Processing Framework Research Articles

Transitioning to Agile Organizational Structures: A Contingency Theory Approach in the Financial Sector

The primary objective of this investigation is to explore the key factors within a large corporation that facilitate the transition from a conventional hierarchical organizational structure to a more agile one. Although there are studies in the literature on the different agile organizational structures, there are no clear guidelines for a large company to transform to an agile framework. This study employs contingency theory as its framework and specifically focuses on the financial sector to identify both opportunities and challenges encountered during this transformation process. Qualitative research methods were employed, involving an analysis of five case studies in which managers served as respondents. This research contributes to the identification of a model as a guideline for the transformation of the organizational structure towards agility that can provide valuable information for companies undergoing such a transformation. The managers surveyed provided their experience and the competencies of their profiles were validated, in addition to the application of the Delphi method to obtain more objective information. The findings underscore that large companies should refrain from adopting a completely flat hierarchical structure. Moreover, there exists a set of strategic-level elements that serve as a guiding framework for the transformation process. Furthermore, it is essential to acknowledge that the transformation process itself does not follow a one-size-fits-all approach; rather, it is contingent upon the unique context of each individual case, and it is also a cultural challenge.

Management Evaluation in the Achievement Development Program of Football Sport Branch at FC UNY Academy

This research aims to assess the management's evaluation of the FC UNY Academy football achievement development program using the Context, Input, Process, and Product (CIPP) framework. Additionally, it seeks to determine if the ongoing achievement development program at the FC UNY Academy is being effectively managed. This qualitative research employs the CIPP evaluation model, with a purposive sampling technique to select three program administrators for study. Data collection involves interviews and documentation. The research findings indicate that the management evaluation of the FC UNY Academy achievement development program met adequate criteria across all aspects, signifying a moderate level of performance. However, the management's effectiveness in optimizing the program remains suboptimal. Specifically, the context aspect meets sufficient criteria, the input meets adequate criteria, the process meets good criteria, and the product meets adequate criteria. Key considerations are identified in the context, input, and product aspects. Recommendations for administrators include: Context aspect: Increase the number of administrators to enhance management performance. Input aspect: Enhance promotional activities and counselling during player recruitment to showcase player potential effectively. Process aspect: Optimize processes for better outcomes. Product aspect: Despite having tier 2 players, coaches should maximize player potential to achieve championship results in tournaments.

A reliable knowledge processing framework for combustion science using foundation models

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.

Team usability testing: development and validation of a groupware usability evaluation method

As virtual teamwork became widespread, the importance of the usability of groupware has increased, as groupware must have high usability in order to properly support teamwork. Several studies have focused on the usability evaluation of groupware, yet, both in terms of science and practice, there is a lack of a method that, like existing empirical single-user methods, can be routinely applied in software development practice. The new Team Usability Testing method differs from previous methods in that it is an empirical method for evaluating synchronous (real-time) collaborative software that explores the team usability problems with the help of real or potential users. The method consists of questionnaires, screen recording videos and group interviews, and the framework of data processing is based on the theory of the mechanics of collaboration. In the four steps of the development of the method, empirical studies under laboratory conditions and real working conditions, and then, for validation purposes, analytical, heuristic evaluation with the involvement of usability experts were performed. During the development and the application of the method, the communication patterns of different teams were examined in terms of their relationship to software usability. In this paper we present the results of the second laboratory study and the heuristic evaluation in details. Overall, the method is able to explore team usability problems of different types of teams, making it a valuable element in existing methods for evaluating the usability of groupware.

Explicit machine learning-based model predictive control of nonlinear processes via multi-parametric programming

Machine learning-based model predictive control (ML-MPC) has been developed to control nonlinear processes with unknown first-principles models. While ML models can capture nonlinear dynamics of complex systems, the complexity of ML models leads to increased computation time for real-time implementation of ML-MPC. To address this issue, in this work, we propose an explicit ML-MPC framework for nonlinear processes using multi-parametric programming. Specifically, a self-adaptive approximation algorithm is first developed to obtain a piecewise linear affine function that approximates the behaviors of ML models. Then, multi-parametric quadratic programming (mpQP) problems are formulated to generate the solution map for states in discretized state–space. Furthermore, to accelerate the implementation of explicit ML-MPC, a neighbor-first search algorithm is developed. Finally, an example of a chemical reactor is used to demonstrate the effectiveness of the explicit ML-MPC.

