Traditional Approaches Research Articles

Apocalyptic praxis in Evagrius of Pontus and Francis of Assisi

Abstract The Christian mystical tradition approaches the apocalyptic as praxis – a way of living that renounces the world as it is, lives proleptically into a counter-world of God's reign and practices indifferent freedom in the meantime to love God and neighbour. Although concerns about the ethical viability of such a disposition have merit, this essay demonstrates its constructive possibility through recourse to two archives: the writings of Evagrius of Pontus and the witness of Francis of Assisi. By recovering a scriptural distinction between world and creation, and by emphasising the posture of holy indifference, apocalyptic praxis offers a resource and guide.

Transfer Dynamic Latent Variable Modeling for Quality Prediction of Multimode Processes.

Quality prediction is beneficial to intelligent inspection, advanced process control, operation optimization, and product quality improvements of complex industrial processes. Most of the existing work obeys the assumption that training samples and testing samples follow similar data distributions. The assumption is, however, not true for practical multimode processes with dynamics. In practice, traditional approaches mostly establish a prediction model using the samples from the principal operating mode (POM) with abundant samples. The model is inapplicable to other modes with a few samples. In view of this, this article will propose a novel dynamic latent variable (DLV)-based transfer learning approach, called transfer DLV regression (TDLVR), for quality prediction of multimode processes with dynamics. The proposed TDLVR can not only derive the dynamics between process variables and quality variables in the POM but also extract the co-dynamic variations among process variables between the POM and the new mode. This can effectively overcome data marginal distribution discrepancy and enrich the information of the new mode. To make full use of the available labeled samples from the new mode, an error compensation mechanism is incorporated into the established TDLVR, termed compensated TDLVR (CTDLVR), to adapt to the conditional distribution discrepancy. Empirical studies show the efficacy of the proposed TDLVR and CTDLVR methods in several case studies, including numerical simulation examples and two real-industrial process examples.

Nondestructive assessment of ZnO surge arrester operating condition based on time-domain dielectric characteristics

Given the expansion of the economy and the rise in electricity demand, it is imperative to ensure the safe operation of the power system. Zinc oxide (ZnO) surge arresters play a crucial role in preventing overvoltage that can potentially harm the grid. The study presents an innovative non-destructive assessment approach for evaluating the working condition of ZnO surge arresters based on time-domain dielectric characteristics, aiming to guarantee their safe functioning within the power grid. The proposed technique provides a platform for conducting aging experiments updates the procedure for preparing piezoresistors in ZnO surge arresters and examines their performance. Furthermore, a test bed was utilized to investigate polarization/depolarization currents. The findings revealed that the pressure-sensitive voltage exhibited a minimum value of 693.09 U1mA ·cm2 using the traditional solid-phase approach while employing the chemical precipitation method with a 2:1 alcohol-to-water volume ratio resulted in a maximum value of 1028.41 U1mA ·cm2. Under the traditional solid-phase method, the voltage gradient varied from 411.04 V·mm−1 to 585.12 V·mm−1 when using the chemical precipitation method with an alcohol-to-water volume ratio of 2:1 V ·mm−1. The condition of ZnO surge arresters can be evaluated non-destructively by the polarization/depolarisation charge differential, which increases with the average service life. In conclusion, this study provides methodological criteria for assessing ZnO surge arrester conditions to ensure safe operation of power grids.

Towards Effective and Efficient Sparse Neural Information Retrieval

Sparse representation learning based on Pre-trained Language Models has seen a growing interest in Information Retrieval. Such approaches can take advantage of the proven efficiency of inverted indexes and inherit desirable IR priors such as explicit lexical matching or some degree of interpretability. In this work, we thoroughly develop the framework of sparse representation learning in IR, which unifies term weighting and expansion in a supervised setting. We then build on SPLADE—a sparse expansion-based retriever—and show to which extent it is able to benefit from the same training improvements as dense bi-encoders by studying the effect of distillation, hard negative mining, as well as the Pre-trained Language Model’s initialization on its effectiveness , leading to state-of-the-art results in both in- and out-of-domain evaluation settings (SPLADE++). We furthermore propose efficiency improvements, allowing us to reach latency requirements on par with traditional keyword-based approaches (Efficient-SPLADE).

