Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Abstract This study delved into the application of various machine learning (ML) models for the classification of lung cancer levels. Through meticulous monitoring of parameters such as minimum child weight and learning rate, efforts were made to mitigate overfitting while optimizing model performance. The Deep Neural Network (DNN) emerged as a standout performer, showcasing robust performance across training, validation, and testing stages. Ensemble methods like voting and bagging also demonstrated promising results. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges in achieving satisfactory performance. Overall, the investigation sheds light on the efficacy of different ML models in lung cancer level classification and underscores the importance of parameter tuning to address overfitting concerns.

Cancer diagnosis stands at the cusp of a profound transformation, driven by the burgeoning capabilities of Artificial Intelligence (AI) and Machine Learning (ML). This comprehensive review illuminates the extraordinary impact of AI and ML in the field, unraveling their multifaceted roles in the realm of oncology. In the arena of cancer diagnosis, AI and ML serve as invaluable allies, empowering healthcare professionals with unparalleled tools for precision and efficiency. Notably, AI's prowess in analyzing medical images, including radiological scans and pathology slides, elevates the early detection of malignancies to new heights. Coupled with its ability to dissect genomic data, AI tailors therapeutic strategies to the individual, promising optimized treatment outcomes. However, the incorporation of AI and ML into clinical practice necessitates a careful navigation of ethical considerations, data privacy, and regulatory landscapes. Safeguarding patient data, ensuring transparency, and addressing algorithmic biases emerge as pivotal challenges that require vigilant attention. Yet, the future of AI and ML in cancer diagnosis is brimming with promise. The integration of multi-modal data, real-time monitoring, and Explainable AI (XAI) methods holds the potential to enrich diagnostic capabilities and engender patient trust. Global collaboration and data sharing initiatives are fostering the development of robust AI models. Furthermore, AI's role in cancer prevention, identifying high-risk individuals and enabling targeted preventive strategies, is poised to revolutionize healthcare. AI and ML are forging a path toward an era of unparalleled accuracy, efficiency, and personalization in cancer diagnosis. Challenges notwithstanding, these technologies bear the promise of fundamentally reshaping patient care, elevating outcomes, and advancing the fight against cancer. The ongoing exploration and responsible implementation of AI and ML in oncology will be pivotal in harnessing their full potential and paving the way for a brighter future for cancer patients.

This review investigates the integration of machine learning (ML) and blockchain technology, highlighting their combined potential to drive transformation across sectors such as healthcare, finance, and supply chain management. ML offers data-driven insights through predictive analytics, automation, and intelligent decision-making, while blockchain provides a secure, decentralized framework with transparency and immutability. Together, these technologies present solutions to critical challenges, including fraud detection, operational inefficiencies, and data privacy, promising significant advancements across various industries. ML enhances diagnostics and treatment personalization in healthcare, while blockchain protects sensitive medical data. In finance, blockchain fosters transparency within decentralized finance (DeFi), while ML strengthens fraud detection and trading strategies. In supply chain management, blockchain ensures traceability, while ML optimizes logistics and demand forecasting. Despite their synergies, integrating ML with blockchain faces challenges, notably in scalability, computational efficiency, and privacy. This review critically examines recent advancements, practical use cases, and barriers to adoption, synthesizing strategies that can address these challenges to unlock the transformative potential of these emerging technologies.

Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to address challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.

Although, Artificial intelligence (AI) helps experts with crime analysis, research, and understanding, it has a favourable influence on cyber security. It strengthens the tools that businesses use to safeguard their networks, clients, and workers against dangerous online behaviour. However, artificial intelligence is infamous for requiring a lot of resources. It may not, however, always be relevant. Additionally, it can provide hackers a new tool and advance their abilities. Actually, the VPN industry benefits from AI in the same way. The threat posed by machine learning in AI to user data privacy may be lessened by using a VPN on all of your devices. Because they use machine learning algorithms, VPNs are better equipped to shield their users from internet-based threats. Artificial intelligence (AI) has reportedly being investigated as a means of enhancing internet security for a considerable amount of time, according to Smart Data Collective. We anticipated that AI and machine learning will have a substantial impact on the future of cyber security around two years ago.

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. Model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies.

Machine learning (ML) is changing cybersecurity by enabling progressed discovery, anticipation and reaction instruments. This paper gives a comprehensive survey of ML's part in cybersecurity, looking at both hypothetical systems and down to earth usage. It diagrams the rising dangers focusing on ML models, such as ill-disposed assaults, information harming and show reversal assaults and examines state-of-the-art defense procedures, counting ill-disposed preparing, vigorous models and differential protection. Furthermore, the paper investigates different ML applications in cybersecurity from interruption location to malware classification, highlighting their affect on improving security measures. An peculiarity induction calculation is proposed for the early discovery of cyber-intrusions at the substations. Cybersecurity has ended up a imperative investigate range. The paper concludes with a discourse on the key inquire about bearings and best hones for making secure and versatile ML frameworks in a data-driven world. This paper dives into how Machine Learning (ML) revolutionizes cybersecurity, enabling progressed discovery, avoidance, and reaction components. It offers a exhaustive investigation of ML's urgent part in cybersecurity, enveloping hypothetical systems and viable applications. It addresses rising dangers like ill-disposed assaults and information harming, nearby cutting-edge defense techniques such as antagonistic preparing and strong models.

Machine Learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a Support Vector Machine (SVM) for classification. Results showed that this approach achieved an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates the mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

Objective: With just ten questions, the Berlin questionnaire (BQ) stands out as one of the simplest and most widely implemented non-invasive screening tools for detecting subjects at high risk for Obstructive Sleep Apnea (OSA), a still underdiagnosed syndrome characterized by partial or complete obstruction of the upper airways during sleep. The main aim of this study was to enhance the diagnostic accuracy of the BQ through Machine Learning (ML) techniques. Methods: A ML classifier (hereafter, ML-10) was trained using the ten questions of the standard BQ. A simplified variant of the BQ, BQ-2, which comprises only two questions out of the total of ten, was also assessed in a ML context. A 10-fold cross validation scheme was used. Ground truth was provided by the Apnea-Hypopnea Index (AHI) measured by Home Sleep Apnea Testing. Model performance was determined comparing ML-10 and BQ-2 with the standard BQ by the Receiver Operating Characteristic Curve (ROC), Area Under the Curve (AUC), sensitivity, specificity, and accuracy. Results: ML-10 demonstrated superior performance in predicting the risk for OSA compared to the standard BQ and was also capable in classifying OSA with two different AHI thresholds (AHI>=15, AHI>=30), typically used in clinical practice. Remarkably, BQ-2 was also better in sensibility to assess moderate to severe OSA (AHI≥ 15) compared to the ML-10. Conclusions: The study underscores the importance of integrating ML techniques for early OSA detection, suggesting a direction for future research to improve diagnostic processes and patient outcomes in sleep medicine.

Objectives. The aim of this study was to demonstrate the ability to visualize the results of the Scilit platform's bibliometric data analysis on the topic "AI & Machine Learning" to identify publications reflecting specific issues of the topic. Data source. Bibliometric records exported from the Scilit platform on the topic "AI & Machine Learning" for the years 2021–2023 were used. For each year, 6,000 records were downloaded in CSV and RIS format. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility, GSDMM algorithm. Used services: Elicit, QuillBot, Litmaps. Results. It has been shown that bibliometric data from the open access abstract database Scilit can serve as a quality alternative to subscription-only databases. Data exported from the Scilit platform require preprocessing to make them available in a format that can be processed by programs such as VOSviewer and Scimago Graphica. The use of GSDMM and FP-growth algorithms is effective for structuring bibliometric data for further visualization. The Scimago Graphica software provides wide possibilities for building compound diagrams, in particular, for representing the network of keywords in such important coordinates for bibliometric analysis as average year of publication and average normalized citation, as well as for building an alluvial diagram of co-occurrence of more than two keywords. The possibility of using such services as elicit.com, quillbot.com and app.litmaps.com to accelerate the selection of publications on the topic under study is shown.

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photo-acoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3) and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a MIR (mid-infrared) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra is a common challenge for gas spectroscopy. To mitigate this, we use a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts-per-million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here in this proof-of-concept experiment we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3 and CH4 and with an absolute sensitivity of approximately 300(±50) ppm/V, 50(±5) ppm/V and 5(±2) ppm/V for water, ammonia and methane, respectively. Thus, demonstrating that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

This study focuses on examining various approaches for Tensor Decompositions (TDs) and their potential applications in satellite imaging (SI) and deep learning (DL). The research highlights how these decompositions can contribute to the advancement of SI and DL, providing valuable insights for future utilization of TDs in research. It explores how these techniques contribute to the advancement of SI and DL methods, providing insights into their potential applications and suggesting future research directions. The study aims to enhance the use of TDs techniques to further advance research efforts in these fields. More importantly, the current investigation offers a thorough analysis of the potential advantages and reasons for employing TDs techniques in different domains, including satellite imaging and deep learning. The aim is to enhance research outcomes by utilizing TDs. The review also identifies unresolved issues and proposes future directions for further investigation in this field. Fundamentally, these proposed open problems will open new grounds to the research community to articulate, innovate, and provide more real-life applications to improve the current state of the art by delving into a wider vision for a higher-level performance of both satellite imaging (SI) and deep learning (DL). Looking at the bigger scenario, this also suggests that TDs could be potentially employed to revolutionize existing machine learning technologies as well as the current space AI industry.