A general framework and decentralised algorithms for collective computational processes

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface (spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

Two Prediction Error Systems in the Nonlemniscal Inferior Colliculus: "Spectral" and "Nonspectral".

According to the predictive processing framework, perception emerges from the reciprocal exchange of predictions and prediction errors (PEs) between hierarchically organized neural circuits. The nonlemniscal division of the inferior colliculus (IC) is the earliest source of auditory PE signals, but their neuronal generators, properties, and functional relevance have remained mostly undefined. We recorded single-unit mismatch responses to auditory oddball stimulation at different intensities, together with activity evoked by two sequences of alternating tones to control frequency-specific effects. Our results reveal a differential treatment of the unpredictable "many-standards" control and the predictable "cascade" control by lemniscal and nonlemniscal IC neurons that is not present in the auditory thalamus or cortex. Furthermore, we found that frequency response areas of nonlemniscal IC neurons reflect their role in subcortical predictive processing, distinguishing three hierarchical levels: (1) nonlemniscal neurons with sharply tuned receptive fields exhibit mild repetition suppression without signaling PEs, thereby constituting the input level of the local predictive processing circuitry. (2) Neurons with broadly tuned receptive fields form the main, "spectral" PE signaling system, which provides dynamic gain compensation to near-threshold unexpected sounds. This early enhancement of saliency reliant on spectral features was not observed in the auditory thalamus or cortex. (3) Untuned neurons form an accessory, "nonspectral" PE signaling system, which reports all surprising auditory deviances in a robust and consistent manner, resembling nonlemniscal neurons in the auditory cortex. These nonlemniscal IC neurons show unstructured and unstable receptive fields that could result from inhibitory input controlled by corticofugal projections conveying top-down predictions.

Introduction to the Work Ability Index – A Guide for Rehabilitation Practitioners

The purpose of this report is to introduce the _Work Ability Index_ to a rehabilitation audience and to provide some background on its scope and features. As an indication of its popularity, (a) it is available in 26 languages; (b) it has been applied for more than twenty years and (c) from 1991 there are 388 records including “Work Ability Index” in PubMed. A summary of the data from 29 previous studies of 31,472 participants is provided and it indicated an overall mean of 38.94. Overall this mean would be categorised as a “good” work ability index. As expected, studies of rehabilitation or illness samples indicate “poor” work ability. Each of the 10 questions in the assessment is introduced. The conceptual nature of work ability is reviewed and some issues are raised about the validity of the scoring of the assessment. A conceptual framework for the work ability process is outlined.

Mineralizations of Nb-Ta-Rb-Zr and rare-earth elements in Boziguoer, South Tianshan, NW China: Geochronology and geochemistry of monazite and bastnäsite

