To address the insufficient mechanical properties of calcined phosphogypsum, this research aims to investigate the utilization of basalt fiber and calcium carbonate whiskers as reinforcing agents. The study examines their impact on the flexural and compressive strength, toughness, water resistance, and tensile strength of calcined phosphogypsum. In the individual tests, basalt fibers with different lengths (3 mm, 6 mm, 9 mm, and 18 mm) were added at dosages of 0%, 0.5%, 1.0%, and 1.5% respectively. The findings demonstrate that basalt fiber effectively enhances the flexural and compressive strength, toughness, and tensile strength of calcined phosphogypsum, albeit potentially compromising water resistance. Among the various fiber lengths, the 6 mm fibers exhibit the most advantageous influence on the performance of calcined phosphogypsum. A subsequent test was conducted to investigate the impact of cross-scale fibers on the properties of calcined phosphogypsum by mixing 6 mm basalt fibers and calcium carbonate whiskers. The results illustrate that calcium carbonate whisker refines the pores, thereby improving the flexural strength and toughness of calcined phosphogypsum. Furthermore, it compensates for the water resistance limitations associated with the sole utilization of basalt fiber while further augmenting the tensile strength and strain capacity. However, it should be noted that increasing the dosage of both calcium carbonate whiskers and basalt fibers concurrently leads to increased porosity of phosphogypsum and reduced compressive strength.

Previous control methods developed by our research team cannot satisfy the high-accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) guidance law for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle, with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain a high-accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS guidance law can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, and reduce the average cross-track error by 10.91%, and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional method.

We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensemble (NODE) and Conditional Continuous Normalizing Flows (CNF). It offers improved modeling capabilities for arbitrary probabilistic distributions, addressing the limitations of traditional parametric approaches. In NodeFlow, the NODE captures complex relationships in tabular data through a tree-like structure, while the conditional CNF utilizes the NODE’s output space as a conditioning factor. The training process of NodeFlow employs standard gradient-based learning, facilitating end-to-end optimization of the NODEs and CNF-based density estimation. This approach ensures outstanding performance, ease of implementation, and scalability, making NodeFlow an appealing choice for practitioners and researchers. Comprehensive assessments on benchmark datasets underscore NodeFlow’s efficacy, revealing its achievement of state-of-the-art outcomes in multivariate probabilistic regression setup and its strong performance in univariate regression tasks. Furthermore, ablation studies are conducted to justify the design choices of NodeFlow. In conclusion, NodeFlow’s end-to-end training process and strong performance make it a compelling solution for practitioners and researchers. Additionally, it opens new avenues for research and application in the field of probabilistic regression on tabular data.

Hypertrophic cardiomyopathy (HCM) is a common form of cardiomyopathy in cats, and heart failure occurs as diastolic dysfunction progresses. HCM in cats is broadly classified as non-obstructive and obstructive hypertrophic cardiomyopathy, depending on the presence or absence of outflow tract obstruction. Measurement of the intraventricular pressure differences (IVPD) using Color M-mode (CMM) has attracted attention as a reliable diastolic index as it correlates with catheterization, the gold standard for assessment of diastolic performance. Since, IVPD is affected by the size of the heart, the intraventricular pressure gradient (IVPG) index, which is unaffected by heart size, is used by dividing IVPD be LV length. In the present study, CMM IVPG was used to non-invasively assess diastolic impairment in cats with obstructive hypertrophic cardiomyopathy. This study was conducted on ten control cats and 18 cats in the HCM group. Although no severe left atrial enlargement was observed in the HCM group, the Basal IVPG was significantly increased in the HOCM group compared to the control group. While IVPD typically suggests impaired diastolic function and reduced ventricular compliance, the significant increase observed in the HOCM group compared to controls may suggest an indirect elevation in left atrial pressure, likely secondary to left ventricular outflow tract obstruction. The increase in IVPG in HOCM, as shown in this study, is a pathological effect of left ventricular outflow tract obstruction that cannot be detected by conventional echocardiographic indices, and evaluating IVPG is useful for evaluating cardiac function from a different perspective than conventional methods.

Dry Fish is a rich source of proteins and nutrients and can be eaten in many forms. Oman is one of the important fish producers in the world and having high demand in neighboring countries for Oman’s fish. Fishery sector is considered as one of the important economic development sector in sultanate of Oman. In the fishery sector, use of technology is not up to the mark because of less awareness of technology application among Omani fishermen. Also, there is a demand for a greater number of cold storages in different regions to store fresh fishes which is not materialized yet. Similarly, there is a demand of perfect and cheap technology in Oman to dry fishes to improve the profit to the fishermen. Solar energy is abundantly available in Oman and there are high possibilities in Oman to use solar energy in domestic and industrial applications. Since the sunlight is available throughout the year, it is possible to convert in to thermal energy or electrical energy. Electricity can be produced from photovoltaic principle (PV) or indirectly with concentrated solar power. Similarly, we can use solar concentrators for many thermal applications. The solar dryer is a device which is used to dry fruits, vegetables, leaves, clothes etc. If any of the items are dried with the help of solar energy, then we can save lot of non-renewable source of energy and can help to improve economy of the country. The sun can, at least seasonally at Oman’s latitudes, provide a substantial part of the energy needed for various applications. In this work, we used solar dryer to make dry fish with high quality. Use of parabolic type concentrator to dry the fish is an effective solution which is an innovative idea to the fishermen. The temperature variation in dryer is directly proportional to the intensity of solar. The tracking mechanism is a combination of electronic and mechanical design which tracks the sun automatically. In this work, a parabolic concentrator with automatic tracking mechanism was designed and the performance analysis have been done. In the design, copper tubes are used for heat transfer and then the hot fluid is circulated in the box which is used to dry the fishes. As per the experiment, fish drying efficiency obtained from solar dryer is 77.9% and it took 3 days to dry king fish of 20kg. There are many advantages in parabolic type solar concentrators relate to the quality of dryness in the fishes and time taken to dry the fishes which are elaborately discussed in this paper. This solar dryer technology will improve green environment and also will save non-renewable sources of sultanate of Oman for longer period of time.

Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight.

Rice has become the second major food among rural people. It is one of the most nutrient and low-cost food available in Asia. Rice is composed of two major subspecies as Japonica and Indica and it belongs to the Poaceae family. The World Bank predicts that rice demand will grow 51% faster than population growth in 2025. It is high time to develop a model which predicts the type of infected paddy leaf and avoids the loss of grains. In this paper, the proposed hybrid CNN model, three CNN networks with different layer properties are trained parallel. The extracted feature from these CNN networks is combined to provide the classification of the type of infected leaf. Fieldwork was carried and about 340 samples are collected from five different types of classes. The proposed hybrid model was tested with both the fieldwork dataset and also with an available dataset on the internet. The experimental results show that the proposed model outperforms the other CNN network and yields an accuracy value of 98%, a precision value of 95%, and a misclassification rate of 4% respectively.

In the industrial sector, intelligent sensors in fault diagnosis are becoming more critical in recent technological improvements. The prediction accuracy can be enhanced in fault diagnosis using 3-dimensional, sequential, real-time, and image data. Sensors that capture the vibration, sound, and image data are more critical in predicting unbalancing, tool wear, crack, misalignment, etc, in the rotating machinery to increase productivity and to provide an effective maintenance management system. Due to the fast development of industry 4.0 techniques, monitoring of mechanical machinery is experiencing explosive growth and getting more attention in the area of fault diagnosis (FD). Machine learning and Deep learning methods give promising results and accuracy in predicting faults in rotating machinery on shop floors. The success of AI-based models is due to the availability of comprehensive labeled data. Federated Learning (FL) is the machine learning subfield aiming to train an algorithm with a heterogeneous dataset. Data transmission from local facilities to a central server in AI models creates data privacy and security issues. Heterogeneous data analysis is a complicated process in predicting the machine fault in the central server because of millions of data during real-time condition monitoring. Decentralized data handling and analysis is mandatory in condition monitoring because of heterogeneity in data processing, data privacy, and security advantages. The application of federated learning in fault diagnosis has been getting more attention in recent days, and this study is a review of FL applications that address fault diagnosis in rotating machinery in the first phase. A comparison between the types of FL approaches in FD and the use of aggregation algorithms and their applications will also be discussed in Phase 1. In phase 2, a novel methodology has been proposed using Federated learning to diagnose rotating machinery faults. The proposed method, FLOACOS, addresses how prediction challenges are solved using federated learning approaches by optimizing and standardizing the data at local facilities. This work will be helpful for future condition monitoring researchers and gives an overview of a novel method of the FL technique used in predicting faults and the progress made in the maintenance management of rotating machinery.

Imitation learning, also known as programming by demonstration, has been shown to be a promising paradigm for intuitive robot programming by non-expert users. However, the classical kinesthetic approach with physical hand guidance suffers from generalizability across different robot types and is impractical for demonstrating tasks with long horizons. Visual imitation learning enables the recording of multi-step tasks as a single continuous video, allowing non-experts to demonstrate tasks naturally. Existing approaches typically require a large amount of data to develop end-to-end deep learning models that map raw pixels to robot actions. This paper explores the application of visual imitation learning from one-shot demonstration, significantly reducing the data requirements and simplifying the programming process. To achieve this target, a framework is proposed to map hand trajectories to the robot end-effector, consisting of four essential components: hand detection; object detection; segmentation of the trajectories into elemental skills; and learning the skills. Methods are developed for each component and evaluated on recorded videos to demonstrate the effectiveness of the proposed framework.

The main goal was to investigate the ability of non-thermal plasma reactor with gliding arc discharge to remove the diesel exhaust particulates (DEPs). A conventional knifed-shaped LTP GA reactor was utilized. Three cases were studied: 140 L/min, 70 L/min and 14 L/min of air drawn through the reactor and diesel exhaust fumes were sampled continuously. They were assayed in terms of concentration and number particle size distribution. The higher the residence time’s, the higher the energy input that may be utilized for DEPs’ removal. Reactor’s performance definitely lowered the concentration of DEPs (250 - 580 nm) and altered their number size distribution. There was no effect on the number concentration, nor on the particle size distribution, of DEP particulates of 10 – 250 nm in size. The effectiveness of DEPs’ removal: Decreasing flow rate from 140 L/min to 70 L/min somehow altered the values. Achieving the airflow of 14 L/min led to a substantial improvement (even to a fourfold increase for 300 – 480 nm particles). Non-thermal plasma reactors with gliding arc discharge may be successfully adapted in the process of DEPs’ treatment. Their performance may be optimized by adjusting the airflow at the inlet of reactor to guarantee the longest aerosol residence times and the highest removal efficiency.

