bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of the isotope mass on pedestal structure, transport and stability in D, D/T and T plasmas at similar β
N and gas rate in JET-ILW type I ELMy H-modes
Authors: Frassinetti L. , Perez von Thun C. , Chapman-Oplopoiou B. , Nyström H. , Poradzinski M. , Hillesheim J.C. , Horvath L. , Maggi C.F. , Saarelma S. , Stagni A. , Szepesi G. , Bleasdale A. , Chomiczewska A. , Morales R.B. , Brix M. , Carvalho P. , Dunai D. , Field A.R. , Fontdecaba J.M. , Sun H.J. , King D.B. , Kos D. , Kowalska E. , Labit B. , Lennholm M. , Menmuir S. , Rachlew E. , Refy D.I. , Schneider P.A. , Solano E.R. , Vianello N. , Vécsei M. ,   JET Contributors
Published: October 2023

The work describes the pedestal structure, transport and stability in an effective mass (A
eff) scan from pure deuterium to pure tritium plasmas using a type I ELMy H-mode dataset in which key parameters that affect the pedestal behaviour (normalized pressure, ratio of the separatrix density to the pedestal density, pedestal ion Larmor radius, pedestal collisionality and rotation) are kept as constant as possible. Experimental results show a significant increase of the density at the pedestal top with increasing A
eff, a modest reduction in the temperature and an increase in the pressure. The variations in the pedestal heights are mainly due to a change in the pedestal gradients while only small differences are observed in the pedestal width. A clear increase in the pedestal density and pressure gradients are observed from deuterium to tritium. The experimental results suggest a reduction of the pedestal inter-edge localized mode (inter-ELM) transport from deuterium to tritium. The reduction is likely in the pedestal inter-ELM particle transport, as suggested by the clear increase of the pedestal density gradients. The experimental results suggest also a possible reduction of the pedestal inter-ELM heat transport, however, the large experimental uncertainties do not allow conclusive claims on the heat diffusivity. The clear experimental reduction of η
e (the ratio between density and temperature gradient lengths) in the middle/top of the pedestal with increasing A
eff suggests that there may be a link between increasing A
eff and the reduction of electron scale turbulent transport. From the modelling point of view, an initial characterization of the behaviour of pedestal microinstabilities shows that the tritium plasma is characterized by growth rates lower than the deuterium plasmas. The pedestal stability of peeling-ballooning modes is assessed with both ideal and resistive magnetohydrodynamics (MHD). No significant effect of the isotope mass on the pedestal stability is observed using ideal MHD. Instead, resistive MHD shows a clear increase of the stability with increasing isotope mass. The resistive MHD results are in reasonable agreement with the experimental results of the normalized pedestal pressure gradient. The experimental and modelling results suggest that the main candidates to explain the change in the pedestal are a reduction in the inter-ELM transport and an improvement of the pedestal stability from deuterium to tritium.
Supervised learning techniques for detection of Lung Carcinoma
Authors: Jalall S K , Harsha K , Dutta K K , Sarita K , Banik S , Sakambari N P
Published: October 2023

Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer
Realtime Evaluation of Cardiac Activities Using Multi Features with KNN Classifier - A Pilot Study
Authors: Arun U , Sriraam N , Srinivasulu Avvaru
Published: October 2023

Monitoring of cardiac episodes is essential for patients with suspected cardiac arrhythmias. The quantitative evaluation of cardiac activities during continuous unattended cardiac monitoring will support clinician to understand the nature of the physical activities. This pilot study is proposed to assess the cardiac activities using multi features with KNN classifier. ECG was collected from healthy volunteers under rest and during exercise. The time and frequency domain features such as BPM, QRS duration, RR interval, SD, CV, RMSRR, NN50, SDNN, Shannon entropy, log entropy, and sample entropy were extracted. Further, multi-classification was employed using KNN classifier. The performance of KNN classifier is tested in terms of classification accuracy and elapsed time and it was found that Euclidean distance for k=2 was showing better performance. Statistical analysis was performed to confirm the suitability of the features using Pearson’s correlation coefficient and box plot. Among the features BPM, QRS, RR, sample and log entropies were found to be significant. Specifically, sample entropy provided to be the best candidate with p<0.01. The study reveals an overall classification accuracy of 90% was obtained.
Automated Recognition of Congenital Heart Defects (CHD) from Ultrasonic Fetal Heart Biometry
Authors: Sushma T.V. , Sriraam N , Suresh S
Published: October 2023

