Unified Framework Research Articles

Enhancing Criminal Analysis through Multi-Model Integration: Addressing Challenges and Ensuring Ethical Implementation

Abstract: Law enforcement agencies face numerous challenges in criminal analysis, from data quality issues to ethical concerns. To address these challenges, this research proposes a novel approach: integrating multiple modelling techniques into a unified framework. By combining regression analysis, decision trees, support vector machines, neural networks, and ensemble methods, this approach aims to provide more robust and accurate solutions. The research seeks to develop a framework for integration, evaluate effectiveness using real-world data, and explore ethical implications. Ultimately, the goal is to advance criminal analysis, promote multi-model integration in law enforcement, and ensure ethical implementation for justice.

Multiple Disease Detection System Using Machine Learning

Abstract: In recent times, there has been a surge of interest in employing machine learning techniques for the early and accurate detection of various diseases. This research introduces a holistic approach to constructing a robust multi-disease detection system that utilizes advanced machine learning algorithms. Our proposed system integrates diverse datasets encompassing a range of medical conditions, enabling the simultaneous detection of multiple diseases within a unified framework. We leverage cutting-edge machine learning models, including, but not limited to, [specify the models used], to analyze and interpret intricate patterns within the data.

Unified Steganography for Concealing and Extracting Secret Messages

Abstract: With the increasing significance of data security in the digital age, steganography has emerged as a crucial technique for concealing information within various forms of media. This paper presents a unified steganographic framework designed to embed and extract secret messages in images, audio, and text files. Utilizing the Least_Significant_Bit (LSB) method, our approach ensures minimal distortion of the host media while maintaining high security and robustness of the hidden messages. The system is implemented in Python, featuring an intuitive graphical user interface (GUI) to facilitate user interaction. Comprehensive experiments and analyses demonstrate the effectiveness of our method in maintaining the imperceptibility and integrity of the steganographic media. This unified solution offers a versatile and efficient tool for secure communication, digital watermarking, and protection of intellectual property. Our findings underline the potential of integrating steganographic techniques across different media formats to enhance data security and privacy.

Depth-Enhancement Network for Monocular 3D object detection

Abstract To accurately obtain 3D information, the correct use of depth data is crucial. Compared with radar-based methods, detecting objects in 3D space in a single image is extremely challenging due to the lack of depth cues. However, monocular 3D object detection provides a more economical solution. Traditional monocular 3D object detection methods often rely on geometric constraints, such as key points, object shape relationships, and 3D to 2D optimization, to address the inherent lack of depth information. However, these methods still make it challenging to extract rich information directly from depth estimation for fusion. To fundamentally enhance the ability of monocular 3D object detection, we propose a monocular 3D object detection network based on depth information enhancement. The network learns object detection and depth estimation tasks simultaneously through a unified framework, integrates depth features as auxiliary information into the detection branch, and then constrains and enhances them to obtain better spatial representation. To this end, we introduce a new cross-modal fusion strategy, which realizes a more reasonable fusion of cross-modal information by exploring redundant, complementary information and their interactions between RGB features and depth features. Extensive experiments on the KITTI dataset show that our method can significantly improve the performance of monocular 3D object detection.

Unified framework for geometric error compensation and shape-adaptive scanning in five-axis metrology systems

In response to the escalating demand for precise shape metrology of complex optical surfaces, this study unveils a unified geometric error compensation and trajectory planning framework tailored for high-accuracy five-axis scanning metrology systems, which remains a notably underexplored field compared to error compensation in machine tools. Founded on a unified geometric model, the proposed framework seamlessly integrates a versatile shape-adaptive trajectory planning strategy, a thorough global error sensitivity analysis approach, and an exhaustive geometric error compensation scheme. Leveraging inverse kinematics, an innovative shape-adaptive scanning trajectory generation strategy is mathematically formulated, thereby facilitating adaptable measurement trajectory generation for diverse surface geometries. Employing forward kinematics, an exhaustive geometric error model is established to extensively address the 53 distinct geometric errors in the metrology system. This proposed error model fundamentally augments conventional geometric error models in machine tool by managing not only the geometric errors from the motion system, but also those from the probe and workpiece. To streamline the error compensation procedure, a novel global error sensitivity analysis approach is introduced, identifying both system-oriented and process-oriented sensitive geometric errors for targeted compensation. Experimental validation using a standard ball, which achieved an exceptional 89.35% reduction in the root mean square of the measurement errors, further confirms the feasibility and effectiveness of the proposed framework. By offering an universal trajectory planning, sensitivity analysis and error compensation trinity for five-axis scanning metrology systems, this study sets the stage for precision advancements and design optimization across diverse configurations of metrology systems.

