Maintenance Protocol Research Articles

Evaluating the effect of using augmented reality (Metaverse) on improving the teaching and academic performance

This study aimed at evaluating the effect of employing the augmented reality (metaverse) on improving the process of teaching and academic performance. An electronic questionnaire containing five questions was distributed to the academic groups that received the lesson using augmented virtual reality (Metaverse). The aim of the questions was to evaluate their experience, and after they answered the questions, the answers were analyzed in the form of percentages and frequencies. The study sample consisted of (97) students from Al-Ain University enrolled in the programs of (special education, nutrition, and applied sociology), to whom the experiment was applied using augmented virtual reality in teaching during their academic course. The results revealed that (83%) of the respondents reported that the augmented virtual reality contributed to understanding the concepts of the lesson well, (79%) felt enjoyment in using this technique, (75%) suggested that the technique enhanced their involvement in the content well, (68%) suggested that augmented virtual reality could be a useful educational instrument in the future, and (77%) confirmed that the lesson's objectives were achieved by using metaverse technique in teaching. However, several challenges were faced by students, including the used language (only English) and the negative effects of bad quality of used glasses. Based on the findings, the study recommended addressing several key areas for optimizing the use of augmented virtual reality (AVR) in teaching. For example, diversifying language options for AVR content is crucial to accommodating the linguistic diversity among students, thereby enhancing accessibility and comprehension. Additionally, we should invest in high-quality equipment and implement regular maintenance protocols to enhance the quality of equipment, specifically addressing issues related to the negative effects of subpar AVR hardware.

Smart manufacturing platform based on input-output empirical relationships for process monitoring

Intelligent monitoring and maintenance protocols are undoubtedly crucial for improving manufacturing processes. Accordingly, machine learning techniques and predictive control models have been customized and optimized to account for the specific characteristics of the processes under investigation. In this context, the management of manufacturing processes in a “smart way” requires the development of specific models based on input-output empirical data. The aim of the proposed research was to develop an easily customizable application integrated into a milling process executed at the laboratory level. The application was designed to identify and record the operator, the order and the specific work sequences. It also supports the operator in setting processing parameters according to the type of work sequence to be performed. The application analyses specific process outputs, such as the wear growth on the inserts of the cutter in relation to the main input process parameters: depth of cut, feed rate, and spindle speed. This analysis is implemented by leveraging empirical evidence.

Maintenance therapy for CTCL: importance for prevention of disease progression

There are no established maintenance protocols for cutaneous lymphomas. We aim to determine patient treatments and outcomes during the COVID-19 pandemic in order to uncover the most effective maintenance protocols for cutaneous lymphomas and impact of treatment interruption. Data was collected retrospectively from nine international institutions, including 149 patients. Younger patients had earlier stages of disease and were most frequently treated with skin-directed therapies including topical steroids, mechlorethamine gel, and phototherapy. Treatment interruption varied by treatment type and stage, with patients on topical therapies and earlier stages of disease being least likely to experience interruption. Treatment interruption was significantly associated with progression of disease and worse outcomes, with twice as many patients progressing who had interruption compared to those without interruption. This study may demonstrate the significance of continuous maintenance therapies, even in younger patients with early stages of disease.

Development of best evidence-based practice protocols for central venous catheter placement and maintenance to reduce CLABSI.

Although evidence-based interventions can reduce the incidence of central line-associated bloodstream infection (CLABSI), there is a large gap between evidence-based interventions and the actual practice of central venous catheter (CVC) care. Evidence-based interventions are needed to reduce the incidence of CLABSI in intensive care units (ICU) in China. Professional association, guidelines, and database websites were searched for data relevant to CLABSI in the adult ICUs from inception to February 2020. Checklists were developed for both CVC placement and maintenance. Based on the Integrated Promoting Action on Research Implementation in Health Services framework, a questionnaire collected the cognition and practice of ICU nursing and medical staff on the CLABSI evidence-based prevention guidelines. From January 2018 to December 2021, ICU CLABSI rates were collected monthly. Ten clinical guidelines were included after the screening and evaluation process and used to develop the best evidence-based protocols for CVC placement and maintenance. The CLABSI rates in 2018, 2019, and 2020 were 2.98‰ (9/3021), 1.83‰ (6/3276), and 1.69‰ (4/2364), respectively. Notably, the CLABSI rate in 2021 was 0.38‰ (1/2607). In other words, the ICU CLABSI rate decreased from 1.69‰ to 0.38‰ after implementation of the new protocols. Additionally, our data suggested that the use of ultrasound-guidance for catheter insertion, chlorhexidine body wash, and the use of a checklist for CVC placement and maintenance were important measures for reducing the CLABSI rate. The evidence-based processes developed for CVC placement and maintenance were effective at reducing the CLABSI rate in the ICU.