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront and the research interest in microgrids which rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper proposes an optimization algorithm based on reinforcement learning in an industrial microgrid that is capable of trading energy with the main grid, providing significant cost savings. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared to logic-based optimization and 86% savings compared to not using any optimization. These findings contribute to digitalization and decarbonization of energy technology and support fundamental goals and policies of the European Green Deal.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

The paper presents the prediction of hydrodynamic characteristics of different rudder -plane configurations behind a submarine in deep water through the Reynolds-Averaged Navier-Stokes method in Ansys Fluent Solver. The hydrodynamic force and moment acting on the Cross-plane, X-plane, and Y-plane rudder configurations are analyzed for estimating hydrodynamic coefficientsfor predicting the advantages of each configuration to control the ship. The obtained results show that X-force of the X-plane rudder configuration is larger than the force acting on the Cross-plane rudder and Y-plane rudder configurations. However, Y-force, and Z-force of the X-plane rudder configuration are significantly greater than the Y-force of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y-axis and z-axis. The roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than the case of the X-plane rudder configuration.

This study investigates the effects of Zinc (4wt.%) and severe plastic deformation on the mechanical properties of AZ61 magnesium alloy through the stir-casting process. The severe plastic deformation (equal channel angular pressing (ECAP)) has been done followed by T4 heat treatment. The microstructural examinations revealed that the addition of 4wt.%Zn enhances the uniform distribution of β-phase, contributing to a more uniformly corroded surface in corrosive environments. Additionally, dynamic recrystallization (DRX) significantly reduces the grain size of as-cast alloys after undergoing ECAP. The attained mechanical properties demonstrate that after a single ECAP pass, AZ61 + 4wt.%Zn alloy exhibits optimal yield strength (YS), ultimate compression strength (UCS), and hardness. This research highlights the promising potential of AZ61 + 4wt.%Zn alloy for enhanced mechanical and corrosion-resistant properties, offering valuable insights for applications in diverse engineering fields.

In steel structures, oblique thin steel plates serve as panel zones in structures spanning large spaces (e.g., warehouses and gymnasiums). Considerable research has been conducted on the shear buckling of panels due to seismic loads acting on a structure. Conversely, under snow or wind loads, the panel zone may experience compressive and tensile stresses simultaneously from two directions. Considering the economic preference for thin steel plates, evaluating the elastic critical local buckling stresses in the panel zone under biaxial normal stress may provide essential information to structural engineers. In this study, an elastic buckling analysis based on the energy method is performed to clarify the impact of panel geometry and boundary conditions on the elastic local buckling stresses of oblique panel zones. As confirmed, the buckling stresses derived from the energy method could simulate the local buckling stresses with accuracy comparable to that of finite element analysis, and an engineer-friendly design formula was proposed. Finally, we determined the correlation of the buckling stresses under biaxial stresses and presented a method for evaluating this correlation. Engineers can utilize the provided design equations to more efficiently and accurately calculate buckling loads, facilitating safer and more economical design of structures with oblique plates.

Domain generalization has led to remarkable achievements in Hyperspectral Image (HSI) classification. Inspired by contrastive language-image pre-training (CLIP), the language-aware domain generalization method has been explored to learn cross-domain-invariant representation. However, existing methods face some challenges: 1) The weak capacity to extract long-range contextual information and inter-class correlation. 2) Due to the inadequacies of the large-scale pre-training for HSI data, the spatial-spectral features of HSI and linguistic features can not be straightforwardly alignment. To address the above problems, a novel network has been proposed with a CLIP framework, which consists of an image encoder, based on an encoder-only transformer to obtain the global contextual information and inter-class correlation, a frozen text encoder, and a cross-attention mechanism, named Linguistic-Interact-with-Visual Engager (LIVE), enhances the interaction between two modalities. Extensive experiments demonstrating superior performance over state-of-the-art methods in HSI Domain Generalization with a CLIP framework.

Technological advancements in the Internet of Things (IoT), unmanned aerial vehicles (UAVs), and novel technological tools have transformed disaster management practices from localized approaches to a global framework. These innovations necessitate a unified understanding and application of diverse methodologies at an international level, ensuring they are accessible and operational across different sectors. This integration is crucial not only for technical personnel but also for medical teams, who play a pivotal role in disaster response and management. By fostering a comprehensive grasp of these methods, we can enhance coordination, efficiency, and effectiveness in addressing disasters on a global scale. The authors aimed to provide a comprehensive overview of emerging techniques that are driving shifts in strategic approaches and treatment technologies. Recognized as a domain of continuous research and development, this overview, by necessity, remains an incomplete snapshot of an evolving field. It highlights the dynamic nature of technological and strategic advancements, underscoring the importance of ongoing investigation and innovation to stay abreast of current trends and future directions. The article also highlights the importance of international collaboration beyond societal and national boundaries A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Biofilm-associated infections caused by Candida species represent a significant clinical challenge due to their inherent resistance to antifungal therapies. This study evaluates the antifungal efficacy of trans-Cinnamaldehyde, a natural compound derived from Cinnamomum cassia, in comparison to the widely used antifungal agent Nystatin, against both planktonic and biofilm forms of Candida albicans, Candida glabrata, and Candida tropicalis. Biofilms were established in 96-well polystyrene plates and treated with various concentrations of trans-Cinnamaldehyde and Nystatin. The Minimum Biofilm Inhibitory Concentration (MBIC) and planktonic minimum inhibitory concentration (PMIC) were determined using the MTT assay and Scanning Electron Microscopy (SEM) was employed to assess structural changes in biofilms. The results demonstrated that Nystatin exhibited superior antifungal efficacy across all tested species, particularly in biofilm inhibition, with MBIC50 values as low as 0.0015 mg/mL for C. albicans and C. glabrata. In contrast, trans-Cinnamaldehyde showed moderate antifungal activity with MBIC50 values of 0.16 mg/mL for C. albicans and C. glabrata and higher resistance observed in C. tropicalis (MBIC50 of 0.32 mg/mL). SEM analysis revealed significant biofilm disruption in C. albicans following exposure to trans-Cinnamaldehyde, but C. tropicalis exhibited more resilient biofilm structures. Overall, while Nystatin remains the gold standard for antifungal treatment, trans-Cinnamaldehyde presents potential as an adjunctive treatment, particularly for biofilm-related infections in C. albicans and C. glabrata. Further research is required to explore synergistic effects between trans-Cinnamaldehyde and conventional antifungal agents and to optimize its therapeutic application in clinical settings. This study reused measurement results data from our previous publication to include in tables and charts.

Adeno-associated viruses (AAV) are widely used as viral vectors for gene delivery in mammalian cells. We focused on the efficacy of transduction of AAV 2/5, 2/6, 2/8 and 2/9 expressing GFP in 3T3-L1 murine preadipocyte cells by live imaging microscopy using IncuCyte S3 and flow cytom-etry. Three transduction modes were assessed: AAV transduction in 3T3-L1 preadipocyte cells with or without further differentiation into mature adipocyte-like cells and injection of AAVs in differ-entiated adipocyte-like cells. AAV2/6 demonstrated the highest transduction efficiency in 3T3-L1 preadipocytes, as it was 1.5–2-fold more effective than AAV2/5, and AAV2/8 in the range of viral concentrations from 2×104 to 1,6×105 VG/cell. AAV2/5 and AAV2/8 showed transduction effi-ciencies similar to each other. The expression of GFP under the CMV promoter remained stable for up to 20 days. The induction of 3T3-L1 differentiation in three days after AAV transduction did not alter the GFP expression level, and AAV2/6 showed the best transduction efficiency. AAV2/6 demonstrated the ability to transduce mature adipocytes. These results were confirmed by in vivo studies on C57BL6 mice AAV2/6 had the highest transducing activity on both inguinal and inter-scapular adipose tissue. Thus, AAV2/6 has demonstrated higher transduction efficacy compared to AAV2/5, AAV2/8 and AAV2/9 both in 3T3-L1 adipocytes and adipose tissue in vivo, which proves its usability along with AAV8 and AAV9 for gene delivery to adipocytes.

Objective: This research was aimed to estimate the anti-fatigue effects of arecoline and elucidate its underlying mechanisms using a murine model of central fatigue precipitated by sleep deprivation (SD). Methods: Seventy-two male C57BL/6 mice were randomly allocated into six groups: a control group, a SD-induced fatigue model group, a group receiving Rhodiola Rosea capsules (2.5 mg/kg), and arecoline groups administered low, medium, and high doses (10, 20, and 40 mg/kg, respectively). Following 28 days of continuous administrations, the effects of arecoline on the mouse fatigue-related behaviors was assessed by the behavioral tests, including grip strength, rotarod performance, and weight-bearing swimming endurance. Enzyme-linked immunosorbent assays (ELISAs) were utilized to measure the levels of the relevant biochemical markers. Western blotting was employed to quantify the expression of nuclear factor erythroid 2-related factor (Nrf2), Kelch-like ECH-associated protein 1 (Keap1), heme oxygenase 1 (HO-1), sequestosome-1 (p62), and NADPH quinone oxidoreductase 1 (NQO1) in the gastrocnemius muscle. Results: Arecoline administration notably enhanced grip strength, delayed the onset of fatigue as evidenced by extended latencies in rotarod tests, and increased the duration of weight-bearing swimming in mice. In the elevated plus maze, arecoline significantly reduced both the number of entries and the total distance traveled in the open arms. Arecoline markedly decreased serum levels of creatine kinase, blood urea nitrogen, lactate dehydrogenase, triglycerides, and cholesterol, while elevated total testosterone, lactate dehydrogenase, and immunoglobulin G levels. Furthermore, it significantly increased superoxide dismutase(SOD), catalase(CAT), and glutathione peroxidase activity in the gastrocnemius muscle, reduced malondialdehyde levels, augmented hippocampal SOD and CAT activity, and elevated glycogen stores in both liver and muscle tissues. Neurotransmitter levels were significantly increased, cytokine levels were markedly reduced, and the expression of Nrf2, Keap1, NQO1, p62, and HO-1 in brain tissue was significantly upregulated. Conclusion: The current study for the first time demonstrated that arecoline has significant anti-fatigue activity, and its mechanism of action is related to elevate the levels of glucose and lipid metabolism, relieve oxidative stress damage, inhibit neuroinflammatory response, regulate neurotransmitter levels and Keap1/Nrf2/HO-1 signaling pathway. The current study provides a new insight into the potential of arecoline in prevention and improvement of fatigue.

