To get more data concerning the influence of fertilizers (organic and mineral) on different forage plants existing in North-eastern Romanian grassland, the mass fractions of 14 essential, enzymatic, or considerd toxic elements was determined by Instrumental Neutron Activation Analysis together with the amount of crude proteins, ash, fibres as well as ether fact extract. Final results showed a significant variance of the content of analysed elements on organic as well as on mineral fertilized experimental plots, i.e. the content increased in 24% of plots, decreased in 23%, and remained unchanged in 45% while in 8% of plots their content was below the detection limits, as the case of Se. At the same time, an increase content of crude protein and ether extract was evidenced in fertilized grasses for all applied fertilizers while other global indicators such as neutral and acid fibre of sufuric lignin content decreased, suggesting significant higher nutritional values of fertilized forage plant.

The accurate wellbore pressure control not only prevents from lost circulation/blowout and fracturing formation by managing density of drilling fluid, but also improves productivity by mitigating reservoir damage. The geothermal pressure calculated by constant parameters for geothermal well would bring big error easily, as the changes of physical, rheological and thermal properties of drilling fluids with temperature were neglected. This paper researches the wellbore pressure coupling by calculating the temperature distribution with existed model, fitting the rule of density of drilling fluid with temperature and establishing mathematical models to stimulate the wellbore pressures, which is expressed as the variation of Equivalent Circulating Density (ECD) under different conditions. With this method, temperature and ECDs in the wellbore of the first medium-deep geothermal well ZK212 Yangyi Geothermal Field in Tibet were determined, and the sensitivity analysis was simulated by assumed parameters, i.e. circulating time, flow rate, geothermal gradient, diameters of wellbore, rheological models and regimes, the results indicated the geothermal gradient and flow rate were the most influence parameters on the temperature and ECD distribution, and additives added in drilling fluid should be careful which would change the properties of drilling fluid and induce the temperature redistribution. To make sure the safe drilling, velocity of pipes tripping into the hole, depth and diameter of wellbore are considered to control the surge pressure.

Given the increased provision of international exchange programmes in higher education, this paper examines students’ perceptions of their learning process in one Erasmus+ programme in Food Innovation and Product Design. Using evaluation model of international exchange programme and triple helix model as the analytical framework, students’ testimonials from 2015–2021 were analysed using a critical discourse analysis. EU and non-EU students had similar perceptions of learning outcomes related to knowledge of the food innovation industry, but non-EU students had a stronger sense of awareness of both the international context and multicultural interaction.

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.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

Predictive biomarkers for immune checkpoint inhibitors (ICI), such as PD-L1 expression (TPS), remain limited in clinical applications. Improved novel ICI predictive biomarkers are urgently needed. We evaluated a live single-cell functional liquid biopsy cytokine profiling platform to track immunotherapy treatment response and outcomes. Peripheral blood mononuclear cell samples of healthy donors and NSCLC patients undergoing ICI-based therapies were collected longitudinally pre-/post-treatment under IRB-approved protocols. Samples were enriched for CD4+ and CD8+ T-lymphocytes and analyzed on the IsoLight platform. The single T-cells were captured in microchambers on IsoCode chips for cytokines profiling. Functional polyfunctionality data from 55,775 single cells were analyzed using the IsoSpeak software, kappa coefficient, and Kaplan-Meier survival plots. We found a significant difference between responders and non-responders in CD8+ T-cells’ changes in overall polyfunctionality and polyfunctional strength index (ΔPSI). ΔPSI score in CD8+ T-cells performed better than PD-L1 TPS and, when combined with PD-L1 TPS, strongly correlated with early ICI treatment response with a kappa coefficient of 1.0 (p=0.003). High CD4+ T-cells ΔPSI predicted a strong trend of improved PFS (3.9-fold) and OS (3-fold). In conclusion, single peripheral T-cell polyfunctionality dynamics analysis is a promising liquid biopsy profiling platform as NSCLC ICI predictive biomarker regardless of oncogene-addiction status.