Does Ultra-Endurance Passion Make Athletes Happy?

Sport psychology research of ultra-endurance (UE) athletes focused predominantly on their psychological characteristics, traits, and behaviors. However, their happiness and passion, as well as a unified framework for UE hobby phenomenon, were not sufficiently investigated. This study aims to: (1) identify the main contributors to happiness and passion of non-professional UE athletes; and (2) explore the possible relationships between types of sport passion, motivation, and athletic identity. During data collection, 116 non-professional UE athletes (mean age 43.66 years, SD = 8.97, 16.4% female) responded to an online questionnaire. Statistical analyses revealed that obsessive UE passion (p < 0.05) and amotivation (p < 0.05) predicted lower levels of happiness. A higher level of obsessive passion was predicted by extrinsic motivation (p < 0.005), amotivation (p < 0.05), and exclusivity identity (p < 0.001); a lower level was predicted by social identity (p < 0.05) and years in sports (p < 0.05). Weekly training hours and age correlated positively with passion strength, while amotivation was strongly negatively related to training volume. These results indicate that happiness of UE athletes depends on the type of sport passion formed and the quality of the underlying motivation: obsessive passion and amotivation seem to be the main enemies of happiness for UE athletes. This novel finding connecting passion, happiness, and motivation contributes to both a better understanding of the psychology of UE athletes and has practical implications for UE athletes, coaches, athletes’ social circles, and sport psychologists. Due to known maladaptive outcomes of obsessive passion, including its negative impact on overall well-being, health, and now also on happiness, its formation in UE athletes needs to be observed and prevented. While the study shows predictors of obsessive passion and high vs. low obsessive passion, future research should investigate how harmonious passion impacts athletes’ happiness, motivation, and identity. Likewise, research among the UE entourage would help to better understand the social impact of UE as a serious hobby and the formation of UE lifestyles. We also suggest our Temporal Framework for Progressive UE Engagement and Passion, which was further developed based on the results of this study, to be used and validated by sport psychologists.

MOST wanted: navigating the MAPK-OIS-SASP-tumor microenvironment axis in primary pediatric low-grade glioma and preclinical models.

Understanding the molecular and cellular mechanisms driving pediatric low-grade glioma (pLGG)-the most prevalent brain tumor in children-is essential for the identification and evaluation of novel effective treatments. This review explores the intricate relationship between the mitogen-activated protein kinase (MAPK) pathway, oncogene-induced senescence (OIS), the senescence-associated secretory phenotype (SASP), and the tumor microenvironment (TME), integrating these elements into a unified framework termed the MAPK/OIS/SASP/TME (MOST) axis. This integrated approach seeks to deepen our understanding of pLGG and improve therapeutic interventions by examining the MOST axis' critical influence on tumor biology and response to treatment. In this review, we assess the axis' capacity to integrate various biological processes, highlighting new targets for pLGG treatment, and the need for characterized in vitro and in vivo preclinical models recapitulating pLGG's complexity to test targets. The review underscores the need for a comprehensive strategy in pLGG research, positioning the MOST axis as a pivotal approach in understanding pLGG. This comprehensive framework will open promising avenues for patient care and guide future research towards inventive treatment options.

A unified dataset for pre-processed climate indicators weighted by gridded economic activity

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count – all proxies of economic activity – before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.

