Effective Solution Research Articles

Efficient labeling of french mammogram reports with MammoBERT

Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports. Current methods for report labeling primarily rely on sophisticated feature engineering based on medical domain knowledge or manual annotations by radiologists. These methods can be labor-intensive. In this work, we introduce a BERT-based approach for the efficient labeling of French mammogram image reports. Our method leverages both the expansive scale of existing rule-based systems and the precision of radiologist annotations. Our experimental results showcase the superiority of the proposed approach. It was initially fine-tuned on a limited dataset of radiologist annotations. Then, it underwent training on annotations generated by a rule-based labeler. Our findings reveal that our final model, MammoBERT, significantly outperforms the rule-based labeler while simultaneously reducing the necessity for radiologist annotations during training. This research not only advances the state of the art in medical image report labeling but also offers an efficient and effective solution for large-scale medical imaging model development.

An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems

Metaheuristic algorithms have gained popularity in the past decade due to their remarkable ability to address various optimization challenges. Among these, the JAYA algorithm has emerged as a recent contender that demonstrates strong performance across different optimization problems, largely attributed to its simplicity. However, real-world problems have become increasingly complex in today’s era, creating a demand for more robust and effective solutions to tackle these intricate challenges and achieve outstanding results. This article proposes an enhanced JAYA (EJAYA) method that addresses its inherent shortcomings, resulting in improved convergence and search capabilities when dealing with diverse problems. The current study evaluates the performance of the proposed optimization methods on both continuous and discontinuous problems. Initially, EJAYA is applied to solve 20 prominent test functions and is validated by comparison with other contemporary algorithms in the literature, including moth–flame optimization, particle swarm optimization, the dragonfly algorithm, and the sine–cosine algorithm. The effectiveness of the proposed approach in discrete scenarios is tested using feature selection and compared to existing optimization strategies. Evaluations across various scenarios demonstrate that the proposed enhancements significantly improve the JAYA algorithm’s performance, facilitating escape from local minima, achieving faster convergence, and expanding the search capabilities.

Application of Mirror Image 3D-Printing Technology Bone Cement in Treatment of Stage IIIb Necrosis of the Lunate Bone.

At present, conventional operations are weak for pain relief and restoring the carpal joint function in the treatment of avascular necrosis of the lunate bone. Mirror image 3-dimensional (3D)-printing technology has rarely been reported for the treatment of lunate bone necrosis, The use of mirror 3D-printed lunar bone replacement and bone cement technology in the treatment of stage IIIb lunar bone necrosis has been rarely reported in previous literature. Personalized customized 3D-printed prostheses have become an effective solution for solving many complex cases, and the flexible application of bone cement technology can also achieve satisfactory clinical results. The authors report 3 patients who underwent mirror printing of lunar bone prostheses based on healthy side lunar bone computed tomography data, 3D printing of lunar bone prostheses replacement through small incisions, and intraoperative selection of alternative bone cement technology based on actual conditions. Mirror image 3D printing relieved the pain and resulted in satisfactory functional recovery for stage IIIb lunate bone necrosis.

Neural Approach to Coordinate Transformation for LiDAR–Camera Data Fusion in Coastal Observation

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.

Strategy formulation of agricultural machinery development for sustainable potato farming in upland of Banjarnegara Regency, Central Java, Indonesia

In the agricultural sector, labor reduction is a significant obstacle to increasing potato production and the use of machinery is considered an effective solution. Therefore, this study aimed to evaluate the potential and profitability of implementing agricultural machinery and develop a specific strategy in Banjarnegara Regency, Central Java, Indonesia. Data collection was carried out from September 2021 to April 2022, and the demonstration farming (demfarm) method was used to test three technology package, with and without agricultural machinery and with existing technology as a control. The study also surveyed farmer characteristics, use the demfarm method, and conducted focus group discussion on internal and external indicators influencing agricultural machinery development. Data analysis included the benefit-cost ratio (BCR), marginal BCR, and strengths, weaknesses, opportunities, and threats (SWOT). The results showed that the development of agricultural machinery for potato cultivation was highly profitable, with a BCR of 1.28, suggesting that the development of agricultural machinery is worthwhile. The agricultural machinery technology demonstrated the best performance among the three packages studied. Based on the results, the policy strategy to support the sustainability of potato farming in Central Java involves the application of efficient agricultural technology by optimizing land availability, involving farmer institutions, and the use of agricultural machinery.