Abstract: : 2,3-dihydro-5,6,7,8-tetranitro-1,4-benzodioxine (TNBD), molecular formula =C8H4N4O10 is a completely nitrated in aromatic ring 1,4-benzodioxane derivative. The con-venient method of TNBD synthesis was developed (yield=81 %). The detailed structure of this compound has been investigated by X-ray crystallography. The results of differential thermal analysis (DTA) obtained with twice re-crystallized material showed onset at 247 and isoterm at 288 0C, with further total degradation at 300 0C. The experimental density 1,85 g/cm3 of TNBD was determined by X-ray crystallography. The spectral properties of TNBD (NMR, FT-IR, Ra-man) were explored. The detonation properties of TNBD calculated by EXPLO 5 code was slightly superior in comparison to standard high energy material - tetryl (detonation velocity of TNBD - 7727 m/s; detonation pressure - 278 kbar; tetryl – 7570 m/s and 226.4 kbar at 1.614 g/cm3, or 260 kbar at higher density - 1.71 g/cm3. The obtained preliminary results might suggest TNBD can be a potential thermostable high energy and density material (HEDM).

Ambitious biodiversity goals to protect 30% or more of the Earth’s surface by 2030 (30x30) require strategic near-term targets. To define areas that must be protected to prevent the most likely and imminent extinctions, we propose Conservation Imperatives—16,825 unprotected sites harboring rare and threatened species and spanning ~164 Mha of the terrestrial realm. We estimate that protecting the Conservation Imperatives would cost approximately US$169 billion [90% probability: $146 — $228 billion]. Globally, 38% of the 16,825 sites are either adjacent to or within 2.5 km of an existing protected area, potentially reducing land acquisition and management costs. These sites should be prioritized for conservation action over the next five years as part of a broader strategy to expand the global protected area network. The expansion of global protected areas between 2018–2023 incorporated only 7% of sites harboring range-limited and threatened species, highlighting a renewed urgency to conserve these habitats. Permanently protecting only 0.74% of land found in the tropics, where Conservation Imperatives are concentrated, could prevent the majority of predicted near-term extinctions once adequately resourced. We estimate this cost to be US$29 billion to US$46 billion per year over the next five years. Multiple approaches will be required to meet long-term protection goals: providing rights and title to Indigenous Peoples and Local Communities (IPLCs) conserving traditional lands, government designation of new protected areas on federal and state lands, and land purchase or long-term leasing of privately held lands.

This study introduces a robust methodology utilizing Multi-Criteria Decision Analysis (MCDA) combined with the Analytic Hierarchy Process (AHP) to repurpose abandoned hydrocarbon fields for energy storage, supporting the transition to renewable energy sources. We use a geostatistical approach integrated with Python scripting to analyze reservoir parameters—including porosity, permeability, thickness, lithology, temperature, heat capacity, and thermal conductivity—from a decommissioned hydrocarbon field in Southeast Hungary. Our workflow leverages stochastic simulation data to identify potential zones for energy storage, categorizing them into high, moderate, and low suitability scenarios. This innovative approach provides rapid and precise analysis, enabling effective decision-making for energy storage implementation in depleted fields.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

Reactions of Co(OAc)24H2O, N‚N'-bis(3-pyridylmethyl)oxalamide (L) and 4,4’-sulfonyldibenzoic acid (H2SDA) afforded four coordination polymers with the same mixed ligands, {[Co(L)(SDA)(H2O)2]·H2O·CH3OH}n, 1, {[Co(L)0.5(SDA)]·2H2O·0.5L}n, 2, {[Co(L)1.5(SDA)(H2O)]·H2O}n, 3, and {[Co2(L)1.5(SDA)2(H2O)2]·4H2O}n, 4, which have been structurally characterized by using single crystal X-ray crystallography. Complexes 1 – 4 are 2D layers, revealing the topologies of sql, 2,6L1, (4,4)Ia and 6L12, respectively, and demonstrating that the metal to ligand ratio, solvent system and reaction temperature are important in determining the structural diversity. Immersion of these complexes into various solvents show that the structural types govern the chemical stabilities of 1 – 4. Reversible structural transformation can be shown for complexes 1 and 2 upon solvent removal and adsorption, while those of 3 and 4 are irreversible.

The exponential growth of multimodal content across social media platforms, comprising text, images, audio, and video, has catalyzed substantial interest in artificial intelligence, particularly in multi-modal sentiment analysis (MSA). This study presents a comprehensive survey of 30 research papers published between 2020 and 2024 by eminent publishers such as Elsevier, ACM, IEEE, Springer, and others indexed in Google Scholar. Our analysis primarily focuses on exploring multimodal fusion techniques and features, with specific emphasis on the integration of text and image data. Additionally, the article offers an overview of the evolution, definition, and historical context of MSA. It delves into the current challenges and potential advantages of MSA, investigating recent datasets and sophisticated models. Furthermore, the study provides insights into prospective research directions. Notably, this review offers valuable recommendations for advancing research and developing more robust MSA models, thus serving as a valuable resource for both academic and industry researchers engaged in this burgeoning field.

