Implementation Complexity Research Articles

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.

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

The complexity of home-based rehabilitation technology implementation for post-stroke motor rehabilitation in the Netherlands

BackgroundRehabilitation technology is a growing field, but the sustainable implementation of these technologies, particularly in home settings, is lacking. The aim of this study was to explore the factors influencing the uptake of stroke rehabilitation technology among various stakeholders, including developers, healthcare professionals, individuals who had strokes, strategic experts, management and innovation staff, health insurers, and the National Health Care Institute.MethodsIn total, 22 semi-structured interviews were conducted with a purposive stakeholder sample. The Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework was used as the theoretical basis for the interview design. The interview content was analysed to generate (sub)themes representing factors influencing the implementation of home-based rehabilitation technology. These (sub)themes were organised according to the NASSS framework domains to ensure a systematic and theoretically grounded analysis.ResultsTen influencing factors emerged, nine of which fell within six of the seven NASSS domains. These factors include: (1) the unpredictable aftermath of stroke, (2) technology (mis)alignment with care delivery processes and end users’ preferences, (3) disparities in the assessment of technology’s value, (4) differences in commercial and university developers’ interests, (5) patient group capabilities, (6) perceived workload, (7) formal implementation plans in rehabilitation centres, (8) laws and regulations, and (9) the financial system. The factor that did not align with a single NASSS domain was: (10) the fragmentation of responsibilities among diverse stakeholders.ConclusionThis study shows that the sustainable implementation of home-based rehabilitation technology faces several challenges across multiple domains of the NASSS framework. Effective collaboration among stakeholders is crucial for addressing these challenges but is currently hindered by fragmented responsibilities. To improve collaboration, it is essential to clearly define the roles and responsibilities of all stakeholders. Additionally, national-level policies adopting a systems approach are necessary to align these responsibilities and foster effective collaboration.

Hybrid MPPT control using hybrid pelican optimization algorithm with perturb and observe for PV connected grid

IntroductionPhotovoltaic systems offer immense potential as a future energy source, yet maximizing their efficiency presents challenges, notably in achieving optimal voltage due to their nonlinear behavior. Operating current and voltage fluctuations, driven by temperature and radiation changes, significantly impact power output. Traditional Maximum Power Point Tracking (MPPT) methods struggle to adapt accurately to these dynamic environmental conditions. While Artificial Intelligence (AI) and optimization techniques show promise, their implementation complexity and longer attainment times for Maximum Power Point (MPP) hinder widespread adoption.MethodThis paper proposes a hybrid MPPT technique that integrates the Pelican Optimization algorithm (POA) with the Perturb and Observe algorithm (P&O) for a grid-connected photovoltaic system (PV). The proposed technique consists of two loops: PO as the reference point setting loop (inner loop) and POA as a fine-tuning (outer)loop. The combination of inner and outer loops minimizes oscillations by adjusting the perturbation direction and enhancing the convergence speed of the MPPT.Results and DiscussionTo validate the efficacy of the proposed MPPT technique for different environmental conditions, a comprehensive comparison is conducted between the proposed hybrid pelican and perturb and observe (HPPO) technique and other MPPT algorithms. The proposed technique has optimized PV and grid outputs with an MPPT efficiency of 99%, best tracking speed, and total harmonic distortion (THD) for all conditions below 5% agree with IEEE 519 standards.

TARGET TRACKING IN THREE-DIMENSIONAL COORDINATES BY COMBINING RADAR AND IRST DATA

Since the tracking of moving targets is a vital issue in military and civilian applications, aerospace industries are always looking for accurate, low-error, computationally light, and uncomplicated algorithms for target tracking. Today, most modern military systems are equipped with various sensors. The ideal operation of such sensors helps to realize target tracking. Due to the nature of the sensor system and the types of noises, one kind of sensor alone cannot be ideally used in target tracking. As a result, several different sensors are used in new systems for tracking. Radar systems are usually used to measure the angle and range of targets. Although they measure the range with high accuracy, radar systems cannot measure the target angle with proper accuracy. On the other hand, IRST data can measure the target angle with high accuracy and determine the direction of the target completely, but they do not provide special information about the target range. Providing a structure to integrate the information of these sensors facilitates the exact location of the target and also tracks the change of the target location. In this study, various structures for target tracking in 3D coordinates have been presented by combining radar data and IRST data. The results of target tracking with radar or IRST are very weak compared to the combination of radar and IRST. The performance of the SVF algorithm is favorable in terms of calculation speed and implementation complexity, as expected. Also, IRST-based tracking alone is expected to require less time and subsequently show poorer performance.