Achieving effective control over microbial contamination necessitates the precise and concurrent identification of numerous pathogens. In this research, we have devised a remarkably sensitive duplex droplet digital PCR (dddPCR) reaction system to simultaneously detect Pseudomonas aeruginosa (P. aeruginosa) and Pseudomonas fragi (P. fragi). Employing comparative genomics, we identified four genes of P. fragi. By specific analysis, RS22680 gene was selected as the detection target of P. fragi and LasR gene was chosed as P. aeruginosa, which were applied to construct a dddPCR reaction. In terms of specificity, sensitivity and anti-interference ability, the constructed dddPCR detection system was verified and analyzed. The assay showed excellent sensitivity and applicability, as evidenced by a limit of detection of 100 CFU/mL. When the concentration of natural background bacteria in milk or fresh meat was 100 times that of the target detection bacteria, the method was still capable of completing the absolute quantification. In the simulation of actual sample contamination, P. aeruginosa could be detected after 3 h of enrichment culture, and P. fragi could be detected after 6 h. The established ddPCR detection system exhibits exceptional performance, serving as a foundation for the simultaneous detection of various pathogenic bacteria in food products.

The recent growth of wearable medical device technology in the form of fitness trackers, smart watches, smartphone apps, and patient monitoring systems has created people-generated health data (PGHD) that may benefit medical science with large amounts of continuous real-world data. The prevalence of these devices speaks to their broad popularity and user-friendliness and may lead us one day to a more fully “connected healthcare system.” Meanwhile, data security, confidentiality, and privacy issues have emerged in these hackable systems. Despite the promise of anonymized data, data can sometimes be re-identified, but even without that step, data breaches may reveal information (name, address, date of birth, social security number, and so on) sufficient for identity theft. Clinicians are often asked about the utility and value of wearable devices or monitors, but most are unaware that data from these systems may be transmitted, stored, and even sold without the user’s specific knowledge. Despite the confidentiality of medical information, cybersecurity surrounding wearables and monitors remains relatively lax, making them comparatively easy targets for cyber-villains. It is also important that efforts be taken to make PGHD more secure, since medical data may have great value to telehealth applications and AI-physician assistants. Clinicians should take an active role in informing patients about both risks and benefits of wearables and similar devices.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

Pretreatment of cells with proteolysis inhibitors diminished intensity and changed shape of DNICs specific EPR signal in a treatment time-dependent manner. The level of DNIC formation was significantly influenced by the presence of protein degradation products. Interestingly, formation of HMWDNICs depended on the availability of LMWDNICs. The extent of glutathione involvement in the in vivo formation of DNICs is minor yet noticeable, aligning with our prior research findings.

The importance of a properly regulated immune response has been explored through various research-oriented methods. It has proved that having an effective and well-regulated immune response helps prevent many minors and serious diseases. Any kind of dysregulation in the working of the immune system may lead to severe consequences. An autoimmune disorder is one such consequence in which the component of the immune system starts targeting self-cells of the body, treating them as self-antigen producing autoantibodies and immune complexes resulting from the failure of immune responses both at a central and peripheral level. Currently, many autoimmune disorders are prevailing across the world, and new diseases need attention.In this review, we have written about the functions of T cell, LncRNAs and also some interactions between the LncRNAs and miRNAs in the pathogenesis of Multiple sclerosis (MS), Systemic lupus erythematosus (SLE), Type 1 diabetes (T1D), Rheumatoid arthritis (RA). To find the functions of these autoimmune disorders we have done intensive literature search during this we also found that GAS5 and MALAT are common in MS, SLE, T1D, RA. And finally, we have also mentioned the therapeutic drugs which neutralizes the different cluster of differentiation (CDs) and also suppresses the T cell differentiation in MS, SLE and RA. For T1D therapeutic strategies and various types of insulin are mentioned.

New setting is introduced to study the alliances. Alliances are about a set of vertices which are applied into the setting of neutrosophic graphs. Neighborhood has the key role to define these notions. Also, neighborhood is defined based on the edges, strong edges and some edges which are coming from connectedness. These three types of edges get a framework as neighborhood and after that, too close vertices have key role to define offensive alliance, defensive alliance, t-offensive alliance, and t-defensive alliance based on three types of edges, common edges, strong edges and some edges which are coming from connectedness. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs containing complete, empty, path, cycle, bipartite, t-partite, star and wheel are investigated in the terms of set, minimal set, number, and neutrosophic number. In this study, there’s an open way to extend these results into the family of these classes of neutrosophic graphs. The family of neutrosophic graphs aren’t study but it seems that analogous results are determined. There’s a question. How can be related to each other, two sets partitioning the vertex set of a graph? The ideas of neighborhood and neighbors based on different edges illustrate open way to get results. A set is alliance when two sets partitioning vertex set have uniform structure. All members of set have different amount of neighbors in the set and out of set. It leads us to the notion of offensive and defensive. New ideas, offensive alliance, defensive alliance, t-offensive alliance, t-defensive alliance, strong offensive alliance, strong defensive alliance, strong t-offensive alliance, strong t-defensive alliance, connected offensive alliance, connected defensive alliance, connected t-offensive alliance, and connected t-defensive alliance are introduced. Two numbers concerning cardinality and neutrosophic cardinality of alliances are introduced. A set is alliance when its complement make a relation in the terms of neighborhood. Different edges make different neighborhoods. Three types of edges are applied to define three styles of neighborhoods. General edges, strong edges and connected edges are used where connected edges are the edges arising from connectedness amid two endpoints of the edges. These notions are applied into neutrosophic graphs as individuals and family of them. Independent set as an alliance is a special set which has no neighbor inside and it implies some drawbacks for this notions. Finding special sets which are well-known, is an open way to purse this study. Special set which its members have only one neighbor inside, characterize the connected components where the cardinality of its complement is the number of connected components. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

In this short communication, we conducted first-principles calculations to explore the stability of boron monochalcogenides (BX, X=S, Se or Te), as a new class of two-dimensional (2D) materials. We predicted BX monolayers with two different atomic stacking sequences of ABBA and ABBC, referred in this work to 2H and 1T, respectively. Analysis of phonon dispersions confirm the dynamical stability of BX nanosheets with the both 2H and 1T atomic lattices. Ab-initio molecular dynamics simulations reveal the outstanding thermal stability of all predicted monolayers at high temperatures over 1500 K. BX structures were found to exhibit high elastic modulus and tensile strengths. It was found that BS and BTe nanosheets can show high stretchability, comparable to that of the graphene. It was found that all predicted monolayers exhibit semiconducting electronic character, in which 2H structures present lower band-gaps as compared with 1T lattices. The band-gap values were found to decrease from BS to BTe. According to the HSE06 results, 1T-BS and 2H-BTe show respectively, the maximum (4.0 eV) and minimum (2.06 eV) electronic band-gaps. This investigation introduces boron monochalcogenides as a novel class of 2D semiconductors with remarkable thermal, dynamical and mechanical stability.

Lymphadenopathy is a common manifestation of both reactive and malignant diseases, and fine needle aspiration (FNA) is an effective and inexpensive screening method. It can prevent unnecessary invasive surgery and excisional biopsy, especially in benign cases. Unfortunately, the lack of universally accepted terminology for reporting results has hindered its widespread support. The Sydney system proposal for lymph node cytopathology categorization and reporting introduced five diagnostic categories to address the lack of universally accepted terminology for reporting results in lymphadenopathy. Our study analyzed 188 lymph node FNC samples from King Abdulaziz University Hospital, Saudi Arabia, examining clinical follow-up data, pathology records, patient information, and final diagnosis from January 2019 to December 2022. Most specimens were from axillary LNs, with 99.5% tissue correlation. The Sydney System Category classification identified 56.9% of cases as malignant, while 26.1% were benign. The final surgical specimen diagnosis revealed a higher percentage of malignant diagnoses, with the highest ROM in malignant/Category V. In conclusion, our study demonstrates that LN-FNAC offers high diagnostic accuracy for LN aspirates, with the Sydney approach potentially aiding risk stratification and achieving consistency in cytologic diagnosis, but further multi-centric research is needed.

In tropical developing regions, typhoid fever and malaria often co-occur, posing serious health risks. The two illnesses have similar symptoms, which makes diagnosis and treatment more difficult. Through a thorough analysis of existing literature on typhoid fever, malaria, and co-infection, including research articles, case studies, and review papers, this review seeks to understand the dynamic interactions between typhoid fever and malaria co-infection. The review highlights important problems with co-infection between typhoid and malaria, such as higher morbidity and death, diagnostics-related challenges, and antibiotic resistance. Overlapping symptoms and limitations in current diagnostic tests impede accuracy. Treatment plans are made more difficult by the advent of typhoid strains that are resistant to drugs. The analysis concludes by highlighting the necessity of developing more sensitive and specific diagnostic methods in order to accurately diagnose co-infections. Going forward, the review emphasizes the need for more investigation into the immunological connections between malaria and typhoid illnesses. Essentially, this paper offers a thorough summary of the difficulties and possibilities related to co-infection between typhoid and malaria. It provides a framework for upcoming studies and initiatives that seek to improve public health and patient outcomes in the face of these intricately linked health problems.

A better understanding of high temperature processes in slags contributes to facilitate knowledge based design of the solidified product. Here, a slag analogue with a nominal composition of 17 wt% LiMnO2 and 83 wt% Ca2SiO4 was synthesized encountering fairly high cooling rates. The Mn species from 1223 K to 1773 K was simulated using a thermodynamic model assuming a homogeneous melt. The micro-composition including the Mn species of the solidified slag was determined experimentally and was used as basis for molecular dynamics (MD) simulation. The MD simulation provides information on structure and viscosity at high temperatures, otherwise difficult to access. These parameters significantly influence oxidation state of redox-active elements and the solidified product. The micro-composition analyzed by electron probe micro analysis (EPMA) and synchrotron based micro-X-ray fluorescence (micro-XRF) showed that Mn-rich and Ca-Si-rich phases are separated. While the Mn-O phases did not contain noticeable Ca, the Ca2SiO4 phase had incorporated 0.6 wt% of Mn. The slag solidified into round shaped and droplet shaped grains of a Li-Mn-oxide, some Mn3O4 and Ca2SiO4. The powder X-ray diffraction (PXRD) confirmed the formation of larnite, the identity of the Li-Mn-oxide however remained inconclusive. The Mn oxidation state (OS) was identified using synchrotron based micro-X-ray absorption near edge spectroscopy (micro-XANES). The Mn-O grains, matched well with Li-Mn-oxides and a Mn OS: +3 e.g. LiMn3+O2. Small areas matching Hausmannite (Mn2+Mn23+O4) were also identified. The OS of Mn in the silicate phase could not be identified. For comparison a slowly cooled slag analogue with similar composition however higher Si content, was also subjected to micro-XANES. The slowly cooled slag formed long Mn-rich needles in a matrix of large calcium silicate crystals. The Mn-rich crystals matched well with the XANES spectrum of a Mn3+ Li-oxide like LiMn3+O2. At the rim of the needles the Mn-spectra matched well the Hausmannite (Mn2+Mn23+O4) reference. In the silicate phases Mn had a OS: +2, unambiguously. The melt structure at different temperatures of two compositions i.e. LiMn3+O2 and Ca2SiO4 was simulated using molecular dynamics (MD). They serve as model compositions assuming a heterogeneous melt. The results show significant different degrees of polymerization and viscosity. Information from MD simulations can support the identification of potentially different oxygen permeability and with that prediction of oxidation states. The bulk composition was identified by inductively coupled plasma optical emission spectrometry (ICP-OES), bulk structure by PXRD and bulk species by lab-XANES. The synchrotron micro analysis including micro-XRD were performed at the microfocus beamline I18 at the Diamond Light Source. Pure reference compounds were prepared and characterized with the same multi-modal approach.