Evaluation of Different Materials used in Prosthetic of Dental Implants: A Comparitive Study

ABSTRACT Background: Implant-supported prostheses have become a cornerstone of restorative dentistry, offering patients an effective solution for missing teeth. However, the choice of material for these prostheses can significantly impact their maintenance requirements and long-term success. Methods: Patient records of individuals who received implant-supported prostheses over a 2-year period were reviewed. Data on maintenance interventions, including adjustments, repairs, and replacements were collected and analyzed for metal alloys, ceramics, and composites. Results: Metal alloys exhibited slightly higher average numbers of adjustments compared to ceramics and composites. Ceramics required a higher mean number of repairs and replacements compared to other materials. Statistical analysis revealed significant differences in adjustment frequency between metal alloys and ceramics (P = 0.047), but no significant differences were observed for repairs or replacements. Conclusion: The outcomes imply that material choice plays a significant role in the maintenance requirements of implant dental prostheses. While metal alloys may require more frequent adjustments, ceramics and composites may necessitate more repairs and replacements over time. Clinicians should consider these factors when selecting materials for implant prostheses and develop tailored maintenance protocols to optimize treatment outcomes.

Modeling the Dynamic Loads Affecting a Bridge Crane during Start-Up

Introduction. The dynamic loads during the start-up of a bridge crane can cause excessive stress in the structure and components, leading to potential safety hazards and increased wear and tear. To reduce the influence of the dynamic loads, various strategies can be implemented including optimization of the acceleration and deceleration profiles, using the soft start controls, implementing the vibration damping systems. It is vital to ensure that the proper crane maintenance and inspection protocols are in place. By reducing the impact of dynamic loads during the start-up, the overall performance and longevity of a bridge crane can be improved, ultimately enhancing safety and efficiency of the industrial operations. The present research offers a new approach to improving the efficiency and safety of industrial operations by providing a more precise account of the dynamic loads during the start-up of a bridge crane. The objective of this study is to develop a mathematical model for investigating the mechanical properties of the bridge cranes by analyzing the dynamic loads that occur during lifting operations.Materials and Methods. The development of the mathematical model was based on the kinetic model of the system, which included three connecting blocks and two flexible connections for a more accurate description of the bridge crane structure. Lagrange’s equations incorporating the information about the geometry and structure of a bridge crane were used. They made it possible to describe the motion of a system with the multiple elements and degrees of freedom. Processing and analysis of the results of the mathematical model were carried out in the MATLAB program using the Runge-Kutta method.Results. As a result of the research, a mathematical model was developed to study the dynamic loads affecting a bridge crane during lifting operations. Graphs describing the dependences of speed, acceleration, load, and rope angle over time, and their influence on the crane beam were plotted. The changes in these parameters over time, including their maximum values, were analyzed. The reasons for load changes and factors influencing the extension of lifting machines’ service life as well as reducing metal consumption during production thereof were identified.Discussion and Conclusion. The developed mathematical model and its numerical solution using the specialized software (MATLAB) allow for conducting the dynamic analysis of the bridge crane structures and determining the optimal design solutions. The analysis of the factors influencing the load changes leads to the conclusion that the use of this model can significantly reduce the load magnitudes and metal consumption, as well as increase the service life of lifting machines. The results obtained with the developed mathematical model and its numerical solution are useful for optimizing the crane structures, providing compliance with the operational requirements, and extending the service life of lifting machines.

Long-Term Adherence and Risk of Allergic Reactions in Patients Who Attained Milk Oral Immunotherapy Maintenance

BackgroundOral immunotherapy (OIT) has emerged as the most popular therapy for food allergy. However, data on the long-term adherence and efficacy of this approach are sparse. ObjectiveWe aimed to assess the long-term adherence rates to OIT protocol and the associated risk of allergic reactions. MethodsPatients who completed milk OIT and reached a maintenance dose of 200 ml of milk were surveyed biannually on their dairy consumption and occurrence of allergic reactions. A survival analysis was performed to evaluate the association between the risk of reaction and adherence to OIT maintenance protocol. ResultsThe cohort consisted of 50 patients. Only 56% of the cohort adhered to protocol, which consisted of ingesting a minimum of 200 ml of milk at least 3 times per week. Adherent patients had a significantly reduced risk of allergic reactions, as well as a reduced incidence of anaphylaxis, healthcare/ER visits, and epinephrine/antihistamine administration. ConclusionThe findings demonstrate the importance of consistent maintenance dose consumption in the management of food allergies, with regular milk consumption contributing to the maintenance of unresponsiveness and decreased risk of allergic symptoms.

