The grinding process by friction at the micro-scale in a mill with stones is considered a variable combination of contacts: with two-body (the asperities of lower millstone in direct contact with the asperities of upper millstone) and the three-body (micro-particles of ground seeds trapped between the asperities of lower and the upper stones of the mill). Three elements are described: (1) the mechanic contact of the asperities of the lower and upper millstone, to predict pressures on asperities, by modeling; (2) tests on a millstone sample covered with grinding particles, and (3) tests on a wafer sample formed by the millstones with the grinding particles between them. The paper highlights the combined effects of the micro-scale friction by individual measurements and to sum these effects an analytical model was used, and to validate the model, several experiments were performed. A suitable grind by friction assumes the grain’s movement and the interaction between the seeds and solid surfaces and is highlighted through theoretical and experimental studies. Topography analysis of the surface of the millstones revealed the model of microscopic frictional force. Endpoint measurements (the traces of the surface topography evolution), enable model verification in the grinding process.

Although data acquisition is a very usual technique, there are several aspects not always considered, as synchronization of acquired measures and evaluation of the errors that derive from this fact. The paper hereby aims to point this thing out, by mathematical determination of the necessary correction and implementing the software meant to evaluate the performances of the acquisition system. As an example, a three-phased acquisition system has been developed in order to monitor the currents and voltages on the three phases. Also other measures have been calculated, such as power and phase. The components on each phase don’t have to be fully identified because a whole system calibration can be made in the first stage. Calibration consists in finding the weighting coefficients for each quantity. The implemented solution for three-phased measures data acquisition starts from the hypothesis of a sampling frequency that respects Shannon theorem. The distance between two samples is small enough to consider a linear evolution between two moments for the same measure. Errors that affect the above mentioned, due to the different moments of time when samples are acquired, are analyzed and brought to the minimum value.

Incisional hernias have a particular dynamic related both to the repair method and to the absence or presence of a prosthetic material. The incidence of incisional hernias is estimated at 9.8% after 21 months and increases to 18.7% after ten years. This study presents a surgical method for the repair of recurrent incisional hernias, through open surgery and the use of existing mesh. The selection criteria regarding the use of this method are: the prosthesis is well integrated, without granulomas, suppurations, defect appearing on the same prosthetic abdominal area. The analyzed factors were the location of the incisional hernias, the duration of the operation, the duration of hospitalization, identified complications, the size of the parietal defect. The retrospective study carried out focused on recurrences of incisional hernias from a group of 631 patients operated on for incisional hernias in a general surgery department of Dr. Constantin Andreoiu County Emergency Hospital, Ploiești, Romania, during January 2018 - December 2022. Of the total of 18.22% relapses, 13.6% were relapsed eventrations after prosthetic procedures. Following the application of the selection criteria, several 8 patients with recurrent eventrations were operated by the open method using the existing mesh. In the case of recurrences after prosthetic procedures of incisional hernias, the recurrence can be solved by open surgery using the existing well-integrated prosthesis, without tension, absence of suppuration in the wound, and the peritoneal cavity. For the presented case, the return to daily activities occurred after 22 hours of hospitalization.

Daniel Yergin’s “The New Map: Energy, Climate, and the Clash of Nations” is a remarkable and groundbreaking work that addresses the ever-changing global energy landscape and its profound geopolitical implications. In this modest book review, we aim to offer a fresh perspective by re-examining the core issues from the standpoint of political geography. The aim of this article is twofold: to enrich the existing literature on political geography and to provide valuable insights to students of geography to broaden their knowledge in this field.

The storm 'Daniel' has caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. Causal relationships between soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. Results of causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and available phosphorus (P-Olsen), as well as between sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25 and 30 cm). Then linear regression equations were fitted across the different levels of deposit depth to determine the effect of deposit depth on CaCO3, P, and Mn. The results show that there is a linear slope of 0.12, -0.16, and 0.13, for CaCO3, P, and Mn with deposit depth, respectively. Statistical analysis indicates that corn growing in soils with sediment over 10 cm requires a 31.8% increase in P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.

Hyperspectral imaging data holds great potential for various stages of the mining life cycle in active and abandoned mines. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite im-agery yields high spectral resolution, high signal-to-noise ratio (SNR), and global availability with breakthrough satellite sys-tems like EnMAP, EMIT, and PRISMA, limited spatial resolution poses challenges for mining sectors, which require decimeter- to centimeter-scale spatial resolution for applications such as reconciliation, ore/waste estimates, geotechnical assessments, and en-vironmental monitoring. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the pit/ mine scale, with the capability for frequent, user-defined re-visit times. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers to detect different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we review of applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging in or appropriate to the mining sector, especially for hyperspectral VNIR-SWIR UASs. If directly applicable, draw on previous insights derived from airborne, satellite, and ground-based imaging systems. We also discuss common practices for UAS survey planning and sampling considerations for interpretation.

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

Abstract: As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs at a larger scale. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data.

The paper presents a new vision on the energy consumption management in the case of the Small and Medium Enterprises (SMEs), integrated into an advanced decision support platform, with technical and economic benefits on increasing the energy efficiency, which contains modules for database management, profiling, forecasting, and production scheduling. Inside each module, Artificial Intelligence and Data Mining techniques were proposed to remove the uncertainties regarding the dynamic of technological flows. Thus, the data management module includes the Data Mining techniques, that extract the technical details on the energy consumption needed in the development of production scheduling strategies, the profiling module uses an original approach based on clustering techniques to determine the typical energy consumption profiles required in the optimal planning of the activities, the forecasting module contains a new approach based on an expert system to forecast the total energy consumption of the SMEs, and production scheduling module integrates a heuristic optimization method to obtain the optimal solutions in flattening the energy consumption profile. The testing was done for a small enterprise from Romania, belonging to the domain of trade and repair of vehicles. The obtained results highlighted the advantages of the proposed decision support platform on the decrease in the intensity of energy consumption per unit of product, reduction of the purchase costs, and modification of the impact whom the energy bills have on the operational costs.

In the paper, an improved smart meter data-based three-stage algorithm to calculate the power/energy losses in the three-phase networks with the voltage level below 0.4 kV (low voltage - LV) is presented. In the first stage, a loading function of input data was built having as main feature the working at the same time with files from the database of smart metering system (SMS) containing the hourly electricity records, and files including the characteristic load profiles established by the Distribution Network Operator (DNO) for the consumers with standard energy meters depending the following factors: consumption class, day and season. In the second stage, a function which is based on the work with the structure vectors was implemented to identify easy the configuration of analysed networks. In the third stage, an improved version of forward/backward sweep-based algorithm was proposed to calculate fast the power/energy losses to three-phase LV distribution networks in balanced and unbalanced regime. A real LV rural distribution network from a pilot zone belonging to a Distribution Network Operator (DNO) from Romania was used to confirm the accuracy of the proposed approach. The comparison with the results obtained using the DigSilent PowerFactory Simulation Package certified the performance of the algorithm, the mean absolute percentage error (MAPE) being 0.94%.

In the electric distribution systems, the “Smart Grid” concept is implemented to encourage energy savings and integration of the innovative technologies, helping the Distribution Network Operators (DNOs) in choosing the investment plans which to lead the optimal operation of the networks and increasing the energy efficiency. In this context, a new phase load balancing algorithm was proposed to be implemented in the low voltage distribution networks with hybrid structures of the consumption points (switchable and non-switchable consumers). It can work in both operation modes (on-line and off-line), uploading information from different databases of the DNO which contain: the consumers’ characteristics, the real loads of the consumers integrated into the Smart Metering System (SMS), and the typical load profiles for the consumers non-integrated in the SMS. The algorithm was tested in a real network, having a hybrid structure of the consumption points, on a time interval by 24 hours. The obtained results were analyzed and compared with other algorithms from the heuristic (Minimum Count of Loads Adjustment algorithm) and the metaheuristic (Particle Swarm Optimization and Genetic Algorithms) categories. The best performances were provided by the proposed algorithm, such that the unbalance coefficient resulted in the smallest value (1.0017). The phase load balancing led to the following technical effects: decreasing the average current in the neutral conductor with 94% and for the energy losses with 61.75 %, and increasing the minimum value of the phase voltage at the farthest pillar with the 7.14 %, compared to the unbalanced case.

Acute peritonitis is a severe complication of abdominal surgery with a high mortality rate. Intes-tinal insufficiency syndrome (IIS) is a key factor in the progression of peritonitis, contributing significantly to morbidity and mortality. The most characteristic and rapid manifestation of IIS is the development of intestinal paresis. Intestinal paresis is a condition characterized by inhibi-tion of intestinal motility, with the inability to evacuate intestinal contents. Intestinal paresis is caused by a cascade of neuroreflex and humoral reactions that lead to severe microcirculatory and metabolic disturbances. In the absence of adequate treatment, IIS can progress to enteritis, a state of complete intestinal insufficiency, with the inability to absorb nutrients and water.

(1) Background: The rapid expansion of 4G and 5G networks has raised public concerns about potential health risks associated with electromagnetic radiation. This study addresses these fears by investigating electromagnetic measurements in crowded squares, where human exposure is heightened. Despite rigorous scientific methodologies showing generally low levels of radiation from these technologies, public apprehension persists; (2) Methods: We conducted 496 measurements in Larissa's major squares, using a portable field strength analyzer and GIS mapping. Squares were categorized by traffic and population density; (3) Results: Low-traffic squares exhibited exposure ratios consistently below ICNIRP limits, with median values of 0.0012 and 0.00173 in non-active and active mobile phone cases, respectively. In high-traffic squares, the median exposure ratio was 0.00335 in non-active mobile phone cases, remaining below recommended limits; (4) Conclusions: These findings indicate no significant risk of electromagnetic radiation in crowded squares in Larissa, emphasizing the importance of factual communication and dispelling public misconceptions.

The best business relationships are built as relationships between people and organizations that work together to achieve common goals. In the current digital era, B2B collaboration relationships are essential for business success. The response time to customer requests can be a key factor in maintaining these relationships and obtaining a competitive advantage. The use of VBA automation for repetitive tasks can help speed up this process, but it is important that computer operators have medium knowledge about using VBA macros. This work deals with issues related to improving response times to requests received by email, using a macro program for repetitive tasks. The empirical study was conducted on an average of 50 daily emails for order confirmation, with an average writing time of 68 s per email, resulting in a potential reduction in working time for the operator of approximately 1 h/day, only for order confirmation. The results show a significant improvement in working time for computer operators, using VBA buttons for repetitive tasks.

