Accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understand their structure-function relationships and electrochemical properties. Current method of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly / partially resolved and unresolved spectra, which is a common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulate heat balances in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of a principal component analysis (PCA) is one such tool that is used to identify the characteristics of seasonal ice variability. The PCA not only identifies “normal” or “dominant” conditions or features, but also extract anomalous spatial features from an archive of long-term sequence of images.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

Since the outbreak of the COVID-19 pandemic, patients infected with COVID-19 may experience abnormal heart rates, posing potential health risks. This study proposes a non-contact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in more accurate diagnosis and treatment. In this study, except for the different image acquisition method, other research processes and methods are similar to the research methods in [15]. The image acquisition method is changed from forehead image to palm image. The new non-contact measurement performance of the proposed method can effectively not only avoid infection concerns but also obtain heart rate and HRV quickly and conveniently, providing higher accuracy for physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to those in recently published papers.

For this research we are selected three indicators of wheat: yield (t/ha), producer price (USD/t) and share of the harvested area under wheat in relation to the total land area of country (%). The mentioned indicators were analyzed for the period 2006-2021 in thirty-two European countries. The purpose of the research is to determine which of the mentioned indicators achieve average annual growth/decrease rates, what is the correlation between them, how European countries are grouped with their similarities and differences. The research examines the impact of the COVID-19 pandemic on the mentioned indicators in a two time phases: Phase I, before the pandemic (2006-2019); Phase II, during the pandemic (2020-2021). The methodological approach to the research topic involved the application of descriptive statistics, as the method of principal components analysis (abbr. PCA) with graphics. A positive correlation exists only between the yield of wheat and the share of the harvested area under wheat in the total area of the land (0.291). PCA method showed that countries are grouped differently depending on the observed indicator and the analyzed time phase.

The ANSYS 2024R1 student edition was used to create a finite element model of a basketball rim and backboard. This finite element model included the use of steel for the rim and its mount, a tempered glass backboard, and an aluminum frame behind the backboard. After a mesh was created, fixed support boundary conditions were applied to the four corners of the aluminum frame. The subsequently calculated mode shapes and frequencies were compared to empirical modal analysis previously done at the United States Military Academy at West Point, New York. Four mode shapes and frequencies agreed rather well between the theoretical finite element analysis and previously published empirical modal analysis, specifically where the rim was vibrating in the vertical direction, which was the direction that the accelerometer was aligned for modal analysis. However, three mode shapes missed by the empirical modal analysis were found where the vibration of the rim was confined to the horizontal plane, which was orthogonal to the orientation of our accelerometer.

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Despite soccer popularity, comparatively little scientific information is available concerning the assessment of youth players’ physiological characteristics. The use of medical software integrating wearable sensors to provide accurate and objective data is necessary. In the present study, the objective assessment of physical performance of 128 adolescent soccer players at two timepoints (pre-season and end-season) has been performed. Participants completed a comprehensive test battery including stability, countermovement jumps (CMJ), plyometric jumps (PJ), agility, and quick feet tests. A single Inertial Measurement Unit (IMU) sensor provided quantitative data on various performance metrics. Principal Component Analysis (PCA) reduced the dataset to four principal components (PCs) explaining over 80% of the variance: power (PC1), balance (PC2), speed/agility (PC3), and stiffness (PC4). Paired samples t-test between the two timepoints showed no significant differences in the PCs between the two timepoints, indicating that the training program did not significantly alter the assessed physical attributes. However, subtle individual changes may still inform personalized training adjustments. The study highlights the importance of continuous monitoring and individualized training strategies to optimize athlete performance and reduce injury risk.

In this study, we investigate the growth of EV ownership among Swedish households. Through detailed socio-economic data and the Swedish vehicle register, we are able compare characteristics of households that bought an EV in 2016 with those that became EV owners in 2020. We are thus able to describe and analyse the transition from the early EV owners to the early majority. Unlike many prior studies in the field, which are based on surveys or interviews, this study relies on registry data, thereby encompassing all car owners in Sweden. In this study, we have also scrutinized the significance of ownership mode by distinguishing between households that have chosen to purchase a car and those that have chosen to lease one. The results show that the demand for EVs has reached new consumer groups, e.g. both younger and older car buyers, and that the option to lease a car privately has broadened the market for new cars in general, and for EV’s in particular.

Regulation of the diet in terms of the content of soluble and insoluble fibre is of great importance from the point of view of dietetics, microbiology of the gastrointestinal tract and immunology of the organism. Dietary fibre plays an important role in maintaining the balance between the con-tinuity of gut mucus production (intestinal mucosa), the stability of the intestinal microbiome composition, the regeneration of the intestinal epithelium and the immune status within the gut-associated lymphoid tissue (GALT) are played by pectin and their metabolic products in the form of pectin oligosaccharides. The main purpose of this review was to focus on pectin, their functions, their impact on the human's health and on some of the most promising prebiotics due to their solubility in the gastrointestinal tract in the form of pectin oligosaccharides. They have the properties of pectin, but have also additional features and postbiotics obtained in fermentation process can affect the intestinal microbiome and GALT-related immunity. They can be obtained from plant agro-wastes.

Abrus precatorius is a medicinal herb originated from Southeast Asia and native to India. The Hindustani centre of origin was found to possess lot of variability especially with respect to seed color. However, no previous researchers have focused on studying the variability by including the entire variability of the region. A total of 99 accessions were analyzed for its biochemical components like moisture content, total phenols, antioxidants, ash content, protein content, total monomeric anthocyanin and flavonols. Principal component analysis (PCA) shown that first three components viz., seed color, phenols and antioxidants are major principal components that contribute to 80.86% of total variability in the germplasm. The dendrogram constructed based on the biochemical data and by using the software Darwin version 6.0. formed three main clusters i.e. cluster 1, cluster 2 and cluster 3 which contain 55 accessions, 40 accessions and 1 accession respectively. Seed color was found to be the main criteria based on which the Abrus accessions were grouped. Accessions from different clusters also differ for their mean phytochemical constituents. Germplasm curators / plant breeders can select such elite genotypes and utilize them in crop improvement programmes.

Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.

The paper presents the problem of damage detection in steel girders. Static displacements at the selected point of the structure play the role of measured variables. Structural response signal decomposition is performed according to the Mallat pyramid algorithm, which is used to perform the discrete wavelet transform (DWT). This procedure allows us to quite well determine the location of structural damage. The geometry and the placement of any defective part of the structure may have a random character. It can be assumed that the random processes occurring in the broadly understood structure mechanics are Gaussian in nature. The first four probabilistic moments are estimated using three approaches independent: semi-analytical (SAM), perturbative (SPT), and Monte-Carlo simulations (MCS). The semi-analytical random approach seems to be the most optimal due to the necessary computation time. The incorporation of the mathematical stochastic apparatus into the classical (deterministic) analysis of the statics of the structure makes it possible to estimate the reliability measures of the analyzed girder.

Principal Component Analysis (PCA) is a method that identifies common directions within multivariate data and presents the data in as few dimensions as possible. One of the advantages of PCA is its objectivity, as the same results can be obtained regardless of who performs the analysis. However, PCA is not a robust method and is sensitive to noise. Consequently, the directions identified by PCA may deviate slightly. If we can teach PCA to account for this deviation and correct it, the results should become more comprehensible. The methods for doing this and an issue with this are presented.

This study presents a comparative analysis of length deformation in Classical and Relativistic Mechanics, specifically investigating 10-gram objects accelerating to 1% of the speed of light. By employing Hooke's Law in Classical Mechanics and the Relativistic Lorentz Factor, the research explores the implications of acceleration dynamics and the limitations inherent in Relativistic Mechanics. The results reveal significant differences in predicted length changes between the two frameworks, emphasizing the necessity of considering relativistic effects beyond velocity alone. This study underscores the critical importance of addressing the incomplete treatment of acceleration dynamics in Relativistic Mechanics to achieve a more accurate depiction of length deformation in high-speed scenarios.

Over the last few decades, advancements in astrophysics have been closely linked to the development of powerful machine-learning models that can accurately classify celestial bodies. At the same time, however, many astronomical datasets are filled with new features collected by increasingly powerful telescopes. These features can cause overfitting, clouding predictive abilities, and dampening the ability of many models to classify images. Therefore, motivation to design more efficient models has skyrocketed—aiming to optimize for lower run times and high accuracies, even with fewer provided features. In our project, we seek to optimize a convolutional neural network model using a technique known as wavelet analysis. This technique allows us to pick the key features of an astronomical image and accentuate niche details, saving time and boosting accuracy. We applied it to the MiraBest Dataset, a dataset compiled from the FIRST sky survey using a virtual telescope. In the end, after training our neural network on the original images and the five filters (approximation, horizontal, vertical, diagonal, and combined), we found that with fewer features and less overfitting, the vertical Daubechies-family wavelet filter outperformed the original runs with the unaltered images by over 10%. Our findings suggest that wavelet analysis can help harvest the most valuable features in images of celestial bodies–leading to enhanced predictions in astronomical applications and perhaps bolstering modern astrophysical theory.

