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.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.

Originally developed to image the shadow region of the central black hole in Sagittarius A* and in the nearby galaxy M87, the Event Horizon Telescope (EHT) provides deep, very high angular resolution data on other AGN sources too. The challenges of working with EHT data have spurred the development of new image reconstruction algorithms. This work briefly reviews the status of the EHT and its utility for observing AGN sources, with emphasis on novel imaging techniques that offer the promise of better reconstructions at 1.3 mm and other wavelengths.

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.

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.

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 & 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.

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.

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.

Keywords: Tendon injuries; Mallet finger; Suture techniques

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.

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.

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.

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.

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.

Background: Rangeland degradation means that a reduction both in rank and status of general floral/ fauna composition, energy flow and biomass of a certain ecosystem. Objective: The review was conducted to assess rangeland degradation, cause and its impact on local livelihood in Yabello district and also suggests appropriate methods used to rehabilitate it. Methods: The data and literatures were collected from different both domestic and abroad researcher research result reviewed and internet sources. The research paper review mainly addresses concepts of rangeland degradation, major causes in tropics and Ethiopia particularly in Yabello rangelands, impacts of rangeland degradation, principles of rangeland restoration and common restoration techniques.Result: Based on those research papers and the current fact situations the major drivers leading to rangeland degradation includes climate change, overgrazing, bush encroachment, both human and livestock population pressure, drought, and government policy . Of all bush encroachment is becoming the major threat to Yabello rangelands. And this led to decline in rangeland condition, water potential, soil status, and animal performance, livestock holding at the household level and community become destitute and in the long run poverty. In spite of these impacts, the action of restoration techniques in the area is highly insufficient. Conclusion: Generally in order to address rangeland degradation problems, there is a strong need to coordination between the local community and the scientific community for sustainable land management trough post management techniques by applying reseeding technology.

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.

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.

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.

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.

Advances in reconfigurable liquid-based reconfigurable antennas are enabling new possibilities to fulfil the requirements of more advanced wireless communication systems. In this review, a comparative analysis of various state-of-the-art concepts and techniques for designing reconfigurable antennas using liquid is presented. First, the electrical properties of different liquids at room temperature commonly used in reconfigurable antennas are identified. This is followed by a discussion of various liquid actuation techniques in enabling high frequency reconfigurability. Next, liquid-based reconfigurable antennas in literature used to achieve the different types of reconfiguration will be critically reviewed. These include frequency-, polarization-, radiation pattern- and compound reconfigurability. The current concepts of liquid-based reconfigurable antennas can be classified broadly into three basic approaches: altering the physical (and electrical) dimensions of antennas using liquid, applying liquid-based sections as reactive loads; and implementation of liquids as dielectric resonators. Each concept and their design approaches will be examined, outlining their benefits, limitations, and possible future improvements.

By introducing a suitable renormalization process the charge carrier and phonon dynamics of a double-stranded helical DNA molecule is expressed in terms of an effective Hamitonian describing a linear chain, where the renormalized transfer integrals explicitly depend on the relative orientations of the Watson-Crick base pairs, and the renormalized on-site energies are related to the electronic parameters of consecutive base pairs along the helix axis, as well as to the low-frequency phonons dispersion relation. The existence of synchronized collective oscillations enhancing the π-π orbital overlapping among different base pairs is disclosed from the study of the obtained analytical dynamical equations. The role of these phonon-correlated, long-range oscillation effects on the charge transfer properties of double standed DNA homopolymers is discussed in terms of the resulting band structure.

With the widespread use of mobile phones, increasing use of mobile applications in different areas of life have gained a large place. Between the reasons of the preference of the rapid increasing number of apps in the application markets of the different platforms, the aesthetic appereance of the icons that apps have perhaps the most important one. In this study, the visual symmetrical side of the icons of the most downloaded apss which are developed for preparing to Public Servant Exam in Turkey is emphasized. Two different types of data are obtained with working on the icons with image processing technique by using Mathworks Matlab program and survey method which is applied on Korkut Ata University students. By comparing the obtained data with the binary logistic regression method, it was determined that the visual symmetry in the apps’ icons partially contributed to the aesthetic appreciation.

