In the extra-thick coal seams and multi-layered hard roofs, the longwall hydraulic support yielding, coal face spalling, strong deformations of goaf-side entry, and severe ground pressure dynamic events typically occur at the longwall top coal caving longwall faces. Based on the Key strata theory an overburden caving model is proposed here to predict the multilayered hard strata behaviour. The proposed model together with the measured stress changes in coal seam and underground observations in Tongxin coal mine provides a new idea to analyse stress changes in coal and help to minimise rock bursts in the multi-layered hard rock ground. Using the proposed primary Key and the sub-Key strata units the model predicts the formation and instability of the overlying strata that leads to abrupt dynamic changes to the surrounding rock stress. The data obtained from the vertical stress monitoring in the 38 m wide coal pillar located adjacent to the longwall face indicates that the Key strata layers have a significant influence on ground behaviour. Sudden dynamically driven unloading of strata was caused by the first caving of the sub-Key strata while reloading of the vertical stress occurred when the goaf overhang of the sub-Key strata failed. Based on this findings several measures were recommended to minimise the undesirable dynamic occurrences including pre-split of the hard Key strata by blasting and using the energy consumption yielding reinforcement to support the damage prone gate road areas. Use of the numerical modelling simulations was suggested to improve the key theory accuracy.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, e.g., low accuracy, slow speed, etc. The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Aerial Pedestrian Dataset, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Aerial Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperforms the most used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

Melanin is extensively distributed in organisms and believed to be synthetized by tyrosinase (TYR), leading to aberrant coloring and skin conditions when over-produced. Loquat (Eriobotrya japonica (Thunb.) Lindl.) flowers contain large amounts of bioactive substances currently few studied on their suppressive capabilities to melanin synthesis. Its isolate product (LFP) was obtained by ethanol solution extraction and resin purification, of which the inhibitory mechanism on TYR activity was elaborated by enzyme kinetics and multi-spectroscopy determinations and the impact on related proteins’ expression in mouse melanoma B16 cells was analyzed with Western blotting. HPLC-MS analysis indicated that LFP was identified into 137 compounds, with 12 including flavonoids (quercetin, isorhamnoin, p-coumaric acid, etc.), cinnamic acid and its derivatives as well as benzene and its derivatives owning TYR inhibitory activities. LFP inhibited TYR activity in a concentration-dependent manner with its IC50 value being 2.8 mg/mL and an anti-competitive mechanism involved altering the enzyme's conformation. LFP reduced the expression of TYR, TRP1, and TRP2 in melanoma B16 cells, hence inhibiting the synthesis of melanin. Considered collectively, LFP would be a viable option for treating hyperpigmentation caused by tyrosinase, providing a theoretical basis for the value-added utilization of loquat flower resource in beauty and aging related functional products.

Eco-labels are an instrument for enabling informed food choices and supporting a demand-sided change towards a more and urgently needed sustainable food system. Lately, novel eco-labels that depict a product’s environmental life-cycle assessment on a multi-level scale are being tested across Europe’s retailers. This study elicits consumers preferences and willingness to pay (WTP) for a multi-level eco-label. A Discrete Choice Experiment was conducted in Austria. Individual partworth utilities were estimated by means of Hierarchical Bayes. Results show higher WTP for a positive evaluated multi-level label, revealing consumers’ perceived benefits of colorful multi-level labels over binary black-and-white designs. Even a negative evaluated multi-level label showed higher WTP compared to no label, pointing towards limited effectiveness of eco-labels. Respondents’ preferences for eco-labels were independent from their subjective eco-label knowledge, health consciousness, and environmental concern. The attribute “protein source” was most important, and preferences for animal-based protein source (beef) was strongly correlated with consumers’ meat attachment, implying that a shift towards more sustainable protein sources is challenging and sustainability labels have only a small impact on the meat product choice of average consumers.

We found an important fact that the beam footprint line of Ping will intersect with the real water depth when the initial incidence angle is about 45°, and sound velocity error hardly has an effect on it. This is contrary to our previous experience that the soundings of the central beam of multi-beam bathymetric system (MBS) is the most accurate. Firstly, the influence of sound velocity errors on central beam and seafloor topography distortion was analyzed. Secondly, the process of proving this fact was given, and the topography correction method was proposed by compensating the sound velocity profile area (SVP area). Finally, we showed in the first simulation of four type errors in SVPs that this fact is verified. The topography correction method used in the second simulation to correct the topography distortion of the first simulation is also verified.

Sustainability in material purchasing is a growing area of research. Goods purchasing decisions strongly affect transportation path flows, vehicle consolidation, inventory levels and related obsolescence costs. Within a global sourcing context, companies experience the need of new decision making approaches capable to consider a large variety of factors, also linked with society and environment. Environmental impact assessment has become a key requirement for materials purchasing and transportation decisions since global warming is a rising concern both in academic and industrial researches. In fact, it is well known that the freight transport industry is responsible for large amounts of carbon emissions contributing to global warming. In this paper, we initially analyse and compare the environmental economic policies established by the International Governments in relation to the carbon trading systems adopted. Then, we develop a multi-objective lot sizing approach useful in practice to define the sustainable quantity to purchase when a Cap and Trade mitigation policy is present. We further analyse the model behaviour according to different carbon price values by demonstrating that carbon prices are still far too low to motivate managers towards sustainable purchasing choices.

Introduction: Anemia and undernutrition are severe public health concerns in Ethiopia. These are the two most common nutritional disorders in pregnant women and frequently co-exist. However, to the best of our knowledge, the evidence is lacking on the co-existence of anemia and undernutrition among pregnant women. Therefore, this study aimed to examine the prevalence of co-existing anemia and undernutrition (CAU) and associated factors among pregnant women. Methods: A community-based cross-sectional study was conducted from June 1–25, 2024, on a sample of 515 pregnant women in the Hawela Lida district of Sidama region, Ethiopia. We utilized a multi-stage sampling method to choose eligible study participants. A pre-tested and structured questionnaire was used to collect data via the online Open Data Kit mobile tool. We controlled the effect of confounders and clustering by using a multi-level mixed-effect modified Poisson regression analysis model. Result: The prevalence of CAU among pregnant women was 25.4% (95% CI: 21.9–28.9). The prevalence of CAU was associated with household food insecurity (adjusted prevalence ratio [APR]: 2.17; 95% CI: 1.43–3.28), training on model family (APR: 0.66; 95% CI: 0.45-0.96), inadequate dietary diversity (APR: 1.51; 95% CI: 1.18–1.95), and having poor knowledge of nutrition (APR: 1.55; 95% CI: 1.06-2.26) at individual levels. Low community-level women’s autonomy (APR: 6.19; 95% CI: 3.42-11.22) and community-level road accessibility (APR: 0.65; 95% CI: 0.43-0.98) were the identified determinants of CAU at the community level. Conclusion: One in four pregnant women was CAU in the study area. Household food insecurity, receiving model family training, inadequate dietary diversity, having poor knowledge of nutrition, low community-level women’s autonomy, and community-level road accessibility were the significant determinants of CAU. Therefore, inter-sectorial collaboration should be required to comprehensively address the determinants of CAU at different levels. Besides, any CAU prevention and intervention programs should provide model family training specifically targeting women who have poor nutritional knowledge and a low level of autonomy in health care decision-making.

Background: Upper extremities’ function impairments (UEFI) and gait impairment are frequently reported even in the early stages of Multiple Sclerosis (MS) disease. These motor deficits can negatively affect the quality of life (QOL) in MS patient. Exercise has been repeatedly introduced as a beneficial adjuvant therapy for MS, attenuating a wide range of physical and psychological symptoms without any side effects or increased risks of relapse or exacerbation in disease symptoms.Aim: This paper investigates the effect of multi-function swing suspension training (MFSST) program on upper and lower extremities’ function (ULEF), and QOL in MS women according to expanded disability status scale (EDSS) score.Design: Experimental method with a pre-test and post-test design with control group. Setting: The department of Sports Injuries and Corrective Exercise at the Shahid Bahonar University of Kerman.Population: Forty-seven MS women.Methods: Eligible subjects who matched the selection criteria were randomly assigned into three groups as follow: i. Intervention group A (EDSS of 2 – 4), ii. Intervention group B (EDSS of 4.5 – 6.5), and iii. Control group (EDSS of 2 – 6.5). The intervention groups have been trained by the SET program for a duration of 8 weeks. At baseline and after the intervention, the upper extremities’ function (UEF) and the lower extremities’ function (LEF) were assessed by 9-hole peg test (9HPT) and the timed 25-foot walk (T25FW) test, respectively. Moreover, participants’ QOL was measured using the Short Form questionnaire (SF-36).Results: A significant difference was found between the pre-test and post-test variables of walking speed and upper extremity endurance in intervention groups, and between the intervention groups and control group (P < 0.05). Compared to control group, both intervention groups showed improvements in all subscales of QOL, except emotional role limitation score.Conclusions: In summary, these results show that the present protocol of MFSST could be effective in improving ULEF and QOL in MS patients, showing the improvements after 4^th week. Moreover, this protocol was more useful for patients with an EDSS score of more than 4.5. Therefore, it seems that exercise therapy should be considered as a beneficial clinical intervention approach to improve ULEF and QOL in MS patients.

