Hrčak - Portal of scientific journals of Croatia
Not a member yet
321498 research outputs found
Sort by
Service learning as a transformative paradigm for improving the resilience and continuity of humanitarian systems
No full textUpravljanje kontinuitetom poslovanja i strateška elastičnost u humanitarnoj logistici nameću
potrebu za razvojem robusnih i prilagodljivih logističkih sustava. Postojeći modeli razvoja
kapaciteta često zanemaruju integraciju teorijskog znanja i praktične primjene, a posebno u
kontekstu strateške otpornosti i kontinuiteta djelovanja. Stoga se u ovom radu problematizira
nedostatak sustavnog pristupa povezivanja visokoobrazovnih institucija i ostalih dionika u
humanitarnoj logistici kroz koncept društveno korisnog učenja (DKU), kao sredstva za jačanje
humanitarne logistike. Svrha rada je predstaviti konceptualni model koji DKU pozicionira kao
transformativni mehanizam za povezivanje akademske zajednice s humanitarnom praksom u
cilju unaprjeđenja otpornosti i strateške elastičnosti humanitarnih sustava. Metodološki, rad se
temelji na sintezi relevantne znanstvene literature iz područja humanitarne logistike,
upravljanja kontinuitetom poslovanja, strateške otpornosti i pedagogije DKU. Predloženi model
prikazuje faze partnerstva između visokoobrazovnih institucija i ostalih dionika u humanitarnoj
logistici, ulogu studenata i nastavnika u primjeni znanja, te očekivane ishode za sve uključene
strane. Doprinos rada ogleda se u teorijskoj integraciji društvenog korisnog učenja s
pojmovima otpornosti i kontinuiteta, kao i u praktičnoj primjenjivosti modela za kreiranje
održivih, dvosmjernih partnerstava što će doprinijeti učinkovitosti i prilagodljivosti humanitarne
logistike u Republici Hrvatskoj, ali i šire.Business continuity management and strategic resilience in humanitarian logistics impose the
need for the development of robust and adaptable logistics systems. Existing capacity
development models often neglect the integration of theoretical knowledge and practical
application, especially in the context of strategic resilience and continuity of operations.
Therefore, this paper addresses the lack of a systematic approach to connecting higher
education institutions and other stakeholders in humanitarian logistics through the concept of
community-based learning, as a means to strengthen humanitarian logistics. The purpose of
the paper is to present a conceptual model that positions service learning as a transformative
mechanism for connecting the academic community with humanitarian practice in order to
improve the resilience and strategic elasticity of humanitarian systems. Methodologically, the
paper is based on the synthesis of relevant scientific literature in the field of humanitarian
logistics, business continuity management, strategic resilience and pedagogy of servicel
learning. The proposed model shows the stages of partnership between higher education
institutions and other stakeholders in humanitarian logistics, the role of students and teachers
in the application of knowledge, and the expected outcomes for all parties involved. The
contribution of the work is reflected in the theoretical integration of service learning with the
concepts of resilience and continuity, as well as in the practical applicability of the model for
creating sustainable, two-way partnerships, which will contribute to the efficiency and
adaptability of humanitarian logistics in the Republic of Croatia, but also beyond
Consumer Trust and Adoption of Digital Payment Systems in Emerging Markets
Get PDFBackground: Digital payment systems have already become the key to transforming financial services in emerging markets, but their usage remains highly dependent on user trust, perceptions of usefulness and convenience, and the availability of favourable conditions. Objectives: This research sought to investigate how consumer trust and adoption of digital payment platforms depend on variables, drawing on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and a multidimensional trust theory. Methods/Approach: A quantitative survey of 500 respondents across four emerging-market regions was conducted. The relationship among performance expectancy, effort expectancy, social influence, facilitating conditions, and the three trust dimensions was analysed using structural equation modelling (SEM). Results: Performance expectancy, facilitating conditions, and trust-related constructs proved to be strong predictors of behavioural intention to use digital payments, and actual system utilisation was strongly predictable by behavioural intention. Trust was identified as mediating the users' technological perceptions and their intentions to adopt. Qualitative implications revealed issues of security, data privacy, and infrastructural inconsistency that continued to influence user reluctance and perceptions of risk. Conclusions: The results highlight the roles of trust-building mechanisms, effective user support systems, and regulatory assurance in promoting digital financial inclusion in emerging markets
The cancer ratio plus in the differential diagnosis of pleural effusions: a scoping review of current evidence
No full textIntroduction: Differentiating between malignant pleural effusion (MPE) and tuberculous pleural effusion (TPE) remains challenging in clinical practice. The cancer ratio plus (CR+), a potential diagnostic tool calculated as serum lactate dehydrogenase/(pleural adenosine deaminase x pleural lymphocyte percentage) has emerged to address this diagnostic challenge. This scoping review maps the available evidence on its diagnostic performance.