The efficacy of combination of sodium hypochlorite (NaOCL)/hypochlorous acid (HOCL) in wound management: A systematic review and network meta-analysis

Introduction Antiseptic and antibacterial solutions are increasingly used in the treatment of various types of wounds. The successful stabilization of wounds using a combination Sodium Hypochlorite/Hypochlorous acid (NaOCl/HOCl) or superoxidized solution (SOS) has been reported, serving as an effective solution without irritating mammal cells and without posing a carcinogenic hazard. Presently, no systematic review or meta-analysis is available that compares the effectiveness of these antiseptic solutions. The present study aims to investigate the role of NaOCl/HOCl in the treatment of wounds. Methods The systematic review with extended to network meta-analysis (NMA) was conducted using NaOCl/HOCl, povidone-iodine, octenidine dihydrochloride, polyhexamethylene biguanide, alcohol, and silver compared with normal saline. The primary outcome was wound healing. Using surface under the cumulative ranking curve (SUCRA), the direct and indirect comparisons were conducted with the probability of selecting the best treatment. Results A total of 507 studies were identified from PUBMED and SCOPUS databases, and 64 articles were duplicates. Among 29 studies included, 22 randomized controlled trials (RCTs) and 7 cohort studies met our inclusion criteria. NaOCl/HOCl had a higher chance of wound healing 1.07-1.30 times compared with all the regimens. NaOCl/HOCl has the probability of providing the best treatment, followed by silver and octenidine with SUCRAs of 36.9, 23.1, and 21.8, respectively. Conclusions NaOCl/HOCl ranks first and proves to have the highest efficacy, followed by silver, octenidine, and PHMB for treatment.

PS‐InSAR post‐processing for assessing the spatio‐temporal differential kinematics of complex landslide systems: A case study of DeBeque Canyon Landslide (Colorado, USA)

AbstractThe complex superimposition of different kinematics and nested sectors within landslide systems amplifies the challenge of interpreting their heterogeneous displacement pattern and targeting effective mitigation solutions. As an example of such peculiar spatio‐temporal behaviour, the DeBeque Canyon Landslide (Colorado, USA) is emblematic of the application of interferometric post‐processing analysis for a detailed, remotely‐based investigation. We employed a multi‐geometry Persistent Scatterers (PS) InSAR dataset to provide continuous information on the spatio‐temporal scale and achieve a solid representation of the segmented kinematics and timings. Using an updated geomorphological map of the landslide system, we performed a two‐dimensional decomposition of the Persistent Scatterers (PS) dataset to determine the displacement orientation and inclination for each internal sector of the landslide system. We then conducted statistical analyses on the displacement vector characteristics and time series data. These analyses enabled us to spatially characterize the segmented activity patterns of the landslide system and identify abrupt changes in trends associated with preparatory and triggering factors. A clear differentiation of the rotational or translational kinematics within the landslide system was accomplished solely using surface displacement measures. Moreover, the application of a Bayesian model on the bi‐dimensional vector time series leads to the identification of significant differences in the deformational behaviour of each sector with respect to precipitation and temperature factors. Our approach represents a replicable method for local‐scale characterization and monitoring of landslides exhibiting complex spatio‐temporal displacement patterns and providing an effective, low‐cost solution for transportation agencies from a risk‐reduction perspective.

Indoor Localization in Commercial 5G Environment with Single BS

Abstract. As commercial 5G systems rapidly expand, indoor positioning using 5G signals holds great potential for serving a large number of users. In this paper, an effective fingerprint solution is proposed for indoor positioning with 5G signal base station by exploring the multi-beam property. Multi-beam channel state information (CSI) and multi-beam reference signal received power (RSRP) are used as the observations for fingerprinting. To assess the effectiveness of the proposed scheme, field tests were conducted across various indoor environments. The results showed that the positioning accuracy is improved by more than 45% compared with the single beam by using the multi-beam characteristics of the 5G signal. Based on the multi-beam RSRP, the proposed scheme can achieve a positioning error of 67% below 1 meter. In the awareness of the two typical indoor deployments of 5G systems, i.e., the digital indoor distribution (DID) and the distributed antenna system (DAS), the paper also compared the 5G positioning performance in these two scenarios. The field tests showed that, the multi-beam in DID has more features than in DAS, which lead to a better positioning performance than that in DAS.

