Reliable Delivery Research Articles

In silico design of a new Podophyllotoxin derivative with potent anti-tubulin activity

Nowadays, there is an increasing focus on cancer as a major cause of death. Therefore, comprehending the chemical reactivity, geometric and electronic structures of inhibitors can aid in the development of better quality and more efficient drugs. In this study, we propose a hybrid compound of Podophyllotoxin (PPT) and vitamin C (VC) as an anti-cancer agent. The PPT and its derivatives are well-known for their anti-tubulin properties. The geometry optimisation and calculation of global chemical reactivity indices (µ = −3.836 eV, η = 2.402 eV, S = 0.416 eV, and ω = 3.061 eV) were performed using the M06/6-311++G (d, p) level of theory. The global reactivity descriptors predict that the PPT-VC compound has more nucleophilic properties compared to Etoposide and Teniposide compounds. Furthermore, the PPT-VC compound exhibits a lower HOMOPPT-VC–LUMOSWCNT gap and a higher adsorption energy (−37.4 kJ mol−1) in the encapsulation process than other derivatives into SWCNT, providing additional evidence of its nucleophilic nature as an effective anti-tubulin agent. Therefore, the PPT-VC@SWCNT compound can be considered a reliable drug delivery system (DDS). Molecular docking analysis reveals that the best cavity of tubulin has an interaction energy of −102.1 kJ mol−1, involving both bonding and non-bonding interactions.

Congestion Avoidance in TCP Based on Optimized Random Forest with Improved Random Early Detection Algorithm

Transmission control protocol (TCP) ensures that data are safely and accurately transported over the network for applications that use the transport protocol to allow reliable information delivery. Nowadays, internet usage in the network is growing and has been developing many protocols in the network layer. Congestion leads to packet loss, the high time required for data transmission in the TCP protocol transport layer for end-to-end connections is one of the biggest issues with the internet. An optimized random forest algorithm (RFA) with improved random early detection (IRED) for congestion prediction and avoidance in transport layer is proposed to overcome the drawbacks. Data are initially gathered and sent through data pre-processing to improve the data quality. For data pre-processing, KNN-based missing value imputation is applied to replace the values that are missing in raw data and [Formula: see text]-score normalization is utilized to scale the data in a certain range. Following that, congestion is predicted using an optimized RFA and whale optimization algorithm (WOA) is used to set the learning rate as efficiently as possible in order to reduce error and improve forecast accuracy. To avoid congestion, IRED method is utilized for a congestion-free network in the transport layer. Performance metrics are evaluated and compared with the existing techniques with respect to accuracy, precision, recall, specificity, and error, whose values that occur for the proposed model are 98%, 98%, 99%, 98%, and 1%. Throughput and latency are also evaluated in the proposed method to determine the performance of the network. Finally, the proposed method performs better when compared to the existing techniques and prediction, and avoidance of congestion is identified accurately in the network.

Redox-responsive Nanomicelles with Intracellular Targeting and Programmable Drug Release for Targeted Tumor Therapy.

Anti-inflammatory medications, in particular aspirin, have chemopreventive and anticancer adjuvant effects on specific types of cancers, according to ongoing anti-tumor research. Additionally, efforts have been made to transform Poly(salicylic acid) (PSA) into delivery-related nanocarriers. to transport anticancer medications into nanocarriers. However, tumor cell targeting and tumor selectivity were lacking in the salicylic acid polymer-based nanocarriers, preventing them from performing to their full potential. The objective of this study is to prepare targeting and reduction-responsive poly pre-drug nanocarriers (HA-ss-PSA NPs) and to investigate the feasibility of delivering adriamycin (DOX) as nanocarriers. The structures of the polymers were confirmed by nuclear magnetic resonance hydrogen spectroscopy (1H-NMR) and infrared spectroscopy (IR); the encapsulation rate and drug loading of DOX-loaded nanoparticles were determined by HPLC; and the anti-tumor effects of the carriers were evaluated by MTT experiments and in vivo</i> experiments. The prepared nanocarriers had uniform particle size distribution. The drug release rate was up to 80% within 48 h in the tumor environment. DOX/HA-ss-PSA NPs showed significant cytostatic effects. In addition, HA-ss-PSA NPs showed significant targeting and inhibition of cell migration in cell uptake and scratch assays. In vivo</i> experiments showed that the prepared carriers had high tumor inhibition rates, good targeting effects on the liver and tumor, and significantly reduced toxicity to other tissues. The prepared HA-ss-PSA NPs could effectively inhibit the growth of HepG2 cells and tumors in vivo</i>, indicating that PSA could be used as a backbone component of a safe and reliable drug delivery system, providing a new strategy for the treatment of liver cancer.

