute a form of contradiction to philosophical and religious ideas, given that religion refers to animals as companions of humans. With regards to the clock of the living organism; there are three kinds of clocks in a living organism: the emotional clock, the cognitive clock and the molecular clock. These three categories actually constitute the temporal layers of our dimension, so they are in a relationship of interdependence. When one has a feeling that time is slower or quicker, they are not fully experiencing that change in the time speed, but only partially. And one can profoundly experience the change of perceived time speed when they experience profound feelings, which sweep them away from reality. Complete, deep mental focus can bring about this profound change in perception, which will make the individual feel as if they are in a different form of reality. When one is more instinctive, the person in cause perceives time as slower and sees more opportunities within the same frame of time and space. When one experiences the “Fight or flight” mode, the person in the cause finds themselves to be in danger and has two options; to try to prevail over the danger or to flee it. Whilst experiencing such a mode, people are much more alert and therefore, time passes more slowly and the physical space dilates. When people are relaxed, however, time and space become smaller, and when they are in a deep state of resting, they become very small. And where there is a smaller dimension, there is a greater speed, just as the circulating air gains speed when it crosses narrow passages.

The theories of Evolution and Big Bang have brought a major impact upon the scientific and societal perception upon the origins, abilities and roles of mankind on Earth. It is conclusively evident that Mother Nature and the physical realm is autonomous and all phenomena in the world occur without an imposition against the “free will” of such events and patterns. Nevertheless, this extent of freedom does not imply that life on Earth represents an accident and has no purpose. By careful analysis of ancient religious texts, it can be suggested even otherwise, that it is especially free will that is offered as a gift from outside of the physical dimensions of existence, with the purpose of a continuous edification and improvement of the overall life conditions and of the human perception. It may be that the multi-generational focus on improvement and progress have been misattributed as a long-term evolution of species, that humans came from animal ancestors and that there is a blurred line associating humans with animals. Furthermore, it may be that scientists based their suggestions that no outside Creator would have brought the Earth into existence on physical perspectives, when the extra-dimensional process of Creation implies that all elements of the physical existence undergo a process of Creation from the outside, where time, space and physical matter do not exist, in spite of such elements possibly reflecting unseen elements of the meta-physical world. Finally, there is the following example that deserves attention within the scientific community; just because all living cells - human, animal and plant - contain DNA chromosomes, ribosomes, mitochondria and a plasma membrane, it does not indicate that humans are animals, just as this does not indicate that plants are animals. In Science, there is a golden statement, that correlation does not imply causation. This example is in complete agreement with the verses of the Book of Genesis in the Holy Scriptures regarding the origins of life; namely, that animals could have been created by an Author of all life on Earth so that man would experience companionship. Another major accordance with the teachings of the Holy Scriptures is the fact that life emerged from the water, and not from the soil, although the soil constituted the second principal means of the commencement and extension of life. Namely, after life first emerged in the seas and the oceans from bacteria, a few bacteria were passed from the marine water into the nearby soil on islands and surrounding land through the circulatory air above the sea level. Afterward, the first life forms emerged from the soil, via the transferred bacteria. Eastern Orthodox Christianity regards water as the principal environment of both physical and spiritual birth. Overall, an improved scientific perception of the timeline of life on Earth may be reached via an approach that combines molecular biology with quantum physics, given that the three layers of Science - Biology, Chemistry and Physics - together with their foundational layer of Mathematics, are in fact situated in a relationship of complete harmony.

We present a novel approach for adapting text written in standard Finnish to different dialects. We experiment with character level NMT models both by using a multi-dialectal and transfer learning approaches. The models are tested with over 20 different dialects. The results seem to favor transfer learning, although not strongly over the multi-dialectal approach. We study the influence dialectal adaptation has on perceived creativity of computer generated poetry. Our results suggest that the more the dialect deviates from the standard Finnish, the lower scores people tend to give on an existing evaluation metric. However, on a word association test, people associate creativity and originality more with dialect and fluency more with standard Finnish.

Background: Peloids are therapeutic agents used in thermal and thalasso centers for curative, preventive, and skin care purposes. Marine peloids are those that include seawater and algae, both macro and/or microalgae. The aim of this study is to investigate the suitability of a marine peloid composed by seawater, clay and microalgae for thalasso-wellness and dermocosmetic uses; Methods: the thermophysical properties of the marine peloid were determined and compared with a non-marine peloid using different techniques as densimetry, conductimetry, calorimetry, and viscometry; skin hydration was also measured by means of Corneometry; Results: marine peloid has been shown to be suitable for thermotherapy and thalaso-wellness treatments as its thermophysical properties were similar to other peloids used for this purpose; marine peloids also showed to improve skin hydration after seven days of treatment; Conclusions: this research provides a comprehensive study of the thermophysical properties of a marine peloid composed by clay, seawater and microalgae for thalasso-wellness and cosmetic uses, as well as its moisturizing properties, highlighting its potential as a valuable natural product for skin care applications as well as for wellness treatments in form of cataplasm, poultices and/or wraps.

