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As the global population continues growing, the demand for textiles also increases, putting pressure on cotton manufacturers to produce more natural fiber from this already undersupplied resource. Synthetic fibers such as polyester (PET) can be manufactured quickly and cheaply, but these petroleum-based products are detrimental to the environment. With increased efforts to encourage transparency and create a more circular textile economy, other natural alternatives must be considered. This article discusses the existing condition and future possibilities for man-made cellulosic fibers (MMCFs), with an emphasis on using non-woody alternative feedstocks as a starting material. This work focuses on conversion technology suitable for producing textile-grade fibers from non-wood-based dissolving pulp, which may be different in nature from its woody counterpart and therefore behave differently in spinning processes. Derivatization and dissolution methods are detailed, along with spinning techniques and parameters for these processes. Existing research related to the spinning of non-woody-based dissolving pulp is covered, along with suggestions for the most promising feedstock and technology combinations. In addition, an emerging method of conversion, in which textile fibers are spun from a hydrogel made of an undissolved nano/micro-fibrillated fiber suspension, is briefly discussed due to its unique potential. Methods and concepts compiled in this review relate to utilizing alternative feedstocks for future fibers while providing a better understanding of conventional and emerging fiber spinning processes for these fibers.

The biodegradation kinetics of paper materials with various chemical additives was studied, focusing on their potential tunability. Paper materials with additives, including retention aid, hydrophobic agent, and wet and dry strength agents, were explored in two forms: disintegrated fiber and paper form. Using the Gompertz equation, biodegradation kinetics were modeled to calculate the lag phase, initial biodegradation rate, and ultimate biodegradation extent. All paper materials showed higher biodegradation extents than microcrystalline cellulose (MCC) due to the highly biodegradable nature of hardwood bleached pulp. Disintegrated paper materials exhibited similar lag phase values and ultimate biodegradation to MCC regardless of treatment, while punched paper materials showed noticeable differences, suggesting that fiber disintegration plays a critical role in initiating biodegradation. Hydrophobic and wet strength treatments, such as alkyl ketone dimer (AKD) and wet-strength agents (PAE), respectively, significantly increased the lag phase, but their ultimate biodegradation extent remained intact. These findings highlight that the biodegradability of paper materials can be preserved even after chemical treatments, underscoring their environmentally friendly potential.

Summary: Microplastics fibers shed from washing synthetic textiles are released directly into the waters and make up 35% of primary microplastics discharged to the aquatic environment. While filtration devices and regulations are in development, safe disposal methods remain absent. Herein, we investigate catalytic hydrothermal carbonization (HTC) as a means of integrating this waste (0.28 million tons of microfibers per year) into the circular economy by catalytic upcycling to carbon nanomaterials. Herein, we show that cotton and polyester can be converted to filamentous solid carbon nanostructures using a Fe-Ni catalyst during HTC. Results revealed the conversion of microfibers into amorphous and graphitic carbon structures, including carbon nanotubes from a cotton/polyethylene terephthalate (PET) mixture. HTC at 200°C and 22 bar pressure produced graphitic carbon in all samples, demonstrating that mixed microfiber wastes can be valorized to provide potentially valuable carbon structures by modifying reaction parameters and catalyst formulation.

Abstract Purpose Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024. Methods The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement. Results From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review’s title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts. Conclusion PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. Note In order to encourage its wide dissemination this article is freely accessible on the web sites of the journals: Health and Quality of Life Outcomes; Journal of Clinical Epidemiology; Journal of Patient-Reported Outcomes; Quality of Life Research.

As the global population grows, the demand for textiles is increasing rapidly. However, this puts immense pressure on manufacturers to produce more fiber. While synthetic fibers can be produced cheaply, they have a negative impact on the environment. On the other hand, fibers from wool, sisal, fique, wood pulp (viscose), and man-made cellulose fibers (MMCFs) from cotton cannot alone meet the growing fiber demand without major stresses on land, water, and existing markets using these materials. With a greater emphasis on transparency and circular economy practices, there is a need to consider natural non-wood alternative sources for MMCFs to supplement other fiber types. However, introducing new feedstocks with different compositions may require different biomass conversion methods. Therefore, based on existing work, this review addresses the technical feasibility of various alternative feedstocks for conversion to textile-grade fibers. First, alternative feedstocks are introduced, and then conventional (dissolving pulp) and emerging (fibrillated cellulose and recycled material) conversion technologies are evaluated to help select the most suitable and promising processes for these emerging alternative sources of cellulose. It is important to note that for alternative feedstocks to be adopted on a meaningful scale, high biomass availability and proximity of conversion facilities are critical factors. In North America, soybean, wheat, rice, sorghum, and sugarcane residues are widely available and most suitable for conventional conversion through various dissolving pulp production methods (pre-hydrolysis kraft, acid sulfite, soda, SO2-ethanol-water, and potassium hydroxide) or by emerging cellulose fibrillation methods. While dissolving pulp conversion is well-established, fibrillated cellulose methods could be beneficial from cost, efficiency, and environmental perspectives. Thus, the authors strongly encourage more work in this growing research area. However, conducting thorough cost and sustainability assessments is important to determine the best feedstock and technology combinations.

Abstract The Northwest European shelf experienced unprecedented surface temperature anomalies in June 2023 (anomalies up to 5 °C locally, north of Ireland). Here, we show the shelf average underwent its longest recorded category II marine heatwave (16 days). With state-of-the-art observation and modelling capabilities, we show the marine heatwave developed quickly due to strong atmospheric forcing (high level of sunshine, weak winds, tropical air) and weak wave activity under anticyclonic weather regimes. Once formed, this shallow marine heatwave fed back on the weather: over the sea it reduced cloud cover and over land it contributed to breaking June mean temperature records and to enhanced convective rainfall through stronger, warmer and moister sea breezes. This marine heatwave was intensified by the last 20-year warming trend in sea surface temperatures. Such sea surface temperatures are projected to become commonplace by the middle of the century under a high greenhouse gas emission scenario.

Quantum measurements are a fundamental component of quantum computing. However, on present-day quantum computers, measurements can be more error prone than quantum gates and are susceptible to nonunital errors as well as nonlocal correlations due to measurement crosstalk. While readout errors can be mitigated in postprocessing, this is inefficient in the number of qubits due to a combinatorially large number of possible states that need to be characterized. In this work, we show that measurement errors can be tailored into a simple stochastic error model using randomized compiling, enabling the efficient mitigation of readout errors via quasiprobability distributions reconstructed from the measurement of a single preparation state in an exponentially large confusion matrix. We demonstrate the scalability and power of this approach by correcting readout errors without matrix inversion on a large number of different preparation states applied to a register of eight superconducting transmon qubits. Moreover, we show that this method can be extended to midcircuit measurements used for active feedback via quasiprobabilistic error cancellation, and we demonstrate the correction of measurement errors on an ancilla qubit used to detect and actively correct bit-flip errors on an entangled memory qubit. Our approach enables the correction of readout errors on large numbers of qubits and offers a strategy for correcting readout errors in adaptive circuits in which the results of midcircuit measurements are used to perform conditional operations on nonlocal qubits in real time.