Downregulation of Hypoxia Inducible Factor-1α in Primary Human Natural Killer Cells Using Small Interfering RNA Delivery with ExPERT ATx by MaxCyte.

Natural killer (NK) cells are innate cytokine-producing and cytolytic effector lymphocytes. Their function is responsive to environmental factors, e.g., hypoxia, a frequent feature of inflamed tissues. Such responses require that the NK cells up-regulate HIF-1α (hypoxia inducible factor-1α), the major mediator of cellular responses to hypoxia that affects cell survival as well as immune responses. Thus, a major approach to the study of NK cell effector function under hypoxic conditions involves the ability to regulate HIF-1α levels in primary human NK cells. One difficulty with this approach, however, is that NK cells are difficult-to-transfect cells and common transfection methods, including electroporation or lipofection, suffer from variable transfection efficiency and cell viability. Moreover, the detection of HIF-1α is technically challenging because of the rapid degradation of the protein under normoxic conditions. Here, using the commercially available ExPERT ATx by MaxCyte, we report a workflow for the reliable delivery of small interfering RNA (siRNA) for targeting HIF-1α expression in primary human NK cells. We further provide a protocol for the detection of HIF-1α by immunoblot analysis demonstrating its efficient downregulation by siRNA. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Isolation of natural killer cells from human peripheral blood mononuclear cells Basic Protocol 2: Delivery of non-coding small interfering RNA and HIF-1α targeting siRNA into natural killer cells using ExPERT ATx Basic Protocol 3: Assessing the downregulation of HIF-1α protein using immunoblot analysis Support Protocol 1: Exemplary assessment of transfection efficiency using fluorescently labeled non-targeting siRNA Support Protocol 2: Exemplary assessment of NK cell viability 20 hr post-transfection Support Protocol 3: Exemplary assessment of HIF-1α knockdown using immunoblot analysis.

An effect of dynamic logistics capabilities on courier’s business performance

Courier companies face environmental challenges influenced by the development of e-commerce businesses, demanding high service quality and reliability in meeting consumer demand. Courier companies demand to&lt;br /&gt;immediately adjust company strategies to be able to survive in competitive market conditions, the only way is to continue to keep dynamic on internal capability. This study develops and tests a model of the relationship between dynamic logistic capabilities, environmental turbulence, and business performance. As quantitative research, a survey was used to collect data. A sample of 70 valid answers from courier company members of the Association of Indonesian Express Delivery, Postal, and Logistics Services Companies(ASPERINDO) was treated through the Partial Least Square (PLS) method. The research results show a full mediation relationship between dynamic logistics capability and business performance, given the proportion of the impact of dynamic logistics capabilities on business performance (72.7%) identified in this study. As a result, the main result points out that the positive correlation between dynamic logistics capabilities and business performance strengthens the relationship evidence in the literature that dynamic logistics capabilities contribute to business performance, that is, sales growth, delivery reliability and security.

Perancangan Aplikasi Pemesanan Bahan Makanan Frozen Toko Makmur Jaya

In this digital era, online buying and selling transactions are increasingly popular with the public. This is also driven by increasing public awareness of the importance of convenience and efficiency in shopping. Toko Makmur Jaya, as a seller of frozen foodstuffs, has a great opportunity to develop in this digital era. By designing a special ordering application, Toko Makmur Jaya can reach customers more easily, increase sales and build customer loyalty. This research aims to design a frozen food ordering application for Toko Makmur Jaya that suits customer needs and desires. This research uses a qualitative research method with a case study approach. Research data was obtained through interviews with the owner of Toko Makmur Jaya and a survey of Toko Makmur Jaya customers. Based on the research results, the Toko Makmur Jaya frozen food ordering application needs to be equipped with features and functions which include: Complete and attractive product catalog, Easy to use and order, Various payments, Fast and reliable delivery, Attractive promotions and discounts Apart from these features and functions, the Toko Makmur Jaya frozen food ordering application also needs to be supported by an efficient and reliable logistics system to maintain the quality of frozen products. The success of the Toko Makmur Jaya application is not only determined by features and functions, but also several other important factors, such as branding and marketing, customer service, and continuous analysis and development..

A safe and effective winged peripheral intravenous cannula for use in clinical practice.

