Modern Techniques Research Articles

Cancer Risk Identification using Machine Learning

In this comprehensive study, we explore the SEER (Surveillance, Epidemiology, and End Result) dataset to meticulously craft a predictive model aimed at understanding patient survivability, focusing keenly on outcomes like patient survival diagnosis and treatments. Employing a good approach involving rigorous data cleaning, intricate feature extraction, and detailed correlation analysis, we identify the dominant attributes that significantly influence patient outcomes. Our methodology integrates classical machine learning models, including decision trees and random forests, with modern techniques such as neural networks. This mix of methodologies allows us to accurately predict patient survivability. Furthermore, our study delves into the intricate relationships between attributes, models, and algorithms, aiming to identify the dominant factors that influence the outcomes. Our groundbreaking findings underscore the enormous potential of integrating these dominant attributes, paving the way for the creation of exceptionally robust predictive models. These models will substantially enhance medical decision-making processes and, in turn, elevate the overall quality of patient care. Keywords - SEER dataset, patient survivability, machine learning, decision trees, random forests, neural networks, data cleaning, feature extraction, clinical data, medical decision-making.

Contemporary Patterns and Progressions in Topologies of DC–DC Converters with Control Methodology

DC–DC converters have achieved great popularity in recent decades due to their immense penetration in various applications. With this motivation, the authors have conducted a thorough review of recent advancements in various topologies of DC–DC converters. The need for DC power has raised further for certain applications like grid integration of distributed generation (DG), solar photovoltaic (PV), wind power generation (WPG), fuel cells (FC), etc. The investigation of converter topology is performed to achieve the desired objective of a specific application. Like in a PV system, to obtain the inherent capability of a DC–DC converter for operating at the maximum power point (MPP) and thereby electronically extracting the maximum power from the source. Hence, a detailed review of topological advancements on the low to medium-voltage and medium-to-high-power DC–DC converters has been carried out. Moreover, a thorough investigation has been carried out on profuse closed-loop strategies and compared with each other for obtaining the optimum or maximum output performance and thereby obtaining the utmost source utilization. The modern control techniques though have relatively more calculation time but, they tend to reduce the steady-state error that leads to the stabilization of the converter. Lastly, certain applications of the DC–DC converters have been explained to get an overall idea of the usefulness of such power converters.

“The Ghostly Terrors of the Nursery”: Joseph Sheridan Le Fanu and Childhood Trauma

This essay contends that emotional oscillations between subordination and emancipation are responsible for the inconclusivity and evasiveness of Le Fanu’s plots, which critics often emphasise but rarely try to explain. The parallels between, on the one hand, the dual structures that pervade Le Fanu’s works and, on the other, the binaries of latent dream thoughts and manifest dream content, the familiar and the uncanny, which Sigmund Freud employs in his explorations of the unconscious, shed significant light on the motivational forces at work in Le Fanu’s oeuvre. Le Fanu uses displacement and condensation, the major operations Freud attributes to dream work, to create an outside world, which reveals itself as an upended world within. This essay tries to demonstrate the psychological significance of central motifs in Le Fanu’s works, such as the double and the closed room. In light of later psychoanalytical insights into the influence of childhood traumatisation on the creativity of writers, Le Fanu appears absorbed by the incorporation of his innermost mortifications and yearnings into the defamiliarisations of his fiction. On the basis of Gothic patterns, he produces psychological fiction avant la lettre. He anticipates George Orwell’s dissection of mental processes in terms of manipulative social engineering, H.P. Lovecraft’s fantastic evocations of a Jungian collective unconscious, and literary interiorisation by modernist techniques, developed not long after Le Fanu’s death by the likes of James Joyce and Virginia Woolf.

