In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

The aim of this study was to compare the effect of using L-PRF in patients undergoing impacted lower third molar surgery with either primary or secondary closure techniques. Methods: This prospective, randomised, double-blind, split-mouth clinical trial was conducted in patients with bilateral lower impacted third molars of similar position. Primary closure was performed in group 1 and secondary closure in group 2. Group 1 closure technique was applied to one side of the patients and group 2 closure technique was applied to the other side at different times. Nine pa-tients were excluded from the study due to alveolitis and failure to attend regular control visits out of the 45 patients evaluated. Results: Of the 36 patients included in the study, 23 were female and 13 were male and the mean age was 22.42±3.36 years. The secondary closure group had lower VAS scores at hour 6 (p

Keywords: Tendon injuries; Mallet finger; Suture techniques

(1) Background: The aim of the research was to present and emphasize the importance of magnetic resonance imaging (MRI) in diagnosing Achilles tendon (AT) ruptures by presenting case reports. (2) Materials and Methods: The sample of respondents included 7 patients (4 males and 3 females, 2 athletes and 5 non-athletes), aged 25 to 58 years, with AT injuries, who were examined and treated at the Institute for Treatment and Rehabilitation "Niška Banja", Niš, Serbia. Ankle examinations with MRI were conducted using the Esaote O-scan 0.3 T machine. The examinations were performed without the application of a paramagnetic contrast agent and without the use of anesthesia. Results: The MRI method's sensitivity and overall diagnostic accuracy were excellent, with 100% true positives. In our study, the method accurately detected the presence, location, and severity of AT injuries in all cases. MRI is an important and reliable method for detecting AT injuries, classifying them, monitoring the course of recovery, and assessing the effects of therapy. Conclusions: An unrecognized or delayed diagnosis of an AT injury can lead to chronic changes. Timely diagnosis (clinical examination, ultrasonography, magnetic resonance imaging) and appropriate treatment (conservative or operative) enable patients to return to normal activities, including sports. Unfortunately, the use of MRI is limited by restricted access and the cost of the examination.

This study delves into Personal Narratives (PN), which encompass both oral and written recounting of individual experiences, encompassing facts, events, people, and thoughts. Traditional emotion recognition and sentiment analysis primarily focus on broader categories like utterances or documents. Our research, however, centers on identifying Emotion Carriers (EC), which are specific segments within speech or text that elucidate the narrator's emotional state (e.g.,"losing a parent", "decision-making moments"). Extracting these ECs enriches the representation of a user's emotional state, thereby enhancing natural language understanding and the sophistication of dialogue models. While previous studies have utilized lexical attributes to identify ECs, we argue that incorporating spoken narratives offers a more nuanced view of the context and emotional state. This paper explores the integration of speech and textual embeddings at the word level, alongside both early and late fusion techniques, for improved EC detection in spoken narratives. We employ Residual Neural Networks (ResNet), initially pre-trained on diverse speech emotion datasets and subsequently fine-tuned for EC detection. Our experimental findings demonstrate that late fusion, in particular, significantly enhances EC detection capabilities.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

The delivery of therapeutical molecules through the skin, particularly to its deeper layers is impaired due to the stratum corneum layer, which acts as barrier to foreign substances. Thus, for the past years, scientists have focused on the development of more efficient methods to deliver molecules to skin distinct layers. Microneedles, as a new class of biomedical devices, consists on an array of microscale needles. This particular biomedical device had been drawing attention due to their ability to breach the stratum corneum, forming micro-conduits to facilitate the passage of therapeutical molecules. The microneedle device has several advantages over conventional methods, such as better medication adherence, easiness and painless self-administration. Moreover, it is possible to deliver the molecules swiftly or over time. Microneedles can vary in shape, size and composition. The design process of a microneedle device must take in account several factors, like the location delivery, the material and manufacturing process. Microneedles have been used in a large number of fields from drug and vaccine application, cosmetics, therapy, diagnosis, tissue engineering, sample extraction, cancer research, wound healing, among others.

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.

Sperm capacitation involves biochemical and physiological changes that enable sperm to fertilize the oocyte. It can be induced in vitro under controlled conditions that simulate the environment of the oviduct. While extensively studied in mammals, its approach in lizards remains absent. Understanding the mechanisms that facilitate reproduction is essential for advancing the implementation of assisted reproductive technologies in this group. Fifteen males of Sceloporus torquatus were collected during the early stage of the reproductive season. The sperm were isolated from the seminal plasma and then diluted up to a volume of 150 μl using BWW medium to incubate with 5% CO2 at 30 °C for a maximum duration of 3 hours. A fraction was retrieved hourly for ongoing sperm assessment. We found that total motility is maintained up to 2 hours post-incubation, after which it decreased. However, sperm viability remained constant. From that moment on, we observed a transition to a deeper and less symmetrical flagellar bending in many spermatozoa. The chlortetracycline assay indicated a reduction in the percentage of sperm showing the F pattern and an increase in those exhibiting the capacitated (B) and reactive (RA) patterns, accompanied by an elevation in the percentage of damaged acrosomes as revealed by the lectin binding assay. In mammals, these changes are often associated with sperm capacitation. These observations support the notion that this process may also occur in saurian.

Mycobacteria can be one of the main contaminants of biological products, and their presence can have serious consequences on patients’ health. Thus, specific testing is necessitated by regulations such as the European Pharmacopoeia. The current pharmacopeial reference i.e., microbial culture method cannot ensure an exhaustive detection of mycobacteria due to their growth characteristics. Additionally, the method is time-consuming and requires a continuous supply of culture media, which could be further challenging. Thus, to overcome these challenges, pharmaceutical industries need to consider alternative non-microbiological techniques to detect these fastidious, slow-growing contaminating agents. This review provides an overview of alternative methods, which could be applied within a quality control environment of biological products, and underlines advantages and limitations of these methods. Nucleic acid amplification techniques or direct measurement of mycobacteria stands out as the most suitable alternatives for mycobacterial testing in biological products.

Cardiomyopathies (CMPs) are a group of myocardial disorders that are characterized by structural and functional abnormalities of the heart muscle. These abnormalities occur in the absence of coronary artery disease (CAD), hypertension, valvular disease, and congenital heart disease. CMPs are an increasingly important topic in the field of cardiovascular diseases due to the complexity of their diagnosis and management. In 2023, the ESC guidelines on cardiomyopathies were first published, marking significant progress in the field. The growth of techniques such as cardiac magnetic resonance imaging (CMR) and genetics has been fueled by the development of multimodal imaging approaches. For the diagnosis of CMPs, a multimodal imaging approach, including CMR, is recommended. CMR has become the standard for non-invasive analysis of cardiac morphology and myocardial function. This document provides an overview of the role of CMR in CMPs, with a focus on tissue mapping. CMR enables the characterization of myocardial tissues and the assessment of cardiac functions. CMR sequences and techniques, such as late gadolinium enhancement (LGE) and parametric mapping, provide detailed information on tissue composition, fibrosis, edema, and myocardial perfusion. These techniques offer valuable insights for early diagnosis, prognostic evaluation, and therapeutic guidance of CMPs. The use of quantitative CMR markers enables personalized treatment plans, improving overall patient outcomes. This review aims to serve as a guide for the use of these new tools in clinical practice.

The aging mechanism of 10% and 30% nickel substituted manganese hexacyanoferrate cathode material in aqueous zinc-ion batteries has been explored through the advanced synchrotron-based two-dimensional x-ray fluorescence technique. Thanks to the two-dimension modality, not only the metal concentration dynamics throughout the entire electrodes was followed during the aging process, but their spatial distribution was also revealed, suggesting the route of the material transformation. The dissolution of Mn and Ni, and the penetration of Zn inside the framework, alongside the Mn aggregations outside the hexacyanoferrate framework were detected. Additionally, the possibility of conducting x-ray absorption spectroscopy measurements on the regions of interest made it possible to explore the chemical state of each metal, and furthermore, synchrotron-based powder x-ray diffraction demonstrated the gradual structural modification in 30% Ni-containing sample series, in terms of the different phase formation.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Chinese texts often feature a substantial volume of normative content, and their analysis has increasingly become a focal point in recent semantic analysis endeavors. Enhancing the effectiveness of text classification and clustering is pivotal in unveiling the intricate semantics within extensive textual data. Empirical research within the realm of computational social science has underscored the need for improved explanatory power when it comes to general text clustering methods applied to normative texts. This study introduces an innovative hierarchical clustering method that incorporates part-of-speech (POS) features and pioneers a novel semantic network model. It achieves this by amalgamating a POS-based feature weight calculation method with hierarchical clustering techniques. To assess its efficacy, the method is rigorously evaluated across three diverse text datasets, encompassing normative reports, news articles, and social media content. The experimental results unequivocally demonstrate the superior performance of the POS-HC method, surpassing the traditional TF and TF-IDF feature extraction methods in terms of clustering accuracy, and surpassing some existing clustering methods. Furthermore, its classification effectiveness exhibits notable advantages, particularly in the semantic classification of normative texts.

In this work, an innovative numerical approach for polylinear approximation (polylinearization) of non-self-intersecting compact sensor characteristics (transfer functions) specified either pointwise or analytically is introduced. The goal is to optimally partition the sensor characteristic, i.e. to select the vertices of the approximating polyline (approximant) along with their positions on, the sensor characteristics so that the distance (i.e. the separation) between the approximant and the characteristic is rendered below a certain problem-specific tolerance. To achieve this goal, two alternative non-linear optimization problems are solved, whose essential difference is in the adopted quantitative measure of the separation between the transfer function and the approximant. In the first problem, which relates to absolutely integrable sensor characteristics (their energy is not necessarily finite, but they can be represented in terms of convergent Fourier series), the polylinearization is constructed by numerical minimization of the L^1--metric (a distance-based separation measure), concerning the number of polyline vertices and their locations. In the second problem which covers the quadratically integrable sensor characteristics (whose energy is finite, but they do not necessarily admit a representation in terms of convergent Fourier series), the polylinearization is constructed by minimizing numerically the L^2-metric (area-, or energy-based separation measure) for the same set of optimization variables –the locations, and the number of polyline vertices.

We investigate the shear quasinormal modes (QNMs) in the context of electron stars, focusing on the Lifshitz geometry and exploring the behavior in the small star limit. The study begins by analyzing the shear sector modes in the infrared (IR) Lifshitz limit, where the geometry approaches that of a pure Lifshitz space. We derive and solve the differential equations governing the shear modes, revealing the asymptotic behavior in terms of the Lifshitz exponent. In the special case of integer values of the exponent, we obtain analytic corrections to the solutions to extract hydrodynamical QNMs. We then explore the cases with a Lifshitz exponent equal to or greater than three, providing analytical corrections to the Lifshitz IR solutions. A special case with an exponent of two is also considered, leading to modified solutions. The study extends to finding hydrodynamical QNMs, emphasizing the role of the Lifshitz geometry. Further investigations involve the flux with real and complex frequencies, considering the off-shell Lagrangian and deriving conserved flux expressions. Our analysis focuses on the exterior of the star, modeled by the Reissner-Nordström-AdS geometry. The small star limit is examined, revealing interesting features in the behavior of fermionic excitations. In summary, we provide a detailed exploration of shear quasinormal modes in electron stars, shedding light on their behavior in Lifshitz geometry, small star limits, and the interplay between real and complex frequencies. Our findings contribute to our understanding of the dynamics of electron stars and their gravitational properties.

