Sophisticated Evaluation Research Articles

Embedded Bioprinting of Breast Cancer-Adipose Composite Tissue Model for Patient-Specific Paracrine Interaction Analysis.

The interaction between breast cancer and stromal tissues varies significantly from patient to patient, greatly impacting cancer prognosis. However, conventional models struggle to accurately replicate these patient-specific interactions. Herein, a novel breast cancer-adipose composite tissue model capable of precisely adjusting the inter-tissue interaction is developed. The composite tissue model is produced through precise embedded bioprinting of breast-cancer spheroids and live-adipose-tissue ink. This model possessed not only precisely patterned cancer spheroids but also well-preserved intrinsic extracellular matrices (ECMs) and heterogeneous cell populations of adipose tissue (AT). Evaluation results successfully demonstrated that the bioprinted composite model can precisely regulate adipokine secretion, drug resistance, and cancer-cell invasion characteristics by adjusting the distance between the cancer spheroids and adipose tissue. The utility of the model is validated using adipokine-targeted therapies (C-compound/SC600125 (SC), AG 490 (AG), and Metformin (MET)). Interestingly, the inhibition of cancer cell proliferation and invasion by these adipokine-targeted drugs nearly doubled as the tissue distance decreased. This suggests that the efficacy of the drugs can be precisely evaluated using the new model. These findings underscore the potential of the developed composite model to replicate patient-specific crosstalk, thereby offering a promising platform for the sophisticated evaluation of various breast-cancer therapies.

Understanding Generative AI: From Basic Principles to Real-World Applications

This comprehensive article examines the foundational principles, technical implementations, and societal implications of generative artificial intelligence (GenAI), a transformative technology that has revolutionized the creation of synthetic content across multiple domains. The article explores the architectural frameworks underpinning GenAI, focusing on Generative Adversarial Networks (GANs) and Transformer-based models, while detailing the sophisticated training methodologies and evaluation metrics that enable their functionality. Through an in-depth analysis of real-world applications in healthcare, creative industries, and software development, this article illuminates the technology's potential to enhance human capabilities and drive innovation. The investigation extends to critical ethical considerations, addressing security concerns surrounding deepfakes, challenges in bias mitigation, and complex intellectual property issues. Furthermore, the article presents a forward-looking perspective on research opportunities, policy implications, and industry best practices, emphasizing the importance of responsible development and deployment of generative AI systems. This article contributes to the growing body of knowledge on GenAI by providing a holistic understanding of its current state, challenges, and future directions, while highlighting the crucial balance between technological advancement and ethical considerations in shaping its evolution.

Cura-LLaMA: Evaluating open-source large language models question answering capability on medical domain

This paper presents the development and evaluation of Cura-LLaMA, an open-source large language model (LLM) tailored for the medical domain. Based on the TinyLlama model, Cura-LLaMA was fine-tuned using the PubMedQA dataset to enhance its ability to address complex medical queries. The models performance was compared with the original TinyLlama, focusing on its accuracy and relevance in medical question-answering tasks. Despite improvements, the study highlights challenges in using keyword detection methods for evaluation and the limitations of omitting non-essential columns during fine-tuning. The findings underscore the potential of fine-tuning open-source models for specialized applications, particularly in resource-limited settings, while pointing to the need for more sophisticated evaluation metrics and comprehensive datasets to further enhance accuracy and

Using polarization sensitive SMLM to infer the interaction strength of dye-plasmonic nanosphere systems

Single-molecule microscopy is an effective tool for the characterization of fluorophore–plasmonic structure interaction. However, sophisticated evaluation is required as specific information is hidden in the emission. Here, we theoretically and experimentally investigated the emission polarization of rotationally mobile fluorophores near plasmonic Au–Ag alloy nanospheres. We developed an elaborate calculation based on the Mie theory, which considers the rotational mobility of dyes near spherical plasmonic nanoparticles of any size. Furthermore, we have created a simplified model that describes the emission’s degree of polarization within 10% accuracy in the case of small nanoparticles, when dipole approximation is valid. Our results indicate the close relation of the polarization degree of the emission, which can reach up to be ∼0.8 in the resonant case, to fluorescence scattering. DNA-PAINT single-molecule localization microscopy experiments conducted using AF488 and Atto647N dyes revealed that the measured polarization degree distribution followed the tendency predicted by calculations.