The Boziguoer alkaline intrusion is located in the South Tianshan Orogenic Belt on the northern margin of the Tarim Craton. The intrusion is entirely mineralized and forms a super-large rare metal (RM) and rare-earth element (REE) deposit dominated by Nb-Ta-Rb, accompanied by Zr-REE. The primary RM minerals include pyrochlore, astrophyllite, and zircon, while the main REE minerals consist of fluocerite, monazite, xenotime, and bastnäsite, which are commonly present as granular aggregates or singularly filling the interstices between gangue minerals. Through a combination of geochronological and geochemical analyses of different types of monazite and bastnäsite in the mineralized alkaline rocks, this study elucidates the role of magmatic-hydrothermal evolution on the Boziguoer RM-REE mineralization and reconstructs the geochronological framework of alkalic magmatic-hydrothermal evolution and mineralization processes, as well as establishing mechanisms responsible for RM-REE enrichment. Petrographic observations and back-scattered electron (BSE) imaging revealed several types of monazite and bastnäsite with different characteristics as follows: (1) type Ia monazite (Mnz-Ia) experienced intense hydrothermal alteration, forming residual cores of monazite mantled by apatite and allanite coronas; (2) type Ib monazite (Mnz-Ib) was partially eroded, forming concentric zoning patterns with a core of monazite, a mantle of apatite, and an outer rim of allanite coronas; (3) type II monazite (Mnz-II) is slightly modified, and commonly associated with fluorite; (4) bastnäsite and fluocerite exhibit a core-rim structure, with the core of fluocerite being brighter than the rim of bastnäsite in BSE images. According to the paragenetic relationships and compositional variations, all two types of monazite are of primary magmatic origin, while bastnäsite is of hydrothermal origin. In addition, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results indicate that Mnz-Ia and Mnz-Ib belong to the same group, different from Mnz-II. The latter has higher (Ce/Gd)N and (Th/U)N ratios, indicating relatively low-temperature crystallization of Mnz-II at late stages. In-situ LA-ICP-MS U-Th-Pb dating of monazite and bastnäsite show that Mnz-Ia and Mnz-Ib have ages of ca. 290 Ma, consistent with the zircon age of the intrusion, whereas the Mnz-II and bastnäsite have younger ages of ca. 280 Ma, representing a post-magmatic hydrothermal mineralization event at Boziguoer. These new ages indicate that both magmatic and subsequent hydrothermal processes played critical roles in the RM-REE mineralization in this deposit. The latest findings also support the model that the formation of the alkaline belt where the Boziguoer ore-bearing intrusion was temporally and spatially linked to the Tarim Large Igneous Province that is genetically related to a mantle plume.

Tapestry of Time and Actions: Modeling Human Activity Sequences Using Temporal Point Process Flows

Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. These activities can range from the simplest daily routines, like walking and sitting, to multi-level complex endeavors such as cooking a four-course meal. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature—the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, and next action recommendation. Existing neural network based approaches that learn a continuous-time activity sequence are limited to the presence of only visual data or are designed specifically for a particular task (i.e., limited to next action or goal prediction). In this article, we present ProActive , a neural marked temporal point process framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems: next action prediction, sequence goal prediction, and end-to-end sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. Moreover, for time-sensitive prediction, we perform an early detection of sequence goal via a constrained margin-based optimization procedure. This in turn allows ProActive to predict the sequence goal using a limited number of actions. In addition, we propose a novel addition over the ProActive model, called ProActive++ , that can handle variations in the order of actions (i.e., different methods of achieving a given goal). We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of our ProActive and ProActive++ over the state of the art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.

A Nano Scale Machine Learning Assisted Natural Language Processing Framework for Sentiment Classification in Smart System Platform

A Nano Scale Machine Learning Assisted Natural Language Processing Framework for Sentiment Classification in Smart System Platform

Cross-Study of English Education and Cognitive Science: Language Processing and Learning Mechanisms

With the acceleration of globalization and the rapid development of information technology, English, as the main language of international communication, has increasingly prominent importance in education and learning. English education, as a discipline that studies the theory and practice of English education, has been exploring more effective teaching methods and learning strategies. At the same time, cognitive science, as a discipline that studies cognitive processes such as human thinking, learning, and memory, provides a profound theoretical foundation for understanding language processing and learning mechanisms. The cross-study of English education and cognitive science helps to build a more complete theoretical framework for language processing and learning mechanisms. By integrating the theoretical achievements of the two disciplines, we can more fully understand the cognitive processes and laws of English learning, revealing the intrinsic connection and essential characteristics of language processing and learning mechanisms. This can not only enrich and develop the theoretical systems of English education and cognitive science but also provide new ideas and perspectives for research in other related fields. In addition, with the continuous development of artificial intelligence and big data technology, research on language processing and learning mechanisms can also provide technical support and innovative ideas for fields such as natural language processing and intelligent education. Therefore, the cross-study of English education and cognitive science not only has far-reaching academic value but also has broad application prospects and social significance.