Architecture-level energy model for high-capacity STT-MRAM memory

ABSTRACT Nowadays, STT-MRAM has emerged as one of the leading contenders for next-generation memory technologies. However, STT-MRAM still exhibits high energy consumption due to its peripheral circuits, including the read/write and the sample amplifier (SA) circuits. Therefore, it is crucial to accurately evaluate the energy consumption of memory architectures during the early stages of the design process. Traditionally, energy evaluation can be conducted using the NVSim platform. However, the peripheral read/write circuitry of STT-MRAM undergoes iterative improvements, which cannot be promptly incorporated into NVSim. To addreGss this limitation and to enable comprehensive energy consumption evaluations spanning from device-level to architecture-level for STT-MRAM, an architecture-level energy consumption model is proposed in this paper. The model offers high accuracy and flexibility for the STT-MRAM memory array structure, abstracting the influences of read/write circuits for power evaluations. Comparative analysis is performed between the proposed model and the traditional approaches. When estimating a memory array with 128 × 64, the deviation using the proposed model is less than 2%, showing improved accuracy compared to that with analytical formulas. Notably, the proposed model offers up to 97% time savings in the whole circuit simulation, thus enhancing efficiency in the design and evaluation process.

Controllable Data Generation by Deep Learning: A Review

Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning has created the opportunity for expressive methods to learn the underlying representation and properties of data. Such capability provides new ways of determining the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationships to generate structural data, given the desired properties. This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation. First, the article raises the potential challenges and provides preliminaries. Then the article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain. After that, the article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works. Finally, this article highlights the promising future directions of controllable deep data generation and identifies five potential challenges.

Whether search directions number affects the efficiency of the path planning algorithm: Taking an improved ACO algorithm with 32 directions for example

An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm.

Demand and growth regimes of the BRICs countries – the national income and financial accounting decomposition approach and an autonomous demand-led growth perspective

We contribute to the recent debate in post-Keynesian economics (PKE), comparative political economy (CPE) and international political economy (IPE) on growth regimes. The paper presents an analysis of changes in demand and growth regimes in the BRICs countries, Brazil, Russia, India, and China, after the Global Financial Crisis and the Great Recession of 2007–2009. It discusses and applies two approaches: a first one based on national income and financial accounting decomposition and a second one based on the Sraffian Supermultiplier (SSM) growth model, distinguishing the dynamics of autonomous expenditure growth from those of the induced components of aggregate demand. It is argued that the SSM approach provides the bridge between the traditional approach based on national income and financial accounting decomposition and the analysis of growth drivers, both in PKE as well as in CPE and IPE. This is illustrated by pointing out some changes in the underlying political economy and economic policy growth drivers in each of the countries.

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

A systematic review of in-memory database over multi-tenancy

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

Demystifying AI: Current State and Future Role in Medical Education Assessment.

Medical education assessment faces multifaceted challenges, including data complexity, resource constraints, bias, feedback translation, and educational continuity. Traditional approaches often fail to adequately address these issues, creating stressful and inequitable learning environments. This article introduces the concept of precision education, a data-driven paradigm aimed at personalizing the educational experience for each learner. It explores how artificial intelligence (AI), including its subsets machine learning (ML) and deep learning (DL), can augment this model to tackle the inherent limitations of traditional assessment methods.AI can enable proactive data collection, offering consistent and objective assessments while reducing resource burdens. It has the potential to revolutionize not only competency assessment but also participatory interventions, such as personalized coaching and predictive analytics for at-risk trainees. The article also discusses key challenges and ethical considerations in integrating AI into medical education, such as algorithmic transparency, data privacy, and the potential for bias propagation.AI's capacity to process large datasets and identify patterns allows for a more nuanced, individualized approach to medical education. It offers promising avenues not only to improve the efficiency of educational assessments but also to make them more equitable. However, the ethical and technical challenges must be diligently addressed. The article concludes that embracing AI in medical education assessment is a strategic move toward creating a more personalized, effective, and fair educational landscape. This necessitates collaborative, multidisciplinary research and ethical vigilance to ensure that the technology serves educational goals while upholding social justice and ethical integrity.

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of "doubly robust" nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs). In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR). We compare our model to benchmark statistical and deep learning models for causal inference in multiple experiments on semi-synthetic derivations of our dataset with various types and intensities of confounding. In order to further test the reliability of our approach, we test our model on situations of limited data. We find that our model provides more accurate estimates of relative risk [least sum absolute error (SAE) from ground truth] compared with benchmark estimations. Finally, our model provides an estimate of class-wise antihypertensive effect on cancer risk that is consistent with results derived from RCTs.

Point-to-Point Learning Tracking Control via Fading Communication Using Reference Update Strategy.

The networked structure has attracted significant attention due to high demand for industrial systems and rapid developments of network communication. Among various network randomness, fading is a common phenomenon, which can lead to signal attenuation, distortion, loss, and interference. This study concentrates on the point-to-point tracking problem via fading communications by proposing a reference update strategy. Using this strategy, the tracking performance is continuously improved even with faded information as the number of iterations increases. A learning control scheme is established and proved convergent in both mean-square and almost-sure senses under mild conditions. The convergence rate is accelerated by introducing the virtual reference compared with the traditional update approach. Illustrative simulations verify the theoretical results.