A new semi-analytical approach for nonlinear dynamic responses of functionally graded porous graphene platelet-reinforced circular plates and spherical shells

This paper presents the semi-analytical approach for nonlinear dynamic responses of porous graphene platelet-reinforced circular plates (CiPs) and spherical shells (SpSs) resting on the Pasternak foundation in thermal environment. The graphene platelets (GPLs) are distributed in the functionally graded (FG) distribution patterns and uniform distribution pattern through the CiP and SpS thickness direction. The governing equations of the GPL-reinforced structures subjected to time-dependent external pressure respected to the harmonic and linear functions of time are established based on the geometrically nonlinear higher-order shear deformation theory. A simple and effective semi-analytical solution for the considered problem is established using the Euler-Lagrange equations, and the damping viscosity of the foundation can be counted using the Rayleigh dispassion function. The Runge-Kutta method is employed to determine the dynamic responses of the GPL-reinforced structures, while the fundamental frequencies of free and linear vibration, and frequency-amplitude curves are obtained in explicit form. The numerical results obtained reveal that the GPL and porous distributions, geometrical and material properties can significantly affect the frequency-amplitude curves, and dynamic responses of harmonically loaded and linearly loaded structures, specially, the dynamic buckling phenomenon is clearly observable with the SpSs but not with the CiPs.

Research on position tracking control of electro-hydraulic actuator system combining feedforward inverse model controller and exponential functional link network adaptive controller

The electro-hydraulic actuator system plays a crucial role in indoor high-frequency simulation experiments. Many components of the system exhibit unavoidable nonlinear characteristics, which reduce its dynamic position response capability. Additionally, most algorithms are unsuitable for practical high-frequency conditions due to either local instability or high computational complexity. To improve position tracking accuracy under high-frequency control, a novel combined control strategy is proposed, integrating an appropriate feedforward inverse model controller (FFIMC) with an exponential functional link network adaptive controller (EFLNAC). Initially, the system has reached a stable state under the action of the PID controller. Subsequently, the FFIMC estimates both the model and the inverse model of the PID system by applying the AIC technique developed through the DCT-VSSNLMS adaptive algorithm, which extends the system’s bandwidth. Following this, the EFLNAC, as the outermost loop, acquires the inverse model of the FFIMC system and updates internal parameters in real-time, thereby enhancing the system’s resistance to nonlinear disturbances. Finally, the combined controller is validated through simulations and experiments under various working conditions. The results show a significant increase in the PID system’s bandwidth and demonstrate that the tracking error of the signal is maintained within 0.079 mm. Therefore, the combined controller proves to be an effective solution for high-precision, high-frequency displacement tracking, offering the advantages of low computational complexity, high accuracy, and strong effectiveness.

Controlling for polygenic genetic confounding in epidemiologic association studies

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

Unlocking the Puzzle: Exploring the Declining Interest in Economics among Faculty and Students at East Africa University's Garowe Campus

The purpose of this study is to examine the students’ perceptions of economics, identify the factors responsible for the diminished interest in economics, and determine how to create effective solutions for these problems. East Africa University's (EAU) Garowe campus, which is among the oldest in the state, has been considered the subject of this study. The study adopted basic random sampling to determine the sample size for the survey. The data in this research was collected through online surveys and interviews. The survey was given to the university students. The survey consisted of multiple-choice questions and Likert-scale responses. Participants were given enough time to fill out the survey anonymously, and 28 people responded. When the survey was fully completed, the data was then analysed using Microsoft Excel. The results of the research conducted showed that: (1) there is a lack of public awareness about the importance of this field and the career opportunities associated with it, as proven by 97% of the respondents who chose (agree and strongly agree). (2) The belief that economics is tough because of math causes some students to choose not to major in this field. This belief influences the majority of students' decisions not to pursue degrees in this field, as demonstrated by the 54% of respondents who selected (agree and strongly agree). This is a true belief that could prevent students from learning economics. (3) Economics is not a hard field of study, as proven by 64% of students who chose “no” to the statement of whether economics is a hard field of study, confirming that economics is not a difficult subject. (4) Business and economics was an interesting subject in high school, as proven by 75% of the respondents who chose (agree and strongly agree)

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments

Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA’s potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA’s parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.

Development of micromodels for improvements in driving safety using as a case study a university campus in Panama

This research focuses on addressing critical driving safety issues on university campuses, particularly vehicular congestion, inadequate parking, and hazards arising from the interaction between vehicles and pedestrians. These challenges are common across campuses and demand effective solutions to ensure safe and efficient mobility. To address these issues, the study developed detailed microsimulation models tailored to the Victor Levi Sasso campus of the Technological University of Panama. The primary function of these models is to evaluate the effectiveness of various safety interventions, such as speed reducers and parking reorganization, by simulating their impact on traffic flow and accident risk. The models provide calculations of traffic parameters, including speed and travel time, under different safety scenarios, allowing for a comprehensive assessment of potential improvements. The results demonstrate that the proposed measures significantly enhance safety and traffic efficiency, proving the model’s effectiveness in optimizing campus mobility. Although the model is designed to tackle specific safety concerns, it also offers broader applicability for addressing general driving safety issues on university campuses. This versatility makes it a valuable tool for campus planners and administrators seeking to create safer and more efficient traffic environments. Future research could expand the model’s application to include a wider range of safety concerns, further enhancing its utility in promoting safer campus mobility.

