Implementation Complexity Research Articles

Flexible working arrangement scenario planning for the social security agency for employment

This study investigates the readiness of the Social Security Agency for Employment (BPJS Ketenagakerjaan) to implement flexible working arrangements (FWAs) in response to the evolving work landscape post-COVID-19. With the Indonesian government's reclassification of the pandemic, organizations are reconsidering their work models, making it crucial to assess their preparedness for hybrid work systems. The research employs a case study design with a descriptive approach, utilizing the TAIDA method to develop planning scenarios. Data collection includes primary data from in-depth interviews with experts and secondary data from relevant literature and internal documents. Findings reveal that BPJS Ketenagakerjaan demonstrates a very good level of readiness across various dimensions, including technology and management, with an overall readiness index of 82.58. However, the mobility policy dimension indicated potential improvement areas. The study highlights the importance of establishing clear policies and standard operating procedures to support remote work, particularly for employees with specific health needs. The implications of this research are significant for BPJS Ketenagakerjaan as it provides a structured framework for enhancing organizational readiness for FWAs, ensuring employee engagement and satisfaction. This study contributes to the broader discourse on flexible work arrangements, offering practical guidance for public service organizations navigating the complexities of hybrid work implementation.

Leveraging Asynchronous Processing Tools in Salesforce: A Comprehensive Analysis

This article provides a comprehensive analysis of asynchronous processing tools within the Salesforce platform, exploring their significance in enhancing system performance, scalability, and user experience. It delves into a wide array of server-side and client-side asynchronous processing options, including Asynchronous Apex, Platform Events, Asynchronous Flows, and Lightning Actions, offering detailed insights into their functionalities, use cases, and implementation considerations. The article presents a comparative analysis of these tools, discussing their performance metrics, implementation complexities, and scalability factors. It also outlines best practices for implementing asynchronous processing in Salesforce, addressing key aspects such as resource optimization, platform limit mitigation, and data integrity. Furthermore, the article examines the challenges and limitations associated with asynchronous processing in Salesforce, and explores future trends and developments in this domain. By providing a thorough examination of Salesforce's asynchronous processing capabilities, this article serves as a valuable resource for developers, architects, and decision-makers seeking to leverage these tools effectively in building robust, efficient, and scalable Salesforce applications.

Privacy-Preserving in Machine Learning: Bridging Security and Performance in Modern Applications

Privacy-preserving machine learning (PPML) has emerged as a critical paradigm in the era of data-driven applications, addressing the fundamental tension between leveraging large-scale datasets and protecting individual privacy. This technical article examines recent advances in PPML techniques, focusing on three key approaches: federated learning, which enables distributed model training while keeping data localized; homomorphic encryption, allowing computation on encrypted data; and secure multi-party computation (MPC) for privacy-conscious collaborative learning. Through detailed architectural analysis and real-world case studies in mobile device personalization and healthcare analytics, this article demonstrates how these techniques can be effectively implemented while navigating computational overhead and implementation complexity. This article reveals current PPML approaches successfully preserve privacy in production environments, but they face significant challenges in computational efficiency and system integration. This article concludes by presenting optimization strategies and emerging research directions aimed at making PPML more practical for large-scale deployments.

Telehomecare Monitoring for Patients Receiving Anticancer Oral Therapy: Protocol for a Mixed Methods Evaluability Study.