The growth and development of the fetus is monitored widely using Ultrasound imaging modality and has become synonymous with prenatal scanning. Analysing the images helps in the detection of congenital defects at an early stage which help to plan the delivery appropriately or for taking some corrective measures. The inherent noise of ultrasound imaging makes analysis of the images largely dependent on the expertise of the clinician and is prone to errors. Computer Aided Diagnostic (CAD) tools used in medical diagnosis assist the clinicians in analysing the images and reducing the chances of missing the defect. AI tools, machine learning and deep learning algorithms have shown improved classification performance of medical images in recent times have resulting in widespread implementation of these technologies in CAD tools. The proposed work discusses the use of machine learning for detection of Congenital heart disease (CHD). Frames resulting from the B mode Ultrasound cineloop sequence have been used for the study. A total of 870 frames with 550 normal frames and 320 abnormal frames are used for the study. Law’s texture feature have been extracted from the pre processed frames which in turn is used for classification. The performance of each of these features is compared using different kernels of SVM classifier. Tuning parameters regularization and gamma have been used to optimise the classification performance using grid search algorithm. It is observed that Laws texture feature results in better classification performance with an average accuracy and precision of 95% and specificity, sensitivity of 98%. Precision Recall curves and AUC-ROC plots have been used for comparing the performance of different kernels.
On metric dimensions of flower graphs
Authors: Girisha A , Rajendra P , Vijaya Chandra Kumar U , Pushpa S
Published: October 2023

The metric dimension of a connected graph G is the smallest number of nodes (resolving set) required to identify all other nodes based on shortest path distances uniquely. The notion of resolving set is vital in robotic navigation and to create various plan of action for the mastermind game. If certain vertices of a set S ⊂ V(G) resolve each pair of nodes u and v of G, then that set is a resolving set. A metric basis represents the lowest number of nodes in the set S. In this study, we describe the distance matrix and metric dimension of sunflower graphs and flower snarks.
Survey of the use of AI models and techniques in the analysis and prediction of neuro-degenerative diseases
Authors: Prabhu Srikanth , Rathinam Suryaansh , Rao Chirag , Chowdhury Anurag
Published: October 2023

Neurodegenerative diseases are caused by the gradual breakdown of the nerve connections. The detection and prediction of these diseases has been attempted with a variety of different artificial intelligence techniques, using the wide range of modalities available resulting in variations in accuracy and other metrics. This paper aims to offer a methodical as well as quantitative comparison among the various predictive methodologies that have been conducted for three neurodegenerative diseases, namely Alzheimer’s, Schizophrenia and Dementia. Lastly this paper focuses on the variation in occurrences of these diseases with changing demographics such as age, sex and location.
3T-SAPON technique to reduce Power Consumption
Authors: Sreeja Garlapati , Shama Kumara
Published: October 2023

Scaling down CMOS technology has rapidly improved cost, area effectiveness, switching speed of the circuits & high device reliability. However, scaling has an immense impact on static power consumption in VLSI circuits. For battery operating applications higher power dissipation is not desirable because it reduces the efficiency and cooling effects of the battery. Thus, designing a low-power consuming circuit has become the major criteria in circuit designs. A new circuit model called 3T-SAPON is designed to reduce static power consumption. 3T-SAPON technique power consumption has been reduced around 45%. Using this technique inverter, NAND are implemented and compared with some of the existing standard techniques like LCNT, LECTOR, ONOFIC and SAPON. All these circuits are simulated in Cadence 90nm technology.
An Optimized Reinforcement Learning-Based Routing Protocol Using Spider Monkey Optimization Algorithm
Authors: Radhika G , Radhika N
Published: October 2023

In Wireless Sensor Networks, numerous machine-learning algorithms are used in the packet routing strategy. The recommended routing approach is implemented using adaptive reinforcement learning and a spider monkey optimization algorithm. Data packets are sent over the network via optimum route establishment—the above attempts to save energy by changing the Q-value at each instance. Technology enables adaptive mechanisms to demonstrate intelligent behavior in complex and dynamic situations such as Wireless Sensor Networks (WSN). Recently, new routing methods for WSNs have been devised in response to such a notion. Due to user interference and overall network performance, the numerous routing pathways cause frequent channel-switching occurrences. The spider monkey optimization method obtains routing metrics such as bandwidth, end-to-end latency, throughput, overhead, and packet delivery ratio. Compared to other machine learning algorithms, such as RLBR, the performance of WSN is improved and obtained better performance results.
A Comparative Study of an Energy Efficient Algorithm for Wireless Sensor Networks
Authors: Roy Manish Kumar , Kumar Pramod
Published: October 2023

Wireless Sensor Networks are defined as a group of sensors that are spread over an area to gather data in a coordinated manner and send it to the sink where it is further processed and used for the application. The application could be as simple as gathering information from different parts of a confined accessible area or as extreme as tracking of wild animals of concern around a widely spread rainforest. The wide range of applicability of wireless sensor networks suggests that often, the sensors once deployed might not be accessible ever again and hence it becomes important to ensure that the sensors once deployed are functional for as long as possible. One of the hurdles in enhancing the functional lifetime of sensors is the energy consumption at the sensor level which can be broadly attributed to mainly two reasons: sensing and communicating; wherein the latter takes the bigger chunk. So, the goal would be to try and push the boundary viz-a-viz energy efficiency at the individual sensor nodes and enhance the lifetime of a WSN. In this paper, simulations have been carried out by varying the position of the base node to determine the number of rounds till which the network remains fully functional. It is found out that positioning the base node at the network centre significantly increases the rounds till which the network sustains as compared to positioning the node at a distance.
Implementation of EBG Structure to Reduce Surface Wave Excitations for IoT Range Applications
Authors: Gowri CH , Kumari B Leela
Published: October 2023