A Multifunctional Additive for Long‐Cycling Lithium‐Sulfur Batteries Under Lean‐Ether‐Electrolyte Conditions

AbstractThe failure of lithium‐sulfur (Li‐S) batteries operating under lean‐ether‐electrolyte conditions can be ascribed to the deterioration of electrolytes, the passivation of cathodes, and the degeneration of lithium anodes. Efforts to address these challenges by exploring alternative electrolytes beyond commonly used ether‐based systems often introduce new complications. In this study, the utilization of a multifunctional additive, 2‐Bromoacetamide (BA), for ether electrolytes is proposed to achieve stable Li‐S batteries under lean‐electrolyte conditions. These investigations reveal that the amide bond in BA forms hydrogen bonds with sulfur atoms, promoting the dissolution of lithium sulfides (Li2S) in ether electrolytes and mediating the conversion of lithium polysulfides. This enhanced solubility of Li2S facilitates 3D deposition (vertical to the substrate) rather than the typical 2D deposition (parallel to the substrate) of Li2S, and thus alleviates the passivation of cathodes. Furthermore, the incorporation of BA promotes uniform and dense lithium deposition. The use of the BA‐containing electrolyte enables stable cycling of a Li‐S pouch cell for 40 cycles, even under a low electrolyte‐to‐sulfur ratio of 4 µL mg−1. This multifunctional strategy offers an effective solution to extend the lifetime of Li‐S batteries under lean‐electrolyte conditions, thereby paving the way for practical applications of Li‐S battery technology.

Enhancing thermal diffusion in busbars through heat pipe coupling: A novel approach for alleviating contact overheating in power distribution systems

Busbar serves as the conduit for transmitting electrical energy among electrical devices within the distribution system. However, factors such as poor contact can result in burnt busbar contact area, subsequently leading to overheating faults. Traditional cooling methods such as silver-plating of contact area can alleviate this issue to some extent but tend to escalate equipment costs. In response to this issue, this paper proposes a novel busbar based on heat pipes, which can achieve a lower maximum temperature whilst maintaining the same current carrying capacity. The simulation model of this heat pipe busbar is built through FLUENT and verified experimentally. Various heat pipe structures, busbar lengths, current loads, contact resistances, and coatings were investigated for their effects on the surface temperature distribution of the busbar. The research findings indicate that all heat pipe structures, ranging from Structure I to IX, effectively reduce the maximum temperature of the busbar, with Structure IX performing the best, lowering the maximum temperature by 3.56 K. As the busbar length increases from 300 mm to 600 mm, the effectiveness of the heat pipe structure in reducing the maximum temperature shows a positive correlation. Under varying current loads, the heat pipe structure reduces the maximum busbar temperature by 0.83 % to 3.84 %. When the contact resistance in the busbar joint area increases, the heat pipe structure decreases the maximum temperature by 1.07 K to 7.16 K. These research findings indicate that the newly designed HP-busbar provides an effective solution for mitigating contact overheating. Its low cost and broad applicability across various scenarios are significant advantages.

Load transfer behavior of 3D printed concrete formwork for ribbed slabs under eccentric axial loads

3D printed concrete (3DPC) has primarily been used for non-structural applications, with limited exploration into its potential for structural load-bearing applications. This is mainly due to the layered structure of 3DPC and the lack of compatibility of conventional reinforcement strategies to be integrated within the 3D concrete printing process. The post-tensioning of 3DPC structures presents an effective solution to improve the load-carrying capacity and cracking behavior of 3DPC. However, understanding the load transfer behavior and failure modes of 3DPC structures under a particular post-tensioning configuration are crucial to determine the permissible post-tensioning load for a structure without premature failure and to understand the distribution of stresses within the concrete structure under post-tensioning loads. The current study aims to investigate the load transfer behavior and failure mode of a 30 mm thick 3DPC formwork designed for one-way ribbed slabs when subjected to end-anchorage post-tensioning. For this purpose, a series of mechanical characterization and load transfer experiments were conducted on ribbed 3DPC formwork. The mechanical characterization investigations involved examining the compressive and flexural strength, as well as the elastic modulus of 3DPC, under various loading orientations relative to the print path. In the load transfer experiments, the end anchorage post-tensioning system was simulated by applying the axial load to the specimen via end plates bonded to the specimen. The variable parameters of the load transfer experiments were the boundary conditions, the eccentricity of the axial load from the neutral axis, and the topology of the ribbed 3DPC formwork. A digital image correlation system was used to study the axial and transverse strain evolution during the load transfer experiments. The experimental results showed that, regardless of the eccentricity of the applied load, the ribbed 3DPC formwork specimens exhibited higher axial load capacity when the load was applied near the bottom flange rather than the top flange. This was because of the reduced effective cross-sectional area for compression when the load was positioned near the top flanges, a consequence of the selected formwork topology, where top flanges were discontinuous to allow for pouring of the cast concrete within the formwork.