Telehomecare monitoring (TM) in patients with cancer is a complex intervention. Research shows variations in the benefits and challenges TM brings to equitable access to care, the therapeutic relationship, self-management, and practice transformation. Further investigation into these variations factors will improve implementation processes and produce effective outcomes. This study aims to concurrently analyze implementation and evaluate the effectiveness of TM for patients receiving anticancer oral therapy. The objectives are to (1) contextualize how and why TM is implemented according to (a) site characteristics, (b) team characteristics, and (c) characteristics of patients receiving anticancer oral therapy; (2) assess TM effectiveness for recording electronic patient-reported outcome measures (ePROMs) and patient-reported experience measures (ePREMs) according to the site, implementation process, and patient characteristics; (3) describe the acceptability and feasibility of TM from the perspectives of the people directly or indirectly involved and provide evidence-based actionable guidance in anticipation of provincewide implementation. This type II hybrid effectiveness-implementation study uses a concurrent mixed methods design. Evaluability assessment is integrated into an emerging practice in 3 participating sites to enable the evaluation of implementation strategies on TM clinical outcomes. Quantitative data for ePROMs and ePREMs will be collected using validated oncology questionnaire. Descriptive statistics and repeated measures using multiple linear mixed models and generalized estimating equations analyses will be undertaken alongside interpretive descriptive coding of qualitative data. Qualitative data will be gathered from key informants guided by the RE-AIM (reach, efficacy, adoption, implementation, maintenance) framework and its extension, PRISM (practical robust implementation and sustainability model). The concurrent approach allows results at multiple stages of this study to be integrated iteratively. The methodological choice aims to provide real-world data that are rigorous, rapidly usable in practice, and transferable to other settings. Questionnaires were pretested and the technological platform was codeveloped with members of the cancer care team and patients. Preparatory work was carried out to configure the TM platform and activate coordinating mechanisms between members of the cancer care team, patients, information technology experts, and the research team. A steering committee with 3 working groups was established to oversee the technological, clinical, and evaluation aspects of this study. Recruitment of patients for ePROMs started in February 2024, and data collection is expected to continue until March 2025. Interviews with members of the cancer care team began in November 2024. Full analysis should be completed by September 2025. This study will clarify how, why, for whom, and under what conditions TM can complement current care models. Our evaluability assessment will help to address implementation complexities and better understand intervention-to-practice operationalization so that implementation might be adapted to contextual factors without potentially harmful or inequitable impacts on patients. DERR1-10.2196/63099.

Prioritization of Preventive Measures: A Multi-Criteria Approach to Risk Mitigation in Road Infrastructure Projects

Risk management in construction projects is a critical process aimed at identifying, evaluating, and mitigating potential risks that could impact project performance. Preventive measures play a central role in this process, serving as proactive strategies to minimize the likelihood and impact of risks on project outcomes. This study involved 37 experts from multidisciplinary fields related to road infrastructure, ensuring a diverse and comprehensive perspective on risk evaluation and prevention. The DELPHI method was employed to systematically define key risks and their corresponding preventive measures, providing a structured foundation for further analysis. The experts evaluated 302 preventive measures across 56 risks using 4 predefined criteria: implementation costs, time required for implementation, implementation complexity, and probability of success. A multi-criteria decision making (MCDM) approach was then applied to analyze these evaluations, enabling the prioritization of preventive measures and the allocation of resources toward the most effective strategies. Additionally, fuzzy logic was employed to analyze and validate the results, providing a complementary approach to the MCDM methodology. The results of this research provide a robust framework for risk management, offering practical guidance for decision makers in the construction industry. By integrating expert judgment, systematic evaluation, and advanced analytical methods, this study delivers actionable insights and establishes a reliable methodology for enhancing the effectiveness of risk mitigation in road infrastructure projects.

An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design.

Progressive pulmonary fibrosis (PPF) is a progressive and lethal condition with few effective treatment options. Improvements in quality of life for patients with PPF remain limited even while receiving treatment with approved antifibrotic drugs. Traditional Chinese medicine (TCM) has the potential to improve cough, dyspnea and fatigue symptoms of patients with PPF. TCM treatments are typically diverse and individualized, requiring urgent development of efficient and precise design strategies to identify effective treatment options. We designed an innovative Bayesian adaptive two-stage trial, hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF. An open-label, two-stage, adaptive Bayesian randomized controlled trial will be conducted in China. Based on Bayesian methods, the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial. The adaptive Bayesian trial design will employ a Bayesian hierarchical model with "stopping" and "continuation" criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached. The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented. The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score, reflecting an improvement in cough-specific quality of life. The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF, and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases. However, due to the complexity of the trial implementation, sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response. Moreover, detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. Please cite this article as: Zhang C, Nie YS, Zhang CT, Yang HJ, Zhang HR, Xiao W, Cui GF, Li J, Li SJ, Huang QS, Yan SY. An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design. J Integr Med. 2025; Epub ahead of print.