Currently, Pythagorean fuzzy sets (PFSs) have been widely applied in various fields due to their substantial advantages in expressing and dealing with uncertainty. However, measuring the similarity and difference between PFSs effectively remains an unresolved issue. Inspired by Tanimoto similarity, we propose a novel set of similarity and distance measures for PFSs. We delve into the theoretical properties of the proposed measures and compare it with existing PFSs measures. Numerous numerical examples validate their rationality and effectiveness. Furthermore, our experimental findings suggest that in contrast to existing measures, the introduced measures successfully circumvent various counter-intuitive issues encountered by current measures, and yield more pronounced outcomes in the discrimination of different fuzzy sets. This enhances the uniqueness and superiority of our measures. Finally, we developed two decision models based on the proposed measures and validated their applicability in three applications.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Introduction: In Ethiopia, maternal undernutrition is a major public health concern. However, comprehensive evidence is lacking in the southern part of Ethiopia, specifically the household and community-level related determinants of undernutrition. Besides, the evidence about the prevalence and determinants of undernutrition is not yet documented in the current study setting. Thus, this study aimed to determine the prevalence of undernutrition and identify its determinants among pregnant women in Hawela Lida district of the Sidama region, Ethiopia.Methods: A community-based cross-sectional study was conducted on a sample of 515 pregnant women from June 1–25, 2024. A multi-stage sampling method was utilized to select eligible pregnant women. We collected data using the Open Data Kit smart phone device and exported it to Stata version 17 for further processing and analysis. A multi-level mixed-effects modified Poisson regression analysis with robust variance was used to account for between and with cluster effects.Result: The prevalence of undernutrition among pregnant women was 41.7% (95% CI: 37.3–45.6). The prevalence of undernutrition was associated with planned pregnancy (adjusted prevalence ratio [APR]: 0.80; 95% CI: 0.66-0.98), household food insecurity (APR: 1.64; 95% CI: 1.26-2.13), inadequate dietary diversity (APR: 1.79; 95% CI: 1.43-2.25), and women’s poor knowledge of nutrition (APR: 1.68; 95% CI: 1.32-2.12) at individual levels. The identified determinants of undernutrition at the community level were low community literacy rates (APR: 4.62; 95% CI: 1.13–18.79) and low community wealth status (APR: 1.91; 95% CI: 1.10–3.31).Conclusion: Two in five pregnant women had an undernutrition problem in the study setting. Individual and community-level determinants contributed to the high prevalence of undernutrition. Thus, any prevention and control approaches to undernutrition should consider inter-sectorial collaboration to account for determinants at various levels. Besides, any program must emphasize the delivery of nutrition education about dietary diversity, particularly targeting pregnant mothers who have poor knowledge of nutrition and unplanned pregnancy at the individual level. Moreover, creating a small business reform for the community with low wealth status using agricultural extension workers must be considered.

Finite element (FE) modeling is commonly used as a method to investigate the influence of medical devices, such as implants and screws and their effects on the biomechanical behavior of the spine. Another simulation method is a multi-body simulation (MBS), where the model is composed of several non-deformable bodies. MBS solvers generally require a very short computing time for dynamic tasks compared to an FE analysis. Considering this computational advantage, in this study, we examine whether parameters whose values are not known a priory can be determined with sufficient accuracy using MBS model. Therefore, we propose a Many-at-a-time sensitivity analysis method that allows approximating these a priory unknown parameters without requiring long simulation times. This method enables a high degree of MBS model optimization to be achieved in an iterative process. The sensitivity analysis method is applied to a simplified screw-vertebra model, consisting of an anterior anchor implant screw and vertebral body of C4. An experiment described in the literature is used as a basis for developing and assessing the potential of the method for sensitivity analysis and to validate the models action. The optimal model parameters for the MBS model were determined to be c=823224N/m for stiffness and d=488Ns/m for damping. The presented method of parameter identification can be used in studies including more complex MBS spine models or to set initial parameter values that are not available as initial values for FE models.

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

Remote sensing technology provides a new way to explore the earth for geoscience applications. In the context of continuous deepening of geological research and the vigorous development of geospatial information science, 3D geological modeling technology has become a research hotspot in the intersection of earth science and information science. The traditional 3D geological modeling technology refers to underground 3D modeling using geological data. This modeling method can only display the underground scene in 3D visualization, but cannot display the surface in detail. In this study, remote sensing technology is adopted to improve the traditional 3D geological modeling method, so that geological modeling can be integrated with remote sensing image, and a new 3D geological modeling method based on remote sensing technology is developed. This method can perform integrated 3D visualization of underground and aboveground scenes, which provides a new way for disaster prevention and reduction, geological prospecting and tectonic interpretation. The new 3D geological modeling method is mainly applied in the geological field, and has made new progress in multi-source heterogeneous geological data fusion, large-scale high-precision modeling, geological grid subdivision, attribute modeling technology, remote sensing image fusion and other aspects. Taking the Ya'an area as an example, this paper makes use of the new generation of 3D geological modeling technology to carry out 3D geological modeling and visual display. The 3D visualization in Ya'an area verifies the feasibility and effectiveness of the new 3D geological modeling method based on remote sensing technology in the current data environment.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

This article addresses the complex challenge of simultaneously enhancing contrast and detail in an image, where improving one property often compromises the other. This trade-off is tackled using a multi-objective optimization approach. Specifically, the proposal's model integrates the sigmoid transformation function and unsharp masking highboost filtering with the NSGA-II algorithm. Additionally, a posterior preference articulation is introduced to select three key solutions from the Pareto front: the maximum contrast solution, the maximum detail solution, and the knee point solution. The proposed technique is evaluated on a range of image types, including medical and natural scenes. The final solutions demonstrated significant superiority in terms of contrast and detail compared to the original images. The three selected solutions, although all are optimal, captured distinct characteristics within the images, offering different solutions according to field preferences. This highlights the method's effectiveness across different types and enhancement requirements and emphasizes the importance of the proposed preferences in different contexts.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

In this paper, modelling, simulation and verification of multi-agent manufacturing system with application of bio-inspired techniques are addressed. To this end, the new solution of abstract architecture for control and coordination decentralized systems - CODESA is suggested. Centralized architecture suffers from various problems, such as rigidity, scalability, low fault-tolerance or very limited flexibility, agility, energy efficiency and productivity. Prime is concrete application of CODESA in manufacturing domain. The undesirable characteristics of emergent behaviour are the problem to achieve optimization and impossibility to predict future states of the system. CODESA-Prime has been tested by simulations for automatic guided vehicle (AGV) systems guided by magnetic tape in Ella Software Platform.

This study investigated the presence, distribution, and antimicrobial resistance profiles of extend-ed-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae in a dairy herd located in Northern Italy. A questionnaire was administered, and the feces of clinically healthy pre-weaned dairy calves, their mothers, and the cows treated for mastitis, waste milk, water, and environmental samples, were collected and inoculated on Chromagar ESBL™ medium. The questionnaire identi-fied several risk factors including administration of waste milk to male calves, lack of routine cleaning of calf feeding and housing equipment, and blanket dry cow therapy. Escherichia coli was identified by MALDI-TOF MS in the feces of 28 of 37 (75.67%) calves, 2 of 3 (66.67%) treated cows, 8 of 14 (57.15%) environmental samples, and the waste milk. Six of 14 (42.85%) environmental samples were also positive for ESBL Acinetobacter baumannii, while all fecal samples were negative. ESBL production was confirmed for all E. coli isolates based on the double disk susceptibility test (DDST), while none were carbapenemase producers. Upon minimal inhibitory concentration (MIC) analysis, all of them showed multiple resistances and were categorized as multi-drug resistant (MDR). This study confirms the widespread presence of MDR, ESBL-E. coli in dairy calves and highlights their diffusion in the farm environment.

The hybrid system is analyzed and optimized to produce electric energy in Non-Interconnected Zones in the Colombian Caribbean region, contributing both to the improvement in the reduction of greenhouse gas emissions and to the rational use of energy. A comparative analysis of the performance of these systems was carried using a dynamic model in real wind and solar data. The model is integrated by a Southwest Wind Power Inc. wind turbine. AIR 403, a proton exchange fuel cell (PEM), an electrolyze, a solar panel and a charge regulator based on PID controllers to manipulate oxygen and hydrogen flows in the cell. The transient responses of the cell voltage, current, and power were obtained for the demand of 200 W for changes in solar radiation and wind speed for all days of the year 2013 in the Ernesto Cortissoz airport, Puerto Bolívar, Alfonso Lopez airport and Simon Bolívar airport, by regulating the flow of hydrogen and oxygen into the fuel cell. The maximum contribution of power generation from the fuel cell was presented for the Simon Bolívar airport in November with a value of 158,358W (9.45%). A multi-objective design optimization under a Pareto front is presented for each place studied to minimize the Levelized Cost of Energy and CO₂ emission, where the objective variables are the number of panel and stack in the PV system and PEM.

The optimization of guide vane closing schemes (OGVCS) of pumped storage hydro unit (PSHU) is the research field of cooperative control and optimal operation of pumped storage, wind power and solar power generation. This paper presents a OGVCS model of PSHU considering the rise rate of the unit rotational speed and the specific node pressure of each hydraulic unit, as well as various complicated hydraulic and mechanical constraints. OGVCS model is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., unit rotational speed and water hammer pressure criteria. In order to realize the efficient solution of the OGVCS model, an enhanced multi-objective bacterial-foraging chemotaxis gravitational search algorithm (EMOBCGSA) is proposed to solve this problem, which adopts population reconstruction, adaptive selection chemotaxis operator of local searching strategy and Elite archive set to efficiently solve the multi-objective problem. Especially, novel constraints-handling strategy with eliminating and local search based on violation ranking is used to balance various hydraulic and mechanical constraints. Finally, simulation cases of complex extreme operating conditions (i.e., load rejection and pump outage) of ‘single tube-double units’ type PSHU system are conducted to verify the feasibility and effectiveness of the proposed EMOBCGSA in solving OGVCS problem. The simulation results indicate that the proposed EMOBCGSA can provide lower rise rate of the unit rotational speed and smaller water hammer pressure than other method established recently while considering various complex constraints in OGVCS problem.

The pleiotropic biochemical and antioxidant functions of Vitamin C (Vit C) have recently sparked interest in its application in intensive care. Vit C protects important organ systems such as the cardiovascular, neurologic and renal system during inflammation and oxidative stress. Vit C also influences the systems of coagulation and inflammation and its application might prevent the development of organ damage. The current evidence of Vit C’s effect on the pathophysiological reactions during various acute stress events, such as sepsis, shock, trauma, burn and ischemia-reperfusion injury imposes the question, if the application of Vit C might be especially beneficial for cardiac surgery patients, who are routinely exposed to ischemia/reperfusion and subsequent inflammation, systematically affecting different organ systems. This review covers current knowledge about the role of Vit C in cardiac surgery patients with focus on its influence on organ dysfunctions. The relationships between Vit C and clinical health outcomes are reviewed with special emphasis on its application in cardiac surgery. Additionally, this review pragmatically discusses evidence regarding the administration of Vitamin C in every day clinical practice, tackling the issues of safety, monitoring, dosage and most the appropriate application strategy.

