Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierachical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace are delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm is employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trains foot points to facilitate robot movement along predefined paths. Experimental results demonstrate the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieves a 100% success rate, enhances average overall scores by 6.27%, reduces average stride lengths by 39.04%, and attains a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

The increasing complexity and volume of job listings in the digital era necessitate advanced recommendation systems to match job seekers with suitable opportunities. This paper presents a hybrid job recommendation system that integrates Collaborative Filtering (CF) and Content-Based Filtering (CBF) to enhance recommendation accuracy and user satisfaction. The CF component leverages historical user interactions and similarities between users to suggest jobs, while the CBF component analyzes job descriptions and user profiles to provide personalized recommendations based on individual preferences and qualifications. By combining these two approaches, the proposed system mitigates the limitations of each method and offers a more comprehensive and effective solution. Experimental results on a real-world dataset demonstrate the system's improved performance in terms of precision, recall, and F1-score compared to traditional recommendation models. This study contributes to the field of job recommendation systems by providing a robust framework that effectively bridges the gap between user needs and job market opportunities.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

As modern technologies, particularly home assistant devices, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information in 15-minute or even 5-minute intervals, including weather forecasts, outputs from Renewable Energy Sources (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the Local Energy Market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by Large Language Models (LLM), Scikit-llm and Zero-Shot Classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to address the network dismantling problem effectively.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

In terahertz communication systems, lens antennas used in transceivers are basically plano-convex dielectric lenses. As the aperture increases and the focal length decreases, the thickness and mass of the plano-convex lens increase rapidly. Through theory and simulation, we designed a Fresnel lens suitable for the terahertz band to meet the requirements of large aperture and short focal length, and simulated the performance, advantages and disadvantages of the terahertz Fresnel lens. A 300GHz terahertz wireless communication system was built to verify the gain effect of the Fresnel lens antenna. The experimental results show that the Fresnel lens can amplify the signal in the terahertz band. The theoretical gain of a 30 cm Fresnel lens is 48.83dB, while the actual measured gain is approximately 45dB.

Recently, e-commerce companies have been adopting and continuously enhancing personalized product recommendation systems, and there is active research in this field within academia. However, personalized product recommendations in offline retail stores have not yielded significant outcomes thus far. Especially for small-format offline retail businesses like convenience stores, where customer information might be limited, providing personalized product recommendations poses even greater challenges. To address this issue, this study aimed to find a solution by shifting the perspective on recommendation methods and altering the target of recommendations in existing personalized recommendation models. In this study, recommending products that customers need in an offline store was defined as suggesting products that should be introduced and displayed within the store. In other words, the recommendation focus shifted from individuals to the stores themselves. This recommendation system proposes products that individual stores have not yet introduced but are anticipated to be purchased by customers among the products managed by the headquarters. Building upon this, we devised a store-based product recommendation system. The widely used user-based collaborative filtering model, a common technique in existing recommendation systems, was adapted into a store-based collaborative filtering model. Furthermore, various rules and logic pertinent to store operations and business considerations for convenience stores were integrated to implement this store product recommendation system. The accuracy and effectiveness of the system were demonstrated through its application in actual convenience stores. Results from the pilot implementation of the system showed that 88% of the newly recommended products in individual stores were sold based on one week of sales data, and the sales revenue was 1.75 times higher than the average sales revenue of those products across the entire company. Survey results on business owners' satisfaction yielded a score of 4.2 out of 5, indicating a high level of contentment. Additional observed effects included the expansion of product range and reduction in order lead times. However, 12% of the total recommended SKUs did not sell within one week, and 34% of the SKUs did not meet the overall average sales quantity. This research holds significance in extending the scope of personalized recommendation studies from primarily online platforms to offline retail businesses like convenience stores. It provides tangible methodologies and outcomes that can be implemented in real-world settings through the construction and validation of an actual system. The study also suggests avenues for future research to address some of the identified limitations.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

In this paper, a method is developed for presenting a novel virtual torque sensor based on precise model and position measurements avoids the need of traditional strain gauges and amplifiers. More specifically, the harmonic drive compliance model and the Gaussian process regression (GPR) technique are used together to achieve virtual torque sensor measurement. While the harmonic drive compliance model provides the analytic part, the Gaussian process regression method is used to reconstruct the unmolded part based on motor-side and link-side joint angles as well as motor current. After an automatic offline calibration, the method allows for a lean online implementation. The virtual torque sensor measurement is compared with measurements of a commercial torque sensor, and the results have attested the effectiveness of the proposed method.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

Celiac disease is a chronic small intestinal immune-mediated enteropathy precipitated by exposure to dietary gluten, affecting approximately 1% of the global population and two million Americans. An increasing number of studies have identified a link between celiac disease and adverse maternal and fetal outcomes during pregnancy and after birth. Additionally, both celiac disease and pregnancy are associated with an increased risk for nutrient deficiencies, specifically vitamin B12 and folate. Deficiencies in these key nutrients are linked with several negative maternal and fetal health outcomes including preeclampsia, gestational diabetes, spontaneous abortion/miscarriage, preterm birth, NTDs, Intrauterine Growth Restriction, and low gestational age and birthweight. This review examines the current literature related to the folate trap and vitamin B12 deficiency in patients with celiac disease and pregnant women independently, and to provide rationale for future research to explore the correlation between the folate to B12 ratio in pregnant woman with celiac disease.

News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

The success of deep learning (DL) in offline brain-computer interfaces (BCIs) has not yet translated into efficient online applications. This is due to three primary challenges. First, most DL solutions are designed for offline decoding, making the transition to online decoding complex and unclear. Second, the use of sliding windows in online decoding increases the computational complexity of DL training. Third, DL models typically require large amounts of training data, which are often scarce in BCI applications. To address these issues and enable real-time decoding, even across different subjects without calibration data, we first introduce a novel approach called real-time adaptive pooling (RAP). RAP modifies the pooling layers of existing offline DL models towards the online decoding requirements. Additionally, it significantly reduces the computational demand during training by jointly decoding consecutive windows. To reduce the amount of training data required, our approach leverages different levels of domain adaptation. We show how different settings enable different adaptation solutions. The results demonstrate that our approach is both powerful and can be calibration-free, providing a robust and practical solution for real-time BCI applications. These findings pave the way for the development of co-adaptive and highly efficient DL-based online BCI systems.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

A significant body of research has examined the accuracy and diversity of several types of recommendation systems. However, little is known about the psychological mechanisms underlying the effects of the different types of recommendation systems on user satisfaction. The findings of our experiment showed that user satisfaction is influenced by shopping goal. Specifically, when a user’s shopping goal aligns with the type of recommendation system, the user satisfaction is enhanced. Our study evaluated the mediating role of feeling right and psychological reactance for a better understanding of this relationship. We further tested the moderated mediation effect of feeling right and psychological reactance moderated by the user shopping goal. For goal-directed users, accurate recommendations trigger the activation of feeling right, consequently increasing the user satisfaction. Conversely, when exploratory users face accurate recommendations, they activate psychological reactance, which leads to a reduction in user satisfaction.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

Utilization of wasted heat instead of fuel combustion is effective to reduce primary energy consumption for mitigating global warming problem. Because wasted heat sources are not necessarily located close to areas of heat demand, one of the difficulties is that wasted heat has to be transferred from heat source side to heat demand side, which may require heat transportation over long distance. From this point we proposed and have examined new idea of heat transportation using ammonia-water as the working fluid which system is named Solution Transportation Absorption chiller, in short STA. Our previous studies of STA were mainly the experimental investigation with STA facility which cooling power was 25RT (90kW). As a result, the COP of STA was found almost same value 0.65 with the conventional absorption chiller without depending on the transportation distances. The simulation using AspenHYSYS also examined with same experimental condition. The experimental data showed good agreement with the simulation calculation. In this study, we examined the large-scale cooling power STA on simulation. The examination cooling powers were from 90 kW(25RT) to 3517 kW(1000RT). All cooling power achieved around COP 0.64 including pump power consumptions. In addition, we performed the dynamic simulation. As the results, there was no effect of pipeline size on the cooling capacities and mass flow rates. Furthermore, the stability time of the cooling capacities and mass flow rates were almost same regardless of the pipeline size and cooling capacity. In other words, STA may be achieved the same COP even though having various complex conditions compared with the conventional absorption chiller.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

