Traditional Approaches Research Articles

The Impact of Network Design Interventions on the Security of Interdependent Systems

We study the problem of defending a Cyber-Physical System (CPS) consisting of interdependent components with heterogeneous sensitivity to investments. In addition to the optimal allocation of limited security resources, we analyze the impact of an orthogonal set of defense strategies in the form of network design interventions in the CPS to protect it against the attacker. We first propose an algorithm to simplify the CPS attack graph to an equivalent form which reduces the computational requirements for characterizing the defender's optimal security investments. We then evaluate four types of design interventions in the network in the form of adding nodes in the attack graph, interpreted as introducing additional safeguards, introducing structural redundancies, introducing functional redundancies, and introducing new functionalities. We identify scenarios in which interventions that strengthen internal components of the CPS may be more beneficial than traditional approaches such as perimeter defense. We showcase our proposed approach in two practical use cases: a remote attack on an industrial CPS and a remote attack on an automotive system. We highlight how our results closely match recommendations made by security organizations and discuss the implications of our findings for CPS design.

Online Deep Learning for High-Speed Train Traction Motor Temperature Prediction

As the core component of the traction drive system of high-speed trains, the working condition of the traction motor is directly related to the operational safety of the train. During train operation, the temperature signal of the traction motor is constantly changing. Accurate prediction of the traction motor temperature using real-time data generated by sensors at the train end is beneficial for the early detection of abnormal motor conditions. The traditional approach is to apply prediction models from offline learning to the onboard side of the train for real-time temperature prediction, but its prediction accuracy degrades with the online data distribution changed. Therefore, this paper proposes an online deep learning model (ODL) for real-time traction motor temperature prediction. The ODL model uses DNN as its basic structure and hedging strategy to achieve online learning of the model parameters, which can dynamically adjust the model structure and parameters to adapt to streaming data with changing probability distributions. Finally, the study conducts multiple motor temperature prediction experiments using multi-sensor data from train operations. The results show that the ODL model outperforms the currently popular models in temperature prediction and can be effectively applied to real-time temperature prediction on the train end.

Memory-induced weak dissipation in fractional-time-derivative quantum Lindblad-based model

Modeling the interaction between a quantum system and its environment is crucial for practical quantum technologies. The Lindblad master equation is the simplest equation to understand this interaction. In this paper, we have extended the traditional Lindblad equation by fractionalizing its time derivative to account for the memory-induced dissipation. Using this approach, we show weak dissipation of selected quantum systems can be reproduced by this proposed time-fractional Lindblad equation without introducing any specific dissipation terms in the model. By varying the order of the time-fractional Lindblad equation without dissipation terms, we can reproduce the results with good agreements to three tested cases: (a) dissipative Rabi oscillation, (b) dissipative Ising model, and (c) collapse and revival in the Jaynes–Cumming model. We believe this proposed time-fractional Lindblad equation may be a useful modeling tool to characterize weakly dissipative quantum systems in practical quantum technologies, especially if the complex dissipation mechanism is not completely known from the traditional approaches.

Modeling of heat and mass transfer in the discontinuum approximation

The article presents a theoretical analysis of the governing equations expressing the fundamental conservation laws in the continuum and discontinuum approximations, and methods for solving problems of hydrodynamics as one of the most important subfields of continuum mechanics. This article is an attempt to more accurately describe physicochemical macro-processes. It is shown that the most suitable equations for computer modeling are the conservation laws under natural constraints on the minimum spatial and time scales, i.e., equations without partial derivatives and constraints on the solution smoothness. Using the continuity and thermal conductivity equations, a phenomenological method for constructing and numerically solving the governing equations is presented, and comparison with the traditional approach is given.