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

Abstract Background Context Pelvic ring fractures are becoming more common in the aging population and can prove to be fatal, having mortality rates between 10% and 16%. Stabilization of these fractures is challenging and often require immediate internal fixation. Therefore, it is necessary to have a biomechanical understanding of the different fixation techniques for pelvic ring fractures. Methods A previously validated three-dimensional finite element model of the lumbar spine, pelvis, and femur was used for this study. A unilateral pelvic ring fracture was simulated by resecting the left side of the sacrum and pelvis. Five different fixation techniques were used to stabilize the fracture. A compressive follower load and pure moment was applied to compare different biomechanical parameters including range of motion (contralateral sacroiliac joint, L1-S1 segment, L5-S1 segment), and stresses (L5-S1 nucleus stresses, instrument stresses) between different fixation techniques. Results Transiliac -trans sacral screw fixation at S1 and S2 showed the highest stabilization for horizontal and vertical displacement at the sacral fracture site and reduction of contralateral sacroiliac joint for bending and flexion range of motion by 165% and 121%, respectively. DTSF (Double transiliac rod and screw fixation) model showed highest stabilization in horizontal displacement at the pubic rami fracture site, while the L5_PF_W_CC (L5-Ilium posterior screw fixation with cross connectors) and L5_PF_WO_CC ( L5-Ilium posterior screw fixation without cross connectors) showed higher rod stresses, reduced L1-S1 (approximately 28%), and L5-S1 (approximately 90%) range of motion. Conclusions Longer sacral screw fixations were superior in stabilizing sacral and contralateral sacroiliac joint range of motion. Lumbopelvic fixations displayed a higher degree of stabilization in the horizontal displacement compared to vertical displacement of pubic rami fracture, while also indicating the highest rod stresses. When determining the surgical approach for pelvic ring fractures, patient-specific factors should be accounted for to weigh the advantages and disadvantages for each technique.

Intratumoral immune cytolytic activity (CYT), measured as the geometric mean of the expression of Granzyme-A (GZMA) and Perforin-1 (PRF1), has emerged as a critical factor in cancer immunotherapy, with significant implications for patient prognosis and treatment outcomes. Here, we review how different immune checkpoint pathways, the composition of Tumor Microenvironment (TME), antigen presentation and metabolic pathways, all regulate CYT. We also discuss the various methods through which CYT can be assessed. The detection and analysis of tumor-infiltrating lymphocytes using flow cytometry or immunohistochemistry provide important information on the immune cell populations within the TME. Gene expression profiling and spatial analysis techniques, such as multiplex immunofluorescence and imaging mass cytometry, allow researchers to study CYT in the context of the TME. We further report the significant clinical implications that CYT has, as its increased levels are associated with positive clinical outcomes and a favorable prognosis. Moreover, CYT can be used as a prognostic biomarker and aid in patient stratification. Measuring CYT through different methods offers promising paths for improving treatment responses. Overall, we highlight that the understanding and modulation of CYT levels is critical for improving cancer immunotherapy. Research into CYT and the factors that influence it has the potential to transform cancer treatment and improve patient outcomes.

In reservoir operations rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This paper proposes an approach to optimize the reservoir operation rule curves (RORC) using intelligent optimization techniques from the Firefly Algorithm (FA) and a unique combination method utilizing the Artificial Bees Colony and Cultural Algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORC for flood control. The proposed model was used to determine the Nam-Oon Reservoir (NOR) is optimal flood control RORC in Northeastern Thailand. The minimum frequency and a minimum average of ex-cess water were given as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORC involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data syn-thesized from 1000 events. The results demonstrate that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, situations of excess water were smaller than those using the current RORC. The results indicated that situations of excess water using the obtained RORC from the proposed model were smaller than those using the current RORC situations. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

In the era of digital information consumption, YouTube has emerged as a prominent platform for disseminating knowledge across various domains. One such area that has gathered significant attention is teeth whitening, with a plethora of videos offering insights into professional proce-dures, home remedies, and personal experiences. This study delves into the vast landscape of teeth whitening content on YouTube, meticulously categorizing videos into professional and amateur domains. The aim is to critically analyze the information presented, considering its accuracy, re-liability, and potential impact on public perceptions and oral health choices. A total of 100 videos in Greek (n1= 73) and in English (n2= 27) were studied. The analysis was based on views, likes, publication year, duration, presence of background music, and the presence of a description. The completion date of the search was October 7, 2023. Professional videos predominantly present whitening as a painless, conservative, and economical procedure, emphasizing safety measures. Bleaching mechanisms involving hydrogen peroxide and carbamide are explored, with various application techniques discussed, including whitening strips, splints, and in-office procedures. Contradictory findings on the efficacy of home versus in-office treatments are highlighted. In-ternal and external discoloration factors are delineated, with contraindications specified. Amateur videos, garnering greater public appeal, present personal experiences, product promotions, and homemade remedies, reflecting a concerning trend of misinformation. The analysis incorporates viewer engagement metrics, emphasizing the impact of non-professional content. As social media platforms play an increasingly influential role in shaping healthcare narratives, understanding the nuances of dental discourse on YouTube becomes crucial for both practitioners and the public. This exploration encompasses a spectrum of topics, ranging from the chemical intricacies of bleaching agents to the societal dynamics influencing viewer preferences. By unraveling the complexities of online dental information, this study seeks to contribute valuable insights to the evolving land-scape of oral health education in the digital age. Future research should adopt a multi-platform approach and explore the dynamic interaction between content creators and viewers for a more holistic comprehension of online dental information.

: Digital communication is becoming an integral component of contemporary society and the use of mobile devices is increasing faster than ever. The function of message communication is becoming more popular as for mobile phone users, a short messaging service (SMS) requires an extremely high level of security. Any SMS that enters a person's smartphone must be considered a private affair, with privacy and security safeguarded. This research study found a novel Deoxyribonucleic acid (DNA) Cryptography method that uses a dynamic DNA sequence table to increase security. Cryptography is a popular approach for ensuring the confidentiality of a message. This paper proposed an Efficient Cryptographic Scheme for SMS (ECSS) algorithm to protect texts received from an Android smartphone. ECSS-based encryption has been identified as a novel method of securing information in the form of DNA molecules that employ DNA strands to conceal the information. This Paper provides the DNA cryptography approach for encrypting and decrypting plain texts in this work. The primary goal of ECSS cryptography is to guarantee secrecy when people transfer data across a network. This paper examines DNA Crypto, the distinction between classical cryptography and DNA Data encryption. An Android-based app is created to perform the proposed technique and the results show that the algorithms produce a consistent encryption result in which the length of the message before encryption is always the same as the length of the message after decryption. The suggested security solution has the potential to greatly improve the security of mobile communication. This Paper also provides experimental results for our chat application performance, such as received text message correctness, cipher text, average encryption time and average decryption time. According to the findings/results of this study, the speed of average encryption and decryption of the description is roughly 0.011 and 0.037 sec and prevents plaintext and dictionary attacks.

Mobile Ad hoc Networks (MANETs) play an important role in the technological era by enabling wireless communication in dynamic and infrastructure-free contexts. Because of Manet’s unique qualities and decentralized nature, MANETs are subject to many attacks such as the Blackhole attack, grey hole attack, Byzantine attack, Wormhole attack, and Sybil attack. Sybil attacks pose a substantial risk to MANETs. A Sybil attack occurs when a malicious node impersonates many identities inside the network, resulting in the creation of multiple suspicious nodes that appear as independent entities. This study presents a thorough analysis of the literature on the detection of Sybil attacks on MANETs. To find gaps in the literature, current surveys are also looked at. Also taken into consideration are several contemporary systems built on distinct techniques. In terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end latency, some recent methods-based approaches are also evaluated severely. To address the stated state-of-the-art issues in Sybil attack detection, this paper suggests ML-based strategies as the most suitable answer. Machine learning has huge potential for effective wireless network management. According to the researcher, this is the first comprehensive assessment of ML-based techniques for Sybil attack detection in wireless networks. Finally, we considered the unsolved issues in Sybil assault detection in wireless networks.

This study explores the innovative use of CO2 laser technology in frenuloplasty, a significant shift from traditional methods like scalpel surgery or electrocautery, towards a minimally invasive approach. The research involved 15 patients aged 25 to 50, undergoing frenuloplasty with a CO2 laser system equipped with a 7-inch defocused handpiece, set at 20Hz and 0.3W. This method diverges from conventional laser techniques, focusing on controlled laser passes combined with manual traction to elongate the fibrous tissue of the frenulum. The results demonstrated that the CO2 laser technique allowed for a precise and progressive modification of the frenulum, significantly reducing the risks of hemorrhage and secondary in-tention fibrosis. The healing process was notably expedited, with patients reporting satisfactory outcomes within a two-week period. Statistically significant improvements were observed in pa-tient-reported outcomes, as evidenced by the increases in the Short Form Health Survey (SF-12) scores, with the mean Physical Component Summary (PCS) score rising from 32.5 to 47.5 and the mean Mental Component Summary (MCS) score from 39.3 to 52.3 (p < 0.001 for both). The study concludes that CO2 laser frenuloplasty is an effective and safe technique, offering sub-stantial benefits in terms of reduced healing time and enhanced patient satisfaction. The signifi-cant improvements in SF-12 scores underscore the positive impact on patient quality of life, ad-vocating for the broader application of this technique in clinical practice. Further research is warranted to explore its potential in a wider clinical context.

Pathological Brain Detection (PBD) is a crucial field aimed at identifying and diagnosing structural or functional abnormalities in the brain related to neurological, neurodegenerative, or psychiatric disorders. This detection process typically employs medical imaging technologies such as MRI, CT, or PET scans, along with neurological evaluations, blood tests, and other diagnostic tools. Early detection is essential for effective treatment, improving prognosis, and enhancing the quality of life for affected individuals. Each common computational method provides unique insights into brain pathologies. Despite significant advancements in technology and methodology, PBD faces challenges such as variability in brain anatomy, the complexity of disorders, data quality, standardization issues, interpretability of models, and ethical concerns. Addressing these challenges necessitates collaboration among researchers, clinicians, and policymakers to develop robust and ethical methods for improving the detection and diagnosis of brain disorders.

Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios

Fires are a significant threat to people, property, and environment. One of the requirements for the buildings during fires is ensuring the safe evacuation of persons. The evacuation assessment has usually been based on a comparison of the Required Safe Egress Time (RSET) and the Available Safe Egress Time (ASET). One of the important factors influencing the safe evacuation of persons is smoke, which is an important accompanying phenomenon in the burning of polymers. The smoke layer interface is one basic barrier for effective evacuation. The current calculation technique for assessing the smoke layer interface in the Czech Republic is unsatisfactory. For this reason, a calculation technique CSN has been derived, which has been compared with the selected simple calculation techniques, the zone fire model Consolidated Model of Fire and Smoke Transport (CFAST) and the field type model Fire Dynamics Simulator (FDS). The CSN technique was also compared with a real large-scale experiment. The data were also subjected to statistical evaluation using the root mean square error method (RMSE). The deviations between the NFPA, ISO and CSN calculation techniques, the CFAST and FDS fire models and the implemented experiment ranged from 0.05 to 2.41. The established deviations indicate that the calculation technique CSN will be practically usable.

The pharmaceutical industry is one of the largest pollutants and generator of toxic solvent resi-dues for the environment and human beings. In the last decade, there has been a huge evolution in the design and development of new environmentally friendly products and processes, in re-sponse to the global environmental concern and the growing demand for products with quality ingredients and manufacturing processes aimed at achieving sustainability. Consumers are looking for products with the benefits of natural products, with sustainable and environmentally friendly formulations with few synthetic molecules as possible. Thus, based on the origins of the pharmaceutical industry, it is important to return to Nature and enjoy the benefits that natural products bring to us. Deep eutectic solvents appear as an alternative to toxic solvents commonly used to extract bioactive compounds. These solvents are considered green solvents because they are made up of natural compounds such as sugars, amino acids, and carboxylic acids. In addition, they are simple to prepare, with an atomic economy of 100%, with attractive physicochemical properties. This paper is a comprehensive review in which is described the main properties, characteristics, and production methods of deep eutectic solvents as well as its application to extract from natural sources bioactive compounds with pharmaceutical interest.