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...

Identifying Barriers to Implementing Complementary and Integrative Health Therapies in Rhode Island Health Care Systems: A Qualitative Approach.

Introduction: Complementary and integrative health (CIH) therapies are widely used and make up billions of dollars in out-of-pocket expenditures. This quality improvement project aimed to identify stakeholders' perceptions of implementing CIH therapies in health care systems to examine the barriers and facilitators to CIH implementation in Rhode Island (RI). Methods: A qualitative approach guided by the Consolidated Framework for Implementation Research (CFIR) was used to conduct interviews with key stakeholder groups: health care administrators, health insurance administrators, physicians, and CIH practitioners. Interviews were analyzed by directed content analysis of transcripts with CFIR as the a priori coding framework. Results: Eleven subconstructs within four major domains were most prominent in interviews across all stakeholder groups. Key barriers in the "innovation" domain include a perceived lack of evidence and credibility of CIH therapies, logistical and administrative complexity of implementation, high costs to patients, and little financial incentive for hospitals and insurance companies. The "outer setting" domain highlights the utility of market pressure in decision making to implement CIH and the role of health policy. The "inner setting" domain demonstrates the varied personal beliefs about CIH, credentialing challenges, and the dissonance between medical system priorities and patient needs. Positive facilitation of implementation is marked by personal exposure to CIH and provider satisfaction in the "implementation process" domain. Conclusion: CIH therapies offer the medical system low risk and effective interventions that meet patient needs not currently met by Western medicine. However, implementation is difficult due to poor perception of evidence and lack of financial gain for the medical system. Acupuncture and massage may be modalities better situated for implementation in the RI medical system. Further education, awareness, and advocacy for CIH therapies would help meet patients' needs for CIH availability.

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

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

Tools and methodologies for scientific evaluation: bibliometrics, scientometrics and informatics

This study analyzed the main tools and methodologies used in bibliometrics, scientometrics, informatics and altmetrics, through a descriptive analytical review of secondary sources published between 2019-2024. The research identified and evaluated the most used tools, including VOSviewer (75% usage in network analysis), CiteSpace (60% in trend identification), and Altmetric.com (80% in alternative metrics). The results revealed that fundamental mathematical models such as Lotka's law (70% application), Zipf's law (55%) and Bradford's law (50%) continue to be essential pillars for the quantitative analysis of scientific production. Altmetrics were found to be gaining significant relevance, with Plum Analytics used in 65% of applications to assess the social impact of research. The study concluded that the integration of traditional and emerging tools allows for a more complete and multidimensional evaluation of scientific activity, although there are limitations in the accessibility and complexity of implementation of some tools.

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.

Comparative Analysis of Phase-Field and Intrinsic Cohesive Zone Models for Fracture Simulations in Multiphase Materials with Interfaces: Investigation of the Influence of the Microstructure on the Fracture Properties

This study evaluates four widely used fracture simulation methods, comparing their computational expenses and implementation complexities within the finite element (FE) framework when employed on heterogeneous solids. Fracture methods considered encompass the intrinsic cohesive zone model (CZM) using zero-thickness cohesive interface elements (CIEs), the standard phase-field fracture (SPFM) approach, the cohesive phase-field fracture (CPFM) approach, and an innovative hybrid model. The hybrid approach combines the CPFM fracture method with the CZM, specifically applying the CZM within the interface zone. The finite element model studied is characterized by three specific phases: inclusions, matrix, and the interface zone. This case study serves as a potential template for meso- or micro-level simulations involving a variety of composite materials. The thorough assessment of these modeling techniques indicates that the CPFM approach stands out as the most effective computational model, provided that the thickness of the interface zone is not significantly smaller than that of the other phases. In materials like concrete, which contain interfaces within their microstructure, the interface thickness is notably small when compared to other phases. This leads to the hybrid model standing as the most authentic finite element model, utilizing CIEs within the interface to simulate interface debonding. A significant finding from this investigation is that within the CPFM method, for a specific interface thickness, convergence with the hybrid model can be observed. This suggests that the CPFM fracture method could serve as a unified fracture approach for multiphase materials when a specific interfacial thickness is used. In addition, this research provides valuable insights that can advance efforts to fine-tune material microstructures. An investigation of the influence of interfacial material properties, voids, and the spatial arrangement of inclusions shows a pronounced effect of these parameters on the fracture toughness of the material.

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.