Vascular access continues to be a key factor for the reliable and safe delivery of intravenous (IV) therapy to patients in any healthcare setting. Clinical guidelines advocate for the right vascular access device selection, in order to reduce avoidable complications, eg multiple stabs, phlebitis, thrombophlebitis, insertion site infection, and blood stream infection, while improving efficiency and reducing costs. Peripheral intravenous catheters or cannulas (PIVCs) remain widely used for gaining vascular access in all clinical settings, with both adults and children, because they provide a relatively cheap and simple way to provide blood sampling and the prompt administration of IV medications. Although safe and easy to insert, PIVCs present with associated risks that can be costly to the organisation. The case studies included with this article introduce Nipro's Safetouch Cath Winged with Injection Port as a new cost-effective choice of PIVC, which is now available from NHS Supply Chain.

Alginate microspheres encapsulating hox transcript antisense RNA siRNA regulate the Hedgehog-Gli1 pathway to alleviate epidermal growth factor receptor tyrosine kinase inhibitors resistance.

The long non-coding RNA HOTAIR and the Hedgehog-Gli1 signaling pathway are closely associated with tumor occurrence and drug resistance in various cancers. However, their specific roles in the development of EGFR-TKIs resistance in non-small cell carcinoma remain unclear. To address the issue of EGFR-TKIs resistance, this study utilized the electrospray method to prepare sodium alginate microspheres encapsulating HOTAIR siRNA (SA/HOTAIR siRNA) and investigated its effects on RNA interference (RNAi) in the gefitinib-resistant cell line PC9/GR. Furthermore, the study explored whether HOTAIR could modulate EGFR-TKIs resistance through the Hedgehog-GLi1 signaling pathway. The experimental results showed that sodium alginate (SA) microspheres demonstrated excellent biocompatibility with high encapsulation efficiency and drug-loading capacity, effectively enhancing the silencing efficiency of siRNA. HOTAIR siRNA significantly inhibited the proliferation, migration, and invasion abilities of PC9/GR cells while promoting apoptosis. Additionally, HOTAIR siRNA effectively suppressed tumor growth and downregulated the Hedgehog-GLi1 pathway and anti-apoptotic proteins, which were confirmed in animal experiments. Moreover, SA/HOTAIR siRNA exhibited superior inhibition of cellular and tumor functions compared to using HOTAIR siRNA alone. Clinical research findings indicated that monitoring the expression level of HOTAIR in the serum and urine samples of NSCLC patients before and after receiving EGFR-TKIs treatment can predict the efficacy of EGFR-TKIs to a certain extent. This study provided evidence that HOTAIR siRNA effectively mitigated the development of acquired resistance to EGFR-TKIs by inhibiting the Hedgehog-GLi1 pathway. Furthermore, it introduced a reliable and long-lasting drug delivery system for combating acquired resistance to EGFR-TKIs.

An Exploratory Examination of Sustainable Food Purchasing by Restaurants in Paris, France, by Product Category

While executive chefs invariably search for consistent quality, reliable delivery and a competitive price, few studies have sought to demonstrate how the importance of the supplier selection criteria differs by product category for sustainable food ingredients. For this study, 49 executive chefs in Paris, France, were interviewed using a structured questionnaire to explore the importance of the criteria chefs employed in purchasing five groups of food products: meat, fish, fresh fruit and vegetables, dairy products and baked products. Cluster analysis revealed the presence of two distinctly different groups of chefs: those who were primarily concerned about price and those who sought to purchase sustainably produced food ingredients. For those chefs purchasing primarily on price, there were no significant differences in the importance of the criteria utilized in the supplier selection process across the five product categories. However, for those chefs who sought to buy sustainably produced food ingredients, sourcing product from a desired geographic region was significantly less important for baked products. Similarly, in considering whether the food ingredients had been produced ethically and grown locally and sustainably, each of these criteria were ranked significantly lower for baked products than any other food category. Conversely, the availability of products all year round, proximity to the restaurant and the physical appearance of the product were all ranked significantly higher for baked products than any other food category.

A Survey on Routing Solutions for Low-Power and Lossy Networks: Toward a Reliable Path-Finding Approach

Low-Power and Lossy Networks (LLNs) have grown rapidly in recent years owing to the increased adoption of Internet of Things (IoT) and Machine-to-Machine (M2M) applications across various industries, including smart homes, industrial automation, healthcare, and smart cities. Owing to the characteristics of LLNs, such as Lossy channels and limited power, generic routing solutions designed for non-LLNs may not be adequate in terms of delivery reliability and routing efficiency. Consequently, a routing protocol for LLNs (RPL) was designed. Several RPL objective functions have been proposed to enhance the routing reliability in LLNs. This paper analyses these solutions against performance and security requirements to identify their limitations. Firstly, it discusses the characteristics and security issues of LLN and their impact on packet delivery reliability and routing efficiency. Secondly, it provides a comprehensive analysis of routing solutions and identifies existing limitations. Thirdly, based on these limitations, this paper highlights the need for a reliable and efficient path-finding solution for LLNs.