We have conducted a comprehensive year-long sampling campaign for particulate matter (PM) pollutants at a site near a major highway, following standard protocols. Total mass, and elemental and chemical composition of the fine fractions (PM2.5) of traffic-related pollutants are determined utilizing several complementary techniques. These complementary techniques included gravimetric analysis, X-ray fluorescence (XRF), scanning electron microscopy and energy dispersive spectroscopy, X-ray diffraction (XRD) and black carbon multi-wavelength absorption. The results show that the PM2.5 mass concentrations are within or slightly above international standards and include natural dust and anthropogenic pollutants such as black carbon, sulfates and other traffic-related elements. Anthropogenic sources include the secondary pollutants Mascagnite and Koktaite, traffic emissions such as exhaust emissions and tires, brakes and road erosions. It was found that the sulfates are the highest contributors to PM2.5 (~40%), as evident from XRD results and the S content in the XRF analysis.

The rising popularity of virtual reality has seen a recent push in applications, such as, social media, educational tools, medical simulations, entertainment and training systems. With virtual realitythe ability to engage users for specific purposes, provides opportunities to entertain, develop cognitive abilities and technical skills outside of the standard mediums (e.g., the television or theclassroom) thereby optimizing exposure with realistic (live) opportunities. However, before these applications of virtual reality become more widespread, there are a number of open questions andissues that must be addressed including limitations, challenges, relationships between fidelity, multi-modal cue interaction, immersion, and knowledge transfer and retention. In this article, we begin with a brief overview of virtual reality methods, followed by a discussion of virtual reality and its applications (both historically, currently and in the future). We review virtual reality trends both from the early artistic days through to current day state of the art technological advancements. We explore emerging and futuristic breakthroughs - and their applications in virtual reality - showing how virtual reality will go way beyond anything we could envision. Infact, after reading this article, we hope the reader will agree, that virtual reality, is possibly one of the most powerful mediums of our time. While the earliest mechanistic virtual reality prototypes (e.g., Sensorama) allowed us to view stereoscopic 3D images accompanied by stereosound, smells, as well as wind effect - set the foundation and direction for future pioneers - there have been spearheaded developments which have continually pushed the concept of virtual reality to new domains. As virtual reality evolves, many new and yet-to-be-imagined applications will arise, but we must have understanding and patience as we wait for science, research andtechnology to mature and improve. The article ends with a short overview of future directions based upon recent breakthroughs in research and what this will mean for virtual reality in the coming years.

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.

Monitoring of groundwater quality in today's scenario is very much important. Due to urbanization and population pressure regular monitoring of groundwater for drinking as well as irrigation purposes need a major concern. With this aim, a study has been carried out consisting 26 groundwater samples in May 2017, to access the physiochemical characteristic, water quality index (WQI) of groundwater by using GIS software and to find out the groundwater suitableness for drinking as well as for irrigation purpose. The pH is slightly alkaline and the TDS is much more than prescribed limits of BIS. The trend of cations in groundwater are Ca2+>Na+>Mg2+>K+ while anions trend is HCO3->SO42->Cl->NO3->CO32->F-. The Ca-Mg-HCO3 and Na-K-Cl-SO4 types of groundwater facies were dominant. Generally, the chemical changes in groundwater are administered by the evaporation process with ion exchange, and mixing of particles is the significant source of the solute acquisition process. WQI of the study area suggested that the 15% sample is unsuitable, 69% is poor and remaining is good for drinking uses. The potential salinity of the groundwater sample is nearly high although the majority of the sample is suited for irrigation activities.