“Reference sample comparison method”: A new voltammetric electronic tongue method and its application in assessing the shelf life of fresh milk

Shelf life is a critical comprehensive indicator of food quality. Voltammetric electronic tongue (V-Et), is well-suited for assessing food shelf life, due to its capable of capturing food overall fingerprints. This study designed a “reference sample comparison method” for V-Et to assess the shelf life of fresh milk. Quality differences between milk samples of different shelf lives and reference samples were quantified by differential degree (Dd) values. A new “one-to-one” model of milk shelf life was established based on Dd values, and significantly improved predictive accuracy by 11.14 %–17.17 % and 14.86 %–44.47 % in overall quality shelf life assessment compared to “many-to-one” models based on SVM and DFA. Even in the more sophisticated evaluation of microbial safety and sensory quality shelf life, it attained relative errors of 13.57 % and 7.68 %, respectively. All these findings showed the significant potential of the “reference sample comparison method” in assessing food shelf life with V-Et.

A novel profit-driven framework for model evaluation in credit scoring

Credit scoring is a prominent problem in machine learning (ML). ML classifiers used to assess creditworthiness need to be evaluated using appropriate metrics. Common metrics fall into two categories: cost-sensitive and traditional cost-insensitive. Traditional cost-insensitive metrics like accuracy and area under the curve (AUC) have several drawbacks. They can be difficult for managers to understand, potentially misleading in certain scenarios, and often overlook the business realities of credit scoring. Conversely, existing cost-sensitive metrics are imperfect, leading many studies to rely on classic AUC metrics. In this study, we introduce the expected profit ratio (EPR), a novel framework for evaluating classifiers in credit scoring. EPR does not require a base scenario and accurately calculates the profit. Its flexibility makes it suitable for all types of credit-scoring problems, and its simplicity aids in comprehension and explanation. By enabling accurate profit calculation, EPR helps managers, executives, and administrators make informed decisions and investments. This metric lays the foundation for more sophisticated and precise model evaluation and comparison in credit scoring. Additionally, it can be used as a performance measure, alongside fairness and other criteria needed to achieve trustworthy artificial intelligence (AI).

Seven Years SHM of Offshore Wind Turbine Foundations: Review, Experiences and Outlook

Wölfel Engineering is a leading expert on the design and provision of SHM systems for offshore wind turbines, as well as the assessment of their structural integrity based on the implementation of sophisticated data evaluation algorithms. This contribution aims to provide an insight into the SHM results of offshore wind turbine foundations in numerous wind farms over the last seven years, focusing on presenting an overview of the evolution of SHM concepts, monitoring goals and features monitored for structural assessment – alternatively referred to as key performance indicators (KPIs). Over the years, design approaches have become more accurate, alongside with the continuous increase of the size of the structures. Examples from offshore wind farms are used in order to show the effect of these evolutions to KPIs, such as the natural frequencies and the fatigue life consumption. The challenges met in industrial SHM applications for offshore foundations are discussed. A novel approach to SHM, the agile SHM concept, is Wölfel’s answer to the future requirements on monitoring accuracy and reliability. The proposed concept enables the efficient exchange of information between the results of the SHM system and the design process, so that the design model is at all times an accurate representation of the real structures.