IntroductionThis work presents a method to treat stereotactic body radiation therapy (SBRT) for pancreatic cancer on a magnetic resonance-guided linear accelerator (MR-linac) using daily adaptation, real-time motion monitoring, and abdominal compression.MethodsThe motion management and treatment planning process involves a magnetic resonance imaging (MRI) simulation with cine and 3D images, a computed tomography (CT) simulation with a breath-hold CT and a 4DCT, pre-treatment verification and planning MRI, and intrafraction MRI cine images.ResultsThe results from 26 patients were included in this work. Our motion management process results in consistent motion analysis on the CT simulation, MRI simulation, and each treatment fraction. The liver dome was found to be an overestimate of tumor superior/inferior (SI) motion for most patients. Adding compression reduced SI liver dome motion by 6.2 mm on average. Clinical outcomes are similar to those observed in the literature.ConclusionsIn this work, we demonstrate how pancreatic SBRT can be successfully treated on an MR-linac using abdominal compression. This allows for an increased duty cycle compared to gating and/or breath-hold techniques.

Abstract Transition metal carbides, including both MXene and non‐MXene metal carbides, have enjoyed a soaring reputation in recent years. Benefitting from their intriguing physical and chemical characteristics, they shine in multifarious research fields and currently, they have emerged as promising nanomaterials for photocatalysis in energy and environmental science. Herein, based on the recent theoretical research and experimental studies, a systematic and comprehensive review of the expeditious advances of metal carbides and their nano‐architectures in the flourishing arena of photocatalysis is presented. The fundamental mechanism involved in photocatalysis with metal carbides serving as semiconductors or cocatalysts is thoroughly discussed. Besides, we highlight the main synthetic strategies of MXene and non‐MXene metal carbides and unravel the structural properties of the as‐obtained metal carbides via different fabrication routes to establish and elucidate their intriguing role in ameliorating photocatalytic activity. Moreover, the state‐of‐the‐art advancements of metal carbides in diverse photocatalytic applications, including hydrogen evolution reaction, oxygen evolution reaction, overall water splitting, and carbon dioxide reduction reaction, are summarized. In particular, insights into the structure–activity relationship of metal carbide in photocatalysis are elucidated. Finally, this review concludes with the ongoing challenges and perspectives on the future directions of metal carbides in the realm of photocatalysis.

The preface of the permeated metaverse together with Web 3.0, has confluenced virtual and physical reality and can revolutionize social networks, healthcare, gaming and the educational system. Unfortunately, this assimilation has divulged ways for physical and virtual-reality-synthesized security threats like avatar impersonation and sybil attacks. Furthermore, the divergence in metaverse platforms and the involvement of central intermediaries prompted the demand for interoperability, security provisions and decentralization. Therefore, this paper projects a blockchain-integrated authentication protocol for a seamless interface in a cross-platform metaverse network with Web 3.0. The proposed mechanisms are structured for user and avatar authentication within cross-platforms while furnishing conditional traceability, an avatar-update phase and a revocation phase. The blockchain network hinges on characteristics of a consortium blockchain and the voting-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The security of the proposed network is displayed by harnessing the Real-or-Random (RoR) oracle model and informal analysis. The operational effectiveness of the suggested system is gauged through an assessment of relevant existing work based on metrics such as communication, bandwidth, and computational costs. The study’s results indicate that the proposed system exhibits better computation and communication overhead compared to other systems, making it well-suited for metaverse applications in Web 3.0.

Generative artificial intelligence (GAI) can be broadly described as an artificial intelligence system capable of generating images, text, and other media types with human prompts. GAI models like ChatGPT, DALL-E, and Bard have recently caught the attention of industry and academia equally. GAI applications span various industries like art, gaming, fashion, and healthcare. In healthcare, GAI shows promise in medical research, diagnosis, treatment, and patient care and is already making strides in real-world deployments. There has yet to be any detailed study concerning the applications and scope of GAI in healthcare. Addressing this research gap, we explore several applications, real-world scenarios, and limitations of GAI in healthcare. We examine how GAI models like ChatGPT and DALL-E can be leveraged to aid in the applications of medical imaging, drug discovery, personalized patient treatment, medical simulation and training, clinical trial optimization, mental health support, healthcare operations and research, medical chatbots, human movement simulation, and a few more applications. Along with applications, we cover four real-world healthcare scenarios that employ GAI: visual snow syndrome diagnosis, molecular drug optimization, medical education, and dentistry. We also provide an elaborate discussion on seven healthcare-customized LLMs like Med-PaLM, BioGPT, DeepHealth, etc.,Since GAI is still evolving, it poses challenges like the lack of professional expertise in decision making, risk of patient data privacy, issues in integrating with existing healthcare systems, and the problem of data bias which are elaborated on in this work along with several other challenges. We also put forward multiple directions for future research in GAI for healthcare.

Cloud computing offers dynamic, scalable, and virtualized computing resources to end users over the internet. Load balancing is crucial for efficient resource use, distributing workloads across multiple resources to prevent overloading. Load balancing is crucial for resource utilization and processing time reduction, but traditional algorithms are often stuck at local maxima, leading to unequal allocation and performance decline. A metaheuristic based algorithm is proposed to dynamically adjust load distribution, ensuring resilience and sensitivity to changing workloads while managing energy consumption. This research presents a Rock Hyrax-based load balancing algorithm that addresses local maxima and power efficiency issues using QoS parameters. The algorithm’s performance is evaluated qualitatively and statistically, considering both static and dynamic modes of jobs and virtual machines. Comparing it with existing scheduling algorithms, the algorithm reduces makespan by 10%–15% and total energy consumption in data centers by 8%–13%. These results demonstrate the effectiveness of the Rock Hyrax-based load balancing algorithm in improving performance and energy efficiency in data centers, highlighting its potential impact on optimizing resource allocation and enhancing overall system performance.

A land administration system (LAS) is a structured framework designed to govern the management of land resources in a specific region or country. However, LAS faces challenges like inefficiencies, a lack of transparency, and susceptibility to fraud. The digitization of land records improved efficiency but failed to address manipulation, centralized databases, and double-spending issues. Traditional lease and mortgage management systems also suffer from complexity, errors, and a lack of real-time validation. At present, a significant influx of land transactions produces substantial data, classifiable as big data due to constant minute-to-minute occurrences like land transfers, acquisitions, document verification, and leasing/mortgaging transactions. In this context, we present a Blockchain-driven system that not only tackles alteration and double-spending issues in traditional systems but also implements distributed data management. Current state-of-the-art solutions do not fully incorporate crucial features of Blockchain, such as transparency, prevention of double-spending, auditability, immutability, and user participation. To tackle this problem, this research introduces a comprehensive Blockchain-powered framework for lease and mortgage management, addressing transparency, user involvement, and double-spending prevention. Unlike existing solutions, our framework integrates key Blockchain characteristics for a holistic approach. Through practical use cases involving property owners, banks, and financial institutions, we establish a secure, distributed, and transparent method for property financing. We verify the system by employing smart contracts and assess the cost and security parameters while validating the blockchain-based mortgage and lease functions.

Network management is a crucial task to maintain modern systems and applications running. Some applications have become vital for society and are expected to have zero downtime. Software-defined networks is a paradigm that collaborates with the scalability, modularity and manageability of systems by centralizing the network’s controller. However, this creates a weak point for distributed denial of service attacks if unprepared. This study proposes an anomaly detection system to detect distributed denial of service attacks in software-defined networks using generative adversarial neural networks with gated recurrent units. The proposed system uses unsupervised learning to detect unknown attacks in an interval of 1 second. A mitigation algorithm is also proposed to stop distributed denial-of-service attacks from harming the network’s operation. Two datasets were used to validate this model: the first developed by the computer networks study group Orion from the State University of Londrina. The second is a well-known dataset: CIC-DDoS2019, widely used by the anomaly detection community. Besides the gated recurrent units, other types of neurons are also tested in this work, they are: long short-term memory, convolutional and temporal convolutional. The detection module reached an F1-score of 99@ in the first dataset and 98@ in the second, while the mitigation module could drop 99@ of malicious flows in both datasets.