A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding - Part I: Geometry.

A universal multiscale conditional coding framework, Unicorn, is proposed to compress the geometry and attribute of any given point cloud. Geometry compression is addressed in Part I of this paper, while attribute compression is discussed in Part II. We construct the multiscale sparse tensors of each voxelized point cloud frame and properly leverage lower-scale priors in the current and (previously processed) temporal reference frames to improve the conditional probability approximation or content-aware predictive reconstruction of geometry occupancy in compression. Unicorn is a versatile, learning-based solution capable of compressing static and dynamic point clouds with diverse source characteristics in both lossy and lossless modes. Following the same evaluation criteria, Unicorn significantly outperforms standard-compliant approaches like MPEG G-PCC, V-PCC, and other learning-based solutions, yielding state-of-the-art compression efficiency while presenting affordable complexity for practical implementations.

Trials and tribulations: a qualitative exploration of researcher perspectives on navigating the challenges of health system implementation research

ObjectiveWhile researchers are crucial to the successful delivery of implementation trials, their perspectives are rarely captured in the evaluation of these trials. This qualitative study aimed to capture the experiences...

Reconfigurability evaluation and design for control systems with actuator fault and saturation

The issue of reconfiguration-based fault-tolerant control for systems with actuator fault and saturation is addressed. The basic idea behind this work is to evaluate the system reconfigurability to guide the reconfiguration scheme and system structure design. First, a reconfigurability criterion is derived and utilized to quantify the degree of reconfigurability. On this basis, a passive/active combined reconfiguration method is proposed to balance the effectiveness and implementation complexity of the reconfiguration scheme. Moreover, the system structure is optimized to further improve the comprehensive performance of the system. Finally, the effectiveness of the proposed method is illustrated through an application example of the satellite attitude control system.

Tools for planning large-scale measurement surveys for the assessment of indoor environmental pollutants: the case of radon

Risks from indoor pollutants require the implementation of effective policies to prevent and reduce exposure. To take decisions and actions, competent authorities need relevant information. In the case of indoor radon exposure, surveys are carried out by installing detectors in buildings (homes, workplaces) that measure radon concentration in rooms. Conducting national or large-scale surveys in buildings requires addressing both technical complexities of implementation and economic costs. In order to support the implementation of large-scale radon surveys, procedure and tools are described for defining the sample size of buildings to be measured according to available resources, for planning the sampling of buildings in relation to the specific objectives of the survey, and for obtaining a tool for management and control of sample recruitment and installation of radon detectors in buildings, reducing sources of bias. A particular application is shown for the case of the EU radon regulation.

In Situ Fabrication of Electrospun Magnetic Film under Laparoscopic Guidance for Preventing Postoperative Recurrence of Hepatocellular Carcinoma.

Despite laparoscopic-guided minimally invasive hepatectomy emerging as the primary approach for resecting hepatocellular carcinoma (HCC), there is still a significant gap in suitable biomaterials that seamlessly integrate with these techniques to achieve effective hemostasis and suppress residual tumors at the surgical margin. Electrospun films are increasingly used for wound closure, yet the employment of prefabricated electrospun films for hemostasis during minimally invasive HCC resection is hindered by prolonged operation times, complexity in implementation, limited visibility during surgery, and inadequate postoperative prevention of HCC recurrence. In this study, montmorillonite-iron oxide sheets are integrated into the polyvinylpyrrolidone (PVP) polymer framework, enhancing the resulting electrospun PVP/montmorillonite-iron oxide (MI) film (abbreviated as PMI) with robustness, hemostatic capability, and magnetocaloric properties. In contrast to the in vitro prefabricated electrospun films, the electrospun PMI film is designed to be formed in situ on liver wounds under laparoscopic guidance during hepatectomy. This design affords superior wound adaptability, facilitating meticulous wound closure and expeditious hemostasis, thereby simplifying the operative process and ultimately alleviating the workload of healthcare professionals. Moreover, when exposed to an alternating magnetic field, the film can efficiently ablate residual tumors, significantly augmenting the treatment efficacy of HCC.