Towards gender equality: women’s leadership in higher education

This article aims to provide a systematic review of the scientific literature on women’s leadership in higher education. This study was conducted in April 2024. It was based on scientific articles, selected from the Academia.edu portal. The study employed a non-experimental research design. The representation of women in management and leadership positions in the higher education sector has increased significantly, yet this growth has been limited to academic roles with the requisite authority to teach. Management positions with greater responsibility have not seen the same level of advancement. The prevailing masculine culture that pervades higher education presents an impediment to women's advancement to senior management roles. This is attributable to a confluence of interrelated factors, including structural constraints, competitive job demands, demanding work schedules, and the necessity to balance work and family life. The situation is further complicated by the introduction of accountability measures and sophisticated evaluation instruments, designed to assess and accredit work processes, hyper-productivity, and continuous evaluation. Despite the growing focus on gender issues, the prevalence and intensity of negative gender stereotypes about women appear to be on the rise. The negative dynamics, observed in sociological surveys, can be explained by examining the current state of gender ideology, education, and public awareness within society. The observed trends in economic activity, employment status, and salaries of men and women do not appear to be driving significant changes in gender stereotypes or the socioeconomic status of women. In light of these considerations, it becomes imperative to examine the necessity of implementing a feminist approach to appointing managers and leaders within higher education institutions. It is important to acknowledge that the roles, duties, and responsibilities of leaders within higher education institutions may differ considerably. It can be reasonably argued that the styles of leadership, employed by male and female higher education managers and leaders, exert a profound influence over the nature of organizational cultures. It is of the utmost importance that each higher education institution conducts an assessment of its own to ascertain its current situation and the manner, in which its managers and leaders contribute to the overall organizational success. It is recommended that institutions of higher education implement policies that consider gender aspects, with the aim of enhancing the utilization of women’s potential for leadership. It is imperative to implement a gender-based assessment mechanism to ensure equitable competition between women and men in the selection process for managerial and leadership roles

Bayesian uncertainty evaluation applied to the tilted-wave interferometer

The tilted-wave interferometer is a promising technique for the development of a reference measurement system for the highly accurate form measurement of aspheres and freeform surfaces. The technique combines interferometric measurements, acquired with a special setup, and sophisticated mathematical evaluation procedures. To determine the form of the surface under test, a computational model is required that closely mimics the measurement process of the physical measurement instruments. The parameters of the computational model, comprising the surface under test sought, are then tuned by solving an inverse problem. Due to this embedded structure of the real experiment and computational model and the overall complexity, a thorough uncertainty evaluation is challenging. In this work, a Bayesian approach is proposed to tackle the inverse problem, based on a statistical model derived from the computational model of the tilted-wave interferometer. Such a procedure naturally allows for uncertainty quantification to be made. We present an approximate inference scheme to efficiently sample quantities of the posterior using Monte Carlo sampling involving the statistical model. In particular, the methodology derived is applied to the tilted-wave interferometer to obtain an estimate and corresponding uncertainty of the pixel-by-pixel form of the surface under test for two typical surfaces taking into account a number of key influencing factors. A statistical analysis using experimental design is employed to identify main influencing factors and a subsequent analysis confirms the efficacy of the method derived.

Novel Hydrogen and Biomethanol Production From Pinecone Biomass Using an Integrated Steam Gasification System

This study develops a new biomass-based integrated energy system where a steam gasification process is considered for hydrogen and biomethanol production in a viable manner using woody bioresources from pinecones. The modeling and simulation studies are performed using both The Aspen Plus and Matlab software packages in a combined manner to practically investigate the integrated system's operating and management conditions and potential performance for a strategically identified location in British Columbia where bioresources are widely available, and there is a strong need to create a value out of these. The emphasis is on three main stages: gasification, syngas post-treatment, and methanol production. This comprehensive approach ensures an in-depth understanding of the production process, from resource extraction to end-product progress. A key aspect of the study is estimating essential flow rates, notably CO2, H2, and CH4O, in various operational conditions. The analysis goes beyond product yields, providing a thorough evaluation of mass flow rates for all streams in the system. This enables a more sophisticated evaluation of performance under various operating situations and component varieties. The integrated system's performance is evaluated using rigorous thermodynamic studies. This analytical technique provides details on the efficiencies and environmental impacts of the proposed system. The simulation findings show that the existing system can produce 0.17 kg/s methanol and 0.025 kg/s hydrogen. These findings establish a foundation for further improvement and scalability of the integrated system for practical applications. The study not only helps advance the field of renewable energy era but also provides a practical and sustainable solution to the pressing issue of pine beetle infestation, demonstrating the possibility of combining technology and ecology to benefit the environment.