Fatigue cracks in a wide array of industrial components and structures pose a significant threat to their integrity. Detecting fatigue cracks using ultrasonic inspection techniques is a widespread activity for economic reasons but there are limitations to the techniques due to the morphology of 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 machine learning is very important. This paper presents a new approach to condition the ultrasonic data for machine learning settings so that they can be used effectively and confidently to detect and size fatigue cracks. The new approach, using images termed parameter-spaces, will also aid in conventional inspections as they are able to give information to human operators as to the existence or not of these very dangerous cracks.

Online identity-based theft is known to be one of the most serious and growing threats to victims, such as individuals and organizations, over the last 10 years due to the enormous economic damage these crimes have caused. The availability of personal information on the Internet has increased the chances of this cyber-crime. Online identity theft crime is the result of a combination of cyber-crimes on the one hand and lack of awareness and training of users on the other hand to protect personal data on the other. Education and awareness, which also contributes to early detection, is the strongest tool for consumers to safeguard themselves from online identity fraud. This paper provides a comprehensive explanation of online identity theft, the various approaches that thieves use to attack individuals and organizations and the types of fraud involved in this cyber-crime. The aim of this research is to evaluate the need for a reformulation of the concept of identity theft in order to be compatible with the evolution of behaviors and fraud.

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It~provides an image of the subsurface using the same principles as ultrasound medical imaging. As for any data acquired through hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors), the raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The~QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = under filtering, noise remaining in the data; optimal = good filtering; harsh = over filtering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km²) to QC the results of the swell noise attenuation process in marine seismic data.

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It provides an image of the subsurface using the same principles as ultrasound medical imaging. It is based on emitting a sound (pressure) wave through the subsurface and recording the reflected echoes using hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors). Contrary to medical imaging, which is done in real time, subsurface seismic imaging is an offline process that involves a huge volume of data and needs considerable computing power. The raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = underfiltering, noise remaining in the data; optimal = good filtering; harsh = overfiltering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km2) to QC the results of the swell noise attenuation process in marine seismic data. The results are encouraging and helped identify localized issues that were difficult for a human to spot.

One of the most reliable methods of authentication used today is biometric matching. This authentication process, which is done by using biometrics information such as fingerprint, iris, face, etc. is used in many application areas. Authentication at border gates is one of these areas. However, some restrictions have been introduced to storing and using such data, especially with the General Data Protection Regulation (GDPR). The main goal of this work is to find the practical implementation of fully homomorphic encryption-based biometric matching in border controls. In this paper, we propose a biometric authentication system based on hash expansion and fully homomorphic encryption features, considering these restrictions. One of the most significant drawbacks of the homomorphic encryption method is the long execution time. We solved this problem by executing the matching algorithm in parallel manner. The proposed scheme is implemented as proof-of-concept in the SMILE, and its advantages in privacy preservation has been demonstrated.

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Modern radio spectrometers make measurement of polarized intensity as a function of Faraday depth possible. I investigate the effect of depolarization along a model line of sight. I model sightlines with two components informed by observations: a diffuse interstellar medium with a lognormal electron density distribution and a narrow, denser component simulating a spiral arm or H~{\sc ii} region, all with synchrotron-emitting gas mixed in. I then calculate the polarized intensity from 300-1800 MHz and calculate the resulting Faraday depth spectrum. The idealized synthetic observations show far more Faraday complexity than is observed in Global Magneto-Ionic Medium Survey observations. In a model with a very nearby H~{\sc ii} region observed at low frequencies, most of the effects of a ``depolarization wall'' are evident: the H~{\sc ii} region depolarizes background emission and less (but not zero) information from beyond the H~{\sc ii} region reaches the observer. In other cases, the effects are not so clear, as significant amounts of information reach the observer even through significant depolarization, and it is not clear that low-frequency observations sample largely different volumes of the interstellar medium than high-frequency observations. The observed Faraday depth can be randomized such that it does not always have any correlation with the true Faraday depth.