HealthChain: A blockchain‐based framework for secure and interoperable electronic health records (EHRs)

AbstractCurrently, there is no unified Electronic Health Record (EHR) system connecting major healthcare organizations such as hospitals, medical centers, and specialists. Blockchain technology, with its unique features, provides an ideal platform for developing a large‐scale electronic health record system. In this article, the authors introduce HealthChain, a novel blockchain‐based secure EHR system that integrates advanced encryption techniques, a robust consent management system, cross‐platform interoperability, and enhanced scalability. Unlike existing EHR systems, HealthChain allows patients to have comprehensive control over their health data, ensuring that access is strictly regulated according to their preferences. The experimental results demonstrate several significant improvements over traditional EHR systems. HealthChain reduces data access times by 30%, and its interoperability rate with various healthcare systems is 40% higher than that of other blockchain‐based EHR solutions. Security is greatly enhanced, with HealthChain experiencing 50% fewer data breaches due to its advanced encryption and smart contract‐based access controls. Moreover, patient satisfaction has increased by 35% as a result of better control and access to their health records. These findings highlight HealthChain as not only a feasible and effective solution for managing health records but also a significant advancement over existing systems.

Impact of Various Washing Protocols on the Mitigation of Escherichia coli Contamination in Raw Salad Vegetables.

Vegetables are an essential component of a balanced diet. The consumption of ready-to-eat foods may lead to the risk of infections and illnesses due to microbial contamination. To mitigate the potential of microbial contamination risks, it is critical to promote safe handling practices among consumers. In this study, our research evaluated the efficacy of different vegetable washing methods, specifically with lettuce, tomato, and cucumber, to establish optimal practices for reducing microbial contamination. This study consisted of two phases. Initially, a survey was distributed to 150 volunteers using snowball sampling to assess everyday vegetable handling and washing methods. The survey's results identified four predominant methods: washing with a 5% vinegar solution for 3 min followed by tap water rinse (37.3% of participants), rinsing with tap water for 1 min (29.3%), washing with a 5% salt solution (vegetable soap) for 3 min followed by a tap water rinse (16.6%), and a 3 min tap water rinse (14%). A minor segment (3.33%) reported not washing their vegetables at all. The survey's findings guided the second phase, which tested the aforementioned washing protocols' effectiveness in reducing Escherichia coli (E. coli) levels on spiked contaminated salad vegetables. The tested vegetables were sterilized using UV light, inoculated with 0.5 McFarland E. coli, and then washed using the four identified methods. After that, E. coli enumeration after washing was performed using 3M™ Petrifilm and the comparison was analyzed via one-way ANOVA. During this study, it was revealed that the cucumbers had the highest E. coli contamination levels in comparison to the lettuce and tomato after washing. Interestingly, by comparing the three washing methods, it was found that washing the vegetables with vinegar proved to be the most effective solution for reducing microbial presence on both lettuce and cucumbers. Notably, the natural smoothness of tomato skin led to no significant differences in contamination levels across washing methods. In summary, vinegar washing effectively reduces microbial contamination from salad vegetables, highlighting the need for informed consumer practices to prevent foodborne outbreaks. This study emphasizes the importance of monitoring contamination sources and using safe washing techniques.

How can artificial intelligence be used to detect and mitigate zero-day vulnerabilities?

A zero-day exploit is a cyberattack that uses unknown or unaddressed security flaws in computer software, hardware, or firmware. Zero-day vulnerabilities pose very significant threats to cyber security. While traditional methods have been effective, they are lacking in many aspects due to rapidly evolving cyber threats. Hence, this paper examines artificial intelligence techniques, including machine learning and their application in enhancing cyber security against zero-day vulnerabilities. The research delves into supervised and unsupervised models and algorithms like Naive Bayes. The findings suggest that effective solutions such as artificial intelligence-driven approaches are crucial in the face of rapidly evolving cyber threats.