Total ankle replacement (TAR) implants are an effective option to restore the range of motion of the ankle joint for arthritic patients. An effective tool for analyzing these implants’ mechanical performance and longevity in-silico is finite element analysis (FEA). ABAQUS FEA was used to statically analyze the von Mises stress and contact pressure on the articulating surface of the bearing component in two newly installed fixed-bearing total ankle replacement implants (the Wright Medical INBONE II and the Exactech Vantage). This ultra-high molecular weight polyethylene (UHMWPE) bearing commonly fails and is the cause for surgeon revisions. Six different FEA models for various gait percentages during stance (10%-60%) were created. They varied in magnitude of the compressive load and the ankle dorsiflexion/plantarflexion angle. The results indicated that the von Mises stress and contact pressure distributions increased as compressive load increased, and the largest magnitudes occurred at high dorsiflexion angles (15-30°). High magnitudes occurred in regions where the thickness of the bearing was the least. Additionally, high contact pressures were examined in areas near the talar component's edge or at the bearing's edges. To the author’s knowledge, this is the first study available to the research community that analyzes the Vantage implant with FEA. This study lays an essential foundation for future researchers in presenting a thorough literature review of TAR and methods for a simple FEA model setup. This study provides valuable information that can be beneficial to medical company designers and orthopedic surgeons in understanding the stress response and contact pressure of TAR implants during use.

Keywords: Antibacterial activity, Antioxidant activity, Bioactive compounds, Elementary analysis, Nutritional and phytochemical composition, Seaweed.

This article is devoted to the problem of genetically coding of inherited cyclic structures in biological bodies, whose life activity is based on a great inherited set of mutually coordinated cyclic processes. The author puts forward and arguments the idea that the genetic coding system is capable of encoding inherited cyclic processes because it itself is a system of cyclic codes connected with Boolean algebra of logic. In other words, the physiological processes in question are cyclical because they are genetically encoded by cyclic codes. In support of this idea, the author presents a set of his results on the connection of the genetic coding system with cyclic Gray codes, which are one of many known types of cyclic codes. This opens up the possibility of using for modeling inherited cyclic biostructures those algebraic and logical theories and constructions that are associated with Gray codes and have long been used in engineering technologies: Karnaugh maps, Hilbert curve, Hadamard matrices, Walsh functions, dyadic analysis, etc. Additionally, the connections of these constructions with statistical rules of genomic DNAs and binary-genomic numbers are considered.

The article presents the results of cyanide leaching of gold-containing concentrate using the trichlorocyanuric acid (TCCA) oxidizer. Gold-containing concentrate obtained from a gold tailings sample from a gold recovery factory (GRF) in one of the deposits of Kazakhstan that was not previously studied for concentrability. According to X-ray phase analysis and energy dispersive spectrometry (DSM) data, the main compounds in the tailings sample under study are: pyrite FeS2, quartz SiO2, calcite CaCO3, albite NaAlSi3O8, muscovite KAl2Si3AlO10(OH)8, dolomite CaMg(CO3)2, and oxidized iron compounds. Microscopic studies have established the presence of gold grains in free form, ranging in size from 0.9-10.2 mkm Au-ultrafine and fine gold. Obtaining a gold-containg concentrate with a gold content of 15.95 g / t is possible according to the enrichment scheme, which includes centrifugal separation, classification according to the fineness class -0.05 mm, additional grinding of hydrocyclone sands to a fineness of 90.0-95.0% of the class finer than 0.050 mm, and control centrifugal separation. Since pyrite in technogenic raw materials is the main gold-containing mineral, this paper presents studies on the oxidizability of pyrite with the TCCA oxidizer. The results of studies on the oxidation of pyrite using the TCCA oxidizer show that the products of its hydrolysis oxidize pyrite with the formation of various iron compounds on its surface. Pretreatment of gold-containing concentrate with oxidizer TCCA for 3 hours before the cyanidation process (20 hours) allows to increase the recovery of gold in the solution by 5.8 %.