High dynamic-range dimming control for CFMO-OFDM-based VLC systems utilizing a grouped direct current scheme

Since visible light communication (VLC) uses light-emitting diode (LED) as transmitters, it has dual functions of illumination and communication. To meet flexible lighting and energy-saving needs, we propose a novel, to the best of our knowledge, dimming control scheme for spectrally efficient clipping-free multilayer optical orthogonal frequency division multiplexing (CFMO-OFDM)-based VLC systems. In order to achieve high dynamic-range dimming control for LEDs, the time-domain CFMO-OFDM signals are first grouped based on frequency-domain subcarrier distribution. Then, the periodic grouped direct current (DC) signal can be flexibly chosen and added to the above CFMO-OFDM signals. Simulation results demonstrate that, compared with classical asymmetrical hybrid optical OFDM (AHO-OFDM) and DC bias-based dimming methods, the proposed scheme offers higher dimming range, better flexibility, and lower implementation complexity, as well as superior bit error rate (BER) performance.

Transient Stability Analysis for Grid-Connected Renewable Power Generation Systems Based on LMI Optimization Modelling

Grid-connected renewable power generation systems (RPGSs) may be disconnected from the grid under a transient process, which may possibly induce large-scale power outage accidents. Optimization of parameters based on transient stability analysis of RPGSs would be a feasible solution to such a problem. However, the accurate stability boundary of a grid-connected RPGS are hard to obtain, as the commonly used transient stability analysis methods have the problems of large computation burden with no quantitative solution (numerical method), low analysis accuracy (equal area method), and complexity or impossibility in implementation (Lyapunov function-based methods). In this paper, a modified transient stability analysis method is proposed. By calculating the largest area of the domain of attraction (LEDA) based on the linear matrix inequality (LMI) method and optimization modelling, and then applying parameter sensitivity analysis to the LEDA, the dominant parameters that have high impacts on the LEDA are revealed. A parameter optimization design method that can improve the system’s transient stability is eventually obtained. A hardware-in-the-loop (HIL) simulation system of a 2 MW grid-connected RPGS is established based on the Typhoon HIL 602 device. The theoretical results are verified by using HIL simulation results.

Bubble-Swap Flow Control

Deadlock-free adaptive routing is extensively adopted in both on-chip and off-chip interconnection networks to improve communication bandwidth and reduce latency. Introducing virtual channels (VCs), also known as virtual lanes (VLs), is the mainstream technique to handle deadlocks incurred by adaptive routing, and provides VC preemption for higher priority traffic. However, existing deadlock-free flow control schemes either underutilize memory resources due to inefficient buffer management to simplify hardware implementation, or rely on complicated global coordination and synchronization with very high hardware complexity. Most hardware-friendly schemes use more VCs and memory resources to enable ease of implementation of deadlock-free flow control. In contrast, sophisticated schemes achieve deadlock freedom with minimum VC cost, even eliminating additional buffer requirement through the complicated control mechanisms. In this work, we rethink the root cause of the deadlock problem from a different perspective by considering it as a lack of credit, which makes us find an efficient solution to the deadlock problem. With minor modification of credit accumulation and return, our proposed bubble-swap flow control (BSFC) ensures atomic buffer swap between two adjacent routers only based on local credit status while making full use of the buffer space. BSFC achieves a better tradeoff between implementation complexity and memory overhead and can be easily integrated in the industrial router with no modification on buffer allocation or port arbitration. The simulation results demonstrate BSFC outperforms existing bubble-based deadlock-free methods by average 64% higher throughput. We further propose a credit reservation strategy to eliminate the escape virtual channel (VC) cost for fully adaptive routing implementation. The synthesizing results demonstrate that BSFC along with credit reservation (BSFC-CR) can reduce the area and power consumption by respectively 29% and 26% in contrast to the traditional critical bubble scheme (CBS).