This study, serving as Part-2 of the research titled "Comparative Analysis of Length Deformation in Classical and Relativistic Mechanics," investigates the behaviour of matter within gravitationally bound systems. Through meticulous examination of projected length alterations, the research highlights differences between classical and relativistic mechanics frameworks, emphasizing the necessity of considering relativistic effects beyond velocity alone. Additionally, the study underscores the crucial role of gravitational effects on the effective mass of moving objects, which emerges as a critical factor in predicting length deformation across scientific disciplines. The incomplete treatment of relativistic effects within Relativistic Mechanics, including acceleration and material stiffness, emphasizes the importance of comprehensively understanding gravitational influences on effective mass. This is evident in gravitational equations, where the gravitational force depends not only on the object's mass but also on its effective mass, influenced by kinetic energy. Thus, incorporating the gravitational effect on effective mass enhances the understanding of length deformation phenomena within gravitationally bound systems, enriching scientific discourse.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

A new model for international bed numbers is applied to local area populations for English Clinical Commissioning Groups (CCGs) where the Age Standardized Mortality Rate (ASMR) is used as a measure of population health and hence for total occupied hospital bed demand (elective plus emergency admission types, including acute, rehabilitation, mental health, and maternity), rather than simple bed availability. A 100-unit increase in ASMR (European Standard population) leads to a 15.3 – 30.7 (feasible range) unit increase in occupied beds per 1,000 deaths. ASMR shows why the Australian states of the Northern Territory and Tasmania have an intrinsic higher bed demand than the other states – although in both cases the bed planning methodologies failed to indicate this need and both states show a large bed deficit. Due to the high volatility in bed demand the average bed demand for 2019 was determined by a linear trend using data for 2014/15 to 2019/20 — as the pre-COVID-19 baseline. The slope of the relationship between occupied beds per 1,000 deaths and deaths per 1,000 population shows a power law function. As is to be expected there is scatter around the trend line which will partly arise from uncertainty in the expected occupied beds in 2019, fluctuations in both ASMR and deaths per 1,000 population from one year to another, changes in the number of persons on the elective, outpatient and diagnostic waiting lists, and local area variation in births and consequently upon maternity, neonatal and pediatric bed demand. Additional variation in bed demand will arise from differences in the level of funding for social care, especially elderly care, and to a lesser extent upon patients using private health care, and charitable hospices for palliative care. However, after including the change in bed demand arising from ASMR the model is sufficiently accurate to identify gross differences in bed demand as a prelude to further investigation. The model requires further extension to cover specialty-level demand, and perhaps segregation between elective and emergency types of inpatient demand. A method for benchmarking the whole hospital’s average bed occupancy is given which enables them to run at optimum efficiency and safety. Using this method English hospitals can be shown to operate at highly disruptive and unsafe levels of bed occupancy leading to deleterious ‘turn-away’. Turn-away implies bed unavailability for the next arriving patient. The problems associated with crafting effective bed planning are illustrated using the English NHS as an example.

Redundancy in ship systems is provided to ensure operational resilience through equipment backups which ensures system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems, hence is provided with good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired black outs are common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensure a more robust maintenance and support platform for efficient ship operations. In this regard hybrid reliability and fault detection analysis were conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of 4 marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate overall low system reliability of less than 70 per cent within the first 24 months of the 78 operational months. Component criticality using reliability importance measures was used to identify all components with more than 40 per cent contribution to sub systems failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Networks classification. The ANN has identified failure patten threshold at about 200kva which can be attributed to overheating, hence establishing link between component failure and generator performance.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

The accurate detection of clouds is an important first-step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology have exploded, creating a new era in space exploration. Additionally, initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, more recently cloud screening in satellite acquired data is playing an increasingly critical role in the investigation of cloud free data for the retrieval of soil moisture, vegetation cover, ocean colour concentration and sea surface temperatures, as well as environmental monitoring of a host of products, e.g. atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to both accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this paper, the evolution of cloud detection technology is highlighted with advancement in sensor hardware technology and possible AI algorithmic advances. Additionally, a discussion is presented on methods to obtain the cloud truth data needed to determine the accuracy of these cloud detection approaches.

The paper delves into the critical issue of damage detection within the guy cable of a truss steel mast, a pivotal component in structural integrity. It introduces a model wherein damage manifests as a localized cross-section reduction in a single element of the cable. Employing Discrete Wavelet Transform (DWT), a pioneering methodology, the study scrutinizes the behavior of the affected elements through static structural analyses. Signal decomposition via the Mallat pyramid algorithm facilitates comprehensive examination. Static displacements at the connection point between the cable and the truss mast serve as the measured variables. Through systematic investigation, the paper evaluates the impact of damage size and location, external loading force, and cable tension force on the efficacy of the proposed approach. Utilizing data derived from Finite Element Method (FEM) computations for wavelet analysis the authors substantiate the findings with numerical examples, thus offering valuable insights into damage detection strategies for structural health monitoring and engineering applications.

This paper investigates the importance of enhancing the safety of research reactors, especially in newcomer countries where experience may be limited. The focus is on the role of Independent Assessment (IA) and assessors in addition to using Best Estimate plus Uncertainty (BEPU) approach in enhancing safety measures. By using the best estimate codes to perform uncertainty calculations, the BEPU technique logically connects nuclear thermal hydraulics and nuclear reactor safety. BEPU is based on an understanding of the phenomena related to the reactor state. Reactor circumstances, such as those related to operations or accidents, could be identified in this way. The IA process, involves experts supporting design, installation, safety analysis, decommissioning, and modification. In order to shape the use of IA process in this study feedback from nuclear professionals was collected through questionnaires, revealing a consensus on the necessity of qualified regulatory bodies in newcomer countries. Additionally, the questionnaires highlighted the potential benefits of IA, such as minimizing accident risks and providing valuable insights for future analyses. The results indicate a global anticipation of increased research reactor construction, particularly in newcomer countries. The study discusses the propagation of input uncertainties, emphasizing the importance of considering various factors and parameters in safety assessments. The BEPU method was applied by using the COBRA-TF code by modeling onset of nucleate boiling (ONB) conditions for VVR-K research reactor. For uncertain parameters; ±1% inlet/initial pressure, ±1% inlet temperature, and ±1% linear heat rate, the ONB temperature in the axial region with the average cladding temperature was calculated to be 114.44℃. Table 2 (our references) provided a value of 114.6℃. The close agreement between the reference value and the COBRA-TF result shows that the model used in the calculation of the reference value and the Chen relation used by COBRA-TF are compatible.

Objective. To test the hypothesis that a session of unilateral tSMS over the dominant primary motor cortex (M1) in healthy subjects produces a transient decrease in manual dexterity. Methods. A triple-blinded randomized control trial was conducted in 40 healthy participants who were allocated to real- or sham-tSMS group. Each participant underwent one session of tSMS and were assessed before, immediately after, 15 minutes, and 40 minutes after stimulation. Outcome measures were Finger Tapping Test (FTT), 5-seconds FTT, nine Hole Peg Test, and the hand grip assessment. Results: Significant differences were found between groups 15 minutes after stimulation in favor of the tSMS group in the FTT (2.2 ± 0.99; CI= .18-4.22; d= .84p=.034), and 5-second FTT (4.2 ± 1.98; CI= .199-8.2, d= .68; p=.04). In the intragroup analysis, a significantly lower number of taps 15 minutes after stimulation was observed in sham group (3.3 ± 1.01; CI= .47-6.13; d= .42; p=.015), but not in the real stimulation group. Conclusion: A unilateral session of tSMS in the M1 of the dominant hemisphere of healthy individuals does not reduce manual dexterity or grip strength. This technique could have benefits on the motor performance of the arm contralateral to the stimulated motor cortex

The importance of detecting and diagnosing the imminence of aneurysm rupture before it occurs cannot be overstated. However, this process is often time-consuming and challenging, with variability in conclusions not uncommon among experts. Furthermore, current state-of-the-art models in medical image segmentation suffer from limited availability of large annotated datasets for training, exacerbated by an immense data imbalance. In most segmentation tasks, 3D segmentation is preferred due to its capacity to leverage more contextual information. However, these models require more data, which is constrained by the aforementioned issues. In the realm of current diagnosis models, those lacking geographic context are notably ineffective. To address these issues, the aim is to develop a model pipeline to accurately detect cerebral intracranial aneurysms and diagnose rupture imminence through 3D magnetic resonance angiography (MRA) and tabular input, augmented with the implementation of neuro-symbolic ai. The utilization of data fusion to tackle the lack of context in tabular models is proposed, leveraging extracted geographical features from a segmentation model. To enhance segmentation results and tackle the data imbalance in aneurysm segmentation, a two-stage model pipeline that extracts contextual geographic features before aneurysm segmentation is suggested. Segmentation results are additionally improved through ensemble techniques and novel preprocessing techniques. Finally, a neuro-symbolic aspect is introduced to enhance model diagnosis, interpretability and performance. The multi-step Integrated Neuro-Symbolic Analysis Pipeline for Cerebral Aneurysm Rupture Diagnosis is introduced.

The COVID-19 pandemics has had an unprecedented global impact, and the COVID-19 mass vaccination campaign has been commonly regarded as crucial to overcome the pandemics. Since all-cause mortality is the best way to measure the consequences of a health intervention, the present study was devised to analyze the all-cause mortality data of the United Kingdom (UK), which are made publicly available broken down by vaccination status. Data from April 2021 to May 2023 were retrospectively collected and analyzed according to age groups and vaccination status and the Standardized Mortality Ratio (SMR) for all-cause mortality and for non-COVID-19 were calculated in comparison to the corresponding unvaccinated groups. Results show that, since an age group dependent date, the all-cause mortality SMRs were increasing in any of the age groups considered. Initially, the all-causes death SMRs were less than 1 for all age groups, but due to their growth, for the age groups 18-39, 80-89 and 90+ years by a certain date they exceeded the reference value. For the other age groups, as long as the trend is maintained, it is possible to predict the date in which the SMR would reach the value 1. Non-COVID-19 SMR values show a very similar trend. Their initial values much lower than 1 suggest the presence of significant biases in the ONS dataset which lead to an underestimation of the risks for those vaccinated, as it is not plausible that the vaccine protects from causes other than COVID-19. The finding that all-cause mortality SMRs in vaccinated in comparison to unvaccinated people increases over time requires careful examination to understand the underlying factors. Meanwhile, the other major countries should undertake a systematic collection of all-causes mortality broken down by vaccination status, and should be implemented a moratorium of the promotion of mass vaccination campaigns.

Analyzing big data poses a great challenge for numerous researchers to explore the data structure. Dimension reduction methods can be used to reduce data dimensionality, taking it from occupying a high-dimensional space to existing in a lower-dimensional space while retaining as much information as possible. Principal Component Analysis is one of the most popular used to reduce the dimensional space. The bootstrap sample is obtained by randomly sampling n times with replacement from the original sample, the method provides easy tool to understand the interactive component and develop the process. There are not enough researches discussed the stability of PCA method using bootstrap method. In this paper, the bootstrap method is used to analyze the stability of PCA results and to estimate the number of PCA in efficient way. The method is used to estimate the number of PCA that needed to classify the data set, and the effectiveness of the discussed techniques is demonstrated through real data sets.