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

The impact of propeller effects and power contribution on the aerodynamics of small aircraft is indispensable. The aerodynamic analysis of wings in flight varies from rigid wing analysis due to wing deflection caused by transferred aerodynamic loads. This paper investigates the intertwined influence of propeller effects and elasticity on the aerodynamics of small propeller-driven aircraft. Through a detailed methodology, a twin-engine propeller-driven aircraft is analyzed as a case study, providing insights into the proposed approach. Two critical analyses are presented: an examination of propeller effects in rigid aircraft and the incorporation of elastic wing properties. The former establishes a foundational understanding of aerodynamic behaviour, while the latter explores the impact of wing elasticity on performance. Validation is achieved through comparative analysis with wind tunnel test results from a similar rigid structure aircraft. Utilizing NASTRAN software, aerodynamic analysis of the elastic aircraft is conducted, complemented by semi-empirical insights. The results highlight the importance of these factors across different angles of attack. Furthermore, deviations from the rigid aircraft configuration emphasize the considerable influence of static aeroelasticity analysis, notably increasing longitudinal characteristics by approximately 20%, while showing a lower impact of 5% in lateral-directional characteristics. This study contributes to enhanced design and operational considerations for small propeller-driven aircraft, with implications for future research and innovation, particularly for the purpose of efficient concepts in advanced air mobility.

In light of the critical and emerging global challenge posed by the increasing frequency and severity of heatwaves in recent decades, this study demonstrates a practical and robust workflow that bridges the gap in understanding how the effective integration of remote sensing data with ground-based observations can facilitate a comprehensive assessment of spatiotemporal heatwave patterns and trends in the central region of Thailand. This research transcends traditional methods, utilizing air temperature (Tair) and satellite-derived land surface temperature (LST) data from 1981 to 2019. Results exhibit that an analysis of Tair indicates increasing daytime heatwaves in peri-urban areas, with significant trends in heatwave number (HWN), heatwave frequency (HWF), and heatwave duration (HWD), heatwave amplitude (HWA) rises in urban settings while nighttime heatwaves significantly intensify in rural locales. Notably, LST trends reveal varied patterns, with peri-urban areas showing marked daytime increases in heatwave magnitude (HWM), HWA, and HWF. Correlation analysis (p=0.05) highlights strong daytime associations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. Overall, this study advances approaches to the adaptable measurement and spatial-temporal pattern of heatwave-related risk areas, providing insights for decision-makers to develop sustainable practices and strategies for climate change adaptation.

As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of (XAI) techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated a 91% accuracy. Our study utilizes (SHAP), (LIME), Anchors, (ALE), and counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. Consistently, 'Class Duration' and 'Financial Condition' emerge as key factors, while the study also underscores the subtle effects of 'Institution Type' and 'Load-shedding'. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies.

The rapid and accurate detection of viruses and the discovery of single nucleotide polymorphisms (SNPs) are crucial for disease management and understanding viral evolution. In this study, a pipeline for virus detection, validation, and SNP discovery from next-generation sequencing (NGS) data is presented. By integrating state-of-the-art bioinformatics tools with artificial intelligence, the pipeline processes raw sequencing data to identify viral sequences with high accuracy and sensitivity. Before aligning the reads to the reference genomes, quality control measures and adapter trimming are performed to ensure the integrity of the data. Unmapped reads are subjected to de novo assembly to reveal novel viral sequences and genetic elements. The effectiveness of the pipeline is demonstrated by the identification of virus sequences, illustrating its potential for the detection of known and emerging pathogens. SNP discovery is performed using a custom Python script that compares the entire population of sequenced viral reads to a reference genome. This approach provides a comprehensive overview of viral genetic diversity and identifies dominant variants and a spectrum of genetic variations. The robustness of the pipeline is confirmed by the recovery of complete viral sequences, which improves our understanding of viral genomics. This study highlights the synergy between traditional bioinformatics techniques and modern approaches, providing a robust tool for analyzing viral genomes and contributing to the broader field of viral genomics.

Using data from the Swedish National Diabetes Register, this study examines the gender disparity among patients with type I diabetes who have experienced a specific cardiovascular complication, while exploring the association between weight variability, age group, and gender. Fourteen cardiovascular complications have been considered. This analysis is conducted by using three-way correspondence analysis (CA) which allows for the partitioning and decomposition of Pearson’s three-way chi-squared statistic. The dataset comprises of information organized in a data cube, detailing how weight variability among these patients correlates with a cardiovascular complication, age group and gender. The three-way CA method presented in this paper allows for one to assess the statistical significance of the association between these variables and to visualize this association, highlighting the gender gap among these patients. It is worth noting that the association between weight variability, age group, and gender varies among different types of cardiovascular complications.