Leaf area estimation is a critical component in the study of plant growth and productivity within agricultural systems. This research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species: S. quitoense, S. betaceum, P. peruviana, R. fruticosus, P. ligularis and P. edulis. Leveraging response variables such as species type, leaf length and width, the package employs advanced machine learning algorithms to estimate leaf area accurately. The primary focus of the study is to identify the most effective model for describing the relationship between leaf width, length, and area for each plant species. Currently, the LeafArea package utilizes four different machine learning algorithms, namely generalized linear model (GLM), generalized linear mixed model (GLMM), Random Forest and XGBoost. Among these, XGBoost stands out as a top-performing algorithm, exhibiting exceptional predictive accuracy. The evaluation metrics employed in the program provide valuable insights for researchers, aiding in informed decision-making. Specifically, XGBoost demonstrates significantly lower prediction errors and approaches a near-perfect R2 value, emphasizing its potential to enhance predictive accuracy. These results underscore the efficacy of machine learning techniques, as a compelling choice for researchers seeking precise and robust predictions in leaf area estimation. The LeafArea package thus represents a valuable tool for advancing our understanding of plant growth dynamics, resource allocation, and overall productivity within agricultural ecosystems.

Recently, nanotechnologies have become increasingly prominent in the field of bone tissue engineering (BTE), offering substantial potential to advance the field forward. These advancements manifest in two primary ways: the localized application of nanoengineered materials to enhance bone regeneration and their use as nano-vehicles for delivering bioactive compounds. Despite significant progress in the development of bone substitutes over the past few decades, it is worth noting that the quest to identify the optimal biomaterial for bone regeneration remains a subject of intense debate. Ever since its initial discovery, poly(lactic-co-glycolic acid) (PLGA) has found widespread use in BTE due to its favorable biocompatibility and customizable biodegradability. This review provides an overview of contemporary advancements in the development of bone regeneration materials using PLGA polymer. The review covers some of PLGA properties with a special focus on modifications of these properties towards bone regeneration. Furthermore, we delve into the techniques for synthesizing PLGA nanoparticles (NPs), the diverse forms in which these NPs can be fabricated, and the bioactive molecules that exhibit therapeutic potential for promoting bone regeneration. Additionally, we addressed some of the current concerns regarding the safety of PLGA NPs, and PLGA-based products available in the market. Finally, we briefly discussed some of the current challenges and proposed some strategies to functionally enhance the fabrication of PLGA NPs towards BTE. We envisage that the utilization of PLGA NP holds significant potential as a potent tool in advancing therapies for intractable bone diseases.

Globally, between 10% and 15% of couples experience infertility. Given the high demand for Assisted Reproductive Techniques (ART), there is growing interest in exploring modifiable factors influencing their outcomes. In this context, the objective of this review was to investigate the impact of adherence to the Mediterranean Diet (MD), recognized for its benefits in reproductive health, on ART outcomes. Methodology: A systematic review was conducted following the PRISMA methodology. International databases were consulted, selecting studies published between 2010 and 2023, in English and Spanish, involving patients aged 18-55, and examining intermediate and/or clinical outcomes of ART. Systematic reviews, meta-analyses, and animal studies were excluded. Results: Of the 9 included articles, 5 found statistically significant associations between adherence to the MD and positive outcomes in ART, such as a higher number and quality of embryos, clinical and biochemical pregnancy, and live births. However, other studies found no such associations. Conclusions: There is a potential association between adherence to the MD and ART outcomes. The evidence is very limited, necessitating further investigation.

This article examines the evolution of bariatric surgery, with a focus on emerging technologies such as robotics and laparoscopy. In the case of gastric bypass, no significant differences have emerged between the two techniques in terms of hospitalization duration, weight loss, weight regain, or 30-day mortality. Robotic surgery, while requiring more time in the operating room, has been associated with lower rates of bleeding, mortality, transfusions, and infections. In revisional bariatric surgery, the robotic approach has shown fewer complications, shorter hospital stays, and a reduced need for conversion to open surgery. In the case of sleeve gastrectomy, robotic procedures have required more time and longer post-operative stays but have recorded lower rates of transfusions and bleeding compared to laparoscopy. However, robotic surgeries have proven to be more costly and potentially more complex in terms of post-operative complications. The review has also addressed the topic of the single anastomosis duodeno-ileal switch (SADIS), finding comparable results between robotic and laparoscopic techniques, although robotic procedures have required more time in the operating room. Robotic technology has proven to be safe and effective, albeit with slightly longer operating times in some cases.

Nowadays, the combination of fingerprinting methodology with friendly environmental and economical analytical instrumentation are becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) to obtain the edible vegetable oils (sunflower and olive oils) fingerprints was used to evaluate the capability of such fingerprints, obtained quickly, reliable and without any sample treatment, to discriminate/classify the analysed samples. After data treatment, not only HCA and PCA as unsupervised pattern recognition techniques but also SVM, kNN and SIMCA as supervised pattern recognition techniques, showed that the main effect over the discrimination/classification was associated to those regions of RAMAN fingerprint related to the free fatty acids content, especially oleic and linoleic acid. These facts allowed the discrimination attending to the original raw material used in the oil's elaboration. In all the model established, reliable qualimetric parameters were obtained.

This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and gradient boosting techniques (XGBoost, LightGBM, and CatBoost). To mitigate the challenges posed by imbalanced datasets, we adopted different data sampling strategies, namely SMOTE, SMOTE combined with Tomek Links, and SMOTE combined with Edited Nearest Neighbors. Moreover, hyperparameter tuning was employed to enhance model performance. Our evaluation employed standard metrics such as Precision, Recall, F1-Score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). Regarding the F1-Score metric, CatBoost demonstrates superior performance compared to other machine learning models, achieving an outstanding 93% following the application of Optuna hyperparameter optimization. In the context of the ROC AUC metric, both XGBoost and CatBoost exhibit exceptional performance, recording remarkable scores of 91%. This achievement for XGBoost is attained after implementing a combination of SMOTE with Tomek Links, while CatBoost reaches this level of performance after the application of Optuna hyperparameter optimization.

Chatbots are increasingly used as tools for disseminating knowledge and information across various sectors of business, without the need for human intervention. With the continuous advancement of technology and the internet, the field of cybersecurity is also expanding, leading to the development of newer and more sophisticated systems to counter emerging threats. However, a fundamental question arises: how can one determine what to look for, where to acquire the necessary knowledge, and how to initiate this process? Recognizing the need for more effective tools in the market to educate individuals about online threats and self-protection, this paper aims to bridge the gap in the existing literature. The Artificial Intelligence-based Cybersecurity Chatbot (AICyberSec) has been developed to provide a versatile cybersecurity knowledge resource. This initiative is in response to the absence of a comprehensive solution in the market that effectively informs individuals about online threats and offers guidance on how to safeguard themselves. Furthermore, this chatbot serves as a valuable application for identifying potential hazards in cyberspace and can also function as an educational tool to facilitate the learning process.

In this paper, intrusion detection systems are thoroughly investigated utilizing the CSE-CIC-IDS-2018 dataset. The research is divided into three key phases: first, applying Data Cleaning, Exploratory Data Analysis, and Data Normalization techniques (min-max and z-score) for preparing data across distinct classifiers. Second, feature importance is reduced using a combination of Principal Component Analysis (PCA) and Random Forest (RF), with the goal of improving processing speed and decreasing model complexity. This stage comprises a comparison with the entire dataset. Finally, machine learning algorithms (XGBoost, CART, DT, KNN, MLP, RF, LR, and Bayes) are applied to specific features and preprocessing approaches. Surprisingly, the XGBoost, DT, and RF models outperform in both ROC values and CPU runtime. Following evaluation, which includes PCA and RF feature selection, an optimal set is produced.

ABSTRACT(1) Background:. Because of the numerous debates about the training on the use of force provided to police officers, this study intends to evaluate several parameters (Police Ergonomic Parameters, PEP) in connection with such training. (2) Research method: : A smartsuit equipped with a set of 19 wireless sensors including triaxial accelerometers, Inertial Measurement Units (IMU), gyroscopes and geomagnetic sensors has been used. Biomechanics of Body (BoB) software application allowed to evaluate the risks associated to the basic technique number 1 of the Operational Tactical Procedures (OTP) with regard to the established parameters (PEP). The OTP basic technique number 1 was performed by 4 novice police officers. (3) Results: According to the evaluation of a number of specific parameters such as Rapid Entire Body Assessment (REBA), asymmetry over the sagittal plane, compression force at the L5-pelvic junction, shear force at the L5-pelvic junction and total muscle power, it was observed that the measured values clearly varied depending on each individual's performance, although they remained consistent with the values established by Police Ergonomic Parameters, and were significantly different from the values measured when a police officer used a traditional physical intervention procedure, in which case, the values measured were indicative of higher risks. (4) Conclusions: In order to achieve lower and more uniform values in terms of risk, we believe that it is necessary to employ a greater number of hours to the training of police officers on the use of force and also the convenience of conducting further studies on a larger and more representative sample of officers.

Nowadays, semantic segmentation is increasingly used in exploration by underwater robots, for example in autonomous navigation, so that the robot can recognise the nature and elements of its environment during the mission and act according to this classification to avoid collisions. Other applications can be found in the search for archaeological artefacts, in the inspection of underwater structures or in species monitoring. Therefore, it is necessary to try to improve the performance in these tasks as much as possible. To this end, we compare some methods for improving image quality and for data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementations and the SUIM dataset with 8 common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The single result shows that there are 5 classes with an IoU value above 60% and only one class with an IoU value below 30%, which is a more reliable result and easier to use in real contexts.

This article presents the development of an innovative Maximum Power Point Tracking (MPPT) strategy, utilizing a Particle Swarm Optimization (PSO) algorithm to improve the effectiveness of PV systems and expedite convergence. The new MPPT method incorporated a unique Swarm Self-Reinforcement Mechanism (SSRM) within the PSO algorithm, targeting quick convergence and excellent tracking accuracy. This approach enables the PSO to eliminate the fitness function that has the lowest value and subsequently reinforce it in the next iteration, revolving around the global maximum power point (GMPP). By applying this novel PSO-based method, the MPPT performance of PV systems was significantly improved, facilitating the algorithm to proficiently navigate through the solution space and quickly locate the GMPP, even in rapidly changing environmental conditions. The outcomes derived from this novel approach were contrasted with other algorithmic optimization methods, validating its superior convergence speed and tracking accuracy. Different swarm sizes were examined using SSRM, and the optimal swarm size for the system employing MPPT was identified to achieve the lowest convergence time (CT). The results showcased the impressive performance capabilities of this novel strategy, resulting in a time con-traction of up to 28% compared to the conventional PSO technique, where the optimal swarm size was found to be 5. This achievement marks a significant milestone in the evolution of PSO-based MPPT techniques, and paves the way for future advancements in this exciting field.

Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important in targeting spinal column tumors while minimizing harm to adjacent critical structures. This study presents a novel approach utilizing a fusion of Cone Beam Computed Tomography with Magnetic Resonance Imaging to guide percutaneous thermal ablation for four patients with secondary spinal column tumors. The visual analog scale (VAS) evaluated procedure effectiveness during an 18-month follow-up. Percutaneous vertebroplasty was performed in two cases, and a thermostat was used during all procedures. Imaging was performed using the Stealth Station navigation system Spine 8 (SSS8) and a 1.5T MRI machine. The fusion of CBCT with MRI allowed precise tumor localization and guidance for thermal ablation. Initial results indicate successful tumor ablation and symptom reduction, emphasizing the potential of CBCT-MRI fusion in spinal column tumor management. This innovative approach is promising in optimizing therapy for secondary spinal column tumors. Further studies are necessary to validate its efficacy and applicability.

This study aimed to investigate the potential correlations and differences between patients with Acute Coronary Syndrome (ACS) and controls. Emphasis was placed on examining the relationship between arterial blood gas PH value and arterial blood gas lactate value as well as assessing the utility of the Peripheral Perfusion Index (PI) in ACS management. Materials and Methods: A prospective observational study was conducted between January 1, 2023, and July 1, 2023, involving 60 ACS patients and 30 control subjects in an emergency department. The diagnosis of ACS was confirmed by experienced emergency physicians, and various parameters, such as peripheral perfusion index, blood gas values, and cardiac biomarker values, were collected. The statistical analyses included Pearson’s chi-square test, Mann-Whitney U test, Student’s t-test, and others, using SPSS version 18.0. There were significant differences in the mean age, perfusion index, and lactate levels between the patient and control groups. A robust negative correlation was found between age and PI, and a moderate negative correlation was observed between pH and lactate levels (r = -0.301, p=0.004). The perfusion index in the patient group was significantly lower, indicating its potential diagnostic utility. The findings demonstrate the importance of arterial blood gas pH and lactate values in understanding ACS patient profiles, emphasizing the potential of PI as a noninvasive diagnostic method. The negative correlation between pH and lactate levels may offer valuable insights into risk stratification and treatment strategies for patients with ACS.

Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits, and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in Active Distribution Systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies, and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of a more active distribution networks and that the internet of energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS) which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing.

Introduction: The transcranial direct current stimulation (tDCS) is a promising technique for brain modulation after cerebrovascular accident (CVA). This treatment modality has been previously studied in the recovery of patients. The aim of this review is to analyze the evidence in the ap-plication of tDCS in the recovery of gait disturbance in stroke patients. Methods: This review was conducted according to the recommendations of the PRISMA statement. Three different elec-tronic databases were searched for relevant results: PubMed, Scopus, and Cochrane. We included reviews and meta-analyses that only considered randomized controlled trials (RCTs) that inves-tigated the effects of transcranial direct electrical stimulation, in combination or not with other physiotherapy treatment, on gait recovery. Results: Thirteen studies with a total of 195 RCTs were included. Data on population, outcome measures, protocols and outcomes were extracted. The Amstar-2 scale and the GRADE system of certainty of evidence were used. Only one study re-ceived high certainty of evidence, 5 received low certainty of evidence and 7 received critically low certainty of evidence. Conclusions: Although the tDCS produces positive changes in gait recovery in spatio-temporal parameters, mobility, endurance, strength and motor function, there is insuf-ficient evidence to recommend this treatment.

First described in 1760 by the anatomist Morgagni, CoA is a congenital heart defect characterized by narrowing the aorta, typically distal to the left subclavian artery. It accounts for approximately 5-8% of all congenital heart diseases, with an incidence estimated at 4 per 10,000 live births. In 1944, the Swedish surgeon Clarence Crafoord achieved the first successful surgical CoA repair by performing an aortic end-to-end anastomosis on two patients aged 12 and 27 years old. Presently, the most prevalent techniques for surgical repair, particularly in infants and neonates with isolated coarctation, involve resection with end-to-end anastomosis (EEA) and the modified Crafoord technique (extended resection with end-to-end anastomosis (EEEA)). Subclavian flap aortoplasty (SCAP) is an alternative surgical option for CoA repair in patients under two years of age. In cases where the stenosis extends beyond resection and end-to-end anastomosis feasibility, patch aortoplasty (PP) employing a prosthetic patch can augment the stenotic region, especially for older patients. Despite advances in pediatric cardiology and cardiac surgery, recoarctation remains a significant concern after surgical or interventional repair. This comprehensive review aims to provide a thorough analysis of coarctation management, covering the pioneering techniques introduced by Crafoord using head-to-tail anastomosis and now extending to the contemporary era marked by percutaneous interventions as well as the recoarctation rate associated with each type.

Glioblastoma (GBM) is a representative malignant brain tumor characterized by a dismal prognosis, with survival rates of less than 2 years and high recurrence rates. Despite surgical resection and several alternative treatments, GBM remains a refractory disease due to its aggressive invasiveness and resistance to anticancer therapy. In this report, we explore the role of fibronectin type III domain containing 3B (FNDC3B) and its potential as a prognostic and therapeutic biomarker in GBM. GBM exhibited a significantly higher cancer-to-normal ratio compared to other organs, and patients with high FNDC3B expression had a poor prognosis (p < 0.01). In vitro studies revealed that silencing FNDC3B significantly reduced the expression of Survivin, an apoptosis inhibitor, and also reduced cell migration, invasion, extracellular matrix adhesion ability, and stem cell properties in GBM cells. Furthermore, we identified that FNDC3B regulates PTEN/PI3K/Akt signaling in GBM cells using MetaCore integrated pathway bioinformatics analysis and a proteome profiler phospho-kinase array with sequential western blot analysis. Collectively, our findings suggest FNDC3B as a potential biomarker for predicting GBM patient survival and for the development of treatment strategies for GBM.

The paper investigates into the intricacies of semiconductor manufacturing, a highly complex process entailing a wide array of subprocesses and diverse equipment. Semiconductors are miniaturized integrated circuits comprising numerous components. The semiconductor manufacturing process begins with the thin disc-shaped silicon wafers. On each wafer, up to thousands of identical chips can be prepared depending upon the diameter of the wafer to build up the circuits layer by layer in a wafer fab. The size of the semiconductors requires a high number of units to be produced, thus necessitating a large amount of data to control for improving the semiconductor manufacturing process. Therefore, the collection and analysis of the equipment data, process data, and machine history data throughout the manufacturing process are required to diagnose faults, monitor the process, and manage the manufacturing process effectively. This research is focused on improving the semiconductor manufacturing process through a rigorous analysis of collected manufacturing process data, employing statistical process control (SPC), data mining techniques, and data-driven decision models. The project's primary objective is to increase the manufacturing process stability and productivity by utilizing the latest data-driven technologies in the scientific community. A structured review was undertaken, exploring contemporary data-driven methodologies in semiconductor manufacturing process improvement, specifically pertaining to process capability, product yield rate, and process stability. This review accentuates a comprehensive evaluation of data-driven methodologies applicable to conventional semiconductor manufacturing facilities, aiming to drive substantial process improvements. It features a detailed demonstration facilitating the selection of optimal semiconductor manufacturing processes to enhance overall operational performance. This study of process improvement in the semiconductor manufacturing steps through the application of data-driven methodologies will be effective in delivering advanced, real-time, and proactive control decisions throughout the manufacturing facilities. This endeavor is expected to promptly provide critical insights for enterprise manufacturing decision-makers to reduce manufacturing cycle time, improve the product yield rate, and increase the overall efficiency of the manufacturing process.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

This comprehensive analysis thoroughly examines the topic of adversarial attacks in artificial intelligence (AI), providing a detailed overview of the various methods used to compromise machine learning models. It explores different attack techniques, ranging from the simple Fast Gradient Sign Method (FGSM) to the intricate Carlini and Wagner Attack (C&W), emphasising the wide range of adversarial approaches and their intended goals. The discussion also distinguishes between targeted and non-targeted attacks, highlighting the adaptability and versatility of these malicious efforts. Additionally, the study delves into the realm of black-box attacks, revealing the capability of adversarial strategies to compromise models even with limited knowledge. Real-life examples illustrate the tangible consequences and potential dangers of adversarial attacks in various fields such as self-driving cars, multimedia, and voice assistants. These cases highlight the difficulties in ensuring the legitimacy and dependability of AI-powered technologies and programs. The article stresses the importance of ongoing research and innovation to address the growing difficulties posed by advanced methods like deepfakes and disguised voice commands in preserving the security of AI systems. This study provides valuable insights on how different adversarial strategies and defence mechanisms interact within AI. The results emphasise the urgent need for stronger and more secure AI models to combat the increasing number of adversarial threats in today's AI landscape. These findings can guide future research and innovations in developing more resilient AI technologies that can better withstand various adversarial vulnerabilities and challenges.

The need to find the global minimum in multi-dimensional functions is a critical problem in many fields of science and technology. Effectively solving this problem requires the creation of initial solution estimates, which are subsequently used by the optimization algorithm to search for the best solution in the solution space. In the context of this article, a novel approach to generating the initial solution distribution is presented which is applied to a genetic optimization algorithm. Using the k-means clustering algorithm, a distribution based on data similarity is created. This helps in generating initial estimates that may be more tailored to the problem. Additionally, the proposed method employs a rejection sampling algorithm to discard samples that do not yield better solution estimates in the optimization process. This allows the algorithm to focus on potentially optimal solutions, thus improving its performance. Finally, the article presents experimental results from the application of this approach to various optimization problems, providing the scientific community with a new method for addressing this significant problem.

Human prion diseases (HPDs) are fatal neurodegenerative disorders characterized by abnormal prion proteins (PrPSc). Numerous techniques have been devised to detect prion seeding activity, each with limitations. Here, we developed a technology for in vitro amplification of abnormal prion proteins (PrPSc), thereby enabling their sensitive detection. Using the real-time quaking-induced conversion (RT-QuIC) PrPSc amplification assay, we could detect ≥1 fg of PrPSc in diluted human prion disease brain homogenate. Although RT-QuIC assay is suitable for detecting prion seeding activity in a variety of specimens, its accuracy in whole blood, blood plasma, or blood-contaminated tissues is reduced. To circumvent this issue and move toward developing a blood test for prions, we introduced an immunoprecipitation step at the beginning of the RT-QuIC reaction. We refer to this enhanced method as “eQuIC”. eQuIC utilizes three types of human monoclonal antibodies, which significantly increased its sensitivity—making it approximately 1000 times more sensitive than the original RT-QuIC assay. Nevertheless, when we tested the blood (serum, plasma, and whole blood) of six patients with sporadic human prion disease using eQuIC, we were unable to detect prion activity. Therefore, we concluded that the detection of prion seeding activities in blood samples from patients with sporadic prion disease remains challenging; thus, alternative methods beyond RT-QuIC and eQuIC will be necessary for future research.

Diabetes mellitus is a chronic disease that affects millions of people worldwide. Article focuses on detecting the presence of diabetes in patients using microarray gene data obtained from the pancreas. Handle the high-dimensional nature of the data, four different dimensionality reduction techniques, namely Bessel Function, Discrete Cosine Transform (DCT), Least Square Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. After reduced the data, Meta-heuristic algorithms are applied like Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Ten classification techniques are using to classify the data in both the format like without feature selection method and with feature selection method. The classification techniques are Non-Linear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), Support Vector Machine (SVM) with linear kernel, Support Vector Machine (SVM) with polynomial kernel, and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. Results showed that the AAA with SVM(RBF) achieved an accuracy of 90% without feature selection. However, when feature selection was applied, with EHO of AAA with SVM(RBF) exhibited the highest accuracy of 95.714%, followed closely by with DOA of AAA with SVM (RBF) at 94.28%.