The high incidence of psychopathologies recorded in today's human society, correlated with the high percentages of biodiversity loss, point to the need for an interdisciplinary approach of the scientific fields under study – Neurosciences and Biodiversity Conservation. Thus, our approach here pre-sents, in a synergistic manner, the significant correlation between mental health and the increased values of biodiversity in the ecosystems located in the immediate vicinity, especially those located in the middle of cities. Our approach aims to emphasize the importance of biodiversity conservation in the context of preserving mental health and general well-being. There are a series of recent ex-perimental demonstrations that outline the influence of natural elements on the human psyche and implicitly the effects of nature in the prevention and reduction of stress, anxiety and depression. And beyond the cognitive barriers of humanity in relating to the surrounding biodiversity must lie the desire to know the values of biodiversity and the absolute importance of its conservation. The sustainable relationship between man and living nature, seen as a complex of biodiversity, is dealt with by a branch of science called Human Ecology. Therefore, this study emphasizes the crucial need to know and respect the connection between man and nature, based, since time immemorial, on biophilia. And with the regression of ignorance and the correlated approach of several scientific fields, some at the intersection of humanities and natural sciences, one can observe the progress of preserving the dynamic balance within the ecosystems and implicitly the preservation of mental health and human well-being.

Partially state-owned firms are common in both developed and developing countries and contribute around 10% of the world's GDP. This has led to a rich literature exploring various aspects of competition between such “mixed”-ownership firms. This paper contributes to the existing literature by studying the effects of mixed ownership on the intensity of competition in oligopoly markets. Our findings indicate that state ownership exerts an anti-competitive influence, with a higher state ownership share resulting in less aggressive behavior by firms in the product market. This result implies that industries with a higher proportion of state ownership yield lower total surplus. Further, when considering factors such as taxes, soft budget constraints, and weak property rights, the anti-competitive effects of state ownership are accentuated.

The per capita availability of freshwater resources on a global scale is gradually decreasing due to population growth, rapid urbanization, industrialization, and modern lifestyles. In this situation, wastewater management becomes a major concern. Conventional treatment processes used to treat wastewater discharges are not adequate for the advanced removal of pollutants, especially refractory organic pollutants, which are mainly organic compounds that are not easily biodegradable in the environment. The use of photocatalytic membrane reactors (PMRs) for tertiary wastewater treatment has attracted much interest in recent periods, and various configurations have been investigated. The performance of a PMR using an iron doped titanium photo catalyst and Polysulfone-based polymeric membrane for municipal wastewater treatment was evaluated under natural and simulated solar light. Obtained results proved that solar driven PMRs can remove organic loading (COD) with high efficiencies and may represent a suitable alternative for the tertiary treatment step of municipal wastewater.

Emerging data processing techniques brought back into attention the HF range communication as an interesting alternative to third-party solutions for IoT applications, such as data transmission in distributed energy production facilities. The physical size of HF antennas, often comparable to the surrounding objects, require in-situ radiation measurements. Drone-borne measuring systems are already known as a flexible solution, but mostly restricted to higher frequency ranges. In this work, we propose to use an electrically small, folded dipole as a probe for drone-borne measurements on HF antennas. We also propose a calibration approach for the effects related to the near-field zone, and to the drone body proximity. The impact of ground reflection is also investigated. We show that despite its low, realized gain figure such a probe can provide stable results for near-field measurements, even at low input power levels.

Over the last few decades, ocean research and exploration have made underwater mechanical systems a necessity. Underwater vehicles provide a new kind of marine platforms that could represent a great necessity in many areas of oceanographic research. Until now, the underwater vehicles come in a verity of shapes, sizes and means of propulsion. Depending on these characteristics, the type and mission of the vehicle are also determined. The underwater robots are used for different inspection and intervention missions in e.g. the oil and gas industry, ocean science research. Due to multiple applications to which the vehicle can participate, it can be successfully used and to determine methods of re-use of marine energy. Environmental mapping provides accurate information about the main areas of interest of the energy, as well as the exploitation possibilities of that. Most of the time, biomimetic robots were inspired their senso structure, from different kind of animals, such as insects, fish and birds. Nowadays, the concept of a underwater robotic vehicles capable to move independently, autonomously or remotely, has a great potential and a large application. This is the reason that the last studies have been directed on biomimetic robots. The fish and other underwater animals have evolved superior swimming capabilities in many ways and represent a starting point to explain the fluid-mechanical principles. Furthermore, the underwater animals develop and achieve extraordinary propulsion efficiencies, acceleration and maneuverability. They can also achieve high speed under water. Implanting and creating a vehicle through a biomimetic approach reduces the energy used to maneuver the vehicle as it can automatically correct its position and displacement. The paper presents an examination of the state of biomimetic robotic fishes, underlining the reason why bio-inspiration can help us in the underwater locomotion technology.

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods, as well as those based on neural networks. Furthermore, we will explore the artifacts associated with the identification of modified photos and present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all of the above, the final outcome of this study is to provide a comprehensive perspective on the abilities and constraints to detect images for which an inpainting object removal method was applied.

The splitting method developed in a previous study for linear, multi – component, convection-diffusion mass transfer equations is extended to non-linear, multi-component, convection-diffusion mass transfer equations. The diffusion term is non-linear (the diffusion coefficients depend on chemical species concentrations). Theoretical results about the splitting error are presented, as well as numerical simulations for mixtures with n ≤ 5. The numerical experiments show second-order accuracy and stability for the present splitting algorithm.

In recent years, many libraries and archives have started digitizing their collections thus making maps by Indigenous peoples more easily available for scholars to study. While a number of these maps were discussed by G. Malcolm Lewis in the History of Cartography series (volume 2, book 3, Chapter 4: 1984), more have since been found and disseminated. These maps are critical in understanding the historic and current land tenure of Indigenous groups. Further, Indigenous claims to land can be seen in their connections via toponymy. European concepts of territory and political boundaries did not coincide with First Nation/American Indian views resulting in the mistaken view that Natives did not have formal concepts of their territories. Further, Tribes/First Nations with cross-border territory have special jurisdictional problems. This paper will illustrate how many Native residents were very spatially cognizant of their own lands, as well as neighboring nations’ lands, overlaps between groups, hunting territories, populations, and trade networks. Currently, the Sinixt First Nation provides a perfect example of how an Aboriginal people are inputting and using a GIS representation of their territory with proper toponymy and use areas.

The emergence of M. tuberculosis strains resistant to Isoniazid (INH) and Rifampicin (RIF), the two most potent drugs of first-line anti-TB therapy is termed multidrug drug-resistant TB (MDR-TB). Multidrug-resistant tuberculosis has been a serious medical and epidemic problem all over the world. We present here a series of clinical cases consist of two patients diagnosed with isoniazid resistant tuberculosis. Histopathological examination supports the diagnosis of tuberculous granulomas. And the pathology molecular examination revealed the presence of Isoniazid-resistant Mycobacterium tuberculosis1,2 via the following mutation c.947G>A; p.Gly316Asp. Isoniazid-resistance is associated with mutations in the furA-katG and fabG1-inhA operons, as well as mutations in the ahpC gene. 64% of the isoniazid-resistance phenotypes were associated with the katG315 mutation worldwide. The second most common mutation is inhA-15, and it has been reported in 19% of the Isoniazid-resistant isolates. The significant association between the two mutations, inhA c-15 and katG 315 respectively, and the high-level resistance is of interest in the interpretation of current and future molecular diagnostic testing, as an early prediction of the level of Isoniazid-resistance is essential to decide the benefit of high-dose Isoniazid use.

The deregulated markets have replaced the traditional way of trading electricity from the producers to the consumer, via government-owned organizations and regulated tariffs. Nowadays, electricity prices are determined by the offer-demand mechanism and consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are a result of the transactions performed on the wholesale market and the energy mix available in certain geographical regions. In parallel with becoming eligible and participating in retail electricity markets, the consumers use increasingly local generation sources based mostly on renewable energy technologies such as PV panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. Currently, several trading mechanisms for prosumers are available, ranging from the simplest, selling back the surplus to an aggregator at fixed tariffs, to more complex market schemes. This paper proposed a two-tier local market model for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as remote sensing, smart grids, crowdsourcing and energy poverty.

The deregulated markets have replaced the traditional way of trading electricity from the producers to the consumer, via government-owned organizations and regulated tariffs. Nowadays, electricity prices are determined by the offer-demand mechanism and consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are a result of the transactions performed on the wholesale market and the energy mix available in certain geographical regions. In parallel with becoming eligible and participating in retail electricity markets, the consumers use increasingly local generation sources based mostly on renewable energy technologies such as PV panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. Currently, several trading mechanisms for prosumers are available, ranging from the simplest, selling back the surplus to an aggregator at fixed tariffs, to more complex market schemes. This paper proposed a two-tier local market model for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as remote sensing, smart grids, crowdsourcing and energy poverty.

The present study analyses the influence of scratches and fatigue on the mechanical characteristics of aluminium alloys commonly used in the aviation industry: 2024-T4, 6061-T4, 6061-T4 uncoated, 6061-T6 uncoated, 7075-T0, and 7076-T6. 120 specimens were used for the study. Out of these, 60 samples were scratched, and the other 60 samples were also scratched under the same circumstances and then subjected to low-cycle fatigue (LCF) before undergoing the tensile test. Based on the statistical examination of the measured data, mathematical prediction models have been established. Compared to their unscratched counterparts, the results indicate a significant decrease in the mechanical properties of both scratched and scratched-fatigued sample sets. The LCF pre-treatment contributes to the negative impacts of scratches, resulting in a reduced value for the ultimate tensile strength and elongation at break. The results conclude that surface integrity plays an essential role in preserving aircraft components' structural strength.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

This paper presents the results of investigations on the kinetic of Brilliant Blue FCF (BB) discoloration reactions in aqueous solutions with different ozone concentrations and pH conditions. Kinetic studies involve knowledge of the structure and properties of the dye and ozone, as well as the experimental conditions. In general, scientists admit that the predominant oxidation pathway is direct (by free oxygen atoms) or indirect (by free hydroxyl radicals), it will depend on influencing factors such as: the physicochemical properties of the dye, the pH of the aqueous solution, ozone concentration, reaction time and the contact mode with/without stirring. In the experimental research, two pathways were chosen following CBB = f(t): 1. constant dye concentration and different ozone concentration on the concentration range (100 - 250 mg/L) at three pH media (acidic, neutral and basic) with and without stirring; 2. Constant concentration of ozone and different dye in the range of concentrations (2.5 - 10 mg/L) under the conditions of point 1. With obtained experimental data the curves CBB = f(t) were drawn and were processed by the integral method of classical kinetic based on first and second order equations. Unfortunately, this simple procedure this not yield any minim full results and any pH level. The rate constants been negative and/or the reaction order been dependent on initial conditions. Due to its structure, the BB dye has several chromophore groups, thus multiple attack centers, resulting in more oxidation by-products, which is why the 1H - NMR spectrum of BB discoloration with ozone was recorded.

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, assure accuracy and reliability for auditing, and adhere to tax compliance. The design involves data selection from Web of Science Core Collection (WoS) and Scopus, published between 2007 - 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers' interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data was retrieved, 9-11 May 2024. This review might need periodic updates because the CA landscape is constantly changing.