This paper presents the design of antenna for Internet of Things (IoT) applications. The performance of conventional antenna is limited by surface waves due to which it produces a low gain. An attempt is made to reduce the surface wave excitations in conventional antennas with the use of integration of Electromagnetic Band Gap (EBG) cells. The proposed work involves the design of Edge via EBG unit cell at 5.2 GHz and it is simulated with High Frequency Structure Simulator (HFSS) software. The simulation results show that the gain is enhanced by 10% and S11 is reduced by 72%, approximately in the rectangular microstrip patch antenna with Edge via EBGs.
Electronic, structural and optical properties of rare-earth based TbNiSb half Heusler compound
Authors: Mehta Shreya , Saini Sapan Mohan
Published: October 2023

Half-Heusler (HH) compounds have potential applications in many fields including spintronics and future energy applications. Rare earth based Heusler materials are best suitable for thermoelectric materials. To calculate some physical properties (the electronic, optical-properties) of rare earth (RE) based ternary HH TbNiSb compound, we applied full potential linearized augmented plane wave (FPLAPW) method which is based on Density Functional Theory (DFT). We use the Coulomb corrected generalized gradient approximation (GGA+U) for this study. The calculation of total density of states (DOS) of TbNiSb shows that Ni-3d states dominate in valence band (VB) around -2 eV below fermi energy level (EF) in both spin-up and spin-down configurations, while spin up Tb-5d & spin down Tb- 4f states constitute the conduction band (CB) around 4 eV above the EF. We found a narrow energy gap 0.115 eV is opened showing the semiconducting nature of the TbNiSb compound. Tb –4f states lie around -8 eV below EF and not participate to the DOS near EF. We have also calculated the optical properties like dielectric constant, extinction coefficient, refractive index and reflectivity of TbNiSb compound.
Photometric Study of the nuclear region of the galaxy NGC 5689
Authors: Verma Mahendra , Singh Avinash , Chaware Laxmikant
Published: October 2023

The nuclear regions of the galaxies are fascinating. It contains a vital clue about galaxy formation and evolution. This letter presents a detailed photometric study of the galaxy NGC 5689. The Hubble space telescope WFC2 PC and 3.6 micron Spitzer image are adopted for the analysis. A one-dimensional photometric study is used to analyze the nuclear galaxy profile. The two-dimensional analysis(GALFIT) is performed to study bulge and disc counterparts. Our study shows that the nuclear region of the NGC 5689 is incarnated with a colossal nuclear dust lane, which causes drastic changes in the position angle and ellipticity profile of the galaxy.
Design and development of rover with pick and place Mechanism
Authors: Jadhav Dhairyashil , Kurane Niraj , Mali Om , Pawar Rohan , Jadhav Anurag , Korwar Ganesh
Published: October 2023

This work presents a unique rover design that can traverse over a rough terrain and incorporate a pick-and-place mechanism over there. In the fields of robotics and space technology, rovers are crucial. Moreover, the rocker bogie design has established itself as a reliable mobility solution recognized for its improved vehicle stability and capability to traverse over obstacles. ‘Mars Exploration Rover’ has played a vital role in space exploration because it was an apparent choice because of its design of rocker-bogie suspension. The rover’s design has been modelled with CAD software and validated through simulation testing. Thus, the various improvements ensures mechanical sustainability, structural soundness, stability, and lightweight design.
Design and Development of Multi-axis Manipulator for Data Acquisition for Neurosurgery Applications.
Authors: Sanap Madhuri U. , Tiwari Pratham B. , Anasane S. S.
Published: October 2023

Neurosurgery is the specialty of medical operations that deals with the nervous system such as the brain, spinal cord, etc. Stereotactic neurosurgery is an important technique in neurosurgery as it enables precise targeting of specific areas in the brain with minimal damage to surrounding tissues. Neuro-navigation is one of the most important techniques used in stereotactic neurosurgery to help accurately target specific brain locations during the operation. It involves a computer-based system that assists to create 3D images of the patient’s face using imaging processes such as CT/MRI. In the existing process, considerable time is required to reach a specific target location of the tumor. The study presented in this manuscript consists of some mechanical assembly surrounding the patient’s head and a preoperative imaging system. There are several problems associated with the technique. But the main challenge is an inoperative brain shift. There is an error in deviations from the target anatomy from the operative plan brain shifts up to 20 - 50 mm. It may affect the accuracy of targeting and treatment effectiveness. The paper represents the development of a multi-axis manipulator for the neurosurgery application. The main goal of the machine is to provide good accuracy at a low cost. This multi-axis manipulator can assist the neuro registration and perform physical movement of the surgical instrument. In this, the movement of the manipulator is controlled by using a microcontroller with different motors and drivers.
Automated smart waste segregation system using IoT technology
Authors: Chitale M R , Chitpur S J , Chivate A B , Chopade P D , Deshmukh S M , Marathe A A
Published: October 2023