Uncoupling of Damped Linear Potential Multi-degree-of-freedom Structural and Mechanical Systems

Abstract This paper provides the necessary and sufficient conditions for a multi-degree-of-freedom linear potential system with an arbitrary damping matrix to be uncoupled into independent subsystems of at most two degrees-of-freedom using a real orthogonal transformation. The incorporation of additional information about the matrices, which many structural and mechanical systems commonly possess, shows a reduction in the number of these conditions to three. Several new results are obtained and are illustrated through examples. A useful general form for the damping matrix is developed that guarantees uncoupling of such systems when they satisfy just two conditions. The results provided herein lead to new physical insights into the dynamical behavior of potential systems with general damping matrices and to robust computational procedures. It is shown that the dynamics of a damped potential system in which the damping matrix may be arbitrary is identical to that of a damped gyroscopic potential system with a symmetric damping matrix. This brings, for the first time, these two systems, which are seen today as belonging to different categories of dynamical systems, under a unified framework.

Curiosity is more than novelty seeking.

The novelty-seeking model (NSM) does not offer a compelling unifying framework for understanding creativity and curiosity. It fails to explain important manifestations and features of curiosity. Moreover, the arguments offered to support a curiosity-creativity link - a shared association with a common core process and various superficial associations between them - are neither convincing nor do they yield useful predictions.

University lecturers’ lived experiences of teaching critical thinking in Australian university: a hermeneutic phenomenological research

AbstractThe study explores insights into the phenomenon of Australian lecturers’ lived experiences of teaching standalone critical thinking units within associate degree courses at one university in Victoria, Melbourne, Australia. The study makes an original contribution by focusing upon the experiences of teaching staff in Australian universities in relation to teaching critical thinking, particularly from a Heideggerian hermeneutic phenomenological and Gadamerian hermeneutic theoretical and conceptual framework. At present, there are no unified methods, frameworks, or models of teaching critical thinking in Australian higher education. This problem for lecturers is an important aspect of a university education that is not well understood. This is a global educational issue and is a matter of teaching and learning concern worldwide in tertiary education (e.g. United States of America, New Zealand, Canada, and the UK). Although, several studies have been conducted on teaching critical thinking from the perspective of university lecturers. There is limited research that focus on teaching staff in Australian universities’ experience with teaching critical thinking that has used Heidegger’s hermeneutic phenomenology and Gadamer’s hermeneutic circle, interpretive approach in gathering data. Using interviews, data is conducted with three first-year undergraduate Australian university Ph.D. lecturers. During the analysis of the empirical data, three themes were significant in revealing the key findings: (a) Dwelling; (b) Sorge, and (c) Concern. The comprehensive understanding of the results was that the challenges university lecturers faced in developing students to thinking critically provided new pedagogical curriculum insights for the teaching and learning of a standalone critical thinking unit within the associate degree course.

A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China

Land use and land cover change (LUCC) prediction of cities in Western China requires higher accuracy in quantitative demand and spatial layout because of complex challenges in balancing relationships between urban constructions and ecological developments. Considering city-level areas and various types of land use and land cover, existing LUCC models without constraint or with only loose demand constraints were impractical in providing evidence of high accuracy and high-resolution predictions in areas facing fierce land competition. In this study, we proposed a two-layer SD-ANN-CA model to simulate and explore the LUCC trend and layout predictions for 2018, 2028, and 2038 in Ya’an City, Western China. The two-layer structure with an upper layer of the SD model and a lower layer of the ANN-CA model, as well as the advantages of all three methods of system dynamics (SD), artificial neural network (ANN), and cellular automata (CA), have allowed us to consider the macro-level demand constraints, meso-level driving factors constraints, and the micro-level spatial constraints into a unified model framework. The simulation results of the year 2018 have shown significant improvement in the accuracy of the ANN-CA model constructed in our earlier work, especially in types of forest land (error-accuracy: 0.08%), grassland (error-accuracy: 0.23%), and construction land (error-accuracy: 0.18%). The layout predictions of all six types of land use in 2028 and 2038 are then carried out to provide visual evidence support, which may improve the efficiency of planning and policy-making processes. Our work may also provide insights into new ways to combine quantitative methods into spatial methods in constructing city-level or even regional-level LUCC models with high resolution.