Anthracnose and root rot are major foliar and root diseases of strawberry, respectively that cause most yield and quality losses. Anthracnose is caused by multiple species belonging to Colletotrichum spp complex. This disease alone can causeup to 70% yield loss in North America. Colletotrichum spp. cause several disease symptoms on strawberries, including root, fruit, and crown rot, lesions on petioles and runners, and irregular black spots on the leaf. In many cases, a lower level of infection on foliage remains non-symptomatic (quiescent), posing a challenge to growers as these plants can be a significant source of inoculum for the fruiting field. Reliable detection methods for quiescent infection should play an important role in preventing infected plants' entry into the production system or guiding growers to take appropriate preventative measures to control the disease. The selection of highly effective fungicides and including them in a schedule to prevent the development of fungicide resistance in fungal populations will remain an area of continued research. Disease management methods should entail sensitive molecular testing of suspected fungal isolates for resistance, rotation of products, and monitoring of quiescent infections in transplant materials. Testing and the inclusion of non-chemical methods such as biologicals and biorational treatments especially for soilborne pathogens will also be necessary to make disease management economically feasible and sustainable.

The study aimed to manage and to analyse the results of the laboratory tests, routinally practice for hepatitis C diagnosis, using blood tests. Comparison of ELISA method results (Enzyme-Linked Immunosorbent Assay) and chemiluminescence methods results. Statistical analysis of this study results, was performed using the laboratory informatic system. The results of the study are substantial and intricate reffering to comparision between two methods concretlly ELISA method (Enzyme-Linked Immunosorbent Assay) and chemiluminescence methods and their results. For this purpose, the results of preliminary EСL screening method of patients at risk for HCV who took part in the study, are presented in specific tables. More than good to mention that the correlations of results were used in order to identify a possible relationships between indicators of ELISA method and ECL index. In addition, correlations antibodies detected in electrochemiluminescence, ECL and Enzyme-Linked Immunosorbent Assay ELISA are pointed out. EСL and ELISA method results, are relevant for screening and for diagnostic confirmation in HCV risk patients. Unfortunately in the present study, were impossible to conclude about false-negative results. Good to know our opinion that RT-PCR technique, it is considered proper for the diagnosis of HCV.

From December 2021 to May 2022, MeteoSwiss and Meteomatics conducted a proof of concept to demonstrate the capability of Meteodrones to provide data of sufficient quality and reliability on a routine operational basis. Over 6 months, Meteodrones MM-670 were operated automati-cally 8 times per night at Payerne, Switzerland. 864 meteorological profiles were measured and compared to co-located standard measurements including radiosoundings and remote-sensing instruments. To our knowledge, it is the first time that Meteodrone’s atmospheric profiles are evaluated in such an extensive campaign. The paper highlights two case studies that showcase the performance and challenges of measur-ing temperature, humidity, and wind with a Meteodrone. It also focuses on the overall quality of the drone measurements. Throughout the campaign, the availability of Meteodrone measure-ments was 75.7%, with 82.2% of the flights reaching the nominal altitude of 2000m above sea level. To assess the quality of the Meteodrone measurements, the radiosondes were used as a refer-ence, comparing them to the WMO (World Meteorological Organization) requirements [1]. The temperature measurements by the Meteodrone met the "breakthrough" target, while the humid-ity and wind profiles met the "threshold" target for high-resolution numerical weather predic-tion. The temperature measurement quality was comparable to that of a microwave radiometer, and the humidity quality was similar to that obtained from a Raman Lidar. However, the wind measurements by a Doppler Lidar were more accurate than the estimation provided by the Me-teodrone. During this campaign, the data quality could be improved and at the end of the campaign, Me-teodrone quality reached the “goal” target for temperature and the “breakthrough” target for humidity. This campaign marks a significant step towards the operational use of automatic drones for me-teorological applications.

A considerable number of infective endocarditis (IE) patients require cardiac surgery with an increased risk for postoperative sepsis. Intraoperative hemoadsorption may diminish the risk of postoperative hyperinflammation with potential economic implications for intensive care unit (ICU) occupation. The present study aimed to theoretically investigate the budget impact of a reduced length of ICU stay in IE patients treated with intraoperative hemoadsorption in the German healthcare system. Data on ICU occupation were extrapolated from a retrospective study on IE patients treated with hemoadsorption. An Excel-based budget impact model was developed to simulate the patient course over the ICU-stay. A base-case scenario without therapy reimbursement and a scenario with full therapy reimbursement were explored. The annual eligible German IE patient population was derived from official German Diagnostic Related Group (DRG) volume data. One-way deterministic sensitivity analysis and bootstrap analysis were performed to evaluate the uncertainty over the model results. Use of intraoperative hemoadsorption resulted in 2,298€ saved per patient in the base-case scenario without therapy reimbursement. Savings increased to 3,804€ per patient in case of full device-specific reimbursement. Deterministic and probabilistic sensitivity analyses confirmed the robustness of savings, with a probability of savings of 99% and 100% in the base-case and full reimbursement scenario, respectively. Intraoperative hemoadsorption in IE patients might have relevant economic benefits related to reduced ICU stay, resulting in improved resource use. Further evaluations in larger prospective cohorts are warranted.

This study focused on the optimization of the flexural strength of bio-composite samples of palm kernel, whelks, clams, periwinkles shells and bamboo fiber, reinforced with resin for engineering applications. The aim of the study was to: formulate different samples of bio-composite reinforce with resin for engineering applications and to evaluate the flexural strength, of the fabricated composite. The hand lay-up technique was used for the composites produced by incorporating different percentage compositions of the shells/fiber (10%, 15%, 20%, 25% and 30%) into varied proportions of epoxy resin and catalyst. The cured samples after 24hours were subjected to tensile, impact, flexural and water absorption test. The experiments were conducted using Taguchi optimization method L25 (5x5) with five design parameters and five level combinations in Minitab 18 statistical software. The results showed that the average values of flexural was 114.87MPa when compared to the unreinforced of 72.33MPa bio-composite. The study recommended that agricultural waste like palm kernel shells, whelk shells, clams, periwinkle shells and bamboo fiber, should be converted into important engineering applications.

The most frequent, noticeable, and frequent natural calamity in the karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram highway, particularly during the monsoon, causing a major loss of life and property. Therefore, it was necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper's major goal is to provide new integrative models for assessing landslide susceptibility in a prone area of north of Pakistan. To do this, the training of an artificial neural network (ANN) is supervised using metaheuristic and Bayesian techniques: particle swarm optimization algorithm (PSO), Genetic algorithm (GA), Bayesian optimization Gaussian process (BO_GP), and Bayesian optimization Gaussian process (BO_TPE). 304 previous landslides and the eight most prevalent conditioning elements combine to form a geographical database. The models are hyper-parameter optimized, and the best ones are employed to generate the susceptibility maps. The area under the receiving operating characteristic curve (AUROC) accuracy index found demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying artificial neural networks (ANNs) for landslide mapping, susceptibility analysis, and forecasting are studied in this research it’s observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE are relatively small, ranging from 0.3166% to 1.8399%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it's important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the KKH. The algorithms considered include Information Gain, Gain Ratio, OneR Classifier, Subset Evaluators, Principal Components, Relief Attribute Evaluator, Correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide susceptibility. By applying these feature selection algorithms, we aim to identify the most influential geospatial variables that contribute to landslide occurrences along the KKH. The algorithms encompass a diverse range of techniques, such as measuring entropy reduction, accounting for attribute bias, generating single rules, evaluating feature subsets, reducing dimensionality, and assessing correlation and information sharing. The findings of this study will provide valuable insights into the critical geospatial variables associated with landslide susceptibility along the KKH. These insights can aid in the development of effective landslide mitigation strategies, infrastructure planning, and targeted hazard management efforts. Additionally, the study contributes to the field of geospatial analysis by showcasing the applicability and effectiveness of various feature selection algorithms in the context of landslide susceptibility mapping.

In recent years, significant progress has been observed in cloud computing. As the number of companies using the cloud increases, so does the need to protect user data. Today, the major challenges facing cloud computing include the security, protection, and processing of data belonging to users. This study aims to investigate security issues and identify appropriate security techniques in the world of cloud computing. Also, the identification of security challenges that may arise in the field of cloud computing in the future has been evaluated. In this research, we used two different research methods: systematic literature review (SLR), and survey and interview with different security experts who work in the field of cloud computing. All the security challenges, most compromised attributes, and identified mitigation techniques from both SLR and survey were presented and discussed.

Objectives: To compare the volumes and shapes of the coagulation zone (CZ) of a multi-probe RFA system (three RFA electrodes) and a single-probe MWA system from the same vendor in an ex-vivo bovine liver model. Material &amp; Methods: A total of 48 CZs were obtained in bovine liver specimens with three different ablation system configurations (single-probe MWA vs. multi-probe RFA with 20 mm inter-probe distance [confluent CZ] vs. multi-probe RFA with 50 mm inter-probe distance [three individual CZ]) at 4-, 6-, 8-, and 10-min ablation time using a fixed ablation protocol. Ablation diameters were measured and ellipticity indices (EI) and volumes calculated. Calculations for all systems/configurations were compared. Results: Volumes and diameters increased with ablation time for all configurations. At 4- and 6-min ablation time volumes obtained with the RFA 50 mm setup, and at 8 and 10 min with the RFA 20 mm setup were the largest with 26.5 ± 4.1 mL, 38.1 ± 5.8 mL, 46.3 ± 4.9 mL, 48.4 ± 7.3 mL, respectively. The single-probe MWA could not reach the volumes of the RFA setups for any of the ablation times evaluated. EI were very similar and almost round for RFA 20 mm and single-probe MWA and differed significantly to the more ovoid ones for the RFA 50 mm configuration. Conclusion: The multi-probe RFA system employing three electrodes achieved significantly larger ablation volumes in both configurations (confluent CZ and three individual CZ) per time as compared to a single-probe MWA system in this ex-vivo bovine liver model.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. So, this paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Non-Linear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) Methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

With the rapid development of urbanization, the chemical evolution of groundwater is significantly affected by human activities. However, the driving mechanisms of groundwater chemical evolution at different stages of urbanization remain unclear, severely impacting the implementation of groundwater protection. This study investigated the driving mechanisms of groundwater chemical evolution based on the long-term series (from 1985 to 2015) of hydrochemical data from 19 groundwater monitoring sites in rapidly urbanizing areas (Shijiazhuang, Hebei Province, China). The results show that the concentrations of various chemical components in groundwater gradually increase with the acceleration of urbanization process, especially NO3-, which has increased from 13.7 mg/L in the primary stage of urbanization (PSU) to 65.1mg/L in the advanced stage of urbanization (ASU), exceeding the WHO drinking water standard (50mg/L), indicating that the groundwater chemistry has been significantly affected by human activities. It is interesting to find that the main hydrochemical types have changed from HCO3•SO4-Ca•Mg type water in the PSU to SO4•HCO3-Ca•Mg type water in the ASU. It is worth noting that there were obvious differences in driving factors of groundwater chemical evolution at different urbanization stages. In the PSU, the driving factors of groundwater chemical evolution were carbonate and rock salt dissolution, cation exchange, and industrial activities. However, in the intermediate stage and advanced stage, the driving factors of groundwater chemical evolution were changed to carbonate and gypsum dissolution, groundwater over-exploitation, agricultural fertilization, and domestic sewage. Based on the above conclusions, it is suggested that future groundwater management should control the amount of agricultural fertilizers, apply scientific fertilization, and prohibit the discharge of various types of non-compliant sewage, while strengthening the supervision of groundwater extraction to reduce the impact of urbanization development on the groundwater chemical evolution process.