Enhancing Energy Management in New Energy Vehicles and Energy Storage Systems Through Advanced Data Analysis and Machine Learning

This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.

Enhancing Energy Management in New Energy Vehicles and Energy Storage Systems Through Advanced Data Analysis and Machine Learning

This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.

SCREEN-HOUSE REARING OF THE CASHEW MIRID HELOPELTIS SCHOUTEDENI REUTER (HETEROPTERA: MIRIDAE) ON THREE VEGETABLES FOR ITS MANAGEMENT ON CASHEW

Helopeltis schoutedeni (Heteroptera: Miridae) is a significant cashew and cotton pest in East and West Africa. To date, viable laboratory rearing and maintenance protocols have been lacking to facilitate biological investigations on the bug. This drawback has largely hindered its successful management. The potential of three vegetable intercrops, cucumber fruit (Cucumis sativus L.), fresh French bean (Phaseolus vulgaris L.) pods, and pigeon pea (Cajanus cajan L.) were the suitable host plants for establishing laboratory colonies of H. schoutedeni. The developmental time, survival, oviposition, and feeding behaviors of H. schoutedeni under screen-house conditions were monitored. Newly hatched nymphs reared on these substrates successfully developed into adults with varying average developmental times of 25.72, 25.81, and 33.26 days for French beans, pigeon peas, and cucumbers, respectively. Similarly, survival varied significantly on the different plants, with the highest survival occurring on pigeon pea seedlings (97-100%) and the lowest on cucumber (48-69%; P < 0.05). Significantly more feeding lesions were recorded on pigeon peas (349.6 ± 82.56) than on cucumbers (52.6 ± 13.62) in the feeding preference test. The proportion of adult feeding during the scotophase was significantly higher than in the photophase (Pigeon pea seedling: t = -3.545; df = 18; P < 0.05, French beans: t = -2.684; df = 28; P < 0.05 and Cucumber t = 3.713; df = 18; P < 0.05). Oviposition was only recorded on pigeon pea seedlings. Present findings demonstrate the potential for using specific vegetables as suitable host plants for H. schoutedeni under confined and open field settings to aid future research studies toward effective management.. KEYWORDS :Cashew, Helopletis Schoutedeni, Legumes, Pest Management, Rearing

Earthworm coelomocytes as a soil health assessment tool

Different pollutants can disrupt earthworm coelomocytes integrity and functions, and their responses can be applied as biomarkers of sublethal contaminant exposure. In this context, the aim of this study was to develop an in vitro protocol for coelomocyte extraction, maintenance and analysis with regard to soil health status and chemical toxicity profile assessments. The extrusion technique was first tested comparing previously depurated (purged stomach content) and non-depurated and resampled earthworms. After testing, earthworms were exposed to different 2,4D and chloroacetamide concentrations for methodology validation. The values of viability were not affected by food restriction since no statistical difference was observed between non-depurated (sample A) and depurated (sample B) organisms. Regarding to cell density, a significant (p<0;05) reduction of 22% was observed between non-depurated and depurated organisms, indicating that food restriction may affect cell density. However, the non-depurated resampling did not show a significant reduction, indicating that this assessment may not be affect by resampling of the same organism. For both chemical compounds, no change in cell viability was observed at all assessed concentrations and exposure times. However, for cell density, a mainly time-dependent effect was observed for organisms exposed to chloroacetamide, and concentration-dependent effect for organisms exposed to 2,4D. The proportion of immune system cells was altered, mainly after 24 h, with the increasing of granular amoebocytes proportion. The difference in the proportion of granular amoebocytes in earthworms exposed to 2,4D can be explained by the existence of recognition and elimination mechanisms for this chemical substance. Thus, assessments of pollutant responses with in vitro coelomocytes seem to be a powerful tool for ecotoxicological studies.

Effects of glycopyrrolate and atropine for oral secretions and perioperative hemodynamics in children undergoing tonsillectomy and adenoidectomy: a prospective, single-center, randomized, double-blind, controlled trial.