This study addresses the causal links between external factors and the main hydro-climatic variables. There is a gap in the literature on the description of a complete chain in addressing the structures of direct causal links of solar activity on the terrestrial variables. This is why, the present study uses the extensive facilities of the application of information theory in view of recent advances in different fields. Also, by other methods (e.g. neural networks) first are tested the existence non-linear links of solar-terrestrial influences on hydro-climate system. The results are promising related to the solar impact on terrestrial phenomena which is discriminant in space-time domain. The implications prove robust for determining the causal measure of climate variables under direct solar impact which makes it easier to consider solar activity in climate models, by appropriate parametrizations. This study found that hydro-climatic variables are sensitive to solar impact only for certain frequencies (periods) and these have a coherence with the Solar-Flux only for some lags of the Solar-Flux (in advance).

The purpose of this paper is threefold: (i) to introduce and study the Euler-Lagrange prolongations of flatness PDEs solutions (best approximation of flatness) via associated least squares Lagrangian densities and integral functionals on Riemannian manifolds; (ii) to analyze some decomposable multivariate dynamics represented by Euler-Lagrange PDEs of least squares Lagrangians generated by flatness PDEs and Riemannian metrics; (iii) to give examples of explicit flat extremals and non-flat approximations.

The study investigates the impact of globalization, renewable energy consumption and economic growth on CO2 emissions in 26 European Union (EU) countries for the period 1990-2020. The second-generation panel unit root tests are applied, the Westerlund cointegration test is used, and panel Fully Modified Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) techniques are employed to estimate the long-term relationship between variables. The causality relationship among variables under investigation is identified by the heterogeneous Dumitrescu-Hurlin causality test. It is found that globalization and renewable energy consumption contributed to the carbon emissions mitigation, while economic growth induced their increase. The results are robust when control variables (i.e., financial development, foreign direct investment and urbanization) are added in the model. Foreign direct investment and urbanization are contributors to carbon emissions increase whereas financial development induce their decrease. The effect of variables under consideration on carbon emissions is differentiated by the economic development and institutional quality level. Unidirectional causalities relationships were identified from globalization to carbon emissions and from carbon emissions to foreign direct investment and bidirectional relationships between economic growth, renewable energy consumption, financial development and carbon emissions. Policy implications of the findings are also discussed.

Simultaneously with the development of industrial society, wastewater with organic pollutants has caused various environmental problems. The most majority of organic pollutants in water and wastewater are persistent, reason which can cause serious problems for human health, animal health, and for the whole environment. Therefore, efficient treatment methods for wastewater with highly concentration of organic compounds are immediately necessary. During the last years, the prescribed and non-prescribed consumption of antibiotics has grown a lot worldwide. Big quantities of antibiotics are discharged into wastewater because their incomplete absorption by living organisms, but at small concentrations present in aquatic environments represents a major risk for the human health and environment protection. The paper presents the main advantages and disadvantages of advanced oxidation processes, but also current state and new perspectives in the field of environment protection. Advanced oxidation processes (AOPs) are often used in the field of treatment of different types of wastewater. AOPs are based on physicochemical processes that create significant structural changes in chemical species, their commercialization at a wide scale may result in cost reductions that are desirable for environmental applications. The majority of antibiotics may be eliminated using physicochemical processes, such as photo-Fenton, photolysis, ozonation, electrooxidation, heterogeneous catalysis, and other bio processes. In comparison to conventional chemical processes, AOPs provide superior oxidation efficiency, ideal operating costs, and zero secondary pollutants.

Flood protection infrastructures are crucial for enhancing the resilience of societies exposed to natural hazards. Newly designed infrastructures are evaluated for sustainability using a coherent and internationally recognized method defined by the International Hydropower Association (IHA). However, older structures require a different assessment approach. Popescu et al. (2024) proposed a modified IHA protocol, mHSAP, which identifies opportunities for improvement and develops a sustainability evaluation framework for existing infrastructures. This paper applies the modified protocol to evaluate the sustainability of two types of flood protection structures: a unique canal system for flood-drought protection of an urban area and a flood protection dyke. The time of operation of over 250 years, and respectively over 50 years. The application of the modified framework demonstrates its advantages in identifying areas for improvement in operating the flood protection structure, such that it is maintained sustainable. It also illustrates how Romanian water boards can use such tools to facilitate collaboration between structure owners and stakeholders, allowing them to assess the risks and effects of flooding on society. Potentially water boards, could use the results of the method to account for the effects of climate change and addressing the issue in a coordinated and efficient manner. Through these two examples from Romania, we also show that the mHSAP framework has the potential to actively support the fulfillment of the United Nations Agenda 2030 Sustainable Development Goals (SDGs).

Four-wire low voltage microgrids supply one-phase consumers with continuously changing electricity demand. For addressing climate change concerns, governments implemented incentive schemes for residential consumers, encouraging the installation of home PV panels for covering self-consumption needs. In the absence of sufficient storage capacities, the surplus is sold back by these entities, called prosumers, to the grid operator or in local markets, to other consumers. While these initiatives encourage the proliferation of green energy resources, and ample research is dedicated to local market designs for prosumer-consumer trading, the main concern of distribution network operators is the influence of power flows generated by prosumer surplus injection on the operating states of microgrids. The change in power flow amount and direction can greatly influence the economic and technical operating conditions of radial grids. This paper proposes a metaheuristic algorithm for prosumer surplus management that optimizes the power surplus injections using the automated control of three-phase inverters, with the aim of improving the active power losses and balancing the phase voltage profiles. A case study is performed on two real distribution networks with distinct layouts and load profiles and the algorithm shows its efficiency in both scenarios.

A growing number of households benefit from the government subsidies to install renewable generation facilities such as PV panels, used to gain independence from the grid and provide cheap energy. In the Romanian electricity market, these prosumers can sell their generation surplus only at regulated prices, back to the grid. A way to increase the number of prosumers is to allow them to make higher profit by selling this surplus back into the local network. This would also be an advantage for the consumers, who could pay less for electricity exempt from network tariffs and benefitting from lower prices resulting from the competition between prosumers. One way of enabling this type of trade is to use peer-to-peer contracts traded in local markets, run at microgrid (μG) level. This paper presents a new trading platform based on smart peer-to-peer (P2P) contracts for prosumers energy surplus trading in a real local microgrid. Several trading scenarios are proposed, which give the possibility to perform trading based on participants’ locations, instantaneous active power demand, maximum daily energy demand and the principle of first come first served implemented in an anonymous blockchain trading ledger. The developed scheme is tested on a low-voltage (LV) microgrid model to check its feasibility of deployment in a real network. A comparative analysis between the proposed scenarios, regarding traded quatities and financial benefits is performed.

This study conducts a comprehensive review of the integration of Hybrid Electric Turbochargers in mass-produced road vehicles. The primary objective is to inform the readers about the potential advantages and innovative applications of HETs. Employing a SWOT analysis, the research compares the performance of HETs on thermal engines versus traditional turbochargers and HETs on thermal engines versus HETs on hybrid engines based on a synthesis of multiple research findings. The study elucidates the fundamental mechanics of turbochargers, emphasizing their inherent benefits. Additionally, it introduces a novel approach with the integration of the hy-brid-electric turbocharger, demonstrating its capacity, supported by practical examples. The in-corporation of turbochargers with energy recovery mechanisms, including HETs, is shown to significantly contribute to emissions reduction. Moreover, the electric drive facilitates the utili-zation of hybrid turbines, particularly on low-displacement engines, resulting in enhanced effi-ciency, torque, and liter power. In conclusion, the research underscores the potential of HETs to substantially mitigate the environmental impact of the transportation sector by reducing emis-sions and conserving energy. Their ability to optimize the fuel efficiency and power output of internal combustion engines, coupled with the feasibility of downsized engines, positions HETs as an attractive option for both manufacturers and consumers. However, it is acknowledged that the implementation of HETs is in its early stages, necessitating further research to comprehensively understand their environmental impact. Despite this, the promising positive impact on the en-vironment designates HETs as a technology deserving close attention in the forthcoming years.

The paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. The method minimizes the communication network (between vehicles and the database servers) load and represents a reasonable trade-off between communication network load and forecasting accuracy. Traffic prediction leads to the choice of less congested routes and therefore to the reduction of energy consumption. The traffic is forecasting using a LTSM neural network with a regression layer. The inputs of the neural network are sequences - obtained from graph that represent the road network - at specific moments of time that are read from traffic sensors or the outputs of neural network (forecasting sequences). The input sequences can be filtered to improve the forecasting accuracy. This general framework is based on Contiki IoT operating system that ensure support for wireless communication and efficient implementation of processes in a resource constrained system and it is particularized to implement a graph neural network. Two cases are studied: one case in which the traffic sensors are periodically read and the other case in which the traffic sensors are read when their values changes are detected. A comparison between the cases is made and the influence of filtering is evaluated. The obtained accuracy is very good, very close to the accuracy obtained in infinite precision simulation, and the computation time is low enough and the system can work in real time.

This paper presents results on tribological characteristics for polymer blends made of polybutylene terephthalate (PBT) and polytetrafluoroethylene (PTFE). This blend is relatively new in research as PBT has restricted processability because of its processing temperature near the degradation one. Tests were done block-on-ring tribotester, in dry regime, the variables being the PTFE concentration (0%, 5%, 10% and 15%wt) and the sliding regime parameters (load: 1 N, 2.5 N and 5 N, the sliding speed: 0.25 m/s, 0.5 m/s and 0.75 m/s, and the sliding distance: 2500 m, 5000 m and 7500 m). Results are encouraging as PBT as neat polymer has very good tribological characteristics in terms of friction coefficient and wear rate. SEM investigation reveals a quite uniform dispersion of PTFE drops in the PBT matrix.