A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes

A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes

How does leader emotional labor influence employee voice: the mediating roles of psychological safety and perceived voice efficacy

Purpose This study aims to explore the impact of leader emotional labor on employee voice. According to the emotion as information theory and the voice as a deliberate decision-making process framework, this study develops and tests a model that examines the mediating effects of psychological safety and perceived voice efficacy in this relationship. Design/methodology/approach This study conducted two studies to test hypotheses. Study 1 used a quantitative research methodology using a two-wave survey of 435 employees and 58 leaders in China. The research model was analyzed using multilevel path analyses. Study 2 collected 301 full-time employees from Prolific Platform. Hypotheses were tested using Mplus. Findings The results in Study 1 reveal that leader deep acting has a positive indirect relationship with employee voice via psychological safety. Conversely, leader surface acting has a negative indirect effect on employee voice through psychological safety. The results in Study 2 supported the hypotheses. Originality/value This study contributes to the voice as a deliberative process literature by introducing leader emotional labor as an antecedent of voice behavior. Additionally, this study indicates that perceived psychological safety and perceived voice efficacy are two important mediating mechanisms for implementing voice behavior.

Optimisation-based strategies to identification of material parameters of hygro-thermo-mechanical problems

Applications involving hygro-thermo-mechanical models are found in engineering fields as diverse as electronic packaging, concrete structures, composite materials and wood structures. Within the framework of wood processing, hygro-thermo-mechanical problems account for the cross-dependence of moisture content, temperature and dimensional changes of wooden components. Numerical simulation plays an important role in predicting the behaviour of wooden structures and, therefore, use of appropriate process and material parameters is essential for a successful prediction. The present work is inserted in this context and addresses application of optimisation techniques to identification of heat transfer, moisture diffusion and moisture-dependent swelling/shrinkage parameters. The direct problem is formulated based on a fully coupled transient solution of the energy, moisture and momentum transfer conservation laws. A global–local hybrid optimisation technique is proposed combining swarm intelligence and deterministic approaches, respectively based on particle swarm optimisation and the Nelder–Mead (NM) technique. The first stage aims at reducing the search space, so that the NM algorithm is able to determine the global minimum with acceptable accuracy. The strategy is both robust and efficient, being able to avoid local minima with reduced number of fitness computations. An illustrative example featuring a moisture-gaining process is also discussed, in which special attention is placed on the elastic and material swelling coupled effects caused by moisture transfer.

Current Approaches to Improving Consensus Decision-Making Mechanisms in International Regional Organizations

This study is aimed to study a key element of the activity of any international organization — the problem of consensus decision-making and the corresponding practice of solving it both in integration bodies (Eurasian Economic Union, European Union) and in interstate organizations devoid of a supranational component (Shanghai Cooperation Organization, North Atlantic Treaty Organization treaty, Collective Security Treaty Organization).Aim. Identify current approaches to improving the application of the rule of consensus in interstate (intergovernmental) structures and their bodies.Tasks. Investigate and generalize the practice of consensus decision-making within the international regional organizations, highlight actual ways to increase the efficiency of their decision-making mechanisms.Methods. The study used general scientific methodology. The comparative analysis method is used to compare existing decision-making practices. Analysis, synthesis, generalization, as well as the formal logical method were used to formulate the conclusions of the work.Results. The collective decision-making based on consensus follows from the international legal principle par in parem non habet imperium. It emphasizes the equality of all participants in an international organization and excludes the imposition of obligations non-approved by a sovereign state. From this point of view, consensus is a way to strengthen unity and achieve organizational stability. Simultaneously the practice of using this form in various international organizations gives evidence to its low efficiency in decision-making. Achieving consensus requires intensive efforts to coordinate the positions of the parties, and the emergence of “one-vote veto” situations cannot be ruled out. A possible way to increase the efficiency of decision-making within the framework of integration processes (EU, EAEU) lies through the development of supranational mechanisms.Conclusions. Analysis of the practice of functioning of international regional organizations (SCO, NATO, CSTO) allows to identify two approaches aimed at overcoming the negative features of consensus decision-making. The first involves the development of an international organization with formal obligations of members into a community associated with the establishment of a set of shared values among its participants. The second presumes the creation of mechanisms that allow interested states to use the institutions of such a structure to make collective decisions without a not-participating state. Both approaches do not exclude but complement each other.