Addressing escalating energy demands and greenhouse gas emissions in the oil and gas industry has driven extensive efforts in carbon capture and utilization (CCU), focusing on power plants and industrial facilities. Utilizing CO2 as a primary feedstock for industrial fuels and petrochemical materials offers a favorable alternative to sequestration, with byproducts substituting traditional chemical feedstocks. This comprehensive review explores the current landscape of CO2 capture technologies, examining opportunities, economic viability, and efficiency challenges. It provides a detailed analysis of various CO2 utilization methods and the combined process of capture and utilization. The review highlights primary obstacles in planning, production, and widespread implementation, emphasizing the need for strategic integration of materials research, process performance, and cost-effectiveness to advance new technologies.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

BUB1 is overexpressed in most human solid cancers including breast cancer. Higher BUB1 levels are associated with poor prognosis especially in patients with triple-negative breast cancer (TNBC). Women with TNBC often develop resistance to chemotherapy and radiotherapy, which are still the mainstay of treatment for TNBC. Our previous studies demonstrated that BUB1 kinase inhibitor (BAY1816032) reduced tumor cell proliferation and significantly enhanced radiotherapy efficacy in TNBC. In this study, we evaluated the effectiveness of BAY1816032 with a PARP inhibitor (olaparib), platinum agent (cisplatin), and microtubule poison (paclitaxel) alone or in combination with radiotherapy using cytotoxicity and clonogenic survival assays. BUB1 inhibitor sensitized BRCA1/2 wild-type SUM159 and MDA-MB-231 cells to olaparib, cisplatin and paclitaxel synergistically (combination index; CI

Texas cotton production is facing challenges arising from increased temperatures and extended droughts. One potential solution to mitigate these environmental stresses lies in enhancing soil health through measures aimed at increasing soil organic matter and microbial biomass. Our field study investigated the effectiveness of adding residue on soil surface to increase soil organic matter content, microbial biomass, and cotton production under experimental warming with open-top chambers (OTCs). The OTCs raised the air temperature by 2 °C but did not affect soil temperature. OTCs also increased microbial biomass and soil respiration rate. Residue addition increased moisture content in non-irrigated (i.e., dryland) soils under experimental warming and concurrently reduced the daily temperature range in warmed plots. Furthermore, residue addition increased microbial biomass and soil respiration, particularly under OTC treatments in dryland soils. We also observed increased soil organic matter, microbial biomass, cotton biomass, and yield in irrigated fields compared to dryland. Our findings suggest that residue retention can mitigate the adverse effects of warming by stabilizing soil microclimate and enhancing soil organic matter and microbial biomass levels, especially in drylands. This, in turn, can help attenuate the potential impacts of future climate change on soil health and crop production in semiarid environments.

The early 2-factor (E2F) family of transcription factors, including E2F1-8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment.To predict the possibility of GBM patient survival and progression, we classified and comparedthe expression of E2Fs according to BVZ-responsive subtypes and employed a machine learning method-TreeBagger, one random forest algorithm. Multiple bioinformatics analyses have shown that the significant increased E2F8 post BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, indicating one of the mechanisms of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. Our findings suggest that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers and therapeutic targets and recommending precise BVZ treatment for GBM patients.

The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, making estimation of downwelling surface longwave radiation (DSLR) with satellite data difficult. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we simulate the outgoing longwave radiation (OLR) and DSLR under different land surface and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols, that aerosol longwave radiative forcing (ALRF) increased with increasing aerosol optical depth (AOD), and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model that yields the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it estimates the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/M² and 24.2 W/M² for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/M² and 9.6 W/M², respectively. For the Arvaikheer site, the RMSEs are 23.2 W/M² and 19.8 W/M² for Terra and Aqua, respectively, and the biases are 7.8 W/M² and 10.5 W/M², respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.

Neonatal hypoxic ischemic encephalopathy (HIE) occurs in 1.5 per 1000 live births, leaving affected children with long-term motor and cognitive deficits. Few animal models of HIE incorporate maternal immune activation (MIA) despite the significant risk MIA poses to HIE incidence and diagnosis. Our non-invasive model of HIE pairs late gestation MIA with postnatal hypoxia. HIE pups exhibited a trend toward smaller overall brain size and delays in the ontogeny of several developmental milestones. In adulthood, HIE animals had reduced strength and gait deficits, but no difference in speed. Surprisingly, HIE animals performed better on the rotarod, an assessment of motor coordination. There was significant upregulation of inflammatory genes in microglia 24 hours after hypoxia. Single cell RNAseq revealed two microglia subclusters of interest following HIE. Pseudobulk analysis revealed increased microglia motility gene expression and upregulation of epigenetic machinery and neurodevelopmental genes in macrophages following HIE. No sex differences were found in any measures. These results support a two-hit noninvasive model pairing MIA and hypoxia as a model for HIE in humans. This model results in a milder phenotype compared to established HIE models; however, HIE is a clinically heterogeneous injury resulting in a variety of outcomes in humans. The pathways identified in our model of HIE may reveal novel targets for therapy for neonates with HIE.

The study of underwater vehicle wake detection is of significant importance within the field of target detection, localisation and tracking of underwater vehicles. Given that propellers are the propellers of modern ships and underwater vehicles, the propeller wake field represents the principal target source for wake detection in underwater vehicles. The objective of this paper is to propose a method for measuring the wake of an underwater propeller based on a position-sensitive detector. A theoretical model of the relationship between the laser spot displacement and the change of refractive index of the wake field is established on the basis of the principle of laser beam deflection. A prototype experimental setup for underwater propeller wake measurement was constructed based on the aforementioned optical measurement method. Furthermore, the simulation of the propeller wake flow field with strong density stratification and linear density stratification was conducted based on the experimental setup. In the experiment, a three-bladed propeller with a diameter of 39mm was selected, the water tank size was 980mm×380mm×380mm, and the laser acted at a distance of 550 mm from the propeller. The experimental results indicate that the wake dissipation time of the propeller in a strong density-stratified water environment is approximately 800s. Following the stabilization of the wake field density, the laser spot position is observed to be stable at 0.341 mm, with a corresponding refractive index change of 2.99×10-6 RIU. The wake dissipation time of the propeller in a linear density-stratified water environment is approximately 750 s. Once the wake field density has reached a stable state, the laser spot position is found to be 0.441mm, corresponding to a refractive index change of 3.87×10-6 RIU. These experimental results are found to be in general agreement with the simulation results of the propeller wake field. A comparison of the experimental device-based wake measurements with the CTD-based wake measurements reveals a consistent trend.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

This study aims to apply a Recommendation System with Content Based Filtering method with Jaccard Similarity and Cosine Similarity algorithms on the E-Learning Platform. Recommendation systems deal with how to provide personalized recommendations to users efficiently. The Content Based Filtering method with Jaccard Similarity and Cosine Similarity algorithms can be used to calculate the similarity value between E-Courses on the E-Learning Platform. Implementation for Recommendation System using Google Colaboratory with Python programming language. In the application of the Recommendation System dataset, Coursera Free Dataset consists of 975 instances. The recommendation results use the Jaccard Similarity algorithm with an average similarity value of 0.3 while the value of Cosine Similarity with the average similarity value is 0.6 where the similarity value of Cosine Similarity is higher. Based on the results of the Mean Absolute Error in the low recommendation system, the average MAE value for all iterations of Jaccard Similarity algorithm is 0.013 and for the Cosine Similarity algorithm the average MAE value for all iterations is 0.014. This shows that the Recommendation System with Jaccard Similarity and Cosine Similarity algorithms can be used on the E-Learning Platform to provide efficiency solutions for personalized recommendations.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. A seawater refractive index measurement system based on position-sensitive detectors is an effective means to achieve high-precision measurement of seawater refractive index. However, in the actual seawater refractive index measurement process, the impact of the measurement error caused by the jitter signal obtained from the measurement on the measurement results cannot be ignored. In this paper, we theoretically analyse the causes of the long jitter signals during seawater refractive index measurement and quantify the influencing factors. Through the analysis, it can be seen that the angle between the two windows in the refractive index measurement area of seawater causes a great error on the measurement results, so the angle between the two windows should be controlled within 2.06° in order to effectively reduce the measurement error. At the same time, the factors affecting the measurement accuracy of the position-sensitive detector are analysed, and the factors leading to the reduction of the measurement accuracy of the position-sensitive detector are mainly the background light, the size of the photosensitive surface, and the change of the temperature of the working environment. For the above factors, we add a narrow-band filter with a bandwidth of 0.9nm in front of the photosensitive surface of the position-sensitive detector during the construction of the measurement system, so as to filter out stray light other than the signal light source. On the premise of ensuring the measuring range of the refractive index of seawater, a position-sensitive detector with a photosensitive surface size of 4 mm×4 mm was selected. In order to reduce the temperature variation of the working environment of the position-sensitive detector, the measurement system was partitioned. In order to validate the testing error range of the optimised test system, standard seawater samples under the same conditions were measured. It can be seen that the jitter signal of the measurement system has been reduced from 0.0022mm before the optimisation to 0.0011mm after the optimisation, and the deviation of the refractive index has been reduced by 1.09×10−8RIU. The experimental results show that the refractive index of seawater has been effectively reduced by adjusting the optical path and the structure of the seawater refractive index measurement system.