This study contributes to the Sybil nodes detecting algorithm in Online Social Networks (OSNs). As major communication platforms, online social networks get significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. Additional exploration of distinct reputation systems and their practical application led to the study’s discovery of machine learning algorithms, i.e., the KNN, Support Vector Machines, and Random Forest algorithms. The study details the data cleansing process for the employed dataset in its process for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to explaining and analysis of conducting experiments and comparing their results. Such experiments demonstrate the algorithm’s ability to detect Sybil nodes in OSNs (100% accuracy in SVM, 99.6% in Random Forest, and 97% in KNN algorithms); thus concluding by proposing future research opportunities.

In the rapidly evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a critical role in safeguarding networks and systems from unauthorized access and malicious activities. Traditional IDS approaches, relying heavily on predefined rules and signature-based detection, often struggle to keep pace with the dynamic nature of modern cyber threats. Deep learning, with its ability to automatically learn complex patterns and representations from large datasets, offers a promising solution to this challenge. This paper explores the application of deep learning techniques in enhancing the effectiveness and accuracy of IDS. By leveraging advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, deep learning-based IDS can detect both known and unknown (zero-day) attacks with higher precision. We discuss the advantages of deep learning in IDS, including its capability to handle large-scale data, adaptability to evolving threats, and reduced dependency on human intervention. Furthermore, this paper reviews recent advancements in deep learning for IDS, highlights the challenges associated with their implementation, and suggests future directions for research to overcome these hurdles. The integration of deep learning into IDS frameworks represents a significant step forward in the development of intelligent and autonomous cybersecurity systems.

The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) at-tacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands or more to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantitative and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms Opti-mization with TOPOT-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using evolutionary algo-rithms, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.

Introduction and Purpose: Nurses are expected to be competent in practice based on the domain of standards and interpersonal soft skills (ISS). Nurses are faced with inadequate physical assessment 28%, formulating diagnoses 56%, plans 34%, implementation 31%, evaluation 37,5%, and communication skills 41%. Aims to develop an ISS learning model based on transformative learning theory and digitalization (TLTD) to improve nurses’ competencies. Method: Used Explanatory cross-sectional involved 190 practitioner nurses. Variables of individual characters (X1), facilities (X2), Social environment support/SES (X3), TLTD (X4), ISS (X5), and nursing competencies (Y) were measured. Data was collected using a checklist questionnaire, conducted on 6 June to 10 August 2022 at Dili, Baucau, and Maliana hospitals. Used descriptive and inferential analysis with SEM-PLS. Result: Five of the 11 hypotheses of statistical p-value < 0,05 and T-Test > 1,96, including X2>X4, X3>X5, X4>X5, and X5>y, to form ISS learning model. The characteristic of respondents were 81,8% young adults, 67,9% women, 57,8% undergraduates, judging the previous training 35% was unsuccessful, and 45,8% did not meet expectations. Learning facilities that are still lacking include policies 23,7%, curriculum 23,4%, guideline 22,7%, modules 27,9%, infrastructure 20,3%, social environment support 24,2% low, organizational support, 30,0% low peer support and 26,3% low family support. The average TLTD is 25,3% to 34,2%, ISS 22,1% to 30,5%, and nurse competence 25,3% to 30,5%.Suggestion: Result of the study become base to improve nurse’s competencies. Conclusion: Learning model was formed by facilities, SES, TLTD, ISS, and nurses’ competencies.

Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions becomes a challenge due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts where an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live-video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal has been implemented and assessed in a laboratory context, analyzing the model performance and QoS metrics. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% of accuracy. Also, obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.

Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

The opportunities and advantages of implementing Artificial Intelligence (AI) and Machine Learning (ML) to optimize the process control of wastewater treatment in the Oil and Gas industry are discussed in this work. Discussion is centered around five major points: 1) Various types of Oil and Gas wastewater (Sour, Oily, Produced) and industry standards/methods for treatment, including specific case studies and challenges when treating certain wastewater. This section aims at the common types of wastewater in the Oil and Gas industry and how they are generally treated. 2) How AI/ML has been implemented in wastewater treatment in various industries; summary of which AI/ML languages were used along with a brief overview of how they work. This article focused on an understanding of the specific case studies and AI explanations to understand the effectiveness of wastewater treatment using AI/ML. 3) AI that is currently implemented in Oil and Gas such as personal protective equipment (PPE), smartwatches radiofrequency identification etc. This showed that AI/ML already plays a successful role in the Oil and Gas industry and has proven to help optimize many processes and components. Many of these AI/ML implementations are now industry standards and have greatly benefitted the industry. This section demonstrates that the implementation and the use of AI/ML have been proven to be a successful endeavor and that expanding the role of AI/ML would be beneficial. 5) Using the established points from the previous sections the paper outlines why AI/ML is ideal for Oil and Gas wastewater treatment; this argument centers around fit-for-purpose treatment, and the possible uses of the treated water. A determination of the best-suited AI languages, possible implementation scenarios, and how this would shift the way Oil and Gas industries treat and approach wastewater is presented to the reader.

The current COVID19 pandemic has raised huge concerns for outdoor air quality due to expected lungs deterioration. These concerns include the challenges in the scalable prediction of harmful gases like carbon dioxide, iterative/repetitive inhaling due to mask and environmental temperature harshness. Even in the presence of air quality sensing devices, these challenges lead to failed planning and strategy against respiratory diseases, epidemics, and pandemics in severe cases. In this work, a dual time-series with bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and optimization responses that use automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e. multi-variate dual time-series of environmental and US Environmental Protection Agency standard sensor variables for air quality index measured from air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor air quality and their data was collected at Ubidots IoT platform over GSM. Results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 seconds for normal and RMSE of 1.646 in the training time of 28.53 seconds for optimization. The estimated R-Squared error of 0.03 with Mean-Square Error for temperature 1.0084 ᵒC and 293.98 ppm for CO2 was observed. Furthermore, the Mean-Absolute Error (MAE) for temperature 0.66226 ᵒC and 10.252 ppm for CO2 at a prediction speed of ~5100 observations/second for temperature 45000 observations/second for CO2 due to iterative optimization of the training time 469.28 seconds for temperature and 28.53 seconds for CO2 was very promising in forecasting COVID19 countermeasures before time.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed before being applied to 7 nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data was used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in be-havioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.

Artificial General Intelligence (AGI) represents a pivotal advancement in AI with far- reaching implications across technological, ethical, and societal domains. This paper addresses the following: (1) an in-depth assessment of AGI’s transformative potential across different sectors and its multifaceted implications, including significant financial impacts like workforce disruption, income inequality, productivity gains, and potential systemic risks; (2) an examination of critical ethical considerations, including transparency and accountability, complex ethical dilemmas and societal impact; (3) a detailed analysis of privacy, legal and policy implications, particularly in intellectual property and liability, and (4) a proposed governance framework to ensure responsible AGI development and deployment. Additionally, the paper explores and addresses AGI's political implications, including national security and potential misuse. By analyzing and considering computer science, philosophy, economics, and policy perspectives, we offer a multidisciplinary view of AGI’s challenges and opportunities, advocating for proactive measures to align AGI development with human values and societal interests.

In this paper, various machine learning techniques are discussed. These algorithms are used for many applications which include data classification, prediction, or pattern recognition. The primary goal of machine learning is to automate human assistance by training an algorithm on relevant data. This paper should also serve as a collection of various machine learning terminology for easy reference.

In this work, an IoT system with edge computing capability is proposed facilitating postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises are executed correctly, which is critical since, it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages Internet of Things (IoT) paradigm, in combination with Deep Learning and Edge Computing to classify the extend-flex movement of one’s knee via embedded Machine Learning (ML) classification algorithms. The contribution of the proposed work is multilayered. Furthermore as an outcome of this work a dataset of labeled knee movements is freely available on https://www.kaggle.com/datasets/billskarm/knee-range-of-motion to the research community. It also provides real time movement detection with an accuracy reaching 100%, which is achieved with a ML model trained to fit a low cost, off the shelf, Bluetooth Low Energy platform. The proposed Edge Computing approach allows predictions to be performed on-device rather than solely relying on a cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy, while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99,9% wireless data transfer reduction, a critical 39% prediction delay reduction and a valuable 17% increase of prediction event. Finally, enhanced privacy comprises another significant benefit from the implemented Edge Computing ML model as sensitive data can be processed on-site and only events or predictions are shared with the medical personnel.