This study reports rapid hydrolysis, acylation, and lipolysis with lipase B from Candida antarctica (CalB) and nanofructosome encapsulated CalB (CalB@NF) enzymes immobilized on silica-coated magnetic nanoparticles (Si-MNPs). Enzymatic hydrolysis was confirmed for conversion to p-nitrophenol (p-NP, λex=400 nm) from p-nitrophenyl butyrate (p-NPB, λex=270 nm), and various catalytic parameters such as Km, Vmax, and Kcat were calculated from Michaelis-Menten and Lineweaver-Burk plot. Efficiency of enzymatic hydrolysis was in the order of CalB > Si-MNPs@CalB > CalB@NF > Si-MNPs@CalB@NF, and reusability was much better with the enzyme immobilized on Si-MNPs than with the native enzyme. Enzymatic acylation was confirmed by the formation of benzyl benzoate (λex=229 nm) from benzoic anhydride (λex=240 nm), and efficiency exceeded 99% in all samples. Finally, enzymatic lipolysis was confirmed by the decomposition of olive oil to fatty acid such as oleic acid (λex=230 nm) using thin-layer chromatography (TLC), column chromatography, and proton nuclear magnetic resonance (1H -NMR). The lipases immobilized on Si-MNPs have the advantage of good thermal stability and reusability and are thus applicable to a wide range of industries.

In order to deepen students' understanding of the deep learning network development and application process, this paper designed a deep learning-based teaching experiment simulation method for C. oleifera diseases and pest segmentation. The experimental process includes training data collection and preprocessing, network model training, result verification and analysis, etc., throughout the whole process of deep learning-based image segmentation task development. To this end, CDM-DeeplabV3+ network was proposed to segment C. oleifera leaf diseases fast and accurately, which was composed of a Residual Attention ASPP(CBAM-ASPP) and Dual Attention Encoder (DAM-Encoder). CBAM-ASPP obtained multiscale information by extending to five dilated convolutions and focused on the edge features using Convolutional Block Attention Module. The DAM-Encoder structure, which was connected with CBAM-ASPP in parallel, highlighted the tiny features by combining global and local features. Then, the C. oleifera diseases and pest dataset was built, and the model was compared with the traditional segmentation models DeeplabV3+, UNet, HrNetV2 and PSPNet. The experimental results show that the mIoU of CDM-DeeplabV3+ reaches 85.63%, which is 5.82% higher than that of the original model and the model parameters are reduced by nearly five times, proving the effectiveness and feasibility of segmenting pests and diseases on C. oleifera. The design of the experimental teaching content is inspired by the scientific research experiments of artificial intelligence, following the frontier of the discipline and easy to realize, reflecting the teaching concept of the integration of science and education.

Nowadays, rotor-stator mixers are widely used in the chemical process industry to carry out the dispersion and de-agglomeration processes for manufacturing the homogenous electrode slurries. Development of the process simulation models for the turbulent mixing of these slurries helps to optimize the mixing parameters such as rotational speed and dispersion time. In this work, computational fluid dynamics (CFD) approach through ANSYS Fluent are explored to determine and analyze the flow patterns, velocity fields and viscosity distribution during the dispersion of anode slurries. It utilizes a non-homogeneous Eulerian multiphase approach and the typical k-ε model to create turbulence fields and compute the mixing rate. This work significantly improves our understanding of the relationship between rotor speed, dispersion time, viscosity distribution, and mixer geometry in rotor-stator mixers.

The characteristics of gap plasmon formed by nanoparticle-on-mirror (NPOM) structure composed of metal nanoparticles (MNP) and metal thin films have aroused the interest of various optoelectronic devices. The resonant spectrum obtained by the internal coupling effect of gap can be flexibly controlled by the polarization of incident light and the thickness of the dielectric layer between MNP and metal thin films. We have theoretically studied the resonance spectra of polarization-dependent gold ellipsoidal nanoparticles (GENP) and gold thin films in the gap region of NPOM structures. GENP and gold thin films are separated by a dielectric layer with a refractive index of 1.36. We observe that the intensity of the local electric field resonance peak in the gap region is inversely proportional to the polarization angle. Similarly, the intensity of the local electric field resonance peak in the gap region is inversely proportional to the thickness of the dielectric layer. We have obtained more than 2200 V/m local electric field intensity (dielectric layer thickness 0.3nm). Finally, the resonant peak wavelength of the electric field in the gap region of the NPOM structure is also controlled by the polarization angle of the incident light and the thickness of the dielectric layer.

Analyses of the three predominant types of sedimentation – clastic, carbonate and black shale – revealed their discontinuity/continuity in different facies environments of the Middle and Late Devonian of the central part of the Volga-Urals petroleum province, namely (1) in depressions, both condensed and non-condensed, (2) on the slopes of depressions and (3) in shallow water environments represented by tectonic arches or localised biohermal uplifts. The regional zonation of conodonts, correlated with the global chronostratigraphic scheme, substantiates the stratigraphic completeness of the general Devonian sequence in this area. In particular, the sedimentation of organic-rich black shales, traditionally considered as source rocks, lasted more than 29 million years, starting from the Late Eifel and ending at the Devonian-Carboniferous boundary. There was only one hiatus in the deposition of both black shales and carbonate sediments, lasting about 2.5 million years at the end of the Frasnian. That interval is dominated by clastic sediments, which are considered to be the most productive reservoirs of the Volga-Ural province. In the time interval from the beginning of the Eifel to the end of the Frasnian (about 17 Ma), (a) clastic sediments show the highest accumulation rates; (b) the most complete sequences occur in depressions; and (c) arches and uplifts show the most significant gaps in the geological record. During Famennian, (a) clastic sedimentation has completely ceased; (b) differences between the slopes of troughs and shallow-water environments have diminished, resulting in stable accumulation of carbonate sediments in both settings; (c) the decrease in sequence thickness (up to 50 m) in depressions suggests reduced accumulation rates of deep-water organic-rich black shales. The results help to understand the complex spatial relationship between different coexisting sedimentation types and focus attention to the incompleteness of the geological record in different environments.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Optical wireless video transmission technology combines the advantages of high data rates, enhanced security, large bandwidth capacity, and strong anti-interference capabilities inherent in optical communication, establishing it as a pivotal technology in contemporary data transmission networks. However, video data comprises a large volume of image information, resulting in substantial data flow with significant redundant bits. To address this, we propose an adaptive block sampling compressive sensing algorithm that overcomes the limitations of sampling inflexibility in traditional compressive sensing, which often leads to either redundant or insufficient local sampling. This method significantly reduces the presence of redundant bits in video images. First, the sampling mechanism of the block-based compressive sensing algorithm was optimized. Subsequently, a wireless optical video transmission experimental system was developed using a Field-Programmable Gate Array chip. Finally, experiments were conducted to evaluate the transmission of video optical signals. The results demonstrate that the proposed algorithm improves the Peak Signal-to-Noise Ratio by over 3 dB compared to other algorithms, with an enhancement exceeding 1.5 dB even in field tests, thereby significantly optimizing video transmission quality. This research contributes essential technical insights for the enhancement of wireless optical video transmission performance.

The article addresses the development and evaluation of a novel environmentally-friendly cutting fluid prepared using sunflower oil and ZnO nanoparticles, specifically developed for use in metal machining processes. An experimental investigation was conducted to examine the impact of three adjustable input process factors: spindle speed, feed rate, and depth of cut on Mild steel. The investigation was carried out under two conditions: one with a 1%(weight) ZnO nano cutting fluid and the other with a 1.5%(weight) ZnO nano cutting fluid. The study focused on two factors: surface roughness and material removal rate (MRR), which were measured during the process of face-milling mild steel. The research aims to optimize the milling machine settings to improve the efficiency of machining mild steel as the work material. This will be achieved by using Response Surface Methodology (RSM). The ZnO nano-cutting fluid was created using a meticulous technique and its stability was assessed by several investigations. An investigation indicates that a ZnO nano-cutting fluid with a weight concentration of 1.5% is more stable. The process was enhanced by the use of a newly created integrated approach grounded on response surface methodology (RSM). The suitability of the model was confirmed by an analysis of variance (ANOVA). The analysis revealed that, when considering equal weights of responses, the ideal values for the input parameters of spindle speed, feed rate, and depth of cut were determined to be 408.081 rpm, 146.667 m/min, and 0.4878 mm accordingly, under the circumstances of using 1.5% weight of ZnO nano cutting fluid. The projected output responses were as follows: 777.920 for the MRR (Material Removal Rate) and 3.5297 for surface roughness. The composite attractiveness was calculated to be 0.61433. The suggested model in this research shows a fitting of over 80% with our studied values. This model may be used to discover the ideal states and enhance the efficiency of the milling process. The enhanced milling parameters exhibit the potential to enhance machining performance, offering useful insights for firms seeking environmentally sustainable alternatives for metal-cutting operations. This work contributes to the advancement of sustainable machining practices by developing a novel formulation for cutting fluid and providing a systematic approach to improving machining parameters.