In the United States, several land use and land cover (LULC) data sets are available based on Landsat. These data sets often fail to accurately represent the features on the ground despite having several advantages over other data. Detailed mapping of complex heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences between scenes, low spectral depth of imagery, landscape heterogeneity, and computational limitations. Machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study are to examine and evaluate bagging (Random Forest; RF), Boosting (Gradient Boosting Machines [GBM] and Extreme Gradient Boosting [XGB]), and stacking ensemble models on sentinel 2A fusion data on NAIP imagery. We also compared the accuracy based on random cross-validation (80% / 20% split) without accounting for spatial autocorrelation and target-oriented validation (10 clusters) accounting for spatial structures of the training data set. To make the LULC map of a portion of Tom Green and Irion counties in central Texas, we used a time series of sentinel data and NAIP orthoimagery. We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. The random and target-oriented validation results show that autocorrelation in the training data offers over-optimistic results. In our case, the overestimation ranged from 2 % to 3.5 %. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base-learner. The main contribution of this research is that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use land cover classification using Geographic Object-Based Image Analysis (GEOBIA).

One of the most crucial elements in deeply understanding humans on a psychological level is manifested through facial expressions. The analysis of a human behavior can be informed by their facial expressions, making it essential to employ indicators such as expression (Expr), valence-arousal (VA), and action units (AU). In this paper, we introduce the method proposed in the Challenge of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) at CVPR 2024. Our proposed method utilizes the multi-modal Aff-wild2 dataset, which is splitted into spatial and audio modalities. For the spatial data, we extract features using a SimMiM model that was pre-trained on a diverse set of facial expression data. For the audio data, we extract features using a WAV2VEC model. To fusion the extracted spatial and audio features, we employed the cascaded cross-attention mechanism of a transformer.

Pancreatic cancer presents formidable challenges due to rapid progression and resistance to conventional treatments. Oncolytic viruses (OVs) selectively infect cancer cells and cause cancer cells to lyse, releasing molecules that can be identified by the host’s immune system. Moreover, OV can carry immune-stimulatory payloads such as interleukin-12 which when delivered locally can enhance immune system-mediated tumor killing. OV’s are very well tolerated by cancer patients due to their ability to selectively target tumors without affecting surrounding normal tissues. OV’s have recently been combined with other therapies including chemotherapy and immunotherapy to improve clinical outcomes. Several OVs including adenovirus, herpes simplex viruses (HSV), vaccinia virus, parvovirus, reovirus, and measles virus have been evaluated in preclinical and clinical settings for the treatment of pancreatic cancer. We evaluated the safety and tolerability of a replication-competent oncolytic adenoviral vector carrying two suicide genes (thymidine kinase, TK; and cytosine deaminase, CD) and human interleukin-12 (hIL12) in metastatic pancreatic cancer patients in a phase 1 trial. This vector was found to be safe and well-tolerated at the highest doses tested without causing any significant adverse events (SAE). Moreover, long-term follow-up studies indicated an increase in the overall survival (OS) in subjects receiving the highest dose of the OV. Our encouraging long-term survival data provide hope for patients with advanced pancreatic cancers, a disease that has not seen a meaningful increase in OS in the last five decades. In this review article, we highlight several preclinical and clinical studies and discuss future directions for optimizing OV therapy in pancreatic cancer. We envision OV-based gene therapy to be a game changer in the near future with the advent of newer generation OVs that have higher specificity and selectivity combined with personalized treatment plans developed under AI guidance.

Despite the growing adoption of Mobility as a Service (MaaS) in urban transportation systems, standard monitoring methods for evaluating its impact and effectiveness still need to be developed. This study proposes a quantitative state of MaaS analysis based on mobility market indicators and macroeconomic metrics to generate a MaaS Status Index (MSI). The intention is to introduce a standardised quantitative methodology for systematically assessing and comparing the state of MaaS in urban mobility systems. The MSI aims to quantitatively capture the economic, social, technological, and infrastructural conditions relevant to MaaS implementation. The methodology includes four steps: identifying relevant mobility markets, defining mobility market metrics, integrating macroeconomic metrics, and deriving the MSI formula. We apply the MSI methodology to the Austrian mobility market as a case study, demonstrating its practicality in assessing MaaS readiness and highlighting specific challenges and opportunities within the Austrian mobility system. The analysis covers the present (2017-2022) and the projected future (2023-2028). The findings indicate that the proposed MSI is an effective tool for evaluating MaaS implementation readiness.

Triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors, human epidermal growth factor receptor 2, and progesterone receptors (PR). TNBC has the poorest prognosis among breast cancer subtypes and is more likely to respond to immunotherapy due to its higher expression of PD-L1 and a greater percentage of tumor-infiltrating lymphocytes. Immunotherapy has revolutionized TNBC treatment, especially with the FDA's approval of pembrolizumab (Keytruda) combined with chemotherapy for advanced cases, opening new avenues for treating this deadly disease. Although, immunotherapy can significantly improve patient outcomes in a subset of patients, achieving the desired response rate for all remains an unmet clinical goal. Strategies that improve responses to immune checkpoint blockade, including combining immunotherapy with chemotherapy, molecularly targeted therapy, or radiotherapy may improve response rates and clinical outcomes.  In this review, we provide a short background on TNBC and immunotherapy and explore the different types of immunotherapy strategies that are currently being evaluated in TNBC. Additionally, we review why combination strategies may be beneficial, provide an overview of the combination strategies, and discuss the novel immunotherapeutic opportunities that may be approved in the near future for TNBC.

Studies have shown that vehicle trajectory data are effective for calibrating microsimulation mod-els. Light Detection and Ranging (LiDAR) technology offers high-resolution 3-D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastruc-tures, and moving objects such as vehicles, cyclists, and pedestrians. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and accu-rately calibrating for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point cloud collected at six urban signalized intersections in Lubbock, Texas in USA. Each study intersection was modelled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model's parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the com-bined use of LiDAR-based vehicle trajectory data, microsimulation and surrogate safety assessment tools to transportation professionals for proactive safety evaluation.

In this paper, the entire downhole fluid sucker rod pump system is replaced with a viscoelastic vibration model, namely a third-order differential equation with an inhomogeneous forcing term. Both Kelvin and Maxwell viscoelastic models can be implemented along with the dynamic behaviors of a mass point attached to the viscoelastic model. By employing the time-dependent polished rod force measured with a dynamometer as the input to the viscoelastic dynamic model, we have obtained the displacement responses, which match closely with the experimental measurements in actual operations, through an iterative process. The key discovery of this work is the feasibility of the so-called inverse optimization procedure which can be utilized to identify the equivalent scaling factor and viscoelastic system parameters. The proposed Newton-Raphson iterative method with some terms in the Jacobian matrix expressed with averaged rates of changes based on perturbations of up to two independent parameters provides a feasible tool for optimization issues related to complex engineering problems with mere information of input and output data from either experiments or comprehensive simulations. The same inverse optimization procedure is also implemented to model the entire fluid delivery system of a very viscous non-Newtonian polymer modeled as a first-order ordinary differential system similar to the transient entrance developing flow. The convergent parameter reproduces transient solutions which match very well with those from full-fledged computational fluid dynamics models with the required inlet volume flow rate and the outlet pressure conditions.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

Dengue infection is caused by the Dengue virus (DENV) and is transmitted to humans by infected female Aedes aegypti and Aedes albopictus mosquitoes. There are nearly 100 million new dengue cases yearly in more than 120 countries with a five-fold increase in incidence over the past four decades. While many patients experience a mild illness, a subset suffer from severe disease which can be fatal. Dysregulated immune responses are central to the pathogenesis of dengue, and haematologic manifestations are a prominent feature of severe disease. While thrombocytopaenia and coagulopathy are major causes of bleeding in severe dengue, leucocyte abnormities are emerging as important markers of prognosis. In this review, we provide our perspective on the clinical aspects and pathophysiology of haematologic manifestations in dengue. We also discuss the key gaps in our current practice and areas to be addressed by future research.