Micelle-based nanoparticles with stimuli-responsive properties for drug delivery

Abstract Cancer treatment often causes adverse effects and toxicity, as chemotherapy drugs affect both cancerous and healthy cells. Scientists seek to target tumor cells specifically and minimize harm to normal cells. Smart nanoparticles (NPs) are a modern technique that can release drugs when triggered by internal or external stimuli, such as temperature, pH, ultrasound, etc. This review covers stimuli-responsive micelle-based nanoparticles (SRM-NPs), a promising drug delivery platform that can enhance drug efficacy and reduce toxicity. It discusses the recent developments and applications of SRM-NPs, their responsiveness to different stimuli, and their potential to overcome drug resistance and adaptive responses. It also addresses the challenges and issues related to their stability, reproducibility, biocompatibility, safety, and optimization. The study concludes that SRM-NPs have great potential for drug delivery, but more research and development are needed to improve their clinical utility.

Comparison of Traditional and Modern Techniques for Betalains Extraction from Amaranth Agro-Industrial Waste: The Recovery of High Value By-Products

Comparison of Traditional and Modern Techniques for Betalains Extraction from Amaranth Agro-Industrial Waste: The Recovery of High Value By-Products

Modern Hip Arthroscopy for FAIS May Delay the Natural History of Osteoarthritis in 25% of Patients: A 12-Year Follow-up Analysis.

Little is known about the effect of modern hip arthroscopy on the natural history of femoroacetabular impingement syndrome (FAIS) with respect to joint preservation. To (1) characterize the natural history of FAIS and (2) understand the effect of modern hip arthroscopy by radiographically comparing the hips of patients who underwent only unilateral primary hip arthroscopy with a minimum follow-up of 10 years. Cohort study; Level of evidence, 3. Between 2010 and 2012, 619 consecutive patients were reviewed from the practice of a single fellowship-trained hip arthroscopic surgeon. Inclusion criteria were FAIS, bilateral radiographic findings of femoroacetabular impingement, primary unilateral hip arthroscopy (labral repair, femoroplasty, or capsular closure), and minimum 10-year follow-up. The preoperative and minimum 10-year postoperative radiographs of patients were evaluated at each time point. Both operative and nonoperative hips were graded using the Tönnis classification or the presence of hip arthroplasty by 2 independent reviewers. Subgroup analyses were performed. A total of 200 hips from 100 patients were evaluated at a mean follow-up of 12.0 years. Preoperatively, 98% and 99% of operative and nonoperative hips were evaluated as Tönnis grades 0 and 1, respectively; 5% of nonoperative hips had worse Tönnis grades than operative hips. The nonoperative hip advanced to a worse Tönnis grade in 48% (48/100) of cases compared with 28% (28/100) among operative hips. At follow-up, Tönnis grades between hips were equal in 70% (70/100) of the cases, the operative hip had a better grade 25% (25/100) of the time, and the nonoperative hip had a better grade 5% (5/100) of the time. Modern hip arthroscopy was associated with a relative risk reduction of 42% in osteoarthritis progression. Impingement with borderline dysplasia, age, preoperative Tönnis grade, and alpha angle >65° were key risk factors in the radiographic progression of osteoarthritis. Although the majority of patients (70%) undergoing hip arthroscopy for FAIS did not experience differences between operative and nonoperative hips in terms of the radiographic progression of osteoarthritis, the natural history may be favorably altered for 25% of patients whose Tönnis grade was better after undergoing arthroscopic correction. Modern hip arthroscopy indications and techniques represent a valid joint-preservation procedure conferring a relative risk reduction of 42% in the progression of osteoarthritis. Arthroscopy for mixed patterns of impingement and instability were the fastest to degenerate.

Practical tips for starting a successful national postgraduate course

Background Few start national courses, and those that do usually do it once. The aim of this paper is to outline an approach to conduct a successful national postgraduate course. Methods The practical tips were derived from personal experience. Results The 12 tips identified are: define learning needs and curriculum, create a functioning structure, recruit a committed faculty, obtain legitimacy, promote your course, try out the concept, establish administrative support, use modern techniques and accessories, create course-related social activities, keep all on board, collect ongoing evaluation, and stay in control. Conclusion It is hoped that these tips will make it easier for others to take the decisive first step in the exciting task of starting a national course; that is: ‘to know the road ahead - ask those coming back’.

Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus.

The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.