Ammonium ion is a chemical species that is found in abundance in natural waters, whether underground or surface, but also in wastewater resulting from agricultural and industrial activities. Even if the removal of the ammonium ion from water has been studied for a very long time, it has been found that its removal is far from being solved. In this study, we evaluated the performance of the ammonium ions adsorption process on two adsorbents, zeolite clinoptilolite, ZR, (manufacturer Zeolite Develop-ment SRL, Rupea, Brasov, Romania) and the other granular activated carbon type Norit GAC 830 W. In the first step the paper presents the mineralogical (XRD) and structural (SEM and EDX) characterization of the ZR and determination of the pH zero-point charge, pHZPC, for all the adsorbents. Studies were carried out in equilibrium and kinetic conditions. The efficiency of the adsorbent was investigated in different experimental conditions by varying the initial concentration, particle size, temperature, pH, ionic strength and contact time. The mathematical models and parameters specific to the adsorption isotherms that best describe the experimental results were identified. Langmuir, Freundlich, Temkin and Dubinin-Radushkevich mathematical models were used for comparison. The Langmuir isotherm proved to be the most appropriate to describe the adsorption of ammonium ions on all types of adsorbents used. The adsorption capacity of ammonium ion from synthetic solutions, at 20 °C, pH = 6.09, for the range of initial concentrations 0 - 50 mg/L for Rupea Zeolite is in the range of 10.46 mg/g -12.34 mg/g and for granular activated carbon GAC W830 is 16.64 mg/g. It was found that the increase in temperature and pH have a positive effect on the adsorption capacity of the ammonium ion on the studied adsorbents, and the ionic strength determines the decrease in the adsorption capacity. Kinetic models were also identified that best describe the experimental processes. In this sense, pseudo first order, pseudo second order, intraparticle diffusion and the Elovich model were used. The results of the investigation showed that second-order kinetics governs the adsorption process on ZR zeolites and pseudo-first order for activated carbon.

With the rapid development of new energy vehicles and the digital electronics industry, the demand for lithium has surged, necessitating advanced lithium extraction technologies. Electrochemical methods, noted for their high selectivity and efficiency in extracting target ions from liquid sources in an environmentally friendly manner, have become increasingly vital. These methods are versatile, applicable in scenarios such as lithium extraction from saline lakes, mother liquor separation, and lithium enrichment. They include electrochemical deintercalation, electrochemical ion pumps, and electrodialysis, each offering unique benefits and challenges depending on the application context. This review provides a detailed exploration of the research progress in lithium extraction using electrochemical methods and discusses future prospects for these technologies, emphasizing their potential to meet the growing demand for lithium.

High-precision displacement sensing has been widely used across from both scientific researches and industrial applications. The recent interests in developing micro-opto-electro-mechanical systems (MOEMS) have given rise to an excellent platform for miniaturized displacement sensors. Advancement in this field during past years is now yielding integrated high-precision sensors which show great potential in applications ranging from photoacoustic spectroscopy to high-precision positioning and automation. In this review, we briefly summarize different techniques for high-precision displacement sensing based on MOEMS, and discuss the challenges for future improvement.

The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on paired-samples t-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance.

Nowadays, there is an increasing concern about environmental challenges ranging from emissions to overconsumption. Most of the environmental problems today are direct consequences of human consumption. Understanding the factors that affect consumer purchasing decisions is critical to developing demand for and commitment to sustainable consumption. In this regard, this study aims to explore the environmentally sustainable consumption behavior of Ethiopian consumers by using an extended model of the theory of planned behavior. Data were collected using a self-administered questionnaire having open and closed-ended questions. A total of 523 Ethiopian consumers were selected using convenience sampling technique. Structural equation modeling with AMOS V.26.0. was applied for data analysis. The findings show that Ethiopian consumers have very low awareness about the impact of their clothing consumption and disposal. Their consideration of sustainability attributes during purchasing decisions is also low. The structural equation model analysis showed that all variables had a positive and significant effect on the dependent variables at p<0.05, except the effect of personal norms on an environmentally responsible purchasing intention. These results provide additional evidence on the role of environmental labels in purchasing sustainable apparel products. The results of the study allow a better understanding of the factors determining sustainable consumption behavior and suggest practical solutions to their producers. As theory-building activities, with implications for the conceptualization of sustainable consumption behavior are sparse in the literature, this work has also been able to detect sustainable consumption behavior trends and practices by using the availability of a sustainable apparel product and consumers price perceptions of environmentally responsible apparel as a mediating effect of intention and behavior in developing countries, particularly Ethiopia.

As Aotearoa-New Zealand faces increasing cyber threats, this paper comprehensively analyses the nation’s current cybersecurity landscape, revealing novel opportunities and challenges. Through an interdisciplinary approach, the study integrates social, cultural, and technological dimensions—providing a fresh perspective that has been underexplored in previous research. A key focus is integrating cultural values, particularly those of the indigenous Māori, into cybersecurity practices. This not only strengthens the effectiveness and trustworthiness of security measures but also fosters a more inclusive and resilient digital environment, demonstrating our deep respect for the unique contributions of Māori culture. Additionally, this paper introduces critical insights into the country's cybersecurity talent gap, with a special focus on the challenges of developing and retaining cybersecurity expertise in remote regions. Our study also uncovers underexplored opportunities for cross-sector collaboration between private enterprises, government agencies, and indigenous communities, promoting an inclusive and resilient approach to cybersecurity. By positioning Aotearoa-New Zealand as a potential leader in regional cybersecurity innovation, we provide forward-looking recommendations that leverage its distinctive cultural and technological strengths. This research contributes fresh insights to the global discourse on cybersecurity, offering practical strategies tailored to the specific needs of smaller nations.

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.

Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

Satellite navigation spoofing is a major challenge in the field of satellite/inertial integrated navigation security. To effectively enhance the anti-spoofing capability of low-cost GNSS/MEMS-SINS integrated navigation system, this paper proposes a method integrating a dual-antenna global navigation satellite system (GNSS) and a micro-inertial measurement unit (MIMU) to determine the two-dimensional (2D) directions of spoofing signal sources. The proposed method evaluates whether the single-difference carrier-phase measurements conform to the corresponding directions given in ephemeris files, and employs the micro-inertial navigation technology to determine the 2D directions of the signal source. Based on a set of short-baseline dual-station measurements, the accuracy of the proposed method in determining the 2D azimuths of satellites in synchronous orbits is verified, and the deviation from the real value is evaluated. The experimental results show that the proposed method can effectively identify the spoofed satellite signals while providing high-precision direction information at three different distances: 100m, 10km, and 36000km.The two-dimensional angles errors do not exceed 0.2 rad, 0.05 rad, and 0.01 rad, respectively.

This paper proposes an optimization method for the scheduling of a multi-energy virtual power plant supply-demand balance in the power market environment of Jiangxi Province. The objective of this method is to enhance the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method comprehensively considers the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Additionally, real-time market price feedback is integrated to achieve accurate allocation of power supply and demand. Through a case study of a multi-energy virtual power plant in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

This study uses machine learning to identify key factors influencing the cost-effectiveness of over-the-counter (OTC) medications. By developing a novel cost-effectiveness rating (CER) from user ratings and prices, we analyzed data from Amazon. The findings indicate that FSA/HSA eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size significantly impact cost-effectiveness across cold, allergy, digestion, and pain relief medications. Medications eligible for FSA or HSA funds, treating a broader range of symptoms, and having smaller packaging are perceived as more cost-effective. Cold medicines with safety warnings were found to be cost-effective due to their lower average price and effective ingredients like phenylephrine and acetaminophen. Allergy medications with kid-friendly features showed higher cost-effectiveness, and ingredients like calcium, famotidine, and magnesium boosted the cost-effectiveness of digestion medicines. These insights help consumers make informed purchasing decisions and assist manufacturers and retailers in enhancing product competitiveness. Overall, this research supports better decision-making in the pharmaceutical industry by highlighting factors that drive cost-effective medication purchases.

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional Convolutional Neural Networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.

Variable-Angle Tow (VAT) laminates can improve straight fiber composites mechanical properties thanks to the application of curvilinear fibers. This characteristic allows to achieve ambitious objectives for design and performance purposes. Nevertheless, the wider design space and the higher number of parameters result in a more complex structural problem. Among the various approaches that have been used for VATs study, Carrera’s Unified Formulation (CUF) allows to obtain multiple theories within the same framework, guaranteeing a good compromise between the results accuracy and the computational cost. In this article, the linear buckling behavior of VAT laminates is analyzed through the extension of CUF 2D plate models within the Reissner’s Mixed Variational Theorem (RMVT). Results show that RMVT can better approximate the prebuckling non-uniform stress field of the plate when compared to standard approaches, thus improving the prediction of the linear buckling loads of VAT composites.

Microplastics, which are plastic particles smaller than 5 mm in size, are a growing environmental concern since they have been found in oceans, rivers, lakes, and even on land. These tiny particles can escape water filtration systems and end up in water bodies as well as through the degradation of the larger plastic products causing serious environmental and food safety concerns. This research represents the magnitude of this pollution, characterization of microplastic contamination, its type of polymer, and possible sources of this pollution in the rivers around Dhaka, Bangladesh. Quantitative analyses were done following the method suggested by the NOAA and other conventional methods. Among the rivers, Buriganga has the highest microplastic pollution with 3.654 items/L in surface water and 43.125 items/kg in sediment. The concentration in Shitalakshya and Turag rivers are 3.011 items/L and 2.051 items/L in surface water and 34.875 and 17.875 items/kg in sediment respectively. ANOVA test revealed a significant difference between the concentration level of microplastic in the surface water of the Burigana and Turag rivers. A positive correlation between the concentration level of microplastic in the surface water and the sediment was also found. On average 58% of the microplastics were found to be fibrous in surface water. Qualitative analyses were done to find the characteristics and chemical composition of the microplastics. Through the use of FTIR spectroscopy, the study identified different kinds of synthetic polymers in the sample including polyamide (Nylon), polyethylene (PE), polystyrene (PS), polypropylene (PP), polyethersulfone (PES), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) and polyethylene were the most abundant polymer in the samples. This study involved the investigation of three distinct rivers located in the vicinity of Dhaka city

Solid propellants have significant applications in aerospace, and improving their combustion performance has become a hot research topic in recent years. Carbon black (CB) has been widely used in the field of catalytic pyrolysis due to its low cost, controllable morphology, and ease of modification. Herein, we first investigates the structure-activity relationship between the physicochemical properties of nine types of CB with a particle size range of 1-15 μm and a mass fraction of 5%, including oil absorption value, compression oil absorption value, and oxygen content, and the catalytic pyrolysis of ammonium perchlorate (AP), concluding that the function relationship between oil absorption value and high-temperature peak is Y1=1032.67-9.618x+0.038x2, the function relationship between compression oil absorption value and high-temperature peak is Y2=145.537+62.365ln(x), and the function relationship between oxygen content and high-temperature peak is Y3=e(6.085-0.02/x). Based on these, we further uses coal tar, which is abundant in sources, as the raw material to prepare five types of coal-based CB marked as CB1-CB5 at pyrolysis temperatures of 1000~1800 ℃. The relative error between the actual peak value and the predicted value of the CB/AP composite particles is less than 5%, proving that the peak temperature model of CB/AP composite particles can guide actual production. This study contributes to promoting CB application in improving the catalytic pyrolysis performance of ammonium perchlorate and enhancing the high added value utilization of coal-derived products.