Mangrove plant seedling cultivation is crucial for the protection, management and restoration of mangrove ecosystem. In this study, we focused on Kandelia obovata, a typical mangrove, and evaluated nursery cultivation with different combinations of three salinity levels (S1: 0 ppt, S2: 10 ppt, and S3: 20 ppt), three genealogies (EZD, JX, and YZ), and five growth media (M1: 100% loess, M2: 100% sandy, M3: 50% loess + 50% sandy, M4: 40% loess + 40% sandy + 20% peat, and M5: 40% loess + 40% sandy + 20% coir), by measuring the growth parameters such as mortality rate, seedling height, diameter, and biomass partition. These growth indexes were significantly affected by salinity and medium, and genealogies also had significant effects on mortality rate and biomass accumulation. S2 or S3 both had lower mortality and higher growth indexes than S1. M1 was the medium that increased seedlings height, diameter and biomass the most and had the lowest death rate. EZD and JX were also higher levels than YZ in these indicators, but the difference between them was not obvious. S3, M1, and EZD consistently performed well in fuzzy evaluation and quality assessment. Furthermore, combinations involving these treatments also produced highly favorable results. These results furnish both a theoretical and practical foundation for advancing nursery cultivation techniques and germplasm breeding of K. obovata in mangroves.

COVID-19 pandemic disproportionately affected seven South Asian countries because of rampaging virus and weak health system response. Vaccination commenced in all countries during early 2021, once the vaccines became available in the international market. Vaccines were donated by international development partners, friendly countries and procured utilizing domestic resources and financial support from various agencies. All countries opted for a phasing of the vaccination by prioritizing high risk populations which included health care personnel, front line workers and elderly with comorbidities. Seven vaccines that had WHO emergency authorization usage were utilized in South Asia. India produced more than 2.4 billion doses of AstraZeneca vaccine and has indigenously developed five COVID-19 vaccines. Vaccine inequity was obvious and characterized by fewer people in South Asia vaccinated with single dose vis a vis the developed countries. Vaccine hesitancy was another obstacle that kept the vaccination rates low. Difficult to reach areas in Afghanistan, Bhutan, Maldives, Nepal could not fully access vaccines. South Asian countries need to strengthen their health system for efficient deployment of vaccines in future health emergencies. At the same time the global agencies and developed countries must address the issue of vaccine access to low-income countries to mitigate the impact of pandemic all over the world.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

Public transportation scheduling aims to optimize the allocation of transportation resources, enhance transportation efficiency, and increase passenger satisfaction, which is crucial for building a sustainable urban transportation system. As a complement to public transportation, shared bikes provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, such as peak usage periods in the morning and evening and significant fluctuations in demand across different areas, the optimization of shared bike dispatch can better meet user needs, thereby reducing vehicle vacancy rates and increasing operating revenue. Addressing this issue, this paper designs a comprehensive decision-making approach for spatio-temporal demand prediction and bike dispatch optimization. For demand prediction, we designed a T-GCN based bike demand prediction model. In terms of dispatch optimization, we considered factors such as dispatch capacity, distance restrictions, and dispatch costs, and designed an optimization solution based on genetic algorithms. Finally, this paper validates the approach using shared bike operating data, shows that T-GCN can effectively predict short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper, which combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

This work discusses seismic isolation technologies and analyses the concept of geotechnical seismic isolation (GSI) system, which is characterized by new principles of operation, using geomaterial with modified properties to reduce inertial seismic loads on buildings and structures. The aim of the study is to develop a geotechnical seismic isolation system in the form of vertical barriers, with different geometrical characteristics, using rubber-soil mixture (RSM). The novelty of the work consists in determination of effective constructive and technical solutions of vertical seismic barriers from RSM, which are characterized by reliability in providing seismic isolation of construction objects. Soil and superstructure interactions are modeled in Plaxis software, using the Northridge earthquake accelerogram. The results confirm the positive impact of using RSM as an effective material for GSI vertical barriers. The present numerical and experimental results show that the GSI system using RSM can significantly reduce the seismic hazard. The results of computational-experimental modeling will become a research, analytical and design basis for the development of new ways to ensure earthquake resistance of construction objects.