Plant Adaptation and Tolerance to Heat Stress: Advance Approaches and Future Aspects.

Heat stress impacts plant growth at all phases of development, although the particular threshold for heat tolerance varies significantly across different developmental stages. During seed germination, elevated temperatures can either impede or completely halt the process, contingent upon the plant type and the severity of the stress. During advanced stages, high temperatures can have a negative impact on photosynthesis, respiration, water balance, and membrane integrity. Additionally, they can also influence the levels of hormones and primary and secondary metabolites. In addition, during the growth and development of plants, there is an increased expression of various heat shock proteins, as well as other proteins related to stress, and the generation of reactive oxygen species (ROS). These are significant plant responses to heat stress. Plants employ several strategies to deal with heat stress, such as maintaining the stability of their cell membranes, removing harmful reactive oxygen species (ROS), producing antioxidants, accumulating and adjusting compatible solutes, activating mitogen-activated protein kinase (MAPK) and calcium-dependent protein kinase (CDPK) cascades, and, crucially, signaling through chaperones and activating transcription. These molecular-level systems boost the ability of plants to flourish in heat stress. Potential genetic methods to enhance plant heat stress resistance encompass old and modern molecular breeding techniques and transgenic approaches, all of which rely on a comprehensive comprehension of these systems. Although several plants exhibit enhanced heat tolerance through traditional breeding methods, the effectiveness of genetic transformation techniques has been somewhat restricted. The latter results from the current constraints in our understanding and access to genes that have known impacts on plant heat stress tolerance. However, these challenges may be overcome in the future. Besides genetic methods, crops' heat tolerance can be improved through the pre-treatment of plants with various environmental challenges or the external application of osmoprotectants such as glycine betaine and proline. Thermotolerance is achieved through an active process in which plants allocate significant energy to maintain their structure and function to avoid damage induced by heat stress. The practice of nanoparticles has been shown to upgrade both the standard and the quantity of produce when crops are under heat stress. This review provides information on the effects of heat stress on plants and explores the importance of nanoparticles, transgenics, and genomic techniques in reducing the negative consequences of heat stress. Furthermore, it explores how plants might adapt to heat stress by modifying their biochemical, physiological, and molecular reactions.

The local and systemic effects of immune function on fracture healing.

The immune system plays an integral role in the regulation of cellular processes responsible for fracture healing. Local and systemic influences on fracture healing correlate in many ways with fracture-related outcomes, including soft tissue healing quality and fracture union rates. Impaired soft tissue healing, restricted perfusion of a fracture site, and infection also in turn affect the immune response to fracture injury. Modern techniques used to investigate the relationship between immune system function and fracture healing include precision medicine, using vast quantities of data to interpret broad patterns of inflammatory response. Early data from the PRECISE trial have demonstrated distinct patterns of inflammatory response in polytrauma patients, which thereby directly and indirectly regulate the fracture healing response. The clearly demonstrated linkage between immune function and fracture healing suggests that modulation of immune function has significant potential as a therapeutic target that can be used to enhance fracture healing.

Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning

The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate.

Variational Principles in Quaternionic Analysis with Applications to the Stationary MHD Equations

In this paper we aim to combine tools from variational calculus with modern techniques from quaternionic analysis that involve Dirac type operators and related hypercomplex integral operators. The aim is to develop new methods for showing geometry independent explicit global existence and uniqueness criteria as well as new computational methods with special focus to the stationary incompressible viscous magnetohydrodynamic equations. We first show how to specifically apply variational calculus in the quaternionic setting. To this end we explain how the mountain pass theorem can be successfully applied to guarantee the existence of (weak) solutions. To achieve this, the quaternionic integral operator calculus serves as a key ingredient allowing us to apply Schauder’s fixed point theorem. The advantage of the approach using Schauder’s fixed point theorem is that it is also applicable to large data since it does not require any kind of contraction property. These considerations will allow us to provide explicit iterative algorithms for its numerical solution. Finally to obtain more precise a-priori estimates one can use in the situations dealing with small data the Banach fixed point theorem which then also grants the uniqueness.