To obtain carbon dots (CDs) with narrow full width at half maximum (FWHM) and long-wavelength emission, carbon sources with high conjugate sizes and abundant functional groups can be employed to synthesis CDs. In this study, orange-emissive carbon dots (OCDs) were synthesized with phloroglucinol and rhodamine B as precursors. When the molar ratio of them was 30:1 and ethanol was served as solvent, OCDs with optimized emission wavelength at approximately 580 nm, an FWHM of 30 nm and a quantum yield (QY) of 27.31% were obtained. Subsequently, the OCDs were incorporated into polyvinyl alcohol (PVA) to fabricate solid-state OCDs/PVA fluorescent films, which exhibited an FWHM of 47 nm. The PVA matrix facilitated the dispersion of OCDs, thereby suppressing non-radiative energy transfer among the OCDs and enhancing luminescence efficiency. Consequently, compared with OCDs, the OCDs/PVA film exhibited significant luminescent enhancement, and the QY of the composite film was increased to 84.74%. Moreover, OCDs/PVA film show good transmittance and thermal stability. This research offers a solid theoretical and experimental foundation for the potential applications of CDs in the fields of solid-state lighting.

Mortality rates in sub-Saharan Africa remains unacceptably high, and disproportionately affecting economically disadvantaged communities. And healthcare delivery which is considered a major contributor to this is challenged by the limited resources and high service costs, prompting many patients to opt for outpatient department services. This study addresses the need for a data-driven approach to healthcare policy formulation by proposing a model for the implementation of a data warehousing system that integrates outpatients into the national healthcare framework. By leveraging data warehousing, healthcare delivery can be made more efficient through a robust repository that supports decision-making and policy formulation. The research focuses on the outpatient department in resource-scarce settings, proposing a novel multidimensional data model and practical framework implementation. The benefits include improved patient experiences, cost-effective service delivery, and enhanced patient management through electronic health records. Additionally, mobile apps are explored for patient navigation and reminders to enhance treatment adherence. The study’s results indicate that data warehousing can significantly improve healthcare delivery by consolidating critical data and facilitating better planning, resource allocation, and policy formulation. Ultimately, this approach offers valuable insights for optimizing healthcare systems in resource-constrained environments, enabling data-driven policies, and improving health outcomes across sub-Saharan Africa.

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N=5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US:RF, and SMART-US:VMO models (p<0.05). Model validation showed that the Distributed SMART-US model had an R² of 0.80 ±0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.

Focal vibration therapy can provide neurophysiological benefits. Unfortunately, standardized protocols are non-existent. Previous research presents a wide range of protocols with a wide range of effectiveness. This paper is part of a broader effort to identify effective, standardized protocols for focal vibration therapy. The vibration characteristics of four commercially available focal vibration devices that have been used for research and clinically were measured. An accelerometer was used for the measurements. Frequency and peak-to-peak amplitude were measured. Measurements were made when the devices were free and then again when they were strapped to the human body. Vibration frequency ranged from 120 to 225 Hz. Free vibration amplitude ranged from 2.0 to 7.9 g’s (peak-to-peak). When the devices were strapped to the body (constrained), vibration amplitude decreased by up to 65.7%. These results identify effective ranges of focal vibration frequency and amplitude. They illustrate the importance of identifying vibration environment, free or constrained, when quoting vibration characteristics. Finally, the inconsistency of multi-actuator devices is discussed. These results will guide protocol development for focal vibration and potentially better focal vibration devices.

The abstract should be a total of about 250 words and structured to contain the following headings: Background/Objectives, Methods, Results, Conclusions. Background/Objectives: Place the question addressed in a broad context and highlight the purpose of the study; Methods: Describe briefly the main methods or treatments applied. Include any relevant preregistration numbers, and species and strains of any animals used; Results: Summarize the article’s main findings; Conclusions: Indicate the main conclusions or interpretations. The abstract should be an objective representation of the article: it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions. Clinical trial abstracts should include items that the CONSORT group has identified as essential.

We suggest a new solution to the strong CP problem. The solution is based on the proper use of the boundary conditions for the QCD generating functional integral. It obeys the principle of renormalizability of Quantum Field Theory and does not involve new particles like axions.

Nearly half a century has passed since the discovery of cytoplasmic inheritance of human chloramphenicol resistance. The inheritance was then revealed to take place maternally by mitochondrial DNA (mtDNA). Later, a number of mutations in mtDNA were identified as a cause of severe inheritable metabolic diseases with neurological manifestation, and the impairment of mitochondrial functions has been probed in the pathogenesis of a wide range of illnesses including neurodegenerative diseases. Recently growing number of preclinical studies has revealed that animal behaviors are influenced by the impairment of mitochondrial functions and possibly by the loss of mitochondrial stress resilience. Indeed, as high as 54% of patients with one of the most common primary mitochondrial diseases, mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome, present psychiatric symptoms including cognitive impairment, mood disorder, anxiety, and psychosis. Mitochondria are multifunctional organelles which produce cellular energy and play a major role in other cellular functions including homeostasis, cellular signaling, and gene expression, among other. Mitochondrial functions are observed to be compromised and to become less resilient under continuous stress. Meanwhile, stress and inflammation have been linked to the activation of the tryptophan (Trp)-kynurenine (KYN) metabolic system, which observably contributes to development of pathological conditions including neurological and psychiatric disorders. This narrative review discusses the functions of mitochondria and the Trp-KYN system, the interaction of the Trp-KYN system with mitochondria, and the current understanding of the involvement of mitochondria and the Trp-KYN system in preclinical and clinical studies of major neurological and psychiatric diseases.

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

Formaldehyde is generated via the biochemical and chemical pathways, and it widely exists in foods. Its chronic exposure causes carcinogenic, neurotoxic, reproductive, allergic, immunological, genetic and respiratory toxicities. However, food-sourced formaldehyde is usually neglected due to its rapid metabolism after intake. Compared with its residual level in processed foods, a large proportion of formaldehyde disappears because of its high reactivity. Nevertheless, its elimination reaction mechanism and the potential consequences remain unclear. In this review, the disappearing pathway of formaldehyde from two aspects, the formation of formaldehyde-derived toxins and aromas, was reviewed. This review article may help food scientists take appropriate strategies to use the beneficial reactions of formaldehyde or divert its harmful effects to beneficial ones.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the Quality of Experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a Traveling Salesman Problem (TSP). Subsequently, we develop a coordinate descent (CD) based method and an alternating direction method of multipliers (ADMM) based method for network bandwidth and computing resource allocation in the MEC system. The CD-based method is simple to implement and has a low computational complexity, while the ADMM-based method can further enhance the optimization result through joint optimization. Extensive simulations demonstrate that our proposed algorithms perform well in large-scale edge networks and outperform other representative benchmark methods.

Nowadays circular economy trend drives researchers in the recovery of various bioactive compounds from agri-food by-products. Enzyme-assisted extraction (EAE) is shown as an innovative green technology for the effective extraction of various phytochemicals from agri-food section by-products, therefore this study aimed to evaluate the application of EAE as green technology to obtain extracts from olive leaves (Olea europaea) for potential industrial production. EAE was conducted under various enzyme dose combinations, and incubation time (120 min). Obtained extracts were characterized in terms of total polyphenols (TP) and Total Antioxidant Activity (AA). Firstly, the enzyme synergistic effect in enzymatic extraction of polyphenols was evaluated. TP optimal extraction conditions (468.19 mg GAE/L) were achieved after EAE (50-50%) and for AA (69.85 AA%). According to the above results, a second experimental part occurred for the incubation time (min.) and enzyme dose (mL) of optimal extraction conditions of olive leaves. Results indicated EAE as an excellent choice for green extraction of polyphenols from the olive leaves.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative Spatial Augmented Reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It employs a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: 1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects; 2) evaluating the system's fluidity and stability in performing complex interactive tasks; 3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrate that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

Ti and its alloys are the most commonly used materials for biomedical applications. However, bacterial infection after implant placement is still one of the significant rising complications. Therefore, the application of the antimicrobial agents into implant surfaces to prevent implant-associated infection has attracted lots of attention. Scientific papers have shown that inorganic antibacterial metal element (e.g. Ag, Cu, Zn) can be introduced to implant surfaces with the addition of metal nanoparticles or metallic compounds into electrolyte via micro-arc oxidation (MAO) technology. In this review, the effects of the composition and concentration of electrolyte and process parameters (e.g. voltage, current density, oxidation time) on morphological characteristics (e.g. surface morphology, bonding strength), antibacterial ability and biocompatibility of MAO antimicrobial coating were discussed in detail. Anti-infection and osseo-integration can be simultaneously accomplished with the selection of the proper antibacterial elements and operating parameters. Besides, MAO assisted by magnetron sputtering (MS) to endow Ti-based implant materials with superior antibacterial ability and biocompatibility was also discussed. Finally, the development trend of MAO technology in the future was forecasted.

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

Background: This study introduces a novel dexamethasone (DEX) mixed micellar system (DEX-MM) using Soluplus® and Pluronic F-127 (PF127) to enhance ocular drug delivery. The enhancement of ocular application properties was achieved by creating a chitosan-coated DEX-MM (DEX-CMM), which promotes better adherence to the ocular surface, thereby improving drug absorption; Methods: Using the solvent evaporation method, a formulation was developed with a Soluplus® to drug ratio of 1:10, enhanced with 0.25% PF127. After dispersing in water, 1% chitosan (CS) was added. The stability and integrity of DEX within the micelles were verified using attenuated total reflection-fourier transform infrared spectroscopy (ATR-FTIR) and differential scanning calorimetry (DSC). Additionally, in vitro and ex vivo drug release studies were con-ducted; Results: DEX-CMM (F6) demonstrated a particle size of 151.9±1 nm and a polydispersity index (PDI) of 0.168±0.003, suggesting uniformity and high electrostatic stability with a zeta po-tential of +35.96±2.13 mV. The non-Fickian drug release mechanism indicated prolonged drug retention. Comparative analyses showed DEX-CMM outperforming a standard DEX suspension in drug release and ocular tissue permeation, with flux measurements significantly higher than the DEX suspension; Conclusion: The study confirmed the efficacy of DEX-CMM in enhancing drug delivery to ocular tissues, evidenced by improved permeability. Safety evaluations using the HET-CAM test demonstrated that DEX-CMM was non-irritant, supporting its potential for effective ocular drug delivery.