This paper investigates the applicability of deep learning models for predicting the severity of forest wildfires, utilizing an innovative benchmark dataset called EO4WildFires. EO4WildFires integrates multispectral imagery from Sentinel-2, SAR data from Sentinel-1 and meteorological data from NASA Power annotated with EFFIS data for forest fire detection and size estimation. Resulting in a coverage of 45 countries (Figure \ref{Figure1-events}) with a total of 31730 wildfire events from 2018 to 2022. All these various sources of data are archived into data cubes, with the intention of assessing wildfire severity by considering both current and historical forest conditions, utilizing a broad range of data including temperature, precipitation, and soil moisture. The experimental setup has been arranged to test different deep learning architectures’ effectiveness in predicting the size and shape of wildfire burned areas. The study incorporates both Image Segmentation networks and Visual Transformers, employing a consistent experimental design across various models to ensure comparability of results. Adjustments were made to the training data, such as the exclusion of empty labels and very small events, to refine the focus on more significant wildfire events and potentially improve prediction accuracy. The models’ performance was evaluated using metrics like F1 Score, IoU Score, and Average Percentage Difference (aPD). These metrics offer a multi-faceted view of model performance, assessing aspects such as precision, sensitivity, and the accuracy of the burned area estimation. Through extensive testing with Image Segmentation networks and Visual Transformers, the research not only aims at enhancing the accuracy of estimating the burned area but also underscores the significance of quality training data and the comparative effectiveness of traditional segmentation methods over transformer-based models for this specific application. The dataset is published with an Open Access license and is hosted at: \url{10.5281/zenodo.7762564}.

The electricity generated from fossil fuel and nuclear energy has negative impacts on the environment and has shown a need to search for and promote the use of clean, renewable and sustainable energy resources such as wind, solar and geothermal resources. Nowadays, wind energy resource has emerged as the fastest-growing source of energy on an overall scale. However, the selection of the best location for constructing a wind farm is a vital issue that can be considered a site selection problem, which involves various conflicting factors. Therefore, it is classified as a multi-criteria decision-making (MCDM) problem. In this research, the site selection of wind power plants is conducted in Burundi country. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. The main factors affecting optimal wind farm location considered in this study are wind speed, slope, distance from the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map. The obtained results show that 20.91% of the total study area is suitable; nevertheless, only 1.96% is highly suitable for wind farm placement. The western part of Burundi is concluded to be the most suitable area for wind farm construction and the most area is situated in Lake Tanganyika.

The food distribution process is responsible for significant quality loss in perishable products. However, preserving quality is costly and consumes a tremendous amount of energy. To tackle the challenge of minimizing transportation costs and CO2 emissions while also maximizing product freshness, a novel multi-objective model is proposed. The model integrates a vehicle routing problem with temperature, shelf life, and energy consumption prediction models, thereby enhancing its accuracy. Non-dominated sorting genetic algorithm II is adapted to solve the proposed model for the set of Solomon test data. The conflicting nature of these objectives and the sensitivity of the model to shelf life and shipping container temperature settings are analyzed. The results show that optimizing freshness objective degrade the cost and the emission objectives, and the distribution of perishable foods are sensible to the shelf life of the perishable foods and temperature settings inside the container.

Citizen participation has always been of vital importance in decision-making processes and, likewise, joint work in the formulation and execution of projects that respond to territorial needs. This in order to ensure the best impact of public or private investment, optimizing the result and community articulation. For this reason, this investigation proposes a methodology of evaluation and prioritization of projects based on an approach integrated between the modelling in dynamics systems (SDM) and the Analytical process network (ANP), in which the citizen factors are used as qualitative and quantitative variables inside the posed balance sheet model. This is how the model developed by the Stella Architect software, it offers to the decision makers approximate information about the reality of the community, especially those who were affected by the Colombian conflict and where the expectations of a comprehensive repair could disrupt the socioeconomic system and reveal more pressing interests than those of the central government. The methodology is described by the information gotten through the participatory workshops done in the California county that belongs to the Magdalena region, this is a very affected region by the conflict in Colombia. The investigation results confirm the useful of, first of all, the use of the modelling as an information-generating tool by allowing the simulation of relevant variables in the evaluation and prioritization of projects at different moments of time and, in second place, use the knowledge of the experts that expose the ANP method for establishing the importance of each variable compared with other variables and, likewise of each project compared to the rest of the alternatives in each of the different instants of time in which the evaluation is carried out, given that the results could change in any time, the official ranking is showed after an optimization process with partial results.

With the urgent need of precise positioning faced by internet of things (IoT) applications, the universality and cost of indoor positioning devices become key factors. Since the 5G network has been widely deployed, new opportunity is brought by tightly fusing the traditional low-cost sensors, i.e., the magnetometer. In this study, using 5G channel state information (CSI) and geomagnetic data, a multi-input convolutional neural network (CNN) localization system was proposed. First, to generate a data tensor that is easy for CNN processing, the raw data was reconstructed individually. Then, to comprehensively incorporate the features of 5G CSI and geomagnetic strength data, the ReLU function was chosen as the activation function of the convolutional layer. After that, a multi-input CNN was trained using the incorporated geomagnetic strength and CSI amplitude in the off-line side, and the trained CNN was recorded as a location fingerprint, which can be used for the user position prediction. Finally, in the online side and using a multi-input CNN, the 2-D coordinates are estimated and tested indoors in a typical conference room scenario. The results showed that longer sampling time of fingerprint data result in better uniqueness of the reference point, while the data collection time of locating points does not need to be long. Taking the positioning efficiency and accuracy into consideration, a sampling time of 3s at the reference point and 0.2s at the locating point are recommended. The positioning accuracy using the proposed method was 1.41 m, with an improvement of 22.9% compared with the 5G positioning, and an improvement of 18.0% compared with the 5G and geomagnetic fusing positioning using single CNN.

Soil secondary salinization has seriously affected the greenhouse vegetable production in China. To improve the secondary salinization greenhouse soil and enhance the soil physical-chemical properties in an eco-environmental way, different organic amendments (straw, straw biochar, Trichoderma bio-organic manure, commercial organic manure) were applied using pot experiment for 60 day to comprehensively screen the optimal remediation method. In this study, soil nutrient condition and salt movement were assessed, and predictive models using multi-linear regression (MLR) and Random Forest (RF) were developed to estimate soil salinization parameters. The findings indicated a significant decrease in soil salt content following the application of organic materials compared to the control treatment. Specifically, the addition of straw at a rate of 250g/kg resulted in a 59.38% reduction in soil salt levels after 60 days (P < 0.05). Furthermore, the main salt ions exhibited dynamic changes over the course of the experiment, with reductions observed in Na+, Ca2+, Cl-, and NO3- content under the 250 g/kg straw treatment by 83.63%, 74.67%, 63.26%, 59.07%, respectively, compared to CK (P < 0.05). In addition, the SO42- content under 125 g/kg commercial organic manure amendment reduced by 48.94%, respectively (P < 0.05). Contrary to expectations, the addition of organic materials significantly increased the levels of total nutrients (N/P/K) and available potassium (AK) and phosphorus (AP) in the soil. Specifically, the addition of straw at a rate of 250 g/kg resulted in increases of 18.02%-87.38% in total potassium (TK), 66.67%-200% in total phosphorus (TP), and 142.87%-367.8% in AK after a period of sixty days (P < 0.05). Ultimately, the treatment involving the addition of 250 g/kg of straw demonstrated the most pronounced impact on the physical and chemical properties of the soil. The random forest method shows promise in accurately predicting soil salt and soil sodium adsorption ratio (SAR)indicators, thereby providing a valuable tool for estimating soil properties.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2,892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG16, Detectron-121, MobileNet and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10-30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.

With a mild Mediterranean-type climate, Corsica is endemic for canine vector-borne diseases (CVBDs) such as dirofilariosis (a mosquitoes borne-diseases: MBDs), leishmaniosis and ehrlichiosis. The aim of this present study was to evaluate a monthly multi-modal prophylactic strategy (MMP) against CVBDs occurring in Corsica. The study was conducted as a comparative field trial in which eighty dogs allocated into two groups were included: (i) Group 1 consisted of 25 dogs under the MMP [per-os administration of 1.5 tablet of milbemycine oxime-praziquantel and a topical line-on application of a 3.6 mL solution of dinotefuran-permethrin-pyriproxyfen] and (ii) Group 2 under various real-life prophylactic treatment (RLP) based on the use of ectoparasiticide products [different formulations: deltamethrin, fluralaner, fipronil] and/or macrocyclic lactones based-products [milbemycin oxime/praziquantel, milbemycin oxime, moxidectin] during the period ranging from June to October 2017. All animals were followed for one year and had blood drawn at day 0, followed by follow-up at 6 and 12 months. Samples were screened for filariosis using molecular tools as well as for leishmaniosis and ehrlichiosis using indirect immunofluorescence assay (IFA). At the end of the study, no new cases of CVBDs were recorded within Group 1. In Group 2, the cumulative incidence of CVBDs was 20.0% (n= 11; p= 0.015) including dirofilarioses due to Dirofilaria immitis and/or D. repens, with 16.4% (n= 9; p=0.027). Ehrlichiosis was 5.5% (n= 3; p=0.241). No new cases of leishmaniosis were detected in Group 2. The data illustrated that, unlike the RLP treatment which failed to protect at least 20% of dogs, the MMP based on the concurrent administration of milbemycine oxime-praziquantel and dinotefuran-permethrin-pyriproxyfen is efficient to protect dogs against CVBDs in a high-risk area.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Its treatment includes antiarrhythmic drugs (AADs) to modulate the function of cardiac ion channels. However, AADs have been limited by proarrhythmic effects, non-cardiovascular toxicities as well as often modest antiarrhythmic efficacy. Theoretical models showed that combined blockade of Nav1.5 (and its current INa) and Kv1.5 (and its current, IKur) ion channels yield a synergistic anti-arrhythmic effect without effect on ventricles. We focused on Kv1.5 and Nav1.5 to search for structural similarities in their binding site (BS) for flecainide (a common blocker and widely prescribed AAD), as a first step for prospective rational multi-target directed ligand (MTDL) design strategies. We presented a computational workflow for flecainide BS comparison in a flecainide-Kv1.5 docking model and a solved structure of flecainide-Nav1.5 complex. The workflow includes docking, molecular dynamics, BS characterization and pattern matching. We identified a common structural pattern in flecainide BS for these channels. The latter belongs to the inner cavity and consist of a hydrophobic patch and a polar region, involving residues from S6 helix and P-loop. Since the rational MTDL design for AF is still incipient, our findings could advance multi-target atrial-selective strategies for AF treatment.