The survival rates of women with stage 0 or 1 breast cancer stands at almost 100% and around 93% for stage 2. As the disease advances with decreasing survival, the rate detoriates to 72% for stage 3 and about 22% for stage 4 (ACS, 2018). Undoubtedly, earlier presentation is synonymous with better prognosis and survival. In fact data indicates that early stage breast cancer diagnoses has 90% survival rate as compared to 27% for the advanced states (ACS, 2018). In this regard, governmental and non-governmental agencies are working tirelessly towards the realization of improved breast cancer care and survival through early presentation and detection. For instance, the Sustainable Development Goal (SDG) Target 3.4 seeks to reduce by one third, premature mortality from cancers and other non-communicable diseases through prevention and treatment. Clearly, early presentation, diagnoses and treatment will assist in the realization of the SDG target 3.4 goal. To achieve this, research must be focused on early presentation among women with breast cancer.. However, current studies have focused mainly on advanced breast cancer regarding treatment, symptoms experience (emotional and psychological), coping and support systems, and the reasons for delayed diagnoses and treatment. Objective In this review, it is argued that, rather than focusing on advanced breast cancer which has been adequately explored, the attention has to be on what measures or approaches promote early presentations among Ghanaian women. Methods The review included all primary studies that provided answers to the phenomenon of interest. It included both quantitative and qualitative studies. Search scope encompassed studies within the last ten years; thus from 2010 to 2020 irrespective of the country of study.Studies were included if: The paper is of a primary study; the study population included women with early BC or BC symptoms; the study outcome is the association between early stage breast cancer and earlier presentation; the study has a comprehensive data analysis protocol; the results of the studies are unique.A thorough literature search was conducted using the following electronic database: Google scholar and Pubmed. Google scholar alert on new studies was subscribed. This gave update on newly published papers for appraisal and possible inclusion.Manual identified of other potentially eligible publications by searching the reference lists of retrieved studies was also done. After a thorough search, this review included studies from several countries around the Globe such as Ireland, London, Turkey, Iran, Ghana, Kenya, Ethiopia etc Data Collection The title and or abstract of all retrieved papers were independently scanned through to establish which studies should be further evaluated. The full texts of these potential articles were then assessed. Extraction The Cochrane extraction form for data extraction was resorted to. From each eligible article, the following study identifiers/information were extracted: author(s), first author, year, title, objective, article type, location, setting, study design and method, sample size, instruments used, and results. Results Prompt help seekers mainly self-discovered their BC, and this occurred through accidental touch, after menstruation, through breastfeeding, during grooming, and while taking their bath.Notably, women who were termed prompt help seekers had higher seriousness perception of their symptoms. This was likened to their level of understanding and awareness, as well as periodic examination and comparison. Similarly, women who knew they were at risk for breast on account of family history, and increased incidence of BC in their localities, labeled their initial symptoms as requiring prompt attention.Following interpretation and labeling of symptoms, the participants generally resorted to symptom monitoring until the onset of additional and or intense symptoms. Though most women thought of benign growths, a few feared it could be malignant. These emotional reactions led the women to purposively seek clarification by selectively consulting others. Further, participants opened up to social acquaintances such as family relationships, people with prior experience of breast disease, and in some few instances health professionals. Some women also resorted to media items to better their understanding. Remarkably, prompt help seekers sort for help within one month. Last but not least, some women first communed with their creator after discovery and labeling based on their spiritual affiliations.Also, researchers should consider conducting surveys to assess the BC risks for specific smaller groups. This would help tailor specific educational interventions to promote earlier presentation and treatment among the vulnerable.

Monitoring the ratio of forested and deforested areas plays a key role in studying the dynamics of forest areas. Appropriate mapping of anthropogenic forest disturbances is particularly important in the context of sustainable forest management. It provides ecological, social and economic information which is crucial for forest policymakers. In the last two decades, the forest areas of the Moldo-Transylvanian Carpathians have been subject to a high rate of deforestation which at present state lacks proper quantification. We present a novel methodology for monitoring the forest disturbance dynamics in Moldo-Transylvanian Carpathians by use of fractal analysis including entropy, Fractal Fragmentation Index (FFI) and Tug-of-War lacunarity (Λ_(T-o-W)). This was necessary to quantify and identify the disorder (entropy), the fragmentation (FFI) and heterogeneity of the spatial distribution (Λ_(T-o-W)) patterns. Based on satellite images of the forest areas (annually 2000-2014), increased fragmentation was demonstrated by FFI increase, a measure of the degree of disorder (entropy) and heterogeneity (lacunarity). Our results revealed that textural and fractal analysis can be an effective tool for the extraction of quantitative information about the spatiotemporal dynamics of forest disturbance. The methods developed, and results obtained are a complementary approach to forest disturbance mapping (based on traditional image classification) for future development and adaptation of forestry management policies to ensure a sustainable management and exploitation of forest areas.

This study evaluates the efficiency of biodegradable polyurethane foams as thermal insulation materials compared to conventional materials like expanded polystyrene (EPS). Key parameters analyzed include thermal conductivity, energy savings, CO2 emission reductions, and investment payback periods. Biodegradable polyurethane foams demonstrated a thermal conductivity of 0.022 W/(m·K), significantly lower than EPS's 0.035 W/(m·K), leading to higher energy savings. Calculations reveal an annual energy saving of 443,820 kWh for polyurethane foams, compared to 441,330 kWh for EPS. Financially, polyurethane foams save approximately €53,258.4 annually, while EPS saves €52,959.6. In terms of environmental impact, polyurethane foams reduce CO2 emissions by 88,764 kg annually, versus 88,266 kg for EPS. Despite the higher initial cost of polyurethane foams (€50/m² compared to €30/m² for EPS), the payback period is remarkably short at 0.094 years (1.13 months) compared to EPS's 0.057 years (0.68 months). These findings highlight biodegradable polyurethane foams as a superior option for thermal insulation, offering significant energy, financial, and environmental benefits.

The imperative to achieve carbon neutrality has driven significant advancements in the construction sector, particularly in the production and utilization of building materials. This paper explores innovative strategies and practices designed to reduce the carbon footprint of construction materials, with a focus on both the production processes and the application of sustainable materials. Key innovations include the development of low-carbon and carbon-negative materials, the integration of renewable energy sources in production, and the implementation of advanced recycling technologies. Case studies of successful projects demonstrate how these strategies can be applied in real-world scenarios, highlighting both the benefits and challenges encountered. The analysis also delves into the technical and economic aspects of these innovations, addressing the barriers to their widespread adoption and proposing solutions to overcome these challenges. Through a comprehensive examination of current practices and future trends, this paper aims to provide a roadmap for the construction industry to achieve carbon neutrality, contributing to broader climate change mitigation efforts and fostering a more sustainable built environment.

Overweight and obesity, including its prevalence and consequences reflect a leading public health problem. Studies have already shown that physical activity leads to a reduction in body weight in children and adults. However, the university setting has rarely been investigated. The aim of this review is therefore to examine and summarize the effectiveness of physical-activity-based interventions to reduce obesity and overweight in university students. Three databases (PubMed, Scopus & Web of Science) were searched for relevant studies published in English between January 2010 and February 2022. Quantitative studies, conducting a physical-activity-based intervention with overweight or obese university students and reporting changes in BMI, were included. Data were described in a narrative synthesis. Eleven of 16 included studies reported a significant reduction in BMI. However, all studies except one were able to demonstrate some BMI improvements, whereas all studies reported significant changes in at least one health-related indicator. Aerobic exercises were able to demonstrate the greatest reductions in BMI. This review is the first systematic presentation on the effectiveness of physical-activity-based interventions on overweight and obese university students. Future work should reconsider BMI as the primary outcome because it is prone to bias. More interventions are needed to improve strategies.

The medium-voltage DC (MVDC) technology has gained increasing interests in recent years. Power electronics devices dominate those grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system level control model of such a modern MVDC distribution grid. Aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/- 10 kV MVDC grid case study, including Solid State Transformer (SST) and Modular Multilevel Converter (MMC) based Active Front End (AFE). Additional aim of this paper is to supply an educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.

We present the prototype and analytical model of a miniaturized impact actuation mechanism, providing a fast out-of-plane displacement to accelerate objects against gravity, allowing for freely moving objects and hence for large displacements without the need of cantilevers. To achieve the necessary high speed, we chose a piezo electric stack actuator driven by a high-current pulse generator, connected to a rigid support and a rigid three-point contact with the object. We describe this mechanism with a spring-mass model and compare various inertial masses, all of them spheres, from different diameters and materials. As expected, we found that larger flight heights are achieved by harder projectiles, achieving, e.g., approx. 3 mm displacement for a 3 mm steel sphere using a 3x3x2 mm³ piezo stack.

Groundwater is becoming increasingly important in the Pacific Northwest of the USA due to declining snowpack volumes and shifts in precipitation type and timing, all connected with climate change. The Upper Williamson Basin of the Klamath Watershed is a groundwater dominated watershed with massive fluctuations in year-to-year streamflow volumes over the past four decades, including the complete absence of any live flow for several years. The precise relationship between groundwater and streamflow in the basin has been difficult to assess due to a limited number of monitoring wells and significant gaps in the water level time history. To address this challenge, we use a novel imputation technique that leverages Earth observations and machine learning to impute gaps in water level records. We use these more complete datasets to compute a groundwater storage change time series for the basin. We show that groundwater storage is highly correlated to streamflow and that groundwater storage is correlated to rainfall with a three- to four-year delay that appears variable depending on groundwater storage volumes. The tools and relationships we present make it possible for water managers to estimate when streamflows will return to the basin and be more informed to support better management.

This study evaluates the efficiency of biodegradable polyurethane foams as thermal insulation materials compared to conventional materials like expanded polystyrene (EPS). Key parameters analyzed include thermal conductivity, energy savings, CO2 emission reductions, and investment payback periods. Biodegradable polyurethane foams demonstrated a thermal conductivity of 0.022 W/(m·K), significantly lower than EPS’s 0.035 W/(m·K), leading to higher energy savings. Calculations reveal an annual energy saving of 443,820 kWh for polyurethane foams, compared to 441,330 kWh for EPS. Financially, polyurethane foams save approximately €53,258.4 annually, while EPS saves €52,959.6. In terms of environmental impact, polyurethane foams reduce CO2 emissions by 88,764 kg annually, versus 88,266 kg for EPS. Despite the higher initial cost of polyurethane foams (€50/m² compared to €30/m² for EPS), the payback period is remarkably short at 0.094 years (1.13 months) compared to EPS’s 0.057 years (0.68 months). These findings highlight biodegradable polyurethane foams as a superior option for thermal insulation, offering significant energy, financial, and environmental benefits.

The current paper presents a case of a 33-year-old female with an uncommon localization of a leiomyoma in the oral cavity - the anterior palatal fibromucosa and the incisive papilla. The patient referred to the Oro-Maxillo-Facial Surgery Clinic of the Emergency City Hospital Timisoara, Romania accusing a slight discomfort in the act of mastication and the occurrence and persistence of a diastema between the upper central incisors, due to the presence of a tumoral mass located in the anterior palatal mucosa, between the upper central incisors, without any changes of the subjacent bone structure in the anterior hard palate visible on the cone beam computed tomography image (CBCT). The tumor was removed by a surgical excisional biopsy and a histopathological examination was performed using morphological Hematoxylin–Eosin (HE) staining and additional immunohistochemical (IHC) reactions, in order to confirm the diagnosis. On microscopic examination, there were found bundles of spindle cells with eosinophilic cytoplasm and vesicular nuclei, with finely granular chromatin. The immunohistochemically staining found positive results for smooth muscle actin (SMA) and desmin and negative result for vimentin. The treatment of choice for leiomyoma of the oral cavity is surgical excision with clear margins, followed by periodical clinical monitoring.

In modern times, technology has become more than just a tool; it is a vital component of our lives, driving society and humanity towards new horizons, better living standards, and solu-tions for complex, global challenges. This article explores the world of dependable systems, specifically focusing on system design, software solutions, and architectural decisions that fa-cilitate collaborative work. It aims to dive into the intricacies of designing robust and effective document collaboration software. Throughout this paper, readers will gain insights into the proposed design process, enabling them to understand, learn from, and critically evaluate the examined methodologies and outcomes. Like everything else in the software technology field, there are multiple solutions to any given problem, and this work is no exception to this rule. The objective of this study is to provide a detailed architectural overview of the microservices pattern in an orchestrated cloud environment based on independent containers. To examine the pros and cons of such a system, the paper will detail how a real-time document collaboration system can benefit from such an architecture regarding availability, elasticity, scaling, and disaster recovery. Moreover, the paper will analyze how cloud-native technologies can be used to create a scalable platform solution, capable of handling traffic, dynamic resource storage, asynchronous com-munication, and application scaling. The intricate nature of this system renders this paper a valuable resource for prospective investigations within the domain of dependable systems, cloud computing, and distributed systems. This paper not only showcases the design and implementation process but also sets a founda-tion for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.