Sarcopenia is associated with clinical complications, which increase mortality. Effective sarcopenia risk screening in the elderly should be grounded in a comprehensive assessment, encompassing both nutritional status and medication use. Current screening tools, while emphasizing muscular per-formance, exhibit limited sensitivity in identifying elderly individuals at risk of sarcopenia. This cross-sectional study aimed to develop a sarcopenia risk screening tool, named Sarc-Global, with enhanced sensitivity, incorporating factors pertinent to the overall health of the elderly. Utilizing the criteria established by EWGSOP2 for diagnosing sarcopenia, we evaluated 395 communi-ty-dwelling elderly individuals. This evaluation served as a reference for assessing the efficacy of two validated questionnaires, Sarc-F and Sarc-CalF, and our newly developed Sarc-Global. Con-structed through multiple logistic regression, Sarc-Global integrates variables such as sex, age, medication use, body mass index, arm circumference, and handgrip strength – all significantly associated with sarcopenia (p

Chronic stress, culminating in burnout, evolves gradually and is notoriously difficult to detect due to vague symptoms and individuals' hesitance to acknowledge their struggles. To expedite the identification and recognition of this condition, enabling timely psychological intervention, the integration of Artificial Intelligence (AI) is increasingly considered. This research does not assert the feasibility of an AI system managing all aspects of chronic stress's impact. However, it highlights the capability of current technology to detect stress indicators based on quantifiable data like Heart Rate (HR) and organizational climate dimensions. Although other physiological markers such as oximetry, skin galvanic response, and EKG have been explored, they have not shown reliable differentiation between stress and joy. Focused on the unique context of school principals in the Bedouin regions of Israel, this study investigates the interplay between HR, organizational climate, and stress levels. It introduces a novel application of a fuzzy logic tool that combines HR and organizational climate metrics to aid in stress diagnosis. This tool incorporates the psychologist's expertise to provide real-time data crucial for developing effective coping strategies. While the AI-supported fuzzy system does not replace professional psychological intervention, it significantly enhances the speed of condition identification and intervention planning, thus shortening the response time to stress-related issues in educational leadership within culturally specific settings. The application of such AI-driven tools is pivotal for sustaining the well-being and effectiveness of educational leaders, thereby supporting the broader goal of educational sustainability.

Prostate cancer is the second leading cause of death in males in America, with advanced prostate cancers exhibiting a 5-year survival rate of only 32%. Castration-resistance is often developed during the course of treatment, though its influences require further understanding. This study explores the human microbiome for its implications in castration-resistance and metastasis in prostate cancer. RNA sequencing data was downloaded for bone and soft tissue biopsies of patients with metastatic castration-resistant prostate cancer. These sequences were mapped to bacterial sequences to yield species-level abundance approximations. Numerous species were found to correlate to the expression of known markers of castration-resistance, including AR, PI3K, and AKT. Castration-resistance-associated signaling pathways were also enriched with these species, including PI3K-AKT signaling and endocrine resistance. For their implications in cancer aggression and metastasis, cancer stem cell markers were further explored for relation to these species. EGFR and SLC3A2 were widely downregulated with greater abundance of most species. Our results suggest that the microbiome is heavily associated with castration-resistance and stemness in prostate cancer. By considering the microbiome’s importance in these factors, we may better understand the highly aggressive and highly invasive nature of prostate cancer, allowing for needed improvement in the treatment of this disease.

Abstract: This study investigates the transition to sustainable shipping industry within the framework of the IMO's 2050 carbon neutrality objective, focusing on socio-technical systems analysis. The research delves into the sustainable paradigm shift towards alternative marine fuels such as LNG, methanol, ammonia, and hydrogen from a socio-technical transition perspective, analyzing the multi-level per-spective challenges and opportunities. Specifically, it scrutinizes the implications of adopting these al-ternative fuels for global shipping, identifying technical uncertainties, financial constraints, and infra-structure needs. Through a comprehensive review of existing literature on ship emissions and alterna-tive fuels, coupled with socio-technical transition theory, the study addresses the multifaceted chal-lenges and opportunities in meeting the IMO's environmental objectives. It emphasizes the necessity of collaborative governance, innovative niche strategies, and a holistic approach to leverage the current window of opportunity in facilitating the maritime industry's transition to sustainable fuel options. Fi-nally, this research enhances our understanding of the intricate interplay between technological, social, industrial, and cultural dynamics in the greening of shipping and offers insights into the strategic adop-tion of environmentally friendly marine fuels for a sustainable future.

This study examined how Information Use Environments (IUEs) influence the information behavior of people living with neglected tropical diseases (PLWNTDs) and how this affects their participation in diagnosis and treatment programs. The author demonstrated that local information use environments (IUEs) significantly impacted the information behavior of people living with neglected tropical diseases (PLWNTDs'), which can be harnessed to improve their involvement in diagnosis and treatment programs. The author compared user-centered and program-centered approaches within common IUEs in remote and impoverished areas, finding that NTDs persist due to a disconnect between program organizers and the affected individuals’ information use environments (IUEs). Data was sourced from published papers, and OpenAI (ChatGPT) was used to generate list of local sources (IUEs) in affected remote communities. The findings indicated that understanding local IUEs can help program organizers enhance participation rates of individuals with NTDs in diagnosis and treatment initiatives.

In today’s Web 2.0 era, online social media has become an integral part of our lives. In the course of the information revolution, the form of information has undergone a radical change, from simple text information to today’s integrated video, image, text and audio, and there has also been a great change in the way of dissemination and access, as people nowadays do not just rely on traditional media to passively receive information, but more actively and selectively obtain information from social media. Therefore, it has become a great challenge for us to effectively utilize these massive and integrated multi-modal media information to form an effective system of retrieval, browsing, analysis and usage. Unlike movies and traditional long-form video content, micro-videos are usually short in length, between a few seconds and tens of seconds, which allows users to quickly browse different contents and make full use of the fragmented time in their lives, while users can also share their micro-videos to their friends or the public, forming a unique social way. Video contains rich multimodal information, and fusing information from multiple modalities in a video recommendation task can improve the accuracy of the video recommendation task.According to the micro-video recommendation task, a new combinatorial network model is proposed to combine the discrete features of each modality into the overall features of various modalities through the network, and then fuse the various modal features to obtain the overall video features, which will be used for recommendation. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

Previous studies in our lab show that that administration of OT into the fourth ventricle (4V; hindbrain) elicits weight loss and stimulates interscapular brown adipose tissue temperature (T_IBAT) in male DIO rats. What remains unclear is whether chronic hindbrain (4V) OT can impact body weight in female high fat diet-fed (HFD) rodents and whether this involves activation of BAT. We hypothesized that OT-elicited stimulation of sympathetic nervous system (SNS) activation of IBAT contributes to its ability to activate BAT and reduce body weight in female high fat diet-fed rats. To test this hypothesis, we determined the effect of disrupting SNS activation of IBAT on OT-elicited stimulation of T_IBAT and reduction of body weight in DIO rats. We first measured the impact of bilateral surgical SNS denervation to IBAT on the ability of acute 4V OT (0.5, 1, and 5 µg ≈ 0.5, 0.99, and 4.96 nmol) to stimulate T_IBAT in female HFD-fed rats. We found that the high dose of 4V OT (5 µg ≈ 4.96 nmol) stimulated T_IBAT similarly between sham rats and denervated rats (P=NS). We subsequently measured the effect of bilateral surgical denervation of IBAT on the effect of chronic 4V OT (16 nmol/day ≈ 16.1 mg/day) or vehicle infusion to reduce body weight, adiposity and energy intake in female HFD-fed rats (N=7-8/group). Chronic 4V OT reduced body weight gain (sham: -18.0±4.9 g; denervation: -15.9±3.7 g) and adiposity (sham: -13.9±3.7 g; denervation: -13.6±2.4 g) relative to vehicle treatment (P<0.05) and these effects were similar between groups (P=NS). These effects were attributed, in part, to reduced energy intake evident during weeks 2 (P<0.05) and 3 (P<0.05). To test whether these results translate to other female rodent species, we also examined the effect of chronic 4V infusion of OT on body weight and adiposity in two strains of female HFD-fed mice. Similar to what we found in the HFD-fed rat model, we also found that chronic 4V OT (16 nmol/day) infusion resulted in reduced body weight gain, adiposity and energy intake in female DIO C57BL/6J and DBA/2J mice (P<0.05 vs vehicle). Together, these findings suggest that 1) sympathetic innervation of IBAT is not necessary for OT-elicited increases in BAT thermogenesis and weight loss in female HFD-fed rats and 2) the effects of OT to elicit weight loss translate to other mouse models of diet-induced obesity.