The legitimation of private net zero emission building standards in the context of global decarbonization goals

Since the Paris Agreement – where nations committed to decarbonizing their economies – private net zero standards have proliferated. Yet, their limited scope and ability to deliver tangible change threatens their legitimacy. For some scholars, this reflects societies’ limited capacity to envision pathways towards a fossil-free future. This raises questions about how private net zero standards are legitimised. Combining insights from the literature on legitimation, discourse analysis, and sociotechnical transitions, we analyse the legitimation of Net Zero Carbon Buildings by the World Green Building Council, the strongest global sustainable construction network. We find that net zero standard legitimation is a dynamic multilevel, multistakeholder process based on three strategies: vertical nesting, ambivalence, and conflict avoidance. This process perpetuates assumptions about growth, authority, and appropriate scales of action. This incremental, emission-focused approach to decarbonization is shielded from critique through claims that it contributes to broader sustainability objectives. By reproducing institutionalized narratives, global standard setters create a sense of consensus. However, this perception of consensus fails to address the complexities of implementation, contextualization, and integration of broader environmental and social concerns. These findings raise doubts in both theory and practice about the capacity of the construction sector to decarbonize through current strategies.

Optimization of selective powder deposition for multi-material powder bed fusion: process innovations and applications

AbstractAdditive manufacturing processes based on powder bed fusion offer a high degree of design flexibility and enable the processing of a wide range of materials, including metals, ceramics, and polymers, while maintaining minimal porosity. However, production of multi-material components with locally tailored properties to meet specific requirements by incorporating different materials with a high degree of spatial selectivity remains an elusive challenge. Essential prerequisites for achieving this selectivity are specialized selective powder deposition techniques, their development, characterization, and subsequent implementation. In order to investigate optimization potentials and to identify research gaps in the field of selective powder deposition techniques, an evaluation of the current literature is performed in this study, ultimately highlighting promising potentials for vibration-assisted approaches. Key considerations include the reduction of implementation complexity and the downscaling of associated devices to increase their applicability. To achieve implementation simplification, this study derives dimensionless quantities that facilitate a targeted calculation of control parameters by associating powder layer quality metrics with relevant input quantities. The validity of the derived dimensionless quantities is verified by discrete-element method simulations and physical experiments employing a novel miniaturized vibration-assisted device. Metal, ceramic and polymer powders are used as representative samples to demonstrate the versatility of the method for different classes of materials. Ultimately, the presented methods enable a significant improvement in the applicability of vibration-assisted devices and represent an integrative component that provides a suitable basis for further research efforts in the field of combined processing of multiple materials by additive manufacturing technologies that utilize powder beds.

The essence and system of international standards of the legal status of the defendant: from generally recognized principles to acts of "soft law"