Global rapidly evolving events, e.g., COVID-19, are usually followed by countermeasures and policies. As a reaction, the public tends to express their emotions on social media platforms. Therefore, predicting emotional responses to events is critical to put a plan to avoid risky behaviors. This paper proposes a machine learning-based framework to detect public emotions based on social media posts in response to specific events. It presents a precise measurement of population-level emotions which can aid governance in monitoring public response and guide it to put in place strategies such as targeted monitoring of mental health, to react to a rise in negative emotions in response to lockdowns, or information campaigns, for instance in response to elevated rates of fear in response to vaccination programs. We evaluate our framework by extracting 15,455 tweets. We annotate and categorize the emotions into 11 categories based on Plutchik’s study of emotion and extract the features using a combination of Bag of Words and Term Frequency-Inverse Document Frequency. We filter 813 COVID-19 vaccine-related tweets and use them to demonstrate our framework’s effectiveness. Numerical evaluation of emotions prediction using Random Forest and Logistic Regression shows that our framework predicts emotions with an accuracy up to 95%.

Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

While the surface of the Earth plays a key role in weather forecasting through its interaction with the atmosphere, in ensemble numerical weather predictions the uncertainty on the surface is only represented with perturbations in the parameterisations representing the surface processes. Data representing the surface, such as the land cover, are not perturbed. As fully data-driven forecasts without parameterisations are growing in importance, sampling the uncertainty on the land cover data brings a new way of making ensemble forecasts. Our work describes a method of generating ensemble land cover maps for numerical weather prediction. The target land cover map has the ECOCLIMAP-SG labels, used in the SURFEX surface model, and therefore is expected to have all relevant labels for surface-atmosphere interactions. The method translates the ESA WorldCover map to ECOCLIMAP-SG labels and resolution using auto-encoders. The land cover ensemble members are obtained by sampling the land cover probabilities in the output of the neural network. This paper builds upon the work done in a companion paper describing the high-resolution version of ECOCLIMAP-SG, called ECOCLIMAP-SG+, used for the training and evaluation of the neural network. The output map presented here, called ECOCLIMAP-SG-ML, improves upon the ECOCLIMAP-SG map in terms of resolution (from 300~m to 60~m), overall accuracy (from 0.41 to 0.63) and the ability to produce ensemble members.

The presence of potentially pathogenic bacteria on veterinary clinic surfaces may be problematic. In this study we collected swab samples (Fisherbrand, double transport swabs with Stuart’s liquid medium) and water samples from five veterinary rehabilitation clinics. Swabs and water samples were transported to a microbiology lab for processing. At the lab swabs were used to inoculated Hardy’s Cdiff Banana broth (for Clostridium difficile [Cdiff]), and five different types of bacterial growth media, including: Hardy CHROM MRSA agar (methicillin resistant Staphylococcus aureus [MRSA], and S. pseudintermedius [SIM]), Mannitol Salt Agar (S. aureus), Eosin Methylene Blue agar (enterics [ENT]), Pseudomonas Isolation agar (Pseudomonas sp.), and Tryptic Soy Agar [TSA] (non-specific). The most prominent species cultured was Cdiff (on nearly 55% of swabs). Bacillus sp. and Enterics were encountered on nearly 35% of swabs, with MRSA and SIM on just over 10% of swabs. The most contaminated sample site was harnesses/life jackets used with the underwater treadmill (33% of swabs). Underwater treadmill water had total bacterial counts from 1.6 to 2.7 e3 cfu/ml. Of all bacterial species detected SIM tends to be more pathogenic for dogs. Targeted cleaning/disinfecting in these clinics could help reduce risks for both animals and caregivers utilizing these clinics.

Improving the quality of petroleum products and refining processes through the use of artificial intelligence and machine learning techniques is the topic of this article. It shows that expert knowledge and conventional empirical models can't get you where you want to go in a refining process. To effectively capture complex interactions and forecast fuel qualities, machine learning techniques such as principal component analysis (PCA), support vector machines (SVM), artificial neural networks (ANN), and partial least squares (PLS) are suggested. Gasoline and other petroleum products, as well as property prediction, process control, product quality, and operational efficiency in refineries, can all be improved with the help of machine learning applied to spectral or distillation curve data. An exciting new direction in optimizing operations, meeting environmental norms, and precisely estimating gasoline quality is offered by advanced machine learning algorithms.

The paper presents two novel mathematical approaches to prove Goldbach's conjecture. The conjecture states that every even number greater than or equal to 6 can be expressed as the sum of two odd prime numbers. We propose that the conjecture has only four genetic codes, and as long as one of them remains valid, the conjecture continues to hold. Through these new mathematical ideas, we aim to prove the validity of Goldbach's conjecture comprehensively.

The need to increase the energy efficiency of buildings as well as the use of local renewable heat sources has caused that heat meters are used not only to calculate the consumed energy but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyse a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption and unnecessary maintenance. The reliability analysis of heat metres, based on historical data collected over several years, shows some regularities which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past but is a non-linear combination of state variables. To predict meters' failures in the next settlement period, three independent machine learning models are implemented and compared with selected metrics because even the high performance of a single model (87\% True Positive for Neural Network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameters optimisation boosts models' performance by a few percent. Finally, three improved models are used to build an ensemble classifier which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95\%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilised to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework's viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York's Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why these dynamics are necessary for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

Microparticles derived from biomaterials are becoming increasingly popular for various applications, particularly in drug delivery systems. In this study, the water-in-oil (W/O) emulsification-diffusion method was used to create cellulose (C), silk fibroin (SF), and C/SF composite microparticles. We then observed the morphology of all obtained microparticles using scanning electron microscopy (SEM), evaluated their functional groups using attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), and conducted thermogravimetric analysis using a thermogravimetric analyzer (TGA). SEM micrographs indicated that the native SF microparticles have the highest spherical shape with smooth surfaces. With blue dextran, the C microparticle was smaller compared to the native microparticle, while the drug-loaded SF microparticles were bigger than the native microparticle. The morphological surfaces of the C/SF composite microparticles were varied in shape and surface depending on the C/SF ratio used. The spherical shape of the C/SF composite microparticle was increased as SF content increased. Furthermore, the size of drug-loaded C/SF composite microparticles was gradually increased by the SF content. The significant functional groups in the C and SF structures were identified based on the ATR-FTIR data, and a suggestion was made regarding the interaction between the functional groups of each polymer. When compared to both native polymers, the C/SF composite microparticles exhibit improved thermal stability. The C/SF composite microparticle at a 1:3 ratio had the lowest drug release content, whereas the hydrophilicity of the C microparticle affected the highest drug release content. As a result, one crucial factor affecting the medication released from the microparticle is its thermal stability. According to the obtained results, C, SF, and C/SF composite microparticles show promise as delivery systems for drugs with controlled release.

Background: Autonomic balance measurement (ABM) is a valuable tool in clinical practice for assessing the functioning of the autonomic nervous system (ANS), which controls involuntary bodily functions. Here we summarize the current understanding of the utility of autonomic balance measurement in clinical practice. Methods: A narrative review of relevant literature was conducted, focusing on studies investigating the use of autonomic balance measures in various clinical settings. Results: ABM holds potential benefits in clinical practice, including aiding in diagnosing autonomic dysfunction, monitoring treatment efficacy, and predicting potential health complications. ABM can be crucial for diagnosing various conditions, including cardiovascular diseases (Heart failure, arrhythmias, and hypertension), neurological disorders (Parkinson's disease, Alzheimer's disease, and diabetic neuropathy), in sports medicine athletes can undergo ABM to optimize training regimens, enhance performance, assessing the impact of training stress, preventing overtraining, and promoting overall well-being and probably to assess potential health risks, in metabolic syndromes ABM can aid in the early detection of autonomic neuropathy, a diabetes-related complication, allowing for timely intervention and better management of the disease, and psychiatric disorders (Anxiety, depression, and post-traumatic stress disorder). In addition, ABM can be helpful in monitoring of treatment efficacy, by tracking changes in autonomic balance, clinicians can assess if treatments for conditions impacting the autonomic nervous system are effective. ABM can also be helpful in predicting outcomes by facilitating early recognition of future health complications in certain populations. However, limitations exist, including non-specificity, variability in measurements, and limited evidence for routine use in specific clinical settings. Conclusion: Incorporating autonomic balance measurement into routine clinical assessments provides a more comprehensive understanding of a patient's health. It complements traditional diagnostic methods, offering additional insights into the body's regulatory mechanisms. While autonomic balance measurement offers potential benefits, careful interpretation and consideration of its limitations are crucial for its effective use in clinical practice. ABM remains a valuable and versatile tool in clinical practice for a deeper understanding of the autonomic nervous system, enabling early detection, targeted interventions, and improved management of various medical conditions across different specialties.