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

Food systems will need to undergo considerable transformation. To be better prepared for and resilient to uncertainty and disturbances in the future, resource users and managers need to further develop knowledge about the food and farming system, with its dominating feedback structures and complexities, and to test robust and integrated system-based solutions. This paper investigates how participatory system dynamics modeling can be adapted to groups at the community level with low or no formal educational background. The paper also analyzes the refinement of workshop participants’ mental models as a consequence of a participatory system dynamics intervention. For this purpose, we ran two workshops with small-scale farmers in Zambia. Analysis of workshop data and post-workshop interviews shows that participatory system dynamics is well adaptable to support an audience-specific learning-by-doing approach. The use of pictures, objects and water glasses in combination with the basic aspects of causal loop diagramming makes for a well-balanced toolbox. Participants acquire understanding that is also relevant beyond systems thinking in that is offers a range of practical insights such as a critical evaluation of common food security strategies.

The intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance underscores the need for rigorous dataset evaluation and selection protocols to ensure the reliability and generalizability of classification outcomes. This study involved a thorough examination of selected publicly available plant diseases datasets, with an emphasis on how well they performed as measured by GLCM metrics. After first classifying the datasets according to their GLCM metrics, dataset_2 (D2) and dataset_5 (D5) were found, respectively, to be the best-performing dataset in all GLCM analyses. The same datasets were then used to train deep learning models, and their classification performances were assessed. A noteworthy association was observed between the results of training deep learning models and the performance ratings derived from GLCM studies. More specifically, dataset_2 (D2) performed best in both GLCM analysis and deep learning model performance, indicating a strong correlation between the accuracy of classification and the textural qualities that GLCM captured. In the context of plant disease identification, in particular, these results highlight the significance of clearly defined dataset selection criteria in deep learning applications. Scholars can improve the accuracy and dependability of deep learning models for diagnosing plant diseases by giving preference to datasets with favorable GLCM metrics. The research also emphasizes the importance of texture features being taken into account in addition to conventional image features, highlighting the necessity of transparency and rigor in dataset selection procedures.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) persists throughout the world with over 65 million registered cases of survivors with post-COVID-19 sequelae, also known as LongCOVID-19 (LongC). LongC survivors exhibit various symptoms that span multiple organ systems, including the nervous system. To search for neurological biomarkers of LongC, we investigated the soluble proteins present in the plasma and the proteins associated with plasma neuronal-enriched extracellular vesicles (nEVs) in 33 LongC patients with neurological impairment (nLongC), 12 COVID-19 survivors without any LongC symptoms (Cov), and 28 pre-COVID-19 healthy controls (HC). COVID-19 positive participants were infected from 2020-2022, not hospitalized, and were vaccinated or unvaccinated before infection. IL-1b was significantly increased in both nLongC and Cov and IL-8 elevated in only nLongC. Both brain-derived neurotropic factor and cortisol were significantly elevated in nLongC and Cov compared to HC. nEVs from people with nLongC had significantly elevated protein markers of neuronal dysfunction, including amyloid beta 42, pTau181 and TDP-43. This study shows chronic peripheral inflammation with increased stress after COVID-19 infection. Additionally, differentially expressed nEV neurodegenerative proteins were identified in people recovering from COVID-19 regardless of persistent symptoms.

Background and Objectives: In this current state, Type 2 Diabetes Mellitus (T2DM) is more prevalent in the population, and metformin is used as a first-line medication for treating it. There was up surge in the utilization of alternative therapies in the management of diabetes. Especially in diabetes, Herbal medicines are considered safe and reliable by majority of the population. Aim: The purpose of this research is aimed to estimate the safety and efficacy of Polyherbal Metabolite Compounds (PHMC) capsules in adults with T2DM. Materials and Methods: In this study, 60 T2DM patients aged 18-60 years were randomly assigned to Groups A (30) receiving Metformin 500 mg twice a day after food and Group B (30) receiving PHMC capsule 500 mg twice a day after food in a prospective, randomized and open-label clinical study. The in-silico simulation study was performed to selected plants major compounds on target protein Insulin-like Growth Factor-1 (1K3A). Result: The PHMC is found to be safe and effective in the management of T2DM. The FBS, PPBS and HbA1c were significantly lowered (p

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Patient value is an important factor in clinical decision-making, but conventionally, it is not incorporated in the decision processes. In this paper, a value-based therapy recommendation comprehensive model is proposed. A literature analysis is conducted to collect value-based evidence. Categorized values and candidate therapies are used in combination as filtering keywords to build this evidence. Then a formalism model is put forward to integrate the value-based evidence with clinical evidence. The relative importance of two evidence can be adjusted dynamically by decision-makers during the decision-making processes. A prototype system was implemented using a case study for breast cancer and validated for feasibility and effectiveness.

The rapid evolution of drug resistance is one of the greatest health issues of the 21st century. There is an alarming situation to find new therapeutic strategies or candidate drugs to tackle ongoing multi-drug resistance development. The marine environment is one of the prime natural ecosystems on Earth, the majority of which is still unexplored, especially when it comes to the microbes. A wide variety of bioactive compounds have been obtained from a varied range of marine organisms, however, marine bacteria-produced bacteriocins are still undermined. Owing to the distinct environmental stresses that marine bacterial communities encounter, their bioactive compounds frequently undergo distinct adaptations that confer on them a variety of shapes and functions, setting them apart from their terrestrial counterparts. The ribosomally-synthesized and posttranslationally modified peptides (RiPPs), bacteriocins are of special interest because of their variety in structure and diverse potential biological activities. Additionally, the gut microbiome of marine creatures are largely unexplored source of new bacteriocins with promising activities. There is a huge possibility of novel bacteriocins from marine bacterial communities that might come out as efficient candidates to fight against antibiotic resistance, especially in light of the growing pressure from antibiotic-resistant diseases and industrial desire for innovative treatments. The present review summarizes known bacteriocins from marine bacteria, evolutionary aspects, challenges, and huge possibilities of unexplored bacteriocins from marine bacterial communities.

The LTTD process for highly concentrated crude oil-contaminated soil was studied to evaluate the remediation efficiency. In addition, the environmental impact difference between HTTD and LTTD was studied. OpenLCA software obtained from PE International was used to perform the analyses. The total ADP environmental impact of the HTTD process was 1.63E-04 MJ, whereas the total GWP impact was 4.14E+02 kg CO2-eq. In contrast, the LTTD process exhibits total ADP and GWP environmental impacts of 1.29E-04 MJ and 2.78E+02 kg CO2 eq, respectively. Analysis of the overall environmental impact reduction revealed that replacing the HTTD process with the LTTD process resulted in a total ADP reduction of 3.40E-05 MJ (20.5%) and a GWP reduction of 1.36E+02 kg CO2 -eq (32.9%). It is inferred that the LTTD treatment of contaminated soil can significantly reduce the environmental impact compared with conventional incineration and HTTD processes. Additionally, the LTTD-treated soil with coke or carbonized residue could act like biochar as a soil amendment. Approximately 50% of organic hydrocarbon contaminants are sequestered into coke or carbonized residues on the soil surface. With additional analyses of various oil-contaminated soils, the potential utility of LTTD-treated soils is expected to be maximized.

