Dechlorination of PCBs in oil transformers by microwaves catalysed by Fe0, glycerol, and NaOH.

Dechlorination of Polychlorinated Biphenyls (PCBs), carcinogenic substances used as oil in electrical transformers, remains an environmental challenge. This study aims to investigate the dechlorination of PCBs-contaminated transformer oils using microwave catalytic (Fe0, glycerol) pyrolysis and to study the kinetics of PCBs dechlorination. After determining the composition of PCBs-contaminated oil using Gas Chromatography with an Electron Capture Detector (GC-ECD), response surface methodology through a central composite design was used to optimize dechlorination factors (irradiation time, microwave power, NaOH). Finally, a kinetic study of PCBs dechlorination under optimal conditions was conducted. The results showed that the concentration of PCBs in the studied transformer oil was 526 ± 0.01 mg/kg, composed of 14 congeners, with a dominance of hexa-PCBs (70 %) and penta-PCBS (18 %). The optimal PCBs dechlorination yield of 98.87 % was obtained under the following conditions: microwave power of 700 W, irradiation time of 8 min, and 0.3 g of NaOH. The kinetic study showed that PCBs degradation under optimal conditions follows a first-order reaction. These findings suggest that microwave-assisted catalytic pyrolysis is an effective and promising method for PCBs dechlorination in transformer oil, offering a potential solution for environmental remediation.

Supporting appropriate use of extended dual antiplatelet therapy post-myocardial infarction based on an innovative 12-month ticagrelor virtual service.

Purpose: Extended dual antiplatelet therapy (DAPT) with ticagrelor and aspirin is recommended in selected cases after myocardial infarction (MI) but not widely deployed in practice. This study assessed an innovative, cardiology pharmacist-led virtual service for determining eligibility for extended DAPT among patients completing 12 months of initial DAPT in primary care following MI. Methods: Within this model, potentially eligible individuals are reviewed virtually by a cardiology pharmacist for suitability for extended DAPT with reduced-dose ticagrelor [60 mg twice daily (BD)] for up to 3 years. Eligibility is guided by the PEGASUS-TIMI 54 trial criteria (aged ≥50 years and having ≥1 high-risk feature for further ischaemic events). This is balanced against potential ineligibility driven primarily by bleeding risk, assessed using PRECISE-DAPT score. The final recommendation is sent to primary care to action. The present work is a retrospective evaluation of patients referred to the service between July 2018 and December 2021. Results: A total of 200 patients were included [n = 131 (65.5%) male; mean age: 69.4 ± 9.5 years]. Of these, 79 (39.5%) were recommended for extended DAPT based on the balance of risks for further ischaemic events vs. bleeding. Sixty-three patients on high-dose DAPT (ticagrelor 90 mg BD)-which is inappropriate beyond 12 months-were reassigned to reduced-dose DAPT or aspirin monotherapy. Conclusions: This virtual clinic played a key role in medicines optimisation, enabling appropriate patients to benefit from extended DAPT while offsetting bleeding risk. The model could be adapted locally for use elsewhere.

Scalable mesoporous biochars from bagasse waste for Cu (II) removal: Process optimisation, kinetics and techno-economic analysis.

As the world faces the brink of climatological disaster, it is crucial to utilize all available resources to facilitate environmental remediation, especially by accommodating waste streams. Lignocellulosic waste residues can be transformed into mesoporous biochar structures with substantial pore capacity. While biochars are considered a method of carbon dioxide removal (CDR), they are in fact an environmental double-edged sword that can be used to extract metal ions from water bodies. Biochars possess high chemical affinities through chemisorption pathways that are tuneable to specific pH conditions. This work demonstrates how biochars can be enhanced to maximise their surface area and porosity for the removal of Cu (II) in solution. It was found that bagasse derived mesoporous biochars operate preferentially at high pH (basic conditions), with the 1.18 mKOH/mSCB material reaching 97.85% Cu (II) removal in 5 min. This result is in stark contrast with the majority of biochar adsorbents that are only effective at low pH (acidic conditions). As a result, the biochars produced in this work can be directly applied to ancestral landfill sites and carbonate-rich mine waters which are highly basic by nature, preventing further metal infiltration and reverse sullied water supplies. Furthermore, to assess the value in the use of biochars produced and applied in this way, a techno-economic assessment was carried out to determine the true cost of biochar synthesis, with possible routes for revenue post-Cu being removed from the biochar.