One of the major challenges faced not just by India but globally as well is waste management. India is the second most populous country and naturally generates tonnes of garbage daily. It is common to find dustbins overflowing with junk scattered everywhere, not to mention that segregation of waste is conveniently neglected. Many-a-times it is observed that despite providing separate bins for dry and wet trash, the convention is carelessly ignored. With the traditional methods of periodic garbage collection and manual segregation of wet, metallic, non-recyclable, and recyclable waste proving to be inefficient, there is a need for a better solution. With the help of IoT, a more effective solution is proposed to improve the current system and take a step forward toward the vision of smart cities. The proposed system uses smart garbage tracking, segregation, and collection systems, interfaced with ultrasonic sensors, rainfall/moisture sensors, inductive proximity sensors, and servo motors, along with the necessary hardware, with Arduino as the main controlling unit. The Arduino platform is used as a software platform for the working of the model and obtaining the expected results.
Plant disease prediction system using advance computational Technique
Authors: Gulame Mayuresh B. , Thite Trupti. G. , Patil Kranti D.
Published: October 2023

A vital sector of India’s economy is agriculture. Identification of plant infections is crucial to preventing crop damage and further disease. The majority of plants, such as apple, tomato, cherry, and grapes, have leaves that appear to have disease signs. The plant health can be monitored through images to precisely predict the disease and to take early preventative action. The traditional method is to manually inspect the plant leaf to identify the kind of disease, as done by farmers or plant pathologists. In this research, we presented a deep CNN model termed as Decompose, Transfer, and Compose (DTComp) for the classification of plant disease. The deep learning model makes predictions more quickly and precisely than manual plant leaf observation. Out of all the pretrained deep models, the ResNet50 model achieves the highest accuracy for classification. DTComp can handle any anomalies in the images using class decomposition approach to examine the class boundaries. The experimental findings demonstrated DTComp capacity for detecting plant disease instances on dataset gathered from multiple villages using the Kaggel Open Source platform. DTComp can successfully identify plant disease with a high accuracy of 98.30% from images. Additionally, this model can be deployable on real-time systems equipped with a Raspberry Pi and a camera module.
Development of digital twin model for mechanical application
Authors: Ajegaonkar S P , Katoke A R , Bhingare S V , Mane S R
Published: October 2023

The notion of digital twin technology is one that is just starting to gain traction in industry or business and more recently, academia. A virtual depiction of a real process or thing is called a “digital twin.” that may gather data from the real environment in order to represent, validate and simulate the physical twins. For artificial intelligence, the internet of things (IoT), and digital twins, the problems, applications, and enabling technologies are discussed. Particularly in the industrial sector, the development of Industry 4.0 principles has aided in its expansion. In order to adapt the idea to production. It is simulation technology that, by making full use of physical models, sensor data, and operation histories, blends interdisciplinary, multi-physical quantity, multiscale, and multi-probability. Utilizing cutting-edge analytical, monitoring, and predictive capabilities to test digital twin processes and services. It eliminates the expensive failure of physical items. It is a crucial facilitator of object lifecycle management, product validation and simulation, complex systems monitoring, and data-driven decision-making. Using a machine learning technique, this paper examines fault detection and develops a digital replica of the conveyor system of quality analysis of gear.
Modal and harmonic acoustical analysis of a circular rubber membrane and classification of resonant frequencies using deep learning
Authors: Dixit Anirudh , Wani Kiran P
Published: October 2023

Mode shapes of a component or a structure can be determined by conducting Modal analysis. Chladni figures can also be used to determine the mode shapes of a plate. In this research comparison of these mode shapes using Chladni figures and ANSYS simulation has been carried out. Analysis systems used for ANSYS simulation are Harmonic Acoustic Analysis and modal analysis. With the help of wave equation, a mathematical formulation has been achieved. The experimental and simulation results are compared, and an error of not more than 9% for modal and 10% for harmonic acoustics is found. Classification of these Chladni figures which are obtained experimentally, using Deep Learning algorithms like Inception ResNetV2, VGG16 and Xception and have achieved 100 percent accuracy at different converging times.
Real time two-wheeler recognition for Surveillance
Authors: Ganesh M A , Perumaal S Saravana , Preethi S P , Bhavdharani S D
Published: October 2023

In today’s intelligent transportation systems, vision-based traffic tracking systems and automatic vehicle make and model recognition is of significant importance for surveillance. From the literature, vehicle make and model recognition has been implemented for four wheelers easily due to the presence of logo in the frontal and rear parts. But it is not feasible for two wheelers due to the insignificant differential features. Hence in this work, a robust two-wheeler recognition system has been developed with frontal, rear and side images of two wheelers which is implementable in edge devices. The contributions of the paper are (i) Development of a two-wheeler dataset with the frontal, rear and side images of two classes in Indian Scenario. (ii) Development of a two-wheeler recognition framework implementable in Raspberry pi-based devices. (iii) Evaluation of the framework in real time outdoor environment. From the experimental results it is observed that the proposed network classifies two wheelers in real time at 4s with 94% accuracy.
Design of Bidirectional Resonant Converter For EV Application
Authors: Brindha B. , Annapoorani K. Iyswarya
Published: October 2023