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

According to the wide variety of internet of things (IoT) devices within smart environments, many challenges face conventional digital forensic investigation (DFI) in smart environments. Challenges in this environment include heterogeneity, distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time. Furthermore, it significantly slows down or even incapacitates the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine learning (ML) techniques might be a viable option in certain situations, this paper presents the integration of ML into DF. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process.

Software governance is a management structure that guides projects in terms of their accountability and responsibility. Prime motivation of this approach is to improve the skillset of the team members through software governance policies and increase the overall success rate of the software projects. The scope of skill development is across the pillars of governance, such as structure, people, and information. Primary focus of this paper is on the skillset development of the project team members through educational policies in software governance. As part of the governance process, educational policies are defined for the skillset development of project team members. The JIRA dataset was used to determine the skillset development of the team members. Machine learning techniques, such as J48, Random Forest, Decision Table, Logistics, and Naïve Bayes, were used in the JIRA dataset. These machine learning techniques were processed using WEKA open-source software. Based on these results, it was concluded that the J48 algorithm can be applied to multiple projects/programs to monitor and track the skill development process. Machine learning model such as J48 is required to use this model at an organizational level. The skillset development of project team members should be aligned with IT governance and educational policies. Overall upskilling and reskilling strategies are provided to demonstrate the impact of skillset development through software governance.

One of the most popular techniques for determining electrical properties of material is the free-space measurement technique. With the potential for new applications requiring efficient, accurate and broadband material measurement systems using higher millimeter-wave and terahertz frequencies, the free-space measurement method has received renewed interest. This is mainly due to its simplicity, favorable properties as a non-destructive method, and it allows transmission and reflection measurements without any physical contact with the sample. This paper reviews and discusses state- of-the-art free space material measurement systems, starting with the different measurement techniques available for material characterization, their important concepts, post-processing in determining their properties, and progress towards expanding their use at higher frequencies. Also, this paper reviews dielectric material characterization using the free space method focusing on their application extension towards the higher frequencies. Besides, the algorithms for conversion methods and usage of lens in free space material measurement system was discussed in this paper. Finally, a future perspective on the outlook of this free space method is presented prior to the conclusion.

This review article is aimed at exploring the possibility of developing an integrated approach in the management of the different needs of endometrial cancer (EC) patients seeking to become pregnant. Life preservation of the woman, health preservation of the baby, a precocious and – as much as possible – minimally invasive characterization of the health and fertility parameters of the patient, together with the concerns regarding the obstetric, neonatal, and adult health risks of the children conceived via assisted reproduction techniques (ART) are all essential aspects of the problem to be taken into consideration, yet the possibility to harmonize such needs through a concerted integrated approach is still highly challenging. This article aims to illustrate the main features of EC and how it affects the normal physiology of pre-menopausal women. Then, we focus on the prospect for a miR-based, molecular evaluation of patient health status, including both EC early diagnosis and staging and, similarly, the receptivity of the woman, discussing the possible evaluation of both aspects using a single specific panel of circulating miR in the patient, thus allowing a relatively fast, non-invasive way to perform such testing with a significantly reduced margin of error. Finally, the ethical and legal/regulatory complexities arising from such innovative techniques require not only a risk-benefit analysis; respect for patient autonomy and equitable health care access allocation are fundamental issues as well.

In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website's visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website's source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website's performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors.

The objective of this research is to mitigate vibrations in induction motors. To achieve this goal, a discontinuous pulse width modulation (PWM) control strategy based on carrier wave modulation is proposed for multilevel inverters. This study provides justification for the reduction of machine vibrations compared to existing control techniques documented in the technical literature. Additionally, the proposed technique offers the advantage of attenuating the Total Harmonic Distortion of the multilevel inverter's output voltage while simultaneously achieving a higher RMS value for the same DC level. By modifying a parameter of the carrier wave, the control strategy allows for variations in the electrical spectrum while avoiding natural mechanical resonance frequencies, thereby reducing motor vibrations. Laboratory results demonstrating the application of different modulation strategies in a multilevel inverter for an induction motor and a comparison with the presented strategy are provided.

As indicated by standards of modern dentistry, the essence of endodontic treatment is chemo-mechanical disinfection of root canal system. The infected tooth should be eradicated of microorganisms and sealed tight with biocompatible materials. Vital element of this process is irrigation of canals with designated solutions as well as their activation. This review article summarizes various techniques of activation of irrigants for endodontic treatment and compares them. Results were gathered using PubMed and Google Scholar databases by searching keywords: irrigation, activation, sodium hypochlorite, irrigation protocols.

significant security threats to Ransomware attacks provide serious security hazards to personal and corporate data and information. The owners of computer-based resources suffer serious verification and privacy violations, monetary losses, and reputational damage due to a successful ransomware assault. As a result, it is reported critically, accurately, and swiftly identifying ransomware. Numerous methods have been proposed for ransomware, each with pros and cons. The main objective of this study is to discuss current trends and potential future debates on automated ransomware detection. The document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides a comprehensive study of existing methods for identifying, avoiding, minimizing, and recovering from ransomware. An analysis of studies between 2017 and 2022 is another advantage of the study. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection. It also highlights advancements in methods for combating ransomware attacks. In conclusion, this study highlights unanswered concerns and potential research challenges in ransomware detection.

With the spread of compulsory education emerged school management problems continued, and the quality of school management in compulsory education has attracted a great deal of attention in China. However, the application of information technology in the field is not yet detailed and wide, resulting in problems of heavy workload and high difficulty in the whole evaluation process. Accordingly, we use big data technologies such as Apache Spark, Apache Hive, and SPSS to carry out data cleaning, correlation analysis, dynamic factor analysis, principal component analysis, and visual display on 1760 sample data from 40 primary and secondary schools in Q Province in China, and constructs a model school management of quality evaluation in the compulsory education stage, which reduces the 22 management tasks required for previous evaluation to 5, greatly reducing the workload and difficulty of evaluation. It has improved the efficiency and accuracy of evaluation, and further promoted the simultaneous development of education of five domains and education equity in the compulsory education stage.

In order to remain competitive in the highly competitive construction industry, it is crucial for construction organizations to continually improve their overall performance, as project performance is a key determinant of their relevance. Despite the industry's competitiveness and risk, it has been reported that the construction industry has the lowest return margins compared to other industries (Jackson, 1999), suggesting that the project performance of construction organizations in the industry has not been up to par. This issue can be attributed to several factors, with motivation being the primary factor. Unfortunately, motivation is often disregarded in the construction industry due to the short duration of many construction projects and the high rate of employee turnover. The impact of employee dissatisfaction and related work attitudinal problems on performance in the construction industry is a matter of concern, particularly in developing countries where working conditions may be unappealing. The severity of this issue cannot be overstated.The objective of this research is to evaluate the effectiveness of motivational techniques in enhancing project performance in Nigeria. The study employed a survey research design and targeted the management staff of construction firms in Lagos State, Nigeria. The questionnaire was used to collect data, and the respondents were purposively selected from the population. Data analysis was conducted using percentage, mean ranking, and chi-square. The findings reveal that the opportunity to learn new skills, with a mean score of 0.85, and wage increment, with a mean score of 0.82, were the most widely adopted motivational techniques for management staff and skilled labor, respectively, in construction firms. Additionally, the study found that most of the motivational schemes tested did not produce statistically significant results, indicating low adoption and limited effectiveness in enhancing project performance. As a result, it is suggested that construction companies in Nigeria should implement and enforce financial and non-financial incentives to enhance the performance of their employees and consequently, improve project performance.

The awareness of the existence in plants of bioactive compounds namely phytochemicals (PHYs), having health properties is progressively expanding. Therefore, their massive introduction in the normal diet, in food supplements, and their use as natural therapeutics to treat several diseases are increasingly emphasized by several sectors. Particularly, most PHYs possessing antifungal, antiviral, anti-inflammatory, antibacterial, antiulcer, anti-cholesterol, hypoglycemic, immunomodulatory, and antioxidant properties have been isolated from plants. Additionally, their secondary modification with new functionalities, to further improve their intrinsic beneficial effects, is extensively investigated. Unfortunately, although the idea of exploiting PHYs as therapeutics is amazing, its realization is far from simple, and the possibility of exploiting them as effective orally administrable drugs is almost utopic. Most PHYs are insoluble in water and, when introduced orally, they scarcely reach the site of action in therapeutic concentrations. Degradation by enzymatic and microbial digestion, occurring in the mouth, stomach, and intestine, as well as fast metabolism and rapid excretion via the kidney, biliary, or lung, strongly limit their in vivo activity. To overcome these drawbacks, several nanotechnological approaches have been used and many PHYs-loaded delivery systems with dimensions of nanometers have been developed. This paper, also by reporting various recent case studies, reviews the foremost nano-suspension and nano-emulsion-based techniques developed for formulating the most relevant PHYs in more bioavailable nanoparticles (NPs), suitable or promising for clinical application. Also, the acute and chronic toxic effects due to the exposure to NPs reported so far, the possible nanotoxicity which could derive by their massive employment, as well as the ongoing actions to improve the knowledge in the field were discussed. The state of the art concerning the actual clinical application of both PHYs and the nanotechnologically engineered PHYs was also reviewed.

Despite the fact that strong routine separation methodologies can give reliable specificity and validity at usual working pharmaceuticals concentrations, they may fail at very low concentration levels. This poses considerable challenges for researchers inves-tigating product purity and therapeutic drug monitoring. Sensitivity enhancement pro-cedures are thus required to maximize the performance of separation techniques. Large volume injection, solid phase extraction/solid phase enrichment (SPE/SPEn), pre-, post-, and in-column derivatization, as well as the use of sensitive detection devices are the simplest strategies for improving sensitivity of the separation-based analytical techniques. Large volume injection of samples with online SPE/SPEn coupled with separation techniques increased sensitivity and improved detection as well as quantification limits without affecting peak shape and system performance. Although the primary purpose of derivatization is to improve sensitivity and selectivity, greener derivatization is growing in popularity and should be considered in analytical chemistry. In general, two strategies are essential for accomplishing greener derivatization goals. The first is the search for and use of ecologically acceptable derivatizing reagents, solvents, and reaction conditions. The second is miniaturization and automation of analytical methods. This review discusses significant advances in separation-based analytical techniques, specifically enrichment approaches and detector signal improvement for pharmaceutical quantification in various matrices at very low concentration levels. As a result of improved analytical systems setup in drug assays, the possibility of high-throughput analyses was also highlighted.

In the current socio-economic context, pension systems have become a crucial topic in the agenda of governments and international bodies. In this paper, an empirical study regarding the economic and social sustainability of pension systems in Central and Eastern European Countries was performed. The goal of the research is to establish the sustainability of pension systems with econometric modeling techniques on three dimensions as the research hypotheses are revealed: first, by strengthening the financial soundness of pension systems; second, consolidating the economic environment of pension systems; third, increasing the degree of social security systems with a direct effect of reducing poverty. The results led to relevant insights emphasizing the need for more inclusive economic, financial, and social reforms. From a policy perspective, these findings could be a starting point for the enhancement of the financial adequacy and economic and social sustainability of pension systems in Central and Eastern European Countries. The econometric techniques in this study highlight those modern pensions systems that need to be addressed in a more complex and empirical context that must encompass the main technical aspects. The conclusions suggest that in the future it needs judicial, economic, and social adjustments for the enhancement of the financial soundness of the pension systems.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

(1) Background: the use of force by Public and Private Security Forces is currently an issue of great relevance because of the potential injuries that any excessive use of force by either the active or passive subjects or a deficit in the real mastery of the appropriate Physical Intervention Techniques (PIT) may cause. For this reason, certain traditionally used Physical Intervention Techniques have been questioned by scientific research studies and punished by justice. On the other hand, certain media have dealt with this matter in a biased and unfair manner by broadcasting videos where the use of force by police officer is displayed out of context. As a consequence, this problem has been brought under the spotlight, causing general uneasiness of the communities and rapidly spreading over social networks while favoring all sorts of parallel judgments. (2) Research method: A suit equipped with 19 Inertial Measurement Units (IMU) and a Biomechanics of Bodies software application for the Marras’ analysis of the data collected on trajectory, trunk twisting velocity, its sagittal angle, load, nature and severity of the injuries associated to the different intervention techniques that have been examined. (3) Results: according to the data registered, the implementation of Operational Tactical Procedures (OTP) reduces the probability of injuries and leads to a more satisfactory outcome. (4) Conclusions: the implementation of Operational Tactical Procedures, to-gether with the awareness on the risks associated to the excessive use of force by Public and Private Security Forces and Bodies, could reduce the risk of injuries suffered by both officers and citizens.