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, Named Entity Recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the Multiple Fine-Grained Feature Fusion (MFGFF) module with a BiLSTM neural network and employs an Efficient Global Pointer (EGP) for predicts the entity location encoding. In MFGFF module we designed three encoders, character encoder, word encoder and sentence encoder, this design effectively captures fine-grained features and improves the recognition accuracy of nested entities. Experimental results show that the model performs robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of knowledge graph and intelligent question-answering system for chicken diseases but also provides a viable solution for extracting disease information that can be applied to other livestock species.

According to evolutionary cancer cell biology ECCB, cancer stem cells do not arise from normal stem cells, but rather from the functional ACD phenotype of the germline. This phenotype proliferates through asymmetric cell division, producing self-renewing cells and non-proliferating daughter cells with cancer stem cell qualities (CSCs). ECCB posits that cancer stem cells themselves do not proliferate. Similar to protists, there is a close reciprocal relationship between both sister cells that collectively form a germ and stem cell lineage. These sister cells share functional roles: the proliferating cancer germline cells generate stem cells, while the non-proliferating cancer stem cells accumulate progenitor cells for new germline clones. ECCB distinguishes between primary CSCs, which are associated with carcinogenesis and primary tumors, and secondary CSCs, which are linked to metastases. This unicellular stem cell system is homologous to that of parasitic protists, such as amoebae. Both cancer stem cells and amoebae stem cells are generated by an oxygen-sensitive germline and are vulnerable to damage when oxygen levels exceed 6.0% (germline hyperoxia). Oxygen concentrations above this threshold can damage the germline genome. Dysfunctional germline cells do not undergo senescence; instead, they continue to cycle through defective symmetric cell cycles However, to regain functionality, the germline genome requires repair by hyperpolyploid giant cells known as native PGCCs, which are homologous to the multinucleated genome repair structures MGRS found in protists. Native PGCCs differ in some respects from genotoxic-induced PGCCs

This study critically reviews the situation of Language teachers in China, who transited from on-campus face-to-face teaching to online teaching within one week of the official school opening dates, which were postponed due to the outbreak of the COVID-19 pandemic. Presently, the study is located in an interpretive research paradigm, and hence, it takes the human experiences within educational setups to be subjective and complex. In so doing, to elude this educational transition and its complex dynamics, the research uses such qualitative research methods as document analysis and interviews. Painting a picture of great variety in individual experiences and challenges, strategies, and outcomes not only across these two specific institutions but, certainly, more broadly through the lens of previous case studies. First, it values depth over breadth and tries to unearth the varied experiences, challenges, innovations, and achievements faculty made during their transition into online Language Teaching. The results of the study are believed to be able to contribute quite a rich, detailed, and multi-faceted perspective to the pool of knowledge for the complexities of online Language education in China, which may be of interest to educators, policymakers, and researchers.

Gravity forward modeling as a basic tool has been wildly used to topography correction and 3D density inversion. The source region is usually discretized into tesseroid (i.e., spherical prism) to consider the influence of the curvature of planets in global or large-scale problems. Traditional gravity forward modeling methods in spherical coordinates, including the Taylor expansion and Gaussian-Legendre quadrature, are all based on spatial domain, which are mostly low computational efficiency. This study proposes a high-efficient forward modeling method of gravitational fields in spherical harmonic domain, in which the gravity anomalies and gradient tensors can be expressed as spherical harmonic synthesis form of spherical harmonic coefficients of 3D density distribution. A homogeneous spherical shell model has been used to test its effectiveness compared with traditional spatial domain methods. It demonstrates that the computational efficiency of the proposed spherical harmonic domain method has improved by four orders of magnitude and with a similar level of computational accuracy compared with the optimized 3D GLQ method. Finally, the proposed forward method is applied to topography correction of the Moon. The results show that the gravity response of the topography by our method is close to that of the optimized 3D GLQ method, and is also consistent with previous results.

Dielectric elastomers, such as thermoplastic polyurethanes (TPU), are widely used as dielectric layer, emulation layer and substrate of flexible and stretchable devices. To construct capacitors and actuators that work stably upon deformation, it has become an urgent need to investigate the evolution of dielectricity under stress and strain. However, the lack of effective methods for estimating the dielectric constant of elastomers under strain poses a big challenge. This article reports a device for in-situ measuring the dielectric constant of TPU under strain. It is found that upon stretching TPU to a strain of 400%, its dielectric constant decreases from 8.47 to 2.94. In addition, combined Fourier-transform infrared spectroscopy, X-ray scattering technique, and atomic force microscopy have been utilized to characterize the evolution of microstructure under strain. The investigation under tensile strain reveals that a decreased density and average size of polarized hard domains, along with a tendency of the molecular chains to align in parallel with the tensile stress. The evolution of the microstructures results in a reduction of the dielectric constant parallel to the electric field in TPU. Our work provides valuable experimental reference for improving the stability of flexible sensors and actuators based on dielectric elastomers.

This study presents a critical literature review on the models of users' adoption of blockchain applications in supply chains. This review pertaining to blockchain technology adoption by supply chain stakeholders is significant as it is a pre-requisite for the technical sustainability of blockchain technology in supply chains. Through a critical literature review, the previous studies related to technology adoption models were analyzed to explore practices brought about by blockchain applications for supply chain management. This study examined the suitability of the existing models (e.g., the technology-organization-environment (TOE) framework, task technology fit (TTF), unified theory of acceptance and use of technology (UTAUT)) of technology adoption for blockchain applications in supply chains and came up with a new unified model, namely, TOE-TTF-UTAUT. This review provides implications for a more appropriate research design using mixed methods. When adopting the TOE-TTF-UTAUT model in a study, two phases of research using mixed methods can be performed. The first phase is an exploratory sequential design in which a qualitative approach to explore the TOE and supply chain task constructs is followed by a quantitative approach using a survey and quantitative analyses to investigate the factors affecting blockchain adoption in supply chains. The second phase adopts explanatory sequential design in which the quantitative findings from the first phase are reviewed for follow-up by qualitative interviews to obtain explanations.

The relationship between perception of anxiety and quality of life in caregivers of children with autism was verified. To verify quality of life, the Short Form Healthy Survey Item was used; perception of anxiety was assessed using the Beck Anxiety Inventory. 80 caregivers of children with autism participated, with 68.7% of caregivers being mothers. Of the total number of caregivers, 68.8% had a severe level of anxiety. Regarding quality of life, they demonstrated greater impairment in Limitation due to physical aspects, Limitation due to emotional aspects, Vitality and Pain. Caregivers with severe anxiety levels had worse quality of life in the domains of pain (p = 0.012), social aspects (p =&lt; 0.001), limitation due to emotional aspects (p = 0.001) and mental health (p =&lt; 0.001). However, in the functional capacity domain, caregivers with a moderate level of anxiety had a better physical capacity score (p = 0.001). There was a negative correlation between the general anxiety score and the general physical (p = 0.029) and general emotional component of quality of life (p =&lt; 0.001). It was found that caregivers of children with ASD present losses related to psychological factors, quality of life and the lack of social support.

Based on some experiments, the paper describes with the help of polarization the physic-chemical processes that take place in the contact area of friction pairs, during the selective transfer as a result of the change in the input speed of the lubricant (glycerin), as well as how it influences their wear. Through polarization, the quantitative and qualitative transformations of glycerin are studied during frictional stress with selective transfer, the determination of the properties of the selective solution, and the damage to the metals and alloys of the friction pairs, but also after frictional stress.

The study of the tribological behavior of the braking system in auto vehicles requires knowing the characteristics of the material in contact and, in the work process, for the friction pair, brake disc-pads. Materials structural analysis is necessary because the wear process depends on the friction pair chemical composition, and brake disk-pads, respectively on the work process parameters (pressing force, rotational speed, traffic conditions, etc.). The material of the brake discs is generally the same gray cast iron, but the brake pads can be semi-metallic (particles of steel, copper, brass, and graphite, all united with a special resin), organic materials (particles of rubber, glass, and kevlar all joined with the help of a resin), composite materials that contain different constituents and ceramic materials (rarely have small copper particles). Therefore, the purpose of the paper is to know the crystalline structure and the mechanical properties of the brake pad materials, through specific studies and analyses, with important implications on the friction and wear behavior.

In recent years, rapid urbanization has intensified the need for sustainable solutions to address growing demands on urban infrastructure, climate change, and resource constraints. As smart cities strive to enhance citizen experiences by improving infrastructure, increasing accessibility, and accelerating economic growth, artificial intelligence (AI) and the Metaverse offer transformative potential for developing sustainable urban environments. However, integrating AI with the Metaverse for sustainable smart city development remains underexplored amid data management and processing challenges, privacy, security, and technological integration. This study addresses this gap by reviewing advancements in AI and its role within the Metaverse's architecture in smart cities. It explores how AI enables data analysis, enhances user experiences through computer vision and natural language processing, optimizes connectivity with 6G and Edge AI, strengthens security through blockchain, and manages the creation of digital twins. Through a comprehensive analysis of peer-reviewed publications and research projects, the review investigates how AI is integrated into key technologies to realize the Metaverse for sustainable smart cities. Specific applications in smart environments, mobility, energy, health, governance, and the economy are highlighted through AI-enabled use cases, offering a practical roadmap for implementing solutions to enhance citizens' quality of life, promote economic growth, and achieve sustainability. Lastly, future directions are discussed, addressing potential challenges by emphasizing interoperability and scalability and underscoring the need for robust data governance frameworks and AI ethics guidelines to address privacy, security, and ethical concerns. This approach aims to unlock the full potential of AI in enhancing the sustainability of smart cities within the Metaverse.

With the development of the energy sector based on renewable primary sources, structural changes are emerging for the entire national energy system. Initially, it was estimated that energy generation based on fossil fuels would decrease until its disappearance. However, the evolution of CO2 capture capacity leads to a possible coexistence for a certain period with the renewable energy sector. The paper develops this concept of coexistence of the two systems, with the positioning of green hydrogen not only within the renewable energy sector but also as a transformation vector for carbon dioxide captured in the form of synthetic fuels such as CH4 and CH3OH. The paper also analyzes the economic aspects related to price differences for energy produced in the two sectors, as well as their interconnection. The technical aspect, as well as the economic one, for storage through various other solutions besides hydrogen, has been highlighted. The development of the renewable energy sector and its demarcation from the fossil fuel energy sector, even with the transcendent vector represented by green hydrogen, leads to the deepening of dispersion aspects between the electricity sector and the thermal energy sector, a less mentioned aspect in current works but of great importance.

With prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning based methods can achieve outstanding recognition performance in condition that an abundant of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Thereby, SAR target recognition under the situation of few-shot, where only a scarcely few of labeled samples are available, is a fundamental problem that needs to be solved. In this paper, we put forward a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) to specially tackle the few-shot SAR ATR tasks. To be specific, We first develop a feature extraction network based on a customized and region-aware convolution, which can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, we then propose an enhanced prototypical network which can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset and the OpenSARShip dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for emotion recognition that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.

Plastics, once regarded as a revolutionary material shaping modern society, now poses an unprecedented threat to our environment. Household solid waste sorting stations produce several fractions, one of which contains a high concentration of Low-Density Polyethylene (LDPE) film waste (packaging, sunscreen film, etc.). This fraction is difficult to recycle because it contains quite a lot of impurities. Usually, it is sent to the cement factories that burn it together with other fuels. However, with some processing techniques such catalytic pyrolysis this fraction could be valorized. In this paper the experiments which were carried out at a laboratory scale installation, in batches with a processing capacity of 1-3 kg of waste. The pyrolysis reactor is connected to a distillation column giving the possibility to separate the fractions. The gaseous and liquid fractions were characterized by GC-FID-TCD (gases) and GC-MS (liquids) analysis. Natural catalysts such as bentonite or clinoptilolite were studied and used in the melting of plastic mass to simplify the process as much as possible. To test the activity of the catalysts, the pyrolysis of LDPE granules was initially studied. It was found that natural zeolites are much more active than bentonite and that a minimum concentration of 5-10% is needed to have a positive effect on the composition of the fractions (increasing the weight of the light fractions C1-C6, C6-C10 and C11-C13) in relation to the heavy fractions C13-C20 and C20+. Catalytic pyrolysis gives a completely different distribution of light hydrocarbons. The best catalyst selected from LDPE lab experiments, was then tested upon the pyrolysis of plastic film waste obtained by a waste treatment plant. The research objective reported in this paper was to obtain a fraction of combustible gases in the largest possible proportion, which can be much easier exploited by burning in an engine that drives an electric generator.

The communication space allows for the exchange of information and ensures the movement of material flows and passenger flows. By communication space, we mean the movement of information and material flows of various natures based on transport systems, in this case, air transport systems. The direction and intensity of these flows to varying degrees reflect the main social and economic processes. Time series reflecting the intensity of passenger flows and cargo transportation over a given time horizon were selected as a source of information used for analysis. An information-theoretical measure based on the assessment of entropy transfer between time series elements is used as a research tool. Based on the obtained paired estimates of entropy transfer between the selected time series, an entropy transfer matrix is formed, which allows considering a set of time series as a communication system within the framework of system analysis. The resulting matrices are used to form entropy transfer graphs, which allow investigating the properties of time series based on directed graph analysis methods. The article examines examples of the analysis of the cognitive model of transportation, the analysis of the regional communication space, and the analysis of changes in the global communication field.

In this paper, we study the well-posedness and asymptotics of a one-dimensional thermoelastic laminated beam system either with or without structural damping, where the heat conduction is given by Fourier's law effective in the rotation angle displacements. We show that the system is well-posed by using Lumer-Philips theorem, and prove that the system is exponentially stable if and only if the wave speeds are equal, by using the perturbed energy method and Gearhart-Herbst-Prüss-Huang theorem. Furthermore, we show that the system with structural damping is polynomially stable provided that the wave speeds are not equal, by using the second-order energy method.

In this work, we consider a one-dimensional laminated beam in the case of non-equal wave speeds with only one infinite memory on the effective rotation angle. In this case, we establish the general decay result for the energy of solution without any kind of internal or boundary control. The main result is obtained by applying the method used in Guesmia et al. (Electron. J. Differential Equations 193: 1-45, 2012) and the second-order energy.

In this paper, we study a one-dimensional Bresse-Cattaneo system with infinite memories and time-dependent delay term (the coefficient of which is not necessarily positive) in the internal feedbacks. First, it is proved that the system is well-posed by means of the Hille-Yosida theorem under suitable assumptions on the relaxation functions. Then, without any restriction on the speeds of wave propagations, we establish the exponential or general decay result by introducing suitable energy and Lyapunov functionals.

In this study, based on a new quantum integral identity, the authors establish some quantum estimates of Hermite-Hadamard type inequalities for convex functions. These results generalize and improve some known results given in literatures.

Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

“De-[Constructing] Growth” is offered as a deeper and more useful conceptualization that avoids the negative connotations of, and resistance to, “degrowth” by decoupling profit from unsustainable consumption, production, and inequality.

Continuous-wave photocathode guns are capable of delivering electron beams with high repetition rates and high average currents. However, they are limited by relatively low accelerating gradients and cathode fields. This limitation leads to a larger beam size at the gun exit due to space charge forces, making the beam more sensitive to optical aberrations and posing challenges for low-emittance electron beam transmission. This paper focuses on the aberrations induced by multipole fields. We analyze the effects of quadrupole, sextupole, and higher-order fields on beam profile and transverse emittance and propose a beam-based method for the rapid characterization and correction of these multipole fields. Furthermore, we present an experimental study conducted on the DC-SRF-II photocathode gun equipped with dedicated correctors. Both simulation and experimental results demonstrate that our approach effectively mitigates beam aberrations caused by multipole fields, resulting in improved beam symmetry and reduced emittance.

Leydig cells, pivotal for male fertility, undergo significant oxidative stress due to their proximity to macrophages and reactive oxygen species (ROS) generated by extracellular structures. This stress disrupts Leydig cell function, impairing steroidogenesis and spermatogenesis, ultimately leading to male infertility. Oxidative stress triggers apoptosis and inhibits testosterone production, crucial for spermatogenesis regulation. Understanding the mechanisms of oxidative stress and antioxidation on Leydig cells is vital. Some studies highlight the role of antioxidants in mitigating oxidative damage, emphasizing critical pathway of Sirt1 in oxidative stress and Nrf2 in antioxidation. Future research should focus on screening antioxidant substances and optimizing antioxidant dosages for Leydig cell protection and testosterone restoration, thereby enhancing reproductive performance. This review provides insights into combating male infertility by addressing Leydig cell oxidative stress and antioxidation.

To address the instability of wind power caused by the randomness and intermittency of wind generation, as well as the challenges in power compensation by Hybrid Energy Storage Systems (HESS), this paper proposes a State of Charge (SOC) balancing control strategy based on Successive Variational Mode Decomposition and multi-fuzzy control. First, a consensus algorithm is used to enable communication between energy storage units to obtain the global average SOC. Then, the Secretary Bird Optimization Algorithm (SBOA) is applied to optimize the Successive Variational Mode Decomposition (SVMD) and Variational Mode Decomposition (VMD) for the initial allocation of wind power, resulting in the smoothing power for hybrid energy storage and the grid integration power. Finally, considering the deviation between the current SOC of the storage units and the global average SOC, dynamic partitioning is used for multi-fuzzy control to adjust the initial power allocation, achieving SOC balancing control. Simulations of the control strategy were conducted using Matlab/Simulink, and the results indicate that the proposed approach effectively smooths wind power fluctuations, achieving stable grid integration power. It enables the SOC of the HESS to quickly align with the global average SOC, preventing the HESS from entering unhealthy SOC regions.

Deep Eutectic Solvents (DESs) show promising abilities for the delamination of multilayer packaging films that are used in food packaging and in pharmaceutical blister packs. Due to the complexity of their structure, the recycling of these materials is a challenging task, leading to the easiest or cheapest option, landfilling. Towards the development of ‘green’ solvents for the efficient waste management and recycling, the conducted research focuses on the preparation of a range of hydrophobic and hydrophilic DES based on carboxylic acids. Also, their chemical and physical characterization, such as DSC, rheometer and density measurements, was followed for the determination of their properties. The laminate packaging waste products that were tested were Al/PE, PE/Al/PET, Al/PE/paper and PVC/PE/Al. Different batches of solvent systems were tested based on various naturally derived aliphatic and aromatic organic compounds as well as amino acids. The separated films were collected and studied further for the examination of the delamination extend. Results showed full or partial delamination of some materials as well as challenges of solvent systems. Variables such as temperature, agitation rate, mixing time and solvents ratios were monitored and collected during the design of experimental process. The results showed that the used DESs can offer an efficient, low-energy, affordable and green option for the delamination tested packaging materials.

Fagao is one of China's traditional gluten free staple foods made by rice or corn flour. Corn Fagao prepared by co-fermentation with sourdough and yeast exhibits better quality and less staling compared to traditional yeast-fermented Fagao. The physicochemical properties of corn Fagao batter during the sourdough-yeast co-fermentation were investigated. The results showed that compared with yeast fermentation, the gas production and viscosity of the batter increased by co-fermentation. The co-fermented batter shows higher hydrolysis of starch and less amylose content. The integrity of starch granules in co-fermented batter was damaged more seriously, the crystallinity and short-range ordered structure were less than yeast fermented batter, even though the crystal structure type of starch did not change obviously. The peak viscosity, minimum viscosity, final viscosity, decay value and recovery value of corn batter are reduced by co-fermentation, which can improve the thermal stability of the batter and slow down the aging. Co-fermentation also results in a more pronounced reduction in protein subunit contents than yeast fermentation. The changes in the physicochemical properties of corn Fagao batter help explain the quality improvement of corn Fagao made from co-fermentation method, and may provide theoretical references for co-fermentation with sourdough and yeast to other gluten free foods.

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network’s sustainable operation and growth. In this paper, we consider a WPT-MEC network wit user cooperation to migrate the double near-far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Rigorous mathematical analysis shows that our algorithm achieves O(1/V),O(V) trade-off between computation rate and queues stability. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability.

Extreme climates such as droughts and hot extremes, may result in marked damage to crop yields and threaten regional and global food security. Maize is a major grain crop in Liaoning Province, severely affected by drought and hot events. The objective of this study is to advance an approach to estimate the risk of crop yield reduction under the compound dry-hot events (CDHE) in Liaoning Province. Results show that the ratio of stations experiencing compound dry-hot events during the twelfth leaf and maturity stage tended to increase on the time scale. On a spatial scale, the frequency of compound dry-hot events in central Liaoning Province was relatively low during the whole and various reproductive periods of maize. In addition to the twelfth leaf, western Liaoning Province is prone to compound dry-hot events. The high-risk area for the occurrence of the compound dry-hot events in maize is Chaoyang City, located in the western part of Liaoning Province, and the cities in the low-risk area account for about 64.3% of the total number of cities in Liaoning Province, mainly in the central and northern parts of Liaoning Province. Generally, this study emphasizes focusing on the impacts of compound dry-hot events on agricultural production and provides an index to assess the risk of compound dry-hot events on maize production.