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O models from the mul-ti-preprocessing PLSR 5 range method could be used for most applications, including research, whereas the C and H models from GA-PLSR performance is fair and applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving by 4.1839%, 8.1842%, 3.7587%, and 35.9404%, respec-tively. Therefore, it can be considered a reliable and non-destructive alternative method for rap-idly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for a robust application.

For advancing manufacturing, arising AM, with an inverse philosophical approach compared to conventional procedures, has benefits that include intricate fabrication, reduced material waste, flexible design, and more. Regardless of its potential, AM must overcome several challenges due to multi-physical processes with miscellaneous physical stimuli in diverse materials systems and situations, such as anisotropic microstructure and mechanical properties, a restricted choice of materials, defects, and high cost. Unlike conventional experimental work that requires extensive trial and error resources and FEM, which generally consumes substantial computational power, the analytical approach based on physics is an exceptional choice. Understanding the relationship between the microstructure and material properties of the fabricated parts is a crucial focus in AM research. Texture is a vital factor in almost every modern industry. This study first proposed a physics-based model to foreshadow the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions due to the importance of part geometry in real industry. The thermal distribution obtained from this function operates as the information for the single-phase crystallographic texture model. In this model, we forerun and validate the orientations of single-phase materials utilizing three Euler Angles with the principles of CET and thermodynamics, as well as the intensity of the texture by approximating them with published results. Then, we transform the single-phase texture into a dual-phase texture in Bunge calculation, illustrating visualized by pole figures of both BCC and HCP phases. This texture evolution model provides a new paradigm for future researchers to model the texture or microstructure evolution semi-analytically and save many computational resources in a real-world perspective. Others have not yet done this work about simulating the multi-phase texture in an analytical approach, so this work bridges the gap in this field. Furthermore, this paper establishes the foundation for future research on materials properties affected by microstructure or texture in academic and industrial environments. The precision and dependability of the results obtained through this method make it a valuable tool for ongoing research and advancement.

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new dataset called Fish52 (Multi-scene Fish dataset) was constructed, on which the model achieved an mAP of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish dataset) and Fish30 dataset and tested them on the F4k and FishNet dataset. The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet datasets reaches 91.72%, 98.7%, 88.6%,and 81.5% respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses high generalization ability and has reached advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and anew module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection header module, introducing classes with independent parameter sand regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar target sin other environments, and offers assistance for the construction of smart fisheries and digital fisheries.

Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP.

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

Recent studies, typically using patient cerebrospinal fluid (CSF), have suggested that different autoantibodies (Aabs) acting on their respective receptors, may underlie neuropsychiatric disorders. The NR1 subunit of the N-methyl-D-aspartate receptor (NMDAR) has been identified as a target of anti-NMDAR Aabs in a number of central nervous system (CNS) diseases including encephalitis and autoimmune epilepsy. However, the role or the nature of Aabs responsible for effects on neuronal excitability and synaptic plasticity is yet to be established fully. Patient-derived anti-NMDAR Aabs have been shown to bind to specific regions within the NR1 subunit; here, peptide immunisation was used to generate Aabs against selected specific NR1 extracellular sequences. ‘Protein A’ purification was used to obtain total IgG and further peptide purification used to obtain a greater percentage of NMDAR-target specific IgG Aabs. Binding and specificity of these anti-NMDAR Aabs was determined using a range of methodologies including enzyme-linked immunosorbent assay, immunocytochemistry and immunoblotting. Functional effects were determined using different in vitro electrophysiology techniques: two-electrode voltage-clamp in Xenopus oocytes and measures of long-term potentiation (LTP) in ex vivo hippocampal brain slices using multi-electrode arrays. We show that anti-NMDAR Aabs generated from peptide immunisation had specificity for NR1 immunisation peptides as well as target-specific binding to the native protein. Anti-NMDAR Aabs had no clear effect on isolated NMDARs in an oocyte expression system. However, we show that peptide-purified anti-NMDAR Aabs prevented the induction of LTP at Schaffer collateral-CA1 synapses in ex vivo brain slices. Our data demonstrate that anti-NMDAR Aabs cause synaptic NMDAR hypofunction at a network level. This work provides a solid basis to address outstanding questions regarding anti-NMDAR Aabs mechanisms of action and, potentially, development of therapies against CNS diseases.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

Liquid argon technology is widely used by many previous and current neutrino experiments, and it is also promising for future large-scale neutrino experiments. When detecting neutrinos using liquid argon, many hadrons are involved, which can also interact with argon nuclei. In order to gain a better understanding of detection processes, and to simulate neutrino events, knowledge of hadron-argon cross sections is needed. This paper describes a new procedure, which improves upon the previous work with a multi-dimensional unfolding, to measure hadron-argon cross sections in a liquid argon time projection chamber. Through a simplified version of simulation, we demonstrate the validity of this procedure.

In this paper, we investigate a financial market in which asset prices evolve based on a multi-dimensional Brownian motion process and a multi-dimensional Poisson process with different credit and deposit rates We proceed to evaluate European options by establishing upper and lower hedging prices through a transition to a suitable auxiliary market. Additionally, we address the minimization of shortfall risk and no-arbitrage price bounds in incomplete markets within this framework.

In recent decades, porous scaffolds for bone tissue engineering (BTE) have gained significant attention. Considerable research efforts have been dedicated to investigating the impact of scaffold pore size on a diverse array of biological processes, including cell-scaffold interactions, substance transportation, and vascularization. However, the multi-scale hierarchical porosity, a key structural characteristic of natural bone, has rarely been replicated in BTE scaffolds due to the challenges of controlling the scaffold structure across multiple length scales. With the advancement of manufacturing technology, there has been increasing research on biomimetic multi-scale hierarchical materials, which are also gaining favor in the field of BTE. Therefore, there is an urgent need to review multi-scale hierarchical porous BTE scaffolds. This paper aims to review the role and fabrication methods of multi-scale porous BTE scaffolds at different length scales, the design and manufacturing of new BTE scaffolds for bone regeneration.

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) have a tough time adopting new technologies. Numerous technological obstacles nega-tively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0 might resolve several technological challenges. A sustainable city is a difficult structure whose economical, societal, and ecological components interact and compete with one another. There is a dearth of actual methodologies for measuring interaction. The objective of Industry 4.0 is to obtain a better degree of performance effectiveness and profitability, and greater automation. Conse-quently, the purpose of the research is to determine the influence of Industry 4.0 in fostering economic efficiency in small and medium enterprises' sustainability. A Sustainable City using Multi-Criteria Decision Making (SC-MCDM) system is designed in this research to test and achieve sustainable developmental goals. This paper then gives a technique for calculating the interaction between various standards, such as static interactions and dynamical pattern resemblance, as well as the weight variables of every indication generated by the connection. Furthermore, the ap-plication of the suggested technique is proved by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional inequalities in sustainability. Indicator scores suggest that the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit are the most significant limiting factors for most communities. Furthermore, the growth of tertiary industries, the improvement of energy performance, the expansion of green areas, and the reduction of pollution emissions are the key driving forces for enhancing sustainability. Compared to other methodologies, Multi-Criteria Decision Making (MCDM) considers the interplay between conditions, which is an excellent way to assess the sustainability of a city. The experimental findings show the impact of MCDM and sustainability to achieve sustainable development goals.

Some tumors occur in anatomical regions that are hard to biopsy with a needle. Such regions include the brain, spinal cord, liver, and lungs. For the latter two, magnetic nanoparticle-loaded macrophages could be intravenously infused and driven via an MRI machine into the tumor or tumors. Once there, they can be induced to phagocytose whole tumor cells. They would keep their target in a non-digested form by inhibiting phagosome maturation - and be directed via magnetotaxis or chemotaxis to an extraction point in the body where they can be more easily collected via needle.

Recently, deep learning models have been widely applied to modulation recognition, which have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the Iterative Dual Scale Attention Module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5\% at 10dB and 0.6232 at full SNR. The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.

In order to perform multi-degree-of-freedom motion measurements of marine machinery such as ship-borne mechanical platform in an absolute environment without a reference, absolute measurement methods using acceleration sensors and tilt gyroscopes are typically employed. However, due to the influence of wave forces on ship-borne mechanical platform, the coupling between different degrees of freedom can cause mutual interference, resulting in significant sensor measurement disturbances that make efficient computation and real-time analysis challenging. Specifically, when ship-borne mechanical platform swings, the proof mass of the acceleration sensor produces an undesirable signal output in the vertical direction, which leads to imprecise acceleration integration, thereby affecting accurate motion collection and posture estimation. To address these challenges, by analyzing the influence of the inclination angle of the ship-borne mechanical platform on the sensor measurement, and using the working principle of the acceleration sensor and angle sensor, a correction method for the motion measurement of the ship-borne mechanical platform based on multi-sensor fusion is proposed. In this article, we first analyze the influence of inclination angle on the integral effect in the heave direction. Then the configuration using four groups of acceleration sensors to correct the integral effect is proposed. Finally, the optimal inclination angle is determined through Kalman filtering based on the measured value of angle sensors and estimated values from the acceleration sensor sets. Experiments have proved that the average error of the corrected heave displacement signal is 25.34 mm, which is better than the integral displacement signal of a single acceleration sensor. At the same time, the acceleration sensor is used to calculate the roll angle and pitch angle of the ship-borne mechanical platform, and combined with the angle sensor signal to perform Kalman filtering, which filters out the errors caused by the shaking and instability of the mechanical platform, and can more accurately estimate the true inclination of the platform. Therefore, this method can enhance the precision and accuracy of the ship-borne mechanical platform motion signal acquisition, providing more valuable experimental data for research in marine engineering and related fields.