PERFORMANCE OF SCREW HORIZONTAL AND BRIDGE LINKAGE PHOTOVOLTAIC ARRAY CONFIGURATIONS

Due to partial shading effects on the productivity of solar panels which in turn negatively impacts the performance characteristics of photovoltaic systems, researchers work on different studies to overcome this phenomenon and improve solar system productivity. Therefore, this study aims to investigate different techniques to enhance the output power, fill factor, and efficiency of the PV system by reducing the number of local maximum power peaks, power losses, and mismatch losses. The configurations include a novel static reconfiguration technique, called a Screw Horizontal photovoltaic array, and a recently developed technique known as a Bridge Linkage array. Both of these are modeled using MATLAB/Simulink software and examined during six shading patterns. The novelty of this study is that we combined the above static reconfiguration technique with another modern technique called blocking and bypass diode technology to prevent the effect of reverse current and hotspot phenomena respectively. According to the results, the Bridge Linkage configuration performs the most efficiently under partial shading conditions compared to the Screw horizontal PV array configuration.

Green University Model – World Trend and Policy Implications for Vietnam

Green university model is now developing dramastically, spreading and becoming a mainstream trend in world higher education. Main features of this trend are universities integrating sustainable development goals into their operations, implementing their mission in building a prosperous and safe society. With key solutions using modern technology and techniques, universities strive to develop smart educational ecosystems, explore natural resources to improve quality of life, and participate in combating climate change, solving ecological problems of resources and human health. A number of green university rankings have been created, becoming a place to encourage the trend of developing green university model as a breakthrough solution for smart universities. Based on the approach of content and trend of green university development, the article presents some recommendations for green development in Vietnamese universities.

Comparison of Crop Area Estimation for the Winter Season Using the Google Earth Engine (GEE) Platform and Traditional Method of Supervisor Classification

There is a significant development in the methods and techniques for classifying satellite images during these years. One of the most recently launched methods is using the Google Earth Engine platform for its contribution in providing a huge amount of satellite data with different dates and multiple satellite images sources, with a method of work that is easy to deal with, but it needed to learn the computer language such as Java and Python, which is the method of work depends on writing a script that gives application commands to classify the different satellite image. Therefore, this research aims to apply classification of satellite images using the Google Earth Engine platform for three models Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF), then compare the results calculated from using images classification (Supervised classification: Maximum likelihood) in terms of the degree of accuracy of the classification and matching the results of the classified satellite images in addition. to boundary clarify of the classification methods. This study showed that, using the Google Earth Engine (GEE) platform is considered one of the important methods that facilitated researchers interested in using remote sensing techniques and geographic information systems, due to the availability of this platform of free satellite sources with multiple spatial resolutions, in addition to the presence of a powerful system that collects, processes, and classifies satellite images using different classification models and indexes. But we need to apply these modern techniques at a number of levels, such as at Markas, Governorates, International, and regional levels, to judge their accuracy and suitability for large areas.

Rapid Investment Casting: Towards Rapid Die Development

Abstract: Presently the cost of investment casting via wax patterns is an expensive endeavor. Its costs are only justified if the end product is to be produced in high volumes, however for customized products or small Industries, the costs are not viable. In Pakistan, die manufacturing is especially costly due to the use of modern computerized machines like CNC machining and electric discharge machining (EDM). The cost of manufacturing increases significantly with the complexity of the die, the number of cavities, high surface quality requirements, and the number of undercuts. The purpose of this research is to use modern techniques to investigate the feasibility of dies manufacturing through rapid investment casting technique. 3D printing technology is used to make the patterns for casting of die. The research focuses on the differences in dimensions between the actual casted die, and the designed values.