A High-Throughput Microdroplet-Based Single Cell Transfection Method for Gene Knockout Based on the CRISPR/Cas9 System.

The efficient application of the newly developed gene-editing method CRISPR/Cas9 requires more accurate intracellular gene delivery. Traditional delivery approaches, such as lipotransfection and non-viral delivery methods, must contend with major problems to overcome the drawbacks of low efficiency, high toxicity, and cell-type dependency. The high-throughput microdroplet-based single-cell transfection method presented herein provides an alternative method for delivering genome-editing reagents into single living cells. By accurately controlling the number of exogenous plasmids in microdroplets, this method can achieve high-efficiency delivery of nucleic acids to different types of single cells. This paper presents a high-throughput quantitative DNA transfection method for single cells and explores the optimal DNA transfection conditions for specific cell lines. The transfection efficiency of cells at different concentrations of DNA in microdroplets is measured. Under the optimized transfection conditions, the method is used to construct gene-knockout cancer cell lines to determine specific gene functions through the CRISPR/Cas9 knockout system. In a case study, the migration ability of TRIM72 knockout cancer cells is inhibited, and the tumorigenicity of cells in a zebrafish tumor model is reduced. A single-cell microfluidic chip is designed to achieve CRISPR/Cas9 DNA transfection, dramatically improving the transfection efficiency of difficult-to-transfect cells. This research demonstrates that the microdroplet method developed herein has a unique advantage in CRISPR/Cas9 gene-editing applications.

Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising solution to enhance the computing capability of resource-limited networks. A fundamental problem in MEC is efficiently offloading tasks from user devices to edge servers. However, there still exists a gap to deploy in real-world environments: 1) traditional centralized approaches needs complete information of edge network, ignoring the communication costs generated by synchronization, 2) previous works do not consider concurrent computation on edge servers, which may cause dynamic changes in the environment, and 3) the scheduling algorithm should deliver individualized decisions for different users independently and with high efficiency To solve this mismatch, we studied a multi-user task offloading problem where user devices make offloading decisions independently. We consider the concurrent execution of tasks and formulate a non-divisible and delay-aware task offloading problem to jointly minimize the dropped task ratio and long-term latency. We propose a decentralized task scheduling algorithm based on DRL that makes offloading decisions without knowing the information of other user devices. We employ Double-DQN, Dueling-DQN, Prioritized Replay Memory, and Recurrent Neural Network (RNN) techniques to improve the algorithm's performance. The results of simulation experiments show that our method can significantly reduce the long-term latency and dropped task ratio compared to the baseline algorithms.

Performance measurement for a better future

Performance measurement has become increasingly crucial, encompassing both organizational and personal spheres. It assesses how effectively tasks are carried out and is pivotal in determining the value derived from these activities. Tradition-al approaches to performance measurement, rooted in representational realism or social constructivism, present limitations in ensuring trustworthiness. This paper proposes pragmatic constructivism (PC) as an alternative paradigm to enhance the reliability of performance measurement. PC posits that organizational practices are constructed by humans through cognitive processes and offers an epistemolog-ical framework for developing effective measurement systems and a foundation for intentional and valuable outcomes. The paper discusses the shortcomings of traditional paradigms, outlines the principles of PC, and demonstrates its applica-tion in evaluating the three existing measurement frameworks of financial accounting, the Balanced Scorecard, and sustainability framework of Global Report-ing Initiative (GRI).

Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Abstract: As urban environments continue to witness the proliferation of surveillance systems, ensuring public safety and security has become an increasingly challenging endeavor. Anomaly detection in crowded areas has emerged as a crucial task in these settings, aimed at identifying suspicious activities or potential threats. To meet the demands of real-time monitoring and analysis, edge computing has gained prominence as a critical technology. This survey paper provides a comprehensive overview of the state-of-the-art in anomaly detection, its use in crowd surviellance and the role played by edge computing in anomaly detection .The paper reviews an extensive body of literature encompassing various techniques and methodologies employed for anomaly detection in crowded scenes. It explores the evolution of traditional video-based approaches, such as motion analysis and object tracking, and the recent advancements leveraging deep learning, including various models in machine learning. These techniques are examined for their applicability in crowd surveillance scenarios.Various commonly used datasets to measure the quality of anomaly detection are also explored, along with their attributes and descriptions.The survey analyzes how edge computing solutions, such as edge AI accelerators and edge devices, enable faster and more context-aware processing of video data, while also addressing issues related to bandwidth constraints, privacy concerns, and scalability; paving way for a further research in harnessing the power of edge computing in crowd surveillance anomaly detection.