In this study, a novel LLC converter-based battery charger for EVs is presented. With this structure, the major switches may be switched at zero voltage over the whole charging profile. The suggested charger offers a flexible output voltage range suitable for the battery system. Additionally, storage battery-to-battery charging enables the sharing of charge between two batteries if one battery becomes stranded far from a charging station, hence maintaining high-efficiency undercharging. Various types of vehicle-to-vehicle chargers have been examined by researchers. A major drawback of conventional chargers is the inefficiency caused by the several power conversion steps they use. The study describes a DC source to a battery charger that operates independently from the vehicle. The paper discusses the operation modes of DC source-storage battery chargers.
Optimal Power Management for Sustainable Multipurpose Smart EV Charging Stations
Authors: Subashini M , Mohamed Abdullah J , Adarsh JK , Senthil Rethinam , Sumathi V
Published: October 2023

Automation and AI are necessity in today’s fast-moving world. This combination augments human intelligence and skills to make better decisions and improves the quality of services. This project proposes an automated intelligent control algorithm to schedule the EV charging in the closed community parking lots. The intelligent controller uses an optimisation algorithm to allocate the chargers based on the availability of renewable power, ensuring that all cars are charged efficiently and effectively. The system incorporates automatic number plate recognition technology to identify incoming cars, retrieve their charging details, and initiate the charging process. Additionally, provisions are made for manual emergency charging. The system generates monthly charging bills for car owners, which are sent via email, thus promoting transparency and accountability. This feature encourages responsible use of the charging stations and contributes to the adoption of electric vehicles. When the charging slots are free, this facility can be used to charge public vehicles. Overall, this smart e-vehicle charging system provides a sustainable and energy-efficient solution for charging electric vehicles while promoting the use of renewable energy sources.
One-dimensional AB random sequential adsorption with one deposition per site
Authors: do Amaral Charles S , dos Santos Diogo C
Published: October 2023

We investigate a modified version of the AB random sequential adsorption model. Specifically, this model involves the deposition of two distinct types of particles onto a lattice, with the constraint that different types cannot occupy neighboring sites. By restricting the deposition attempts to only one per site, we derive an analytical expression for the average densities of particles of types A and B, at all time instances, for all deposition probabilities of each particle type.
Study on the mechanical properties of Self-Curing Concrete Using Magnetized Water
Authors: Ajay Anju , Hameed Aneesa , Lal Anusree
Published: October 2023

The growth of the population has led to an increase in construction activities, which require water for concrete mixing, curing, and cement hydration. Cement hydration occurs due to extra water that is not part of the mixing water. Magnetized water, which is water subjected to magnetic flux, has become a popular technology in the construction industry due to its potential to increase the lifespan of buildings and improve various technological processes at an economical cost. The use of magnetically processed water reduces the amount of cement needed to achieve the desired compressive strength value. This study investigates the effects of magnetic field generated by Permag N-406 on the properties of water, such as pH and hardness also, cement is partially replaced with 1%-4% of Nano Silica to achieve good characteristic strength. The study determines the optimal magnetization value, the maximum exposure time of water to the magnetic field and, the mechanical properties of self-curing concrete. Polyethylene Glycol (PEG) is used as the self-curing agent. The experiment results shows that the 48 hrs of magnetization of water and replacement of cement by 3% of Nano Silica showed the better performances in mechanical properties.
Numerical investigation on bending behaviour of hollow precast slab with FRP tubes using Ansys
Authors: Hari Aswin , Pillai Abhishanth S , Sooraj S
Published: October 2023

Hollow-core precast concrete slabs are frequently employed in modular building construction to reduce concrete volume and weight. Nevertheless, the presence of the hollow-core increases susceptibility to failure. To address this issue, a composite reinforcing system (CRS) has been developed to support the void in precast slabs. This research aims to investigate the bending behavior of a composite slab comprising precast concrete, steel, and the composite reinforcement system. Bending tests will be conducted on precast slabs with various CRS configurations (including changes in tube material properties and tube cross-section) to assess the structural performance of this innovative construction system. Additionally, a non-linear finite element modeling was employed to gain a comprehensive understanding of the behavior of the internal components of the slab. The findings of this study will offer valuable insights for designing more reliable and cost-effective hollow-core precast concrete slabs.
Barocaloric response of plastic crystal 2-methyl-2-nitro-1-propanol across and far from the solid-solid phase transition
Authors: Salvatori Alejandro , Barrio María , Negrier Philippe , Massip Stéphane , Romanini Michela , Aznar Araceli , Lloveras Pol , Tamarit Josep-Lluís
Published: October 2023

Plastic crystals have emerged as benchmark barocaloric (BC) materials for potential solid-state cooling and heating applications due to huge isothermal entropy changes and adiabatic temperature changes driven by pressure. In this work we investigate the BC response of the neopentane derivative 2-methyl-2-nitro-1-propanol (NO2C(CH3)2CH2OH) in a wide temperature range using x-ray diffraction, dilatometry and pressure-dependent differential thermal analysis. Near the ordered-to-plastic transition, we find colossal BC effects of