Artificial intelligence (AI) driven language models have seen a rapid rise in development, deployment, and adoption over the last few years. This surge has sparked many discussions about their societal and political impact, including political bias. Bias is a crucial topic in the context of large models due to its far-reaching consequences on technology, politics, and society. It significantly influences public perception, decision-making, political discourse, and AI policy governance and ethics. This study investigates political bias through a comparative analysis of four prominent AI models: ChatGPT-4, Perplexity, Google Gemini, and Claude.Through a comprehensive analysis by systematically and categorically evaluating their responses to politically and ideologically charged tests and prompts, utilizing the Pew Research Center’s Political Typology Quiz, the Political Compass assessment, and ISideWith political party quiz, this study identifies significant ideological leanings and the nature of political bias within these models. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate these biases. This analysis also advocates for more robust practices and comprehensive frameworks to assess and reduce political bias in AI, ensuring these technologies contribute positively to society and support informed, balanced, and inclusive public discourse, which will point towards neutrality.The results of this study add to the ongoing discourse about the ethical implications and development of AI models, highlighting the critical need to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex issue of bias in AI.

State Forest Management Organizations (SFMOs) play a crucial role in the European forest sector, managing almost half the forests in the region. SFMOs are often managed for timber production only whereas, being publicly owned, they should play an important role in providing a vast range of public goods (e.g. soil protection, biodiversity conservation). Their management goals depend on the history and current conditions of the forest sector at a national level, as well as different challenges and the potential for development. Although there is a lack of knowledge about the current performance of SFMOs, there have been recent changes to their management goals and practices in response to the new demands expressed by society (e.g. transparency, social inclusion). The main purpose of this study is to analyse the current situation of SFMOs by clustering them according to indicators that reflect three pillars of the common understanding of sustainable forest management (SFM) concept. With the help of Principal Component Analysis (PCA), we grouped countries according to common characteristics of the forest sector at the national level. Results show three main clusters of SFMOs in Europe. The first cluster has rather small but commercially-oriented forestry unit together with other business activities and a strong focus on public services. The second sees itself as the protector of public interest, rather than commercially-oriented organisations. The third is mainly profit-seeking. The existence of diverse SFMO clusters shows the possibility of different approaches for SFM with a focus on different goals (e.g. profit gaining, public service delivery).

This study investigates the microbial composition of a commercially available Swiss fermented milk and colostrum product with a history of use in chronic condition management since 2014. The analysis, performed using the Axiom Microbiome Array, identified a high number of targets corresponding to microbial species classified as probiotics belonging to the Bifidobacteriaceae family. The array provided 28 unique targets, each corresponding to a specific microbe. Next, the amino acid sequences of beta-galactosidase and sialidase from Bifidobacteria and humans were studied because these enzymes are responsible for the production of GcMAF (Gc protein-derived Macrophage Activating Factor) from the Gc protein that is abundant in colostrum. The Bifidobacterial enzymes showed striking functional similarities with their human counterparts. In addition, the study explores the potential health benefits of this product with a focus on GcMAF and chondroitin sulfate, a glycosaminoglycan highly represented in milk and colostrum. The high concentration of specific Bifidobacteria strains contributes to these benefits through two mechanisms: enhanced GcMAF production and activation of chondroitin sulfate, which in turn exhibits GcMAF-like activity. These findings suggest that the Bifidobacteria-rich Swiss product could offer health benefits through these combined mechanisms.

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

This paper presents an approach to the analysis of beams with rectangular cross-sections subjected to Saint-Venant torsion using the Fourier analysis, body forces being not considered. The Saint-Venant torsion of beams, also called free torsion or unrestrained torsion, is characterized by the absence of axial stresses due to torsion; only shear stresses are developed. The solution to this torsion problem consists of finding the Prandtl stress function that satisfies the governing equation and the boundary conditions, the governing equation being such that the Laplacian of the stress function has a constant value within the cross-section. In this paper, Fourier sine series was utilized to describe the constant value of the above mentioned Laplacian, while double sine series or simple sine series was utilized to describe the Prandtl stress function. Closed-form solutions in form of infinite series were derived for shear stresses, torsional constant and warping function. Those solutions were identical to the exact solutions.

This study investigates the efficiency of BEAM (Biologically Enhanced Agriculture Management) inoculant, derived from compost produced under Dr. David Johnson-Su's composting technique, in promoting plant growth. A winter mix of pasture comprising Oats, Tetila, unhulled Couch, and Haifa was used as the test crop. Seeds were soaked in BEAM inoculant, water (control), and a competitor product 'Companion.' Results revealed significant enhancements in germination rates, foliage growth, and root development in the BEAM-treated group compared to controls. Further research aims to identify the microbial components of BEAM and explore its potential as a suppressant for fusarium species in orchards.

A compositional data vector is a special type of multivariate observation in which the elements of the vector are non-negative and sum to a constant, usually taken to be unity. A compositional data does not have zero and only retains relative information. Furthermore, comparisons can only be made between compositional data of the same component. The relevant sample space is the standard simplex. A simplex space is a space that is a generalized form of a triangle. For compositional data, many of the operations defined in Euclidean space are meaningless. Microbiome analyzes have become popular in recent years. Operational Taxonomic Units (OTU) and Amplicon Sequence Variants (ASVs) used in microbiome analysis are one type of compositional data. The microbiome data are counts of different species within a sample and it is compositional. Although there is an order of bacterial abundance within a sample, there is no order of bacterial abundance between samples. Firmicutes and Bacteroidetes, the major phyla in the colon, have been observed in humans worldwide. Gut microbiome analyses often use the Firmicutes/Bacteroidetes ratio and principal coordinates analysis (PCoA). Alpha and beta diversities are used as indicators of bacterial flora diversity. However, misinformation is pervasive in the human microbiome literature and analysis. There is a lot of misunderstanding regarding compositional data and its analysis. An attempt was made to demonstrate how to analyze using the “National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN JMD) (Public Data).” The results showed that PCoA did not work, and principal component analysis (PCA) was useful for analyzing the gut microbiome relative diversity.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

Background and aims: Despite recent advances in advanced prostate cancer treatments, there are no clinically useful biomarkers or treatments for men with such cancers. Targeted therapies have shown promise, but there remain fewer actionable targets in prostate cancer than in other cancers. This work aims to characterize BRD9, currently understudied in prostate cancer, and investigate its co-expression with other genes to assess its potential as a biomarker and therapeutic target in human prostate cancer. Materials and methods: Omics data from a total of 2053 prostate cancer patients across 11 independent datasets were accessed via Cancertool and cBioPortal. mRNA expression and co-expression, mutations, amplifications, and deletions were assessed with respect to key clinical parameters including survival, Gleason grade, stage, progression and treatment. Network and pathway analysis was carried out using Genemania, and heatmaps were constructed using Morpheus. Results: BRD9 is overexpressed in prostate cancer patients, especially those with metastatic disease. BRD9 expression did not differ in patients treated with second generation antiandrogens versus those who were not. BRD9 is co-expressed with many genes in the SWI/SNF and BET complexes, as well as those in common signaling pathways in prostate cancer. Summary and conclusions: BRD9 has potential as a diagnostic and prognostic biomarker in prostate cancer. BRD9 also shows promise as a therapeutic target, particularly in advanced prostate cancer, and as a co-target alongside other genes in the SWI/SNF and BET complexes, and those in common prostate cancer signalling pathways. These promising results highlight the need for wider experimental inhibition and co-targeted inhibition of BRD9 in vitro and in vivo, to build on the limited inhibition data available.

In this study, the autofrettage method, which is used to increase the durability of cylindrical parts, was applied to a barrel made of steel material according to the AISI 4340 standard. Assuming that the barrel works continuously under the pressure of 400 MPa, optimum autofrettage was performed. The stresses occurring on the barrel before and after autofretage were calculated. Additionally, analytical equivalent stress values (Von-Mises) formed on the part's wall were compared with numerical analysis results obtained with a commercial finite element program ANSYS®, and their compatibility was examined. As a result of the calculations and analysis performed, it was observed that autofretage has positive effect on increasing the pressure carrying capacity of the barrel. After autofrettage the cylindrical part, it was determined that the Von-Mises stress values formed in the part wall changed and these stresses took the lowest value at a point (elastic-plastic interface). It has been evaluated that this point gives the optimal autofretage pressure value for a certain operating pressure. After the autofrettage application, it was determined that the Von-Mises Stress value in the barrel wall decreased by 25.27 % and the pressure carrying capacity of the barrel increased without any design or dimensional change. It has been seen that the analytical and numerical analysis results are consistent with each other, and it is expected that this study will guide and contribute to the studies to be conducted on increasing the strength or lightening the weight of the parts.

Principal component analysis (PCA) is used to reduce the dimensionalities of high dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods, and their applications for investigating protein dynamics.

The aim of this work was to determine if the development of low-cost or no-cost finite element analysis (FEA) software has advanced to the point where it can be used in place of trusted commercial FEA packages for linear static structural analyses using isotropic material models. Nonlinear structural analysis will be covered in a separate paper. Several suitable packages were identified, these underwent a process of systematic elimination when they were unable to meet the minimum imposed qualitative criteria. Three packages were chosen to be subjected to performance benchmarking, namely: Code_Aster/Salome Meca; Mecway and Z88 Aurora. SimScale, a browser-based analysis package was included as well because it met all the baseline criteria and has the potential to offer a completely cloud-based approach to computer aided engineering, potentially reshaping the way an engineering business views its operational capabilities. This paper presents the test cases and simulation results for packages that fall under the linear static structural analysis type.

In the era of Big Data and the Internet of Things (IoT), ensuring the security and privacy of patient data within distributed health systems is paramount. This research delves into the challenges posed by these advancements and explores viable solutions, focusing notably on blockchain technology, data encryption methods, and access control mechanisms. Employing a comprehensive literature review approach, the study synthesizes current research findings, complemented by analyses of existing frameworks and case studies from healthcare organizations, offering practical insights. Interviews with cybersecurity experts and healthcare IT professionals provide nuanced perspectives on current security issues and emerging trends. Through qualitative and quantitative data analyses, the study generates recommendations aimed at enhancing patient data security. These include the adoption of new frameworks and innovative technologies to fortify healthcare data systems. By addressing these critical aspects, this research aims to significantly contribute to the advancement of a more secure and efficient healthcare ecosystem in today's digital landscape.