In the present study, a numerical model was developed to predict the flow pattern inside a broiler building. The model intends to predict the velocities fields inside the domain and ammonia (NH3) emitted or released by litter from poultry housing. The numerical model developed in Computational Fluid Dynamics (CFD) commercial code, intends to represent a commercial broiler building, and intends to simulate the 3D and heat transfer, in steady state flow. The evaporative cooling pads were also included in the model. The validation of the model was based in experimental measurements obtained in previous studies. The simulations were focused on Summer, Winter and also Mid-Season situation. The numerical results of NH3 concentration were compared with the experimental measurements, and a quite good agreement was verified. The numerical results allowed the characterization of: the inside flow pattern developed for the summer and winter situation; the inside NH3 distribution, and the velocity field distribution inside the broiler building. It was found that NH3 concentration increases along the tunnel, especially in low flow rate imposed from the exhaust fan. Also, it was verified that the low velocities inside domain are no sufficient to remove the gaseous pollutants.

In this paper, a systematic literature review is presented, through a survey in the main digital databases, about modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of the Artificial Intelligent (AI) in STLF. With these specific objectives, it was developed a conceptual framework on the subject, a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed to the scientific production of AI and STLF. The review of research articles over a 10-year period, which takes place between 2012-2022, uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in 4 (four) databases: Web of Science, IEEE Xplore, Scopus and Science Direct. The research was organized around 3 (three) central themes, which were defined through keywords: STLF, Electricity, Residential and their corresponding synonyms. 334 research articles were analyzed and identified the year of publication, journal, author, geography by continent and country and the area of application. Of the 334 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 37 (thirty-seven) documents are in English (25 are journal articles and 12 are conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance is measured with different metrics and, therefore, cannot be compared directly. Hence, it should be desirable to have a unified data set together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.

Near-neutral pH and low redox potentials (EH) are considered favorable conditions for radionuclides immobilization in deep repository systems in clay geological formations. Cigeo is a future Industrial Centre for Geological Disposal for Nuclear Waste (NW) for high-level and intermediate level long-lived radioactive waste, to be built in France, at 500 m depth within the Callovo-Oxfordian clay formation. Deep knowledge of the mechanisms and kinetics of corrosion occurring at the external surface of tubings made of API 5L X65 carbon steel (CS-X65) in contact with COx pore water, is essential for a reversible NW management of the Cigeo site. By using robust handmade electrodes and electrochemical and gravimetric techniques, this work aimed to determine the corrosion phenomenology and kinetics of CS-X65 in contact with COx pore water flowing continuously through a Multi-Parameter Probe (MPP) device, placed at 500 m depth, for more than 180 days. It also aimed at demonstrating the reliability and the robustness of the measurement methodology for the corrosion evaluation of the metallic liner of the future nuclear waste disposal site. Two iron oxidants were encountered, oxygen, who was depleted, and proton, who was accompanied by hydrogen sulfide. Corrosion mechanisms and kinetics were well established for two CS-X65, electrochemically perturbed and not. The corrosion thickness loss rates stand between 0.016 and 0.032 mm/y.

Jaundice is a common condition for newborns, and its complications can be severe and cause permanent damage to the patient’s brain if no action is taken at its early stages. Current methods for jaundice detection are invasive, which include collecting blood samples from the patient, which can be painful and stressful and may cause some complications. Alternatively, a non-invasive approach can be used to diagnose jaundice through image-processing and artificial intelligence (AI) techniques, requiring a database of infant images to achieve a high-accuracy diagnosis. This data article provides a collection of newborn images, called NJN, with various birthweight and skin tones, with ages ranging from 2 to 8 days, and an excel sheet file in CSV format for the values of RGB and YCrCb channels and the status for each raw which is freely accessible at (https://sites.google.com/view/neonataljaundice). It also provides Python code for data testing using different AI techniques. Thus, this article offers a unique resource for all AI researchers to train their AI system and develop algorithms to help neonatal intensive care unit (NICU) healthcare specialists monitor neonates and provide fast, real-time, non-invasive, and accurate jaundice diagnosis.

Numerous researchers and institutions have been developing in ovo sexing technologies to improve animal welfare by identifying male embryos in an early embryonic stage and disposing of them before pain perception. This review gives a complete overview of the technological approaches reported in papers and patents by performing a thorough search using Web of Science and Patstat databases for papers and patents, respectively. Based on a total of 45 papers and 100 patent families up until 2021 reported worldwide, 11 technology categories were defined: six non-optical and five optical techniques. Every category was described for its characteristics while assessing its potential for application. Next, the dynamics of the publications of in ovo sexing techniques in both paper and patent fields were described through growth curves, and the interest or actual status was visualized using the number of paper citations and the actual legal status of the patents. When comparing the reported technologies in papers to those in patents, scientific gaps were observed, as some of the patented technologies were not reported in the scientific literature, e.g., ion mobility and mass spectrometry approaches. Generally, more diverse approaches in all categories were found in patents, although they do require more scientific evidence through papers or industrial adoption to prove their robustness. Moreover, although there is a recent trend for non-invasive techniques, invasive methods like analyzing DNA through PCR or hormones through immunosensing are still being reported (and might continue to be) in papers and patents. It was also observed that none of the technologies complies with all the industry requirements. Thus, more research and harmony between consumers, industry, and governments is necessary.

Visualization techniques are a powerful communication tool between construction stakeholders. The advancement of technology from 2D drawings to 3D models, Virtual Reality (VR), and Augmented Reality (AR) has emphasized the crucial role of efficient communication of ideas, designs, and concepts in preventing project delays and other issues in the Architecture, Engineering, and Construction (AEC) industry. Contemporary innovations such as web-based virtual reality cloud platforms that facilitate collaborative AEC projects and Head-mounted Displays that enable the viewing of VR environments are some of the latest technological developments that are emerging in the construction industry. These technological advances have the potential to improve communication, increase project efficiency, and enhance the quality of construction work. The purpose of this research is to identify existing trends and potential future directions by appropriating the state of the art of the intellectual growth of Virtual reality and Augmented Reality. The study used scientometric analysis in reviewing existing publications to scientifically map the evolution of VR and AR in the AEC sector. The study observed an emerging but little amount of publications on VR and AR in construction. While Africa contributes little significance, advancement in VR and AR research is led by United States and America. The study expresses concern over the dearth of research and the low amount of collaborations amongst institutions in diverse countries. The study concludes that VR and AR research in the AEC sector must be afforded the needed focus and attention. In recent times, the global Coronavirus pandemic has shown that technology such as VR and AR is inevitable in the success of a project.

Fruits are commonly used, fresh or processed, to prepare different industrial products with superior nutritional and health-promoting properties. Currently, the demand for processed fruit products has motivated the rapid growth of the fruit processing industries, persuading them to produce an enormous amount of by-products with less utilization. Furthermore, people's shifting dietary habits and lack of awareness of nutritional properties result in an avoidable load of fruit by-products. The knowledge of the value of by-products urges exploration with proper documentation, emphasizing the health benefits of some such products. Hence, this review is prepared by carefully analyzing the recent literature on industrial applications of fruit by-products and their nutritional and health-promoting properties. The use of fruit by-products in food industries for various purposes has been reported in the past and has been reviewed and described here. Fruit by-products are a good source of nutrients and bioactive components, including polyphenols, dietary fibers, and vitamins, implying that they could have an important role for novel, value-added functional food properties. Furthermore, fruit by-products are used as the substrate for the production of organic acids, essential oils, enzymes, fuel, biodegradable packaging materials, and preservatives.

This paper presents a comprehensive review that highlights the characteristics of non-isolated step-up converters based on high boost voltage lifting techniques. The paper categorises the high boost techniques: multistage/multilevel, switched capacitor, voltage multiplier, voltage lift, switched inductor, and magnetic coupling. The paper also discusses in detail the advantages and disadvantages for each category such as cost, complexity, power density, reliability and efficiency. The number of passive and active components, voltage gain, voltage stress, switching frequency, efficiency and power rating are also compared. Although the paper considers coupling inductors in the context of the non-isolated converter, the focus of the entire article is on the non-isolated high voltage step-up techniques. The key contribution in this paper is the review of high boosting techniques rather than the DC /DC converters. This allows divergence of new ideas and new power converters that will help provide highly efficient and flexible power converters for several applications where the sending end voltage is very low as photovoltaic systems. In addition, many applications and control techniques of DC/DC converters are summarised in this paper.

Background: Strongyloidiasis, a neglected disease caused by intestinal nematodes of the genus Strongyloides, is endemic to tropical and subtropical areas such as Vietnam. The morphological diagnosis of larvae by Hara Mori culture technique and microscopicare are considered the standard diagnostic procedures in the endemic areas of Strongyloides spp. However, they could only identify the genus, not the species of Strongyloides. DNA-molecular techniques which are highly sensitive and more cost-effective have been increasingly utilized in detection of Strongyloides species. This study aims to determine prevalence and the species of Strongyloides among resident population in Duc Hoa district, Long An province, Southern Vietnam. Methods: A cross-sectional study was conducted using 1,190 stool samples collected in Duc Hoa district, Long An province, Vietnam, from July, 2017 to November 2018. The stool specimens were transported to the Laboratory of Medical Parasitology, Pham Ngoc Thach University of Medicine within two hours of collection at an appropriate temperature of 25 oC. All samples were stored at 2 - 8°C and processed within 48 hours for microscopic examination. Molecular detection was carried out at Laboratory of Molecular Biology, Pham Ngoc Thach University of Medicine, Hochiminh city, VietNam. Results: Of the 1,190 samples tested, Strongyloides spp. larvae were detected in 79 specimens (6.6%) by two classical parasitological methods, namely direct microscopy and the modified Harada-Mori filter paper culture. DNA was extracted from 70 of the 79 samples of Strongyloides spp. larvae, which was subsequently characterized by real-time PCR amplification of the 18S and 28S regions of the rDNA gene. The results showed that 97.1% of the DNA samples were S. stercoralis, 2.9% were co-infections with S. ratti and S. stercoralis, and 2.9% belonged to S. ratti. For all 14 isolates, nucleotide sequencing was compared with other human pathogenic species of Strongyloides whose sequences are available in GenBank. The identity of 12/14 sequences were confirmed as S. stercoralis with a high level of similarity (91.3% - 100%) and over 98% for S. ratti. Between the two co-infection samples, the higher similarity belonged to S. stercoralis. Conclusion: A molecular amplification of small subunit ribosome RNA followed by sequence analysis has been proved to be a suitable method for discrimination of Strongyloides spp. retrieved from stool samples.