Background: Despite progress in access to family planning services in many sub-Saharan 11 African countries in recent decades, advances in early postpartum contraceptive adoption remain 12 low, and the unmet need for early postpartum contraceptives is high. According to the Ethiopia 13 Demographic and Health Survey report, early postpartum modern contraceptive method uptake is 14 still unacceptably low in Ethiopia. Objectives: This study aimed to determine the magnitude of 15 intention to adoption of early postpartum modern contraceptive methods and its associated factors 16 among pregnant women in Dessie and Kombolcha town zones, northeast Ethiopia. Methods: A 17 community-based cross-sectional study was deployed from January 15–February 15, 2023, in the 18 Dessie and Kombolcha zones, north-east Ethiopia among pregnant women. The study involved 780 19 pregnant women using the cluster sampling technique. A census was conducted in 20 randomly 20 selected clusters to identify eligible pregnant women. Actual data were collected home-to-home in 21 the community through face-to-face interviews. Data were collected by Open Data Kit (ODK) and 22 exported to STATA 17 for analysis. A multivariable logistic regression analysis was done and the 23 goodness of the model was checked by Hosmer-Lemeshow's test statistic and rock curve. An ad- 24 justed odds ratio with a 95% confidence interval and P-value < 0.05 was considered statistically sig- 25 nificant. Result: The study revealed that 49.6% of pregnant women lack autonomy and 50% lack 26 knowledge about early postpartum contraception, with participants' wealth index status ranging 27 from rich (36.6%) to poor (33.2%). The study found that 75.8% of pregnant women intended to adopt 28 early postpartum modern contraceptive methods early after childbirth. After controlling the poten- 29 tial confounder’s mothers age (AOR = 6.2 [2.6–14.6], birth interval (AOR = 2.5 [1.6–3.7]), have paid 30 work (AOR = 1.9 [1.3–2.8]), health facility from home (AOR=2.6[1.5-4.4]), last delivery 31 Place(AOR=2.4[1.1-5.7]), knowledge on (AOR = 1.5 [1.1-2.1]), and antenatal care follow-up (AOR = 32 1.9 [1.2-3.3]) were significant associated factors of intention to uptake early postpartum modern con- 33 traceptive methods among pregnant women. Conclusion: The study found that 75% of the partici- 34 pants had the intention to adoption of contraceptive methods during the early postpartum period. 35 Identified factors influencing this intention were age, birth interval, women's employment status, 36 area of residence, distance to health facilities, last delivery place, knowledge of early postpartum 37 modern contraception, gravidity, and antenatal care follow-up. These findings highlight the need 38 for targeted interventions to address these factors, framing the intended users and enabling access 39 to early adoption of postpartum contraceptive methods.

Urea cycle disorders (UCDs) are common inborn errors of metabolism, with an incidence of one in 30,000 births. They are caused by deficiencies in any of six enzymes and two carrier proteins, the most common being Ornithine Transcarbamylase Deficiency (OTCD). OTCD results in impairment to excrete nitrogen, causing toxic buildup of ammonia with resultant encephalopathy. Hyperammonemia (HA) induces the conversion of glutamate to glutamine in the brain. Excess glutamine in the brain causes osmotic changes cerebral edema, changes in astrocyte morphology, and cell death. Acute symptoms of HA include vomiting, hyperventilation, seizures, and irritability. Long-term neurological changes include deficits in working memory and executive function. To date, there are no predictors of prognosis of infants with neonatal onset OTCD outside of plasma ammonia level at presentation and duration of hyperammonemic coma. We provide a comprehensive analysis of a 16-year-old male with neonatal onset of OTCD as an example of how brain biomarkers may be useful to monitor disease course and outcome. This male presented at 8 days post natal with plasma ammonia and glutamine of 677 and 4024 micromol/L and had a missense mutation in Exon 4 (p.R129H). Treatment included protein restriction, sodium benzoate, and citrulline, arginine, and iron. He suffered recurrent acute hyperammonemic episodes despite compliance, triggered by infections or catabolic stressors. We discuss the long-term effects of the hyperammonemic episodes by following MRI based disease biomarkers.

This paper proposes the utilization of large language models as recommendations systems for museums. Since the aforementioned models lack the notion of context, they can’t work with temporal information that is often present in recommendations for cultural environments (e.g. special exhibitions or events). In this respect, the current work aims at enhancing the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations, aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-ware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

The advent of the era of big data will bring more convenience to people and greater development to society. But at the same time, it will also bring people the problem of 'information overload', i.e., when people are faced with huge information data, there are many redundant and worthless data. The redundant and worthless data information seriously interferes with the accurate selection of information data. Even though people can use Internet search engines to access information data, they cannot meet the individual needs of specific users in specific contexts. The personalized needs of a particular user in a particular context. Therefore, how to find useful and valuable information quickly has become one of the key issues in the development of big data. With the advent of the era of big data, recommendation systems, as an important technology to alleviate information overload, have been widely used in the field of e-commerce. Recommender systems suffer from a key problem: data sparsity. The sparsity of user history rating data causes insufficient training of collaborative filtering recommendation models, which leads to a significant decrease in the accuracy of recommendations. In fact, traditional recommendation systems tend to focus on scoring information and ignore the contextual context in which users interact. There are various contextual modal information in people's real life, which also plays an important role in the recommendation process. In this paper we achieve data degradation and feature extraction, solving the problem of sparse data in the recommendation process. An interaction context-aware sub-model is constructed based on a tensor decomposition model with interaction context information to model the specific influence of interaction context in the recommendation process. Then an attribute context-aware sub-model is constructed based on the matrix decomposition model and using attribute context information to model the influence of user attribute contexts and item attribute contexts on recommendations. In the process of building the model, the method not only utilizes the explicit feedback rating information of users in the original dataset, but also utilizes the interaction context and attribute context information of the implicit feedback and the unlabeled rating data. We evaluate our model by extensive experiments. The results illustrate the effectiveness of our recommender model.

Background/Objectives: Dental implants have become a cornerstone of restorative dentistry, providing a long-lasting method for tooth replacement. The level of osseointegration has a big impact on the biomechanical stability at the bone-implant contact (BIC), which determines how long these implants will work. However, the exact consequences of changing osseointegration levels on different implant designs, especially in bones with variable densities, are not well known. Three short dental implants were tested in this study: BioMet 3iT3, Straumann® Standard Plus Short Regular Neck (SPS-RN), and Straumann® Standard Plus Short-Wide Neck (SPS-WN). 3D finite element analysis (FEA) was used to look at how well they worked biomechanically. This paper tests the implants at four stages of osseointegration: 25%, 50%, 75%, and 100% in both high-density (Bone Type III) and low-density (Bone Type IV) cancellous bone. It also created and examined realistic CAD models under static occlusal loading conditions to assess stress distribution and major strains at the bone-implant contact.; Results: The study discovered that as osteointegration increases, von Mises stress and principal strains go down significantly for all implant types. The SPS-WN implant had the lowest strain values, especially for bone with low density. These reductions demonstrate increased mechanical stability as the bone-implant inter-face becomes more capable of dispersing mechanical stresses, minimizing the potential for localized deformation and bone resorption.; Conclusions: The results highlight the importance of achieving optimum osseointegration to reduce mechanical stress and increase the lifespan of dental implants. The SPS-WN type implant performed better in biomechanical tests than the others, especially when bone conditions were not ideal. This makes it a great choice for clinical applications that need long-term implant success.

Typhoid fever is a major public health burden which causes substantial global morbidity and mortality due to lack of decisive diagnostic protocols. The capacity of commonly use diagnostic test to validate the absence of typhoid fever is controversial. This study explores to evaluate new techniques for typhoid diagnosis and proposed a harmonised suitable standardized composite reference to be adopted. Published peer-reviewed articles indexed in PubMed, MEDLINE and Google scholar were reviewed for hospital-based studies. This study reveals new typhoid diagnostic techniques such as proteomics, serology, Rapid Diagnostic tests (RDTs), transcriptomics, genomics, and metabolomics. 34.4% of the studies use prospective study design. The study result establishes that, Widal test has a moderate diagnostic accuracy with average percentage sensitivity (52.9%), specificity (54%), positive predictive value (PPV) (56.8%) as well as negative predictive value (NPV) (55.6%) when compared with 29.4%, 28%, 29.5%, and 27.8% of Typhidot respectively. The findings showed a statistically significant difference on the sensitivity between Widal and Typhidot t (40) = 2.639, p = 0.012 at p<0.05 using independent sample t-test. When there is no perfect reference standard that has an optimal diagnostic accuracy, the need for a harmonised suitable standardized composite reference is essential. Hence, this study recommends that, peripheral blood culture with established sensitivity of 60% and Widal test with average sensitivity of 52.9% be adopted as a consensus composite reference standard for typhoid fever diagnosis in other to improve confidence in prevalence estimates.