Case study on delivery time determination using a machine learning approach in small batch production companies

AbstractDelivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades. In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions. Based on the data provided by two separate manufacturing companies, this paper presents a machine learning-based approach for predicting delivery times as soon as a request for an offer is received considering the desired customer delivery date as a feature.

Facilitators and Barriers of Implementation of Evidence-Based Parenting Support in Educational Settings

Improving child behavior and promoting family well-being is a key objective of evidence-based parenting programs, such as the Triple P–Positive Parenting Program. To achieve this goal, parenting programs are delivered using a multidisciplinary workforce. Previous researchers have collectively examined the entire workforce of parenting practitioners to determine the factors that influence program delivery, primarily using self-report measures. However, these findings did not highlight the unique factors relevant to specific practitioner disciplines. Educators are one practitioner discipline that play an integral role in delivering parenting programs through schools and early childhood learning settings. This study aimed at exploring the facilitators and barriers that impact frequency of program use for educator practitioners using both qualitative and quantitative analyses. Data from 404 Triple P educator practitioners were extracted from a larger dataset of 1202 practitioners from English-speaking countries who completed self-report questionnaires and responded to three open-ended questions. Hierarchical multiple regressions were conducted using eight independent variables (with participant characteristics as control variables), revealing seven positive and one negative predictor for frequency of use. A thematic analysis was then conducted on the qualitative responses, producing 11 themes and 28 subthemes. The quantitative analysis revealed organisational support, perceived usefulness, and practitioner self-regulation were the most important positive predictors. The qualitative analysis supported these findings and revealed novel barriers including Covid-19/work from home, online delivery, parent factors, and specific organisational factors. These findings highlight the need for online resources, reliable virtual delivery methods, improved ways to reach and engage families, and additional trained education practitioners to distribute high workloads.

Multi-comparted microgels delivering human derived probiotics and deferoxamine for multidrug-resistant infection and healing

Infection and vascular dysfunction are two major reasons for delayed wound healing, especially in the scenario of multidrug-resistant bacterial has been a challenge for antibiotics usage. Probiotics are effective in fighting pathogens, while avoiding the destruction of skin microbiome. However, the viability of probiotics is a major obstacle in clinical application. As a non-protein proangiogenic drug, deferoxamine (DFO) has significant advantages over traditional growth factors in terms of cost, storage and transportation conditions, and stability. However, there is still a problem of short half-life that requires reliable drug delivery carriers to maximize its effect. Here, we developed multiple-chambered microgels using electrospray and microfluidics. Lactobacillus fermentum (LF) and DFO, respectively, for the management of multidrug-resistant Pseudomonas aeruginosa (MPA) and promotion of wound healing. We first tested five probiotics isolated from healthy people and found LF inhibiting the growth of MPA by lowering local pH and antimicrobial substances. The multi-chambered microgels effectively inhibited MPA by delivering probiotics, they can also protect probiotics from antibiotics. DFO promoted the angiogenesis of human umbilical venous endothelial cells. The microgel system had good biocompatibility and hemocompatibility. In a MPA infected wound, the microgel system effectively alleviated wound infection by 87.44%, improved angiogenesis by 29.32% and accelerated wound healing. This work suggests that multi-compartmented microgels loaded with probiotics and deferoxamine have great potential in wounds with multidrug-resistant bacteria.

DecentraliDrone: A decentralized, fully autonomous drone delivery system for reliable, efficient transport of goods

As the world becomes more interconnected and globalized, the demand for efficient and reliable delivery systems is on the rise. To meet this demand, we propose DecentraliDrone, an innovative drone delivery system that utilizes advanced technology to transport goods autonomously and efficiently. Our system is fully decentralized, meaning that it operates without the need for human intervention and relies on a distributed network of drones and devices to function. One of the key features of DecentraliDrone is its autonomy. The system operates without the need for human intervention, enabling it to operate 24/7 and reducing the risk of accidents caused by human error. Additionally, our system is highly scalable, allowing it to adapt to changing demands and expand its delivery capabilities. There are several challenges that must be addressed in order to fully realize the potential of DecentraliDrone. These include developing robust communication protocols, ensuring the safety and security of the system, and addressing regulatory and legal issues related to autonomous delivery systems.

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.