On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment

Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system complexity. However, deep learning models are shown to be vulnerable to adversarial examples in the field of computer vision. While this vulnerability has been noticed by the power grid cybersecurity research, the domain-specific analysis on the requirements imposed upon effective attack implementation is still lacking. Although these attack requirements are usually reasonable in computer vision tasks, they can be stringent in the context of power grids. In this paper, we conduct a systematic investigation on the attack requirements and credibility of six representative adversarial example attacks based on a voltage stability assessment application for the New England 10-machine 39-bus power system. We show that (1) compromising about half the transmission system buses' voltage traces is a rule-of-thumb attack requirement; (2) the universal adversarial perturbations regardless of the original clean voltage trajectory possess the same credibility as the widely studied false data injection attacks on power grid state estimation, while the input-specific adversarial perturbations are less credible; (3) the prevailing strong adversarial training thwarts the universal perturbations but fails in defending certain input-specific perturbations. To advance defense to cope with both universal and input-specific adversarial examples, we propose a new approach that simultaneously estimates the predictive uncertainty of any given input of voltage trajectory and thwarts the attacks effectively.

The anatomical location of the great saphenous vein at the thigh and ankle: a neonatal cadaver study

Providing critically ill neonatal patients with parenteral nutrition, medication, fluids, and access to blood sampling is essential in intensive care units. One option for blood sampling is the great saphenous vein within the proximal thigh, and near the medial malleolus in neonates via ‘conventional’ landmark and ultrasound techniques. Practitioners in many countries still use the traditional landmark approach to locate the great saphenous vein in neonates, regardless of access to ultrasound. We aim to provide measurements that accurately describe the anatomy of the great saphenous vein in neonates to aid in cannulation success. The great saphenous vein was exposed in the proximal thigh and near the medial malleolus by reflecting the skin in 31 and 30 formalin-fixed neonate cadavers, respectively. Pins were placed at essential bony landmarks and soft tissue structures. The termination of the great saphenous vein within the proximal thigh can be located 6.8 ± 1.5 mm inferior to the inguinal ligament. The average shortest distance from the medial malleolus to the great saphenous vein was 4.3 mm, 2.0 mm anterior, and 3.1 mm superiorly. The diameter of the great saphenous vein in the proximal thigh and at the medial malleolus ranged between 1.4 mm and 1.6 mm, and 0.9 mm and 1.1 mm, respectively with a 95% confidence level. Our results provide a more accurate description to gain venous access through the great saphenous vein. However, if available, ultrasound should be used to locate and confirm the diameter of the great saphenous vein in the lower limb.

The overtreatment of papillary thyroid microcarcinoma in the community

IntroductionTotal thyroidectomy is the traditional primary approach for papillary thyroid cancer. However, recent evidence supports conservative management for low-risk tumors like papillary thyroid microcarcinomas (PTMCs). This study explores the adoption of these practices in our community, using a cancer database to analyze treatment strategies. MethodsA retrospective review of a 1433-patient institutional database identified 258 ​PTMC cases. Outcomes, including 30-day mortality, reoperation rate, postoperative hypocalcemia, and recurrent laryngeal nerve (RLN) injury, were assessed. ResultsOf PTMC patients, 63.4% underwent total thyroidectomy, with higher rates of RLN injury (8.8% vs. 2.3%) and hypocalcemia (12.4% vs. 0.0%) compared to lobectomy. Non-endocrine surgeons had higher postoperative radioactive iodine administration rates (28.6% vs. 6.1%). Subgroup analysis revealed a shift in total thyroidectomy rates based on tumor size and surgery period. ConclusionOur community favors total thyroidectomy for PTMC, despite associated complications. Enhanced awareness and adherence to PTMC best practice guidelines are warranted.