Complicated grief (CG) predicts decreased mental health over time. Furthermore, feeling connected to nature (CN) is positively associated with beneficial mental health outcomes, such as psychological wellbeing and perceived psychological resilience. Thus, we hypothesized that CN moderates the association between general grief or CG and negative mental health for bereaved people. Further, we hypothesized that one’s physical exposure to nature—that is, estimated time spent in nature and greenness (i.e., vegetation) surrounding one’s residential area—might moderate the association between general grief or CG and negative mental health for bereaved people. To test these hypotheses, we sampled 153 participants who experienced the death of a close other by COVID-19 infection. Participants reported CG, general grief, anxiety symptoms, depression symptoms, CN, estimated time spent in nature, and residential area postal code via a single online survey. We estimated greenness surrounding participants’ residential areas using their self-reported 5-digit U.S. postal code. Cross-sectional analyses indicated that, as predicted, CN attenuated the association between CG and depression, but only trended toward moderating the association between CG and anxiety and did not moderate the associations between general grief and depression or anxiety. Other variables related to the experience of nature—the estimated time an individual spends in nature and the greenness surrounding one’s residential area—did not moderate the association between general grief or CG and depression or anxiety. We thus conclude that a sense of feeling connected to nature—not simply spending more time in nature or being surrounded by nature—may serve an important role in the mental health status of people experiencing complicated grief, perhaps because CN replenishes general belonging when someone significant has passed away.

The PAX8/PPARγ rearrangement, producing the PAX8–PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble docking-based compound screening. Specifically, we investigated which pocket structure should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand binding pocket on the PPARγ side of the PPFP is common to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to PPFP for 97 drug or drug-like compounds with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ, registered in the DrugBank database. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics-Poisson–Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

For personalized recommender systems,matrix factorization and its variants have become mainstream in collaborative filtering.However,the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless,most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper,we propose a metric learning-based social recommendation model called SRMC.SRMC exploits users' co-occurrence pattern to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations.Experiments on three public datasets show that our model is more effective than the compared models.

Event detection provides a crucial foundation for intelligent text analysis and supports numerous downstream applications. However, existing detection models often fail to meet the high accuracy requirements and typically require a human–machine collaborative detection mode involving “machine detection followed by manual audit and correction.” Analysis reveals that the recall rate of machine detection is a major factor limiting the efficiency of human–machine collaboration. To address this at its root and ensure high recall, an event trigger recommendation task is introduced, and a span-based recommendation model for Chinese, SpETR (Span-based Event Trigger Recommender), is developed. This model integrates confidence information from both trigger identification and event classification stages to recommend candidate triggers and their event types for each event. To enhance recall, a semantic boundary smoothing method is proposed, which assigns soft labels to spans around gold triggers based on semantic completeness and part-of-speech, combined with a modified focal loss function to make the model predictions smoother. Experiments on DuEE and a self-built dataset show that SpETR is most effective when the number of recommendations is set to 3, achieving recall rates of 97.76% and 98.70%, respectively, comprising improvements of 4.30% and 5.22% over the optimal baseline models. In terms of human–machine collaborative detection, SpETR saved 30.6% and 25.8% of time compared to traditional detection models on two datasets, and the average F1 score of the annotation results was also improved by 5.35% and 3.48%, respectively.

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graph can introduce user-item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user-item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: 1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and 2) how to merge interaction information from the two graphs while ensuring that user-item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC has strengthened and represented the information of different connecting edges in both graphs, and extracted the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We have conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results.

In this paper, we introduce ResNetMF, a groundbreaking approach that harnesses the power of residual network matrix factorization to revolutionize recommendation systems. ResNetMF integrates residual networks, renowned for their ability to capture intricate patterns and features, with matrix factorization techniques that excel in modelling user-item interactions. This fusion presents a novel solution that surpasses the limitations of traditional recommendation systems. Through comprehensive experimentation and evaluation of diverse datasets, ResNetMF demonstrates remarkable enhancements in recommendation accuracy and efficiency. By effectively capturing both linear and nonlinear relationships in user-item interactions, ResNetMF provides superior recommendation quality. The outcomes from experiments unequivocally highlight the superiority of ResNetMF over existing state-of-the-art recommendation approaches, thereby validating its innovative nature and underscoring its potential to shape the future of recommendation systems. Through the integration of the deep residual network, ResNetMF approach facilitates the training of neural networks, enabling them to explore the underlying data layers more comprehensively. Extensive experimentation and evaluation across various datasets provide compelling evidence for the superiority of ResNetMF. Moreover, the proposed method utilized natural language processing (NLP) techniques for targeted information dissemination in recommendation systems, emphasizing the importance of personalized and relevant recommendations for user satisfaction and engagement.

Bundle recommendation is provided to users by combining related items into a bundle, enhancing the user's shopping experience and increase the merchant's sales revenue. Despite the success of existing solutions based on Graph Neural Networks(GNN), there are still some significant challenges: (1)It is demanding to explicitly model multiple complex associations using standard graph neural networks. (2)Numerous additional nodes and edges are introduced to approximate higher-order associations. (3)The user-bundle historical interaction data is highly sparse. In this work, we propose a Global Structural Hypergraph Convolution model for Bundle Recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles(three entities) into a relational hypergraph without introducing additional nodes and edges. Higher-order associations are already involved in the hypergraph structure which alleviate the training burden of neural networks and the dilemma of scarce data. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, hypergraph convolution propagation between user and bundle nodes generates the learned representations. Experimental results on two real-world datasets demonstrate that SHCBR outperforms the state-of-the-art baselines by 11.07%-25.66\% on Recall and 16.81%-33.53% on NDCG. We publicly release the codes and datasets at https://github.com/Lxtdzh/SHCBR-master/.

The giant octopus caught off the east coast of Korea is an important fishery resource with the largest haul and is mostly caught using net pots. While giant octopus pots have different shapes, drum, rectangular, and cylinder pots are mostly used. However, these pots have not been thoroughly evaluated. Here, we evaluated the fishing performance of three giant octopus pots, their fluid resistance, and underwater movement. Six sea trials were conducted near the Hupo region in Gyeongsangbuk-do. In total, 163 individuals (62,724 g), 190 individuals (49,913 g), and 154 individuals (52,364 g) were caught using drum, rectangular, and cylinder pots, respectively. Drum pots showed significant differences in the amount of catch by weight (ANOVA test, p&lt;0.05). Moreover, the percentage of minimum conservation size was the lowest in cylinder pots (6.5%). Drum pots showed the highest stability in the underwater stability test, while underwater fluid resistance at 2.5 kt fluid speed was in the following order: drum pots (7.66 kgf) &gt; rectangular pots (7.16 kgf) &gt; cylinder pots (7.04 kgf). Therefore, drum pots showed excellent fishing performance. This study provides foundational data for developing managing systems for giant octopus resources and establishing the extent of fishing gear usage.

The autonomous displacement and displacement measurement functions of the maglev ruler are performed by the mover core. The stability of the four magnetic field in air gaps and the levitation position of the maglev ruler is one of the key factors for the stability of the thrust force on the power core, and it is also one of the key factors for ensuring the precision of the maglev ruler. There is cross-coupling between the two ends of the mover core of the maglev ruler, resulting in a strongly coupled, nonlinear, multi-input and multi-output system for the levitating system of the maglev ruler. This paper establishes a mathematical model for the levitating system of the maglev ruler, and designs a levitating control system for the maglev ruler based on ADRC control algorithm to achieve decoupling and disturbance suppression. Through simulation analysis and experimental testing of the levitating system with starting and disturbance, it is proved that the levitating control system of the maglev ruler has good dynamic characteristics, static characteristics, and robustness.

With the rapid development of the Internet industry, the problem of information overload has arisen due to the abundance of information available online. Recommendation algorithms, as the core of recommendation systems, have been attracting much attention and are a hot topic of research for many experts and scholars. The classical recommendation algorithms are mainly divided into three major categories: collaborative filtering recommendation algorithms, content-based recommendation algorithms, and hybrid-based recommendation algorithms. Although these algorithms are widely used in various fields, with the proliferation of information, these traditional recommendation algorithms are no longer able to meet the needs of the times. To address this issue, recommendation systems have been developed to provide users with personalized and relevant information or products. Despite the wide use of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches, traditional recommendation algorithms have limitations and are no longer suitable for meeting the demands of the times. This paper proposes a new recommendation algorithm, SFRRG, that fuses structure and feature information in graph neural networks to improve the performance of the recommendation system in rating prediction. The effectiveness of the proposed algorithm is demonstrated through experiments on various data sets and compared with existing recommendation algorithms.