Over the years, the Egyptian temples at Luxor city have been intensely investigated, but most of these studies just focused on the classical sides of the archaeological and historical descriptions. Many of the environmental problems are inevitable results of the unplanned urban crawling around the monuments temples. This paper aims at assessing the environmental changes around some temples of Luxor City using Remote sensing and GIS techniques. In particular, a historical database made up of Corona and Landsat TM data have been investigated along with the new acquisitions of Quickbird2 and Sentinel2. Results from our investigation highlighted rapid changes in urban and agricultural areas, which adversely affected the Egyptian monumental temples causing serious degradation phenomena. Using the information obtained from our RS&GIS based analysis, mitigation strategies have been also identified for supporting the preservation of the archaeological area.

Electrochemical impedance (EI) measurements were performed to evaluate the nylon 66/-tetra-(para-aminophenyl) porphyrin (H₂T(p-NH₂)PP)/graphene oxide (GO) film coating on stainless steel and compared to the nylon/H₂T(p-NH₂)PP and nylon/GO film samples using 1M H₂SO₄ as electrolyte. The nylon/H₂T(p-NH₂)PP and nylon/GO composite films showed high electrochemical impedance in the order of 109 ohm-cm² and a system controlled by mass transfer, product of a diffusion process at low frequencies with a resistance up to 5 orders of magnitude, indicating the diffusion of protons through the coating and a decrease in the metal dissolution. Otherwise, the nylon/H₂T(p-NH₂)PP/GO film compound evaluated show good ionic conductivity and electrochemical stability in the acid environment, acting porphyrin as a catalyst to the passage of protons through the film, reducing its electrochemical impedance up to 7 orders of magnitude with respect to the compounds nylon/H₂T(p-NH₂)PP and nylon/GO. Likewise, good capacitance values are also shown by modifying the concentrations of porphyrin and GO reinforcing materials. These properties are important for technological applications, such as anticorrosion coating for bipolar plates or membrane in a fuel cell type PEM, super-capacitors, etc.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Increased severity of droughts, due to anthropogenic activities and global warming has imposed a severe threat on agricultural productivity ever before. This has further advanced the need for some eco-friendly approaches to ensure global food security. In this regard, application of plant growth-promoting rhizobacteria (PGPR) can be beneficial. PGPR through various mechanisms viz. osmotic adjustments, increased antioxidant, phytohormone production, regulating stomatal conductivity, increased nutrient uptake, releasing Volatile organic compounds (VOCs), and Exo-polysaccharide (EPS) production, etc not only ensures the plant’s survival during drought but also augment its growth. This review, extensively discusses the various mechanisms of PGPR in drought stress tolerance. We have also summarized the recent molecular and omics-based approaches for elucidating the role of drought responsive genes. The manuscript presents an in-depth mechanistic approach to combat the drought stress and also deals with designing PGPR based bioinoculants. Lastly, we present a possible sequence of steps for increasing the success rate of bioinoculants.

This paper uses the complex regression analysis method to establish the customer’s load regression models, which consider economic indicators, temperature and rainfall. Furthermore, the proposed models are used to study the forecasting feasibility of the future energy sales and summer peak load demand. At first, this paper used least-squares techniques to derive regression models for considering economic indicators and temperature of 34 customer energy sales and total energy sales. Besides, the AMI high voltage customer demand data and system generating capacity for 24 hours were adopted to forecast summer peak load. The above-mentioned data analysis tool is used by EViews software to achieve, in order to verify the feasibility of the research framework. The study found that although its forecasting model accuracy is low only when mixed with temperature and high voltage demands. So, when mixed with high voltage demand data and system generating capacity for 24 hours to forecast peak load, the average error is ± 0.87% and in the majority of its energy sales forecasting model of average error is ±3%. This result can provide power company as future reference.