The Viability of Topic Modeling to Identify Participant Motivations for Enrolling in Online Professional Development

Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify participants’ motivation for enrolling in two successive statistics education professional development online courses. Computational text mining, such as topic modeling, is a learning analytics field that has proven effective in analyzing large volumes of text to automatically identify topics or themes. This contrasts with traditional qualitative approaches, in which researchers manually apply labels (or codes) to parts of text to identify common themes. Combining topic modeling and qualitative research may prove useful to education researchers and practitioners in better understanding and improving online learning contexts that feature asynchronous discussion. Three topic modeling approaches were used in this study, including both unsupervised and semi-supervised modeling techniques. The three topic modeling approaches were validated and compared to determine which participants were assigned motivation themes that most closely aligned to their posts made in an introductory discussion forum. A discussion of how each technique can be useful for identifying topical themes within discussion forum data is included. Though the three techniques have varying success rates in identifying motivation for enrolling in the MOOCs, they do all identify similar themes for motivation that are specific to statistics education.

An Efficient Aspect-based Sentiment Classification with Hybrid Word Embeddings and CNN Framework

Background: As the e-commerce product reviews and social media posts are increasing enormously, the size of the database for polarity/ sentiment detection is a challenging task, and again, predicting polarities associated with respect to aspect terms end to end in a sentence is a havoc in real-time applications. Human behavior is influenced by the various opinions generated in society. Public opinion influences our decisions most often. Businesses and establishments always need to collect the opinion of the society, which they try to obtain using customer feedback forms and questionnaires or surveys, which help them to be aware of the shortcomings if any, and to use suggestions to improve quality. It works in the same way for customers as well and the opinions of other customers about a particular product can come in handy when deciding to buy a product. Objectives: In this work, an efficient Aspect-based Sentiment Classification technique has been introduced with a hybrid, multiple-word embedding methods and implemented using the CNN framework on large databases. Methods: Most of the traditional models have a limitation on the dependency for one or more similar types of aspect words for sentiment classification problem. However, these conventional models such as TF-ID, Word 2Vec and Glove method consumes much more time for word embedding process and Aspect terms generation and further process of aspect level sentiment classification. Further, these models are facing problems of high true negative rate and misclassification rate on large aspect databases in sentiment classification. In this article, we have introduced an efficient Proposed ensemble word embedding model in the CNN network and defined Hybrid Word2 Vec method, Hybrid Glove word embedding method and Hybrid Random Forest model for sentiment classification. Results: Experiments on a widely used benchmark prove that the proposed word embedding method- based classification technique results in to higher true positive rate with minimal misclassifications and also supports better runtime and accuracy than the traditional word embedding-based aspect level classification approaches. Conclusion: In this article, a hybrid ensemble feature ranking-based classification model is proposed on the large aspect databases. In this work, advanced multiple-word embedding methods are implemented to improve the essential feature extraction problem in the aspect level sentiment process. These multiple-word embedding methods are applied to the sentiment databases in the CNN framework.

Rethinking the Standard-Setting Process: The Role of Intangibles

ABSTRACT This think piece looks at the traditional reporting for intangibles and concludes that the measuring and reporting of intangibles needs a structural rethinking to incorporate 21st-century technology involving new forward-looking information and methods to provide information that is consistent with current capabilities (e.g., apps, bots, multiple databases, artificial intelligence). Traditional measurement methods, first published by Fra Luca Pacioli, satisfied business needs for centuries, but they limit modern external stakeholders’ ability to evaluate and compare firms’ current performance or predict their future performance. The traditional concepts of articulation, consolidation, and valuation of intangibles are inadequate, and sometimes blatantly misleading. Further, empirical research has ignored the emergence of new circumstances in business operations and accounting technology. The goal of this think piece is to discuss the traditional backward-looking approach to financial reporting for intangibles and to outline some considerations for developing a new system.

Quantum Dot-based Bio-conjugates as an Emerging Bioimaging Tool for Cancer Theranostic- A Review.