We are interested in studying alterations in the regulatory circuitry that controls mRNA stability and function, and more specifically in the interactions among miRNAs and mRNAs. We have previously reported transcriptome-wide changes in mRNA expression in an animal model of atherosclerosis (ATS) progression, and reversion upon treatment with an αCD40 specific siRNA. In this work, we have used our data on mRNAs downregulated during these processes to study length dynamics as an adaptive mechanism to thwart miRNA activity. We show that ATS progression was characterized by the lengthening of the 5’UTR and 3’UTR regions of these mRNAs. Furthermore, 3’UTR lengthening (but not that of the 5’UTR) was shown to be partially reversed by the treatment with the αCD40 specific siRNA. Study of the exonic composition of the transcriptome of these mRNAs suggested a role for alternative/cryptic/latent splicing in the generation of length variants. This was confirmed by the detection, during ATS progression, of a transcript variant switching process in which specific isoforms were replaced by others that differed in their pattern of alternative splicing, mainly at their 5’ or 3’UTRs. This work highlights alternative splicing as a mechanism contributing to the generation of 5’/3’UTR length and sequence variability during ATS progression and calls attention on factors regulating alternative splicing as possible targets of therapeutical intervention.

Software stands as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity which leads to challenges in quality assurance. Software Defect Prediction (SDP) emerges as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. Machine Learning (ML) and Deep Learning (DL) methodologies exhibit superior accuracy and adaptability compared to traditional statistical approaches catalyzing active researches in this domain. However, it makes us hard to generalize not only because of the disparity between open-source project and commercial project but also the difference on each industrial sectors. Consequently, further research utilizing datasets sourced from diverse real-world sectors becomes imperative to bolster the applicability of these findings. In this study, we utilized embedded software for telecommunication systems of SAMSUNG ELECTRONICS, supplemented by the introduction of nine novel features to train the model, and subsequent analysis of results ensued. Experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolster the application of SDP methodologies within analogous industrial sectors.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML) built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future, uncertain causally-linked benefits. This multi-period, causal-linked process incorporating certainty and uncertainty replaces the single period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modelling and real world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. Causal coupling can also be applied at the macroeconomic level to gauge the effectiveness of policies that deliver various levels of cost and benefit coupling for individuals. With the growing interest in natural experiments in statistics and CML across many fields, such as healthcare, economics and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision making models that focus only on rationality, bounded to various degrees.

This literature review offers a comprehensive analysis of the design and optimization of microstrip patch antennas for wireless communication systems, drawing insights from a variety of research papers. It examines diverse approaches aimed at enhancing antenna performance and functionality. Key topics explored include optimization of resonant frequency, design of antenna arrays, and bandwidth enhancement. Noteworthy optimization techniques, such as genetic algorithms and artificial neural networks, are discussed, highlighting their effectiveness in achieving superior antenna performance. Application-specific solutions tailored to areas such as military communication and RFID systems are emphasized. The review underscores the significant role played by optimization techniques in achieving desired performance metrics and identifies future research directions, including multi-objective optimization, investigation of robustness and reliability, and integration with advanced technologies. Overall, this literature review provides valuable insights into the state-of-the-art in microstrip patch antenna design and optimization, guiding future research endeavors in this dynamic and rapidly evolving field.

Well-exposed fluvio-deltaic Surma Group successions of the northeastern Surma Basin provide a direct analogue to its equivalent subsurface successions—the main hydrocarbon reservoirs in Bangladesh. The various levels of lateral and vertical heterogeneities lead to changes in the subsurface sandbody distributions and their reservoir qualities across fields. The present study aims to identify the heterogeneities in the fluvio-deltaic Surma Group successions by detailed sedimentological analysis of the Mio-Pliocene-aged Surma Group successions. Eleven lithofacies have been identified and based on the recurring pattern of these facies within the Surma Group, five facies associations have been identified. The facies associations indicate that these deposits originated from diverse depositional settings, ranging from the prodelta to the alluvial to delta plain. As a whole, they represent a progressive shallowing upward succession with two transgressive events in the upper and lower parts of the Surma Group. Five levels of heterogeneities such as prodelta mud (HL-I), tidal heterolithic deposits (HL-II & III), interbedded mud units in delta front (HL-IV), and intercalated mud laminae, drapes (HL-V) have been identified in the fluvio-deltaic Surma Group. The study suggests that the various levels of reservoir heterogeneities are controlled by the occurrence and distribution of various facies associations.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

The importance of energy systems and its role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating and liquid fuels is very important. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this study is to evaluate the ML and DL techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations.

Actually, the study aimed to manage and to analyse the results of the laboratory methods, using liver samples, preparing and analizing in pathology service. This mentioned, are known as routinally practice for hepatitis C diagnosis, which conduct to hepatic cirrhosis. In this study direction, chronic liver diseases stimulate a degree of hepatocyte injury. So previously mentioned modifications, alters thet affect the liver architecture and finally ends in cirrhosis are well analyse on microscopic preparates. Folowing morphological steps, it is possible to analyse normal and alterate liver structure, on colored preparates.

Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, making advanced analytics accessible to a broader range of organizations. By automating complex tasks like data preprocessing, model selection, and hyperparameter tuning, AutoML reduces the time and resources needed to develop and deploy machine learning models. This accelerates decision-making and enables quicker responses to market changes. AutoML empowers businesses to build accurate predictive models using sophisticated algorithms, optimizing model performance for reliable insights and better outcomes. A key advantage of AutoML is its accessibility; even organizations without a dedicated data science team can leverage machine learning, reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success. The systematic review will examine the bibliometric literature on how AutoML can boost business, analyzing 74 academic and scientific documents from the Scopus database.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

Background: Tafamidis reduces cardiovascular morbidity and mortality in transthyretin amyloid cardiomyopathy (ATTR-CM), yet availability and access to therapy vary. Objective: To determine how availability and access to tafamidis impact time-to-diagnosis, time-to-therapy, and cardiovascular outcomes in ATTR-CM. Methods and Results: 91 consecutive ATTR-CM patients diagnosed between June 2019 and June 2021 were evaluated for tafamidis. Access to therapy was regulated by compassionate use [n(CU)=42] prior to, and insurance [n(IA)=49] after regulatory approval. Tafamidis was started in 37/42 (88.1%), and 39/49 (79.6%) patients, respectively. At diagnosis, ATTR-CM disease stage (≤stage 2: 88.2% vs. 90.9%, p= 0.92) was similar between groups. Timely access (after tafamidis approval) reduced time-from-first-presentation-to-diagnosis from median 6.2 (IQR: 1.3-28.9) to 2.4 (0.7-21.7) months, and from-first-presentation-to-therapy from 24.4 (10.7-46.8) to 11.8 (6.4-32.4) months. While RV function significantly worsened between diagnosis and therapy initiation in CU patients diagnosed before tafamidis approval (S’-velocity 10.0±2.2 to 9.2±2.2cm/s; p=0.018; TAPSE 17.3±4.7 to 15.7±3.9mm, p=0.008), it remained unchanged in IA patients (S’-velocity 9.6±2.6 to 9.4±2.3cm/s; p=0.83; TAPSE 15.6±4.2 to 16.3±3.1mm, p=0.45). After a median follow-up of 42.3 and 24.9 months in CU and IA patients, respectively, timely availability was associated with a reduction of annual heart failure hospitalizations (0.40 vs. 0.16 per patient, p<0.001) and improved MACE-free survival (HR=0.51; 95%CI: 0.26-1.00; p=0.051). Timely diagnosis (<12-months) prolonged MACE-free survival (HR=0.424; 95%CI:0.22-0.81; p=0.004), and reduced HFH (HR=0.40; 95%CI:0.19-0.81); p=0.011) and all-cause mortality (HR=0.29; 95%CI:0.11-0.74); p=0.009). Conclusions: Availability of tafamidis improves diagnostic efficacy in ATTR-CM patients. Timely diagnosis and initiation of therapy reduces adverse cardiovascular events.

The interaction between gut-residing microorganisms plays a critical role in the pathogenesis of Crohn’s disease (CD), where microbiome dysregulation can alter immune responses leading to unresolved local inflammation. The aim of this study is to analyze the immunomodulatory properties of our recently developed probiotic+amylase blend in the SAMP1/YitFc (SAMP) mouse model of CD-like ileitis. Four groups of SAMP mice were gavaged for 56 days with: 1) probiotic strains + amylase (concentration: 0.25 mg/100 uL PBS); 2) only probiotics; 3) only amylase; PBS (vehicle-treated controls). Ilea were collected for GeoMx Digital Spatial Profiler (DSP) analysis and histological evaluation. Histology assessment for inflammation indicated a significantly reduced level of ileitis in mice administered probiotics+amylase blend. DSP analysis showed decreased abundance of neutrophils and increased abundance of dendritic cells, innate lymphoid cells and macrophages, with a significant enrichment of five intracellular pathways related to apoptosis, in probiotics+amylase treated mice. Increased apoptosis occurrence was confirmed by TUNEL assay. Our data demonstrate a beneficial role of the probiotic and amylase blend, highlighting an increased apoptosis of innate immunity-associated cell subsets, thus promoting the resolution of inflammation. Hence, we suggest that our recently developed probiotic blend may be a therapeutic tool to manage CD and therefore could be a candidate compound to be tested in clinical trials.

