Analyzing the efficacy of trimethylolpropane trioleate oil for predicting cutting power and surface roughness in high-speed drilling of Al-6061 through machine learning.

This study aimed to investigate the impact of a lubricant derived from trimethylolpropane trioleate on power consumption and surface roughness during high-speed drilling of Al-6061, with the goal of developing an environmentally friendly cutting fluid. The study investigated the impact of additive concentration, spindle speed, and feed rate on energy consumption and surface roughness using a Taguchi L27 orthogonal array. Through analysis of the Taguchi experimental outcomes and single-to-noise ratios, the parameters were ranked accordingly.

Taguchi optimization of Wire EDM process parameters for machining LM5 aluminium alloy.

LM5 alloy is suitable for metal castings for marine and aesthetic uses due to its admirable resistance to corrosion. In order to make intricate shapes in the LM5 alloy, this study intends to assess the impact of Wire Electric Discharge Machining process variables, like Pulse on Time (Ton), Pulse off Time (Toff), Gap Voltage (GV) and Wire Feed (WF) on responses like Material Removal Rate (MRR), Surface Roughness (SR), and Kerf Width (Kw). The LM5 aluminium alloy plate was produced through stir casting process.

Stiffness and stability of bamboo stem- A optimal design perspective.

During the evolution process, a bamboo stem achieves a significant height (up to 20 m) to fulfil its phototropic requirements. While on land, the stem is mostly subjected to bending load which makes it liable to fail by uprooting. However, this failure is prohibited by smart structure of bamboo stem which includes graded arrangement of fibre bundles in the cross-section and a tapered cantilever form of the stem.

Experimental investigation on solidification cracking & intergranular corrosion of AISI 321 & AISI 316 L dissimilar weld on pulsed current gas tungsten arc welding (PCGTAW).

Dissimilar metal combinations are frequently employed in the power generation and nuclear industries. Where stainless steel piping systems are connected to pressure vessels made of low-alloy steel, the subsystems of liquid rocket engines also have different, dissimilar material combinations. Dissimilar welding plays a vital role in ensuring the integrity, performance, and reliability of components and structures operating in cryogenic environments, in this study, plates of AISI 316L and AISI 321, each 5 mm thick, were successfully joined using the pulsed current gas tungsten arc welding (PCGTAW) technique with optimized process parameters.

Tensile strength analysis of additively manufactured CM 247LC alloy specimen by employing machine learning classifiers.

Using a cutting-edge net-shape manufacturing technique called Additive Layer Manufacturing (ALM), highly complex components that are not achievable with conventional wrought and cast methods can be produced. As a result, the aerospace sector is paying closer attention to using this technology to fabricate superalloys based on nickel to develop the holistic gas turbine. Because of this, there is an increasing need for the mechanical characterisation of such material.

The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case.

Background: The aim of this paper is to report one of the significant applications of artificial intelligence (AI) and how it affects everyday clinical practice in neurosurgery. The authors present a case in which a patient was diagnosed via an AI algorithm during ongoing magnetic resonance imaging (MRI). According to this algorithm, the corresponding physicians were immediately warned, and the patient received prompt appropriate treatment.

Deep convolutional neural network-the evaluation of cervical vertebrae maturation.

Objectives: This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages using precision, recall, and F1-score.

Methods: A total of 588 digital lateral cephalometric radiographs of patients with a chronological age between 8 and 22 years were included in this study. CVM evaluation was carried out by two dentomaxillofacial radiologists.

Diffusion of Innovations-Informed Knowledge Translation Strategy to Implement Optimal Safe Nursing Workforce Policy in Practice.

Aim: This study aims to develop a free, limited-edition workshop as an effective knowledge translation strategy to enhance nurse leader-perceived self-efficacy for competence using Park's Sweet Spot Theory and to evaluate its effectiveness over time.

Method: This is a study showing the process of developing a study protocol and its details.

Results: A 2-day workshop was developed for innovators and early adopters among nurse leaders with a macro-level influence based on Rogers's diffusion of innovations theory, which consists of an introduction of Park's Sweet Spot Theory, hands-on experience, a summary session, and a presentation of a certificate of completion.

No more unimplementable nurse workforce planning.

: This paper aims to spur thought-provoking practical debates on current nurse workforce staffing and scheduling systems in relation to a critical review of Ang and colleagues' (2018) article entitled "Nurse workforce scheduling in the emergency department: A sequential decision support system considering multiple objectives.": Discussion paper on a practical discourse in connection with the aforementioned published article.: Mathematical Programming (optimisation) (MP)-based nursing research has been published for nearly thirty years almost exclusively in industrial engineering or health business administration journals, demonstrating a widening gap between nursing research and practice.

Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study.

Aim: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time.

Material And Methods: MRIs of 300 healthy subjects were employed as training set. Additionally, test data were consisting anonymized T2-weigted MRIs of 27 healthy subjects and 27 HGG patients.

A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance Images: A Preliminary Machine Learning Study.

Aim: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans.

Material And Methods: A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration.

Diagnosis of Acute Coronary Syndrome with a Support Vector Machine.

Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis.

The rate of mucosal healing by azathioprine therapy and prediction by artificial systems.

Background/aims: We aimed to assess the effect of azathioprine on mucosal healing in patients with inflammatory bowel diseases (IBD). Artificial neural networks were applied to IBD data for predicting mucosal remission.

Materials And Methods: Two thousand seven hundred patients with IBD were evaluated.