This research work focuses on the potential application of an organic compound, santalol obtained from santalum album in the inhibition of the enzyme tyrosinase which is actively involved in the biosynthesis of the melanin pigment. Over-production of melanin causes undesirable pigmentation in humans as well as other organisms that significantly downgrade their aesthetic value. The study is designed to explain the purification of tyrosinase from the mushroom Agaricus bisporus, followed by activity assay and enzyme kinetic to give insight into the santalol modulated tyrosinase inhibition in a dose dependent manner. The multi-spectroscopic techniques like UV-vis, fluorescence, and isothermal calorimetry are employed to deduce the efficiency of santalol as potential candidate against the tyrosinase enzyme activity. Experimental results are further verified by molecular docking. Santalol derived from the essential oils of santalum album, is widely used as remedy for skin disorders and potion for fair complexion since ancient times. Based on enzyme kinetics and biophysical characterization, this is the first scientific evidence where santalol inhibits tyrosinase, which may be employed in agriculture, food, and cosmetic industries by prevent excess melanin formation or browning.

Plant secondary metabolites are organic compounds (alkaloids, flavonoids, phenolics, tannins, anthraquinones, saponins, steroids, lignins, terpenes) produced by plant for self-defense during growth and development in order to protect them from harmful agents. As population is increasing and quality of life is reducing couple with outbreak of some diseases that are posing threat on the economy, a lot of medicinal plants have attracted interest of researchers because of the usefulness of these metabolites in pharmaceuticals, food and cosmetic industries. In this review, the pharmacological activities of some medicinal plants that are rich in secondary metabolites were studied, means of identifying and quantifying using spectrophotometry and chromatography techniques were also discussed. However, this will assist to reduce the uses and dependent on the synthetic drug and the onset of the age related diseases.

Power quality problem is a hot area in power systems and drives that has drawn the attention of many researchers. This is because of the threat it poses on the health of electrical systems and the cost incurred on utility bills. The problem arises as a result of the need for reactive power to be supplied alongside active power in power transmission and distribution. Unfortunately, reactive power is consumed by almost all components of the system, leading to a shortage. To solve this problem, there is a need to compensate for the lost reactive power. Several researchers have proposed various approaches to mitigate this problem such as thyristor switched capacitor bank, series compensator, series reactor, series var compensator (SVC), Static Synchronous Compensator (STATCOM), synchronous condenser and others. In order to further draw attention in this area, this work investigates a number of research papers on power compensation techniques and draw up challenges from each work that can be considered for further investigation. To this end, each research paper reviewed is evaluated in terms of problem solved, technique used, and results obtained. It therefore offers ample opportunities for researchers to explore in improving power quality and power system safety.

Neck circumference (NC), neck circumference to height ratio (NHtR) and neck circumference to weight ratio (NWtR) appear to be good candidates for the non-invasive management of non-alcoholic fatty liver disease (NAFLD). This study aimed to evaluate the ability of routine variables to assess and manage NAFLD in participants with obesity and NAFLD included in a 2-year nutritional intervention program. Anthropometric measurements, biochemical variables and imaging techniques were performed at different study time-points (baseline, 6, 12 and 24 months). The nutritional intervention significantly improved all anthropometric measurements as well as the glucose profile and the hepatic enzymes. NC and neck ratios combined with ALT levels and HOMA-IR showed good prediction ability for the hepatic fat content and hepatic steatosis at all the study time-points in a ROC analysis. The prediction ability of the combination panels improved when the weight loss variable was also considered. NC and neck ratios are easy anthropometric measurements that in combination with routine biochemical variables (ALT and HOMA-IR) showed good prediction ability of NAFLD. More research studies are necessary to validate the utility of these simple and easy variables as surrogate markers of NAFLD since their application could improve the prevention and management of this prevalent disease.

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

The low water solubility of any pharmacoactive molecules limits their pharmacological potential but the solubility parameter cannot compromise, so different approaches are employed to enhance their bioavailability. Pharmaceutically active molecules with low solubility convey a higher risk of failure for drug innovation and development. Pharmacokinetics, pharmacodynamics, and several other parameters, such as drug distribution, protein binding and absorption, are majorly affected by their solubility. Among all pharmaceutical dosage forms, the oral dosage forms cover more than 50 %, and the drug molecule should be water-soluble. For good therapeutic activity by the drug molecule on the target site, solubility and bioavailability are crucial factors. The pharmaceutical industry screening programs identified that around 40% of new chemical entities (N.C.E.s) face various difficulties at the formulation and development step. These pharmaceuticals are attributed to their less solubility and bioavailability. The bioavailability and drug solubility enhancement are significant challenges in the area of pharmaceutical formulations. According to the Classification of Biopharmaceutics, Class II and IV drugs (APIs) exhibits poor solubility, lower bioavailability, and less dissolution. Various technologies are discussed in this article to improve the solubility of the poorly water-soluble drug, for example, complexation of active molecules, utilization of emulsion formation, micelles, microemulsions, cosolvents, polymeric micelles preparation, particle size reduction technologies, pharmaceutical salts, prodrugs, solid-state alternation technique, soft gel technology, drug nanocrystals, solid dispersion methods, crystal engineering techniques and nanomorph technology. This review mainly describes several other advanced methodologies for solubility and bioavailability enhancement, such as crystal engineering, micronization, solid dispersions, nano sizing, use of cyclodextrins, solid lipid nanoparticles, colloidal drug delivery systems and drug conjugates, by some appropriate research reports.

Botnets, a prominent threat to IoT security. ‘botnet’ this word is the composition of robot and network. A network of robots used to commit cybercrime. A bot means a compromised end-host or a device which is a member of a botnet. [2] Governments have become a popular target for malicious attacks. This is due to them holding mass confidential data on their network.

Cracking in a wide array of industrial components and structures pose a significant threat to their integrity. Detecting cracks using ultrasonic inspection techniques is a widespread activity for economic reasons but there are limitations to the techniques due to the morphology of cracks, such as fatigue cracks. In addition to detection there is a need to measure the size of the cracks which are often within the volume of the material. Ultrasonic techniques are well-suited to look inside the volume of the material but achieving sufficient sensitivity to the tip of the cracks in particular is practically difficult. Without an accurate knowledge of where the tip of the crack lies there can be significant uncertainty in sizing measurements. Machine Learning (ML) techniques are being developed to aid in the inspection and monitoring tasks but presenting the ultrasonic data in a suitable way for ML is very important. Following on from recent work presenting the development of the snooker algorithm to create images termed parameter-spaces, this paper presents how these images can be input into neural network based ML systems to automatically size these critical cracks.

The present work presents and discusses experimental performance and gaseous emission results of the use of ternary mixtures of diesel oil, biodiesel and ethanol in an ASTM-CFR engine. Eight different mixtures with biodiesel content equal to 8%, 20% and 60%, and ethanol content equal to 0%, 5% and 8% were analyzed. After the selection and preparation of the mixtures, the determination of the cetane number for each one was performed following the ASTM D613 standard. After cetane number evaluation, two off-standard operating conditions were selected, and performance and emissions were obtained. A thermodynamic analysis and a Bayesian inverse approach were applied and physical quantities of interest, such as indicated work, maximum pressure, rate of heat released by the fuel, ignition delay, and combustion duration, were obtained. In-cylinder pressure data were obtained through an AVL Indicating system installed in the ASTM-CFR engine. The heating values of each mixture were determined using a bomb calorimeter and thermal and indicated efficiencies were evaluated based on the total heat released by the fuel and the indicated work. Finally, a gaseous emissions analysis was conducted to evaluate the influence of the blend compositions, as well as of their cetane numbers, on the formation of NOx, CO and CO₂.

Large power transformers are generally associated with a maximum capacity rating of 100 MVA or higher. These large liquid dielectric power transformers are a custom-built piece of equipment, thus very expensive, and a backbone element of the power grid. In extreme cases as, for example, severe geomagnetic disturbances, permanently monitoring their condition will enhance their electrical reliability and resilience to guarantee efficient management of its life cycle. However, some traditional monitoring/diagnosis techniques have singular features when applied to large power transformers and their interlinked subsystems. In this context, and since that information is hardly put in evidence and compiled in the literature, this paper reviews the particularities of monitoring and diagnosing those assets.

Water is a limited resource that needs to be properly managed and distributed to the ever-growing population of the world. Rapid urbanization and development have drastically increased the overall water demand worldwide. Ageing water distribution networks are vulnerable to deterioration and leakage, thereby causing an estimated annual loss of about 48 trillion liters of water. To address these issues, efficient and reliable leakage detection and management techniques are necessary. In this paper, the results of the experiments performed on a looped water distribution network in AnSeong, Korea are discussed. Transient-based techniques were used and physical data were collected for the detection and localization of leakages in the experimental water pipes. The results obtained from the experiments demonstrated the applicability of transient techniques for leak analysis in looped water distribution networks.

With compact footprint, low energy consumption, high scalability, and mass producibility, chip-scale integrated devices are an indispensable part of modern technological change and development. Recent advances in two-dimensional (2D) layered materials with their unique structures and distinctive properties have motivated their on-chip integration, yielding a variety of functional devices with superior performance and new features. To realize integrated devices incorporating 2D materials, it requires a diverse range of device fabrication techniques, which are of fundamental importance to achieve good performance and high reproducibility. This paper reviews the state-of-art fabrication techniques for the on-chip integration of 2D materials. First, an overview of the material properties and on-chip applications of 2D materials is provided. Second, different approaches used for integrating 2D materials on chips are comprehensively reviewed, which are categorized into material synthesis, on-chip transfer, film patterning, and property tuning / modification. Third, the methods for integrating 2D van der Waals heterostructures are also discussed and summarized. Finally, the current challenges and future perspectives are highlighted.

A quantitative analysis of X-ray emission from an electron cyclotron resonance (ECR) plasma was performed to probe the spatial properties of electrons having energy for effective ionisation. A series of measurements were taken by INFN-LNS and ATOMKI, capturing spatially and spectrally resolved X-ray maps as well as volumetric emissions from argon plasma. Comparing the former with model generated maps (involving space-resolved phenomenological electron energy distribution function and geometrical efficiency calculated using ray-tracing MC routine) furnished information on structural aspects of the plasma. Similarly, fitting a model composed of bremsstrahlung and fluorescence to the volumetric X-ray spectrum provided valuable insight into the density and temperature of confined and lost electrons. The latter can be fed back to existing electron kinetics models for simulating more relevant energies, consequently improving theoretical X-ray maps and establishing the method as an excellent indirect diagnostic tool for warm electrons, required for both fundamental and applied research in ECR plasmas.

We follow the PRISMA extension for scoping reviews to review the emerging international body of empirical evidence on consumers’ attitudes and willingness to pay (WTP) for novel foods produced with New Plant Engineering Techniques (NPETs). NPETs include genome/gene editing, cisgenesis, intragenesis, RNA interference and others. These novel foods are often beneficial for the environment and human health and more sustainable under increasingly prevalent climate extremes. These techniques can also improve animal welfare and disease resistance when applied to animals. Despite these promising attributes, evidence suggests that many, but not all, consumers discount these novel foods relative to conventional ones. Our review sorts out findings to identify conditioning factors that can increase the acceptance of and WTP for these novel foods in a significant segment of consumers. International patterns of acceptance are identified. We also analyze how information and knowledge interact with consumer acceptance of these novel foods and technologies. Heterogeneity of consumers—across cultures and borders and in attitudes towards science and innovation—emerges as a key determinant of acceptance and WTP. Acceptance and WTP tend to increase when socially beneficial attributes—as opposed to producer-oriented cost-saving attributes—are generated by NPETs. NPET-improved foods are systematically less discounted than transgenic foods. Most of the valuation estimates are based on hypothetical experiments and surveys and await validation through revealed preferences in actual purchases in food retailing environments.