Early discharge after childbirth has led to a rise in neonatal readmission, becoming a major concern in recent decades. Our research aimed to identify the risk factors and incidence of neonatal readmission, as well as explore preventive measures. Our study at the Clinical Hospital of Pediatrics in Ploiești, Romania included 108 neonates admitted during the neonatal period. This accounted for 2.06% of all admissions (5226). The most prevalent cases were malnutrition (25%), fever (20.3%) and bronchiolitis (17.5%). Diarrhea and infectious gastroenteritis were also observed (14.8%), along with acute rhinoconjunctivitis (9.2%) and late onset sepsis (3.7%). No deaths were recorded. The most significant characteristics identified were number of children (p<0.001) and age at maternity discharge (p<0.001). By following the prevention rules, malnutrition, feeding errors, and infections can be avoided. This includes practicing proper hand hygiene for both mothers and medical staff, as well as educating and demonstrating to mothers the benefits of breastfeeding. In addition, all newborns discharged from the maternity ward should benefit from follow-up at 7-10 days of life. Our results confirm the effectiveness of a multidisciplinary team and endorse promoting breastfeeding. Implementing quality control measures and regularly evaluating the surveillance program will help improve its effectiveness.

Endometriosis is known to be a chronic, debilitating disease. The pathophysiological mechanisms of endometriosis development include local chronic inflammation and a certain degree of local immune deficit. We investigated the relationship between the endometriosis severity, IL-8, IL-10, BDNF, VEGF-A serum and tissue levels, patient-related pain, and physical activity in a cohort of 46 patients diagnosed with endometriosis who underwent surgery. The same panel of biomarkers was investigated in a control group of 44 reproductive-aged patients with non-endometriotic gynecological pathology who underwent surgical intervention. Our data show a high statistical significance between tissue expression of IL-8, IL-10, patient-related pain and the severity of endometriosis. No relationship was identified between serum or tissue levels of VEGF-A and BDNF and the severity of endometriosis. These results validate the presence of local chronic inflammation and immune deficit, thereby creating, alongside other studies in the field, an opportunity for the development of innovative and personalized treatment approaches in endometriosis.

Digital twins are an emerging technology that could be harnessed for the digitalization of the industry. Steel industry systems contain a large number of electro-hydraulic components, as proportional valves. An input-output model for a novel water proportional cartridge valve is derived from physical modelling based on fluid mechanics, dynamics, and electrical principles. The valve is a two-stage valve with two 2/2-way water proportional valves as the pilot stage and a marginally stable poppet-type cartridge valve as the main valve. The orifice equation, which is based on Bernoulli principles, is approximated by a polynomial which makes parameter estimation easier and makes modelling possible without measuring pressure of the varying control volume situated in the pilot stage part of the valve. Data were collected when the valve was operating as a component in a closed-loop system as part of a press-mill machine in a steel manufacturing plant. Parameter estimation was made by convex optimization with physical constraints to overcome problems caused by poor system excitation. For comparison, a simple linear model was derived and the least squares method used for parameter estimation. A thorough estimation of the parameters relative errors is presented. The model contains 5 parameters related to the design parameters of the valve. The modelled position output is in good agreement with experimental data for training and test data. The model can be used for real time monitoring of the valve’s status by the model parameters. One of the model parameters varies linearly with production cycles. Thus, aging of the valve can be monitored.

Near Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass through the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep CNNs for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We have applied transfer learning and fine-tuning on these pre-existing deep convolutional networks and from the results that we have obtained one can see the potential of using deep convolutional neural networks in the process of asteroid classification. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we loose the least number of valid asteroids.

Despite the relative scarcity of studies focusing on pharmacotherapy in aphasia, there is evidence in the literature indicating that remediation of language disorders via pharmaceutical agents could be a promising aphasia treatment option. Among the various agents used to treat chronic aphasic deficits, cholinergic drugs have provided interesting results. In the current review, we focused on published reports investigating the impact of acetylcholine on language and further cognitive disturbances. It has been suggested that acetylcholine plays an important role in neuroplasticity and is related to several aspects of cognition, such as memory and attention. Moreover, cholinergic input is diffused to a wide network of cortical areas, which have been associated with language sub-processes. This could be a possible explanation for the positive reported outcomes of cholinergic drugs in aphasia recovery, and specifically in distinct language processes, such as naming and comprehension, as well as overall communication competence. However, evidence with regard to functional alterations in specific brain areas after pharmacotherapy is rather limited. Finally, despite the positive results derived from the relevant studies, cholinergic pharmacotherapy treatment in post-stroke aphasia has not been widely implemented. The present review aims to provide an overview of the existing literature in cholinergic pharmacotherapy interventions in post-stroke aphasia, following an integrated approach by converging evidence from neuroanatomy, neurophysiology, and neuropsychology.

The subject of this paper is to analyse the Math Principia of Economic 3D Black Holes in Roegenian economics. This idea is totally new in the related literature, excepting our papers. In details, we study two special problems: (i) math origin of economic 3D black holes, (ii) entropy and internal political stability depending on national income and the total investment, for economic RN 3D black hole. To solve these problems, it was necessary to jump from macroeconomic side to microeconomic side (a substantial approach so different), to complete the thermodynamics-economics dictionary with new entities, to introduce the flow between two macroeconomic systems, to study the Schwarzschild type metric properties on an economic 4D system, together with Rindler coordinates, Einstein 4D PDEs, and economic RN 3D black hole. In addition, we introduce some economic Ricci type flows or waves, for further research.

This paper experimentally reveals some of the resources offered by the evolution of the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor at no-load and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. The paper proposes a procedure for extracting this periodic component description (as a pattern, by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g. a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments which show that a slight change in the no-load running conditions of the motor (e.g. by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern.

The purpose of this paper is to develop a phytosociological, ecological, cytogenetic, eco-protective and economic study of the vegetation of the wetland ecosystems in the Vlădeasa Massif built by the phytocoenoses of the association Carici echinatae-Sphagnetum (Balázs 1942) Soó 1955. In order to attain the aim and objectives put forward we carried out 10 phytocoenological surveys (surveys) in the phytocoenoses of the ecosystems during the optimal vegetation periods related to the summer-serotinal season in the timeframe 2020-2021. The taxonomically inventoried species were included in an association table by the criteria of their belonging to the basic coenotaxa of the association, alliance, order, vegetation class. The outcomes of the phytocoenosis research of the vegetation of the wetland ecosystems were processed, analysed, interpreted based on tables, histograms, diagrams, regarding the numerical and percentage weight of the species in the ecological categories of bioforms, phytogeographic elements (geoelements) and cytogenetic elements. The ecological behaviour of the species confined in ecosystems was also analysed by their relationship with ecological factors, edaphic soil moisture, air temperature and chemical reaction of the soil. The current state of ecosystems, potential threats, sustainable conservation of biodiversity, dynamics of phytocenoses, economic and scientific relevance were also subjected to research. The results obtained in the surveyed territory were analysed and compared numerically and as percentage with the data provided by two reference scientific works belonging to authors who independently carried out research with a similar topic in two different geographical regions of the Western Carpathians.

(1) Background: TNF inhibitors (TNFi) have revolutionized the treatment of rheumatoid arthritis (RA). However, 30-40 % of RA patients do not respond adequately to those biologics. In addition to neutralizing soluble TNF, TNFi have the ability to bind the transmembrane form of TNF, tmTNF. Importantly, tmTNF can act itself as a receptor that induces a “Reverse Signaling” (RS) in cells. We previously showed that certolizumab, a Fab’ TNFi, activates RS in human primary monocytes, at least in part through the transcription factor NRF2 that is known to regulate the expression of genes involved in anti-inflammatory response and oxidative stress. (2) Methods: Here, we have developed an assay for the prediction of clinical response of RA patients to TNF inhibitors. This assay is based on mRNA quantitation of CD36 activation and of 6 genes induced by NRF2 following tmTNF RS in fresh monocytes. (3) Results: We could predict the response to anti-TNF monoclonal antibodies (mAbs) with 93.3 % accuracy. However, our method was not suitable for the prediction of the response to TNF soluble receptor etanercept. (4) Conclusions: We have developed a rather simple, short-term, test that can be standardized. Predicting the response to TNF mAbs will help physicians offer the best available treatment and provide patients with personalized medicine.

The global climate change mitigation efforts have increased the efforts of national government to incentivize local households in adopting individual renewable energy as a mean to help reduce the usage of electricity generated using fossil fuels and to gain independence from the grid. Since the majority of residential generation is made by PV panels that generate electricity at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of locally generated electricity at later times. On the other hand, the presence of distributed generation can affect negatively the operating conditions of low-voltage distribution networks. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation of the network. This paper proposes a metaheuristic approach based on a Genetic Algorithm that considers three different scenarios of using energy storage for reducing the losses in the network. Prosumer and network operator priorities can be considered in different scenarios inside the same algorithm, to provide a comparative study of different priorities in storage placement. A case study performed on a real distribution network provides insightful results.

In the last years, the Distribution Grid Operators (DGOs) assumed transition strategies of the distribution grids towards an active area associated with the "Smart Grids" concept. They are considering the use of Artificial Intelligence techniques, combined with advanced technologies and real-time remote communication solutions of the enormous data amounts, to develop smart solutions into the small size distribution grids, also called microgrids (μGs). These solutions will provide support for the DGOs to ensure an optimal operation of the technical infrastructure of the μGs. In this context, a bi-level methodology for solving the phase load balancing problem in the μGs with complex topologies and a high number of single-phase consumers, considering a clustering-based selection criterion of the consumers for placement of the switching devices, was proposed in the paper. A real μG from a rural area, with 114 consumers integrated into the Smart Metering System (SMS), belonging to the DGO from Romania, was considered in testing the proposed methodology. An implementation degree of 17.5%, corresponding to the phase load balancing equipment installed to only 20 consumers from the μG, led to a faster computational time with 43% and reducing the number of switching operations by 92% than in the case of a full implementation degree (100%). The performance indicators related to the unbalance factor and energy-saving used in the evaluation of the technical benefits highlighted the efficiency of the proposed methodology.

Knowledge about the formation energies of compounds is essential to derive phase diagrams of multi-component phases with respect to elemental reservoirs. The determination of formation energies using common (semi-)local exchange-correlation approximations of density functional theory (DFT) exhibits well-known systematic errors if applied to oxide compounds containing transition metal elements. In this work, we generalize, reevaluate and discuss a set of approaches proposed and widely applied in the literature to correct for errors arising from the over-binding of the O2 molecule and from correlation effects of electrons in localized transition-metal orbitals. The DFT+U method is exemplarily applied to iron oxide compounds, and a procedure is presented to obtain U values, which lead to formation energies and electronic band gaps comparable to experimental values. Using such corrected formation energies, we derive the phase diagrams for LaFeO3, Li5FeO4 and NaFeO2, which are promising materials for energy conversion and storage devices. A scheme is presented to transform the variables of the phase diagrams from the chemical potentials of elemental phases to those of precursor compounds of a solid-state reaction, which represents the experimental synthesis process more appropriately. The discussed workflow of methods can directly be applied to other transition metal oxides.