To decrease oral pathogens and increase the number of beneficial bacteria, probiotics and bioactive compounds made via microbial bioconversion are recently used. In addition, the interest regarding probiotics-mediated bioconversion with popular medicinal plants is increasing. These compounds produced during the process exhibit synergistic effect and enhance a variety of biological activities including pathogen inhibition in host. Therefore, we developed bioconverted milk with high inhibitory activity against oral pathogens and α-glucosidase. Lactiplantibacillus plantarum SMFM2016-RK was chosen as the probiotic due to its beneficial characteristics such as high acid and bile tolerance, antioxidant activity, and α-glucosidase inhibition. Based on the minimal bactericidal concentration against three oral pathogens, the following appropriate concentrations of Artemisia herba-alba extract were added to milk: 5 mg/mL of A. herba-alba ethanol extract and 25 mg/mL of A. herba-alba hot-water extract. Four bioconverted milks (BM), BM1, BM2, BM3, and BM4, were produced by combining L. plantarum SMFM2016-RK alone, L. plantarum SMFM2016-RK and ethanol extract, L. plantarum SMFM2016-RK and hot-water extract, and L. plantarum SMFM2016-RK with both extracts. As a result of antimicrobial activity, BM3 inhibited the growth of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis the most, and BM4 suppressed the growth of Fusobacterium nucleatum the most. In addition, bioconverted milk containing A. herba-alba (BM2, BM3, and BM4) inhibited α-glucosidase more effectively than BM1. In conclusion, A. herba-alba-added milk bioconverted by L. plantarum SMFM2016-RK displayed the growth inhibitory effect on oral pathogens and the α-glucosidase inhibitory activity, thus it necessitates to evaluate the effects on the alleviation of oral diseases and glycemic control through future animal experiments.

Respiratory infections with RSV account for ¼ of hospital admissions for acute respiratory infections. Nirsevimab has been developed to reduce the hospital burden of these infections: Since it has a long shelf life thanks to its high affinity for FcRn (neonatal receptor for the Fc fragment of IgG), it requires only a single injection and can be administered to all children under 2 years of age, unlike palivizumab, which is reserved for at-risk children and requires several injections. With the reservation of a large or unknown number of excluded treated subjects in both clinical trials and post-marketing observational studies, nirsevimab has been shown to be highly effective in reducing hospitalization rates for RSV infections. In rare cases, however, RSV infections were more severe in the treated group than in the placebo group. The 2023-2024 immunization campaign involved 4 countries (USA, France, Spain, Luxembourg). Analysis of the results of the 2023 immunization campaign does not allow us to conclude on the efficacy of nirsevimab in the USA (coverage being too low at around 20%); in the 3 other countries coverage is ≥ 80%. Neither clinical trials nor observational studies point to a reduction in all-cause hospitalizations in the immunized age group in this same season compared with previous seasons. The rate of hospitalization for RSV in the treated age bracket is significantly reduced compared with previous seasons in France, Spain and Luxembourg, but biases (exclusion of a large or unknown number of subjects, and changes in diagnostic criteria in France) may moderate this reduction. In France, there is a significant signal of an increase in newborn deaths between 2 and 6 days of age during the 2023-2024 immunization campaign. This signal could be attributable to ADE (antibody-dependent-enhancement). ADE has been observed with RSV F-protein antibodies in inactivated vaccine trials. The theoretical risk of ADE with an anti-RSV F-protein antibody such as nirsevimab has been eliminated by the EMA following clinical trials. In vitro evaluation of nirsevimab's effector functions on FcγR (cellular IgG Fc receptors) and the properties of FcRn cannot exclude the possibility of an ADE. This risk has been incompletely assessed in preclinical in vivo trials. In clinical trials, pharmacokinetic studies show the possibility in rare individuals of sub-neutralizing circulating levels of nirsevimab in the blood and pulmonary mucosa, in the days following injection and at longer distances. This could explain the rare cases of aggravated RSV infections in treated subjects. ADE by disruption of the immune system has not been studied, and could explain why the all-cause hospitalization rate has not fallen in treated age groups: mAbs are indeed capable of promoting infections by binding to FcRn. Given the high price of nirsevimab, the cost-effectiveness of mass immunization campaigns may therefore be debated from an economic as well as a scientific point of view.

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. However, most of the previous experiments have been carried out under controlled conditions that preclude the overwhelming effect of ambient light. This study demonstrates successful detection of a 3 MBq alpha emitter in an open environment using a newly developed, lightweight compact alpha camera. This camera incorporates a deep-cooled CCD and a specially designed lens system with a low f-number to maximum the signal intensity. The lens was also designed to minimize blue shift effects of filters. Nighttime imaging was achieved with a dual-filter system using a sandwich filters assembly centered at 337 nm and 343 nm for capturing alpha RL and subtracting background light, respectively. At night, the alpha source was detected from 1 meter away within one minute. The system was also evaluated under simulated urban lighting conditions. For daytime imaging, a stack of tilted 276 nm short pass filters minimized sunlight interference, enabling clear detection of the alpha source at 70 cm within 10 minutes under indirect sunlight. This research not only underscores the viability of long-range optical detection of alpha emitters for environmental monitoring and scientific research but also highlights its ability to enhance nuclear safety and public health in real-world settings.

The Armed Criminal Group (KKB) in Papua is still conducting acts of terror such as burning public facilities, killing people, and spreading propaganda through social media, which could threaten the integrity of Papua as part of Indonesia. This study used a critical discourse analysis approach to identify various propaganda for Free Papua, collected from uploads on Facebook owned by KKB Papua and its sympathizers. The propaganda claims that TNI and Polri conduct open operations, form militias, and commit violence and torture against the Papuan people. Non-Papuan Indonesians are labeled with derogatory words, while Papuan students are persecuted. Independence is seen as a way to liberate Papua from Indonesian colonialism, with demands for a referendum and termination of special autonomy. KKB also proclaimed a provisional Constitution (UUD) and rejected Indonesian legal products. The propaganda techniques used by KKB include card stacking, name calling, glittering generalities, and bandwagons. To overcome this propaganda, efforts are needed to provide correct and reliable information, education, and instill a sense of belonging to Indonesia for the Papuan people. By doing so, acts of propaganda and separatism by the Free Papua Movement can be overcome, and the integrity of the nation can be maintained.

This work develops a new method for vector data augmentation. The proposed method applies the Principal Component Analysis (PCA) and determines the Eigenvectors of a set of training vectors for a Machine Learning (ML) method and uses them to generate the distilled vectors. The training and the PCA distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors the extended set trains a ML classifier to achieve a required accuracy. Hence, the novelty of this study is, the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: Neural Network, Logistic Regression, Support Vector Machine with linear and polynomial kernels. For the purpose of augmentation, we conduct several distillations including nested (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We train the classifiers with three sets of vectors: original vectors; original augmented with vectors distilled by PCA; original augmented with distilled and double distilled, by PCA, vectors. The experimental results are presented in the paper, and they confirm the advantage that the PCA distilled vectors increase the classification statistics of ML methods, if the distilled vectors augment the original training vectors.

This research discusses the challenges faced by university students in group work assignments and the importance of effective project management. This article introduces Tasknest, a project management platform that helps teams organize tasks, track progress, and improve communication. This article also reviews related studies on project management applications and discusses the Agile methodology used in the development of Tasknest. The implementation of the task management system was done using Agile methods and Typescript programming language. This web-based system includes a landing page, dashboard page, board page, and settings page. Black box testing was conducted to ensure the system functions as expected, with tests on login, board creation, adding columns and cards, and others. Overall, the system was successfully implemented and tested. Tasknest application development includes user stories, acceptance criteria, sprint backlogs, sprint planning, reviews, and retrospectives. System design analysis includes usecase diagram, activity diagram, sequence diagram, and entity relationship diagram. Application features include task list management, team member management, real-time collaboration, notification system integration, task enhancement, in-task checklists, user authentication, UI optimization, performance improvement, collaboration feature enhancement, database design, front-end implementation, and UI design. The Agile Alliance outlines 12 principles for implementing Agile methodologies, which prioritize customer satisfaction, collaboration, and continuous improvement. Agile methodologies offer high flexibility, rapid iteration, responsiveness to change, improved product quality, stakeholder involvement, transparency, and clear responsibilities. Scrum is a framework that supports Agile principles, with a focus on transparency, inspection, and adaptation. Scrum improves team collaboration, rapid adaptation to change, transparency, visibility, product quality, and team motivation. In the development of the Tasknest application, the Scrum methodology is used, with roles such as Product Owner, Scrum Master, and Development Team. Data collection techniques include interviews, observations, and literature studies, while data testing techniques involve black box testing. Planning using Scrum involves creating a Product Backlog with prioritized features and fixes. Prioritization determines the duration of each sprint.

The software industry has adopted component-based software development (CBSD) to rapidly build and deploy large and complex software systems with significant savings at minimal engineering effort, cost, and time. However, CBSD encounters issues on security trust, mainly with respect to dependability attributes. A system is considered dependable when it can produce the outputs for which it was designed with no adverse effect on its intended environment. Dependability consists of several attributes that imply availability, confidentiality, integrity, reliability, safety, and maintainability. Dependability attributes must be embedded in a CBSD model to develop dependable component software. Motivated by the importance of these attributes, this paper pursues two objectives: to design a model for developing a dependable system that mitigates the vulnerabilities of software components, and to evaluate the proposed model. The model proposed in this study is labelled as developing dependable component-based software (2DCBS). To develop this model, the CBSD architectural phases and processes must be framed and the six dependability attributes embedded according to the best practice method. The expert opinion approach was applied to evaluate 2DCBS framing. In addition, the 2DCBS model was applied to the development of an information communication technology (ICT) portal through an empirical study method. Vulnerability assessment tools (VATs) were employed to verify the dependability attributes of the developed ICT portal. Results show that the 2DCBS model can be adopted to develop web application systems and to mitigate the vulnerabilities of the developed systems. This study contributes to CBSD and facilitates the specification and evaluation of dependability attributes throughout model development. Furthermore, the reliability of the dependable model can increase confidence in the use of CBSD for industries.