Efficient and reliable management of the business operations within the food supply chain is crucial to ensure the quality, safety, traceability, and security of food products. Streamlining different food supply chain management operations by utilizing cutting-edge technologies such as Cloud computing and the Internet of Things (IoT) has gained enough attention in recent years. Most of the systems developed for streamlining food supply chain management operations are centralized and offer limited traceability, reliability, transparency, immutability, provenance, and consumer trust in food products. Furthermore, such systems are prone to single-point failure, as well as data tampering and alteration. Blockchain is a decentralized technology that follows a distributed peer-to-peer (P2P) architecture for storing and processing data. It offers several benefits to participants of the food supply chain in terms of improved data provenance, transparency, traceability, security, and immutability. This article discusses blockchain technology’s potential role and opportunities in transforming food supply chain processes. It identifies and discusses key requirements of food supply chain participants that blockchain technology must meet to efficiently manage the business processes involved in the food supply chain. It explores case studies and research projects in the food sector that are based on blockchain technology, aiming to highlight the applicability of blockchain in the food supply chain. Finally, it identifies and discusses open research issues and challenges in the food industry.

The burgeoning concept of the metaverse as an interconnected virtual space represents the forefront of the next-generation internet. Quantum teleportation, known for its prowess in ensuring secure and reliable communications, stands poised to revolutionize interactions within this immersive digital realm. In this context, we propose a comprehensive interaction protocol tailored for the metaverse environment. The designed protocol entails two fundamental components: first, the interaction between a user and their avatar, facilitated by a secure seven-qubit entangled state; and second, the interaction between two avatars, enabled through an efficient two-qubit entangled state. To fortify the protocol’s resilience, quantum key distribution (QKD) is employed for secure key sharing, while a trusted certificate authority serves as an essential entity for signature verification. Through rigorous safety and efficiency analysis, we demonstrate the protocol’s robustness and performance, ensuring adherence to fundamental security properties such as unforgeability, undeniability, verifiability and traceability. By seamlessly integrating quantum teleportation principles with the metaverse environment, our protocol not only enhances security but also unlocks novel avenues for interaction and exploration within this digital frontier.

Track quality and train operating safety depend on routine inspections of track components. According to statistics from the Federal Railroad Administration (FRA), train accidents in the United States are primarily caused by the failure of track components. Existing technologies, such as fault detection and fault segmentation, have been used to increase efficiency. However, these models sometimes fail when dealing with small or broken cracks. To address this issue, we suggest implementing a fusion model that uses a deep convolutional neural network for fault detection and semantic segmentation on railway lines. The ResNet50 network is used for fault identification in this model, while the Deep Residual U-Net network is applied for semantic segmentation. The identified defects are isolated, and decisions can be made from resulting binary image segments on whether to inspect or disregard detected faults. These defects include damages observed on railway tracks and any absent or damaged components such as spikes, clips, and rails. Comprehensive tests carried out on a track dataset obtained from the Kaggle repository demonstrate that the suggested approach is capable of accomplishing the following: 1) surpassing current leading models by achieving 94.3% mean average precision (mAP) for fault detection and 79 frames/sec, and 2) achieving 95.6% accuracy in semantic segmentation. Therefore, this fusion model helps inspect tracks by detecting and segmenting faults, thereby reducing derailment cases and other railroad-related accidents.

Objective: To describe the creation of a scalable framework for a Remote Patient Monitoring (RPM) program tied to a large medical practice. Patients and Methods: In July 2020, at Mayo Clinic Rochester, we initiated the process to combine human-centered design, health systems engineering, and clinical expertise to develop and refine RPM programs and explore new medical conditions where RPM could be beneficial. We report on the creation of the overarching RPM program, the development of a hybrid nursing model, the technology used for each population, and the lessons learned throughout the process. Results: Four pilot programs for hospitalized patients with cirrhosis, acute kidney injury (AKI), post-pancreatectomy, and post-gastroenterology (GI) procedures were launched. The 4 programs enrolled a range from 0 patients (post-GI procedures) to 91 patients over 5 months (AKI) for RPM programs ranging from 30 days to 96.5 days in average duration. Three of the programs continue enrollment (cirrhosis, AKI, and post-pancreatectomy), and one program was discontinued. Conclusion: RPM can uniquely facilitate the transition from hospital to home, decrease hospital length of stay, and optimize hospital capacity and staffing resources in select scenarios. To achieve positive results at the health care system level, RPM interventions require a change in the model of care. However, not all care models or medical conditions lend themselves to RPM, which should be explored in pilot testing.

In Mexico, breast cancer (BC) is the principal cause of death in women over 30 years old, with an annual mortality rate of 14.61 deaths per a 100,000 population. Chemotherapy, in combination with trastuzumab (TTZ), improves the survival of cancer patients; however, cardiotoxicity (CT) is the principal consequence. CT prevalence occurs between 10% and 30% of patients; however, there are no data about the prevalence of CT in the Mexican population. This study aims to establish the prevalence of CT in patients treated with anti-HER-2 therapy among BC women in southeast Mexico. A retrospective cross-sectional study was carried out from January 2015 to July 2019. The records of 46 patients diagnosed with HER-2-positive BC who attended the Mexican Social Security Institute in the Ambulatory Care Medicine Unit were analyzed. The diagnostic criterion for CT was a decrease in LVEF > 10% from baseline or a final LVEF < 53%. CT prevalence was observed in 19 (41.3%) of women with cancer, with an average decrease in LVEF of 13%. In the population, we found an association between weight, surface area, and the loading dose of TTZ with CT. Nutritional follow-up and the administration of cardioprotective drugs are necessary to recover LVEF and avoid cardiovascular failure in women with BC and survivors.

BackgroundRates of melanoma have increased dramatically in the United States over the past 25 years, and it has become among the most prevalent cancers for young adult women. Intentional skin tanning leads to a pattern of intense and intermittent UV radiation exposure that is associated with increased risk of melanoma. Frequent tanning is most common among young women and is linked to a variety of sociocultural pressures that negatively impact body image and drive appearance control behaviors. Unfortunately, there are no established interventions designed for frequent tanners. This intervention addresses this gap with unique content informed by body image and acceptance-based interventions. The intervention is delivered using Facebook secret groups, an approach designed to support behavior change and ensure scalability. ObjectiveThis study aims to describe the rationale and methodology of a randomized controlled trial of a melanoma prevention program targeting young women engaged in frequent indoor or outdoor UV tanning. MethodsParticipants are women aged 18-25 years who report high-risk tanning (ie, at least 10 indoor tanning sessions in the past 12 months or 10 outdoor sessions in the previous summer). After recruitment and screening, participants completed a baseline survey and were randomly assigned to receive the intervention or an attention-matched control condition. Both conditions were 8-week-long Facebook groups (approximately 25 members each) with daily posting of content. Follow-up surveys are administered at 3, 8, and 18 months after baseline. The primary trial outcome is the combined number of indoor and outdoor tanning sessions reported at the 8-month follow-up. Hypothesized intervention mediators are assessed at the 3-month follow-up. ResultsThis project was funded by a National Cancer Institute award (R01 CA218068), and the trial procedures were approved by the University of Kentucky Institutional Review Board in February 2020. Trial recruitment and enrollment occurred in 6 waves of data collection, which started in February 2022 and closed in May 2023. The study is closed to enrollment but remains open for follow-ups, and this protocol report was prepared before data analyses. As of February 2024, all participants have completed the 8-month follow-up assessment, and data collection is scheduled to close by the end of 2024 after the collection of the 18-month follow-up. ConclusionsThis trial will contribute unique knowledge to the field of skin cancer prevention, as no fully powered trials have examined the efficacy of an intervention designed for frequent indoor or outdoor tanning. The trial may also contribute evidence of the value in translating principles of body image and acceptance-based interventions into the field of skin cancer prevention and beyond. If successful, the use of the Facebook platform is intended to aid in dissemination as it provides a way to embed the intervention into individuals’ everyday routines. Trial RegistrationClinicalTrials.gov NCT03441321; https://clinicaltrials.gov/study/NCT03441321 International Registered Report Identifier (IRRID)DERR1-10.2196/56562