Microalgae are photosynthetic microorganism able to receive light from sunlight and external light sources for their growth. Despite the simplicity of cultivation and their widespread presence, the growth of microalgae is affected by several factors, light intensity being a crucial one as it is linked to external environmental conditions. On the other hand, microalgae cultivation in wastewater with high nitrogen content has become a promising alternative as a low cost nutrients substrate method. However, effluents may contain many compounds, making therefore difficult the assessment of light intensity on cells growth profile and especially on nutrients assimilation rate. The aim of this study was the examination of the effect of intensity of light source on the growth and nutrient uptake rates by a freshwater microalgae, Chlorella sorokiniana in the effluent from an anaerobic digestion plant, with nitrogen content reaching up to 5 g/L. Microalgae were cultivated using three light intensities (1,500, 5,000 and 12,000 lx) in 10% digestate mixtures in water. Samples were collected and analyzed for the presence of microalgae cells, while aqueous phase samples were subjected to the analysis of nitrogen and phosphorous content. Biomass productivity followed the light intensity pattern: the highest the light the highest the cells growth. A similar pattern was observed in the reduction rate of nitrogen and phosphorous concentrations; however, nutrients reduction rate per gram of microalgae were mainly associated to biomass growth rate, indicating that the effect of light intensity was significant for biomass growth, without however changing the main nutrients assimilation pathway. In addition, results indicated that under high light intensity, microalgae might change their metabolism towards formation of lipids against nitrogenous compounds.

Cancer-specific monoclonal antibodies (CasMabs) that recognize cancer-specific antigens with in vivo antitumor efficacy are innovative therapeutic strategies for minimizing the adverse effects. We previously established a cancer-specific anti-human epidermal growth factor receptor 2 (HER2)monoclonal antibody (mAb), H₂Mab-250/H₂CasMab-2. In flow cytometry and immunohistochemistry, H₂Mab-250 reacted with HER2-positive breast cancer cells but did not show the reactivity to normal epithelial cells. In contrast, a clinically approved anti-HER2 mAb, trastuzumab, strongly recognizes both breast cancer and normal epithelial cells in flow cytometry. The human IgG₁ version of H₂Mab-250 (H₂Mab-250-hG₁) possesses compatible in vivo antitumor effects against breast cancer xenografts with trastuzumab despite the lower affinity and effector activation than trastuzumab in vitro. This study compared the antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cellular cytotoxicity (CDC) between H₂Mab-250-hG₁ and trastuzumab. Both H₂Mab-250-hG₁ and trastuzumab showed ADCC activity against CHO/HER2 and breast cancer cell lines (BT-474 and SK-BR-3) in the presence of human natural killer cells. Some tendency was observed that trastuzumab showed a more significant ADCC effect compared to H₂Mab-250-hG₁. Importantly, H₂Mab-250-hG₁ exhibited superior CDC activity in these cells compared to trastuzumab. Similar results were obtained in mouse IgG_2a types of both H₂Mab-250 and trastuzumab. These results suggest the different contributions of ADCC and CDC activities to the antitumor effects of H₂Mab-250-hG₁ and trastuzumab and indicate the future direction for the clinical development of H₂Mab-250-hG₁ against HER2-positive tumors.

Fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive imaging modalities, such as Coronary Computed Tomography Angiography (CCTA), and invasive coronary angiography or intravascular imaging. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy in comparison to the invasive FFR using pressure wires. These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice. This review aims to present all the data on the studies and methodologies that have been presented for the virtual estimation of FFR, and the diagnostic execution of the virtual tools for the hemodynamic assessment of coronary lesions compared to the invasive FFR.

This study investigated the development of novel probiotic formulations specifically designed for rainbow trout aquaculture. The project evaluated the effects of two formulations containing potential probiotic strains isolated from rainbow trout itself. These effects were assessed on various parameters including growth performance, immune response, liver enzyme activity, gene expression related to immunity, and the composition of the bacterial gut microbiota in rainbow trout (Oncorhynchus mykiss). In a completely randomized experimental design, a total of 450 rainbow trout were randomly divided into 70-liter fiberglass tank. The study included six treatments of three replicates (n=18 tanks). Six dietary treatments were included (A) and (B) containing three strains of Pediococcus sp P15, Lactococcus lactis ml3, Weissella cibaria ml6 at two dosages (300 and 500 mg/kg), treatments (C) and (D) containing five strains of P. sp P15, L. lactis ml3, W. cibaria ml6, Enterococcus faecium E10 and Lactobacillus curvatus 13A at two dosages (300 and 500 mg/kg), a commercial probiotic (E) at 300 mg/kg, and a control group (F) without probiotics. Treatments of (A), (B) and (E) resulted in the highest final weights in compared to control group. Fish fed treatments (A) and (B) had the lowest FCR than other groups. Treatments (A) and (D) showed a significant increase in immunoglobulin M (IgM) concentration compared to other groups. The expression of tumor necrosis factor (TNF) was significantly lower in group (A) than the control. In contrast, the expression of interleukin 6 (IL6) and immunoglobulin T (IgT) was increased 4.4-fold and 41-fold, respectively, in group (A) compared to control group. Among the probiotic fed treatments, group (A) showed a significant higher number of lactic acid bacteria compared to other groups during the experiment. This study highlights the significant role of treatment (A) containing three strains of P. sp P15, L. lactis ml3, and W. cibaria ml6 at a dosage of 300 mg/kg as a potential probiotic product for the rainbow trout industry.