Materials and methods: We conducted a systematic search of PubMed, Scopus, and Web of Science databases from inception to April 2025. Eligible studies assessed the accuracy of CR+ in distinguishing MPE from TPE. Data on study design, cut-off values, sensitivity, specificity, area under the curve (AUC), and likelihood ratios were extracted and synthesized narratively.
Results: Six studies comprising 881 patients were included. Reported cut-off values varied widely (5.7 - 41.0), as did sensitivity (74.3 - 97.6%) and specificity (36.6 - 94.1%). Most studies, however, reported good discriminatory power with AUC values generally above 0.80. The highest diagnostic accuracy was observed in one study, which reported a sensitivity of 97.6%, a specificity of 94.1%, and an AUC of 0.86. Differences in cut-off thresholds, study populations, local tuberculosis epidemiology, and laboratory methodology (particularly lymphocyte quantification) likely contributed to this heterogeneity.
Conclusions: The CR+ appears promising as a non-invasive tool using routine parameters for differentiating MPE from TPE, but diagnostic performance varies across settings. The heterogeneity in optimal cut-off values highlights the need for local validation before clinical adoption. Future research should standardize methodology and assess its impact on decision-making and patient outcomes
Parallel and Distributed Multi-level Entropy- Based Approach for Adaptive Global Frequent Pattern Mining in Large Datasets
No full textFrequent pattern mining in distributed settings remains a significant challenge due to predominantly high computational expenses and high communication overhead. This paper presents AGFPM (Adaptive Global Frequent Pattern Mining), a novel solution that integrates an extensible Master-Slave architecture with an advanced pruning technique that relies on binary entropy and statistical quartiles. AGFPM proposes two primary data structures: the LP-Tree (Local Prefix Tree) and the GP-Tree (Global Prefix Tree). A single pass of each local Slave site is used to build one LP-Tree, and low information value branches are pruned early on by entropy and quartile thresholds. Rather than transferring complete trees, only succinct metadata is sent to the Master site, where the GP-Tree is built from globally sorted items in order of their entropy rankings. A significant aspect of AGFPM is the flexible pruning approach: either the GP-Tree is pruned or not pruned, based on user criteria. This provides a dynamic adjustment between the performance and generality of results, thereby allowing control over the level of compression applied when generating global patterns. Global frequent patterns are then recursively mined from the GP-Tree based on conditional sub-GP-Trees. Frequent patterns are extended at each level of the hierarchy by intersecting the common prefix paths, guided by a Global Header Table. AGFM demonstrates improved performance in execution time, scalability, and robustness against low support thresholds relative to existing methods
Numerical Simulation Study on the System Dynamics of Sea Buckthorn Harvester
No full textTo investigate the mechanical characteristics of the vibrational air-sucking sea buckthorn harvester, a system dynamics model integrating the harvester and the sea buckthorn tree was developed in this study. The model incorporates the adsorption force of the sucking device and the mechanical interactions between branches and fruits. Through numerical simulations, the influence of excitation frequency, lifting height, vibration isolation spring stiffness, crank radius, and adsorption force on the displacement of each component in the vibration system was systematically analyzed. Unlike vibration-only or suction-only prototypes, the proposed model simultaneously incorporates vibratory excitation and vacuum suction, which yields a 15 % higher detachment rate at 25 % lower trunk acceleration, thereby mitigating tree damage. The dynamic analysis demonstrates the vibration displacement of all components peaks at an excitation frequency of 10 rad/s. Increasing the stiffness of vibration isolation springs enhances the harvester's anti-vibration performance while amplifying the relative displacement between the tree trunk, branches, and fruits. Notably, the lifting height exhibits minimal impact on the harvest rate, whereas enlarging the crank radius significantly increases vibration displacement. Additionally, the application of adsorption force promotes fruit detachment by augmenting vibrational amplitude. This study provides theoretical insights for optimizing the design of vibration-adsorption combined harvesters
Exploration of ESG Audit Adaptive Decision-Making and Anomaly Analysis Driven by Reinforcement Learning in Artificial Intelligence
No full textThe dynamic nature of Environmental, Social, and Governance (ESG) audit strategies, particularly in responding to evolving environmental regulations and shifting corporate sustainability practices, necessitates robust methodological innovation. Reinforcement learning (RL) presents a transformative pathway for refining ESG audit processes through continuous interaction with environmental performance data, regulatory updates, and real-time ecological compliance feedback. Nevertheless, the application of RL to optimize ESG auditing remains significantly underexplored. Addressing this gap, our study develops an RL-driven model designed to strategically recalibrate ESG auditing mechanisms, with enhanced focus on responsiveness to emerging environmental compliance requirements and ecological risk factors. We adopt a dual-method research framework integrating theoretical and empirical approaches. The theoretical investigation establishes the structural compatibility between RL algorithms and environmental ESG audit optimization, ensuring alignment with the complexities of sustainability decision-making. Empirical validation employs: (1) large-scale simulations using synthetic corporate environmental datasets to evaluate model performance across diverse operational scenarios, and (2) real-world applications to quantify the model's efficacy in improving ecological audit efficiency, mitigating environmental compliance risks, and addressing the challenges of modern sustainability auditing. Results demonstrate that the RL-driven model outperforms conventional methods in adapting to environmental data variability, achieving a 40% increase in resource efficiency and a 30% improvement in predicting ecological compliance risks. Practical implementations further reveal a 25% reduction in audit cycle duration and significantly fewer errors in environmental disclosure assessments. These findings highlight RL's potential to revolutionize environmentally focused ESG auditing while underscoring ongoing challenges in data reliability and model interpretability for sustainability applications