In recent years, how to combine intelligent prospecting algorithms such as random forest with a large number of geological and mineral data for quantitative prediction of exploration geochemistry has become an important topic of concern to quantitatively improve the accuracy of target delineation. The ore-forming geological conditions in the central Kunlun area of Xinjiang are great and have good prospecting prospects. However, due to the exhaustion of shallow deposits and the lag of geological prospecting work in the past ten years, there has been no expected breakthrough in the search for large and super-large metal deposits for many years. There has been a serious shortage of reserve resources. The use of new theories, new methods and new technologies for mineral resources investigation and evaluation has become an urgent need in the current prospecting work. In view of this, based on the existing spatial database of geological and mineral resources in the central Kunlun of Xinjiang, combined with the geological characteristics, genesis and metallogenic regularity of the area, this paper carried out a series of studies on gold polymetallic minerals with the help of geographic information system and data science programming software platform. The researchers integrated geological and regional geochemical data, and constructed a random forest metallogenic discriminant model based on two different sampling methods (integrated random undersampling and selection of training samples) to predict the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineate the metallogenic target area. The quantitative prediction of gold polymetallic mineral resources in the central Kunlun area of Xinjiang by two random forest models is compared and discussed: the known ore spots, fault structures and geochemical information are extracted, and the known gold polymetallic ore spots and geochemical data are used to form a training set and a prediction set to construct a machine learning random forest model. The results of prediction evaluation and metallogenic prospect division show that for different sampling methods, the performance evaluation parameters of the training process show that the prediction accuracy of the selected training samples is higher, and the selected training samples are more reliable because they can fully learn the complex information of the original data. In the metallogenic prospect prediction and metallogenic potential division, the random forest model of selecting training samples has more reference value and further exploration research significance in the production problem considering the actual exploration cost because of its small area of high potential prediction area and high proportion of ore bearing per unit area. At the same time, this study innovatively improves the prediction accuracy, reduces the exploration risk, and expands the prospecting idea of machine learning algorithm in mathematical geology in the central Kunlun area of Xinjiang. The delineated metallogenic potential area has positive guiding significance for the actual gold polymetallic prospecting work in this area.

Missing observation is a common problem in scientific and industrial experiments, particularly in a small-scale experiment. They often present significant challenges when experiment repetition is infeasible. In this research, we propose a multi-objective genetic algorithm as a practical alternative for generating optimal mixture designs that remain robust in the face of missing observation. Our algorithm prioritizes designs that exhibit superior D-efficiency while maintaining a high minimum D-efficiency due to missing observation. The focus on D-efficiency stems from its ability to minimize the impact of missing observations on parameter estimates, ensure reliability across the experimental space, and maximize the utility of available data. We study problems with three mixture components where the experimental region is an irregularly shaped polyhedral within the simplex. Our designs have proven to be D-optimal designs, demonstrating exceptional performance in terms of D-efficiency and robustness against missing observations. We provide a well-distributed set of optimal designs derived from the Pareto front, enabling experimenters to select the most suitable design based on their priorities using the desirability function.

Unmanned construction machinery vehicles mostly carry work in bridges, tunnels, and outdoor open spaces. Obtaining accurate pose estimation of the entire vehicle and establishing a map of the surrounding environment is of great significance for path planning and control in the later stage. Traditional simultaneous localization and mapping (SLAM) schemes, which mostly use a single sensor, but there are problems with localization drift and mapping failure in scenarios where there are few geometric features and the environment is prone to degradation. Currently, the multi-sensor fusion strategy has been proven to be an effective solution and widely used in the field of unmanned vehicle localization and mapping. This paper proposes a SLAM framework that tightly couples a LiDAR, IMU and camera to achieve accurate and reliable pose estimation. The framework is based on LiDAR-inertial system(LIS) and factor graph optimization theory. Texture information provided by vision is integrated into the LiDAR-inertial odometry to generate a new visual-inertial subsystem(VIS).The two subsystems, VIS and LIS, can assist each other and work jointly. Through real vehicle tests, the system can perform incremental, real-time state estimation, reconstruct dense 3D point cloud maps,and effectively solve the problems of localization drift and mapping failure in the lack of geometric features or challenging construction environments. Meanwhile, the system has a safety redundancy mechanism. When any subsystem fails, the system can also operate normally, to ensure the reliability and robustness of vehicle positioning.

Target detection and segmentation in synthetic aperture radar (SAR) images are vital steps for many remote sensing applications. In the era of data-driven deep learning, this task is extremely challenging due to the limited labeled data. Few-shot Learning has the ability to learn quickly from few samples with supervised information. Inspired by this, a few-shot learning framework named MSG-FN is proposed to solve the segmentation of ship targets in heterologous SAR images with few annotated samples. The proposed MSG-FN adopts a dual-branch network consisting of a support branch and a query branch. The support branch is used to extract features with an encoder, and the query branch uses a U-shaped encoder-decoder structure to segment the target in the query image. The encoder of each branch is composed of well-designed residual blocks combined with filter response normalization to capture robust and domain-independent features. A multi-scale similarity guidance module is proposed to improve the scale adaptability of detection by applying hand-on-hand guidance of support features to query features of various scales. In addition, a SAR dataset named SARShip-4i is built to evaluate the proposed MSG-FN and the experimental results show that the proposed method achieves superior segmentation results compared with the state-of-the-arts.

Water pollution is the main cause of global ecological degradation and seriously affects people's water supply. In order to respond to the water environmental protection policy and provide management departments with a basis for refining water quality, this paper used the environmental Gini coefficient (EGC) method based on four indicators such as water environmental capacity, population, land area, and gross domestic production (GDP) to represent social factors, economic factors, and environmental factors, respectively. After the optimization, for COD, the EGC based on the land area was 0.30, EGC based on population was 0.21, EGC based on environment capacity was 0.02, and the EGC based on GDP was 0.45, and the sum of EGC was 0.962. From this result, we observed that the change in the Gini coefficient of each indicator was not very big. Hence, the most significant change in the Gini coefficient was that of GDP, with a higher rate than other criteria. Then, the COD, AND, and TP discharge allocation model was constructed to obtain the total allocated discharge permit in Lushui Basin. The results found that the total discharge permit allocation of COD, AN, and TP to the Lushui basin were 51483.304 tons/year, 843.119 tons/year, and 340.926 tons/year, respectively. Based on the GIS spatial analysis technology, the distribution of unfair factors that cause pollution inequity was investigated. Finally, the reduction measures were proposed to implement environmental supervision and strengthen water environment management strictly.

As a new distributed machine learning (ML) approach, federated learning (FL) shows the great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning assisted FL framework. Then the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a deep Q-netwok (DQN) based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but has stronger robustness to resist malicious nodes.

Fast convergence routing is an important issue for LEO constellation network, due to its dynamical topology changing and time varying transmission requests. Most of existing research focus on the OSPF routing algorithm, which cannot handle the frequently links state changing of network. In this paper, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, in which the satellite gets the network links status fast and adjusts its routing strategy. In FRL-SR, each satellite node is regarded as an agent. The agent selects the port for packet forwarding according to its own routing policy. When the satellite network state changes, agent would send ’hello’ packets to the neighbor node to update the neighbor node’s routing policy. Compared with traditional reinforcement learning, FRL-SR can perceive network information faster, and then converge faster. Also, FRL-SR can mask the dynamics of satellite network topology and adaptively adjust the forwarding strategy according to the link state. Various simulation is constructed, the results show that the proposed FRL-SR algorithm out performance the Dijkstra algorithm in performance of average delay, packet arriving ratio, network load balance.

The study sought to investigate the effect of Multi-choice Goal programming in project Portfolio Analysis, using Lagos State Project Portfolio from between 2017-2021 as a study case. Specifically, the objectives examined the effectiveness of multi-choice goal programming in analyzing the project Portfolio of Lagos State Government and investigated the possible lapses in the use of Multi-choice Goal programming in analyzing the project Portfolio of Lagos State Government. Data were gathered from the State financial Statements and analyzed using Lingo-17 Software. Based on the analysis, the study discovered that the goals formulated can be optimally and minimally attained as the positive deviational and negative deviational variables depict zero. This indicates that the cost incurred on the project portfolio was absolutely minimized during the period and at the same time; Lagos State Government derived the most preferred benefits from it. The study therefore concluded that the State minimized the project costs and at the same time maximized benefits while meeting its overall objectives.

Background: While mankind is still dealing with the COVID-19 pandemic, on May 7, 2022, a case of monkeypox virus (MPXV) has been reported to the WHO. Monkeypox is a viral zoonotic disease with characteristics comparable to those seen in smallpox cases in the past. It has been a public health threat, particularly in Africa, but recently have been circulating the world, consequently, may become a global public health threat in a very short period. Thus, the current work was planned and then constructed a multi-epitope vaccine that can evoke an immunological response against MPXV utilizing cell surface-binding protein as a target in order to develop a novel vaccine that is both safe and almost free of side effects. Results: The proposed vaccine composed of 304 amino acids and was shown to be antigenic in Vaxijen server (0. 5311) and nonallergenic in AllerTop server. The 3D structure of the designed vaccine is predicted, refined and validated by various in silico tools to assess the stability of the vaccine. Moreover, solubility of the vaccine construct was found greater than the average solubility provided by protein-Sol server indicating the solubility of the vaccine construct. Moreover, the most promising epitopes bound to MHC I and MHC II alleles were found having good binding affinities with low energies ranging between ₋7.0 - ₋8.1kcal/mol. Conclusion: We conclude from our research that the cell surface-binding protein is one of the primary proteins involved in MPXV pathogenesis. The most promising epitopes were selected using a rigorous procedure and used for vaccine design. As a result, our study will aid in the development of appropriate therapeutics and prompt the development of future vaccines against MPXV, which could serve as an important milestone in the production of an antiviral vaccine against MPXV.

Existing Reynolds Averaged Navier-Stokes based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this study, a novel framework based on computational fluids dynamics driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model.

A multi-methodic analysis was performed on 5 samples of fly ashes coming from different biomasses. The aim of the study was to evaluate their possible re-use and their dangerousness for men and environment. Optical granulometric analyses indicate that the average diameter of the studied fly ashes is around 20 µm, whereas only ~1 vol% has diameter lower that 2.5 µm. The chemical composition, investigated with electron probe microanalysis, indicates that all the samples have a prevalent Ca composition, followed by Si and Al. A large content in K and P was observed in some samples, whereas the content in potentially toxic elements is always below the Italian law thresholds. Polycyclic aromatic hydrocarbons are completely absent in all the samples coming from combustion plants, whereas they are present in the fly ashes from the gasification center. Quantitative mineralogical content, determined by Rietveld analysis of X-ray powder diffraction data, indicates that all the samples have a large amorphous content, likely enriched in Ca, and several K and P minerals, such as sylvite and apatite. The results obtained from the performed chemo-mineralogical study allowed to point out that the biomass fly ashes could be interesting materials (1) as amending in clayey soils, in substitution to lime, to stimulating pozzolanic reaction and improve their geotechnical properties, on the one hand, avoiding to mine raw materials and, on the other hand, re-cycling wastes; (2) as agricultural fertilizes made by a new and ecological source of K and P.