Glycopyrrolate is commonly researched as a preoperative medication or in conjunction with cholinesterase inhibitors to counteract the lingering muscarinic effects of non-depolarizing muscarinic agents. However, studies have yielded inconsistent results regarding the superiority of glycopyrrolate over other anti-cholinergic drugs, such as atropine, particularly its effect on heart rate, blood pressure (BP), and glandular secretions. This study aimed to evaluate the differences in perioperative oral secretions, hemodynamics, and recovery quality with glycopyrrolate versus those with atropine before anesthesia induction in children undergoing tonsillectomy and adenoidectomy. In this prospective, single-center, randomized, double-blind, controlled trial, a total of 103 children were randomly assigned to group A (n = 51, glycopyrrolate 0.005mg/kg) or B (n = 52, atropine 0.01mg/kg). The follow-up anesthetic induction and maintenance protocols were the same in both groups. Vital signs, duration of surgery, extubation time, degree of wetness around the vocal cords during tracheal intubation, weight of oral secretions, and perioperative complications were recorded. No significant differences were observed in the degree of wetness around the vocal cords during tracheal intubation, as well as in the weight of oral secretions, duration of surgery, or extubation time, between the two groups. The intraoperative and postoperative heart rates were lower in group A than in group B (110.18 ± 10.58 vs. 114.94 ± 11.14, p = 0.028; 96.96 ± 10.81 vs. 103.38 ± 10.09, p = 0.002). The differences observed in the intraoperative and preoperative heart rates were lower in group A than in group B (23.84 ± 9.62 vs. 29.65 ± 8.75, p = 0.002). The differences observed in the postoperative and preoperative heart rates were lower in group A than in group B (10.63 ± 9.97 vs. 18.09 ± 9.39, p = 0.000). Glycopyrrolate showed a smoother change in heart rate than atropine during and after tonsillectomy and adenoidectomy, with no effect on BP or recovery quality, and did not increase oral secretions. The findings indicate that glycopyrrolate can serve as an alternative to atropine to prevent secretions in anesthesia induction for tonsillectomy and adenoidectomy in children. Trial registration: This study was registered with the Chinese Clinical Trial Registry (Registration Number: ChiCTR2200063578; Date of Registration: 12/09/2022).

Development of the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course.

Manual wheelchairs (MWC) users have limited mobility during winter months as they encounter many environmental barriers that restrict their community participation. This paper outlines the creation and standardization of an outdoor environment designed to simulate the real-life conditions and obstacles experienced by MWC users in winter. This study consisted of four phases. In Phase 1, researchers used a qualitative ethnographic approach to document the specific challenges and adaptive strategies used by MWC users in winter conditions. In Phase 2, key informants with expertise in MWC winter mobility were invited to co-design the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course. Participants reviewed draft design solutions and offered their input and suggestions to expand upon the initial design. A second co-design workshop included additional key informants, including service providers, policymakers, and professionals with expertise in landscape architecture and engineering, to validate the design solution. The workshops resulted in a detailed illustration of the SNOWMAN course, including five sections: platforms with side slopes, a miniature ice rink, curbs and curb cuts, a path with uneven winter surfaces, and modular ramps at various slopes. Phases 3 and 4 marked the conclusion of the study and involved fabrication of the SNOWMAN course and establishment of a standardized protocol for course setup and maintenance. The project aimed to offer several additional potential benefits, supported by the various stakeholders across the study phases, that extend beyond creation of a controlled and safe environment for wheelchair users to develop their winter mobility skills. Practicing wheelchair skills in this area may assist wheelchair users in gaining confidence which may ultimately translate to increased participation in the community.

Percutaneous tibial nerve stimulation for the overactive bladder: A single-arm trial.

We hypothesise that PTNS is a safe and effective treatment for OAB. Overactive bladder (OAB) is estimated to affect 11.8% of women worldwide, causing diminished quality of life. Lifestyle modifications, muscarinic receptor antagonist and beta-adrenoreceptor agonist remain the mainstay of treatment but are limited by their efficacy and adverse effects. Access to third-line therapies of intravesical botulinum toxin type A or sacral neuromodulation is limited by their invasive nature. Percutaneous tibial nerve stimulation (PTNS) has emerged as a non-invasive treatment option for OAB. This study was a single-arm trial of women requesting third-line treatment for OAB. The primary treatment outcome was patient-reported visual analogue score (VAS) improvement of at least 50%. Secondary outcome measures were Urinary Distress Inventory Short Form (UDI-6) score and two-day bladder diary. Patients also provided feedback on adverse effects encountered. In the 84 women recruited, initial treatment protocol showed a success rate of 77.2% among those who completed treatment based on VAS, with a statistically significant improvement in mean UDI-6 score of 20.13 (P < 0.01, standard deviation: 12.52). Continued success following tapering protocol of 60.8% and a mean maintenance protocol of 14.2 months was achieved. No adverse effects were reported. The results from this study are in concordance with previously published literature on the effectiveness and safety of PTNS as a treatment modality for OAB. Further randomised controlled trials to evaluate the optimal treatment protocol are warranted to establish a standardised regime.