≃

400 J K−1 kg−1 and

≃

5 K upon pressure changes of 100 MPa. Although reversible effects at the transition are obtained only from higher pressure changes due to hysteretic effects, we do obtain fully reversible BC effects from any pressure change in individual phases, that become giant at moderate pressures due to very large thermal expansion, especially in the plastic phase. From our measurements, we also determine the crystal structure of the low-temperature phase and estimate the contribution of the configurational disorder and the volume change to the total transition entropy change.
Redesigning planet carrier for material conservation using whale optimization
Authors: Shinde Harshvardhan , Barpande Girish , Mali Arun , Kuldharan Amol
Published: October 2023

Planet carriers are one of the important and heavy components in planetary gearboxes. Planet carriers of heavy gearboxes used for large applications are not produced in numbers. However, the increasing cost pressures has forced designers to apply weight reduction and optimization methods for components like these as a one-time obligatory exercise. In the presented case, a candidate planet carrier used in the gearbox of a sugar mill is considered for weight reduction exercise without compromising its strength. 3D CAD model of the given planet carrier is prepared and analyzed to understand the operating stresses. The knowledge of predictable stress values at various locations are used to guide the optimization. Numerical simulations are carried out with AI based Whale optimization algorithm for optimization. Obtained dimensions of optimized planet carriers are verified by the stress analysis again. The results predicted that a weight reduction of 8% can be possible with the optimized design without compromising the design constraints. The work demonstrates that a relatively simple whale optimization algorithm can be used to optimize weight for many such heavy components.
Sensitivity enhancement of an antenna sensor by using sextuple notch bands for application in cancer detection
Authors: Thakur Shivkant , Mishra Rajan , Soni Sanjay Kumar
Published: October 2023

In this study, a sextuple band-notched ultra-wideband (UWB) antenna sensor with slots in the feedline and parasitics is proposed. This is used for cancer detection in breast and skin phantom. The antenna sensor operates a bandwidth of 2.45–16 GHz. The dielectric properties of malignant cells are completely distinct from healthy cells, EM waves passing through the cells recognize a malignant. S11 parameters of the antenna are analyzed with the help of a Vector Network Analyzer (VNA) after placing the antenna on the phantoms. Further, the statistical machine learning concept of Principal Component Analysis (PCA) is utilized for more vivid detection of malignant cells. The bands WiMax (2.8–3.4 GHz), radio astronomy (4.75–5.8 GHz), satellite communication (6.7–7.5 GHz, 9.2–10.9 GHz and 12.95–13.55 GHz) and SHF band (15.43–15.63 GHz), create interferences within the UWB spectrum. The interfering bands are removed by slot loading and parasitics. Thus, the sensitivity of the antenna sensor improved.
Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods
Authors: Nguyen Tri-Long , Debray Thomas P A , Youn Bora , Simoneau Gabrielle , Collins Gary S
Published: October 2023

Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g., positivity). The disease risk score (DRS) is an alternative confounding score that can relax some of these assumptions. Like the propensity score, the DRS summarizes multiple confounders into a single score, on which conditioning by matching allows the estimation of causal effects. However, matching relies on arbitrary choices for pruning out data (e.g., matching ratio, algorithm, and caliper width) and may be computationally demanding. Alternatively, weighting methods, common in propensity score analysis, are easy to implement and may entail fewer choices, yet none have been developed for the DRS. Here we present 2 weighting approaches: One derives directly from inverse probability weighting; the other, named target distribution weighting, relates to importance sampling. We empirically show that inverse probability weighting and target distribution weighting display performance comparable to matching techniques in terms of bias but outperform them in terms of efficiency (mean squared error) and computational speed (up to >870 times faster in an illustrative study). We illustrate implementation of the methods in 2 case studies where we investigate placebo treatments for multiple sclerosis and administration of aspirin in stroke patients.
Clinical Features and Outcomes of Hospitalized Adult Patients With Breakthrough COVID-19 Infections: A Propensity-Score–Matched Observational Study
Authors: Niu Jianli , Samuels Shenae , Sareli Candice , Mayer Daniel , Visbal Alvaro , Sareli Aharon E
Published: October 2023

In this study, we aimed to evaluate the impact of vaccination on intensive care unit (ICU) admission and in-hospital mortality among breakthrough coronavirus disease 2019 (COVID-19) infections. A total of 3,351 adult patients hospitalized with COVID-19 in the Memorial Healthcare System (Hollywood, Florida) between June 1 and September 20, 2021, were included; 284 (8.5%) were fully vaccinated. A propensity-score–matched analysis was conducted to compare fully vaccinated patients with unvaccinated controls. Propensity scores were calculated on the basis of variables associated with vaccination status. A 1:1 matching ratio was applied using logistic regression models, ensuring balanced characteristics between the two groups. The matched samples were then subjected to multivariate analysis. Among breakthrough infections, vaccinated patients demonstrated lower incidences of ICU admission (10.3% vs. 16.4%; P = 0.042) and death (12.2% vs. 18.7%; P = 0.041) than the matched controls. Risk-adjusted multivariate analysis demonstrated a significant inverse association between vaccination and ICU admission (odds ratio = 0.52, 95% confidence interval: 0.31, 0.89; P = 0.019) as well as in-hospital mortality (odds ratio = 0.57, 95% confidence interval: 0.34, 0.94; P = 0.027). Vaccinated individuals experiencing breakthrough infections had significantly lower risks of ICU admission and in-hospital mortality. These findings highlight the benefits of COVID-19 vaccines in reducing severe outcomes among patients with breakthrough infections.
High endoreduplication after drought-related conditions in haploid but not diploid mosses
Authors: Zumel D , Diéguez X , Werner O , Moreno-Ortiz M C , Muñoz J , Ros R M
Published: October 2023