Congenital heart disease (CHD) is a prevalent congenital disorder diagnosed with high frequency, affecting approximately 0.8% to 1.2% of all live births. Research has demonstrated that the use of pulse oximetry and clinical examination as a screening tool for CHD diagnosis has proven extremely valuable. We conducted a retrospective longitudinal study over a 3-year period that involved screening 1188 newborns admitted to the NICU during the first 72 hours of life within this timeframe. 201 newborns were diagnosed with CHD. Almost all (n=198) had a positive screening test during the 72-hour period, while 3 had a negative screening test, but were later diagnosed using echocardiography. The prevalence rate was 16.67%. A confusion matrix was constructed with 198 true positives (TP), 3 false negatives (FN), no false positives (FP), and 987 true negatives (TN). The sensitivity of the screening test was 98.51%, while the specificity 100%, with no false positives. In our study, a total of 9 deaths occurred. Using the Kaplan-Meier method, the survival probability appeared to be slightly above 0.9 (95.5%), indicating that almost all patients survived past 72 hours after being diagnosed with CHD. Pulse oximetry is an effective, low cost and easy-to-use method and a useful tool to assess and lead to the diagnosis of CHD.

Background: Peritoneal sclerosis (PS) and its most severe form, encapsulating PS (EPS), are rare entities that can occur in various procedures (liver transplantation, intraperitoneal chemotherapy), secondary to medications ( beta-blockers); however, PS or EPS typically occur in patients undergoing peritoneal dialysis as a form of renal function substitution. Medical or surgical treatments can be applied, but morbidity and mortality have high rates . This condition typically presents clinically as an intestinal obstruction caused by the inclusion of the intestinal loops in the peritoneal fibrous membrane. Methods: Herein we present data from a single tertiary surgery center that has dedicated teams for patients receiving dialysis. In 12 years we analyzed a group of 63 patients admitted for catheter replacement/removal or for acute surgical pathology. In 5 cases (7,9%) we diagnosed EPS . Two patients with EPS presented with atypical abdominal pathologies requiring emergency surgery: one case of hemoperitoneum caused by a ruptured ovarian cyst and one case of uterine fibroids and metrorrhagia. Results: The definitive diagnoses were established intraoperatively and by analyzing the morpho-pathological changes of the peritoneum. The possible intraoperative challenges included laborious dissection, difficulties in restoring the correct anatomical landmarks, increased duration of the surgical intervention and a high rate of incidents and accidents Conclusions: The aim of the present study was to emphasize the possibility of other surgical pathologies overlapping with EPS, increasing the complexity of the surgical intervention. Also, we sought to determine whether there is a correlation between the morpho-pathological results of these patients who underwent emergency surgery and the common EPS morpho-pathological changes.

The analysis of the radon-seismicity relationship has been carried out so far in the Vrancea (Ro-mania) seismic zone, with monitoring stations positioned on tectonic faults. The article analyzes the evolution of radon under the conditions of the existence of deep and surface seismicity and the presence of mud volcanoes along with live fires caused by gases emitted from the soil. The monitoring area was extended to the Black Sea and the area of the Făgăraș Câmpulung fault, where a special radon detection system was set up, which was proposed for patenting. A case study is the effect of the earthquakes in Turkey (7.8 R and 7.5 R on 2023/02/06) on the seismically active areas in Romania in terms of gas emissions (radon, CO2). The main analysis methods on radon (we also included CO2) are applied to integrated time series and the use of anomaly detection algorithms. Data analysis shows that the effects of global warming introduce deviations in seasonal gas emissions compared to previous years. This makes it difficult to analyze the data and correlate them with seismicity. The main conclusions related to the development of a radon monitoring network and in general the emission of gases in seismic areas refer to the importance of the choice of equipment, the monitoring location and the installation method.

G4v is a four-vector theory of gravitation proposed by Mead (2015, 2023a,b) which reinterprets several major results commonly celebrated for the tensor-based approach of General Relativity (GR). Here, we show that G4v obeys the correspondence principle, an important test of any relativistic theory. We derive the non-relativistic Lagrangian and Energy expressions at the limit of low speeds and potentials, complete with the correct terms for potential energy (something traditional Special Relativity is incapable of achieving). G4v could represent a new interpretation of the gravitational field with essentially the same mathematics as electromagnetism (Mead 2023a). It is also possible to interpret G4v as a very interesting (and useful) approximation of the Einstein field equations (Mead 2023b).

Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and in the same time can reduce environmental impact. Investment in high tech technologies frequently involves additional costs, but an efficient water management can increase the lifetime of the equipment. The main objective of this article is to reduce the energy consumption by one thousand cubic meters pumped and automatically to increase the economic efficiency of the pumping groups. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and in the same time identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group. This methodology is based on the principles of an Expert System to perform the optimization process of the energy consumption in pumping groups. The resulting methodology avoids the combinatorial explosion of the solutions to be analyzed and determines the point of maximum efficiency without violation of any of the system constraints under any operating condition. The proposed methodology is tested on an irrigation system that includes a pumping group with 5 pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden.

In this work, we propose an approach for the autonomous navigation of mobile robots using fusion of sensor data by a Double Deep Q-Network with collision avoidance by detecting moving people via computer vision techniques. We evaluate two data fusion methods for the proposed autonomous navigation approach: Interactive and Late fusion strategy. Both are used to integrate mobile robot sensors through the following sensors: GPS, IMU and, an RGB-D camera. The proposed collision avoidance module is implemented along with the sensor fusion architecture in order to prevent the autonomous mobile robot from colliding with moving people. The simulation results indicate a significant impact on the success of completing the proposed mission by the mobile robot with the fusion of sensors, indicating a performance increase (success rate) of 27% in relation to navigation without sensor fusion. With the addition of moving people in the environment, deploying the people detection and collision avoidance security module has improved about 14% the success rate when compared to that of the autonomous navigation approach without the security module. Video was developed with robot navigation using the DDQN-Late Fusion https://www.loom.com/share/684afa6a5b0148afadc9a200ab9f3483.

The present study investigates the impact of electrode surface roughness on the electrofreezing of water. This research focuses on how the electrode roughness induced by a laser treatment affects the nucleation and growth of ice crystals under controlled electric fields. To achieve this, a series of experiments were conducted using deionized water samples placed between electrodes with varying surface roughness. The electrodes, fabricated with the Al6061 T6 alloy were structured via the Laser Shock Processing (LSP) method. For this purpose, the pulsed densities during the LSP process were varied from 900 to 2500 pulses/cm2. The increase of the pulse density was correlated to the surface structuring and to the microstructural features and average roughness of the LSP-treated Al6061 alloy. A wavy-like microstructure was induced upon the LSP treatment with average roughnesses between ~ 3.5 and 6 µm at the selected pulse densities. The results indicate that electrode roughness significantly influences the electrofreezing process. Rougher electrodes were found to lower the nucleation temperature compared to smoother ones, suggesting enhanced ice nucleation activity. The findings are attributed to the increased electric field concentration at the asperities of the rough surfaces, which may facilitate the alignment of water molecules and the formation of critical ice nuclei.

Near-infrared spectroscopy (NIRS) has become a key modality in medical imaging, finding application in both brain and breast imaging. This paper discusses the current trends in NIRS for brain and breast imaging, exploring advances in multi-modal integration with modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Challenges related to spatial resolution, depth sensitivity, and the impact of extracerebral tissues on signal specificity are examined. In addition, ongoing efforts to enhance hemodynamic measurements’ quantitative accuracy. Challenges, including limited spatial resolution and tissue heterogeneity, are discussed. The discussion extends to diffuse optical tomography and ongoing instrumentation development, clinical trials and studies validating NIRS diagnostic efficacy in breast imaging. The paper emphasizes the need for standardization, integration into routine clinical practice, and motivates for future work.

It is well established the complex exponential and logarithm are multivalued functions, both failing to maintain most identities originally valid over the positive integers domain. Moreover the general case of complex logarithm, with a complex base, is hardly mentionned in mathematic litterature. We study the exponentiation and logarithm as binary operations where all operands are complex. In a redefined complex number system using an extension of the C field, hereafter named E, we prove both operations always produce single value results and maintain the validity of identities such as log_u (w v) = log_u (w) + log_u (v) where u, v, w in E. There is a cost as some algebraic properties of the addition and subtraction will be diminished, though remaining valid to a certain extent. In order to handle formulas in a C and E dual number system, we introduce the notion of set precision and set truncation. We show complex numbers as defined in C are insufficiently precise to grasp all subtleties of some complex operations, as a result multivaluation, identity failures and, in specific cases, wrong results are obtained when computing exclusively in C. A geometric representation of the new complex number system is proposed, in which the complex plane appears as an orthogonal projection, and where the complex logarithm and exponentiation can be simply represented. Finally we attempt an algebraic formalization of E.

Serendipity favors the prepared mind, but how does the brain make that lucky find? Analyzing the cerebral mechanisms behind an exceptional (albeit trivial) discovery, the author suggests that a combination of ‘concept cells’ and the Global Neuronal Workspace Hypothesis could explain how we make great discoveries.

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability and low costs. These subsystems are usually part of the thermal power plant's coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 is utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model's effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system's status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.

The advent of modern satellite technology has transformed observational astronomy and astrophysics, offering unprecedented insights into the large-scale behavior of gravitation and challenging established cosmological models. This technological progress has reinvigorated the study of relativistic cosmology, leading to a critical reassessment of foundational assumptions, particularly the cosmological principle, which posits that the universe is homogeneous and isotropic on large scales. While this principle underpins the Standard Cosmological Model and the Friedmann-Lemaitre-Robertson-Walker metric, emerging data has increasingly been challenging its validity. Central to this investigation are the redshift-distance and light intensity-distance relations, essential for testing cosmological models. The integration of both parametric and nonparametric redshift models provides a more comprehensive analysis, addressing discrepancies in our understanding of the universe's structure and evolution. However, unresolved mysteries, particularly concerning dark matter and dark energy, complicate these models. This research critically examines the cosmological principle using the latest observational data and scrutinizes the Friedmann model's assumptions. The study shows that galaxy formation was most rapid in the early universe, especially within the redshift range of , with notable peaks around at and These two characteristic redshift values, predicted by our standard Friedmann redshift model used in this work, are confirmed by the observational data utilized in this research. The research also highlights that dark matter plays a significantly more critical role than dark energy in the process of galaxy formation. While dark energy primarily affects the large-scale expansion of the universe, dark matter seems to go beyond the domination of local galaxy formation and evolution of cosmic structures. These findings underscore the limitations of current models and contribute to the ongoing refinement of cosmological theories, offering a clearer understanding of the universe’s evolution.