Global Industrial Efficiency Revolutionized by Automated Lathe

The implementation of an automatic lathe, utilizing hydraulic drive and PLC programming, stands as a significant advancement in industrial engineering aimed at augmenting productivity while circumventing additional labor requirements. Through meticulous research, it was discerned that the predominant issue plaguing the production process was the occurrence of stoppages due to irregular or unstable dimensions in the output, particularly concerning the Item Bush K0JA Left. Employing methodologies such as SIPOC analysis and the 4M framework revealed that the root cause primarily stemmed from machine-related factors, exacerbated by fluctuations in the installation, resulting in misalignments and subsequent dimension inaccuracies. Addressing this, a targeted intervention involving the addition of locking bolts to the material fixture mitigated the stoppage occurrences significantly, with the operator rectifying positioning irregularities on average 1200 minutes per month. This intervention not only enhanced operational efficiency but also yielded tangible cost savings, amounting to Rp 578,000 monthly or Rp 6,936,000 annually, attributed to the prevention of production downtime. The implications of this study underscore the importance of proactive measures in streamlining manufacturing processes, thereby optimizing resource utilization and economic viability. Further research avenues could delve into the optimization of maintenance protocols for automated machinery and the exploration of advanced sensor technologies to preemptively detect dimensional inconsistencies, thus bolstering overall production resilience and efficacy. Highlight: Advanced machinery integration boosts manufacturing efficiency. Root cause analysis reveals process optimization insights. Cost savings demonstrate practical impact of production enhancements. Keywoard: Automatic lathe, Industrial engineering, Productivity enhancement, Dimensional accuracy, Cost savings

Long-Term Mouse Spinal Cord Organotypic Slice Culture as a Platform for Validating Cell Transplantation in Spinal Cord Injury.

Resolutive cures for spinal cord injuries (SCIs) are still lacking, due to the complex pathophysiology. One of the most promising regenerative approaches is based on stem cell transplantation to replace lost tissue and promote functional recovery. This approach should be further explored better in vitro and ex vivo for safety and efficacy before proceeding with more expensive and time-consuming animal testing. In this work, we show the establishment of a long-term platform based on mouse spinal cord (SC) organotypic slices transplanted with human neural stem cells to test cellular replacement therapies for SCIs. Standard SC organotypic cultures are maintained for around 2 or 3 weeks in vitro. Here, we describe an optimized protocol for long-term maintenance (≥30 days) for up to 90 days. The medium used for long-term culturing of SC slices was also optimized for transplanting neural stem cells into the organotypic model. Human SC-derived neuroepithelial stem (h-SC-NES) cells carrying a green fluorescent protein (GFP) reporter were transplanted into mouse SC slices. Thirty days after the transplant, cells still show GFP expression and a low apoptotic rate, suggesting that the optimized environment sustained their survival and integration inside the tissue. This protocol represents a robust reference for efficiently testing cell replacement therapies in the SC tissue. This platform will allow researchers to perform an ex vivo pre-screening of different cell transplantation therapies, helping them to choose the most appropriate strategy before proceeding with in vivo experiments.

Opioid-Sparing Anesthesia Versus Opioid-Free Anesthesia for the Prevention of Postoperative Nausea and Vomiting after Laparoscopic Bariatric Surgery: A Systematic Review and Network Meta-Analysis.