Identification of the impact of electric pulse action on the disintegration of a natural mineral

The work is devoted to the study of the electric pulse disintegration of a natural mineral. The object of the study is the natural mineral quartzite of the Aktas deposit of the Republic of Kazakhstan. For the destruction and grinding of quartzite, a working cell of an electric pulse unit was developed. Electric pulse crushing is a modern technique for grinding a variety of materials, which provides the desired degree of grinding with a certain granulometric composition of the product and has a high ability of selective crushing. With the help of this technology, quartzite grinding was carried out with an increase in the capacity of capacitor banks from 0.25 mF to 1 mF, the pulse discharge voltage changed from 20 kV to 30 kV, the number of pulse discharges from 500 to 1,000, the inner diameter of the working cell from 60 mm to 80 mm. The results of the disintegration of a natural mineral by the electric pulse method allowed us to determine the degree of grinding of the finished product. The obtained results can be used in the study and optimization of the extraction of natural minerals, which is important for ensuring the sustainable use of natural resources and balanced economic development. Crushed quartzite is used in various industries, including the production of optical fibers, electronics and photovoltaic devices. A material with a particle diameter of 0.1 to 0.4 millimeters is used to create glass, ceramic and porcelain products, as well as insulation materials. Due to its homogeneous composition containing up to 98 % silicon oxide (SiO2) and excellent absorbent properties, quartz sand is also used as a filter material for water purification

The Impact of Technology on Jewellery Design and Manufacturing: Exploring Traditional and Modern Techniques

The Impact of Technology on Jewellery Design and Manufacturing: Exploring Traditional and Modern Techniques

A Comprehensive Analysis of Technology in Aeroponics: Presenting the Adoption and Integration of Technology in Sustainable Agriculture Practices

Aeroponics is a soilless farming method in which plants are grown by sprinkling a nutrient-rich fluid over their roots. Aeroponics has changed as a result of technology, which now offers advantages including automated nutrient supply, environmental management, and plant health monitoring. A key area of concern in light of the rising global food demand, climate change, and environmental issues is sustainable agriculture. This article highlights the integration of technology, sustainability, and socio-economic elements in modern agriculture by methodically examining the development of aeroponics practices from conventional ways to innovative modern techniques. When agronomic practices are viewed historically, they show a shift from labor-intensive, antiquated techniques to precision-based, technology-driven processes. Even though they were sustainable, traditional methods frequently had efficiency and scalability issues. A big change came with the arrival of the Green Revolution, which brought synthetic inputs and high-yield crop varieties. However, a reevaluation towards more sustainable practices was spurred by the approaches' long-term ecological implications. Precision agriculture, which makes use of sensor-based monitoring systems, artificial intelligence (AI), satellite and drone technologies, is a major feature of modern agronomy breakthroughs. According to the systematic research, Industry 4.0 and sensor technology are the most often used technologies in aeroponics. Numerous advantages, including time efficiency and sustainability, have come from these technologies. On the other hand, the research focused on power dependence and technical complexity as the primary obstacles in technology-assisted aeroponics.

On propagation in networks, promising models beyond network diffusion to describe degenerative brain diseases and traumatic brain injuries.

Introduction: Connections among neurons form one of the most amazing and effective network in nature. At higher level, also the functional structures of the brain is organized as a network. It is therefore natural to use modern techniques of network analysis to describe the structures of networks in the brain. Many studies have been conducted in this area, showing that the structure of the neuronal network is complex, with a small-world topology, modularity and the presence of hubs. Other studies have been conducted to investigate the dynamical processes occurring in brain networks, analyzing local and large-scale network dynamics. Recently, network diffusion dynamics have been proposed as a model for the progression of brain degenerative diseases and for traumatic brain injuries. Methods: In this paper, the dynamics of network diffusion is re-examined and reaction-diffusion models on networks is introduced in order to better describe the degenerative dynamics in the brain. Results: Numerical simulations of the dynamics of injuries in the brain connectome are presented. Different choices of reaction term and initial condition provide very different phenomenologies, showing how network propagation models are highly flexible. Discussion: The uniqueness of this research lies in the fact that it is the first time that reaction-diffusion dynamics have been applied to the connectome to model the evolution of neurodegenerative diseases or traumatic brain injury. In addition, the generality of these models allows the introduction of non-constant diffusion and different reaction terms with non-constant parameters, allowing a more precise definition of the pathology to be studied.