The COVID-19 pandemic struck unexpectedly, emergency services and chronic care institutions, including dialysis centers, were overloaded. An important problem was the care of positive COVID patients, but also the care of chronically dialyzed patients who presented emergencies. In our Hospital, which has become a COVID support for dialysis patients with severe forms of the disease, we had to care for PD patients with dialysis-related emergencies. We presented 2 cases of patients managed on an outpatient basis or 1 day hospitalization, treated successfully and without compromising the quality of the care provided. We used remote monitoring, we worked in a multidiscipli-nary team, we shortened the hospitalization of patients (and implicitly the risk of contact). In pandemic conditions, the advantage of PD was represented by the possibility of patient isolation, so that in the first 6 months of the pandemic, we recorded no deaths in this category of patients. In case of hemodialysis patients, in-fection and mortality rates were high. Although we expected an increase in the number of peritoneal dialysis patients in the post-pandemic period, this did not happen. We continue to plead for the popularization of the PD method among patients and doctors, which has proven its advantages under the conditions of the pandemic.

Background/Objectives: Peritoneal dialysis as a form of renal function sub-stitution is not as widespread as hemodialysis. However, it has recognized advantages such as preservation of the residual renal func-tion, no need for vascular access , can be performed at home, but requires a proper in-sertion of a peritoneal dialysis (PD) catheter and maintaining its patency. Methods: We presented a 10-year retrospective study on a group of 133 patients hos-pitalized for peritoneal dialysis catheter insertion/replacement. We routinely implant the catheter laparoscopically, but there were cases in which the insertion was performed open route. We analyzed the patient group by age and gender criteria, number of complications and time when they appeared, duration of surgical intervention and the surgical technique used (126 were performed laparoscopically and 7 were inserted via open route). The open technique was performed mainly in patients with contraindications of general anesthesia. Results: We encountered a number of 23 complications in 18 patients, in only 1 case the patient was switched to permanent hemodialysis (at his own wish). We highlighted in the study the indisputable advantages of laparoscopy, supported by the results obtained: correct placement of the catheter, the possibility of adhesiolysis, preservation of the peritoneal surface through minor op-erative trauma, low number of complications(bleeding, migration, hernia 0,8%, leakage 4%, lower than those found in the literature). Even if infection was the most frequent complication (peritonitis 35%, catheter exit site infection 30.4%), only 1 case appered in the pretraining period. All complications were managed by medical treatment or by temporary cessation of peritoneal lavage, except of two cases that required reimplantation of the catheter. Conclusions: Laparoscopic catheter insertion for PD is a safe method, with reduced post-procedural complications, thus ensuring a long duration of the functionality of the catheter.

The existence of the exclusion zone (EZ), a layer of water in which plastic microspheres are repelled from hydrophilic surfaces, has now been independently demonstrated by several groups. A better understanding of the mechanisms which generate EZs would help with understanding the possible importance of EZs in biology and in engineering applications such as filtration and microfluidics. Pollack theorizes that EZ water exists in a new phase which has a layered structure. We note several problems with Pollack's proposed structure, some of which were noted in the 1960's when a similar structure was proposed for ``polywater''. We argue that Schurr's diffusiophoresis based theory presents a compelling alternative explanation for the core EZ phenomena, although there remain other findings from Pollack's lab it does not explain. Among other things, Schurr's theory makes predictions about the growth of the EZ with time which have been confirmed by Florea et al.\ and others. In this work we also introduce and explore a novel hypothesis - that some aspects of EZ phenomena may be attributable to repulsive van der Waals forces. Finally, we touch on several possible confounding factors that make experimentation on EZs difficult, such as charged surface groups, dissolved solutes, and adsorbed nanobubbles.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two data sets: our institutional DE-CTPA data set D1 comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7,892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism Detection Challenge data set D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak-signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naive approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

The Golgi apparatus (GA) dysfunctions in Parkinson’s Disease (PD), neurodevelopmental disorders (NDDs), cancer, and organelle structural biology (OSB) can provide insights into therapeutic targets, gene therapy, and drug design. Primary defects and fragmentation within the GA are implicated in a wide range of neurodegenerative diseases. GA defects typically result in mislocation of proteins, accumulation of undegraded proteins, and impaired glycosylation of proteins. Inhibition of vesicular trafficking by α-synuclein (aSyn) may affect the dopamine-producing neurons and neuromodulators. GA regulates apoptosis during pathological mechanisms of neurological diseases and could provide new avenues in treatments through translation research. PD patients bearing the hereditary E46K disease mutation manifest the clinical picture of parkinsonism. How do we provide high resolution nanoimages of the GA during disease to capture dysfunction? Could we visualize the aSyn traffic jam between vesicles in the organelles ER and GA? OSB is emerging as a field as more technology advances and is more accessible. Structural studies of the GA will advance the field of neurological disease forward with an in depth understanding of dysfunction, fragmentation, and defects. Discoveries of the GA in PD, NDDs, and cancer would break new ground and provide translational medicine data of these diseases. Future research could be visualizing high angle annular dark field-STEM (HAADF-STEM) tomograms, cryogenic electron tomography (cryo-ET), multiplex correlative light and electron microscopy (cryo-CLEM), nanobody-assisted tissue immunostaining for volumetric EM (NATIVE) and using soft X-ray tomography (SXT) and computational reconstruction of the GA.

Multiple social and environmental justice concerns are linked to urban form such as the distribution of socioeconomic class populations, healthcare spending, air pollution exposure, and human mobility. This study used 1m resolved LIDAR data to characterize land use in Salt Lake County, Utah and associate it with sociodemographic and air quality data at the census block group and zip code levels. We found that increasing tree cover was associated with higher per capita income and lower minority populations while increasing built cover was linked to lower per capita income and higher minority populations. Air quality showed less strong correlations, however, decreased non irrigated cover, increased built cover, and higher amounts of households living under poverty was related to higher long-term PM2.5 exposure. Several policy efforts have been undertaken to improve air quality and reduce negative health outcomes in Utah which are being informed by regulatory and research grade air quality sensors.

Mangroves and salt marshes are among the most productive ecosystems in the global coastal ocean. Mangroves store more carbon (739 Mg CORG ha-1) than salt marshes (334 Mg CORG ha-1), but the latter sequester proportionally more (24%) net primary production (NPP) than mangroves (12%). Mangroves exhibit greater rates of gross primary production (GPP), above-ground net primary production (NPP) and plant respiration (RC) with higher PGPP/RC ratios, but salt marshes exhibit greater rates of below-ground NPP. Mangroves have greater rates of subsurface DIC production and, unlike salt marshes, exhibit significant microbial decomposition to a soil depth of 1 m. Salt marshes release more soil CH4 and export more dissolved CH4 , but mangroves release more CO2 from tidal waters and export greater amounts of POC, DOC and DIC to adjacent waters. Both ecosystems contribute only a small proportion of GPP, RE (ecosystem respiration) and NEP (net ecosystem production) to the global coastal ocean due to their small global area, but contribute 72% of air-sea CO2 exchange from the world’s wetlands and estuaries and contribute 34% of DIC export and 17% of DOC + POC export to the world’s coastal ocean. Thus, both wetland ecosystems contribute disproportionately to carbon flow of the global coastal ocean.

Large ruminant production is associated with high greenhouse gas emissions (GHGe) intensity, with the highest in southeast Asia being Laos at 102.9 CO2eq/kg meat produced, compared with average global meat emissions intensity of 33 CO2eq/kg. As recent studies in Laos identified emissions control molasses blocks (ECBs) achieved abatement of 470kg CO2eq/20kg block consumed, a study was conducted with a supplement mix with reported methane reducing properties, containing plant tannins and citral extract, salt, molasses and water. Groups of housed dairy buffalo (n=7) and pasture-fed grazing beef cows (n=11) were randomly selected, body condition scores estimated (BCS:1-5) and baseline nasa-oral methane eructation measured daily for 2 weeks. All animals were offered 300-400g/day of the mix for a month, with buffalo accessing abundant fresh cut forages whereas the grazing cows accessing rice straw and pasture declining in quality and quantity due to severe drought. The buffalo consumed all of the mix and retained their BCS of 3, whereas the cattle consumed between 200-300g/day of the mix and their BCS declined from 1.5 to 1.0 during the trial. Analysis of data points (buffalo n=309; cattle n=378) found average methane concentration (AMP) per animal of both cohorts increased over the 2 week baseline period, then declined during the supplementation period by 36% in the buffalo and 18% in cattle cohorts.

The manuscript studies the release kinetic of fluorescein from colloidal liquid crystals made up from monoglyceride and different non-ionic surfactants. The release experiments were carried out under sink conditions, and mathematical models were described as extrapolations from solutions of diffusion equation in different initial and boundary conditions imposed by pharmaceutical formulations. The diffusion equation was solved, using Laplace and Fourier transformed functions for the release kinetic from infinite reservoirs in a semi-infinite medium. Solutions represents a general square root law and can be applied for the release kinetic of fluorescein from monoglyceride colloidal liquid crystals. Akaike, Schwartz and Imbimbo criteria were used to establish the appropriate mathematical model and the hierarchy of performances of different models applied to the release experiments. The Fisher statistic test was applied to perform significance of differences among mathematical models. Differences evaluated by mathematical criteria demonstrated that small or no significant statistic differences were carried out between various applied models and colloidal formulations. Phenomenological models were preferred over to empirical and semi-empirical ones. The general square root model shows that the diffusion-controlled release of fluorescein is the mathematical models extrapolated for monoglyceride colloidal liquids, in the first part of the process.

Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air-conditioning systems (HVACS). This is done using a specific range of controllers, which have more than thirty loops of proportional, integral and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen, oxygen, and maintaining the desired temperature of the battery storage room within the recommended limits (i.e. 25°C ±1°C tolerance) without compromising quality as set out in the user requirement specification in Appendix-A. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database amongst others. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and ensure that the HVACS extract the gas content to the outside environment. The results of the system show that the network control panel effectively operates as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, the effective operation of HVACS enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead acid Batteries (LABs).

The acquisition of adverbs in French as L2 (FSL) by learners with different L1s, at various levels of proficiency, has been extensively studied within various SLA projects (ESF, Interfra etc.). In the generativist tradition, it has been noted that “high category adverbs” such as those related to Mood-evaluative, Mood-epistemic and Tense are present early in FSL data. In the functionalist school, the early presence of time adverbs and scope particles in learner varieties has been analysed in relation with the informational and syntactic structures observed in these varieties and in terms of their contribution to temporal reference and other semantic domains in discourse.

The phytoplankton community in 66 reservoirs ubicated at the hydrographic basin of the Ebro River (Spain) was analyzed. Samples were collected in the summer season during the period 2010-2015, with the main purpose of evaluate the Trophic State and Ecological Potential according to the Water Framework Directive of the European Union. In this work we presented a representative overview of several consecutive years of the state of the reservoirs and their associated trophic status; methodology was to obtain statistically significant relationships between the phytoplankton community and main physicochemical water quality variables (water transparency, pH, chlorophyll a, total and soluble phosphorus, anoxic zone depth, nitrite, nitrate and ammonium concentrations). We provide data for retrospective comparative of the state of the reservoirs with previous and current studies in order to improve the management of these reservoirs, and to serve as data reference for future works in a context of global change (climate and pollution).