Abstract The multi-objective grasshopper optimization algorithm (MOGOA) is a relatively new algorithm inspired by the collective behavior of grasshoppers, which aims to solve multi-objective optimization problems in IoT applications. In order to enhance its performance and improve global convergence speed, the algorithm integrates simulated annealing (SA). Simulated annealing is a metaheuristic algorithm that is commonly used to improve the search capability of optimization algorithms. In the case of MOGOA, simulated annealing is integrated by employing symmetric perturbation to control the movement of grasshoppers. This helps in effectively balancing exploration and exploitation, leading to better convergence and improved performance. The paper proposes two hybrid algorithms based on MOGOA, which utilize simulated annealing for solving multi-objective optimization problems. One of these hybrid algorithms combines chaotic maps with simulated annealing and MOGOA. The purpose of incorporating simulated annealing and chaotic maps is to address the issue of slow convergence and enhance exploitation by searching high-quality regions identified by MOGOA. Experimental evaluations were conducted on thirteen different benchmark functions to assess the performance of the proposed algorithms. The results demonstrated that the introduction of simulated annealing significantly improved the convergence of MOGOA. Specifically, the IDG (Inverse Distance Generational distance) values for benchmark functions ZDT1, ZDT2, and ZDT3 were smaller than the IDG values obtained by using MOGOA alone, indicating better performance in terms of convergence. Overall, the proposed algorithms exhibit promise in solving multi-objective optimization problems.

Using near-term quantum computers to achieve a quantum advantage requires efficient strategies to improve the performance of the noisy quantum devices presently available. We develop and experimentally validate two efficient error mitigation protocols named ``Noiseless Output Extrapolation" and ``Pauli Error Cancellation" that can drastically enhance the performance of quantum circuits composed of noisy cycles of gates. By combining popular mitigation strategies such as probabilistic error cancellation and noise amplification with efficient noise reconstruction methods, our protocols can mitigate a wide range of noise processes that do not satisfy the assumptions underlying existing mitigation protocols, including non-local and gate-dependent processes. We test our protocols on a four-qubit superconducting processor at the Advanced Quantum Testbed. We observe significant improvements in the performance of both structured and random circuits, with up to $86\%$ improvement in variation distance over the unmitigated outputs. Our experiments demonstrate the effectiveness of our protocols, as well as their practicality for current hardware platforms.

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b.

ObjectiveThis study examined the effects of a peer-led integrated nutrition education intervention with maternal social support using Care Groups on infant growth among South Sudanese refugees in Uganda.MethodsA community-based cluster-randomized trial (RCT) was conducted among 390 pregnant women (third trimester). Two intervention study arms were Mothers-only(n = 131) and Parents-combined (n = 142) with a Control (n = 117). WHO infant growth standards defined length-for-age z-scores (LAZ) for stunting, weight-for-age z-scores (WAZ) for underweight and weight-for-length z-scores (WLZ) for wasting. The Medical Outcomes Study (MOS) social support index was a proxy measure for social support. A split-plot ANOVA tested the interaction effects of social support, intervention, and time on infant growth after adjusting for covariates. Further, pairwise comparisons explained mean differences in infant growth among the study arms.ResultsThe mean infant birth weight was 3.1 ± 0.5 kg. Over the study period, infant stunting was most prevalent in the Control (≥ 14%) compared to Mothers-only (< 9.5%) and Parents-combined (< 7.4%) arms. There were significant interaction effects of the Care Group intervention and social support by time on infant mean LAZ (F (6, 560) = 28.91, p < 0.001), WAZ (F (5.8, 539.4) = 12.70, p = < 0.001) and WLZ (F (5.3, 492.5) = 3.38, p = 0.004). Simple main effects by the end of the study showed that the intervention improved infant mean LAZ (Mothers-only vs. Control (mean difference, MD) = 2.05, p < 0.001; Parents-combined vs. Control, MD = 2.00, p < 0.001) and WAZ (Mothers-only vs. Control, MD = 1.27, p < 0.001; Parents-combined vs. Control, MD = 1.28, p < 0.001).ConclusionMaternal social support with an integrated nutrition education intervention significantly improved infant stunting and underweight. Nutrition-sensitive approaches focused on reducing child undernutrition among post-emergency refugees may benefit from using Care Groups in programs.Trial registrationClinicaltrials.gov, NCT05584969.

Objectives As life expectancy increases, older people are living longer with multimorbidity (MM, co-occurrence of ≥2 chronic health conditions) and complex multimorbidity (CMM, ≥3 chronic conditions affecting ≥3 different body systems). We assessed the impacts of MM and CMM on healthcare service use in Australia, as little was known about this.Design Population-based cross-sectional data linkage study.Setting New South Wales, Australia.Participants 248 496 people aged ≥45 years who completed the Sax Institute’s 45 and Up Study baseline questionnaire.Primary outcome High average annual healthcare service use (≥2 hospital admissions, ≥11 general practice visits and ≥2 emergency department (ED) visits) during the 3-year baseline period (year before, year of and year after recruitment).Methods Baseline questionnaire data were linked with hospital, Medicare claims and ED datasets. Poisson regression models were used to estimate adjusted and unadjusted prevalence ratios for high service use with 95% CIs. Using a count of chronic conditions (disease count) as an alternative morbidity metric was requested during peer review.Results Prevalence of MM and CMM was 43.8% and 15.5%, respectively, and prevalence increased with age. Across three healthcare settings, MM was associated with a 2.02-fold to 2.26-fold, and CMM was associated with a 1.83-fold to 2.08-fold, increased risk of high service use. The association was higher in the youngest group (45–59 years) versus the oldest group (≥75 years), which was confirmed when disease count was used as the morbidity metric in sensitivity analysis.When comparing impact using three categories with no overlap (no MM/CMM, MM with no CMM, and CMM), CMM had greater impact than MM across all settings.Conclusion Increased healthcare service use among older adults with MM and CMM impacts on the demand for primary care and hospital services. Which of MM or CMM has greater impact on risk of high healthcare service use depends on the analytic method used. Ageing populations living longer with increasing burdens of MM and CMM will require increased Medicare funding and provision of integrated care across the healthcare system to meet their complex needs.