Extraction optimisation and characterisation of Artocarpus integer peel pectin by malonic acid-based deep eutectic solvents using response surface methodology.

Traditional pectin extraction methods involve strong acids, which are environmentally harmful. This study explores an innovative approach using Malonic Acid (MA)-based Deep Eutectic Solvents (DES) to extract pectin from Artocarpus integer Peel (AIPP), optimised through Response Surface Methodology (RSM). The extracted AIPP-A and AIPP-B from ChCl-MA and ChDHCit-MA DES, respectively, were characterised for yield, pH, solubility, Degree of Esterification (DE), Water and Oil Holding Capacity (WHC and OHC). The experimental values aligned with RSM model predictions, with low standard deviations: 0.7300 for ChCl-MA and 0.1531 for ChDHCit-MA. Optimal extraction conditions for AIPP-A were 3.27 % ChCl-MA, 1.28 h extraction time, 50.44 °C temperature, and a 1:40 g/mL solid-to-liquid ratio. For AIPP-B, the conditions were 4.95 % ChDHCit-MA, 2.04 h extraction time, 79.65 °C temperature, and a 1:50 g/mL solid-to-liquid ratio. ChCl-MA yielded 30.97 % AIPP, which was higher than that of ChDHCit-MA (27.99 %). Both AIPP demonstrated desirable properties such as low pH, high solubility, and significant DE. AIPP-A exhibited a greater DE (58.40 %) compared to AIPP-B (32.4 %) contributed to its lower WHC and higher OHC. In conclusion, RSM-based optimisation of AIPP extraction with DES is effective in producing pectin that is suitable for use as a gelling agent, preservative, and stabiliser in the food industry.

Cycling position optimisation - a systematic review of the impact of positional changes on biomechanical and physiological factors in cycling.

Bike positional configuration changes strongly affect cycling performance. While consensus has emerged on saddle height optimisation, there is none for the relationship between other bike positional variables and cycling performance. Accordingly, this systematic review examines the effect of all major positional variables on performance in cycling, assessing differences between cycling disciplines and sex where possible. The systematic review, conducted per PRISMA guidelines, searched databases including Embase, Web of Science, Medline, and CINAHL, screening 16,578 studies. Of these, 47 were fully analysed. Study quality assessment using the NIH tool revealed none rated "good", 5 "fair" and 33 "poor". The analysis involved 724 participants (90 female, 454 male, 180 sex unstated). Studies focused on trunk angle/upper body position, handlebar height, Q factor, foot position, saddle fore-aft/height, seat tube angle and crank length. Participant cycling disciplines were often unspecified and few papers address women cyclists specifically. Key findings were associated with changing saddle height, trunk angle and saddle fore-aft. For trunk angle, accounting for the biomechanical and physiological effects as well as aerodynamic changes is important. Saddle fore-aft affects the hip angle and trunk angle. There are no clear recommendations for crank length, handlebar height, Q factor or cleat position.

Multi-criteria optimisation of subcritical wet oxidation for sludge treatment.