Hubble diagrams are examined for SN1a supernovae in the redshift range z = 0.01–1.3 and for gamma ray bursts in the range z = 0.034–8.1. It is shown that in the low redshift range, the Hubble diagram shows an innate equivocality between the ΛCDM and the tired light model. This means that the strong agreement between the z/µ data, calculated with the parameters of the ΛCDM model, and the experimentally measured z/µ values cannot be considered as definite evidence for the expansion hypothesis. The exponential function , which is characteristic of the tired light redshift mechanism, fits the data with similarly high accuracy. Hence, on the premise of low redshift data, a decision for or against either model is completely arbitrary. The Hubble diagram for high redshift gamma ray bursts shows poor agreement with the ΛCDM model, but concurs with the exponential energy decay following from the tired light redshift hypothesis.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

In this paper we review dynamics and roughness of bonds in proteins on example of albumin, that is important from the physiological point of view. We have performed computer simulations of albumin chain. Statistics were collected by performing many simulations realizations for each experimental setting. We concentrate on hydrogen bonds, cation-π and π- π interactions and NP contacts. Histograms of hydrogen bonds length are positively skewed in contrary to histograms of interactions and HP contacts that are negatively skewed. Scaling exponents of power spectra of energies of bonds / interactions /contacts are in range -0.2 to -0.5 and significantly differ between various hydrogen bonds or interactions. Varying scaling of such spectra can be used to classify between distinct bonds or contacts. Concerning particular amino-acids, largest amount of HBO H20 bonds are between Glutamate (GLU) amino-acids and water particle, while large amount of HBO bonds are formed with Lysine (LYS). For HP contacts the mayor role plays Phenylalanine (PHE) and Leucine (LEU) amino-acids. From decay curves HBO H2O bonds decays in fastest rate, while HBO bonds and HP contacts at slowest rate. We present as well decay curves of bonds formed by particular amino-acids, that gives interesting results.

The severe acquired respiratory coronavirus–2 (SARS–CoV-2) infection has initiated both acute and chronic COVID–19 disease between 2020 and 2023, currently evolving with other homologous prior coronavirus strains of the Nidoviridae order, which encompasses other prevalent alpha/ beta coronaviruses, but also the Middle East Respiratory Syndrome (MERS-CoV) and SARS-CoV-1, with recent SARS–CoV–2 variants, increasing demands for effective immunogens and therapeutic approaches that will reduce global disease burden and further infection from SARS–CoV-2 affected individuals that may experience post acute sequelae (PASC) or “Long COVID”. Following a worldwide programme of prophylactic vaccination, there is still a dilemma in the efforts to find prophylactic and early therapeutic approaches that would treat novel SARS-CoV-2 variants and prevent future epidemics or pandemics within host human and animal populations, where zoonotic or cross species transfer naturally occurs. Concerns about viral immune escape intersect at a specific point; a gained evolutionary ability of several viruses to co–infect and compete against previous scientific advances since 1796 that remain undetected or asymptomatic during the early stages of infection progressing to symptomatic and severe disease via the double methylation of the 5' end of eukaryotic DNA or RNA-based viral genomes, the 7-MeGpppA2’-O-Me cap, and its double methylation capping process is performed by the activated viral 2’ - O - Methyltransferase (MTase) enzyme, a complex of two viral non-structural proteins (NSPs) joined together through an activation process (NSP10/16) and by N7-Methyltransferase (N7-MTase/NSP14), respectively. Moreover, it was discovered that polymorphic viruses translate NSP1, which prevents the activation of various Pattern Recognition Receptors (PRRs), and consequently, detection of Pathogen-Associated Molecular Patterns (PAMPs) and Damage-Associated Molecular Patterns (DAMPs) alike. NSP1 also silences important interferon-encoding genes (INGs) and interferon-stimulated genes (ISGs), is signalled in a paracrine manner to neighbouring cells, and that induces the apoptosis of host cells, inducing an effect of “trace erase” effect and making the viral infection as immunologically “invisible” as possible during the initial, key stages of viral replication and distribution, all such mechanisms occurring independently of the viruses in cause. Another important viral NSP is NSP14, as it plays two functional roles that are independent of each other; to produce new viral genetic material for the purpose of maintaining the validity of the viral genome as well, and not just transfer a methyl group to the 5’ end of the viral genome. Other viral NSPs share a role with NSP1, 10, 14 and 16 in directly suppressing the activation of PRRs and ISGs, and all such viral proteins help the virus in its process of self-camouflaging against first- and second-line immunity, thereby often severely impacting the quality of the produced adaptive immune responses. The outcome of all such phenomena is the sharp decrease in the host Type I and Type III interferons' (IFNs) rate of synthesis by the host cells, that would usually occur and affect homeostatic cellular pathways, resulting in further viral replication and induced apoptosis. Nonetheless, effects of microbial immune evasion during the development of other viral or carcinogenic pathologies are not widely known. In short, polymorphic viruses developed a proportionate evolutionary response against developed adaptive immune responses, by currently relying on gaps mostly situated in the natural immune system in their process of molecular self-camouflaging. Scientists developed numerous approaches of early treatment that generally showed good success rates and fewer risks of adverse events, and the still early present stages of COVID-19 research should also be taken into consideration whilst filtering for the most appropriate solutions. For example, the administration of recombinant human interferons I and III into the nasal mucosa cellular layer, as key mediators of anti–viral activity, can simulate intracellular infection and stimulate cellular activity in a timely manner, training the innate and adaptive immune system cells to develop and appropriately stimulate an adequate immune response through B and T cells. Another example could involve the treatment of natural and adaptive lymphocytes with a low dose of IFNs I and possibly III, prior to their insertion into the host lymphatic system, possibly alongside additional recruitment of plasmacytoid dendritic cells (pDCs) as further interferon “factories”, all with the purpose of early infection management. It might be that focusing on directly offering the immune system the information about the genetics and protein structure of the pathogen, rather than training its first-line mechanisms to develop faster, excessively increases its specificity, making it reach a level that brings the virus the opportunity to evolve and escape previously-developed host immune mechanisms. It is until the scientific community realises this potentially crucial aspect that large proportions of the world population will probably continue to face serious epidemics and pandemics of respiratory diseases over the coming several decades, evidenced with dengue fever and more recently, monkeypox and possibly avian flu. Of note, it has been indicated that IFN I and / or III display significant immunising, early therapeutic and clinical disease onset-attenuating effects for many other microbial evoked diseases, as well as for a number of oncological diseases.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

This qualitative research explores the transformative impact of technological innovations on retail marketing effectiveness. Through thematic analysis of interviews with industry professionals, the study identifies key themes including enhanced customer engagement, personalized marketing strategies driven by AI and machine learning, data-driven decision-making leveraging big data and analytics, the rise of e-commerce platforms, integration of mobile technologies, adoption of augmented reality (AR) and virtual reality (VR), development of omnichannel strategies, and implementation of loyalty programs. These themes illustrate how technology has reshaped retail marketing by enabling real-time customer interactions, personalized shopping experiences, and targeted marketing campaigns that enhance customer satisfaction and loyalty. The findings highlight the critical role of digital platforms like social media in fostering community engagement and brand loyalty. AI and machine learning facilitate personalized marketing efforts, tailoring product recommendations and enhancing customer experiences. Data-driven strategies optimize marketing ROI by providing insights into consumer behavior and market trends. E-commerce platforms expand market reach and streamline transactions, while mobile technologies and AR/VR innovations create immersive shopping experiences that captivate consumers. Omnichannel strategies integrate physical and digital channels, delivering seamless brand experiences and enhancing customer retention through CRM systems and loyalty programs. Looking ahead, retailers must navigate challenges such as rapid technological change, the need for continuous adaptation, and concerns about data privacy. By embracing technological advancements strategically and maintaining ethical standards, retailers can sustain competitive advantage, meet evolving consumer expectations, and drive growth in the digital era.

The Jakarta LARP community, established in March 2020 and primarily active in Gelora Bung Karno, fosters inclusivity, diversity, and physical role-playing activities. It adheres to a policy of excluding ultra-nationalist ideologies and integrates new members despite language barriers. Bruhn (2011) defines this kind of community sharing the same goals, values, and lifestyles, while McMillan & Chavis (1986) outline four aspects of the sense of community.The research discovered that communities endure by utilizing solidarity built on the basis of their peculiarities. This study employed ethnographic methods alongside grounded research to identify the community's culture and their efforts to sustain and utilize it as community solidarity.

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

Some studies investigated the relationship between frequency domain analysis of heart rate variability and the cognitive performance at the Stroop task. We propose a combined assessment also of trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac and motor variables based on a principal component analysis. We also assessed the change on this result after an intervention of two months aiming at improving the perception-action link. At baseline, the PCA correctly identified 3 components: one related to cardiac variables, one to trunk motion and one to the performance of Stroop task. After the intervention, only two components were found with trunk symmetry and range of motion, accuracy and time to complete the Stroop task and low frequency heart rate variability aggregated in a single component by PCA. It suggested that this protocol was effective in investigating the embodied cognition and we defined this approach as “embodimetrics”.

The pollution of the bays in Abidjan is a major concern for the Ivorian policy makers. In fact, the pollution of the bays induce high costs to the society while impacting population health dramatically. As a result, pollution reduction management of production activities has been undertaken in the Cocody Bay area. To our knowledge, no study has yet proposed a model to evaluate the cost-effectiveness of these pollution management strategies. A cost-effectiveness model, based on Monte Carlo simulation, was developed to assess the economic and environmental impacts of various scenarios characterized by a set of production practices, both in the short term and in the long term. The authors discuss the steps and input parameters of the model presented. The proposed model may serve as the basis for identifying an optimal production scenario defined as the scenario with the best incremental cost-effectiveness ratio considering a willingness to pay (WTP) threshold. The WTP, to be estimated based on the gross domestic product of Côte d’Ivoire, represents the opportunity costs associated with selecting the optimal scenario. The current framework can also be applied to other settings facing similar challenge.