Introduction Potentially inappropriate medicine prescriptions and low-value diagnostic testing pose risks to patient safety and increases in health system costs. The aim of the Clinical and Healthcare Improvement through My Health Record usage and Education in General Practice study was to evaluate a scalable online quality improvement intervention, integrating online education regarding a national shared electronic health record and rational prescribing, pathology and imaging ordering by Australian general practitioners (GPs).Methods The study was a parallel three-arm randomised trial comprising a prescribing education arm, a pathology education arm and an imaging education arm. Currently practising GPs in Australia were eligible to participate and randomised on a 1:1:1 basis to the study arms after consenting. The response to the intervention in reducing potentially unnecessary medicine prescriptions and tests in each arm was assessed using the other two arms as controls. The primary outcome was the cost per 100 consultations of predefined medication prescriptions, pathology and radiology test ordering 6 months following the intervention, compared with 6 months prior. Outcomes were assessed on intention-to-treat and post hoc per-protocol bases using multilevel regression models, with the analysts blinded to allocation.Results In total, 106 GPs were enrolled and randomised (prescribing n=35, pathology n=36, imaging n=35). Data were available for 97 GPs at the end of trial (prescribing n=33, pathology n=32, imaging n=32) with 44 fully completing the intervention. In intention-to-treat analysis, there were no significant differences in the rates of change in costs across the three arms. Per protocol, there was a statistically significant difference in the rate of change in pathology costs (p=0.03). In the pathology arm, the rate of increase in pathology costs was significantly lower by $A187 (95% CI −$A340, −$A33) than the prescribing arm, and non-significantly $A9 (95% CI −$A128, $A110) lower than the imaging arm.Discussion This study provides some evidence for reductions in costs for low-value pathology test ordering in those that completed the relevant online education. The study experienced slow uptake and low completion of the education intervention during the COVID-19 pandemic. Changes were not significant for the primary endpoint, which included all participants. Improving completion rates and combining real-time feedback on prescribing or test ordering may increase the overall effectiveness of the intervention. Given the purely online delivery of the education, there is scope for upscaling the intervention, which may provide cost-effectiveness benefits.Trial registration number ACTRN12620000010998.

Background. Well-written and transparent case reports (1) reveal early signals of potential benefits, harms, and information on the use of resources; (2) provide information for clinical research and clinical practice guidelines, and (3) inform medical education. High-quality case reports are more likely when authors follow reporting guidelines. During 2011–2012, a group of clinicians, researchers, and journal editors developed recommendations for the accurate reporting of information in case reports that resulted in the CARE (CAse REport) Statement and Checklist. They were presented at the 2013 International Congress on Peer Review and Biomedical Publication, have been endorsed by multiple medical journals, and translated into nine languages.Objectives. This explanation and elaboration document has the objective to increase the use and dissemination of the CARE Checklist in writing and publishing case reports.Article design and setting. Each item from the CARE Checklist is explained and accompanied by published examples. The explanations and examples in this document are designed to support the writing of high-quality case reports by authors and their critical appraisal by editors, peer reviewers, and readers.Results and conclusion. This article and the 2013 CARE Statement and Checklist, available from the CARE website [www.care-statement.org] and the EQUATOR Network [www.equator-network.org], are resources for improving the completeness and transparency of case reports.Source. This article is a translation of the original paper «CARE guidelines for case reports: explanation and elaboration document» in the Journal of Clinical Epidemiology (doi: 10.1016/j.jclinepi.2017.04.026), prepared under the permission of the copyright holder (Elsevier Inc.), with supervision from the Scientific Editor by Professor E.G. Starostina, MD, PhD (translator) (Moscow, Russia). Present translation was first published in Digital Diagnostics. doi: 10.17816/DD105291. It is published with minor changes related to the literary editing of the translation itself. Keywords: case report; case study; EQUATOR network; health research reporting guidelines; CARE guideline; timelines; N-of-1 For citation: Riley David S., Barber Melissa S., Kienle Gunver S., Aronson Jeffrey K., von Schoen-Angerer Tido, Tugwell Peter, Kiene Helmut, Helfand Mark, Altman Douglas G., Sox Harold, Werthmann Paul G., Moher David, Rison Richard A., Shamseer Larissa, Koch Christian A., Sun Gordon H., Hanaway Patrick, Sudak Nancy L., Kaszkin-Bettag Marietta, Carpenter James E., Gagnier Joel J. CARE Guidelines for Case Reports: Explanation and Elaboration Document. Translation into Russian. Voprosy sovremennoi pediatrii — Current Pediatrics. 2023;22(2):88–108. (In Russ). doi: https://doi.org/10.15690/vsp.v22i2.2540

Abstract Background Evidence suggests that forced migration and refugee status may adversely impact mothers’ breastfeeding choices. Furthermore, suboptimal breastfeeding practices have been reported among vulnerable populations including those living in refugee settlements. Therefore, this study investigated the barriers and facilitators of breastfeeding in protracted settlements in Adjumani district, in the West Nile region in Uganda. Methods This study was conducted among refugees living in protracted settlements located in Uganda in July 2019. Participants, originally from South Sudan, included mothers (n = 63) and fathers (n = 32) of children less than 24 months of age. Agojo, Ayilo-I, and Nyumanzi were randomly selected among the 17 refugee settlements in Adjumani. Participants formed a total of six focus group discussions (FGDs); four FGDs for mothers and two FGDs for fathers. Each FGD consisted of 15–16 participants. Data were transcribed verbatim and back-translated into English. Thematic analysis was used and data were analyzed using NVivo, v. 12. Results Facilitators of breastfeeding included knowledge of breastfeeding benefits, support from husband/father, support from the community, and support from non-governmental organizations. Mothers and fathers noted that breastfeeding protected children from diseases and breastfed children grew well. Fathers, the community, and organizations provided material support for breastfeeding mothers. Four themes were identified as barriers to breastfeeding: physical, socioeconomic, knowledge, and psychosocial. Mothers and fathers described physical barriers such as mothers stop breastfeeding when they are sick or they feel they are not producing enough breastmilk. Mothers reported that working or educated mothers may use other milk to feed their infant. Some mothers and fathers believed infants under six months needed more than breastmilk. Fathers described psychosocial barriers such as mothers’ fear of pain during breastfeeding and maternal mental health issues. Conclusion Interventions and policies that aim to improve breastfeeding in protracted settlements should consider addressing the barriers to breastfeeding at each level: physical, socioeconomic, knowledge, and psychosocial. Involving and encouraging support from husbands/fathers, relatives, and the community may increase adherence to breastfeeding recommendations.

The Internet of Things (IoT) based smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

In the ever-expanding Internet of Things (IoT) domain, the production of data has reached an unparalleled scale. This massive data is processed to glean invaluable insights, accelerating a myriad of decision-making processes. Nevertheless, the privacy and security of such information present formidable challenges. This study proposes an innovative methodology for resolving these challenges, by augmenting the privacy and efficacy of big data analytics through federated learning in the IoT ecosystem. The proffered approach amalgamates a hierarchical structure, a scalable learning rate, and a rudimentary cryptographic mechanism to foster learning while ensuring robust privacy and security. Additionally, we introduce a novel communication protocol - SEPP-IoT, designed to facilitate efficient, secure, and confidential interactions between IoT devices and a central server. In our pursuit of optimizing communication overhead, we propose an adaptive data compression algorithm, aimed at curbing the volume of data transferred between IoT devices and the central server. To fortify resilience and fault tolerance, our approach incorporates multiple mechanisms such as data replication, error correction codes, and proactive fault detection and recovery. Trust management, a salient feature of our framework, bolsters the security and integrity of federated learning. We recommend a unique technique that gauges the dependability of IoT nodes using four trust parameters. We employ the FedSim simulator to evaluate our method’s effectiveness. The results indicate a notable enhancement in privacy and efficiency of big data analytics within the IoT.