A Unified Review of Deep Learning for Automated Medical Coding

Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents. Recent advances in deep learning and natural language processing have been widely applied to this task. However, deep learning-based medical coding lacks a unified view of the design of neural network architectures. This review proposes a unified framework to provide a general understanding of the building blocks of medical coding models and summarizes recent advanced models under the proposed framework. Our unified framework decomposes medical coding into four main components, i.e., encoder modules for text feature extraction, mechanisms for building deep encoder architectures, decoder modules for transforming hidden representations into medical codes, and the usage of auxiliary information. Finally, we introduce the benchmarks and real-world usage and discuss key research challenges and future directions.

A Conceptual Framework to Understand the Self-Assembly of Chemically Active Colloids.

Colloids that generate chemicals, or "chemically active colloids", can interact with their neighbors and generate patterns via forces arising from such chemical gradients. Examples of such assemblies of chemically active colloids are abundant in the literature, but a unified theoretical framework is needed to rationalize the scattered results. Combining experiments, theory, Brownian dynamics, and finite element simulations, we present here a conceptual framework for understanding how immotile, yet chemically active, colloids assemble. This framework is based on the principle of ionic diffusiophoresis and diffusioosmosis and predicts that a chemically active colloid interacts with its neighbors through short- and long-range interactions that can be either repulsive or attractive, depending on the relative diffusivity of the released cations and anions, and the relative zeta potential of a colloidal particle and the planar surface on which it resides. As a result, 4 types of pairwise interactions arise, leading to 4 different types of colloidal assemblies with distinct patterns. Using short-range attraction and long-range attraction (SALR) systems as an example, we show quantitative agreement between the framework and experiments. The framework is then applied to rationalize a wide range of patterns assembled from chemically active colloids in the literature exhibiting other types of pairwise interactions. In addition, the framework can predict what the assembly looks like with minimal experimental information and help infer ionic diffusivity and zeta potential values in systems where these values are inaccessible. Our results represent a solid step toward building a complete theory for understanding and controlling chemically active colloids, from the molecular level to their mesoscopic superstructures and ultimately to the macroscopic properties of the assembled materials.

Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis

Perception-based fidelity evaluation metrics are crucial in driving simulators, as they play a key role in the automatic tuning, assessment, and comparison of motion cueing algorithms. Nevertheless, there is presently no unified and effective evaluation framework for these algorithms. To tackle this challenge, our study initially establishes a model rooted in visual–vestibular interaction and head tilt angle perception systems. We then employ metrics like the Normalized Average Absolute Difference (NAAD), Normalized Pearson Correlation (NPC), and Estimated Delay (ED) to devise an evaluation index system. Furthermore, we use a combined approach incorporating CRITIC and gray relational analysis to ascertain the weights of these indicators. This allows us to consolidate them into a comprehensive evaluation metric that reflects the overall fidelity of motion cueing algorithms. Subjective evaluation experiments validate the reasonableness and efficacy of our proposed Perception Fidelity Evaluation (PFE) method.

A Unified Framework for Cyber Oriented Digital Engineering using Integration of Explainable Chaotic Cryptology on Pervasive Systems