Subcritical wet oxidation (SWO) is an environmentally-friendly solution for sewage sludge volume reduction. However, little study has comprehensively optimised SWO conditions across various aspects. This study developed a multi-objective model using genetic algorithms (GAs) to optimise SWO conditions, considering sludge deconstruction, emissions, energy balance, and resource recovery. The multi-criteria optimisation approach highlights the significant environmental benefits of SWO, including substantial sludge volume reduction and effective pollutant removal. An in-depth analysis of temperature, reaction time, and severity factor revealed their critical roles in enhancing sludge deconstruction and resource recovery efficiency. GAs predicted optimal conditions at 271 ± 2 °C and 51 ± 1 min, with confirmation experiments showing only 12% discrepancy between predicted and actual outcomes. This study provides practical insights for efficient sewage sludge treatment and sustainable wastewater management.

The validation of short eating disorder, body dysmorphia, and Weight Bias Internalisation Scales among UK adults.

BACKGROUND: When collecting data from human participants, it is often important to minimise the length of questionnaire-based measures. This makes it possible to ensure that the data collection is as engaging as possible, while it also reduces response burden, which may protect data quality. Brevity is especially important when assessing eating disorders and related phenomena, as minimising questions pertaining to shame-ridden, unpleasant experiences may in turn minimise any negative affect experienced whilst responding. METHODS: We relied on item response theory to shorten three eating disorder and body dysmorphia measures, while aiming to ensure that the information assessed by the scales remained as close to that assessed by the original scales as possible. We further tested measurement invariance, correlations among different versions of the same scales as well as different measures, and explored additional properties of each scale, including their internal consistency. Additionally, we explored the performance of the 3-item version of the modified Weight Bias Internalisation Scale and compared it to that of the 11-item version of the scale. RESULTS: We introduce a 5-item version of the Eating Disorder Examination Questionnaire, a 3-item version of the SCOFF questionnaire, and a 3-item version of the Dysmorphic Concern Questionnaire. The results revealed that, across a sample of UK adults (N = 987, ages 18-86, M = 45.21), the short scales had a reasonably good fit. Significant positive correlations between the longer and shorter versions of the scales and their significant positive, albeit somewhat weaker correlations to other, related measures support their convergent and discriminant validity. The results followed a similar pattern across the young adult subsample (N = 375, ages 18-39, M = 28.56). CONCLUSIONS: These results indicate that the short forms of the tested scales may perform similarly to the full versions.

This manuscript introduces short versions of existing measures of eating disorders and body dysmorphia, specifically the Eating Disorder Examination Questionnaire, the SCOFF Questionnaire, and the Dysmorphic Concern Questionnaire. We further investigate the properties of the recently introduced 3-item short version of the modified Weight Bias Internalisation Scale. Across analyses including measurement invariance testing and bivariate correlations aiming to assess convergent and discriminant validity, we find support that the short scales may perform similarly to their longer versions. These short scales may contribute in meaningful ways to research where the brevity of questionnaire-type measures may make a difference by contributing to data quality.

Optimisation of dry heat treatment conditions for modification of faba bean (Vicia faba L.) starch.

Faba bean is a protein-rich starchy grain that is underutilised in the UK. The starch of faba bean can be modified using environmentally friendly methods like dry heat treatment (DHT) to enhance functional and its physicochemical properties. This study investigated the impact of dry heat temperature and time on the structure, functional and physicochemical properties of faba bean starch (FBS) using a two-factor central composite rotatable design. Factors (DHT temperature:100-150 °C and DHT time:0.5-5 h) with their respective α mid-point values led to 13 experimental runs. Selected pasting and functional properties were measured as response variables. Corn starch was included as a reference and compared with the FBS modified using the optimized conditions. DHT increased peak (approx. 2205-2267 cP), final (approx. 3525-3642 cP) and setback (approx. 1887-1993 cP) viscosities but decreased the amylose content of FBS. Colour, as measured by lightness value, morphology and crystalline type were not altered but the starches showed a loss of order and an increase in crystallinity after DHT. FBS appeared resilient to DHT but showed higher swelling power and pasting properties compared to the corn starch control. The optimum DHT conditions to produce starch with desirable properties are a temperature of 100 °C for 0.1716 h, with a desirability factor of 66 %.