Abstract Ternary quantum information processing in superconducting devices poses a promising alternative to its more popular binary counterpart through larger, more connected computational spaces and proposed advantages in quantum simulation and error correction. Although generally operated as qubits, transmons have readily addressable higher levels, making them natural candidates for operation as quantum three-level systems (qutrits). Recent works in transmon devices have realized high fidelity single qutrit operation. Nonetheless, effectively engineering a high-fidelity two-qutrit entanglement remains a central challenge for realizing qutrit processing in a transmon device. In this work, we apply the differential AC Stark shift to implement a flexible, microwave-activated, and dynamic cross-Kerr entanglement between two fixed-frequency transmon qutrits, expanding on work performed for the Z Z interaction with transmon qubits. We then use this interaction to engineer efficient, high-fidelity qutrit CZ† and CZ gates, with estimated process fidelities of 97.3(1)% and 95.2(3)% respectively, a significant step forward for operating qutrits on a multi-transmon device.

Background: Obesity is associated with increased complications after total joint arthroplasty (TJA), leading some surgeons to recommend nutrition counseling and weight loss. We aim to evaluate the effect of preoperative nutritionist referral on weight loss and likelihood of surgery in obese patients seeking primary TJA. Methods: A retrospective cohort of patients seeking primary TJA who were referred to a licensed nutritionist for weight loss was matched by age, sex, and body mass index (BMI) to an unreferred control group. BMI change was compared between groups up to 1 year of follow-up. Differences were determined using 2-tailed t-tests and chi-squared tests with a significance cutoff of P < .05. Results: A total of 274 referred patients and 174 controls were included in our analysis. Patients who were referred to a nutritionist achieved significantly greater average BMI change (−1.5 kg/m2) than controls (−0.8 kg/m2) by 6 months after first contact (P = .01) although significance was lost at 1 year after first contact (P = .21). Thirty-eight percent of referred patients went on to TJA compared with 28% of controls (P < .01). Conclusions: Referral to a licensed nutritionist modestly improves early weight loss and is associated with a higher rate of surgery in obese patients seeking primary TJA.

Abstract Background Total hip arthroplasties (THA) are cost-effective interventions for patients with osteoarthritis refractory to physical therapy or medical management. Most individuals report positive surgical outcomes with reduction in pain and improved joint function. Multiple recent studies demonstrated the influence of patient mental health on surgical success. We sought to determine the relationship between patient preoperative psychological factors and postoperative THA outcomes, specifically pain and function. Methods PubMed, EMBASE and Cochrane Reviews databases were queried using terms “(mental OR psychological OR psychiatric) AND (function OR trait OR state OR predictor OR health) AND (outcome OR success OR recovery OR response) AND total joint arthroplasty).” A total of 21 of 1,286 studies fulfilled inclusion criteria and were included in the review. All studies were analyzed using GRADE and Risk of Bias criteria. Results Overall, compared to cohorts with a normal psychological status, patients with higher objective measures of preoperative depression and anxiety reported increased postoperative pain, decreased functionality and greater complications following THA. Additionally, participants with lower self-efficacy or somatization were found to have worse functional outcomes. Conclusions Preoperative depression, anxiety and somatization may negatively impact patient reported postoperative pain, functionality and complications following THA. Surgeons should consider preoperative psychological status when counseling patients regarding expected surgical outcomes. Level of evidence 3.

We proposed the idea of editing a special issue that would compile the fruitful research that resulted from the stimulating discussions that occurred during the workshop that was held during the 5th International Conference on Intelligent Computing, Chennai on 25th & 26th March 2022. The objective of this special issue is to call for high-quality papers covering the latest data analytic concepts and technologies of big data and artificial intelligence. This special issue serves as a forum for researchers across the globe to discuss their work and recent advances in this field. The best papers from Artificial intelligence and Big Data Analytics (BAM) in the domains of Product, Finance, Health, and Environment were invited, peer-reviewed. The best high-quality papers were selected based on the innovativeness and relevance of the theme. The amount of data being generated and stored in various fields such as education, energy, environment, healthcare, fraud detection, and traffic is increasing exponentially in the modern era of Big Data. Simultaneously, there is a significant paradigm shift in business and society worldwide due to rapid advancements in fields such as artificial intelligence, machine learning, deep learning, and data analytics. This creates significant challenges for decision-making and the potential for transformation in areas such as the economy, government, and industry. Artificial Intelligence tools, techniques, and technologies, in conjunction with Big Data, improve the predictive power of the systems created and allow the government, public, and private sectors to discover new patterns and trends, as well as improve public values such as accountability, safety, security, and transparency to enable better decision-making, policies, and governance. They also have a wide range of capabilities to perform complex tasks that humans cannot. They could be used to collect, organize, and analyze large, diverse data sets to discover patterns and trends that address a variety of problems related to the development of the economy, such as identifying new sources of revenue, expanding the customer base for business, product reviews, and promotion, disease prediction and prevention, climatic variation prediction, and the provision of energy solutions. The wide variety of subject areas discussed at the 5th International Conference on Intelligent Computing is reflected in the seven accepted papers presented in the following section.

Background: Evidence-based strategies to improve outcomes in minority children with uncontrolled asthma discharged from the emergency department (ED) are needed. Objectives: This multicenter pragmatic clinical trial was designed to compare an ED-only intervention (decision support tool), an ED-only intervention and home visits by community health workers for 6 months (ED-plus-home), and enhanced usual care (UC). Methods: Children aged 5 to 11 years with uncontrolled asthma were enrolled. The change over 6 months in the Patient-Reported Outcomes Measurement Information System Asthma Impact Scale score in children and Satisfaction with Participation in Social Roles score in caregivers were the primary outcomes. The secondary outcomes included guideline-recommended ED discharge care and self-management. Results: Recruitment was significantly lower than expected (373 vs 640 expected). Of the 373 children (64% Black and 31% Latino children), only 63% completed the 6-month follow-up visit. In multivariable analyses that accounted for missing data, the adjusted odds ratios and 98% CIs for differences in Asthma Impact Scores or caregivers’ Satisfaction with Participation in Social Roles scores were not significant. However, guideline-recommended ED discharge care was significantly improved in the intervention groups versus in the UC group, and self-management behaviors were significantly improved in the ED-plus-home group versus in the ED-only and UC groups. Conclusions: The ED-based interventions did not significantly improve the primary clinical outcomes, although the study was likely underpowered. Although guideline-recommended ED discharge care and self-management did improve, their effect on clinical outcomes needs further study.

ABSTRACT The importance of gut microbiomes has become generally recognized in vector biology. This study addresses microbiome signatures in North American Triatoma species of public health significance (vectors of Trypanosoma cruzi) linked to their blood-feeding strategy and the natural habitat. To place the Triatoma-associated microbiomes within a complex evolutionary and ecological context, we sampled sympatric Triatoma populations, related predatory reduviids, unrelated ticks, and environmental material from vertebrate nests where these arthropods reside. Along with five Triatoma species, we have characterized microbiomes of five reduviids (Stenolemoides arizonensis, Ploiaria hirticornis, Zelus longipes, and two Reduvius species), a single soft tick species, Ornithodoros turicata, and environmental microbiomes from selected sites in Arizona, Texas, Florida, and Georgia. The microbiomes of predatory reduviids lack a shared core microbiota. As in triatomines, microbiome dissimilarities among species correlate with dominance of a single bacterial taxon. These include Rickettsia, Lactobacillus, “Candidatus Midichloria,” and Zymobacter, which are often accompanied by known symbiotic genera, i.e., Wolbachia, “Candidatus Lariskella,” Asaia, Gilliamella, and Burkholderia. We have further identified a compositional convergence of the analyzed microbiomes in regard to the host phylogenetic distance in both blood-feeding and predatory reduviids. While the microbiomes of the two reduviid species from the Emesinae family reflect their close relationship, the microbiomes of all Triatoma species repeatedly form a distinct monophyletic cluster highlighting their phylosymbiosis. Furthermore, based on environmental microbiome profiles and blood meal analysis, we propose three epidemiologically relevant and mutually interrelated bacterial sources for Triatoma microbiomes, i.e., host abiotic environment, host skin microbiome, and pathogens circulating in host blood. IMPORTANCE This study places microbiomes of blood-feeding North American Triatoma vectors (Reduviidae) into a broader evolutionary and ecological context provided by related predatory assassin bugs (Reduviidae), another unrelated vector species (soft tick Ornithodoros turicata), and the environment these arthropods coinhabit. For both vectors, microbiome analyses suggest three interrelated sources of bacteria, i.e., the microbiome of vertebrate nests as their natural habitat, the vertebrate skin microbiome, and the pathobiome circulating in vertebrate blood. Despite an apparent influx of environment-associated bacteria into the arthropod microbiomes, Triatoma microbiomes retain their specificity, forming a distinct cluster that significantly differs from both predatory relatives and ecologically comparable ticks. Similarly, within the related predatory Reduviidae, we found the host phylogenetic distance to underlie microbiome similarities.