Timely diagnosis of Alzheimer's diseases(AD) is crucial to obtain more practical treatments. In this paper, a novel approach Based on Multi-Level Fuzzy Neural Networks (MLFNN) for early detection of AD is proposed. The focus of study was on the problem of diagnosing AD and MCI patients from healthy people using MLFNN and selecting the best feature(s) and most compatible classification algorithm. In this way, we achieve an excellent performance using only a single feature i.e. MMSE score, by fitting the optimum algorithm to the best area using optimum possible feature(s) namely one feature for a real life problem. It can be said, the proposed method is a discovery that help patients and healthy people get rid of painful and time consuming experiments. Experiments shows the effectiveness of proposed method in current research for diagnosis of AD with one of the highest performance (accuracy rates of 96.6%), ever reported in the literature.

A heterogeneous disease like cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), patients’ survival vary significantly and show different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score. We observed that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we also compared the effect of feature selection and similarity measures for subtype detection. To evaluate the results obtained, we selected the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes identified by the autoencoders; the obtained results coincide well with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

Due to the tremendous progress in the automotive industry, the growth of the urban population, the number of vehicles is increasing and this creates parking challenges. Intelligent parking management systems offer an optimal solution for finding empty parking space so that drivers can quickly find their car parking space. To solve these problems, it is necessary to design an intelligent parking system, in addition to providing comfort to drivers, which is also economically viable. This paper proposes an intelligent multi-storey car parking system with the help of RFID technology and examining user preferences that can effectively solve car parking problems. The proposed method is a multi-objective decision-making method to reduce the problem of car parking, which is called MODM-RPCP. Therefore, the proposed MODM-RPCP method can allocate the best space for their stopping place by using the decision-making system and based on the priorities considered by the users. The simulation results show that the MODM-RPCP reduces the average booking time more than 19.2% and 27.1%, and decreases the response time of central parking management server more than 20.1% and 29.78% compared to MOGWOLA and ODPP approaches.

Stability considerations play a central role in structural dynamics to determine states that are robust against perturbations during the operation. Linear stability concepts, such as the complex eigenvalue analysis, constitute the core of analysis approaches in engineering reality. However, most stability concepts are limited to local perturbations, i.e. they can only measure a state’s stability against small perturbations. Recently, the concept of basin stability has been proposed as a global stability concept for multi-stable systems. As multi-stability is a well-known property of a range of nonlinear dynamical systems, this work studies the basin stability of bi-stable mechanical oscillators that are affected and self-excited by dry friction. The results indicate how the basin stability complements the classical binary stability concepts for quantifying how stable a state is given a set of permissible perturbations.

This review provides a snapshot of chronic bacterial infections through the lens of Burkholderia pseudomallei; detailing its ability to establish multi-nucleated giant cells (MNGC) within the host, leading to the formation of pyogranulomatous lesions. We explore the role of MNGC in melioidosis disease progression and pathology by comparing the similarities and differences of melioidosis to tuberculosis, outlining the concerted events in pathogenesis that lead to MNGC formation, discussing the factors that influence MNGC formation and how they fit into clinical findings reported in chronic cases. Finally, we speculate about future models and techniques that can be used to delineate the mechanisms of MNGC formation and function.

Many clustering approaches succeed in pattern segmentation in many applications. This unsupervised segmentation should be effective to reduce an expert labelling time: i.e, they must be able to detect the number of patterns and identify them in a sequence or map with the right cuts. Several direct and hierarchical clustering approaches are compared for this task. A divisive spectral clustering architecture with a no-cut criteria is also proposed. This new algorithm achieves promise segmentation of spatial UCI databases and marine time series compared to other approaches.

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

The commonly used words ‘transformation’ and ‘transition’ tend to lose their edge when used for any significant change process as is rather often the case. Partners and wider stakeholders in initiatives related to ‘sustainability transitions’ therefore often entertain different perspectives on what the strategic orientation of an initiative is or is meant to be. Common planning and design processes such as situation analysis and theories of change, however, often do not sufficiently cater to this dynamic. As a result, different actors may be pulling the initiative in different directions, undermining the overall partnership efforts. In this short contribution a strategic scoping canvas and an associated facilitation process are presented as a way of addressing such situations. Illustrations are provided of initial application in three cases related to food system transitions in Peru, Ethiopia, and Bangladesh, exploring the connectivity with approaches commonly used in the context of system transformations, including the Multi-Level Perspective on sustainability transitions, the Leverage Points approach, Capability Approach, and the theory of Large System Change. We conclude that the canvas and associated facilitation approach has proved useful in different contexts, offering opportunities for complementing existing methodologies, and potentially enhancing their efficacy in facilitated multi-stakeholder processes.

Marine mortar was the goal material and its multi-scale physical-chemical-mechanical characteristics were the principal interest in this study. The on-line multi-scale damage detection experiments art was designed to quantify the characteristics mathematically and graphically. The normal cylinder specimen with 70-day age was produced and investigated by dynamically global MSHCT scan and local detection of EDS, SEM and XRD. The experiments results indicated that the marine mortar offered the appreciable strength at the early age at least, although some saline minerals were generated during the preparation. The micro-interfacial behavior and the parental foci controlled the damage development of the marine mortar the performance of which was still the adjustable one by the composition optimization.

The aim of this article is to extend the local as well as the semi-local convergence analysis of multi-point iterative methods using center Lipschitz conditions in combination with our idea, of the restricted convergence region. It turns out that this way a finer convergence analysis for these methods is obtained than in earlier works and without additional hypotheses. Numerical examples favoring our technique over earlier ones completes this article.

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

Accurate assessment of the breast cancer deterioration degree plays a crucial role in making medical plan, and the important basis for degree assessment is the number of mitoses in a given area of the pathological image. We utilized deep multi-scale fused fully convolutional neural network (MFF-CNN) combing with conditional random felid (CRF) to detect mitoses in hematoxylin and eosin stained histology image. Analyze the characteristics of mitotic detection ----scale invariance and sparsity, as well as the difficulties ---- small amount of data , inconsistent image staining and sample class unbalanced. Based on this, mitotic detection model is designed. In this paper, a tissue-based staining equalization method is used, and to establish an effective training sample set, we select training samples by using CNN. A mitotic detection model fusing multi-level and multi-scale features and context information was designed, and the corresponding training strategy was made to reduce over-fitting. As preliminarily validated on the public 2014 ICPR MITOSIS data, our method achieves a better performance in term of detection accuracy than ever recorded for this dataset.

This article addresses reinsurance decision making process, which involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. In contrast to existing literature on pure proportional reinsurance or stop-loss reinsurance, this article focuses on the combination into Proportional-Stop-loss reinsurance design which better addresses interest of both parties. In terms of methodology, the significant contribution of the study is to incorporate Multiple Attribute Decision Making (MADM) into modelling the reinsurance selection. The Multi-Objective Decision Making (MODM) model is applied in designing reinsurance alternatives. Then MADM is applied to aid insurance companies in choosing the most appropriate reinsurance contract. To illustrate the feasibility of incorporating intelligent decision supporting system in reinsurance market, the study includes a numerical case study using simulation software @Risk in modeling insurance claims, and programming in MATLAB to realize MADM. Managerial implications could be drawn from the case study results. More specifically, when choosing the most appropriate reinsurance, insurance companies should base their decision on multiple measurements instead of single-criteria decision making models for their decisions to be more robust.

Globally, we are undergoing compelling changes in virtually every branch of the society and every aspect of our daily lives over the last couple of years due to the COVID-19 pandemic. In the words of Melinda Gates, the famous American Philanthropist, the pandemic has literally “magnified every existing inequality”. There has been much talk and an ongoing debate on the origin of SARS-CoV-2 (or, the novel coronavirus) as to whether have evolved purely naturally or has the contaminating element of human intervention somewhere in its trajectory. Proponents of the pure ‘natural evolution theory’, some of whom are bigshots in the field of virology have seem to take up great effort to stamp their authority right from the very early days of the pandemic on the slow natural evolution of the virus from its ancestrors by several publications in high-profile journals. These well-cited papers however provide rationals that are somewhat contradictory and are backed up with data which maybe argued as far from being complete and/or all-inclusive. As time progressed, alternative theories to that of the pure natural evolution of the virus emerged with strong counter-logics and gradually compiling genomic and epidemiological data along with detailed structural studies of relevant molecular interactions. All said and done, till date, compelling evidences lack on either end of the spectrum making the COVID origin debate still very mucg wide open. Regardless of whether purely natural or human-intervented the damage caused by the virus is unmistakable and has been an alarming concern to medical science lately. To that end, a section of the scientific community is devoting committed efforts using immuno-informatics to design and develop possible multi-epitope combination vaccines to cover a plethora of viral antigens and their plausible future mutations, alongside with the vaccines that are currently available.

Pseudo-nitzschia spp. blooms are a recurrent problem in many coastal areas globally imposing some significant threats to the health of human, ecosystems and economy. Monitoring programmes have been established, where feasible, to mitigate the impacts caused by Pseudo-nitzschia spp. and other harmful algae blooms. The detection of such blooms from satellite data could really provide timely information on emerging risks but the development of taxa-specific algorithms from available multispectral data is still challenged by coupled optical properties with other taxa and water constituents, availability of ground data and generalisation capabilities of algorithms. Here, we developed a new set of algorithms (PNOI) for the detection and monitoring of Pseudo-nitzschia spp. blooms over the Galician coast (NW Iberian Peninsula) from Sentinel-3 OLCI reflectances using support vector machine (SVM). Our algorithm was trained and tested with reflectance data from 260 OLCI images and 4607 Pseudo-nitzschia spp. match up data points, of which 2171 were of high quality. The performance of no bloom/bloom model in the independent test set was robust, showing values of 0.80, 0.72 and 0.79 for area under the curve (AUC), sensitivity and specificity, respectively. Similar results were obtained by our below detection limit/presence model. We also present different model thresholds based on optimisation of true skill statistic (TSS) and F1-score. PNOI outperforms linear models, while its relationship with in-situ chlorophyll-a concentrations is weak demonstrating poor correlation with the phytoplankton abundance. We showcase the importance of PNOI algorithm and OLCI sensor for monitoring the bloom evolution between the weekly ground sampling and during periods of ground data absence, such as due to Covid-19.