Parameter extraction of proton exchange membrane fuel cell based on artificial rabbits' optimization algorithm and conducting laboratory tests.

Proton exchange membrane fuel cell (PEMFC) parameter extraction is an important issue in modeling and control of renewable energies. The PEMFC problem's main objective is to estimate the optimal value of unknown parameters of the electrochemical model. The main objective function of the optimization problem is the sum of the square errors between the measured voltages and output voltages of the proposed electrochemical optimized model at various loading conditions. Natural rabbit survival strategies such as detour foraging and random hiding are influenced by Artificial rabbit optimization (ARO). Meanwhile, rabbit energy shrink is mimicked to control the smooth switching from detour foraging to random hiding. In this work, the ARO algorithm is proposed to find the parameters of PEMFC. The ARO performance is verified using experimental results obtained from conducting laboratory tests on the fuel cell test system (SCRIBNER 850e, LLC). The simulation results are assessed with four competitive algorithms: Grey Wolf Optimization Algorithm, Particle Swarm Optimizer, Salp Swarm Algorithm, and Sine Cosine Algorithm. The comparison aims to prove the superior performance of the proposed ARO compared with the other well-known competitive algorithms.

Benefit of range uncertainty reduction in robust optimisation for proton therapy of brain, head-and-neck and breast cancer patients.

Background and Purpose: The primary cause of range uncertainty in proton therapy is inaccuracy in estimating the stopping-power ratio from computed tomography. This study examined the impact on dose-volume metrics by reducing range uncertainty in robust optimisation for a diverse patient cohort and determined the level of range uncertainty that resulted in a relevant reduction in doses to organs-at-risk (OARs). Materials and Methods: The effect of reducing range uncertainty on OAR doses was evaluated by robustly optimising six proton plans with varying range uncertainty levels (ranging from 3.5% in the original plan to 1.0%), keeping setup uncertainty fixed. All plans used the initial clinical treatment plan's beam directions and optimisation objectives and were optimised until a clinically acceptable plan was achieved across all setup and range scenarios. The effect of reduced range uncertainty on dose-volume metrics for OARs near the target was evaluated. This study included 30 brain cancer patients, as well as five head-and-neck and five breast cancer patients, investigating the relevance of reducing range uncertainty when different setup uncertainties were used. Results: Lowering range uncertainty slightly reduced the nominal dose to surrounding tissue. For body volume receiving 80% of the prescribed dose, reducing range uncertainty from 3.5% to 2.0% resulted in a median decrease of 4 cm3 for the brain, 17 cm3 for head-and-neck, and 27 cm3 for breast cancer patients. Conclusions: Reducing range uncertainty in robust optimisation showed a reduction in dose to OARs. The clinical relevance depends on the affected organs and the clinical dose constraints.

[Preliminary evaluation of the interest of a therapeutic optimization cell on the titration of treatments for heart failure with reduced left ventricular ejection fraction and the quality of life of patients]. / Évaluation préliminaire de l'intérêt d'une cellule d'optimisation thérapeutique sur la titration des traitements de l'insuffisance cardiaque à fraction d'éjection ventriculaire gauche réduite et la qualité de vie des patients.

INTRODUCTION: A multidisciplinary therapeutic optimization unit (COT) was created in January 2023 at Versailles Hospital, aimed at therapeutic optimisation of patients with chronic heart failure with reduced left ventricular ejection fraction. The objective of the study was to assess the impact of the first year of COT activity on the sequential implementation and titration of heart failure treatments, the clinical evolution, and improvement of patients' quality of life. METHODS: This prospective study included consecutive patients treated by the COT after hospitalisation for acute heart failure, from January to December 2023. Clinical, biological, titration, and tolerance data were analysed. Quality of life was assessed at baseline and at the end of the follow-up by COT, using standardized SF-12 and EQ-5D questionnaires. RESULTS: We included 90 patients (men 73%, mean age 67 years). The mean left ventricular ejection fraction was 34 ± 10 %. At final visit (median number of visits 4 ; median follow-up duration 156 days), 76.7% of patients achieved optimisation with respect to maximum individually tolerated doses, but only 13.3% with respect to theoretical maximum doses for the four therapeutic classes. At 1-year follow up, total mortality was 4.4% (4/90), and 9 patients (10%) were rehospitalised unplanned for acute heart failure. COT monitoring was associated with significant improvement in NYHA class, left ventricular ejection fraction, and SF-12 and EQ-5D-5L quality of life scores. CONCLUSION: Although titration of heart failure treatments remained suboptimal, significant improvement was observed for NYHA class, left ventricular ejection fraction, and patient quality of life parameters.