Patients who undergo laparoscopic bariatric surgery (LBS) are susceptible to postoperative nausea and vomiting (PONV). Opioid-free anesthesia (OFA) or opioid-sparing anesthesia (OSA) protocols have been proposed as solutions; however, differences between the 2 alternative opioid protocols for anesthesia maintenance in obese patients remain uncertain. A network meta-analysis was conducted to compare the impacts of OFA and OSA on PONV. Systematic searches were conducted using Embase, PubMed, MEDLINE, and Cochrane Library databases to identify randomized controlled trials (RCTs) comparing OFA and OSA strategies. After screening according to the inclusion and exclusion criteria, we used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to assess the credibility of the evidence. The main concern of this review was the difference between OFA and OSA in reducing PONV. The primary outcome was any PONV occurrence within 24 hours. Secondary outcomes included postoperative pain intensity, opioid consumption, opioid-related adverse events, and length of hospital stay. Fifteen RCTs involving 1310 patients were identified for a network meta-analysis from 1776 articles that compared OFA, OSA, and traditional opioid-based anesthesia (OBA) strategies in LBS. Twelve RCTs (80%) with 922 participants (70%) were eligible for the occurrence of PONV. These included 199 (22%) patients who received OFA and 476 (52%) and 247 (27%) patients who received OSA and OBA, respectively. OFA was more effective at reducing PONV (relative risks [RR], 0.6, 95% confidence interval [CI], 0.5-0.9, moderate-quality evidence) compared to OSA. No differences were observed in postoperative pain control or opioid consumption between the OFA and OSA strategies (very low-to high-quality evidence). Notably, OFA is associated with a higher risk of bradycardia than OSA (RR, 2.6, 95% CI, 1.2-5.9, moderate-quality evidence). OFA is more effective than OSA in reducing the occurrence of PONV during the early postoperative period of LBS, although it may associate with an increased risk of bradycardia. Patients who received either opioid-alternative strategy demonstrated similar effects in reducing postoperative opioid consumption and alleviating pain intensity.

Predictive maintenance in oil and gas facilities, leveraging ai for asset integrity management

This paper explores the application of AI in predictive maintenance within oil and gas facilities, discussing its benefits, challenges, and future prospects. Through the integration of AI-driven analytics and real-time data monitoring, oil and gas companies can enhance their asset integrity management practices, ultimately driving cost savings and operational excellence. Predictive maintenance has become indispensable in the oil and gas industry, serving as a pivotal strategy to uphold operational efficiency and preserve asset integrity. This paper delves into the profound impact of artificial intelligence (AI) technologies on predictive maintenance, ushering in a new era of proactive equipment management. By harnessing AI capabilities, oil and gas companies can preempt equipment failures, curtail downtime, and refine maintenance protocols, thereby optimizing overall operational performance. The integration of AI in predictive maintenance marks a paradigm shift, offering a proactive approach to asset management. Leveraging AI-driven analytics and real-time data monitoring, oil and gas facilities can fortify their asset integrity management practices. Through predictive algorithms and machine learning models, these technologies empower companies to forecast equipment malfunctions with unprecedented accuracy, allowing for timely interventions and mitigating potential risks the benefits of AI-powered predictive maintenance in the oil and gas sector are multifaceted the future of predictive maintenance in the oil and gas industry is brimming with promise. As AI technologies continue to evolve, we can anticipate further advancements in predictive analytics, fault detection, and decision support systems. By embracing innovation and collaboration, oil and gas companies can harness the full potential of AI-driven predictive maintenance, cementing their position as industry leaders in asset management and operational efficiency.

Clearance rates of sand-burrowed and laterally pressed unburrowed Pismo clam Tivela stultorum (Mawe 1823) in a laboratory open-flow system.

Pismo clam extraction is currently banned in Mexico to help the recovery of natural populations. Thus, the primary objective of this study was to gain insight on its basic biology and husbandry protocols. Growth and clearance rate (CR) of sand-burrowed and sediment-free, laterally pressed adult Pismo clams were quantified in the laboratory as a function of burrowing condition, flow, temperature, and microalgal concentration using open-flow chambers. After 40 days, clams remained healthy regardless of burrowing condition and showed a hyperbolic CR response pattern to increased flow, with CR directly proportional to flows lower than 1000 ml min-1. Maximal asymptotic CR values (300 to 400 ml min-1org-1) were observed from 1000 to 2000 ml min-1. No significant CR differences were observed between burrowed and laterally pressed clams, yet microalgal concentration effects were detected, with constant maximal CRs of ∼250 ml min-1 in the range of 50 to 200 cells µl-1 and decline at higher concentrations. Maintenance protocols of laterally pressed organisms were validated in the laboratory with both weight and CR data. To our knowledge, this is the first study providing whole-body physiological data translated into effective husbandry protocols for Pismo clams. This approach represents a fresh perspective to traditional research areas, opening the possibility for continued experimentation under controlled conditions.

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems.

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.