Presence detection is essential in many applications, including smart homes, office automation, and public space management. Traditional technologies like infrared sensors and cameras frequently encounter obstacles due to privacy concerns, high costs, and complexity. The study provides a novel way to detect presence based on Wi-Fi signals and machine learning algorithms, utilizing the ESP8266 microcontroller. The suggested system attempts to reliably detect the presence of people in a given place by evaluating fluctuations in Wi-Fi signal strength and patterns. The theoretical basis of this strategy is investigated, including the ESP8266’s capabilities for Wi-Fi data gathering and the use of machine learning methods for data analysis. The methodology includes data collecting with the ESP8266, preprocessing, feature extraction, and training and evaluating machine learning models. This system’s potential uses include smart home automation, office occupancy tracking, and crowd management in public settings. The paper also discusses potential issues such as signal interference, data privacy concerns, and the limitations of Wi-Fi detection. Future work is also mentioned, including the suggested system’s installation and testing and enhancement proposals to improve accuracy and scalability. This research aims to provide a cost-effective, non-intrusive, and scalable presence detection system by harnessing the power of Wi-Fi signals and machine learning, enhancing intelligent environments

Additive manufacturing (AM) has revolutionized the field of biopolymers by providing a range of opportunities to create complex and intricate structures. Here we provide an overview of developments in biopolymer AM, first in the context of how natural systems have impacted the synthetic processes of AM, and secondly in the context of how synthetically derived structures can be used for biomedical applications. In particular, we explore the range of biopolymers developed for AM, AM techniques for biopolymers, and potential biocompatible medical applications with a focus on four categories of tissue in the human body: skin, bone, heart, and nerve. Despite the current challenges of the technology, exciting results in tissue regeneration, synthetic replacements for natural grafts, and complex dynamic organoids have already come to pass - with many more possible in the future.

In this article, we present two topics related to mathematical economics. We offer an intriguing proposal for securing cost and production functions in the face of uncertainty and risk. We discuss the relationship between individual and social preferences. This is important because traditional revealed preference theory does not account for such factors.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Background and aims: Brain metastases are prevalent in the late stages of malignant melanoma. Multimodal therapy remains challenging. Patient-derived organoids (PDOs) represent a valuable pre-clinical model, faithfully recapitulating key aspects of the original tumor, including the heterogeneity and the mutational status. This study aimed to establish PDOs from melanoma brain metastases (MBM-PDOs) and to test the feasibility of using them as a model for in vitro targeted-therapy drug testing.Methods: Surgical resection samples from eight patients with melanoma brain metastases were used to establish MBM-PDOs. Samples were enzymatically dissociated followed by seeding into low-attachment plates to generate floating organoids. The MBM-PDOs were characterized genetically, histologically, immunohistologically and compared with the parental tissue. The MBM-PDO cultures were exposed to dabrafenib (BRAF inhibitor) and trametinib (MEK inhibitor) followed by cell viability assessment.Results: Seven out of eight cases were successfully cultivated, maintaining the histological, immunhistological phenotype, and the mutational status of the parental tumors. Five out of seven cases harbored BRAF V600E mutations and were responsive to BRAF and MEK inhibitors in vitro. Two out of seven cases were BRAF wild type, one case harboring an NRAS mutation and the other harboring a KIT mutation, and both were resistant to BRAF and MEK inhibitor therapy. Conclusion:We successfully established PDOs from melanoma brain metastases surgical specimens, which exhibited a consistent histological and mutational profile with the parental tissue. Using FDA-approved BRAF and MEK inhibitors, our data demonstrates the feasibility of employing MBM-PDOs for targeted-therapy in vitro testing.

In this paper, we propose an assumption of which the relation between electron and phonon is discussed and investigated based on the experiment of γ photon and electric interconverting with each other. We calculated the charge of a particular standing wave topology with Maxwell’s equation rotated in accordance space with the SO3 transformation. The derived charge value is 1.6035 × 10E−19C around 0.0826% different from the experimental data, indicating the proposal is a promising theory to explain the origin of charge and spin of electron particle. Electron spin angular momentum comes from the orbital angular momentum of photon rotation (electromagnetic field rotation), and electron charge comes from photon, that is, the space-time rotation effect of electromagnetic wave.

Weathering and erosion transport minerals and organics toward the sea or lake bottoms over geologic time. The finest solids are deposited in lower, less turbulent areas, such as lake bottoms and continental shelves. They sometimes stack up to thicknesses of kilometers, and begin compacting. These sediment sections are called shales, and as initially deposited in water, shales can have porosities up to 50-80{\%} water, As they are buried, many alteration products from oil to slate are produced due to overburden and temperature increasess, making them important to study. A preceeding paper \cite{1} showed that pressure solution is the primary mechanism for porosity reduction, with possible mechanical compaction at shallow depths. Without naming the mineral(s) involved, it postulated that the greater the product of the water and pore interfaces, the faster the reaction would proceed. This term is $ \varphi^{4m/3}(1-\varphi)^{4n/3}$ , where $\varphi$ is porosity and m and n are numbers close to unity. The large exponents, {\em 4/3}, recognize that the reaction occurs at the molecular scale at which the surfaces are rough. A second term, $\exp^{(-E/RT)}$ , indicates that the reaction is impeded by a quantum energy barrier, E, with diminished impeding power as increased available thermal energy, represented by the absolute temperature, T, becomes available at greater depths in the Earth. These two factors combine to allow porosity $\varphi$ to reduce with time, or equivalently for the fraction of solids, $(1-\varphi)$, to increase with time,$\left. \frac{\partial(ln(1-\varphi) ) }{\partial t } \right |_{\sigma} = (\varphi )^{4m/3}( 1-\varphi )^{4n/3}Ae^{-E/(RT)}$. { \em Here it is shown that this equation can cover quantitatively any actual deposition rates which may have been experienced by the six sections studied, the actual deposition rates being unknown for these cases. Hence a time-depth depositional history for any new shale section, known in detail, would allow determination of the parameters m, n, E and a lumped proportionality constant A. This was accomplished by showing that, for a wide range of depositional rates, r, the range of E for any of the studied sections is small compared to laboratory measurements of quartz solution\cite{2}, 24+-15kJ/mol. Results were obtained over this range of r's using the previously determined m and n, and porosity and temperature profiles. The presently existing porosity profiles necessarily incorporate any overpressure or underpressure conditions that may have existed in the past or currently, as the net difference between overburden and pore pressure is a primary driving force for pressure solution. $SiO_2$ usually comprises 20-50{\%} of shales. In conclusion, pressure solution of quartz can account for vertical compaction of shales quantitatively in the studied examples. Separately, a quasi-eqivalent method for discussing reduced porosities in Macron 2 versus Macron 1 as increased vertical stress, rather than additional horizontal stress, is illustrated. Experiments are suggested to clarify the pressure solution mechanism. The role of horizontal forces is discussed. From a global point of view, shale compaction by pressure solution is a quantified step in the cycling of continental rocks, beginning with rifting.

This paper focuses on the application of principal component analysis (PCA) to conduct a source apportionment of atmospheric aerosols from 8 sampling locations along the Marilao-Meycauayan-Obando River System (MMORS). Aerosols were collected on May 2016 during the same time that water samples were collected. Elemental analysis was conducted using a scanning electron microscope coupled with energy dispersive x-ray (SEM-EDX). Carbon (C), nitrogen (N), oxygen (O), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), bromine (Br), niobium (Nb), barium (Ba), mercury (Hg), and lead (Pb) concentrations were measured and used as inputs in Principal Component Analysis (PCA). The aerosol samples showed the presence of heavy metals Pb and Hg, elements that were also detected in trace amounts in the water measurements. Concentrations of heavy metals Fe, Pb, Hg in the aerosols were attributed to industrial sources. However, it was determined that the primary source of aerosols in the area were traffic and crustal emissions (C, N, O, Si, Al, Ca). Thus, control of traffic emissions would be more beneficial in reducing aerosol emissions in Meycauayan.

Citrus essential oils (EOs) have various bioactivities like antioxidants, with many applications. Antioxidant activities depend on the chemical compositions of the EOs, which are affected by climate, soil, and geographical region. Thus, investigations on chemical compositions and antioxidant activities of Citrus EOs in different countries are valuable. In this study, we distilled EOs from peels of Indonesian-grown Citrus, including C. nobilis, C. limon, C. aurantifolia, C. amblycarpa, and Citrus spp.Chemical compositions of EOs were analyzed using Gas Chromatography-Mass Spectrometer (GC-MS), whereas the antioxidant activities were determined by employing 2,2-diphenyl-2-picrylhydrazyl (DPPH) method. Furthermore, principal component analysis (PCA) was applied to elucidate the main contributing compounds for antioxidant activity. The results show that all EOs possess unique chemical characteristics, with limonene as the majority constituent. For antioxidant activities, C. limon and C. amblycarpa EOs are the two strongest, IC50 values below 7.00 μL/mL. PCA approach suggests that -terpinene mainly contributes to the high antioxidant activities of C. limon and C. amblycarpa. Moreover, o-cymene, thymol, p-cymene, and α-pharnesene may also be responsible for the antioxidant activity of C. limon EO. These results are valuable information for the applications of Citrus EOs as antioxidant sources.

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern. It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

The inherited ‘EIA’ process at Brexit is based on a ‘Project’ period from Inception tothe start of construction works. The ‘Incompleteness’ of the process covers andidentifies stages that will impact on the environmental cycle: construction period,operational life, de-commissioning and a return to acceptable environmental footprint.A master environment structure-plan framework is required to commence with theupgrading of the complete ‘EIA’ process, embracing separate limbs for the UK’spriority environmental targets for climate change, and other major adverse effectswhich result in serious ‘harm’ to the UK environment. The identification of the processfor ‘incompleteness’ requires effective sustainability, and inclusion of effectivemanagement tools, for the essential ‘monitoring/control/recording’ proceduresthroughout the project life and the natural environmental cycle. This will allow theopportunity for a new approach to a structured ‘Suite of EIAR Directives’, to enable a‘EIAR’ regime that ‘will remain fit and proper for UK purpose’.