Cancer is the most widely studied disorder in humans, but proper treatment has not yet been developed for it. Conventional therapies, like chemotherapy, radiation therapy, and surgery, have been employed. Such therapies target not only cancerous cells but also harm normal cells. Conventional therapy does not result in specific targeting and hence leads to severe side effects. The main objective of this study is to explore the QDs. QDs are used as nanocarriers for diagnosis and treatment at the same time. They are based on the principle of theranostic approach. QDs can be conjugated with antibodies via various methods that result in targeted therapy. This results in their dual function as a diagnostic and therapeutic tool. Nanotechnology involving such nanocarriers can increase the specificity and reduce the side effects, leaving the normal cells unaffected. This review pays attention to different methods for synthesising QDs. QDs can be obtained using either organic method and synthetic methods. It was found that QDs synthesised naturally are more feasible than the synthetic process. Top or bottom-up approaches have also emerged for the synthesis of QDs. QDs can be conjugated with an antibody via non-covalent and covalent binding. Covalent binding is much more feasible than any other method. Zero-length coupling plays an important role as EDC (1-Ethyl-3-Ethyl dimethylaminopropyl)carbodiimide is a strong crosslinker and is widely used for conjugating molecules. Antibodies work as surface ligands that lead to antigen- antibody interaction, resulting in site-specific targeting and leaving behind the normal cells unaffected. Cellular uptake of the molecule is done by either passive targeting or active targeting. QDs are tiny nanocrystals that are inorganic in nature and vary in size and range. Based on different sizes, they emit light of specific wavelengths. They have their own luminescent and optical properties that lead to the monitoring, imaging, and transport of the therapeutic moiety to a variety of targets in the body. The surface of the QDs is modified to boost their functioning. They act as a tool for diagnosis, imaging, and delivery of therapeutic moieties. For improved therapeutic effects, nanotechnology leads the cellular uptake of nanoparticles via passive targeting or active targeting. It is a crucial platform that not only leads to imaging and diagnosis but also helps to deliver therapeutic moieties to specific sites. Therefore, this review concludes that there are numerous drawbacks to the current cancer treatment options, which ultimately result in treatment failure. Therefore, nanotechnology that involves such a nanocarrier will serve as a tool for overcoming all limitations of the traditional therapeutic approach. This approach helps in reducing the dose of anticancer agents for effective treatment and hence improving the therapeutic index. QDs can not only diagnose a disease but also deliver drugs to the cancerous site.

The Southeast Florida Surface Urban Heat Island and Hispanic Ethnic Exposure Inequalities

abstract: Urban heat islands (UHI) present serious health risks as urban growth continues and thermal conditions warm. However, UHI studies on urban agglomerations are comparatively fewer while presenting notable challenges not encountered when examining individual cities. This analysis exploring the Southeast Florida urban agglomeration surface UHI (SUHI) quantified SUHI intensity (SUHII) using five measurement approaches. Multiple regression illuminated the influence of demographic and socioeconomic variables on respective SUHII indicators. Since prior urban heat analyses considered His-panics broadly and Southeast Florida features a diverse Hispanic population exceeding two million, a Hispanic ethnicity analysis was performed granularly examining exposure differences. Results demonstrated positive statistical correlation yet noteworthy numerical disparities between SUHII indicators, while demographic and socioeconomic variables explained 40-73 percent of observed variation. Collectively, Hispanics were disproportionately exposed to higher intensity with Cubans, Hondurans, Nicaraguans, and Other Central Americans exceedingly at risk. Results underscore a need to advance beyond traditional SUHII measurement approaches when analyzing urban agglomerations and emphasize the importance of distinguishing ethnicities when conducting urban-environmental studies in places with diverse Hispanic populations.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

The Role of Mindfulness in the Relationship Between Social Interest and Psychological Health

ABSTRACT: Social interest is the cornerstone of psychological health. Recently, the psychological construct of mindfulness, which is closely linked to psychological health, has received a great deal of attention in counseling research and practice. Mindfulness plays a prominent role in traditional and modern Buddhist approaches that emphasize social justice and equality, as does Adlerian theory. In this regard, this study explores the mediating role of mindfulness in the relationship between social interest and psychological health. The sample consisted of 261 Turkish university students. The participants were administered the Demographic Information Form as well as three other instruments. Overall, results indicate that mindfulness positively mediates the relationship between social interest and psychological health.