Marine clay deposits are commonly found worldwide. Considering the cost of dumping and the related environmental concerns, an alternative solution involving the reuse of soils that have poor conditions is crucial. In this research, the authors examined the durability of marine deposited clays and compiled a corresponding database. The use of slag alone as a binder, at any percentage, increased the accumulated mass loss (ALM) up to 2%. However, the use of lime as the third binder seemed to accelerate the chemical reactions associated with the hydration of clay and cementitious material and to enhance the chemical stability, i.e., samples that included both lime and slag experienced the same ALM as samples treated with cement only. Scanning electron microscopy analysis confirmed the durability improvements of these clays. The proposed unconfined compressive strength and accumulated mass loss relationship yielded practical approximation for the fine- and coarse-grained soils blended with up to three binders until 60 days of curing.

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.

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.

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.

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.

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.

It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

Profiling technologies, such as proteomics, allow the simultaneous measurement and comparison of thousands of plant components without prior knowledge of their identity. The combination of these non-targeted methods facilitates a more comprehensive approach than targeted methods and thus provides additional opportunities to identify genotypic changes resulting from genetic modification, including new allergens or toxins. The purpose of this study was to investigate unintended changes in GM Bt maize grown in South Africa. In the present study, we used bi-dimensional gel electrophoresis based on fluorescence staining, coupled with mass spectrometry in order to compare the proteome of the field-grown transgenic hybrid (MON810) and its near-isogenic counterpart. Proteomic data showed that energy metabolism and redox homeostasis were unequally modulated in GM Bt and non-GM maize variety samples. In addition, a potential allergenic protein – pathogenesis related protein -1 has been identified in our sample set. These finding highlight the suitability of unbiased profiling approaches to complement current GMO risk assessment practices worldwide.

Dynamic visualizations have been developed to exchange information that transforms over time across a broad range of professional and academic contexts. However, these visual tools may impose substantial demands on the learner’s cognitive resources that are very limited in current knowledge. Cognitive load theory has been used to improve learning from dynamic visualizations by providing certain design techniques to manage learner cognitive load without adding any oral/written explanations. This systematic review examined a series of experimental studies assessing the roles of these design techniques in learning tactical scenes of play through dynamic visualizations. Electronic databases PubMed and Google Scholar were used to search relevant articles. Eleven studies were eventually included for the systematic review based on the eligibility criteria. The present review revealed that adapting design techniques to the level of learners’ expertise, type of depicted knowledge, and level of content complexity is a crucial part of effective learning.

Risk assessment management have been a hot topic for the researchers since a very long time. Software risk management is an important part of project management as it contains the identification, analysis, estimation and monitoring of different risks present in the system. This helps developers in decision making while assessing the problems that could arise in the software systems. Risk management is very complex in large scale system as these systems have very complex development. The paper describes risk management techniques for large scale system. Furthermore we have provided a detailed comparative analysis of these techniques with commonly identified risks in software systems and have provided a systematic order for risk management process to ensure risk mitigation.

Background: Oral squamous cell carcinoma (OSCC), with poor prognosis and high mortality rates, is one of the most diagnosed head and neck cancers. Cancer stem cells (CSCs) - epithelial-to-mesenchymal transition (EMT) loop is increasingly implicated in the therapy-resistance, relapse, and metastasis of OSCC patients. Accumulating evidence indicate that aberrantly expressed CD47 is associated with cell-death evasion, invasion and cancer metastasis; however, the role of CD47 in the modulation of CSCs-like phenotypes, including therapy-resistance and metastasis, with its underlying mechanism in OSCC remains largely underexplored. Methods: This study investigated the CSCs- modulating potential of CD47 in OSCC cell lines SAS, TW2.6, HSC-3 and FaDu using bioinformatics approach, immunoblotting, immunofluorescence staining, migration, invasion, colony and orosphere formation, as well as radiosensitivity assays. Results: We demonstrated that the characteristic ectopic expression of CD47 in OSCC patients was associated with ~ 20% 2-year survival disadvantage (p = 0.01) and positively correlated with the expression of pluripotency factors; while shRNA silencing of CD47 significantly suppressed cell viability and markedly inhibited orosphere formation, resulting in smaller and fewer orospheres, and downregulated CD133, SOX2, OCT4 and c-Myc mRNA and protein expression levels. We also showed that CD47 downregulation attenuates EMT, migration and clonogenicity of OSCC cells, with associated E-cadherin upregulation and suppression of Vimentin, Slug, Snail, and N-cadherin expression. Conclusion: Of therapeutic relevance, combined with radiotherapy, CD47 knockdown enhanced the anti-OSCC effect of radiotherapy. Thus, we demonstrate the therapeutic feasibility of a CD47-mediated anti-CSCs strategy, and suggest a role for CD47 suppression in potentiating the therapeutic efficacy of radiation therapy in OSCC patients.