Abstract Objective Bile and its individual components, mainly bile acids, are important for digestion and drive bacterial community dynamics in the upper gastrointestinal tract of chickens. However, specific responses to bile acids have been characterized in only a few commensal bacteria, and it is unclear how other members of the microbiota respond to biliary stress. Here, we used label-free LC–MS/MS to assess the proteomic response of a common inhabitant of the chicken small intestine, Turicibacter bilis MMM721, to 24 h of growth in anaerobic growth media supplemented with 0.1% whole chicken bile, 0.1% taurochenodeoxycholic acid (TCDCA), or 0.1% taurocholic acid (TCA). Results Seventy, 46, and 10 differentially expressed proteins were identified in Turicibacter bilis MMM721 cultured with supplements of chicken bile, TCDCA, and TCA, respectively, when compared to unsupplemented controls. Many differentially expressed proteins were predicted to be involved in ribosomal processes, post-translational modifications and chaperones, and modifications to the cell surface. Ultimately, the T. bilis MMM721 response to whole bile and bile acids is complex and may relate to adaptations for small intestine colonization, with numerous proteins from a variety of functional categories being impacted.

Abstract Background Systematic reviews of outcome measurement instruments are important tools in the evidence-based selection of these instruments. COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) has developed a comprehensive and widespread guideline to conduct systematic reviews of outcome measurement instruments, but key information is often missing in published reviews. This hinders the appraisal of the quality of outcome measurement instruments, impacts the decisions of knowledge users regarding their appropriateness, and compromises reproducibility and interpretability of the reviews’ findings. To facilitate sufficient, transparent, and consistent reporting of systematic reviews of outcome measurement instruments, an extension of the PRISMA (Preferred Reporting of Items for Systematic reviews and Meta-Analyses) 2020 guideline will be developed: the PRISMA-COSMIN guideline. Methods The PRISMA-COSMIN guideline will be developed in accordance with recommendations for reporting guideline development from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. First, a candidate reporting item list will be created through an environmental literature scan and expert consultations. Second, an international Delphi study will be conducted with systematic review authors, biostatisticians, epidemiologists, psychometricians/clinimetricians, reporting guideline developers, journal editors as well as patients, caregivers, and members of the public. Delphi panelists will rate candidate items for inclusion on a 5-point scale, suggest additional candidate items, and give feedback on item wording and comprehensibility. Third, the draft PRISMA-COSMIN guideline and user manual will be iteratively piloted by applying it to systematic reviews in several disease areas to assess its relevance, comprehensiveness, and comprehensibility, along with usability and user satisfaction. Fourth, a consensus meeting will be held to finalize the PRISMA-COSMIN guideline through roundtable discussions and voting. Last, a user manual will be developed and the final PRISMA-COSMIN guideline will be disseminated through publications, conferences, newsletters, and relevant websites. Additionally, relevant journals and organizations will be invited to endorse and implement PRISMA-COSMIN. Throughout the project, evaluations will take place to identify barriers and facilitators of involving patient/public partners and employing a virtual process. Discussion The PRISMA-COSMIN guideline will ensure that the reports of systematic reviews of outcome measurement instruments are complete and informative, enhancing their reproducibility, ease of use, and uptake.

Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air–oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli’s equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives.

BACKGROUND: Well-written and transparent case reports (1) reveal early signals of potential benefits, harms, and information on the use of resources; (2) provide information for clinical research and clinical practice guidelines, and (3) inform medical education. High-quality case reports are more likely when authors follow reporting guidelines. During 20112012, a group of clinicians, researchers, and journal editors developed recommendations for the accurate reporting of information in case reports that resulted in the CARE (CAse REport) Statement and Checklist. They were presented at the 2013 International Congress on Peer Review and Biomedical Publication, have been endorsed by multiple medical journals, and translated into nine languages. OBJECTIVES: This explanation and elaboration document has the objective to increase the use and dissemination of the CARE Checklist in writing and publishing case reports. ARTICLE DESIGN AND SETTING: Each item from the CARE Checklist is explained and accompanied by published examples. The explanations and examples in this document are designed to support the writing of high-quality case reports by authors and their critical appraisal by editors, peer reviewers, and readers. RESULTS AND CONCLUSION: This article and the 2013 CARE Statement and Checklist, available from the CARE website [www.care-statement.org] and the EQUATOR Network [www.equator-network.org], are resources for improving the completeness and transparency of case reports. SOURCE: This article is a translation of the original paper CARE guidelines for case reports: explanation and elaboration document in the Journal of Clinical Epidemiology (doi: 10.1016/j.jclinepi.2017.04.026), prepared under the permission of the copyright holder (Elsevier Inc.), with supervision from the Scientific Editor by Professor E.G. Starostina, MD, PhD (translator) (Moscow, Russia).

The hygiene tissue industry has an extensive global market that is quickly growing. Market research has indicated that softness is one of consumers’ most highly desired properties. For certain hygiene tissue products (specifically bath tissue), this property can influence prices. A better understanding of the science of softness would allow companies to engineer soft tissue more economically and efficiently. Softness is a subjective perception related to physical aspects that make it challenging to express and measure. Human handfeel panel testing, which ranks the specimens through physical tests, has been recognized as the most reliable method to measure tissue softness. Much effort has been expanded in correlating the panel test results with some measurable properties. In this regard, equipment has been recently developed by combining several different mechanical, surface, and acoustic properties to characterize softness. In comparison with panel tests, these instruments (e.g., tissue softness analyzer) have been found to give equivalent softness metrics. A combination of materials selection and manufacturing operations are used to create softer tissue sheets. This paper reviews the sensation of softness as perceived by the human touch, techniques for measuring softness, the influence of fiber on softness, manufacturing techniques, and additives used for softness enhancement.

Context: Living with diabetes can be difficult since it can affect the patient in many ways. Diabetic foot syndrome (DFS) is described as a group of symptoms where neuropathy reduced blood supply and infection leads to tissue breakdown and morbidity. Aim: This study aims to determine the prevalence of DFS and associated sociodemographic and treatment-related factors among adults living with type 2 diabetes mellitus in a rural community. Setting and Design: A cross-sectional study was conducted in an area under the rural health training centre of department of Community Medicine. Methods and Material: The study was conducted to determine DFS by measuring neuropathy, peripheral vascular disease using Michigan neuropathy screening instrument, and clinical examination. Statistical Analysis Used: The data collected was analyzed using SPSS 25. Results: The prevalence of DFS among those with type 2 diabetes mellitus was high (51.7%). DFS was associated with advanced age (>75 years), duration of diabetes for more than 5 years and with foot ulcer. Smoking and alcohol consumption were not associated with DFS. Conclusion: Half of those with diabetes had DFS. People with DFS were more likely to be older and living with diabetes for longer duration. This underscores the need for early identification of DFS by the primary care physicians. Further research on the role of health professionals at the primary care level in educating and screening DFS in people with diabetes are required.