Review on Vertical Farming in Agricultural Area

Vertical farming has the capability to produce the food or any other services in urban areas which are related to it. The goals and vision of vertical farming is planned with the aim of generating sustainable cities around the world. The recent traditional farming approaches due to an imbalance in the environment. At the end it appears that the concept of the vertical farm in the city centre of urban area could solve various issues that is related to food production and environmental degradation. No crop harvests would fail by severe weather like droughts, floods and hurricanes etc. Hence the vertical farming aims at the making of the sustainable city environment that inspires the people to live there for the safe and the healthy environment that encourages the people to live there for the safe and healthy environment, cleaner air, safe drinking water, safe usage of public liquid waste, new employment chances, vertical farming has the benefit of a seasonally wet and warm weather. They can easily minimize cooling and heating water. Use of indoor temperature and artificial light and also have a plentiful amount of natural resources such as long hours of sunlight and enough water from daily rain to cultivate.

The spiritual essence of Chinese seascape art

The article "The Spiritual Essence of Chinese Seascape Art" explores the evolution and specifics of seascape in Chinese painting, in the context of the spiritual and cultural life of China. The genre of seascape in Chinese art began to form relatively late, at the end of the 20th century, and became significant for modern landscape painting. The article pays special attention to the rarity of the depiction of the sea and ocean in traditional Chinese painting, due to philosophical and aesthetic grounds. The changes in the perception of the sea element are analyzed, which reflect the fundamental transformations in the thinking of Chinese society. The research covers the historical period from the end of the XX century to the beginning of the XXI century, revealing the contribution of individual authors and the influence of Western art on the development of the genre. The presented analysis demonstrates how, through the study of marine painting, a unique combination of traditional Chinese and Western aesthetics is revealed, forming a new artistic language and approach to the perception of nature and the sea element. The article uses an integrated approach, including historical and comparative analysis, stylistic interpretation, to explore the evolution of the seascape in Chinese painting and its spiritual and philosophical and aesthetic content. The article explores the spiritual essence of Chinese seascape art, describing its development as a late phenomenon in comparison with Western art and as an important element in modern Chinese painting. Special attention is paid to rethinking traditional artistic methods and approaches to depicting the sea, considering it as evidence of fundamental changes in the spiritual life of Chinese society. The article discusses how the seascape reflects philosophical and aesthetic changes, especially in the last third of the 20th and early 21st centuries. The authors analyze the works of Chinese artists seeking to find unique artistic languages by integrating traditional and Western elements. The novelty of the research lies in a deep analysis of the impact of socio-cultural changes on the formation and development of the seascape in Chinese painting, which allows for a better understanding of the uniqueness and dynamics of this genre in the context of global artistic and cultural trends.

Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions

Deep neural networks have become the benchmark in diverse fields such as energy consumption forecasting, speech recognition, and anomaly detection, owing to their ability to efficiently process and analyse data. However, they face challenges in managing the complexity and variability in time series data, often leading to increased model complexity and prolonged search duration during parameter tuning. This paper proposes a novel anomaly detection approach through evolutionary neural architecture search (AD-ENAS), which is specifically designed for time series data. The proposed approach focuses on the search for the optimal and minimal neural network architecture. The AD-ENAS method consists of two main phases: architecture evolution and weight adjustment. The architecture evolution phase highlights the importance of neural network architecture by evaluating the fitness of each network agent using shared weight values. Subsequently, the convolutional matrix adaptation technique is used in the next phase for optimal weight adjustment of the neural network. The proposed AD-ENAS method operates without relying on differentiable functions, thus expanding the scope of neural network design beyond traditional backpropagation-based approaches. Various non-differentiable loss functions are explored to facilitate effective architecture search and weight adjustment. Comparative experiments are conducted with five baseline anomaly detection methods on three well-known datasets from reputable sources such as NASA SMAP, NASA MSL and Yahoo S5-A1. The results demonstrate that the AD-ENAS approach effectively evolves neural network architectures, outperforming baseline methods with F1 scores across the three datasets (MSL: 0.942, SMAP: 0.961, Yahoo S5-A1: 0.988) with non-differentiable loss functions, showcasing its efficacy in detecting anomalies in time series data.