In this paper, stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system's average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to simplicity to implement and no presumptions to simplify. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is applied to determine the decision variables defined as the network's optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization, some objectives are weakened compared to their base value, while the results of the MOIF indicated a fair compromise between different objectives, and all objectives are improved. The results of MOIF based on the IMTBO cleared that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is proved compared to the conventional MTBO, particle swarm optimization, and artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag, and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively compared to the deterministic MOIF model.

This study explores the impact of multi-user interaction, group work, and simulations within virtual worlds on language learning and the development of intergroup empathy. Two distinct research projects were conducted, involving a total of 241 participants aged 19-45. The language learning study engaged 116 participants in diverse interactive experiences, while the intercultural study had 125 participants collaborating in multicultural groups and participating in perspective-taking simulations. Both studies employed qualitative data collection methods, including surveys, interviews, and observations. The findings suggest that the combination of networking strategies, collaborative learning, and simulations within virtual worlds contributes to improvements in learners' language proficiency, confidence, and empathy towards diverse social groups. Participants reported increased motivation and engagement, attributed to the immersive and interactive nature of the virtual environments. The studies highlight the importance of collaboration and reflection in facilitating language acquisition and intercultural understanding. Technical challenges were identified as potential barriers to implementation. The results demonstrate the potential of virtual worlds to enhance language education and foster empathy in diverse societies, offering valuable insights for educators and researchers. However, the findings may be limited by the specific contexts and sample sizes of the studies, warranting further research to explore the generalizability and long-term impact of virtual world interventions. not exaggerate the main conclusions.

A new method for multi-shot reconstruction of high-energy photon distributions in the context of studying the interaction between a beam and a plasma in plasma wakefield acceleration (PWFA) experiments is presented. The study investigates the effects of beam perturbations on betatron radiation and analyzes how these perturbations can lead to hosing, a transverse instability that can degrade the quality of the beam. The potential of betatron radiation spectroscopy as a non-invasive diagnostic technique for PWFA experiments is also emphasized.

Although different assessments and evaluations of Dual-Miller cycle performed, specified output power and thermal performance associated with engine determined. Besides, multi objective optimization of thermal efficiency, Ecological Coefficient of performance ( ) and Ecological function ( ) by the mean of NSGA-II technique and thermodynamic analysis performed. The Pareto optimal frontier obtaining the best optimum solution is chosen by fuzzy Bellman-Zadeh, LINMAP, and TOPSIS decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved.

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman Neural Network (ENN), Boosted Tree Algorithms (BTA), Multi-layer Perceptron (MLP), and Gaussian Processes Regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages their strengths for accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction, the fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out, the first scenario is done for PV array fault detection with DC power (DCP) as output. The second scenario is done for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. Real-world solar energy dataset is used to evaluate the proposed technique, with results compared to existing detection techniques and obtained results show that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified reliably among all the models with MAPE=0.0393, and MAE=0.002 for inverter fault detection and MAPE=0.091, and MAE=0.000 for PV array fault detection.

Windows account for a significant proportion of the total energy lost in buildings. The interaction of window type, Window-to-Wall Ratio (WWR) scheduled and window placement height would influence the natural lighting and heat transfer through windows. This is a pressing issue for non-tropical regions considering their high emissions and distinct climatic characteristics. A limitation exists in the adoption of common simulation-based optimisation approaches in the literature, which are hardly accessible to practitioners. This article develops a numerical-based window design optimisation model using a common Building Information Modelling (BIM) platform adopted throughout the industry, focusing on non-tropical regions of Australia. Three objective functions are proposed; the first objective is to maximize the available daylight, and the other two emphasize on the undesirable heat transfer through windows in summer and winter respectively. The developed model is tested on a case study located in Sydney, Australia, and a set of Pareto-optimum solutions is obtained. Through the use of the proposed model, energy savings of up to 16.43% are achieved. Key findings on the case example indicate that leveraging winter heat gain to reduce annual energy consumption should not be the top priority when designing windows for Sydney.

Providing safely-managed sanitation/hygiene requires key competencies for education, training, service delivery, enterprise development and management, product and infrastructure design and development, construction and installation, governance, financing, research, etc. These strategic sanitation capacities will have to be built from higher education’s academic and professional programmes structured and designed to produce skilled and knowledgeable professionals and practitioners. This study aims to investigate the quality of the sanitation/hygiene management content of Environmental Health programmes; the adequacy of the existing central curricula; and the perception of environmental health sanitation/hygiene-trained professionals to determine the next phase for building knowledge and capacity of sanitation professionals in Nigeria through higher education institutions. A multilevel mixed method concurrent study was used for sampling and data collection with a multilevel perception analysis to examine the perceptions of students, lecturers and graduate alumni as well as employers/supervisors and clients/service-users of sanitation/hygiene related graduates. The main findings of this paper show limited understanding of the concepts of contemporary issues of sanitation/hygiene management; the central/national teaching and curricula were found to be seriously out of date; and clients/service-users were dissatisfied with the skills and knowledge levels of the graduates. The study concludes by recommending a national sanitation management higher education pathway to guide the provision of integrated sanitation/hygiene management education at higher education institutions (HEIs), to build effective, efficient, competent, and sufficient workforce for the country.

The feasible, reliable and selective multi-template molecularly imprinted polymers (MT-MIPs) based on SBA-15 (SBA-15@MT-MIPs) for the selective separation and determination of the trace level of ginsenoside Rb₁ (Rb₁), ginsenoside Rg₁ (Rg₁) and notoginsenoside R₁ (R₁) (Panax notoginseng saponins, PNS) from biological samples were developed. The polymers were constructed by SBA-15 as support, Rb₁, Rg₁, R₁ as multi-template, acrylamide (AM) as functional monomer and ethylene glycol dimethacrylate (EGDMA) as cross-linker. The new synthetic SBA-15@MT-MIPs were satisfactorily applied to solid-phase extraction (SPE) coupled with high performance liquid chromatography (HPLC) for the separation and determination of trace PNS in plasma samples. Under the optimized conditions, the limits of detection (LODs) and quantitation (LOQs) of the proposed method for Rb₁, Rg₁ and R₁ were in the range of 0.63-0.75 ng mL^-1 and 2.1-2.5 ng mL^-1, respectively. The recoveries of R₁, Rb₁ and Rg₁ were obtained between 93.4% and 104.3% with relative standard deviations (RSDs) in the range of 3.3-4.2%. All results show that the obtained SBA-15@MT-MIPs could be a promising prospect for the practical application in the selective separation and enrichment of trace PNS in the biological samples.

Differential Evolution (DE) is an evolutionary algorithm that has become increasingly popular due to its effectiveness and efficiency. However, its adaptation for multi-objective optimization requires further investigation. To address this issue, a novel algorithm called ILSDEMO has been proposed, which incorporates indicator-based selection and local search into a self-adaptive DE. ILSDEMO employs an archive population to store non-dominated solutions, generating an initial population uniformly distributed over the feasible solution space using orthogonal design. Additionally, two variants of DE are used to expedite convergence, while q-Gaussian mutation is utilized to exploit better trial individuals. The k-nearest neighbor rule is used to eliminate crowded solutions, and indicator-based selection is employed to generate a new parent population without diversity preservation. The performance of ILSDEMO was investigated on the test instances from the ZDT series and DTLZ series in terms of the selected indicators. The results suggest that ILSDEMO accurately and evenly approximates the true Pareto front compared to NSGAII, IBEA, and DEMO.

Is it true that the nuclear technology applied to electric energy generation offers a clean, safe, reliable and affordable i.e. sustainable alternative? Yes it is, but its impact on the environment strongly depends on the implementation bearing residual risks due to a human factor, technical failures or natural catastrophes. A full response is therefore difficult and can first be given when the wicked multi-disciplinary problems get well formulated and “solved”. These problems have multi-dimensional nature lying at the interface between: necessary R&D effort, the industrial deployment and the technology impact in view of the environmental sustainability including the management of produced hazardous waste. This enormous complexity indicates that just a description of the problem might represent a problem. The paper proposes a holistic approach to assess the nuclear energy systems potential with respect to sustainable performance applying Multi-criteria decision analysis with a suitable objective tree and a multi-level criteria structure and examines the trading-off techniques for ranking of the alternatives. The framework proposes a multi-criteria and multi-stakeholders treatment which can be used as a pre-decisional support towards an implementation of nuclear fuel cycles adapted to national preferences and priorities. Proposed approach addresses some aspects of the environmental footprint of nuclear energy systems. Advanced nuclear fuel cycles, previously investigated by the NEA/OECD expert group WASTEMAN, are analyzed as a case study. Sustainability facets of waste management, resource utilization and economics are in focus.

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.

Object-level Simultaneous Localization and Mapping (SLAM) has gained popularity in recent years since it can provide a means for intelligent robot-to-environment interactions. However, most of these methods assume that the distribution of the errors is gaussian. This assumption is not valid under many circumstances. Further, these methods use a delayed initialization of the objects in the map. During this delayed period, the solution relies on the motion model provided by an Inertial Measurement Unit (IMU). Unfortunately, the errors tend to accumulate quickly due to the dead-reckoning nature of these motion models. Finally, the current solutions depend on a set of salient features on the object’s surface and not the object’s shape. This research proposes an accurate object-level solution to the SLAM problem with a 4.1 to 13.1 cm error in the position (0.005 to 0.021 of the total path). The developed solution is based on Rao-blackwellized Particle Filtering (RBPF) that does not assume any predefined error distribution for the parameters. Further, the solution relies on the shape and thus can be used for objects that lack texture on their surface. Finally, the developed tightly coupled IMU/camera solution is based on an undelayed initialization of the objects in the map.

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

In the traditional power system, customers respond to their primary electricity consumption pattern based on price or incentive to take additional advantages. By developing energy hubs (EHs) where electricity, heat, natural gas and other forms of energy are coupled together, all types of energy customers even the inelastic loads can participate in demand response (DR) program. This novel vision has led to the concept of "integrated demand response (IDR)". IDR programs (IDRPs) in EHs involve coordinating multiple DR activities across different energy systems, such as buildings, industrial complexes, and transportation networks. The main purpose of IDR is that multi-energy users can respond not only by shifting or reducing energy consumption from the demand side, but also by changing the type of energy consumed, in response to the dispatching center. The integration of IDRPs in EHs can help to reduce energy costs, improve grid stability, and increase the penetration of renewable energy sources (RES) in the power system. Moreover, by synchronizing DR activities across different energy systems, IDRPs can provide additional benefits, such as improved energy efficiency, reduced greenhouse gas emissions, and increased resilience to power outages and other disruptions. In this paper, we provide a review, assessment, and classification of fundamental principles, modeling techniques and optimization methods for IDR programs in EHs.