In the context of the Lubumbashi Coal Production Basin (LCPB), the socio-economic repercussions of the COVID-19 pandemic have exacerbated pressures on populations dependent on forest resources for their subsistence. This study employs a comprehensive methodological approach, integrating advanced remote sensing techniques, including image classification, mapping, and detailed landscape analysis, to quantify alterations in forest cover within the LCPB during the pandemic period. Our findings reveal a consistent trend of declining forested area, characterized by processes of attrition and fragmentation observed throughout various study phases, spanning from May 2019 to November 2023. This reduction in forest cover, notably more pronounced in the vicinity of Lubumbashi city and the northern zone of the LCPB, proved to be less pronounced between November 2019 and September 2020, underscoring the influence of COVID-19 pandemic-induced confinement measures on forest management practices in the region. However, subsequent to this period of restriction, deforestation activity intensified, leading to significant landscape transformations within the LCPB, primarily attributable to expanded human activities, consequently resulting in a notable decrease in the proportion of land occupied by these natural ecosystems. Consequently, the size of the largest forest patch declined substantially, decreasing from 14.62% to 8.20% between May 2019 and November 2023, thereby fostering a heightened density of forest edges over time. Our findings provide a significant contribution to understanding the complex interactions between the COVID-19 pandemic and deforestation phenomena, emphasizing the urgent need to adopt adaptive management strategies and appropriate conservation measures in response to current economic challenges.

Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.

Background: Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complications following percutaneous coronary intervention (PCI) or coronary angiography (CAG), presenting significant clinical challenges. Machine learning (ML) models offer promise for improving patient outcomes through early detection and intervention strategies. Methods: A comprehensive literature search following PRISMA guidelines was conducted in PubMed, Scopus, and Embase from inception to June 11, 2024. Study characteristics, ML Models, performance metrics (AUC, accuracy, sensitivity, specificity, precision), and risk of bias assessment using the PROBAST tool were extracted. Statistical analysis used a random-effects model to pool AUC values, with heterogeneity assessed via the I² statistic. Results: From 431 initial studies, 14 met the inclusion criteria. Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) models showed the highest pooled AUCs of 0.87 (95% CI: 0.82-0.92) and 0.85 (95% CI: 0.80-0.90), respectively, with low heterogeneity (I² < 30%). Random Forest (RF) had a similar AUC of 0.85 (95% CI: 0.78-0.92) but significant heterogeneity (I² > 90%). Multilayer Perceptron (MLP) and XGBoost models had moderate pooled AUCs of 0.79 (95% CI: 0.74-0.84) with high heterogeneity. RF showed strong accuracy (0.83, 95% CI: 0.70-0.96), while SVM had balanced sensitivity (0.69, 95% CI: 0.63-0.75) and specificity (0.73, 95% CI: 0.60-0.86). Age, serum creatinine, left ventricular ejection fraction, and hemoglobin consistently influenced model efficacy. Conclusions: GBM and SVM models, with robust AUCs and low heterogeneity, are effective in predicting AKI and CIN post-PCI/CAG. RF, MLP, and XGBoost, despite competitive AUCs, showed considerable heterogeneity, emphasizing the need for further validation.

Heavy metals represent a class of chemical elements that includes metalloids, bases and transition metals, lanthanides and actinides. They are distinguished for their toxicity in small concentrations and for their negative effects on the environment and human health. They are present in various samples, mainly in water, industrial waste and food. The determination of heavy metals is carried out mainly by analytical methods, using spectroscopy, spectrometry and electroanalysis, while studies are being carried out for the development of new and faster methodologies and techniques for their analysis, with particular emphasis on voltammetry. This review deals with the determination of heavy metals mainly in environmental samples (water) using voltammetry. In particular, the methods of their determination are evaluated in terms of sensitivity, selectivity and applicability.