Streamlining trajectory map-matching: a framework leveraging spark and GPU-based stream processing

Real-time online trajectory map-matching has emerged as a critical component in the era of location-based services (LBS) and intelligent transportation systems (ITS). It refers to the process of aligning a user’s GPS trajectory data with the corresponding road network in real-time. This technology has significant implications for various industries and applications. As our reliance on LBS and ITS continues to grow, the demand for faster, more accurate, and more reliable trajectory map-matching methods becomes increasingly important. Contemporary online map-matching predominantly employs stream processing techniques. Based on stream processing frameworks, we propose a heterogeneous hybrid architecture for map-matching. The architecture integrates Spark Streaming and graphics processing unit (GPU) heterogeneous computing for the first time. The hidden Markov model is employed as the map-matching algorithm, and Spark Streaming serves as the distributed processing platform. We conduct map-matching experiments using a GPS taxi trajectory dataset in Beijing’s Haidian District. The results demonstrate that in comparison to other analogous research, our framework’s performance has increased by over ten times, possessing a superior data processing capability and lower latency. This research provides a novel approach of stream-based heterogeneous computation for processing large-scale geographic data.

Dynamic changes of physicochemical parameters, antioxidant activity, organic acids, polyphenols, and volatile components in prune vinegar during fermentation

To investigate the quality variations of prune vinegar during fermentation, multivariate statistical analysis (MSA) was used in conjunction with high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). The results showed that the pH decreased slightly, there was a rapid decline in sugar content, and a gradual increase in total acid content, which achieved stabilization at 3.68 g/100 mL. The content of organic acid increased, while the total phenol content decreased. The total flavonoid content did not change significantly. Furthermore, the antioxidant capacity initially decreased and then increased. Prune vinegar contains important phenolics such as rutin, resveratrol, salicylic acid, trans-ferulic acid, and (+)-catechin. In addition, the flavor of prune fruit vinegar underwent changes during the fermentation process. During the fermentation of fruit vinegar, large quantities of 2,3-butanediol, glacial acetic acid, nonanoic acid, ethyl acetate, ethyl heptanoate, ethyl caproate, and linalool were produced. These compounds give prune vinegar its distinctive fruity and floral aroma. This study provided a comprehensive framework for the efficient processing of prunes and a strategy for enhancing the quality characteristics of prune vinegar or other fermented products.

Protection and Utilization of Historical Sites Using Digital Twins

Historical sites are incomplete relics left by human activities and are also valuable resources for human society, with most of them buried deep underground. Because the protection of original historical sites is difficult, very few sites choose this method. Many funerary objects that have been in historical sites are scattered in museums, losing their original context and complicating their utilization. Thus, using digital technology to protect and utilize historical sites and to explore their outstanding value from historical, aesthetic, and anthropological perspectives is a foremost concern. Therefore, this study aims to develop a comprehensive method for the protection and utilization of historical sites, that is, digital protection and utilization based on a digital twin. We constructed a historical site digital twin model using qualitative and vertical methods, including a physical entity, virtual entity, twin data center, digital twin service, and connection. We also established a technical framework of data acquisition and processing, digital protection, and digital utilization, forming a layered management and application of digital resources. In digital protection, information in the real world and the virtual world are connected to monitor risks, collect data, create simulations, and propose protection strategies, quickly and accurately. In digital utilization, the knowledge graph is constructed to associate seemingly unrelated information, explore potential knowledge, and improve information sharing. In addition, the method is validated by means of case studies of historical sites in China. In this paper, the historical sites of the Northern Qi Dynasty in Taiyuan, Shanxi, especially the Xuxianxiu Tomb and Lourui Tomb, are discussed in detail. The results indicate that this method is effective for the protection and utilization of historical sites.

Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints.

Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.