Wearable antennas are increasingly vital in modern healthcare, facilitating continuous monitoring and communication for medical purposes. These antennas are integrated into wearable devices, allowing for the transmission and reception of vital health data, such as heart rate, body temperature, and blood pressure, to medical professionals or monitoring systems in real time. This instantaneous data transmission enables timely interventions, thus improving the quality of healthcare services. This review aims to comprehensively synthesize recent research from reputable databases, focusing on the functionality and effectiveness of wearable antennas in medical applications. The primary objectives of each study were categorized, including antenna design and integration, the performance of medical wearable antenna technology, safety considerations in wearable antenna biomedicine, and the use of antennas in clinical applications and their impact. This synthesis provides valuable insights for researchers and practitioners aiming to develop and deploy wearable antennas in medical settings, ultimately enhancing healthcare delivery and patient outcomes.

This article argues that philosophy and its influential narrative should take and think "food" - the gastro-world - in order to radically renew itself and develop a gastrocentric view of the world and the future of humanity. It discusses first some of the basic ideas of a gastrocentric understanding of the world, second the adventure of becoming a Terran, and third the daily practice of what I will call "terra madre forming". It tells us this: If there is to be a future of human survival and well-being on Earth, if we are to achieve the goals of sustainable development, then we have a clear agenda for tomorrow with the re-humidification of the soil - madre terra - that nourishes us. What needs to be done is quite simple and down-to-earth: We, each of us, if possible, must participate in the regeneration of the earth. In conclusion, it is time for the Slow Food movement to blossom into a geo-gastro-activism. The future task of this organization is to make itself electable as a political party to support the much-needed transformation of humans into Terrans.

In this paper, the transmutation operator method is used to construct an exact solution to the initial boundary value problem for the equation of multidimensional free transverse vibration of a thin elastic plate with a singular Bessel operator acting on geometric variables. We emphasize that multidimensional Erdélyi–Kober operators of fractional order have the property of a transmutation operator, allowing one to transform more complex multidimensional partial differential equations with singular coefficients acting over all variables into simpler ones. If formulas for solutions are known for a simple equation, then we also obtain representations for solutions to the first complex partial differential equation with singular coefficients. In particular, it is successfully applied to the singular differential equations, especially with Bessel-type operators. Using this operator, the considered problem is reduced to a similar problem without the Bessel operator. Based on the solution to the auxiliary problem, an exact solution to the original problem is constructed and analyzed.

This study investigates the pivotal areas where Artificial Intelligence (AI) integration in healthcare warrants further exploration, leveraging insights from Boston Specialists, a forefront healthcare provider in AI adoption. Employing a qualitative case study approach, enriched by in-depth interviews and comprehensive literature reviews, we identify three main domains for potential AI expansion: scheduling efficiency, clinical documentation, and AI-driven medical research. Our results underscore the substantial enhancements AI brings to operational workflows and patient care, notably through improved scheduling systems, accuracy in medical documentation, and the facilitation of data-driven research methodologies. These findings suggest a critical need for healthcare institutions to further invest in AI technologies, focusing on these key areas to harness AI's full potential in personalizing care, optimizing efficiency, and advancing medical knowledge. The study concludes with a call for continued investigation into AI's integration within healthcare settings, emphasizing the importance of a supportive organizational infrastructure, ethical considerations, and data security in realizing AI's transformative impact on healthcare delivery and patient outcomes.

In this paper we investigate some properties of square-free numbers. In particular, we study a function that counts the number of square-free numbers no larger than the given number n. We show that this function has asymptotics consistent with the predictions of the Riemann hypothesis and use this to obtain new and better estimates of the density of square-free numbers. Finally, we present a well-known generalization of the concept of a square-free number to monoids and research on square-free factorizations.

In this article, we present solutions for one of the oldest mathematical problems, the Collatz Conjecture, and provide a proof for the Riemann Hypothesis, utilizing a new number theory based on newly discovered number properties, which will also be presented in this paper. This is made possible through the Sophy-Peter mathematical framework, built upon this new number theory. The Collatz Conjecture will be disproven, but as an alternative, the Oscillating Theorem will be introduced, with its correctness proven within this article. Furthermore, we present the general version of the Riemann zeta function, developed based on the new number theory. The correctness of this function is verified by comparing it with existing results of the zeta function. Using this approach and the Sophy-Peter framework, we have successfully proven the Riemann Hypothesis, long considered a millennium problem. Moreover, since the general zeta function is proven, this implies the correctness of the new number theory and the Sophy-Peter framework.

This study presents the evaluation of an ion exchange resin-based installation designed for the recycling of engine coolants, specifically ethylene glycol (EG) and propylene glycol (PG). The recycling process aims to restore the coolant to meet the stringent ASTM standards for both new and recycled coolants. A combination of physical-chemical methods, gas chromatography-mass spectrometry (GC-MS), and inductively coupled plasma mass spectrometry (ICP-MS) was employed to analyze and validate the purity and performance of the recycled product. The experimental setup included performance tests to assess the efficacy of the recycled coolants in comparison to new coolant standards. The results demonstrate that the recycled EG coolants exhibit comparable quality to new coolants, with all critical parameters falling within the acceptable ASTM limits. This indicates that the ion exchange resin method is a viable and efficient solution for the recycling of engine coolants, offering an environmentally friendly alternative to the disposal of used coolants while ensuring compliance with industry standards.

Background: Sexual dysfunction is the most common side effect encountered by cervical cancer patients during and after treatment, and rehabilitation strategies are needed to improve their quality of life. A variety of rehabilitation strategies have been reported in previous studies, varying by country, race and even continent. However, the common rehabilitation strategies used by cervical cancer survivors in Tanzania and their outcomes are unknown. The study aimed to unfold sexual rehabilitation strategies and outcome practiced by cervical cancer survivors who experienced sexual dysfunction during and after treatment in North-western Tanzania. Methods: Descriptive qualitative study were employed. Phenomenology study design used with a sample of 7 participants and data corrected via in-depth interview guided by 11 questions. Audiotaped and transcribed to get main themes and sub themes through deductive analysis. Results: Most participants reported inability to achieve an orgasm and low libido and dyspareunia after treatment. Most participants were eagerly to have their sexual activity recovered. However, they were not familiar with any rehabilitation strategy after the experience of sexual dysfunction. Even though, some of the participants attempted various interventions, but all were unsatisfactory leading to a significant decline in sexual interest and activity. Conclusion: Cervical cancer survivors are not familiar with any sexual rehabilitation strategies. Health education concerning various sexual rehabilitation strategies should be provided to survivors during and even after treatment.

The Cosmic Theory of Structure (CTS) model presents a comprehensive framework that integrates gravitational waves, neutrinos, dark matter, and quantum field interactions. This model aims to provide a unified understanding of the universe's formation and evolution. The CTS model has undergone rigorous validation using the latest datasets, demonstrating its robustness and predictive power. Key updates include detailed mathematical formulations, refined theoretical frameworks, and enhanced descriptions of fundamental cosmic phenomena. This manuscript presents the final validated version of the CTS model, highlighting its alignment with observational data and its implications for future research in cosmology and quantum physics.

Background: Population studies have shown that prehypertension is associated with adverse cardiometabolic risk profile even among apparently healthy populations. Although associations between serum triglyceride (TG) concentrations and the risk of atherosclerotic cardiovascular disease have been reported, there are lack of data on the association of hypertriglyceridemia with cardiovascular risk (CVR) in a low risk prehypertensive population. Aim: Our aim was to analyze the cardiorenometabolic characteristics of prehypertensives (PHT) compared to normotensives (NT) and untreated hypertensives (HT) and its associations with CVR. Materials and methods: In our cross-sectional study we included 323 apparently healthy untreated subjects 18-45 years who underwent basic anthropometric and standardized blood pressure measurement, fasting blood sampling, spot urine collection and transthoracic echocardiogram. Results: Higher BMI, waist circumference, HOMA-IR, LDL-cholesterol, TG, uric acid and LVMI contribute to higher CVR in PHT compared to true NT (p

In Bangladesh, climate change is a major concern because of its geophysical location and climate dependent agriculture. As sessile organisms, crops plants have to face difficulties often in this environmentally vulnerable country. Therefore, this study examines the seasonal trend of two climatic parameters viz. temperature (maximum and minimum) and rainfall over a period of 1983 to 2013. Besides, this study provides insight into the relationship between climatic parameters and crop yield of two major crops viz. rice and wheat during 1997-2013. To assess the relationship of climatic parameters with time and yield using Pearson correlation analysis, time series data used at an aggregate level. SPSS software utilized for this analysis. The cropping seasons such as rice growing seasons Aus (summer rice), Aman (autumn rice) and Boro (winter rice) exhibited a significant increase in maximum and minimum temperature. Rainfall found to have a decreasing trend for all the seasons. This study also revealed that the climatic parameters had significant effects on rice yield, but these results varied among three rice crops. Maximum temperature had positive effects on all rice yields, especially on Aus and Aman. Minimum temperature had a negative effect on Aman rice yield but a positive effect on Aus rice yield. Wheat yield negatively associated with temperature. Rainfall exhibited negative relation with both rice and wheat yield.

In this digital era, laundry applications come as an innovative solution to meet the laundry needs of modern people who often have busy schedules and limited time. This research aims to make a significant contribution to our broader understanding of how digital innovations can revolutionize mundane tasks. This research uses the Waterfall model, which is one of the methods in the Software Development Life Cycle (SDLC). The development process begins with the Requirement Analysis stage, where application requirements and specifications are collected and thoroughly analyzed to ensure that the developed application will meet user expectations. After that, the System Design stage is conducted, where the application architecture, user interface design, and database structure are designed in detail to ensure conformity with the previously identified needs.The next stage is Implementation, where program code is written and application features are developed according to the design that has been made. The final stage is Integration and Testing, where all application components are combined and tested thoroughly to detect and fix any errors or bugs that may exist. The results of this study produced a web-based shuttle laundry service application using the Laravel framework called "All Fresh Laundry". This application can order clothes washing services according to its membership, where there are 3 types of membership based on price and of course have different benefits. In addition, the testing phase of this application using black box testing shows that all menu features function properly according to their functions. Each component of the "All Fresh Laundry" app was thoroughly tested to ensure that no errors or malfunctions occurred. The results of this testing make it clear that every feature, from online ordering to laundry status tracking, works well and meets the specifications that have been set, so that this application can provide an optimal and reliable user experience.