A COMPREHENSIVE REVIEW OF LITERATURE ON MARMA THERAPY ANDAGNIKARMA THERAPY

This abstract explores the research on Marma Therapy and Agnikarma Therapy, two traditional Ayurvedic healing modalities, in great detail. Marma Therapy emphasizes harmony in the body, mind, and spirit through the manipulation of essential energy points for overall health. Agnikarma Therapy uses heat applications to treat pain and musculoskeletal problems. The review delves into their concepts, historical underpinnings, effectiveness as treatments, and incorporation into contemporary healthcare. Though they have Ayurvedic roots, Marma and Agnikarma offer unique perspectives on focused pain management and holistic wellness, respectively. Comprehending their distinct characteristics facilitates a sophisticated evaluation of these treatments within the framework of complementary and alternative medicine.

Assessing the quality of experience in wireless networks for multimedia applications: A comprehensive analysis utilizing deep learning-based techniques

In the context of the burgeoning progression of wireless network technology and the corresponding escalation in the demand for mobile Internet-based multimedia transmission services, the task of preserving and augmenting user satisfaction has emerged as an imperative concern. This necessitates a sophisticated and accurate evaluation of multimedia service quality within the sphere of wireless networks. To systematically address the nuanced issue of user experience quality, the present study introduces a novel method for evaluating multimedia Quality of Experience (QoE) in wireless networks, employing an advanced deep learning model as the underlying analytical framework. Initially, the research undertakes the task of modeling the video session process, giving due consideration to the status of each temporal interval within the session's architecture. Subsequently, the challenge of QoE prediction is dissected and investigated through the lens of recurrent neural networks (RNNs), culminating in the proposition of an all-encompassing QoE prediction model that harmoniously integrates video information, Quality of Service (QoS) data, user behavior analytics, and facial expression analysis. The empirical segment of this research serves to validate the efficacy of the suggested video QoE evaluation method, engaging both quantitative and qualitative comparison metrics with contemporaneous state-of-the-art QoE models, employing the RTVCQoE dataset as the empirical foundation. The experimental findings illuminate that the QoE model elucidated in this study transcends competing models in performance metrics such as PLCC, SRCC, and KRCC. Consequently, this investigation stands as a seminal contribution to academic literature, furnishing an exacting and dependable QoE evaluation methodology. Such a contribution augments the user experience landscape in multimedia services within wireless networks, and instigates further scholarly exploration and technological innovation in the mobile Internet domain.

Dimensionless quantities in discrete element method: powder model parameterization for additive manufacturing

Powder bed-based additive manufacturing processes offer an extended freedom in design and enable the processing of metals, ceramics, and polymers with a high level of relative density. The latter is a prevalent measure of process and component quality, which depends on various input variables. A key point in this context is the condition of powder beds. To enhance comprehension of their particle-level formation and facilitate process optimization, simulations based on the Discrete Element Method are increasingly employed in research. To generate qualitatively as well as quantitatively reliable simulation results, an adaptation of the contact model parameterization is necessary. However, current adaptation methods often require the implementation of models that significantly increase computational effort, therefore limiting their applicability. To counteract this obstacle, a sophisticated formula-based adaptation and evaluation method is presented in this research. Additionally, the developed method enables accelerated parameter determination with limited experimental effort. Thus, it represents an integrative component, which supports further research efforts based on the Discrete Element Method by significantly reducing the parameterization effort. The universal nature of deducting this method also allows its adaptation to similar parameterization problems and its implementation in other fields of research.

Grid neighbourhood based three way clustering (3WC)

The key idea behind three-way clustering (3WC) is to group items into three regions to reveal patterns that might not be apparent using two-way clustering. Three-way clustering, contrasting with two-way, considers data across three regions instead of two regions. The three regions are referred to as inside, outside, and partial regions, contrary to the two-way clustering that uses inside and outside regions. The objects in the inside region are members of a cluster, whereas those in the outside region are not, and partial objects may be members of a cluster. Three-way clustering encounters two challenges: 1) Investigation of sophisticated evaluation functions. 2) Finding an optimized pair of thresholds used to identify three regions for a cluster. In the context of Three-Way Clustering (3WC), applying conditional probability can be beneficial for assessing the likelihood of patterns or relationships within each region. In this study, the first challenge is addressed by employing a grid-based approach for a sophisticated evaluation function based on conditional probability. Whereas the second challenge is addressed by employing the elbow-based method to determine a pair of thresholds within a grid-based histogram. Further, two approaches namely the OGN3 and RGN3 are introduced to discover clear and crisp clusters. Experimental results on UCI and Kaggle datasets indicate improvements for commonly used F1 measures for OGN3 and RGN3 up to 4.97% and 12.88%, respectively