Steel damper columns (SDCs) are energy-dissipating members that are suitable for reinforced concrete (RC) moment-resisting frames (MRFs) and those often used for multistory housing. In a previous study, an energy-based prediction procedure for the peak and cumulative response of an RC frame building with SDCs was proposed. In this procedure, the accuracy of the equivalent velocity of the maximum momentary input energy (V_ΔE1^*)–peak equivalent displacement (D₁^*_max) relationship is essential for improved prediction. In this article, the seismic capacity curve (V_ΔE1^*–D₁^*_max relationship) of RC MRFs with and without SDCs is evaluated using incremental critical pseudo-multi impulse analysis (ICPMIA). In the ICPMIA, which is based on a study by Takewaki and coauthors, the structure is subjected to various intensities of critical pseudo-multi impulsive lateral force. An ICPMIA of four 8- and 16-story RC MRFs with and without SDCs is performed to obtain their structural behaviors under various intensities of pulsive input. Then, the seismic capacity curve obtained from the ICMPIA results are compared with the predicted results based on the simplified equations. The main findings of this article are as follows. (i) The seismic capacity curve of RC MRFs without SDCs strongly depends on the number of impulsive lateral forces (N_p). As N_p increases, the seismic capacity decreases. The predicted seismic capacity curve severely underestimates that obtained from the ICMPIA in the case of large N_p. This trend is notable when the level of pinching behavior in the RC members is severe. (ii) In the case of an RC MRF with SDCs, however, the influence of N_p on its seismic capacity curve is small. The predicted seismic capacity curve agrees very well with that obtained from the ICMPIA. The influence of the level of pinching behavior in RC members on the seismic capacity curve is small.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

This paper presents the methodology to detect and identify the type of fault that occurs in shunt connected static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes classifier. To study this, the network model is designed using Mat-lab/Simulink. The different faults such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and three-phase (LLLG) fault are applied at different zones of system with and without STATCOM considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using daubechies mother wavelet of db4 to extract the features such as standard deviation and Energy values. The extracted features are used to train the classifiers such as Multi-Layer Perceptron Neural Network (MLP), Bayes and Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE) and root-relative square error (RRSE) than MLP and Bayes classifier.

The introduction of the Internet of Things (IoT), i.e. the interconnection of embedded devices over the Internet, has changed the world we live in from the way we measure, make calls, print information and even the way we get energy in our offices or homes. The convenience of IoT products, like CCTV cameras, IP phones, and oscilloscopes, is overwhelming for end-users. In parallel, however, security issues have emerged and it is essential for infrastructure providers to assess the associated security risks. In this paper, we propose a novel method to detect IoT devices and identify the manufacturer, device model, and the firmware version currently running on the device using the page source from the web user interface. We performed automatic scans of the large-scale network at the European Organization for Nuclear Research (CERN) to evaluate our approach. Our tools identified 233 IoT devices that fell into eleven distinct device categories and included 49 device models manufactured by 26 vendors from across the world.

Underwater Wireless Sensor Networks (UWSN) enables various oceanic applications which require effective packet transmission. In this case, sparse node distribution, dynamic network topology and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a Relay based Void Hole Prevention and Repair protocol (ReVOHPR) by multiple Autonomous Underwater Vehicles (AUV) for UWSN. ReVOHPR efficiently identifies and avoids void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as Ocean Depth (levels) based Equal Cluster Formation, Dynamic Sleep Scheduling, Virtual Graph based Routing, and Relay Assisted Void Hole Repair. For energy efficient cluster forming, Entropy based Eligibility Ranking (E2R) is presented which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented Dynamic Kernel Kalman Filter (DK2F) algorithm in which Sleep and Active modes based on the nodes current status. Inter Cluster Routing is performed by maximum matching nodes which selects by Dual criteria and also data transmitted to AUV. Finally, void holes are detected and repair by Bi-Criteria Mayfly Optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes, data packet loss and maximizes Quality of Service (QoS) and energy efficiency of the networks. This protocol is timely deal with node failures in packet transmission via multi-hop routing. Simulation is implemented by NS3 (AquaSim module) simulator that evaluates the performance in network simulation for following metrics as average energy consumption, delay, packet delivery rate and throughput.

Microscale laser dynamic forming, as a novel high-speed microforming technique, can overcome the shortcomings of traditional microforming methods. However, in practical applications, laser dynamic microforming technology is often affected by the rebound behavior of the workpiece surface, limiting the further improvement of processing quality and efficiency. This paper aims to reduce the rebound effect in laser dynamic microforming by using multi-pulse laser shock loading. The forming results of workpieces under different laser energies and laser impact numbers were studied using experimental and numerical simulation methods. Numerical simulations of the forming results after multiple laser shocks were conducted using ANSYS/LS-DYNA software. These numerical simulation results were then experimentally validated and compared. Surface morphology and microstructure of the workpieces were characterized using a confocal microscope and scanning electron microscope, and energy dispersive spectroscopy (EDS) was used to analyze the chemical element content changes in the collision regions at the bottoms of the workpieces after multi-pulse loading, revealing the collision behavior patterns during the forming process. Finally, the forming laws of workpieces under multiple laser shocks were summarized.

Extra virgin olive oil (EVOO) is recognized for its numerous health benefits attributed to its rich phenolic components. NMR has emerged as a prevalent technique for precisely identifying these compounds. Among Mediterranean countries, Greece stands as the third-largest producer of olives, with the Epirus region notably advancing in olive cultivation, contributing significantly to the dynamic growth of the region. In this study, an NMR method was employed based on the acquisition of an 1H NMR spectrum along with the multiple resonant suppression in order to increase the sensitivity. Using the above method, 198 samples of extra virgin olive oil, primarily sourced from the Epirus region were analyzed and both the qualitative and quantitative aspects of phenolic compounds were acquired. In addition, we examined the effect of various factors, such as, variety, harvest month and region origin on the phenolic compounds’ concentration. The results revealed an average total phenolic content of 246 mg/kg, closely approaching the EU health claim limit of 250 mg/kg. Approximately 15% of the samples were confidently characterized as high-phenolic olive oil. The highest concentrations were observed in Thesprotia samples, with several Lianolia variety exceeding the total phenolic content of 400 mg/kg. Statistical tests demonstrated a significant influence of the olive variety and the month of fruit harvest on phenolic component concentration, followed by the region of origin. A very strong correlation was noted between the total phenolics content and the levels of oleocanthal and oleacein, with a correlation coefficient (r) of 0.924. Upon optimization of all factors affecting olive oil quality, the majority of EVOO from the Epirus region have the potential to be characterized as high in phenolic content.

Selection of the most suitable material is one of the key decisions to be taken at the design stage of a manufacturing process. Traditional approaches as Ashby maps based on material properties are widely used in the industry. However, in the production of multimaterial components, the criteria for the selection can include antagonistic approaches. The aim of this work is the implementation of a methodology based on the results of process simulations for several materials and classify them by applying an advanced data analytics method based on Machine Learning (ML), in this case the Support Vector Regression (SVR) and Multi-Criteria Decision Making (MCDM) methodolo-gies, in this case Multi-criteria Optimization and Compromise Solution (VIKOR) combined with Entropy weighting methods. In order to do this, a Finite Element Model (FEM) has been built to evaluate the extrusion force and the die wear in a multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR combined with Entropy weighting methodologies, a comparison has been established based on the material selection and complexity of the methodology used, resulting that material chosen in both methodologies is very similar and MCDM method is easier to implement because there is no need of evaluate the error of the pre-diction model and the time for data preprocessing is less than the time needed in SVR.

The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well-informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we introduce an application of an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs, through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first application of policy graphs in the context of explaining agent behaviour in collaborative multi-agent scenarios and presents experimental results that sets this kind of scenario apart from previous application in single-agent scenarios: when requiring collaborative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations.

The coupling of AC and DC power will impact the protective actions on the AC side and pose a threat to the stable operation of the interconnection system. Therefore, a new longitudinal protection method is proposed based on the comprehensive distance similarity of voltage waveforms. Initially, the measured voltage and current data are extracted to calculate the reference voltage, and the voltage waveform fitting is optimized. Subsequently, the Euclidean dynamic time warp (DTW) distance and entropy weight method are utilized to process the voltage waveform, enabling the calculation of its comprehensive distance similarity. This similarity is adopted to determine fault location. A hybrid DC multi-feed AC/DC interconnection system, incorporating line commutated converter-voltage source converter (LCC-VSC) and line commutated converter-modular multilevel converter (LCC-MMC), was established in PSCAD, and fault data was simulated and output. The effectiveness of the protection scheme was validated using MATLAB. Simulation results demonstrate that the proposed method can accurately distinguish between faults inside and outside a region. When compared to existing protection methods, it demonstrates superior performance in resisting transition resistance and noise interference, while also mitigating the impact of data asynchronicity. The speed and reliability of the method are further enhanced.

In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a binocular vision SLAM/UWB tightly coupled localization method. The core of this algorithm is based on an Extended Kalman Filter (EKF), which utilizes the displacement increment output by binocular vision ORB-SLAM and the positioning coordinates calculated via UWB positioning as measurement information to achieve data fusion. The research utilized the constructed experimental platform to collect images and UWB ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that this algorithm outperforms the use of the individual UWB or visual SLAM localization methods in terms of positioning accuracy. Compared with traditional single positioning algorithms, this algorithm exhibits better practicality and stability, and can effectively suppress the impact of UWB non line of sight errors. The average error under normal line of sight conditions is 0.058 m, the root mean square error (RMSE) is 0.080, and the average error under non-line-of-sight conditions is 0.081 m, with an RMSE of 0.084. There is no significant jump or drift. Through the tightly coupled processing of binocular vision SLAM and UWB positioning, more reliable and accurate mobile robot positioning can be achieved, providing a useful reference for the autonomous navigation of mobile robots in complex environments.