Potato peel waste-its nutraceutical, industrial and biotechnological applacations

With ever increased food processing in the new millennia, production of agro-industrial waste has been increased tremendously. Although, these waste are rich sources of essential bioactive compounds. In developing countries, the waste materials from agro-industries can help to obtain valuable components. The objective of this manuscript is to manage industrial potato peel waste and to highlight its nutritional and industrial uses. Potato peel waste, by various procedures such as fermentation, extraction and other treatments, can ensue into the products such as bio-fuels, dietary fibre, biofertilizer, biogas, biosorbent, antioxidants and food additives. This paper enlightens the application of potato peel utilization in food and nonfood purposes for example extraction, utilization of bioactive components, biotechnological usage, livestock feed and miscellaneous use. The present article summarized the literature pertaining to the studies of the last twenty years

Adaptive Backstepping Integral Sliding Mode Control for 5DOF Barge-Type OFWT under Output Constraint

This article presents a new control solution for a dynamical model of a translational oscillator with a rotational actuator (TORA) based on multi-body dynamics for a barge-type offshore floating wind turbine (OFWT). TORA has been employed as an active structural control strategy. The solution of bounding the output movements of platform pitch and tower bending angle to a certain limit, along with mitigating the OFWT vibrations due to environmental disturbances and uncertainties, is presented in this novel control framework. This new control algorithm consists of a high-gain observer (HGO)-based adaptive backstepping integral sliding mode control (ISMC) and a barrier Lyapunov function (BLF). This guarantees satisfying the constraints on the states and effectively resolves the problem of the unavailability of the system states. The proposed control law based on the BLF has been compared with an adaptive backstepping ISMC to show the efficiency of the output-constraint control scheme. Through MATLAB/SIMULINK numerical simulations and their numeric error table, the effectiveness of the proposed control scheme has been examined. The results confirm the validity and efficiency of the proposed control approaches

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Big Data Analytics (BDA) has garnered significant attention in both academia and industries, particularly in sectors such as healthcare, owing to the exponential growth of data and advancements in technology. The integration of data from diverse sources and the utilization of advanced analytical techniques has the potential to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, and enhancing patient outcomes. In this paper, we aim to provide a comprehensive literature review on the application of big data analytics in healthcare, focusing on its ecosystem, applications, and data sources. To achieve this, an extensive analysis of scientific studies published between 2013 and 2023 was conducted and overall 180 scientific studies were thoroughly evaluated, establishing a strong foundation for future research and identifying collaboration opportunities in the healthcare domain. The study delves into various application areas of BDA in healthcare, highlights successful implementations, and explores their potential to enhance healthcare outcomes while reducing costs. Additionally, it outlines the challenges and limitations associated with BDA in healthcare, discusses modelling tools and techniques, showcases deployed solutions, and presents the advantages of BDA through various real-world use cases. Furthermore, this study identifies and discusses key open research challenges in the field of big data analytics in healthcare, aiming to push the boundaries and contribute to enhanced healthcare outcomes and decision-making processes

Do we practice what we preach? Dialysis modality choice among healthcare workers in the United Kingdom

Background: In the United Kingdom, over 80% of end‐stage kidney disease patients receive in‐center hemodialysis. We conducted a survey of UK renal healthcare workers on their preferred dialysis modality if they needed dialysis themselves. Methods: An anonymized online survey was disseminated to all renal healthcare workers in the United Kingdom. We asked “Assume you are an otherwise well 40‐year‐old (and, separately, 75‐year‐old) person approaching end stage kidney disease, you have no living kidney donor options at present. There are no contraindications to any dialysis options. Which dialysis therapy would you choose?” We also asked about factors influencing their choice. Results: 858 individuals with a median age of 44.3 years responded. 70.2% were female, 37.4% doctors, and 31.1% were senior nurses. There was a preference for peritoneal dialysis over in‐center hemodialysis (50.47% v. 6.18%; p < 0.001 for 40‐year‐old and 49.18% v. 17.83%; p < 0.001 for 75‐year‐old assumption) and home hemodialysis (50.47% v. 39.28%; p < 0.001 for 40‐year‐old and 49.18% v. 18.41% for 75‐year‐old assumption). There was a preference for home hemodialysis over in‐center hemodialysis for 40‐year‐old (39.28% v. 6.18%; p < 0.001) but not for 75‐year‐old. On logistic regression, senior doctors were more likely to opt for PD when compared to nurses. Nurses, allied healthcare professionals, and those of Asian/British Asian ethnicity were more likely to choose in‐center hemodialysis. Conclusions: Most healthcare workers in renal medicine would choose home‐based treatment for themselves although the majority of end‐stage kidney disease patients receive in‐center hemodialysis in the United Kingdom; the reasons for the discrepancy need to be explored

Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks

The Internet of Things (IoT) is a complex and diverse network consisting of resource-constrained sensors/devices/things that are vulnerable to various security threats, particularly Distributed Denial of Services (DDoS) attacks. Recently, the integration of Software Defined Networking (SDN) with IoT has emerged as a promising approach for improving security and access control mechanisms. However, DDoS attacks continue to pose a significant threat to IoT networks, as they can be executed through botnet or zombie attacks. Machine learning-based security frameworks offer a viable solution to scrutinize the behavior of IoT devices and compile a profile that enables the decision-making process to maintain the integrity of the IoT environment. In this paper, we present a machine learning-based approach to detect DDoS attacks in an SDN-WISE IoT controller. We have integrated a machine learning-based detection module into the controller and set up a testbed environment to simulate DDoS attack traffic generation. The traffic is captured by a logging mechanism added to the SDN-WISE controller, which writes network logs into a log file that is pre-processed and converted into a dataset. The machine learning DDoS detection module, integrated into the SDN-WISE controller, uses Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) algorithms to classify SDN-IoT network packets. We evaluate the performance of the proposed framework using different traffic simulation scenarios and compare the results generated by the machine learning DDoS detection module. The proposed framework achieved an accuracy rate of 97.4%, 96.1%, and 98.1% for NB, SVM, and DT, respectively. The attack detection module takes up to 30% usage of memory and CPU, and it saves about 70% memory while keeping the CPU free up to 70% to process the SD-IoT network traffic with an average throughput of 48 packets per second, achieving an accuracy of 97.2%. Our experimental results demonstrate the superiority of the proposed framework in detecting DDoS attacks in an SDN-WISE IoT environment. The proposed approach can be used to enhance the security of IoT networks and mitigate the risk of DDoS attacks

Image‐based malware classification using VGG19 network and spatial convolutional attention

In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state‐of‐the‐art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image‐based classification of 25 well‐known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image‐based malware detection with high performance, despite being simpler as compared to other available solutions

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Radiative and magnetohydrodynamics flow of third grade viscoelastic fluid past an isothermal inverted cone in the presence of heat generation/absorption

A mathematical analysis is presented to investigate the nonlinear, isothermal, steady-state, free convection boundary layer flow of an incompressible third grade viscoelastic fluid past an isothermal inverted cone in the presence of magnetohydrodynamic, thermal radiation and heat generation/absorption. The transformed conservation equations for linear momentum, heat and mass are solved numerically subject to the realistic boundary conditions using the second-order accurate implicit finite-difference Keller Box Method. The numerical code is validated with previous studies. Detailed interpretation of the computations is included. The present simulations are of interest in chemical engineering systems and solvent and low-density polymer materials processing

Renal artery sympathetic denervation:observations from the UK experience

Background: Renal denervation (RDN) may lower blood pressure (BP); however, it is unclear whether medication changes may be confounding results. Furthermore, limited data exist on pattern of ambulatory blood pressure (ABP) response—particularly in those prescribed aldosterone antagonists at the time of RDN. Methods: We examined all patients treated with RDN for treatment-resistant hypertension in 18 UK centres. Results: Results from 253 patients treated with five technologies are shown. Pre-procedural mean office BP (OBP) was 185/102 mmHg (SD 26/19; n = 253) and mean daytime ABP was 170/98 mmHg (SD 22/16; n = 186). Median number of antihypertensive drugs was 5.0: 96 % ACEi/ARB; 86 % thiazide/loop diuretic and 55 % aldosterone antagonist. OBP, available in 90 % at 11 months follow-up, was 163/93 mmHg (reduction of 22/9 mmHg). ABP, available in 70 % at 8.5 months follow-up, was 158/91 mmHg (fall of 12/7 mmHg). Mean drug changes post RDN were: 0.36 drugs added, 0.91 withdrawn. Dose changes appeared neutral. Quartile analysis by starting ABP showed mean reductions in systolic ABP after RDN of: 0.4; 6.5; 14.5 and 22.1 mmHg, respectively (p &lt; 0.001 for trend). Use of aldosterone antagonist did not predict response (p &lt; 0.2). Conclusion: In 253 patients treated with RDN, office BP fell by 22/9 mmHg. Ambulatory BP fell by 12/7 mmHg, though little response was seen in the lowermost quartile of starting blood pressure. Fall in BP was not explained by medication changes and aldosterone antagonist use did not affect response