Variable-Angle Tow (VAT) laminates can improve straight fiber composites mechanical properties thanks to the application of curvilinear fibers. This characteristic allows to achieve ambitious objectives for design and performance purposes. Nevertheless, the wider design space and the higher number of parameters result in a more complex structural problem. Among the various approaches that have been used for VATs study, Carrera’s Unified Formulation (CUF) allows to obtain multiple theories within the same framework, guaranteeing a good compromise between the results accuracy and the computational cost. In this article, the linear buckling behavior of VAT laminates is analyzed through the extension of CUF 2D plate models within the Reissner’s Mixed Variational Theorem (RMVT). Results show that RMVT can better approximate the prebuckling non-uniform stress field of the plate when compared to standard approaches, thus improving the prediction of the linear buckling loads of VAT composites.

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.

Today, Colebrook’s equation is mostly accepted as an informal standard for modeling of turbulent flow in hydraulically smooth and rough pipes including transient zone in between. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of inner pipe surface (ε/D). It is implicit in unknown friction factor (λ). Implicit Colebrook’s equation cannot be rearranged to derive friction factor (λ) directly and therefore it can be solved only iteratively [λ=f(λ, R, ε/D)] or using its explicit approximations [λ≈f(R, ε/D)]. Of course, approximations carry in certain error compared with the iterative solution where the highest level of accuracy can be reached after enough number of iterations. The explicit approximations give a relatively good prediction of the friction factor (λ) and can reproduce accurately Colebrook’s equation and its Moody’s plot. Usually, more complex models of approximations are more accurate and vice versa. In this paper, numerical values of parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After this improvement computational burden stays unchanged while accuracy of approximations increases in some of the cases very significantly.

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain the company's operational capacity at the required level. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology is a systematic literature review using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2024) and studies published in English. This resulted in the selection of 201 of the most relevant papers in the area of investigation. Finally, selected articles in this review were categorized into seven groups: a) air transportation, b) railway transportation, c) land transportation (road), d) in-house logistics, e) water and intermodal transportation, f) supply chain operation, and g) other applications. One of the main advantages of the study is that the results are obtained from different scientific sources/databases thanks to using the multi-search tool. Moreover, a bibliometric analysis of the selected papers was performed. The results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives. In addition, based on the review results, a framework for DT in transportation system maintenance was developed. The study ends with conclusions and future research directions.

Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicators metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity, emphasizing its utility in interpreting productivity indicators within the context of varying industrial compositions across OECD countries. This study underscores the inadequacies of the current national productivity estimating method, pinpointing areas requiring refinement. Specifically, the method for estimating the auxiliary indicator for carbon productivity guarantees enhanced rationality when integrated with current methodologies. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to each unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development.

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

The work of this paper presents multiple novel findings from a comprehensive analysis of about 150,000 tweets about exoskeletons posted between May 2017 and May 2023. First, findings from content analysis and temporal analysis of these tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of tweets, the lowest number of tweets, tweets with the highest number of hashtags, and tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of tweets posted per hour and different characteristics of these tweets. Third, the paper presents a multiple linear regression model to predict the number of tweets posted per hour in terms of these characteristics of tweets. The R2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these tweets was performed using VADER and the DistilRoBERTa-base library. The results show that the percentage of positive, neutral, and negative tweets were 46.8%, 33.1%, and 20.1%, respectively. The results also show that in the tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by the sentiments of joy, disgust, sadness, fear, and anger. Furthermore, analysis of hashtag-specific sentiments revealed several novel insights, for instance, for almost all the months in 2022, the usage of #ai in tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, text processing-based approaches were used to detect possibly sarcastic tweets and tweets that contained news. Finally, a comparison of positive tweets, negative tweets, neutral tweets, possibly sarcastic tweets, and tweets that contained news, in terms of different characteristic properties of these tweets are presented. The findings reveal multiple novel insights, for instance, the average number of hashtags used in tweets that contained news has considerably increased since January 2022.

Ghana, blessed with abundant solar resources and has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one-year monitoring data of the climatic and AC energy output fed into the grid. This data was used to analyze PV performance on each month's sunniest and cloudiest days. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analysis showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous domains, including patient monitoring, security surveillance, and observation of human-machine interactions. While numerous algorithms have been proposed in an attempt to distinguish between a myriad of activities, most practical applications necessitate highly accurate detection of specific activity types. This study proposes a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from human skeleton datasets. The powerful graph convolutional network, designated as GA-GCN, developed in this study, notably outperforms the majority of existing state-of-the-art methods when evaluated on two common datasets, namely NTU RGB+D and NTU RGB+D 120. On the first dataset, the proposed approach achieved accuracies of 92.3\% and 96.8\% for the cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8\% and 90.4\% for the cross-subject and cross-set evaluations, respectively.