Waiting for surgery after hip fracture-Health and/or economic risk?

OBJECTIVES: Waiting for surgery is a disconcerting experience. It can have a negative impact on patients' outcomes and length of stay (LOS) as driver for treatment costs. Process-optimisation may be a strategy to improve quality and cost-efficacy. The study investigates the correlation between waiting for hip fracture surgery and patient characteristics, organisational variables, outcomes, LOS, and the distribution of waiting times and LOS over time, including cost estimates. Thereby the study aims to identify the potential for organisational improvements with respect to managing the waiting time. METHODS: Ten-year routine health data (patient characteristics and follow-up information) and process-indicators that is, waiting time and LOS from a Swiss trauma-centre were analysed retrospectively. Cost-estimates were calculated based on Swiss diagnosis related groups and daily costs to evaluate hospital revenues. RESULTS: In total, 2572 patients aged ≥60 years with low-energy hip fractures were included. Waiting times >48 h were associated with sub-optimal outcomes. Over the years long waiting times decreased. This reduction was not reflected by a reduction in LOS which remained stable around 10 days, primarily driven by late discharge to in-patient rehabilitation. Reimbursement persisted at an average revenue in the low 4-5-digit range, depending on implant costs. CONCLUSIONS: While there has been a reduction of waiting times, this has not translated into a reduction of LOS or potential savings in health care costs, due to the various dependencies along the patient journey. Managing waiting times may be an area for improvement, increasing cost-efficacy, especially since long waiting times are still associated with inferior outcomes and LOS.

Soy whey and brewer's spent grain hydrolysates wholly replace conventional medium for microalgae growth: Process performance and economic considerations.

To enhance circularity in heterotrophic microalgal bioprocesses, this study completely substituted glucose and Bold's basal medium (BBM) with brewer's spent grain (BSG) and soy whey (SW) hydrolysates. Mild acid hydrolysis conditions of BSG (0.2 M H2SO4, 130 °C, 36 min) and SW (0.1 M HCl, 95 °C, 30 min) were optimised for glucose release, and their hydrolysates were optimally mixed (15 % SW-85 % BSG) to obtain a medium that best supported Auxenochlorella protothecoides growth. Maximum biomass production (Xmax) and productivity (PXmax) obtained in the hydrolysate medium containing 50.75 g/L endogenous glucose (Xmax: 22.17 g/L; PXmax: 7.06 g/L/day) were comparable to that in BBM containing 50.44 g/L exogenous glucose (Xmax: 20.02 g/L; PXmax: 6.34 g/L/day). Moreover, estimated hydrolysate medium production costs were within an order of magnitude to BBM. Overall, the integrated approach of tailored hydrolytic treatments and complementary side-streams presents a promising technical and economic feasibility, with applications extending beyond A. protothecoides.

Conductivity optimisation of graphene oxide-M13 bacteriophage nanocomposites: towards graphene-based gas micronano-sensors.