LC–NMR combines the advantage of the outstanding separation power of liquid chromatography (LC) and the superior structural elucidating capability of nuclear magnetic resonance (NMR). NMR has proved that it is a standout detector for LC by providing maximum structural information about plant originated extracts particularly in its isolating ability of isomeric (same molecular formula) and/or isobaric (same molecular weight) compounds as compared to other detectors. The present review provides an overview of the LC–NMR developmental trends and its application in natural products analysis. The different LC–NMR operational modes are described, as well as how technical improvements assist in establishing this powerful technique as an important analytical tool in the analysis of complex plant-derived compounds. On-flow, stop-flow and loop-storage modes, as well as the new offline mode LC–SPE–NMR and capLC-NMR configurations that avoid the ingestion of expensive deuterated solvents throughout the experiment are mentioned. Utilization of cryogenic probe and microprobe technologies which are the other important promising approaches for guaranteeing the sensitivity issues are also described. Concluding remarks and future outlooks are also discussed.

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.

: 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.

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.

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.

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.

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.

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.

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.

Exercises for lower trapezius (LT) often use overhead positions, causing compressive forces to the subacromial space. Scapular retraction would be an alternative to activate LT muscle. Electromyography of infraspinatus (IS), upper (UT) and LT was recorded during scapular retraction under progressive adduction loads of 42 participants, divided in 2 groups: with (SP, n=26) and without shoulder pain (nSP, n=16). The adduction loads of 20, 30, 40 and 50% of the maximal voluntary contraction were applied using a load cell. Normalized electromyography and the ratio between UT and LT (UT:LT) were used for statistical analysis. No differences were observed between groups, but a condition effect occurred for all muscles: UT showed higher values at 50% vs. 20% (p=0.004); LT showed higher values on 40% and 50% (p=0.001; 0.006). Higher values for IS were noted at 40% (vs 20%; p=0.04), and at 50% (vs. 20%; p=0.001, vs. 30%, p=0.001; vs. 40%; p=0.001). UT:LT showed lower values at 50% (vs. 20%; p=0.001, and vs. 30%; p=0.016). Scapular retraction with adduction loads at 40-50% is an alternative to overhead exercises aiming to activate the LT and the IS muscles. The exercise ensures higher levels of LT and IS excitation, without increasing UT excitation.

Brain, the most complex object known in the universe, uses few watts of power. To mimic it a nuclear power plant will be required and this power house controls the human body, single handedly. Surprisingly, “On the left side, nothing is right and on the right side there is nothing left”. Typically, there are two lateral halves of the brain: Left hemisphere and right hemisphere working distinctly. Left hemisphere is inclined towards logic; Right hemisphere is the root cause of imagination added with critical thinking. In situations like the current pandemic, COVID-19, it is the right half which tends to dominate the processing. This gives birth to mental stress, anxiety thus, aggravating the existing medical condition. Considering this pattern, a survey was conducted in Durg district of Chhattisgarh, which is one of the most hard hit epicentre of the COVID-19 second wave in India. According to this survey, it was revealed that largely women of all age groups (10-25, 26-40, 40 above) were right brained i.e. dominance of right over left hemisphere. Being more imaginative and creative thinkers they are more likely to suffer from mental issues than males. The aim of this research is to improve the mental wellbeing of the citizens in such threatening conditions. To prevent this situation awareness is a must and some stress relieving games have also been created.

Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. In this paper, following a short introductory summary and definitions, this paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of NDT techniques in aerospace industry. In addition to this, within a some of significant NDT application extents, prognosis of composites is also briefly discussed.

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.

Brain Tumor is one of the severe diseases and occurrence of this disease threats human life. Detection of brain tumor in advance can secure patient’s life from unwanted loss. Well-timed and swift disease detection and treatment strategy can lead to improved quality of life in these patients. This paper attempts to use Machine Learning based ensemble approaches for recognising patients with brain tumor. Ensemble technique based AdaBoost classifier and 10-fold stratified cross-validation method are assembled in single platform is proposed in this paper for prediction of brain tumor. This prediction is compared against three baseline classifiers such as Gradient Boost, Random Forest and Extra Trees classifier. Experimental result implies the superiority of this model with an accuracy of 98.97%, f1-score of 0.99, kappa statistics score of 0.95 and MSE of 0.0103.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

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.

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.

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.

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.

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.

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.

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.

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 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.

Blood-brain-barrier is a major obstacle in treating brain-related disorders as it does not allow to deliver drugs in the brain. In order to facilitate delivery of drugs in brain, we developed a method for predicting blood-brain-barrier penetrating peptides. These blood-brain barriers penetrating peptides (B3PPs) can act as therapeutic as well as drug delivery agents. We trained, tested, and evaluated our models on blood-brain-barrier peptides obtained from the B3Pdb database. First, we compute a wide range of peptide features then we select relevant peptide features. Finally, we developed numerous machine learning-based models for predicting blood-brain-barrier peptides using selected features. Our model based on random forest performed best on the top 80 selected features and achieved a maximum 85.08% accuracy with 0.93 AUROC. We also developed a web server, B3pred that implements our best models. It has three major modules that allow users to; i) predict B3PPs, ii) scanning B3PPs in a protein sequence, and iii) designing B3PPs using analogs. Our web server and standalone software is freely available at https://webs.iiitd.edu.in/raghava/b3pred/.

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

This paper presents multidisciplinary and innovative research concerning fighting against coronavirus through wastewater collection and treatment. Studies suggest that coronavirus exists in the wastewaters. Untreated wastewater is proved to spread the virus. Coronavirus is attacking people globally and shrinking the economy. This paper highlights the idea that the coronavirus shall be defeated with the help of wastewater collection and treatment as well. The question addressed by this paper is will communities defeat the coronavirus without well-collected and treated wastewaters? This research aims to display the role of wastewaters in the spread of coronavirus in cities and to require their collection. The methods to achieve the goals are theoretical surveys, case study strategy, mathematical modeling, statistical procedures, forecasting of future, and dialectical discussions. The findings of this research demonstrate the need for carefully collected and treated wastewaters to overcome the coronavirus. This paper gives suitable techniques to collect and treat wastewater such as wastewater stabilization ponds, bacterial reactors, and anaerobic ponds. The innovative idea of this paper, its suggested indicators to select a certain wastewater treatment technique in every city, and its outcome will assist the global community to fight the coronavirus more effectively.

We used population reconstruction techniques to assess for the first time the population dynamics of a seagrass, Cymodocea nodosa, exposed to long-term elevated CO₂ near three volcanic seeps and compare them with reference sites away from the seeps. Under high CO₂, the density of shoots and of individuals (apical shoots), and the vertical and horizontal elongation and production rates, were higher. Nitrogen effects on rhizome elongation and production rates and on biomass, were stronger than CO₂ as these were highest at the location where the availability of nitrogen was highest. At the seep where the availability of CO₂ was highest and nitrogen lowest, density of shoots and individuals were highest, probably due to CO₂ effects on shoot differentiation and induced reproductive output, respectively. In all three seeps there was higher short- and long-term recruitment and growth rates around zero, indicating that elevated CO₂ increases the turnover of C. nodosa shoots.

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.

(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.

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

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.

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.

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.

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.

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.

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.