This study presents the first genome assembly of the freshwater saprobe fungus Neonectria lugdunensis and a comprehensive phylogenomics analysis of the Nectriaceae family, examining genomic traits according to fungal lifestyles. The Nectriaceae family, one of the largest in Hypocreales, includes fungi with significant ecological roles and economic importance as plant pathogens, endophytes, and saprobes. The phylogenomics analysis identified 2684 single-copy orthologs, providing a robust evolutionary framework for the Nectriaceae family. We analyzed the genomic characteristics of 17 Nectriaceae genomes, focusing on their carbohydrate-active enzymes (CAZymes), biosynthetic gene clusters (BGCs), and adaptations to environmental temperatures. Our results highlight the adaptation mechanisms of N. lugdunensis, emphasizing its capabilities for plant-litter degradation and enzyme activity in varying temperatures. Comparative genomics of different Nectriaceae lifestyles revealed significant differences in genome size, gene content, repetitive elements, and secondary metabolite production. Endophytes exhibited larger genomes, more effector proteins, and BGCs, while plant pathogens had higher thermo-adapted protein counts, suggesting greater resilience to global warming. In contrast, the freshwater saprobe shows less adaptation to warmer temperatures and is important for conservation goals. This study underscores the importance of understanding fungal genomic adaptations to predict ecosystem impacts and conservation targets in the face of climate change.

The microstructure of the substrate plays a crucial role in the anodizing process. Anodizing cast aluminum alloys is quite challenging due to the higher levels of alloying elements present compared to pure aluminum. Elements such as silicon, iron, and copper significantly impact the growth and quality of the anodic layer. Additionally, anodizing parameters such as electrolyte composition, current density, and temperature are critical in determining the morphology and thickness of the anodic film. The casting process, surface condition, and post-treatment also affect the properties of the anodic layer. Optimizing these parameters is essential to achieve a durable and high-quality anodic layer. This work aims to provide a comprehensive understanding of the various factors affecting the anodizing of cast aluminum alloys and the properties of the anodic layer, including its thickness, corrosion resistance, and wear resistance.

Obesity has increasingly become a worldwide epidemic, as demonstrated by epidemiological and clinical studies. Obesity may lead to development of a broad spectrum of cardiovascular diseases (CVDs), such as coronary heart disease (CHD), hypertension, heart failure (HF), cerebrovascular disease, atrial fibrillation (AF), ventricular arrhythmias, and sudden cardiac death (SCD). In addition to hypertension, there are other cardiometabolic risk factors (CRFs) such as visceral adiposity, dyslipidemia, insulin resistance, diabetes, elevated levels of fibrinogen, and C-reactive protein and others, all of which increase the risk of CVD events. Mechanisms involved between obesity and CVD mainly include insulin resistance, oxidative stress, inflammation, and adipokines dysregulation, which cause maladaptive structural and functional alterations of the heart, particularly left ventricular (LV) remodeling and diastolic dysfunction. Natural products of plants, provide a diversity of nutrients and different bioactive compounds, including phenolics, flavonoids, terpenoids, carotenoids, anthocyanins, vitamins, minerals, fibers, and others, which possess a wide range of biological activities including antihypertensive, antilipidemic, antidiabetic, and other activities, thus conferring cardiometabolic benefits. In this review, we discussed the main therapeutic interventions using extracts from herbs and plants in preclinical and clinical trials with protective properties targeting CRFs. Molecular mechanisms and therapeutic targets of herbs and plants extracts for the prevention and treatment of CRFs are also reviewed.

Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

Keyhole laser beam welding (LBW) of stainless steel sheets with a gap in between is numerically simulated with a three-dimensional, transient multi-physical model for laser material processing. At first, the model’s ability to reproduce experimental results on a relatively coarse computational mesh within reasonable computing time, so as to serve as process optimization tool, is presented. An example of process optimization, where a given set of weld seam quality criteria is fulfilled by iteratively optimizing a secondary laser beam, is shown. The relatively coarse mesh, in combination with a good model calibration to the experimental conditions, allows for sufficiently fast simulations to use this approach for optimization tasks. Finally, using a finer spatial and temporal discretization, the dynamic processes in the vicinity of the keyhole leading to the formation of pores are investigated. The physical phenomena predicted by the simulation are coherent with experimental observations found in literature.

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky and the analysis of the performance of the proposed classifier. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.

In this paper, we present a method for estimating GPS signals from visual information captured by a monocular camera mounted on a fixed-wing Unmanned Aerial Vehicle. The main challenge in visual odometry from aerial images is the computation of scale due to irregularities in the elevation of the terrain. That is, it is not possible to accurately convert from pixels in the image to meters in space, and the error accumulates. The contribution of this work is to reduce the accumulated error by comparing the images from the camera with satellite images. The algorithm has been tested in real-time experiments, and the results show that it is possible to eliminate the accumulated error using satellite images.

In the context of global warming and increasing scarcity of fresh water resources, it becomes significant to evaluate the contribution and evolution of non-rainfall waters such as dew. This study therefore aims to evaluate the relative dew and rain contributions in three sites of south-western of Madagascar (Ifaty, Toliara and Andremba), a semi-arid region which suffers from a strong water deficit. The studied period is 1/1991 – 7/2023, with extrapolation to 8/2033. Dew is calculated from meteo data by using a well-established energy model. The extrapolation of dew and rain follows an artificial neural network approach. It is found that dew forms regularly (2-3 days in average between events), in contrast to rain (10-15 days). The evolutions of dew and rain are similar, with an increase from 1991 to 2000, a decrease up to 2020 and a further increase until 2033. These oscillations follow the Indian Ocean dipole variations and should be influenced by the climate change. Dew contribution to the water balance remains modest on a yearly basis (3-4%) but is important during the dry season (Apr.-Oct.), up to 30%. Dew therefore appears to be a reliable and sustainable resource for plants, small animals and population, especially during droughts.

Conclusions: HRCT is a valuable tool in preoperative assessment of cholesteatoma, helping in making surgical decisions. It can accurately predict the extent of disease and is helpful for detecting unapparent dangerous situations. However it is not very accurate in detecting Fallopian canal and stapes erosion.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

L-cysteine, a precursor of essential components for plant growth, is synthesized by the cysteine synthase complex, which includes O-acetylserine(thiol) lyase (OAS-TL) and serine acetyltrans-ferase. In this work, we investigated how S-benzyl-L-cysteine (SBC), an OAS-TL inhibitor, affects the growth, photosynthesis, and oxidative stress of Ipomoea grandifolia plants. SBC impaired gas exchange and chlorophyll a fluorescence, indicating damage that compromised photosynthesis and reduced plant growth. Important parameters such as light-saturated net photosynthetic rate (PNmax), light saturation point (LSP), maximum carboxylation rate of Rubisco (Vcmax), electron transport rate (J), and triose phosphate utilization (TPU) decreased as a result. The quantum yield of photosystem II photochemistry (ϕPSII) and the electron transport rate through PSII (ETR) also decreased, as did the stomatal conductance (gs) and the photochemical quenching coefficient (qP). Additionally, SBC decreased the maximum fluorescence yield (Fm), variable fluorescence (Fv), and chlorophyll index, indicating possible damage to the photosynthetic apparatus. SBC triggered oxidative stress by increasing reactive oxygen species, malondialdehyde, and conjugated dienes. We hypothesize that dysfunctions in the sulfur-containing components of the photosynthetic electron transport chain, such as the cytochrome b6f complex, ferredoxin, and iron-sulfur (Fe-S) centers are the cause of these effects, which ultimately reduce the efficiency of electron transport and hinder photosynthesis in I. grandifolia plants. In short, our findings suggest that targeting OAS-TL with inhibitors such as SBC could be a promising strategy for the development of novel herbicides.

There are currently worldwide efforts to reduce sugar intake due to the various adverse health effects linked with the overconsumption of sugars. Artificial sweeteners have been used as an alternative to nutritive sugars in numerous applications; however, their long-term effects on human health remain controversial. This led to a shift in consumer preference towards non-caloric sweeteners from natural sources. Thaumatins are a class of intensely sweet proteins found in arils of the fruits of the West-African plant Thaumatococcus danielli. Thaumatins’ current production method through aqueous extraction from this plant and uncertainty of the harvest from tropical rainforests limits its supply while the demand is increasing. Despite successful recombinant expression of the protein in several organisms, no large-scale bioproduction facilities exist. We present preliminary process design, process simulation, and economic analysis for a large-scale (50 metric tons/year) production of thaumatin II variant by several different molecular farming platforms.

The ESKAPE pathogens are public health concerns worldwide, defining the present major antibiotic resistance issues: E. faecium vancomycin-R, S. aureus methicillin-R, Ps. Aeruginosa and A. baumannii carbapenem-R, resistance to 3rd generation cephalosporins of Kl. pneumoniae and Enterobacter spp. Covid-19 pandemic disturbed the health care usual procedures, contributing to the spread of antibiotic-resistant bacteria. The present study retrospectively assessed the prevalence and antimicrobial resistance (AMR) profile of ESKAPE microorganisms during 2020-2022 in a multidisciplinary emergency hospital from Romania, based on microbiological reports. The AMR issues mainly interest the urology department. ESKAPE group accounts for 33.5% of the total bacterial isolated. The specific AMR of ESKAPE pathogens cumulates 30.9%, while antimicrobial multidrug resistance characterizes 21% from all ESKAPE strains. Kl. Pneumoniae, followed by S. aureus, and Ps. aeruginosa are the main hospital problems concerning the ESKAPE surveillance. The directions of the near future antibiotic stewardship program focus to revise the clinical protocols for diagnostic and treatment in each department, to intensify the bacteriological samples collection, to improve the microbiological diagnostic techniques for AMR identification, to continue the epidemiological current actions of surveillance, control, and education.

The Siret River crosses NE Romania from the north to the south and it discharges into the Danube, near the city of Galati. During the period, 17^th June - 10^th July 2010, significant amounts of precipitations in the mountainous basin of Siret were recorded. The floods comprised two periods with four bimodal cycles and they were counted among the strongest on the Romanian territory. The exceptional floods occurred in the rivers of Siret, Suceava, Moldova, Bistrita, Trotus and so on. The most important compound flood wave was determined by the precipitations which fell within the period, 29^th June to 1^st July 2010, when significant amounts of rain were recorded, sometimes, exceeding 80 mm. The high discharges on the Bistrita River – downstream from the Bicaz Reservoir – were controlled by complex hydro technical works. The maximum discharge for the year 2010 summer floods was recorded at Dragesti hydrometric station: 2,884 m³/s (historic discharge) compared to the preceding historic discharge of the year 2008 (2,850 m³/s). The effects of floods were strongest in the counties of Suceava, Neamt, and Bacau. The floods on the main course of the Siret River were analyzed in correlation with the tributaries within the mountainous sector.