Nowadays, it is common for applications to require servers to run constantly and aim as close as possible to zero downtime. The slightest failure might cause significant financial losses and sometimes even lives. For this reason, security and management measures against network threats are fundamental and have been researched for years. Software-defined networks (SDN) are an advancement in network management due to their centralization of the control plane, as it facilitates equipment setup and administration over the local network. However, this centralization makes the controller a target to denial of service attacks (DoS). In this study, we aim to develop a network anomaly detection and mitigation system that uses gated recurrent unit (GRU) neural networks combined with fuzzy logic. The neural network is trained to forecast future traffic, and anomalies are detected when the forecasting fails. The system is designed to operate in software-defined networks since they provide network flow information and tools to manage forwarding tables. We also demonstrate how the neural network’s hyperparameters affect the detection module. The system was tested using two datasets: one with emulated traffic generated by the data communication and networking research group called Orion, from computer science department at state university of Londrina, and CICDDoS2019, a well-known dataset by the anomaly detection community. The results show that GRU networks combined with fuzzy logic are a viable option to detect anomalies in SDN and possibly in other anomaly detection applications. The system was compared with other deep learning techniques.

Low-Power Wide-Area Network (LPWAN) is a new type of wireless technology that offers long range communication for devices in the Internet of Things (IoT) and LoRaWAN is one of the main technologies currently available to enable LPWAN environments. In the LoRaWAN architecture, the Join Server is a key component and is responsible for security tasks, such as authentication and key management. However, the Join Server acts as a Single Point of Failure (SPOF) since all encryption keys are stored centrally. Then, this paper presents a secure and fault-tolerant architecture to increase the levels of security and availability in LoRaWAN. A permissioned blockchain and smart contracts are used to replace the Join Server and solve the SPOF problem. A working prototype was created using open-source tools in order to evaluate the feasibility of the proposed architecture. Additionally, the performance of a blockchain network was analyzed in a cloud environment under multiple workloads and fault-tolerance experiments were performed to evaluate the impact of network failures. The results show a trade-off between availability and performance when choosing the number of blockchain peers in small scenarios. However, this behavior is reversed in large scenarios where the performance of multiple peers is best suited.

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be very helpful for the prediction of some phenomena, such as chances of getting a heart attack and possibility of a tumor, from the collected and analysed healthcare data. To mitigate these issues, in this paper, a new AI-enabled lightweight, secure communication scheme for an IoMT environment has been designed and named as ASCP-IoMT, in short. The security analysis of ASCP-IoMT is performed in different ways, such as an informal way and a formal way (through the random oracle model). ASCP-IoMT performs better than other similar schemes and provides superior security with extra functionality features as compared those for the existing state of art solutions. A practical implementation of ASCP-IoMT is also performed in order to measure its impact on various network performance parameters. The end to end delay values of ASCP-IoMT are 0.01587, 0.07440 and 0.17097 seconds and the throughput values of ASCP-IoMT are 5.05, 10.88 and 16.41 bits per second (bps) under the different considered cases, respectively. For AI-based Big data analytics phase, the values of computation time (seconds) for decision tree, support vector machine (SVM), and logistic regression are measured as 0.19, 0.23, and 0.27, respectively. Moreover, the different values of accuracy for decision tree, SVM and logistic regression are 84.24%, 87.57%, and 85.20%, respectively. From these values, it is clear that decision tree method requires less time than the other considered techniques, whereas accuracy is high in case of SVM.

Abstract The spacing effect refers to the improvement in memory retention for materials learned in a series of sessions, as opposed to massing learning in a single session. It has been extensively studied in the domain of verbal learning using word lists. Less evidence is available for connected discourse or tasks requiring the complex coordination of verbal and other domains. In particular, the effect of spacing on the retention of words and music in song has yet to be determined. In this study, university students were taught an unaccompanied two-verse song based on traditional materials to a criterion of 95% correct memory for sung words. Subsequent training sessions were either massed or spaced by two days or one week and tested at a retention interval of three weeks. Performances were evaluated for number of correct and incorrect syllables, number of correctly and incorrectly pitched notes, degree notes were off-pitch, and number of hesitations while singing. The data revealed strong evidence for a spacing effect for song between the massed and spaced conditions at a retention interval of three weeks, and evidence of no difference between the two spaced conditions. These findings suggest that the ongoing cues offered by surface features in the song are strong enough to enable verbatim recall across spaced conditions, as long as the spacing interval reaches a critical threshold.

Abstract Currently, there is a huge production in the automotive industries and energy consumption resulted into spike in the cost and emissions of greenhouse gases. Hence, an efficient energy utilisation mechanism has become a prime most important factor. Energy and its conservation problem also have raised and become one of the prominent critical factors needed to address. Therefore, the development of smart grid infrastructure is one of the solutions to address the above issue.This article discusses different methods and mechanisms require to manage energy efficiently within the smart grid network using communication technologies and protocols and proposed an integration method of electric vehicles and smart grid using communication networks for charging or discharging electricity and exchanging relevant information. In addition, this paper discusses different integration strategies and multi‐agent system implementation in the smart grid network. The proposed multi‐agent system shows the efficient method for monitoring power flow and maintaining the stability of the grid. The paper also presents the optimal scheduling of charging of electric vehicles in the smart grid network. Finally, the authors investigated and presented many standard communication protocols and their comparisons with respect to different scenarios. This paper discuss different methods and concepts which would require to manage energy efficiently by using recent communication technologies, communication protocols, and sensor technologies.

Abstract Evidence on the efficacy of women's empowerment to improve child growth and minimum dietary diversity (MDD) in the Eastern Africa (EA) region is limited. This cross‐sectional study used recent Demographic and Health Survey data of mother–child dyads from seven countries in EA to examine the associations between women's empowerment measures, child growth and MDD. Length‐for‐age z‐scores, weight‐for‐length z‐scores and weight‐for‐age z‐scores were used to categorize growth indicators of 6–23 months old children. Multivariable logistic regression was used to identify significant associations. Among all countries, 32%–59% of children experienced growth failure. Children meeting MDD were 18%–45%. Women having self‐esteem were associated with lower odds of stunting (adjusted odds ratio [AOR] = 0.62 in Rwanda), wasting (AOR = 0.38 in Uganda), underweight (AORs = 0.60 and 0.57 in Tanzania and Uganda, respectively) and growth failure (AOR = 0.64 in Rwanda). Having health decision control in Burundi was associated with lower odds of stunting (AOR = 0.49) and child growth failure (AOR = 0.52) and higher odds of meeting MDD (AOR = 2.50). Having Legal empowerment among women increased the odds of stunting (AOR = 1.79 in Burundi), underweight (AOR = 1.77 in Uganda) and growth failure (AOR = 1.87 in Burundi). Economic empowerment showed mixed associations with child growth and MDD among some countries. Women's self‐esteem and health decision control were associated with better child growth and MDD for some countries in EA. Nutrition‐sensitive interventions aimed at improving child growth and MDD should consider local contexts when addressing women's empowerment.