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

Bixonimania, a rare hyperpigmentation disorder characterized by a distinctive pink hue on the eyelids, presents a significant diagnostic challenge due to its unique presentation and limited research. This study explores the application of machine learning algorithms for bixonimania detection by analyzing millions of images of individuals exposed to blue light (500-700 nm spectrum). Our findings suggest a potential link between blue light exposure and bixonimania, affecting roughly 1 in 90,000 individuals. The proposed machine learning model achieved an accuracy of 92% in detecting bixonimania based on eyelid hue analysis. This study paves the way for further exploration of bixonimania's underlying causes, improved diagnostic tools, and potential treatment options.

The global textile sector, pivotal to economic development, significantly contributes to environmental degradation, prompting a shift towards sustainable raw material sources. Embracing circular economy principles, this industry is exploring the valorization of agricultural by-products. This study introduces an innovative approach to repurposing mango waste into valuable textile fibers, a byproduct of the fruit processing industry, predominantly generated during juice and jam production. The focus is on the lignocellulosic constituents of mango waste, specifically the peel, seed, and fibrous material, traditionally considered as refuse. Previous research has predominantly utilized mango peel in pectin extraction for food applications, whereas the potential of seed and fibrous content remains underexplored. This investigation endeavors to transform these fibrous by-products into high-value materials suitable for textile manufacturing. The process involved washing, drying, and comprehensive physicochemical characterization of mango fibers, emphasizing lignin quantification, revealing a composition of 12.2% lignin. An experimental design was employed to elucidate the impact of variables such as liquid-to-solid ratio, reaction duration, and sodium hydroxide concentration on lignin dissolution through alkaline hydrolysis. Optimal conditions—10% NaOH concentration, 30 mL/g liquid-to-solid ratio over six hours—achieved an 83% reduction in lignin content. The resulting fibers exhibited properties akin to conventional natural fibers, including a favorable aspect ratio indicative of their suitability for textile applications. A subsequent softening process using propylene glycol enhanced the fibers' spinability, producing a durable, textured fabric ideal for eco-conscious fashion applications, including bags, footwear, and accessories. This research demonstrates the feasibility of converting mango waste into a textile-worthy material and underscores the importance of waste revalorization in promoting sustainability within the circular economy framework.

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

The Transformer architecture, while adept at capturing context through self-attention, falls short in encapsulating complex syntactic structures effectively. Addressing this gap, we introduce the Linguistic Structure through Graphical Interpretation with BERT (LSGIB) approach in Machine Translation (MT) frameworks. Combining the strengths of Graph Attention Network (GAT) and BERT, LSGIB intricately captures syntactic dependencies as explicit knowledge from the source language. This enhances the source language representation and aids in more accurate target language generation. Our empirical analysis leverages gold-standard syntax-annotated sentences and employs a Quality Estimation (QE) model. This approach enables us to assess translation improvements in terms of syntactic accuracy, extending beyond traditional BLEU score metrics. The LSGIB model demonstrates superior translation quality across diverse MT tasks, maintaining robust BLEU scores. Our study delves into the optimal sentence lengths benefiting from LSGIB and identifies which syntactic dependencies are more precisely captured. We observe that GAT's ability to learn specific dependency relations directly influences the translation quality of sentences with those relations. Additionally, we discover that incorporating syntactic structure into BERT's intermediate and lower layers offers a novel approach to modeling linguistic structure in source sentences.

To support the decision making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications. Three artificial intelligence techniques were implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The results of the different algorithms were compared to each other using the metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.

The study evaluated the regenerative responses of the lacrimal functional unit (LFU) after Lacrimal Gland (LG) ablation. The LG of Wistar rats were submitted to G1) partial LG ablation, G2) partial ablation and transplantation of an allogeneic LG, or G3) total LG ablation, (n=7-10/group). The eye wipe test, slit-lamp image, tear flow, and histology were evaluated. RT-PCR analyzed inflammatory and proliferation mediators. The findings were compared to naïve controls after 1 and 2 months (M1 and M2). G3 presented increased corneal sensitivity, and the 03 groups have shown corneal neovascularization. Histology revealed changes in LG, and corneal inflammation. In the LG, there was an increase in MMP-9 mRNA of G1 and G2 at M1 and M2 and RUNX-1 at M1 and M2 in G1 and RUNX-3 mRNA at M1 in G1, and in G2 at M2. TNF-α mRNA rose in the corneas of G1 and G2 at M2. There was an increase in the IL-1β mRNA in the Trigeminal Ganglion of G1 at M1. Without changes in tear flow or evidence of LG regeneration, LG ablation or grafting are unreliable models for dry eye or LG repair in rats. The surgical manipulation extended inflammation to the LFU.

This study explored what motivates university counselors. By analyzing various factors like personal values and work environment, they found counselors experience greater career satisfaction when their work aligns with their own values and societal impact, and when they receive recognition and opportunities for growth. Universities can utilize these findings to create a more supportive environment for counselors by highlighting their impact, offering development opportunities, and fostering a positive work culture. This can ultimately lead to a more motivated and effective counseling team, benefiting both the counselors and the students they serve.

As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, the generalizability of these models across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the model's applicability across diverse development environments by integrating various datasets from distinct projects into a unified, using a more interpretable ML approach. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to seven times more defective modules with the same testing effort as a benchmark.

Analysis of the construction of tall buildings is important because of the changes in the microclimate in its vicinity. Ensuring wind comfort and pedestrian safety are critical requirements in urban environments. This paper reports on the wind flow field at the main campus of the Dhaka University of Engineering & Technology (DUET), Gazipur, using computational fluid dynamics (CFD) to investigate pedestrian wind comfort and safety. The simulations were conducted in ANSYS Fluent employing the Reynolds-averaged Navier-Stokes (RANS) equation. The vertical profile of the streamwise wind speed was validated using the existing observations. The results revealed that the wind speed at the building corners was higher than the air inlet velocity, whereas the wind speeds in the walkways among the buildings were found to vary. The velocity vectors surrounding the building were densely packed, with a recirculation vortex formed in the upstream flow. Furthermore, walkways can display a lower mean wind velocity coupled with high turbulence kinetic energy (TKE). The turbulent kinetic energy is seen to be as high as 1.52 m2 s-2 which exceeds the threshold limit, while wind velocity is 1.81 m/s at the same point. These findings highlight potential pedestrian safety risks and underscore the need for urban design enhancements.

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI). This study aims to explore the contributions of news media towards this phenomenon - we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines. Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.

Non-convex optimization is a critical tool in advancing machine learning, especially for complex models like deep neural networks and support vector machines. Despite challenges such as multiple local minima and saddle points, non-convex techniques offer various pathways to reduce computational costs. These include promoting sparsity through regularization, efficiently escaping saddle points, and employing subsampling and approximation strategies like stochastic gradient descent. Additionally, non-convex methods enable model pruning and compression, which reduce the size of models while maintaining performance. By focusing on good local minima instead of exact global minima, non-convex optimization ensures competitive accuracy with faster convergence and lower computational overhead. This paper examines the key methods and applications of non-convex optimization in machine learning, exploring how it can lower computation costs while enhancing model performance. Furthermore, it outlines future research directions and challenges, including scalability and generalization, that will shape the next phase of non-convex optimization in machine learning.

Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems and has enjoyed significant interest from the research community. This paper discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINN) in particular are highlighted. Additionally, we explore recent Works toward utilizing neural operator learning to intuit relationships in physics systems traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics-problems are served by the physics-informed learning machines, and assisting in identifying apt targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.

A long time has passed since COVID-19 was discovered and widely disseminated. Both machine learning and deep learning have moved towards the research of diagnosing COVID-19. Compared to deep learning, traditional machine learning-based methods can also achieve good diagnostic results if they are improved based on innovative points. And deep learning remains a more popular research object. Based on the deep neural network in the field of deep learning, the convolutional neural network, recurrent neural network, long and short-term memory network, and Transformer model have been extended. These models improve the performance and processing power of neural networks by introducing new structures and algorithms. This paper will introduce the basic concepts of machine learning and deep learning, as well as the details of using the relevant methods of both to help diagnose COVID-19.