Fusion of Centrality Measures with D-OWA in Neutrosophic Cognitive Maps to Develop a Composite Centrality Indicator

This study utilized Neutrosophic Cognitive Maps (NCMs) integrated with the D-OWA operator to analyze the nutritional rights of pregnant women in Ecuador, with a focus on the crucial role of nutrition education. The innovative application of the D-OWA operator enabled the computation of a composite centrality measure by merging key centrality indicators—degree, closeness, and betweenness—each appropriately weighted according to its relevance to the analysis. This methodology provided a sophisticated evaluation of the factors impacting maternal nutrition, demonstrating how combining various centrality measures offers a deeper and more comprehensive insight into the dynamics of complex systems. The calculated composite centrality measures revealed the system’s intricate structure, pinpointing critical nodes and pathways that could be targeted most effectively through interventions. The findings underscore the significant benefits of using composite centrality measures to enhance decision-making in public health and other sectors characterized by complexity and uncertainty. The potential for refining and expanding this approach in future research suggests that it could be further supported by technological advancements, enabling more efficient analysis and scalability across diverse complex systems.

부동산 임대료의 현금 흐름에 대한 리스크 분석

Investment decisions in real estate development projects need to be made more flexibly in uncertain environmental changes such as legal and economic conditions in competitive business in the rapidly changing real estate market. In analyzing the economic feasibility of real estate development projects, the valuation method of the investment plans using the traditional NPV method is evaluated based on a fixed standard planned in advance. Therefore, it was not possible to flexibly cope with uncertain changes in the business environment. In the face of growing uncertainty in the real estate development market, a more sophisticated evaluation method that can evaluate investment plans is needed according to changes in the market. Accordingly, the valuation of real estate development projects was analyzed using real options approach. In this study, an analytical framework was presented to evaluate the return value of commercial rental properties from a financial engineering perspective and to analyze the risk of rent cash flows. Real estate rate assessment methods, risk analysis methods, rent derivatives valuation methods, and real estate option valuation methods were analyzed for uncertainty in rent, tenant residence, vacancy, and real estate price using Monte Carlo simulation. In terms of financial engineering probability models, research in the field of valuation and risk analysis of real estate is now in its infancy, and the framework for analysis itself is not sufficiently established. Based on these studies, various analysis methods will be developed, and in this sense, this study can play a fundamental role.

AN ANALYTICAL DISCUSSION ON METHODOLOGICAL RELEVANCE OF DEVELOPMENT ASSISTANCE COMMITTEE’S EVALUATION CRITERIA

Monitoring and evaluation are modern approaches used to study project logic and these help in solving project problems. Taking a few muscles and bones from social research these studies are growing in the fields. Monitoring and evaluation studies take key methods and tools from social research, particularly operational research. Therefore, these are deemed identical to social research but monitoring and evaluation are not social research. These are deserted children of social research who are growing independently in the fields based on needs. This discussion paper doesn’t explore the history of evaluation but explains the conceptual trajectory of evaluation starting from its mandate to the practice. Development or humanitarian sector is not running based on the theory of demand and supply. The development sector is based on the logic of need and fulfillment. Therefore, evaluation in the development sector is also grounded in need. While explaining the conceptual roots and practices of evaluation in the development sector. Commonly used OECD/DAC evaluation criteria are taken for discussion. Hence the evaluation is an abductive approach of study that focuses on project logic and helps in solving project problems or measuring project results. Therefore, an abductive approach to examination is used in this paper. Coming with the deductive approach, the review starts with theory and ends at practice, and following the inductive approach review starts with practice to help generalizing concepts. Both approaches have limitations when applied to examine evaluation practices and mandate. Because the practices and mandate are already existing in the vacuum of evaluation. Therefore, this specific analysis is an attempt to cognize the linkages between existing evaluation theory and practice. Vivid cognition in building trajectory between evaluation ideology and practice. Leads to recognize the scope of evaluation that ultimately contributes to sophisticated evaluation practices. The journey of this analysis embarks with fundamental questions, why the OECD/DAC criteria are being used for evaluations in the development sector, and considered useful? What are the logical links among evaluation mandate, evaluation approach, evaluation frame “OECD/DAC criteria”, and evaluation methods?<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0081/a.php" alt="Hit counter" /></p>