Strategic human resource management for sustainability in creative industries: A review and framework proposal

Strategic Human Resource Management (SHRM) has emerged as a critical approach for fostering sustainability within the dynamic landscape of creative industries. This review presents a comprehensive review of existing literature on SHRM in the context of creative industries, culminating in a proposed framework aimed at enhancing sustainability in these sectors. The review begins by examining the unique challenges faced by creative industries, characterized by rapid technological advancements, evolving consumer preferences, and intense competition. In this context, traditional HRM approaches are often insufficient, necessitating a strategic alignment between HR practices and organizational objectives to ensure long-term viability. Key themes identified in the literature review include talent management, organizational culture, employee engagement, and leadership development. Effective talent management strategies are crucial for attracting, retaining, and developing skilled individuals who drive innovation and creativity within creative firms. Furthermore, fostering a supportive organizational culture that values diversity, collaboration, and experimentation is vital for promoting sustainability in these industries. Employee engagement emerges as a critical factor influencing organizational performance and resilience, particularly in creative environments where intrinsic motivation plays a significant role. Moreover, leadership development programs tailored to the unique needs of creative professionals can empower individuals to navigate uncertainty and drive strategic initiatives that contribute to organizational sustainability. Building upon these insights, this review proposes a framework for SHRM in creative industries, integrating strategic planning, talent management, culture development, and leadership practices. The framework emphasizes the alignment between HR strategies and business goals, emphasizing the importance of agility, innovation, and adaptability in response to dynamic market conditions. Strategic Human Resource Management plays a pivotal role in enhancing sustainability within creative industries by fostering a culture of innovation, empowering employees, and cultivating leadership capabilities. The proposed framework provides a roadmap for organizations seeking to navigate the complexities of the creative landscape while maximizing long-term success and resilience.

Integrating AI in education: Opportunities, challenges, and ethical considerations

Integrating Artificial Intelligence (AI) in education presents a promising frontier with manifold opportunities, yet it also poses significant challenges and necessitates ethical considerations. This review explores the multifaceted landscape of AI integration in education, highlighting its potential to revolutionize traditional pedagogical approaches, personalize learning experiences, and streamline administrative tasks. However, it also addresses the challenges pertaining to implementation, including issues related to accessibility, data privacy, and the digital divide. The opportunities afforded by AI in education are vast and transformative. AI-driven technologies have the capacity to adapt instruction to individual learning styles, thereby enhancing student engagement and academic outcomes. Additionally, AI-powered tools can automate administrative tasks, allowing educators to allocate more time to meaningful interactions with students. Moreover, AI holds promise in facilitating the creation of immersive learning environments through virtual reality and augmented reality applications, enriching the educational experience. Nevertheless, the integration of AI in education presents ethical considerations that warrant careful examination. Concerns regarding data privacy and security arise as educational institutions collect and analyze vast amounts of student data. Moreover, there are apprehensions about the potential for AI algorithms to perpetuate biases or reinforce inequalities if not implemented with conscientious oversight. Furthermore, questions surrounding the ethical use of AI in assessing student performance and making consequential decisions underscore the importance of establishing transparent and accountable practices. While the integration of AI in education offers unprecedented opportunities for innovation and improvement, it is imperative to navigate the associated challenges with diligence and ethical foresight. By addressing these challenges thoughtfully, stakeholders can harness the full potential of AI to cultivate equitable, inclusive, and effective educational environments.

Determination of the Optimum Test Conditions for Measurement of Glucose Level in Liquids

Diabetes is a disease that affects more than 400 million people worldwide and currently lacks a cure. Monitoring blood sugar levels is crucial in minimizing the effects of this disease and protecting against its complications. Invasive and minimally invasive methods are commonly used traditional approaches for detecting and monitoring blood sugar levels. However, these methods bring along psychological and infectious risks. Currently, efforts are being made to develop a non-invasive method for determining blood sugar levels. Microwaves offer the possibility of non-invasive glucose measurement as they do not cause any harmful effects on human tissue. Furthermore, the complex permeability of blood is sensitive to glucose concentration in the microwave band. In literature, most of the studies are done with vector network analyzers (VNA) to detect blood sugar level noninvasively. In this study, an expensive and bulky VNA is replaced by an affordable microwave source and RMS power detector. The influence of the type and diameter of the test tube material used for non-invasive determination of sugar levels is examined with this setup. Additionally, the effect of the distance between the Vivaldi antennas used during measurements and the test tube is investigated. The results indicate that measurements performed using plastic test tubes yield better results compared to glass test tubes. Moreover, reducing the diameter of the test tube leads to improved outcomes. It has been observed that accurate results cannot be obtained if the antennas and the test tube are too close (4.5cm) from each other.