Background: Significant national and global costs were incurred from recent emerging infectious disease outbreaks, notably H1N1 highly pathogenic influenza, Zika and Severe Acute Respiratory Disease (SARS), both in the medical sphere and within the socioeconomic context around it. With forecasts expecting an increase in direct and indirect burden of novel infectious disease events, costs of future outbreak events are likely to be exacerbated. In this scope, it is critical for countries to identify this trend and work towards minimizing costs while continuing to improve prevention, treatment and mitigation. Objectives: The impacts of the West Africa Ebola crisis have largely focused on the response to the outbreak abroad. The aim of this paper was to determine the direct and indirect domestic costs of the Ebola Virus Disease (EVD) introduction into the United States (U.S.), compare the economic consequences of the epidemic with other disease outbreaks and identify the primary drivers of such costs to leverage more global health preparation to prevent, prepare for, and respond to disease threats more effectively. Methods: I conducted a literature review of 1) EVD cases documented by the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO), 2) scientific articles identifying the costs of Ebola for the U.S. and countries around the world and 3) grey literature on treatments and public reactions. Extrapolating data from these sources, I then performed a cost-analysis of direct costs (preparation, treatment and contact tracing) and indirect costs (loss of productivity owing to disease, death, and risk avoidance behaviors) of the disease. Findings: The country spent between $300-$700 million in direct costs. Additionally, roughly $200-$340 million were forgone as results of loss of productivity from to disease, death or social risk avoidance behaviors. The cost-per-case for the United States was vastly higher than for any other country. Conclusions: High direct costs suggest that either the United States took disproportionate preparative measures to mitigate a potential American outbreak or were unprepared for a potential outbreak of this magnitude. The significant risk avoidance behavior estimates hint that forces having the ability to shape social attitudes and norms – namely traditional and online media – played a significant role in driving social behavior towards fear of exposure to others. The findings reinforce that outbreaks occurring in a single part of the world can have dramatic economic effects globally, even in areas with existing public health capacity and other preventive measures, highlighting the need for change in global health systems to shift from a passive responder and incorporate other societal actors to effectively reduce the ever-increasing costs of disease threats.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

A sensory profile provides important information on the qualitative and quantitative properties of cheeses. Thus, tasters must be trained with reference standards that allow the calibration of the panel around shared sensory definitions. The ideal standard is simple to prepare, easily reproducible, discriminant and specific for the product evaluated. Therefore, commercial products and pure chemicals, which are currently implemented as reference standards, are not suitable. Furthermore, the lack of common practices in the dairy sectors has been widely highlighted. This study aimed to establish new and reproducible reference standards for the gustatory and olfactory descriptors of cheeses. Using casein, maize starch, powder skimmed milk and k-carrageenan, a tasteless and odourless product like a fresh cheese was produced. At this matrix, 20 odour and taste compounds have been added to obtain standards that have been evaluated by 60 tasters so as to define their recognisability and perceived intensity. These cheese standards are easy to reproduce, are stable and exhibited good recognisability. Overall, the reference standards proposed are suitable for the panel training and are a starting point for standardising practices and lexicon, thus enabling the comparability of sensory studies on cheese.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

Sequence analysis is a robust methodological framework that has gained popularity in various fields, including transportation research. It provides a comprehensive approach to understanding the dynamics and patterns of individual behaviors over time. In the context of the Metropolitan Region of Barcelona, applying sequence analysis to the mobility surveys offers valuable insights into the sequencing and order of travel activities and modes, shedding light on the complex interrelationship between individuals, their travel choices, and the built environment. The Barcelona travel surveys collect detailed data on individuals' travel behavior, such as trip purpose, duration, mode of transportation, and origin-destination pairs. Sequence analysis allows for examining travel behaviors as dynamic processes, unveiling travel patterns' underlying structure and evolution in a day. A data analytics methodological approach is described; it enables the identification of common travel patterns and the exploration of variations across different demographic groups or geographical regions. Sequence analysis reveals insights into the factors influencing mode choice and potential opportunities for sustainable transport interventions. The paper proposes a methodological approach to discover homogeneous travel behavioral segments from diaries included in travel surveys in order to refine transport policies to selected segments by transportation planners and authorities