Implementing a Hybrid Model using K-Means Clustering and Artificial Neural Networks for Risk Prediction in Life Insurance

Accurate assessment of policy holder risk is critical for life insurance companies to properly price policies and manage long-term liabilities. However, the complexity of risk factors makes reliance solely on traditional actuarial models inadequate, especially with the proliferation of big data and unstandardized data from diverse sources. This study investigated the development and performance of a hybrid machine learning model combining artificial neural networks (ANN) and K-means clustering for enhanced risk prediction in life insurance underwriting. The exponential growth of unlabelled data presented challenges for predictive modelling. The proposed hybrid model leveraged the strengths of artificial neural networks in modelling nonlinear relationships and K-means clustering in unsupervised for pattern recognition to handle unstandardized data. Using anonymized life insurance application data from Kaggle, the hybrid model was evaluated against the artificial neural network algorithm alone. The results demonstrated that integrating K-means clustering and artificial neural network together with principal component analysis for pre-processing led to superior model performance, with testing accuracy improving from 90% for artificial neural network to 98% for the hybrid technique. Additional metrics like precision, recall and AUC also showed enhancements. The improved predictive capability highlighted the potential of the hybrid approach in transforming legacy underwriting practices towards a more sophisticated data-driven analytical evaluation of policy holder risk. However, limitations existed including the use of single sourced insurance dataset due to data privacy concerns. Further research on integrating diverse algorithms can help insurers unlock more value and gain a competitive edge through advanced analytical modelling and testing on larger real-world datasets. While challenges remain, this study provided key insights into a promising new technique for modernizing risk prediction in the life insurance industry in the era of big data.

Comprehensive evaluation of the effect of regional groundwater overexploitation restoration

Abstract The traditional groundwater overexploitation restoration only considers the impact on groundwater, on the basis of which, this paper also comprehensively considers the impact on economic society and the ecological environment. Based on analyzing the effect of groundwater overexploitation restoration on the groundwater–economic society–ecological environment (GESEE) system, an evaluation indicator system covering the GESEE system was constructed, an evaluation method for the effect of groundwater overexploitation restoration was established, and the effects were evaluated for Hebei Province, China, from 2014 to 2019. The results showed that the effect of groundwater overexploitation restoration in Hebei Province was obvious, and the grade of the groundwater overexploitation restoration effect improved from fair to good. The grade of the groundwater overexploitation restoration effect in most cities was improved to good. The measures that most affected the effect of groundwater overexploitation restoration in Hebei Province are strict control of extraction and reduction of consumption, while measures such as the replacement of water sources also play a significant role. An evaluation indicator system constructed in this paper can help to promote sophisticated effect evaluation of groundwater overexploitation restoration and improve the understanding and management of groundwater overexploitation restoration.

Keyboard Layout Optimization and Adaptation

Since the keyboard is the most common method for text input on computers today, the design of the keyboard layout is very significant. Despite the fact that the QWERTY keyboard layout was designed more than 100 years ago, it is still the predominant layout in use today. There have been several attempts to design better layouts, both manually and automatically. In this paper we improve on previous works on automatic keyboard layout optimization, by using a deep neural network to assist in a genetic search algorithm, which enables the use of a sophisticated keyboard evaluation function that would otherwise take a prohibitive amount of time. We also show that a better choice of crossover routine greatly improves the genetic search. Finally, in order to test how users with different levels of experience adapt to new keyboard layouts, we conduct some layout adaptation experiments with 300 participants to examine how users adapt to new keyboard layouts.