The article carries out a comprehensive analysis of the essence and system of international standards of the legal status of the defendant. The problem of terminological uncertainty of the concept of «international standards» in the context of criminal proceedings and protection of the defendant’s rights is investigated. It is revealed that the absence of a single legal definition of this term and clear criteria for classifying norms as generally recognized creates significant obstacles to their effective application in practice. Various doctrinal approaches to understanding international standards of the defendant’s rights are analyzed. It is established that in the scientific literature there is a tendency to identify international standards with various legal categories: human rights, obligations of states, principles of international law. The specifics of European standards as a special subcategory of international standards of the defendant’s rights are investigated. The system of sources of international standards is determined, which includes generally recognized international documents (UN Charter, Universal Declaration of Human Rights, 1966 Covenants), regional standards and acts of «soft law». Particular attention is paid to the analysis of the recommendation documents of international organizations, which, despite the lack of binding legal force, play an important role in the development of the system of standards. The main problems of the practical application of international standards are identified: the dispersion of norms in various international documents, theabsenceofauniversalinterpretation mechanism and the complexity of implementation in national legal systems. The differences between the European and universal systems of protection of the rights of the defendant are analyzed in the context of the availability of mechanisms for official interpretation of standards. It is substantiated that compliance with international standards of the rights of the defendant is an important indicator of the democratization of society, the quality of relations between the individual and the state and the development of civil society. It is proposed to consider international standards as a dynamic system that is constantly evolving in accordance with the development of social relations and the needs for the protection of human rights. Proposals are formulated to improve the mechanisms of implementation and interpretation of international standards, to increase the level of their understanding and application by lawyers in practice. Prospects for the development of the system of international standards of the rights of the defendant in the context of modern trends in international law are determined.

Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms

One of the major problems of cyclostationary spectrum sensing (CSS) system in cognitive radios (CR) are the implementation complexity. One possible way to reduce CSS complexity is to use efficient algorithms for performing Fast Fourier Transformation (FFT). Over the years, a lot of different FFT algorithms have been created. This includes the Split-Radix algorithm., the Fast Hartley Transform (FHT), and slide DFT. This paper investigates the suitable FFT algorithm among the aforementioned techniques, cyclostationary feature detection (CFD)-based spectrum sensing stands out. The methods have been thoroughly compared based on computational time, object size, code size, data dependence (real or complex), and the amount of mathematical operations involved in the computations. Simulation results show that slide FFT is the suitable frequency domain transformation algorithm to use in implementing cyclostationary spectrum sensing in cognitive radios as compared to the other considered algorithms where it provides a significant reduction in FFT stage computation complexity reach to 17% in SRFFT , 78% in FHT and 82% in SDFT while keeping the detection probability at satisfactory level.

Lean healthcare: a science mapping of current progress and future research avenues.

This study intends to reveal the existing research stream and predict future research avenues for lean healthcare (LH). This paper analyzes the progress of LH research and identifies key trends, research gaps and future directions. By mapping the knowledge structure, the study offers insights into the effects of LH on healthcare efficiency, patient care and organizational performance. The review used the bibliometric approach, involving 319 journal articles retrieved from the Web of Science database. Two science mapping approaches (i.e. bibliographic coupling and co-word analysis) were performed to investigate the current knowledge structure and future research direction in LH. The current research trend in LH focuses on developing frameworks and strategic implementation by considering critical determinants and decision-making strategies. In the future, the research on LH will emphasize the holistic frameworks and efficient strategies for healthcare excellence, along with strategies to overcome barriers to its implementation. This study will benefit researchers and practitioners by advancing their understanding and applying LH principles. This study provides valuable practical implications for healthcare managers to navigate the complexities of lean implementation, optimize processes and drive sustainable improvements in the healthcare context.

Advancements in Predictive Maintenance: A Bibliometric Review of Diagnostic Models Using Machine Learning Techniques

This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearings. Using data from Scopus and Web of Science, the review analyses key trends, influential publications, and significant contributions to the field from 2000 to 2024. The findings highlight the performance of ANNs in handling large datasets and modelling complex, non-linear relationships, as well as the high accuracy of SVMs in fault classification tasks, particularly with small-to-medium-sized datasets. However, the study also identifies several limitations, including the dependency on high-quality data, significant computational resource requirements, limited model adaptability, interpretability challenges, and practical implementation complexities. This review provides valuable insights for researchers and engineers, guiding the selection of appropriate diagnostic models and highlighting opportunities for future research. Addressing the identified limitations is crucial for the broader adoption and effectiveness of machine learning-based predictive maintenance strategies across various industrial contexts.