Camping offers invaluable experiences in nature exploration, but the expense of acquiring camping gear can deter potential enthusiasts. To mitigate this, camping gear rental services emerge as a practical solution, promoting accessibility and reducing financial barriers. However, existing rental systems often encounter issues such as limited payment options, inconvenient pickup/return processes, and opaque management. In response, this paper introduces "Rent-n-Play," a web-based camping gear rental platform built on Laravel framework. It enables cashless transactions, offers pickup/delivery services, and employs a real-time admin system for transparent management. User feedback guided platform enhancements, including review systems and responsive support. The "Rent-n-Play" platform enhances user experience, fosters trust, and promotes sustainable camping practices, thus contributing to a more accessible and enjoyable camping culture.

The issue of sufficient nutrition and appropriate distribution in school lunch programs presents a significant challenge that must be addressed. Many schools struggle to provide food that meets all of their students’ nutritional needs. Additionally, inconsistencies in nutritional information and administrative difficulties often hinder the efficient operation of these programs. Developing technology to better manage school lunch programs is critical to addressing these issues. The goal of this project is to create a web application that ensures the fair distribution of nutritional information while also increasing the efficiency of lunch program administration. The methodology used in this research is Agile. Agile approaches are employed throughout the development process, allowing for rapid iteration and responsiveness to user feedback. The end result is a web tool called Nourishify, designed to streamline the management and dissemination of nutritional information. Implementing Nourishify led to substantial improvements in access to nutrition information and operational efficiency, making it an innovative solution for school lunch programs. This project demonstrates that integrating technology can significantly enhance the administration and transparency of nutrition programs, ultimately benefiting students' health and well-being. Moreover, Nourishify’s user-friendly interface and real-time data capabilities ensure that all stakeholders, including parents, administrators, and nutritionists, can easily collaborate and access crucial information.

Point of Sales (POS) systems are an important component of sales and inventory management for many businesses. This article discusses the development of a POS application designed to simplify the process of selling products in stores. The application uses PHP for the server, JavaScript for the client, Bootstrap for responsive design, and MySQL as the database. Its main features include a dashboard with daily sales summary, visual graphs, product category management, product addition and editing, admin and access rights management, ease of sales transaction processing, access to sales reports and analyses by leaders, and provision of sales reports in various formats. The clean and easy-to-use interface is expected to improve the efficiency of sales and inventory management, and assist businesses in making data-driven decisions.

The inherent capabilities of the K-means++ algorithm to approximate system dynamics within complex systems are subjected by constructing a network structure that captures interconnections among identified components, extending its original purpose as a data clustering method and transforming it into a tool for systems analysis. Leveraging advanced measurement technologies and sophisticated data collection systems, the K-means++ algorithm unveils hidden relationships among components and identifies critical elements. This study explored the algorithm's potential, facilitating the identification of critical components to enhance system operation, control, optimization, and decision-making and examining the practicality and resiliency of the method in real-world application with noisy and limited data. A case study conducted on power systems (IEEE 39-bus and IEEE 300-bus systems) exemplifies K-means++'s capacity to accurately identify critical components and approximate system dynamics, supported by performance metrics affirming its effectiveness and robustness in system analysis through measurements of bus similarity within clusters based on standard deviation and comparison of net tie-line flows in equivalent networks with the original network across scenarios. Performance metrics, including the Silhouette Score, Davies-Bouldin Index, and Variation of Information (VI) score, further validated K-means++'s performance, yielding reliable and consistent results.

Coenzyme Q (CoQ) is a lipidic compound widely distributed in nature with crucial functions in metabolism, protection against oxidative damage and ferroptosis, and other processes. CoQ biosynthesis is a conserved and complex pathway involving several proteins. COQ2 is a member of the UbiA family of transmembrane prenyltransferases that catalyzes the condensation of the head and tail precursors of CoQ, a key step in the process because its product is the first intermediate that will be modified in the head by the next component of the synthesis process. Mutations in this protein have been linked to primary CoQ deficiency in humans, a rare disease predominantly affecting organs with a high energy demand. The reaction catalyzed by COQ2 and its mechanism are still unknown. Here we aimed at clarifying COQ2 reaction by exploring possible substrate binding sites using a strategy based on homology, comprising the identification of ligand-bound homologs with solved structures available in the Protein Data Bank (PDB) and their subsequent structural superposition to the AlphaFold predicted model for COQ2. The results highlight some residues located on the central cavity or the matrix loops that may be involved in substrate interaction, some of them mutated in primary CoQ deficiency patients. Furthermore, we analyze the structural modifications introduced by the pathogenic mutations found in humans. These findings shed new light on the understanding of COQ2 function and, thus, CoQ biosynthesis and pathogenicity of primary CoQ deficiency.

With the deterioration of the bridge performance and ever-increasing amount of traffic, the bridge safety is becoming a concern for engineering community. A method that can assess the bridge's condition in real-time is urgently needed. The main factors that hinder the bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA) based method is developed for structural damage detection of bridges in operational environments in this paper. Two main operational environmental factors: the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate to assess the bridge condition in operational environments.

Cosmetic surgery is the subfield of plastic surgery that enhances appearance mainly for aesthetic reasons. Currently, cosmetic surgery interventions are highly considered appealing and popular all around the world. Several factors have contributed in this rapidly evolving field such as scientific and economic development, breaking down barriers and cultural norms, globalization, social media and mental disorders. Nowadays, the morality of cosmetic surgery is facing many dilemmas due to its benefits and drawbacks. It is proven that the advantages of cosmetic surgery include improved body confidence, mental health, and function. Additionally, aesthetic corrections can upscale the quality of life and create career opportunities for special targeted groups. Also, cosmetic surgery has a major impact in gender affirmation procedures. Although, herein we also describe the disadvantages of this widely developing field such as minor and major medical complications, potential postoperative risks and addiction in plastic surgeries associated with damaging economic issues. Lack in self-confidence, altered mental health status and ethical crisis consist another negative aspects of aesthetic surgery’s impact.

RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.

The use of credit cards and online banking is expanding exponentially. As more individuals use debit cards, credit cards, and internet banking, the likelihood of falling victim to various forms of fraud also rises. Due to ignorance, credit card company customers have provided their card details, personal information, and one-time password to an unknown bogus caller on several occasions in the recent past. One of the biggest issues facing financial institutions and customers alike is credit card theft. It may result in large financial losses and harm to the financial institution's image. While there are several ways to identify credit card fraud, one of the best is machine learning.By using past data, machine learning algorithms may be trained to recognize trends and abnormalities that point to fraud. The state-of-the-art in machine learning for credit card fraud detection is reviewed in this study. Numerous research on the same subject have been conducted in the past with a variety of well-known machine-learning techniques. However, with a grasp of the notion of hybrid machine learning, we will now examine how well hybrid machine learning performs on the same problem statement.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

Visualising textual content could be helpful to professionals as well as amateurs across several fields. However, training a text-to-image generator in the mainstream domain requires large amounts of paired text-image and data, which is too expensive to collect since labeling millions of images and videos can be tiresome. GANs like StackGAN and StyleGAN can be considered as solutions to generate images from text. But the images generated may be of low accuracy and resolution, and the entire processing can be highly time-consuming. Moreover, image generation is a notion that is still being researched. Hence, the process of developing a Video Generation model necessitates substantial research. Despite the need for such a model, modern technology has lagged behind the solutions to this problem. This proposal suggests combining two methods, Text modification for Action Definition (TexAD) and SeQuential Image Generation for Video Synthesis (SQIGen). The proposed solution synthesises a sequence of images from textual information feeds and combines these images to create a video. TexAD uses Natural Language Processing and Deep Learning techniques to process, classify and modify text data. SQIGen is an extension of the VQGAN+CLIP neural network architecture that generates a sequence of images from the modified text data

Abstract: Comorbidities of patients may be related to the duration of mechanical ventilation, survival, and Candida isolation during intensive care unit (ICU) stay. To examine the frequency of Candida isolation and significant comorbidities in the surgical intensive care unit (ICU) in Osijek University Hospital, Croatia, the data from the electronic database from May 2016 to June 30, 2023, were analyzed. In a cross-sectional study examining 15,790 microbiological samples, different strains of Candida were observed in 581 samples from 236 patients. The control group was 160 consecutive patients from March-May 2019, and 161 in the same months of 2020 (pre- and post-COVID-19). Comorbidities, duration of mechanical ventilation and survival were compared. Patients with isolated Candida were more often non-elective, had significantly more heart, kidney, liver diseases and sepsis (P<0.001). The duration of mechanical ventilation was 9.2 [2.2-9.24], 96 [24-146], 160 [19.5-343] and 224 [73.5-510] hours in controls, and patients with Candida albicans, non-albicans, and in patients with ³2 Candidas isolated. The mortality was significantly higher and reached 42% in patients with Candida isolated, while in the control group, it was 19% (P<0.001). In a multivariate analysis adjusted for patient’s age, SAPS II score, days of ICU, and type of admission, only sepsis was independent predictor of mortality (OR = 2.27).

Large Language Models (LLMs) have proved significant proficiency when comes to code generation especially in Structured Query Language (SQL) for databases and recent successful Text-to-SQL method involves fine-tuning pre-trained LLMs for SQL generation tasks. Transforming natural language text into SQL queries, has been attempted to solve with various learning techniques including Few-shot learning[1], fine tuning. In this paper we propose Supervised fine-tuning (SFT) as a better alternative for learning technique for text-to-SQL generation task using Code-Llama that pushes state of art accuracy on spider test suite to 89.6% on dev set which represent first instance of surpassing the earlier best-in-class with 5.5% higher score and 86.8% of exact match accuracy on dev set. Furthermore, we demonstrate that properly prompted LLM along with SFT provides far fewer hallucinations and much more robust LLM that can be used as a general tool for any text-to-SQL generation use case.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.