Integration Challenges and Solutions for Solar-Powered Electric Vehicle Charging Infrastructure: From Panel to Battery

The integration of solar power with electric vehicle (EV) charging infrastructure presents a promising avenue to foster sustainable transportation. This study delves into the multifaceted challenges encountered in the synthesis of solar-powered EV charging stations and proffers solutions that span the complete energy transfer chain from photovoltaic panels to EV batteries. Initial concerns address the intermittent nature of solar energy and its impact on the reliability of power delivery. Advanced energy management strategies are explored, incorporating predictive analytics and real-time adjustment mechanisms to enhance the consistency and efficiency of power flow. The second part of the investigation scrutinizes the power electronics interface, emphasizing the need for high-efficiency converters that can operate effectively over varied solar insolation levels. Novel circuit topologies are presented, alongside adaptive control algorithms designed to optimize the power conversion process. Collectively, the findings underscore the potential of solar-powered EV charging infrastructure to not only support the decarbonization of transportation but also to contribute to the stability and efficiency of the power grid. The intersection of power electronics, energy management, and grid integration forms the cornerstone of this endeavor, with each domain providing critical components to the holistic solution. It culminates in a set of recommendations for policy, design, and operation that can accelerate the adoption of this technology.

The Smart Trust framework for WBAN: An AI-driven approach for node trust assessment

The primary contribution of this research lies in its innovative use of artificial intelligence to automate the trust assessment process in WBANs, providing a dynamic solution to the challenge of maintaining data integrity and network reliability. The SmartTrust (SmTr) framework uses advanced machine learning techniques to accurately analyze historical and behavioral data of network nodes. Thus, computer trustworthiness scores allow one to effectively distinguish between trustworthy nodes and potentially malicious nodes. WBANs and their services are rapidly gaining popularity, but they pose unprecedented security challenges. These requirements are being met with WBAN as it evolves. In an increasingly complex, heterogeneous, and evolving mobile environment, completing these tasks can be difficult. A more secure and adaptable WBAN environment can be achieved by using trust management to meet WBAN security requirements. The reliability of a wireless sensor network is evaluated through behavioral evidence. Researchers use the results of node behavior almost directly or combine them with the results of third-party evaluation, instead of studying the original evidence of node behavior and ignoring the analysis of the history of node behavior, which leads to low confidence, rationality, and reliability. SmartTrust (SmTr) is a new approach based on artificial intelligence (AI) to improve trust and reliability over wireless body area networks (WBAN). As a modern healthcare system, this technology can be considered. Experimental results from implementing the SmTr framework demonstrate its effectiveness in improving network resilience against security threats, improving resource allocation, and thus increasing the quality and reliability of healthcare delivery.

SFC active reconfiguration based on user mobility and resource demand prediction in dynamic IoT-MEC networks.

To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge. In this paper, we study the SFC Reconfiguration Strategy (SFC-RS) based on user mobility and resource demand prediction in IoT MEC networks, aiming to minimize the end-to-end delay and reconfiguration cost of SFCs. First, we model SFC-RS as Integer Linear Programming (ILP). Then, a user trajectory prediction model based on codec movement with attention mechanism and a VNF resource demand prediction model based on the Long Short-Term Memory (LSTM) network are designed to accurately predict user trajectories and node computational and storage resources, respectively. Based on the prediction results, a Prediction-based SFV Active Reconfiguration (PSAR) algorithm is proposed to achieve seamless SFC migration and routing update before the user experience quality degrades, ensuring network consistency and high quality service. Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.

Opportunistic Block Validation for IoT Blockchain Networks

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.

Pay practices and safety organizing: Evidence from hospital nursing units.

Our understanding of how highly reliable care delivery is brought about remains elusive, in part, because there is limited evidence regarding the organizational practices that enable safety organizing-the behaviors and processes underlying high reliability. Because safety organizing relies on discretionary effort and lowering barriers to sharing expertise and discussing threats to safety and errors, we investigate three pay practices and their effects on information sharing and, in turn, safety organizing. Specifically, we examine average pay level, minimum pay rates, and pay dispersion on nursing units and their relationship with information sharing and safety organizing. Cross-sectional analyses of survey data from 1,461 registered nurses in 45 nursing units in three Midwestern hospitals on safety organizing linked to administrative data on pay practices from the organization's human resource systems. Pay data and survey responses were aggregated to the nursing unit level. PROCESS and structural equation modeling were used to simultaneously test for direct and indirect effects of pay variables on information sharing and safety organizing. PROCESS and Mplus path analysis indicated that paying a higher minimum rate in the unit and having lower pay dispersion have indirect, desirable associations with safety organizing through information sharing. Pay practices can help organizations enhance safety organizing. In particular, higher pay rates for the lowest level nurses and lower pay dispersion among nurses are associated with unit-level information sharing and safety organizing. Having pay practices associated with lower within-unit variation and higher pay for the lowest paid members of a unit may be viable strategies for greater information sharing and safety organizing.