This work is concerned with studying the change of some Optical and Electrical Properties of Ba_xFe_(1-x)TiO₄ thin films due to the change of the nano crystal size. Simple theoretical explanation based on the string theory was developed. The empirical results obtained indicated that the absorption coefficient, electrical conductivity, and the magnetic permeability increase upon decreasing the nano crystal size. However, the transmission coefficient, and the refractive index increase upon increasing the nano crystal size. All these changes are explained using a simple string model with the aid of Newton's laws. The capability of the string theory to explain this nano quantum behavior comes from the fact that the string theory recognize both the particle and the wave nature of the nano system. This is similar of course to the quantum approach which is based mainly on the dual wave particle nature of atomic systems and entities.

Portable Document Format (PDF) is one of the most widely used files types worldwide in data exchange, this has encourage hackers to utilize such files to spread any malicious content through PDF, utilizing different methods and techniques to accomplish that, on the other hand, security researches kept trying to improve detection methods to cope up to the rapidly increasing number of malwares daily, one of the commonly used detection technique nowadays is by utilizing artificial intelligence and Machine learning classificat; thision to help detecting PDF Malwares, in this paper, we utilize machine learning classifier Random Forest on a newly released PDF Malware dataset CIC-Evasive-PDFMal2022 to achieve the main goal of detecting malicious PDF documents, results showing a detection accuracy of around 99.5%

Machine Learning (ML) and Artificial Intelligence(AI) have revolutionized almost all fields that are linked to the acquisition of intelligent behavior in the real world. It is an attractive alternative for a researcher of artificial intelligence. Contrary to rule-based programming, ML is an algorithmic approach in which learning comes from existing data. The more data we have these computer systems look at, we say we’re ‘training’ the computer system, and as the computers begin to identify patterns in the data, identify abnormalities in the data from these abnormalities we improve the system architect according to the requirement. This article introduces the use of comprehensive concepts of machine learning, in general, particular, and their potential applications in communications. Furthermore, the current state and futuristic potentials of enabling universal communication with implications of machine learning methods have been explained. In this review paper, we offer a comprehensive talk on distinctive methods/techniques of information analytics, artificial intelligence (AI), and machine learning (ML) moved forward the contact aware communication system.

Artificial Intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade has been remarkable. Specifically, Machine Learning (ML) and Deep Learning (DL) techniques in medicine have been increasingly adopted thanks to the growing abundance of health-related data, improved suitability of such techniques for managing large data-sets, and more computational power. The Italian scientific community has been instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years.

This paper presents a novel method for automatic modulation classification (AMC) for cognitive radio (CR) networks based on a simple classifier that is trained with high-order cumulant. The proposed method focuses on the statistical behavior of modulated signals and includes analog modulation and digital schemes, which received less attention in the literature. The effectiveness of the proposed method is demonstrated through simulation results using high-quality generated signals under different signal-to-noise ratios (SNRs) and channel conditions. The classification performance achieved by the proposed method is superior to that of the more complex deep learning methods, making it well-suited for deployment in end units of CR networks, particularly in military and emergency service applications. The proposed method offers a cost-effective, high-quality solution for AMC that meets the stringent requirements of these critical applications.

Gastrointestinal cancers are still the leading cause of death worldwide. This is related, among other things, to non-specific symptoms, especially in the initial stages, and also to limited possibilities of treatment. Therefore, research is still being conducted to improve the detection of this type of cancer and increase the effectiveness of therapy. The potential application of natural compounds in cancer management deserves special attention. In the group of such products there are polyphenolic compounds which reveal e.g. anti-oxidative, anti-carcinogenic, anti-inflammatory, anti-diabetic and neuroprotective properties. One of such polyphenols is rosmarinic acid, commonly found in plants such as Boraginaceae and Nepetoideae subfamilies of the Lamiaceae (mint) family, including Origanum vulgare, Rosmarinus officinalis Thymus masticmasti chinaythia koreana, Ocimum sanctum, and Hyptis pectinate. A number of studies considers the positive effects of rosmarinic acid in the treatment of many cancers, including gastrointestinal ones such as oral, stomach, pancreas, colon, and liver. The main aim of this paper was to focus on the summarized mechanisms of the action of rosmarinic acid in gastrointestinal cancers.

A genome represents the complete collection of genetic material in an organism or cell. This genetic information is encoded in nucleic acids, which can be either single or double-stranded and arranged in linear or circular forms. DNA, the most common type of genetic material, consists of just four nucleotides: adenine, guanine, thymine, and cytosine. These nucleotides are organized in a double helix structure, a discovery made by Watson and Crick. The sequence of these nucleotides forms the basic blueprint of a gene or genome. The process of determining this sequence is known as genome sequencing. The field of genome sequencing has evolved dramatically over time, advancing from sequencing short DNA fragments to analyzing millions of base pairs. Initially, efforts were focused on determining the sequence of individual genes, but now whole genome sequencing is both rapid and widely accessible. Recent improvements in sequencing technology emphasize faster, more accurate results, lower costs, and better data analysis. Sanger sequencing, which became the gold standard for DNA sequencing for about thirty years, was instrumental in completing various genome sequences, including the human genome. It laid the groundwork for newer sequencing methods. Today, these newer methods, known collectively as “Next-Generation Sequencing” (NGS), include several generations of technology. Second-generation methods, such as Illumina, Pyrosequencing, ABI/SOLiD, and Ion Torrent Sequencing, and third-generation methods like PacBio and Helicos Sequencing, have advanced the field further. Fourth-generation technology, such as Nanopore sequencing, is also emerging. Most modern research now relies on NGS for analyzing biological sequences, and understanding these technologies offers insight into current methods and future developments.

Introduction. Anesthesia is widely used in various fields of dentistry to reduce patient discomfort when the treatment would be painful. The dentist may need to infiltrate the anesthetic into multiple locations and this is usually done with the same needle which passes through the soft tissues several times to distribute the anesthetic solution over a larger area. The present study aims to evaluate, through a morphological analysis under a scanning electron microscope (SEM), how the beveled tip of a 30G needle, used to inject anesthetic several times into different areas of the vestibular mucosa, changes. Materials and methods. 30G needles with two different brands were tested, in vivo used for a variable number of sequential injections from 1 to 6 on 192 patients. The specimens were analyzed under SEM observation to evaluate any deformations induced by contact with the vestibular soft tissues. Results. The amount of deformation, measured along the axis parallel to the needle, was proportional to the number of injections. The protrusion of deformations, measured on an axis perpendicular to the needle, were maximum on the needles used 3 times. For the needles used 4,5 and 6 injections, no further increase in deformations was noted. Conclusion. From the data it seems that the tested needles are not produced to withstand multiple injections consequently after the first injection on soft tissues, they undergo a deformation which could increase the risk of injury to the mucosa itself or to nerves and vessels with which they come into contact. It would be advisable to use the single needle for a reduced number of injections.

Abstract: Since the establishment of multiparty democracy in the 1990s, Malawi has experienced significant political shifts, with elections playing a crucial role in shaping the political landscape. However, there is a lack of comprehensive understanding of the spatio-temporal dynamics of election results, especially regarding voting patterns and their effects on electoral outcomes. This gap hinders political parties, politicians, and electoral stakeholders in effectively managing regional and temporal changes in voting behaviors. Geographic Information Systems (GIS) are underutilized in Malawi for studying geo-graphical and temporal election data. This study utilized GIS to analyze the spatial pat-terns of election results in Malawi since the advent of multiparty democracy. By collecting geographic data on election outcomes, including vote counts, candidate performance, voter turnout, and other relevant data, the study examined how the geographic distribution of votes impacts electoral results. GIS techniques such as spatial analysis, mapping, and spatial statistics were employed to assess the data and create maps illustrating spatial patterns in Malawi's election outcomes. The study revealed that voting patterns in Malawi are significantly influenced by regional lines. These insights can inform political campaigns, voter mobilization efforts, and strategies for electoral reform.

Triple-negative breast cancer (TNBC), lacking specific receptors found in other breast cancer subtypes, poses significant treatment challenges due to limited therapeutic options. Therefore, it is necessary to develop novel treatment approaches for TNBC. In the last few decades, many attempts have been reported for alternative tools for TNBC treatment: immunotherapy, radiotherapy, targeted therapy, combination therapy, and nanotechnology-based therapy. Among them, combination therapy and nanotechnology-based therapy show the most promise for TNBC treatment. This review outlines recent advancements in these areas, highlighting the efficacy of combination therapy (immunotherapy paired with chemotherapy, targeted therapy, or radiotherapy) in both preclinical and clinical stages and nanotechnology-based therapies utilizing various nanoparticles loaded with anticancer agents, nucleic acids, immunotherapeutics, or CRISPRs in preclinical stages for TNBC treatment.