Background and Aims

Endoreduplication, the duplication of the nuclear genome without mitosis, is a common process in plants, especially in angiosperms and mosses. Accumulating evidence supports the relationship between endoreduplication and plastic responses to stress factors. Here, we investigated the level of endoreduplication in Ceratodon (Bryophyta), which includes the model organism Ceratodon purpureus.

Methods

We used flow cytometry to estimate the DNA content of 294 samples from 67 localities and found three well-defined cytotypes, two haploids and one diploid, the haploids corresponding to C. purpureus and Ceratodon amazonum, and the diploid to Ceratodon conicus, recombination occurring between the former two.

Key Results

The endoreduplication index (EI) was significantly different for each cytotype, being higher in the two haploids. In addition, the EI of the haploids was higher during the hot and dry periods typical of the Mediterranean summer than during spring, whereas the EI of the diploid cytotype did not differ between seasons.

Conclusions

Endopolyploidy may be essential in haploid mosses to buffer periods of drought and to respond rapidly to desiccation events. Our results also suggest that the EI is closely related to the basic ploidy level, but less so to the nuclear DNA content as previously suggested.
Gestational intermittent hypoxia induces endothelial dysfunction and hypertension in pregnant rats: role of endothelin type B receptor
Authors: Song Ruolin , Yadav Pankaj , Dangudubiyyam Sri Vidya , Hofmann Alissa , Mishra Jay S , Kumar Sathish
Published: October 2023

Obstructive sleep apnea is a recognized risk factor for gestational hypertension, yet the exact mechanism behind this association remains unclear. Here, we tested the hypothesis that intermittent hypoxia, a hallmark of obstructive sleep apnea, induces gestational hypertension through perturbed endothelin-1 signaling. Pregnant Sprague–Dawley rats were subjected to normoxia (control), mild intermittent hypoxia (10.5% O2), or severe intermittent hypoxia (6.5% O2) from gestational days 10–21. Blood pressure was monitored. Plasma was collected and mesenteric arteries were isolated for myograph and protein analyses. The mild and severe intermittent hypoxia groups demonstrated elevated blood pressure, reduced plasma nitrate/nitrite, and unchanged endothelin-1 levels compared to the control group. Western blot analysis revealed decreased expression of endothelin type B receptor and phosphorylated endothelial nitric oxide synthase, while the levels of endothelin type A receptor and total endothelial nitric oxide synthase remained unchanged following intermittent hypoxia exposure. The contractile responses to potassium chloride, phenylephrine, and endothelin-1 were unaffected in endothelium-denuded arteries from mild and severe intermittent hypoxia rats. However, mild and severe intermittent hypoxia rats exhibited impaired endothelium-dependent vasorelaxation responses to endothelin type B receptor agonist IRL-1620 and acetylcholine compared to controls. Endothelium denudation abolished IRL-1620-induced vasorelaxation, supporting the involvement of endothelium in endothelin type B receptor-mediated relaxation. Treatment with IRL-1620 during intermittent hypoxia exposure significantly attenuated intermittent hypoxia-induced hypertension in pregnant rats. This was associated with elevated circulating nitrate/nitrite levels, enhanced endothelin type B receptor expression, increased endothelial nitric oxide synthase activation, and improved vasodilation responses. Our data suggested that intermittent hypoxia exposure during gestation increases blood pressure in pregnant rats by suppressing endothelin type B receptor-mediated signaling, providing a molecular mechanism linking intermittent hypoxia and gestational hypertension.
Clinical Outcomes of Patients With Nontyphoidal Salmonella Infections by Isolate Resistance—Foodborne Diseases Active Surveillance Network, 10 US Sites, 2004–2018
Authors: Francois Watkins Louise K , Luna Sarah , Bruce Beau B , Medalla Felicita , Reynolds Jared L , Ray Logan C , Wilson Elisha L , Caidi Hayat , Griffin Patricia M
Published: October 2023

Background

Nontyphoidal Salmonella causes an estimated 1.35 million US infections annually. Antimicrobial-resistant strains are a serious public health threat. We examined the association between resistance and the clinical outcomes of hospitalization, length-of-stay ≥3 days, and death.

Methods

We linked epidemiologic data from the Foodborne Diseases Active Surveillance Network with antimicrobial resistance data from the National Antimicrobial Resistance Monitoring System (NARMS) for nontyphoidal Salmonella infections from 2004 to 2018. We defined any resistance as resistance to ≥1 antimicrobial and clinical resistance as resistance to ampicillin, azithromycin, ceftriaxone, ciprofloxacin, or trimethoprim-sulfamethoxazole (for the subset of isolates tested for all 5 agents). We compared outcomes before and after adjusting for age, state, race/ethnicity, international travel, outbreak association, and isolate serotype and source.