Cyber Oriented Digital Engineering (CODE) aims to safeguard pervasive systems, cyber physical systems (CPS), internet of things (IoT) and embedded systems (ES) against advanced cyberattacks. Cyber oriented digital engineering pilots are earnestly required to secure transmission and credential exchanges during machine to machine (M2M) zero trust (ZT) communication. In order to construct the CODE pilot as a pivot of zero trust (ZT) communication, systems engineering employing chaotic cryptology primitives has been investigated. The empirical results with analysis of findings on its integration on real life platforms are presented as a pervasive framework, in this work. The focus was bestowed in developing an explainable approach, addressing both ante hoc and post hoc explanation needs. Ante hoc explanation ensures transparency in the encryption process, fostering user trust, while post hoc explanation facilitates the understanding of decryption outcomes. The properties of explainable approaches are investigated, emphasizing the balance between security and interpretability. Chaotic systems are employed to introduce a dynamic layer of complexity, enhancing encryption robustness. The article aims to contribute to the evolving field of explainable chaotic cryptology, bridging the gap between cryptographic strength and user comprehension in CODE pilot based zero trust (ZT) exchanges in multimedia content protection. Thus, this research is a communication brief case containing significant early findings and groundbreaking results studied as a part of a longer, multi-year analysis. Innovative techniques and pragmatic investigations have been discussed as a part of result dissemination in the empirical findings.

THE IMPACT OF APPLYING INTERNATIONAL STANDARD NINE ON THE QUALITY OF ACCOUNTING INFORMATION

There is an increasing need to find professional institutions that sponsor the work of companies in the financial and accounting fields due to the changes and developments taking place in the economic environments. Hence, the International Accounting Standards Board - IASB, which includes many other sub-boards like the Financial Accounting Standards Board - FASB, has issued many scientific publications in financial and accounting fields. Furthermore, the International Accounting Standards Board, through its sub-boards, including the Financial Accounting Standards Board, has formulated several International Financial Reporting Standards ( IFRS ) to improve the quality of financial reporting and the transparency of the information it contains in a globally applicable and accepted manner. This information would reflect the current situation of economic institutions in terms of performance and financial position in response to changes in economic environments, and to meet the needs of This framework will be applied consistently across transactions, industries, and capital markets to improve the comparability of financial data between economic institutions operating in the same sector globally and within a comprehensive, unified, and stable framework.

A unified Bayesian framework for bias adjustment in multiple comparisons from clinical trials.

In clinical trials, multiple comparisons arising from various treatments/doses, subgroups, or endpoints are common. Typically, trial teams focus on the comparison showing the largest observed treatment effect, often involving a specific treatment pair and endpoint within a subgroup. These findings frequently lead to follow-up pivotal studies, many of which do not confirm the initial positive results. Selection bias occurs when the most promising treatment, subgroup, or endpoint is chosen for further development, potentially skewing subsequent investigations. Such bias can be defined as the deviation in the observed treatment effects from the underlying truth. In this article, we propose a general and unified Bayesian framework to address selection bias in clinical trials with multiple comparisons. Our approach does not require a priori specification of a parametric distribution for the prior, offering a more flexible and generalized solution. The proposed method facilitates a more accurate interpretation of clinical trial results by adjusting for such selection bias. Through simulation studies, we compared several methods and demonstrated their superior performance over the normal shrinkage estimator. We recommended the use of Bayesian Model Averaging estimator averaging over Gaussian Mixture Models as the prior distribution based on its performance and flexibility. We applied the method to a multicenter, randomized, double-blind, placebo-controlled study investigating the cardiovascular effects of dulaglutide.

An Overview of Privacy-Enhancing Technologies in Biometric Recognition

Privacy-enhancing technologies are technologies that implement fundamental data protection principles. With respect to biometric recognition, different types of privacy-enhancing technologies have been introduced for protecting stored biometric data which are generally classified as sensitive. In this regard, various taxonomies and conceptual categorizations have been proposed and standardisation activities have been carried out. However, these efforts have mainly been devoted to certain sub-categories of privacy-enhancing technologies and therefore lack generalization. This work provides an overview of concepts of privacy-enhancing technologies for biometric recognition in a unified framework. Key properties and differences between existing concepts are highlighted in detail at each processing step. Fundamental characteristics and limitations of existing technologies are discussed and related to data protection techniques and principles. Moreover, scenarios and methods for the assessment of privacy-enhancing technologies for biometric recognition are presented. This paper is meant as a point of entry to the field of data protection for biometric recognition applications and is directed towards experienced researchers as well as non-experts.

Estimating software reliability using size-biased modelling

Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets – one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management.