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. The study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, the study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX-method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX-method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

Recommendation systems are crucial in navigating the vast digital market. However, user data’s dynamic and non-stationary nature often hinders their efficacy. Traditional models struggle to adapt to the evolving preferences and behaviours inherent in user interaction data, posing a significant challenge for accurate prediction and personalisation. Addressing this, we propose a novel theoretical framework, the Non-stationary Transformer, designed to capture and leverage the temporal dynamics within data effectively. This approach enhances the traditional transformer architecture by introducing mechanisms accounting for non-stationary elements, offering a robust and adaptable solution for recommendation systems. Our experimental analysis, encompassing deep learning and reinforcement learning paradigms, demonstrates the framework’s superiority over benchmark models. The empirical results confirm the efficacy of our proposed framework, which not only provides significant performance enhancements, approximately 8% in Logloss reduction and up to 2% increase in F1 score but also underscores its potential applicability across accumulative reward scenarios. These findings advocate adopting Non-stationary Transformer models to tackle the complexities of today’s recommendation tasks.

Abstract—Online learning platforms provide diverse course resources, but this often result in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, Existing course recommendation methods face the challenge in accurately recommending the target course that is most relevant to the user, because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the Actor-Critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendation effectively. Furthermore, we propose a policy gradient based on temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and improves the accuracy of the recommendation. We evaluate the proposed model on two real datasets, and the experimental results show that the proposed model is significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user-item rating matrix input of collaborative filtering. This reviews focuses on algorithmic techniques of existing E-commerce recommendation systems that are sequential pattern based such as ChoRec05, ChenRec09, HuangRec09, LiuRec09, ChoiRec12, Hybrid Model RecSys16, Product RecSys16, SainiRec17, HPCRec18 and HSPCRec19. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potentials for solving more important problems in this domain. The review showed that integrating sequential pattern mining of historical purchase and/or click sequences into user-item matrix for collaborative filtering (i) improved recommendation accuracy (ii) reduced user-item rating data sparsity (iii) increased novelty rate of recommendations and (iv) improved scalability of the recommendation system.

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations’ influence on customer clicks and buys, three target areas—customer behavior, data collection, user-interface —will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations’ true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget’s positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer’s interactions influenced by the recommendation model in the context of standard industry metrics such as Click-through rates, Buy-through rates, and Conversion revenue.

In this paper, we provide a study on the effects of applying classical clustering algorithms, such as k-Means to free text recommender systems. A typical recommender system may face problems when the number of items from a database goes from a few items to hundreds of items. Currently, one of the most prominent techniques to scale the database is applying clustering, however clustering may have a negative impact on the accuracy of the system when applied without taking into consideration the underlying items. In this work, we build a conceptual text recommender system and use k-Means to partition its search space into different groups. We study how the variation of the number of clusters affects its performance in the light of two performance measurements: recommendation time and precision. We also analyze if this clustering is affected by the representation of text we use. All the techniques used in this study uses word-embeddings to represent the document. One of the main findings of this work is that using clustering we can improve the recommendation time in up to almost 30 times without affecting much off its initial accuracy. Another interesting finding is that the increment of the number of clusters is not directly translated into linear performance.

Social recommendation is almost as the integration of the business platform and social platform, and gradually become a top in recommendation system. Social recommendation algorithm solves the problem of cold start and data sparseness for traditional commodity, while the internal structure of the relationship graph in social relations has not been fully excavated. This paper proposes two models of Micro Relation Transfer Model and Macro Relation Transfer Model of social relations, and applies the social relations transfer models into the social recommendation system. A relationship graph is built from the relationship between customers on the Internet. Micro Relation Transfer Model establishes the transfer activation function by calculating the relationship between the two customers using the similarity of interests set. Micro Relation Transfer Model spreads the relationship of friends by calculating the proportion of common neighbors held by the customer's social relations. In order to effectively control the transmission range and effect of social relations graph, we introduce pruning algorithm based on Monte Carlo Decision Tree convergence algorithm. The experimental results show that SRRTC algorithm enhances the success rate and stability significantly.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

Background: We conducted research on the newly developed personalized recommendation service (PRS) of the global network of Korean scientists and engineers (KOSEN) in order to explore the information usage behavior and importance of the PRS used by Korean scientists and engineers. Methods: In order to understand information usage behavior, we gathered data from 513 survey results and analyzed them in terms of information usage behavior and the corresponding importance in each of the service quality areas. Results: We analyzed the 321 outcomes that indicated non-use of the PRS in order to understand the underlying reason(s); we employed 192 results that demonstrated the use of functionality to examine information usage behavior and importance. They found that the predominant motive for non-use of the service resulted from the respondents not knowing how to use it. According to demographic characteristics, the usage behavior of the PRS showed a difference regarding the purpose of using the service in the categories of gender and major field of study. Furthermore, users were concerned with various components of the PRS such as ease of use, design, relevance of content, user support, and interactivity. Conclusions: We suggest reinforcing user education degree and promotion to enhance the PRS. Since users were concerned with ease of use, design, relevance, user support, and interactivity, we recommend these as major points for improvement.

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

This study evaluates the viability of n-octanol as an alternative fuel in a direct-injection diesel engine, aiming to enhance sustainability and efficiency. Experiments employed varying blends of n-octanol (10%, 30%, and 50% by volume) with pure diesel, analyzing impacts on engine performance and emissions. The methodology involved testing each blend in a single-cylinder engine, measuring engine performance parameters such as brake torque and brake power under full load conditions across an engine speed range from 1400 to 2500 rpm. Comparative assessments of performance and emission characteristics at a constant engine speed of 1700 rpm were also conducted with varying loads. Results indicated that while n-octanol blends consistently improved brake thermal efficiency, they also increased brake specific fuel consumption due to the lower energy content of n-octanol. Consequently, while all n-octanol blends reduced nitrogen oxides (NOx) emissions compared to pure diesel, they also significantly decreased carbon monoxide, hydrocarbons, and smoke opacity, presenting a comprehensive reduction in harmful emissions. However, the benefits came with nuanced trade-offs: notably, higher concentrations of n-octanol (especially the 50% blend) led to a relative increase in NOx emissions as the octanol ratio increased. The study concludes that n-octanol significantly improves engine efficiency and reduces diesel dependence, but optimizing the blend ratio is crucial to balance performance improvements with comprehensive emission reductions.

Pump Sizing is the process of dimensional matching an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large amount of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This work presents the use of complex networks in the construction of a prototype to recommend interference during the Pump Sizing process in a Progressive Cavity Pump. For this, data from different expert applications were used in addition to individual control Excel spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the Betweenness Centrality metric were used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. The model was evaluated using the mean squared error (MSE), obtaining a MSE of 0.28 for the cases where there were recommendations for order confirmations. Based on the results obtained, it was realized that there is a similarity with the dimensional defined by design engineers during the Pump Sizing process.

There is a considerable amount of research in online social networks, most of which focuses on the structural analysis of social graphs. The interpersonal relationships of social networks, especially friend circle, can solve the cold start and sparsity problems, and through the relationship between social networks can effectively recommend users' favorite items (items), such as music , videos, brands/products, preferred tags, location, services, etc. User relationships in social networks are diverse and there are many different perspectives on different social networks. Associations among users can form multi-layered composite networks, and multi-layered social networks present new challenges and opportunities. Different relationships can influence users' preferences to different degrees, which in turn affects their behavior. Therefore, fusing multiple social networks is an effective way to improve recommendation. Although some studies have started to address multiple social network recommendations, simple linear superposition cannot reflect the coupling and nonlinear association between multiple social networks. In this paper, we propose a graph neural network recommendation model under social relationships based on this background. We first propose to compute the 2nd order collaborative signals and their intensities directly from the neighboring matrix for updating the node embedding of the graph convolution layer. Secondly by embedding historical evaluations, various social networks constituting different dimensions, the attention integration of user preferences by different social networks is achieved, and its effectiveness and scalability are demonstrated in theoretical derivation and experimental validation. The theoretical derivation and experimental validation demonstrate its effectiveness and scalability.