Graphene oxide (GO) and M13 bacteriophage can self-assemble to form ultra-low density porous structures, known as GraPhage13 aerogels (GPA). Due to the insulating nature of GPA and the challenges in producing highly conductive aerogels, it is paramount to explore ways to enhance the conductivity of GPA. Herein, we have developed a method to enhance the conductivity of GPA, via the integration and optimisation of 5 nm and 20 nm diameter gold nanoparticles (AuNPs) into the aerogel structure and systematically analysed the morphology, composition and spectroscopic properties of the resulting GPA-Au nanocomposite. The fabricated GPA-Au nanocomposites exhibited remarkable increases in conductivity, with the integration of 5 nm AuNPs leading to a 53-fold increase compared to GPA, achieving a performance of up to 360 nS/cm, which is within the range suitable for miniaturised semiconductor devices. The mechanism behind the conductivity enhancement was further investigated and attributed to GO-AuNP interactions increasing the carrier density by introducing new energy levels in the GO band gap or shifting its Fermi level towards the conduction band. These findings demonstrate the potential of functionalised AuNPs to significantly improve the electrical properties of GPA, paving the way for their application in gas sensors for biological and chemical detection and a new range of advanced semiconductor devices.

Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization.

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

Digital Twin Methodology in Food Processing: Basic Concepts and Applications.

PURPOSE OF REVIEW: The goal of this article is to present a concise review about digital twin (DT) methodology and its application in food processing. We aim to identify the building blocks, current state and bottlenecks, and to discuss future developments of this approach. RECENT FINDINGS: DT methodology appears as a powerful approach for digital transformation of food production, via integration of modelling and simulation tools, sensors, actuators and communication platforms. This methodology allows developing virtual environments for real-time monitoring and controlling of processes, as well as providing actionable metrics for decision-making, which are not possible to obtain by physical sensors. So far, main applications were focused on refrigerated transport and storage of fresh produces, and thermal processes like cooking and drying. DT methodology can provide useful solutions to food industry towards productivity and sustainability, but requires of multidisciplinary efforts. Wide and effective implementation of this approach will largely depend on developing high-fidelity digital models with real-time simulation capability.

In silico medical device testing of anatomically and mechanically conforming patient-specific spinal fusion cages designed by full-scale topology optimisation.

A full-scale topology optimisation formulation has been developed to automate the design of cages used in instrumented transforaminal lumbar interbody fusion. The method incorporates the mechanical response of the adjacent bone structures in the optimisation process, yielding patient-specific spinal fusion cages that both anatomically and mechanically conform to the patient, effectively mitigating subsidence risk compared to generic, off-the-shelf cages and patient-specific devices. In this study, in silico medical device testing on a cohort of seven patients was performed to investigate the effectiveness of the anatomically and mechanically conforming devices using titanium and PEEK implant materials. A median reduction in the subsidence risk by 89% for titanium and 94% for PEEK implant materials was demonstrated compared to an off-the-shelf implant. A median reduction of 75% was achieved for a PEEK implant material compared to an anatomically conforming implant. A credibility assessment of the computational model used to predict the subsidence risk was provided according to the ASME V&V40-2018 standard.

Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems.

BACKGROUND AND OBJECTIVE: The Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT). METHODS: The study's methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage. RESULTS: The RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods. CONCLUSIONS: The efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.

A novel AES-DES with improved Cheetah optimisation algorithm for secured medical data transmission in cloud environment.

In recent years, transmitting medical data has been a regular process. Although strong, safe, and dependable encryption techniques are necessary for medical data, cryptography is largely a computational process. The research presents a selective encryption approach for the transfer of sensitive data. This study proposes a novel technique for selecting the optimal keys to offer more security to medical data. Initially, the medical data is encrypted using the hybrid AES-DES technique. To make an efficient encryption method, the most optimal keys are selected utilising an improved Cheetah optimisation algorithm (ICO). Finally, the keys are optimised, and the input medical data is safely kept in the cloud system according to the established model. As a result, the proposed approach utilises the Python tool to evaluate the results. The simulation results show that the proposed method outperforms others in terms of encryption time 96 s, decryption time 92 s, memory usage (16), and latency (0.006).