Due to the advancement and availability of internet in present age, device tend to become wireless as well. Mobile ad-hoc network is a wireless environment that consist of advanced agility of nodes and does not contain on any central administrative authority. As it does not contain any central administrative authority so being wireless environment, the network is vulnerable to security threats and suffers in performance as well. Security of MANET is the core issue in this era, meanwhile the data is sent over untrustworthy network. Several techniques were proposed to secure data over Mobile Adhoc Network though cryptography that is one of the most reliable techniques to protect data. In our proposed methodology a Secure Cryptographic technique for Mobile Adhoc Networks (SCTMA) we proposed A Collusion attack prevention (CAP) scheme with ElGamal key generator along with DNA encryption Algorithm is used to make it more secure. The proposed scheme protects the network against collusion attacks and improves the performance as well. Performance analysis will be measured on the basis of key generation time, response time, encryption time and decryption time. The proposed scheme also protects the network against collusion attacks.

Nowadays, there is extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through degradation processes of the plastics themselves, can contaminate the ecosystem with micro and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) to predict different microplastic/water partition coefficients (log Kd) were developed using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients upper than 0.92 in the query phase, which indicate that these type of models could be used for a rapid estimation of the absorption of organic contaminants on microplastics.

Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

Undoubtedly, there is a potential impact of large language models like ChatGPT in revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine tune perplexity AI, which sets a giant step ahead towards accurate next generations AI. Having started this world leading discovery, the upper and lower bounds of perplexity AI, namely , , respectively, are obtained for the first-time ever. Concluding remarks combined with emerging open problems and next research phase are provided.

Cricket, the most popular sports in the world to chase by the fans. Many cricket fans are interested in analyzing the elements and activities that affect the outcomes of matches. Machine learning in sports analytics is a relatively new topic in computer science. The purpose of this survey is to anticipate the outcome of a PSL champion team and build a winning strategy. Machine learning (ML) has exhibited favorable outcomes in a variety of sectors for diverse prediction using classification, regression, and so on, and is proven to be accurate. The innovative frameworks based on ML have the ability to learn from previous experiences. The cricket pitch is the most important aspect, alongside home game an edge, coin toss, innings of play, day/night match, physical conditioning, and dynamic plans, among other things. The dataset from previously ESPN Cricifno was used for this purpose. Recent classification approaches including, Random Forest, Gradient Boost, Deep Neural Networks used to conduct a comparative analysis based on their results and performances, as well as team strategies.

Maintaining the judiciary's power requires a high level of budgetary expenditure, but the specifics of this relationship have yet to be fully explored. Although several studies have analyzed the impact of spending in the judiciary through productivity and performance-related measures, none have employed machine learning techniques. This study examines the productivity of the judiciary based on spending and other variables using machine learning techniques, including clustering and neural networks. The final neural network model supports the results of Pearson's parametric correlation test, which found no significant correlation between expenditure and productivity. This study's findings demonstrate the importance of understanding that increased public budgetary expenditure alone is insufficient for improving the judiciary's efficiency. Instead, other administrative and technical measures are necessary to meet the demands of the Brazilian judiciary and improve service delivery rates. These findings offer important theoretical and managerial contributions to the field.

Rationale: Genetically modified (GM) foods have the potential to reduce food insecurity and address economic and environmental issues. Despite these benefits, GM foods continue to face significant criticism and there is limited information on public awareness and understanding of their nutritional and environmental health benefits in Nigeria. This study aims to fill this gap by investigating public knowledge and perceptions of GM foods in Victoria Island, Lagos. Methods: A convenience sampling method was used to recruit 369 respondents from Victoria Island, Lagos. Data were gathered using a structured questionnaire. The target population comprised employed adults either residing or working on Victoria Island. Statistical analyses were conducted to examine the relationship between sociodemographic factors and awareness of GM foods. Results: The study found that a majority of respondents (62.9%, n=232) were knowledgeable about GM foods and their associated issues. However, many believed that their community was not well-informed on the topic. Awareness of GM foods was significantly associated with sociodemographic factors such as marital status and education (P<0.01). Most respondents were well-informed about the environmental and health benefits of GM foods, with 61.8% believing that GM food production could sustainably reduce reliance on chemical pesticides, fertilizers, and other energy inputs, thus mitigating their ecological impacts. Additionally, most respondents did not feel that purchasing or consuming GM foods conflicted with their beliefs or would affect their food choices. Conclusion: The study concludes that while individual awareness of GM foods is relatively high, there is a perception that community awareness is low. Sociodemographic factors play a significant role in shaping public awareness and perceptions of GM foods. Recommendations: This includes government should actively educate the public on the ecological and health benefits of GM foods and address any safety concerns; Implement strict border controls and food labeling for GM products to protect consumer rights and preferences for organic foods, provide sufficient technical and financial assistance to farmers who participate in organic seed programs to enhance national food security. Significant Statement: This study highlights the importance of public education on GM foods and underscores the need for government intervention to ensure informed consumer choices and support for sustainable agricultural practices.

In today's digital landscape, cybersecurity has become a priority, with attacks becoming increasingly sophisticated. Traditional security approaches are no longer enough, requiring a more dynamic and advanced response. In this context, integrating Artificial Intelligence (AI) and Machine Learning (ML) appears key to addressing this growing threat. However, despite their high effectiveness, there is a developed problem: the integration of various data sources and technologies for comprehensive protection. This article presents an in-depth review of integrating Artificial Intelligence and Machine Learning in cybersecurity, focusing particularly on Open Extended Detection and Response (Open XDR) technology. The literature review in this article was meticulously conducted with specific criteria in mind. It focused on sourcing peer-reviewed journals, authoritative cybersecurity publications, and recent conferences that primarily concentrate on the advancements in AI and ML within the cybersecurity domain, particularly from 2018 to 2023. This timeframe was chosen to ensure the inclusion of the most current and relevant developments in Open XDR technology, Intrusion Detection Systems, Endpoint Detection and Response, and Security Information and Event Management systems. Additionally, the review gave special attention to studies and reports highlighting practical implementations and real-world applications of these technologies, thereby ensuring a comprehensive understanding of their impact and effectiveness in enhancing cybersecurity resilience. The methodology used is a detailed literature review, examining how various cybersecurity components interact and function. These components include Intrusion Detection Systems (IDS), which monitor networks for malicious activities; Endpoint Detection and Response (EDR), which focuses on detecting and investigating security incidents on endpoints; and Security Information and Event Management (SIEM), systems that provide real-time analysis of security alerts. The review also considers the role of Active Directory, a directory service for Windows domain networks, and the process of log forwarding, where log files are transmitted to a central server for analysis, in the context of AI and ML. The paper delves into the development of AI and ML, underscoring their roles in cybersecurity for advanced data processing, pattern recognition, and predicting threats. It explores both supervised (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data) in ML, and how these techniques bolster cybersecurity measures. The article highlights the significance of Open XDR as a critical innovation that integrates data from multiple sources for comprehensive security analysis. Further, the review discusses how the integration of AI and ML into various cybersecurity tools, such as IDS, EDR, and SIEM, augments capabilities in threat detection and response. It addresses the challenges and opportunities that AI and ML present in the cybersecurity domain, focusing on ethical issues, data privacy concerns, and the necessity for ongoing professional development in this rapidly advancing field. The paper concludes by affirming the effectiveness of merging AI and ML with these cybersecurity tools within the Open XDR framework.

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilized the supervised machine learning approach to predict students’ research competency, as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills— can be used to predict students’ research competency, as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. Findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

During a multi-detector computed tomography (MDCT) examination, it is crucial to efficiently organize, store, and transmit medical images in DICOM standard, which requires significant hardware resources and memory. Our project processed large amounts of DICOM images by classifying them based on cross-section views that may carry important information about a possible diagnosis. We ensured that images were retained and saved in PNG format to optimize hardware resources while preserving patient confidentiality. Furthermore, we have developed a graphical, user-friendly interface that allows physicians to visualize specific regions of interest in a patient's brain where changes may indicate disease. Our proposed method enables quick classification of medical images into predefined classes of confirmed diseases of brain parenchyma, contributing to swift decision-making for further diagnosis for more precisely evaluating and characterizing brain changes, and it can lead to the rapid application of adequate therapy, which may result in better outcomes.

The exponential growth of biomedical data in recent years urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling automatic feature extraction, selection and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology and disease genomics. We outline the challenges posed for machine learning, and in particular, deep learning in biomedicine and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Supervised machine learning (SML) is a powerful method for predicting a small number of well-defined output groups (e.g., potential buyers of a certain product) by taking as input a large number of known well-defined measurements (e.g., past purchases, income, ethnicity, gender, credit record, age, favorite color, favorite chewing gum). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known to be true. SML has had enormous success in the world of commerce, and this success may have prompted a few scientists to employ it in the study of molecular and genome evolution. Here, we list the properties of SML that make it an unsuitable tool in certain evolutionary studies. In particular, we argue that SML cannot be used in an evolutionary exploratory context for the simple reason that training datasets that are known to be a priori true do not exist. As a case study, we use an SML study in which it was concluded that most human genomes evolve by positive selection through soft selective sweeps (Schrider and Kern 2017). We show that in the absence of legitimate training datasets, Schrider and Kern (2017) used (1) simulations that employ many manipulatable variables and (2) a system of cherry-picking data that would put to shame most modern evangelical exegeses of the Bible. These two factors, in addition to the lack of methodological detail and negative controls, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S-HIC) should be taken with a huge shovel of salt.