Results

Twenty percent of isolates (1105/5549) had any resistance, and 16% (469/2969) had clinical resistance. Persons whose isolates had any resistance were more likely to be hospitalized (31% vs 28%, P = .01) or have length-of-stay ≥3 days (20% vs 16%, P = .01). Deaths were rare but more common among those with any than no resistance (1.0% vs 0.4%, P = .01). Outcomes for patients whose isolates had clinical resistance did not differ significantly from those with no resistance. After adjustment, any resistance (adjusted odds ratio 1.23, 95% confidence interval 1.04–1.46) remained significantly associated with hospitalization.

Conclusions

We observed a significant association between nontyphoidal Salmonella infections caused by resistant pathogens and likelihood of hospitalization. Clinical resistance was not associated with poorer outcomes, suggesting that factors other than treatment failure (eg, strain virulence, strain source, host factors) may be important.
Can Chatbot Artificial Intelligence Replace Infectious Diseases Physicians in the Management of Bloodstream Infections? A Prospective Cohort Study
Authors: Maillard Alexis , Micheli Giulia , Lefevre Leila , Guyonnet Cécile , Poyart Claire , Canouï Etienne , Belan Martin , Charlier Caroline
Published: October 2023

Background

The development of chatbot artificial intelligence (AI) has raised major questions about their use in healthcare. We assessed the quality and safety of the management suggested by Chat Generative Pre-training Transformer 4 (ChatGPT-4) in real-life practice for patients with positive blood cultures.

Methods

Over a 4-week period in a tertiary care hospital, data from consecutive infectious diseases (ID) consultations for a first positive blood culture were prospectively provided to ChatGPT-4. Data were requested to propose a comprehensive management plan (suspected/confirmed diagnosis, workup, antibiotic therapy, source control, follow-up). We compared the management plan suggested by ChatGPT-4 with the plan suggested by ID consultants based on literature and guidelines. Comparisons were performed by 2 ID physicians not involved in patient management.

Results

Forty-four cases with a first episode of positive blood culture were included. ChatGPT-4 provided detailed and well-written responses in all cases. AI’s diagnoses were identical to those of the consultant in 26 (59%) cases. Suggested diagnostic workups were satisfactory (ie, no missing important diagnostic tests) in 35 (80%) cases; empirical antimicrobial therapies were adequate in 28 (64%) cases and harmful in 1 (2%). Source control plans were inadequate in 4 (9%) cases. Definitive antibiotic therapies were optimal in 16 (36%) patients and harmful in 2 (5%). Overall, management plans were considered optimal in only 1 patient, as satisfactory in 17 (39%), and as harmful in 7 (16%).

Conclusions

The use of ChatGPT-4 without consultant input remains hazardous when seeking expert medical advice in 2023, especially for severe IDs.
Cardiac amyloidosis and aortic stenosis: a state-of-the-art review
Authors: Jaiswal Vikash , Agrawal Vibhor , Khulbe Yashita , Hanif Muhammad , Huang Helen , Hameed Maha , Shrestha Abhigan Babu , Perone Francesco , Parikh Charmy , Gomez Sabas Ivan , Paudel Kusum , Zacks Jerome , Grubb Kendra J , De Rosa Salvatore , Gimelli Alessia
Published: October 2023

Cardiac amyloidosis is caused by the extracellular deposition of amyloid fibrils in the heart, involving not only the myocardium but also any cardiovascular structure. Indeed, this progressive infiltrative disease also involves the cardiac valves and, specifically, shows a high prevalence with aortic stenosis. Misfolded protein infiltration in the aortic valve leads to tissue damage resulting in the onset or worsening of valve stenosis. Transthyretin cardiac amyloidosis and aortic stenosis coexist in patients > 65 years in about 4–16% of cases, especially in those undergoing transcatheter aortic valve replacement. Diagnostic workup for cardiac amyloidosis in patients with aortic stenosis is based on a multi-parametric approach considering clinical assessment, electrocardiogram, haematologic tests, basic and advanced echocardiography, cardiac magnetic resonance, and technetium labelled cardiac scintigraphy like technetium-99 m (99mTc)-pyrophosphate, 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid, and 99mTc-hydroxymethylene diphosphonate. However, a biopsy is the traditional gold standard for diagnosis. The prognosis of patients with coexisting cardiac amyloidosis and aortic stenosis is still under evaluation. The combination of these two pathologies worsens the prognosis. Regarding treatment, mortality is reduced in patients with cardiac amyloidosis and severe aortic stenosis after undergoing transcatheter aortic valve replacement. Further studies are needed to confirm these findings and to understand whether the diagnosis of cardiac amyloidosis could affect therapeutic strategies. The aim of this review is to critically expose the current state-of-art regarding the association of cardiac amyloidosis with aortic stenosis, from pathophysiology to treatment.