Magnetorheological Damper Force Prediction using Particle Swarm Optimization and Long Short-Term Memory Model

Smart materials, like magnetorheological (MR) fluid, are gaining attention for their ability to rapidly change properties under magnetic influence, making them useful in vibration control systems for vehicles, medical devices, and civil engineering structures. Common parametric models, such as Bouc-Wen and Bingham, are traditionally employed to model MR damper dynamics behavior. However, the manual tuning of numerous parameters in these models increases complexity and hinders the identification of inverse models, potentially leading to unpredictable optimum target forces. In response to these challenges, this study suggested a non-parametric approach using Long Short-Term Memory (LSTM) models to predict the optimum target force of MR dampers. Unlike parametric models, LSTM models capture dynamic behavior without the need for extensive manual tuning. To optimize the LSTM model, Particle Swarm Optimization (PSO) is employed to fine-tune hyperparameter values, ensuring robust performance. The proposed non-parametric method, specifically the PSO-LSTM model, demonstrates faster processing times compared to traditional parametric approaches. The proposed model produced an accurate damping force prediction with a root mean square error of less than 5%, This novel approach simplifies the modeling process and offers an efficient and precise alternative to traditional parametric methods.

Abstract IA014: Personalized adjuvant treatment in endometrial cancer

Abstract The majority of endometrial cancer patients are cured by surgical removal of the uterus. Adjuvant treatment is recommended for a subset of patients with tumor characteristics associated with increased risk of recurrence. Historically pathologists have played a central role in defining histopathological factors that together make-up a risk profile which directs the type of adjuvant treatment. This traditional approach suffers from interobserver variability and lacks a biological basis. With the advance of molecular pathology, we have been able to define molecularly distinct endometrial cancer subsets which also carry distinct prognostic information and may form a basis for targeted adjuvant treatment. The RAINBO program exploits this concept by running 4 molecular subclass specific clinical trials. During my talk I will go over the past, the present and the future of adjuvant treatment in endometrial cancer with an emphasis on the role of pathology. Citation Format: Tjalling Bosse. Personalized adjuvant treatment in endometrial cancer [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr IA014.

Research on digital assembly method of large-caliber optomechanical systems

Developing optomechanical systems with large-aperture output beams is increasingly important for deep space exploration, homeland defense, and modern warfare. However, the assembly process for such systems is becoming more challenging due to their complexity and size. Considering that traditional computer-aided assembly methods for optomechanical systems primarily prioritize debugging and testing, often overlooking the importance of feasibility analysis and verification in the assembly process, this paper presents a digital assembly method for large aperture optomechanical systems that integrates virtual assembly technology with misalignment error detection technology. By combining these two technologies, the proposed method aims to enhance the assembly process by providing a more comprehensive approach that includes feasibility analysis and verification, in addition to debugging and testing. Firstly, virtual assembly technology is utilized to simulate and validate the assembly process design of optomechanical systems. Secondly, a second-order sensitivity matrix method is proposed to achieve higher accuracy in solving misalignment compared to traditional methods. The new method is validated through simulation experiments on an off-axis beam expander system, showing superior solving accuracy compared to traditional approaches. Lastly, an actual assembly and adjustment platform is constructed, and the obtained assembly result exhibits a wavefront aberration RMS value better than 0.016λ (λ=632.8nm).