Research on Digital and Intelligent Innovation Transformation Strategy of International Chinese Language Education Reshaped by Generative Artificial Intelligence
No full textGenerative artificial intelligence possesses outstanding language recognition and generation capabilities. It can automatically create diverse data forms based on user input instructions and is widely applied in multiple aspects of international Chinese language education, including student recruitment promotion, classroom teaching, educational administration management, and learning assessment. First of all, it is necessary to clarify the core connotation of the independent knowledge system of international Chinese language education, systematically explain the significant meaning of constructing this system from three dimensions: contemporary development needs, historical evolution context, and global perspective, and analyze its key constituent elements. On this basis, innovative construction strategies in the process of digital and intelligent transformation are proposed: oriented by practical problems, promote the systematic construction of an autonomous knowledge system for international Chinese language education. Based on the fine traditional Chinese culture, enrich its ideological connotation and cultural value. Oriented towards the practical application of Chinese, strengthen the functional attributes of this system in terms of language services. Driven by digital transformation, promote its continuous innovation and dynamic development. In addition, through literature review and in-depth interviews with students, internal and external factors influencing the participation in online videos of international Chinese language education were identified. A participation influencing factor model based on the Interpretive Structure Model (ISM) and the Cross-Influence Matrix (MICMAC) was constructed to clarify the hierarchical relationship and mechanism of action among various factors, including direct factors, indirect factors, and fundamental factors. Furthermore, it provides countermeasures and suggestions for video designers and learners respectively in terms of production and learning. The introduction of generative artificial intelligence technology can achieve the automated production of digital resources for international Chinese language education. Efforts should be made to build a dedicated generative artificial intelligence system for the field of international Chinese language education, integrate multi-source data resources in this field, and develop intelligent generation assistants that can be embedded in various application software, so as to enhance the intelligent level of teaching resource development and application
Automatic Detection Method for Daytime Traffic Flow Based on Background Difference and Edge Extraction
No full textReal-time monitoring and analysis of traffic flow have become critical as urban congestion intensifies. To address the current challenges of low accuracy and insufficient detection efficiency in daytime traffic monitoring, this paper introduces an advanced method utilizing background difference and edge extraction techniques. Specifically, it employs the Gaussian Mixture Model (GMM) for dynamic background modeling and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance input images. Furthermore, the Faster Regional Convolutional Neural Network (Faster R-CNN) and an optimized Canny edge detection algorithm are utilized for effective target detection and tracking. Experimental evaluations indicate significant improvements over existing benchmarks, achieving accuracy, recall, and F1 scores of 0.99, 0.97, and 0.98, respectively. Practical application tests demonstrate an average vehicle detection accuracy of 99.49%, with robustness assessments confirming the model's reliability under diverse datasets and environmental conditions. This research provides substantial improvements in automatic traffic flow detection, supporting more efficient urban traffic management
Gauging Urban Security Advances: A Fuzzy Hierarchical TOPSIS Model with MOPSO Optimization
No full textIn the era of rapid urban digitalization, intelligent analysis systems are pivotal for urban safety management, yet their applicability lacks a robust evaluation framework. This study develops a comprehensive model integrating the Delphi method, Fuzzy Hierarchical TOPSIS, and MOPSO. Through two-round expert consultations, 36 indicators across four dimensions, technical performance, functionality, interaction modes, and cost-effectiveness, are identified. The MOPSO algorithm dynamically optimizes weights in the fuzzy TOPSIS framework, addressing static evaluation limitations and balancing conflicting objectives like reliability and cost-efficiency. Empirical results show a 12% average improvement in the closeness index and enhanced scenario adaptability, such as an 18% reduction in warning latency during emergencies. This hybrid approach bridges qualitative expert insights with quantitative optimization, offering a systematic tool for evaluating system applicability. The research enriches multi-criteria decision-making methodologies and supports evidence-based urban safety governance, facilitating resource allocation and sustainable planning