A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

Global population projected to reach 10 billion by 2050 emphasizing the need for sustainable food production. Traditional protein sources present environmental and scalability challenges, demanding the diversification of protein sources. Edible insects, such as house crickets (Acheta domesticus), emerged as a promising solution due to their high nutritional value, efficient feed conversion rates, and lower environmental impact compared to conventional protein sources. Incorporating insect powders into new food products can improve consumer acceptance, but often lead to poor functionality and/or undesirable organoleptic characteristics. However, protein isolates were revealed to be effective in enhancing functionality and consumer acceptance. This study aimed to develop and compare the yield of three different protein extraction methods, using sodium hydroxide, ascorbic acid or alcalase, from house crickets (Acheta domesticus) for food applications. Protein extraction was performed on cricket powder with a mean protein content of 57.95 g/100 g, and the results were evaluated. The enzymatic method showed the highest protein extraction rate of 85.97% with a mean protein content of 74.03 g/100 g, while extraction with NaOH or ascorbic acid resulted in rates of 74.32 and 56.99%, respectively. Further studies on the functionality and sensorial evaluation of products developed with this protein extract are recommended.

The survival rates of women with stage 0 or 1 breast cancer stands at almost 100% and around 93% for stage 2. As the disease advances with decreasing survival, the rate detoriates to 72% for stage 3 and about 22% for stage 4 (ACS, 2018). Undoubtedly, earlier presentation is synonymous with better prognosis and survival. In fact data indicates that early stage breast cancer diagnoses has 90% survival rate as compared to 27% for the advanced states (ACS, 2018). In this regard, governmental and non-governmental agencies are working tirelessly towards the realization of improved breast cancer care and survival through early presentation and detection. For instance, the Sustainable Development Goal (SDG) Target 3.4 seeks to reduce by one third, premature mortality from cancers and other non-communicable diseases through prevention and treatment. Clearly, early presentation, diagnoses and treatment will assist in the realization of the SDG target 3.4 goal. To achieve this, research must be focused on early presentation among women with breast cancer.. However, current studies have focused mainly on advanced breast cancer regarding treatment, symptoms experience (emotional and psychological), coping and support systems, and the reasons for delayed diagnoses and treatment. Objective In this review, it is argued that, rather than focusing on advanced breast cancer which has been adequately explored, the attention has to be on what measures or approaches promote early presentations among Ghanaian women. Methods The review included all primary studies that provided answers to the phenomenon of interest. It included both quantitative and qualitative studies. Search scope encompassed studies within the last ten years; thus from 2010 to 2020 irrespective of the country of study.Studies were included if: The paper is of a primary study; the study population included women with early BC or BC symptoms; the study outcome is the association between early stage breast cancer and earlier presentation; the study has a comprehensive data analysis protocol; the results of the studies are unique.A thorough literature search was conducted using the following electronic database: Google scholar and Pubmed. Google scholar alert on new studies was subscribed. This gave update on newly published papers for appraisal and possible inclusion.Manual identified of other potentially eligible publications by searching the reference lists of retrieved studies was also done. After a thorough search, this review included studies from several countries around the Globe such as Ireland, London, Turkey, Iran, Ghana, Kenya, Ethiopia etc Data Collection The title and or abstract of all retrieved papers were independently scanned through to establish which studies should be further evaluated. The full texts of these potential articles were then assessed. Extraction The Cochrane extraction form for data extraction was resorted to. From each eligible article, the following study identifiers/information were extracted: author(s), first author, year, title, objective, article type, location, setting, study design and method, sample size, instruments used, and results. Results Prompt help seekers mainly self-discovered their BC, and this occurred through accidental touch, after menstruation, through breastfeeding, during grooming, and while taking their bath.Notably, women who were termed prompt help seekers had higher seriousness perception of their symptoms. This was likened to their level of understanding and awareness, as well as periodic examination and comparison. Similarly, women who knew they were at risk for breast on account of family history, and increased incidence of BC in their localities, labeled their initial symptoms as requiring prompt attention.Following interpretation and labeling of symptoms, the participants generally resorted to symptom monitoring until the onset of additional and or intense symptoms. Though most women thought of benign growths, a few feared it could be malignant. These emotional reactions led the women to purposively seek clarification by selectively consulting others. Further, participants opened up to social acquaintances such as family relationships, people with prior experience of breast disease, and in some few instances health professionals. Some women also resorted to media items to better their understanding. Remarkably, prompt help seekers sort for help within one month. Last but not least, some women first communed with their creator after discovery and labeling based on their spiritual affiliations.Also, researchers should consider conducting surveys to assess the BC risks for specific smaller groups. This would help tailor specific educational interventions to promote earlier presentation and treatment among the vulnerable.

Thyroid hormones are essential for regulating the metabolic rate, growth, development, and maintenance of many physiological processes in vertebrates. They are synthesized by the thyroid gland, which is an ancient organ that evolved early in vertebrate evolution. This study aims to characterize the evolutionary path of polymorphic variants associated with the hypothalamus-pituitary-thyroid (HPT) axis by comparing ancient DNA (aDNA) sequences of Late Pleistocene-Holocene hominin populations with those of modern humans. We evaluated the genetic sequences of several populations, including Neanderthals, Denisovans, Palaeolithic hunter-gatherers from European Russia and Sunghir, and Neolithic hunter-gatherers/farmers from Anatolia focusing on genes involved in the development of the thyroid gland and thyroid hormone (TH) biosynthesis, secretion, and transport. Results showed interesting variants in the DIO2 (rs225014), TPO (rs4927611), and TG (rs2069556 and rs1133076) genes. All SNPs seemed to confirm that Neanderthals, and in part Denisovas, were physiologically hypothyroidic. Probably they had lower T3 levels due to defective production or peripheral conversion. From the moment that the most favourable alleles in terms of T3 production appear during the Paleolithic, we are inclined to assume that their selection was linked to environmental pressure.Our study supports insight into the evolutionary history of the endocrine system and can help in providing understanding into the evolution of physiological systems and their adaptation to changing environments. Understanding the evolution of thyroid hormones can also shed light on the mechanisms underlying thyroid disorders in humans.

Background: As the most invasive breast cancer (BrCa), triple-negative BrCa (TNBC) has the worst survival. The use of dexmedetomidine potentially affected BrCa surgery and dexmedetomidine was reported to have direct effects on TNBC cells. The objective of this study is to explore the mechanisms underlying the effect of dexmedetomidine on TNBC. Methods: Dexmedetomidine targets were predicted using The Cancer Genome Atlas data SwissTargetPrediction. Cell lines MDA-MB-231, MCF7, and MCF10A were used to validate the targets in TNBC with both clinical samples and cell lines. Cancer cell lines and normal breast cell lines were grouped in cancer and normal groups respectively. Both groups were exposed to dexmedetomidine treatment. Cell Counting Kit-8 was used to determine the effect of dexmedetomidine on cells with target silencing. The binding model of the candidate targets was docked and critical amino acids were mutated to validate the binding model. Results: Dexmedetomidine selectively inhibits cancer cells. Catalytic subunit of the DNA-dependent protein kinase (PRKDC), indoleamine 2,3-dioxygenase 1 (IDO1), opioid receptor kappa 1 (OPRK1), glutaminyl-peptide cyclotransferase (QPCT), macrophage migration inhibitory factor (MIF), potassium voltage-gated channel, subfamily H (Eag-related), member 2 (KCNH2), cholinergic receptor, muscarinic 3 (CHRM3), and potassium intermediate/small conductance calcium-activated channel, subfamily N, member 4 (KCNN4) were identified as dexmedetomidine targets in TNBC. The expression levels of PRKDC, IDO1, MIF, KCNH2, CHRM3, and KCNN4 were found to be upregulated in TNBC tissues compared to non-TNBC tissues(p<0.05). Silencing of these genes was found to reduce the sensitivity of TNBC cells to dexmedetomidine(p<0.05). This effect was counteracted when the silenced genes were overexpressed, resulting in an increase in the sensitivity of cells to dexmedetomidine (p<0.05). Furthermore, a direct interaction between dexmedetomidine and IDO1 and CHRM3 was observed, which regulated the sensitivity of cells to dexmedetomidine(p<0.05).Conclusion: IDO1 and CHRM3 are direct targets of dexmedetomidine in TNBC.

We review the basics of nuclear binding energy scheme assumed to be associated with the existence of a new strong elementary charge associated with square root of reciprocal of the strong coupling constant.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.