Background: During pregnancy, major physiological changes occur in every system. Understanding the hemodynamic changes in different stages of pregnancy can help to identify abnormal states during pregnancy. Harmonic analysis could be a useful tool in monitoring the state of the ventricular-arterial system during pregnancy. The aim of this study was to investigate the specific statistical relationships between changes in harmonic indices during different stages of pregnancy. Methods: A Total of 380 women (190 pregnant women and 190 age-matched non-pregnant women), with ages ranging from 20 to 45, were enrolled in the study. The visit measured the subject’s hemodynamic parameters including heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP) and photoplethysmography (PPG). SBP and DBP were evaluated by an automatic blood pressure monitor. PPG signal were continuously recorded for 12-seconds using a PPG sensor. Spectrum analysis of the PPG signal was performed to evaluate the first five harmonic components (C1 – C5) and harmonic phases (P1 – P5). Result: A comparison between pregnant women and non-pregnant women showed that the non-pregnant women had lower DBP, C3, C5 and P5 (p<0.05). Waist Circumference, Heart rate, C2, C4 and P2 were higher in pregnant women. Logistic regression analysis was used to determine the associations between pregnancy and harmonics indices. C2, C4 and P3 were positively and P1 and P4 were negatively associated with pregnancy after controlling age, BMI, waist circumference, heart rate, SBP and DBP. Conclusion: The results of this study provide information on the changes in the harmonic frequency components during various stages of pregnancy. The harmonic components of the PPG signal have independent predictive value for changes in a woman's physiology during pregnancy. Since non-invasive PPG measurements can be performed frequently and easily, in the future, it is possible that the hemodynamic changes from some pregnancy-related disorders could be studied by harmonic analysis.

This paper deals with identifying the accident black spots and the influencing factors causing accidents using factored analysis in the medium-sized city (Tirunelveli) in India. From the literature review, the geospatial technique to identify the black spots and the factors causing accidents was used for analysis. The most influencing factors driving the accident were identified and ranked based on the repetitive occurrence of accidents in the black spot area. The spearman ranking system obtained the correlation among the factors causing accidents. The factor analysis technique was utilized to identify the key factors driving the repetitive accidents and group them. This study will help transportation planners to understand the factors causing accidents and take appropriate measures to reduce the casualties in the road construction planning stage and existing conditions.

Wearable sensors have become increasingly popular for assessing athletic performance, but the optimal methods for processing and analysing the data remain unclear. This study investigates the efficacy of discrete and continuous feature extraction methods, separately and in combination, for modelling countermovement jump performance using wearable sensor data. We demonstrate that continuous features, particularly those derived from Functional Principal Component Analysis, outperform discrete features in terms of model performance, robustness to variations in data distribution and volume, and consistency across different datasets. Our findings underscore the importance of methodological choices, such as signal type, alignment methods, and model selection, in developing accurate and generalisable predictive models. We also highlight the potential pitfalls of relying solely on domain knowledge for feature selection and the benefits of data-driven approaches. Furthermore, we discuss the implications of our findings for the broader field of sports biomechanics and provide practical recommendations for researchers and practitioners aiming to leverage wearable sensor data for athletic performance assessment. Our results contribute to the development of more reliable and widely applicable predictive models, facilitating the use of wearable technology for optimising training and enhancing athletic outcomes across various sports disciplines.

This study comprehensively explores Serbia's Disaster (Risk) Management System, aiming to critically evaluate its inherent strengths and weaknesses. Using descriptive-analytical methods and qualitative interpretation, SWOT analysis was applied to collect data from scientific papers, books, and institutional reports. This study delves deeply into the nuances of Serbia's Disaster Management Framework, revealing a nuanced landscape of strengths, weaknesses, and strategic improvement opportunities. Identified strengths underscore a robust foundation characterized by proficient professionals, state-of-the-art technology, and cohesive teamwork, all pivotal for effective emergency preparedness and response. Conversely, weaknesses underscore pressing imperatives for enhancements in regulatory enforcement, resource allocation, and infrastructure upgrades, presently hampering the framework's efficacy and response readiness. This research contributes to the ongoing discourse on disaster risk management and serves as a catalyst for further scholarly inquiry and policy formulation. By mapping out the structure of Serbia’s Disaster (Risk) Management Framework and integrating diverse data sources, the study enriches theoretical models and lays the groundwork for practical policy interventions. Its findings, including precise recommendations for policy reform and strategic improvements, provide valuable insights for policymakers, emergency management experts, and stakeholders seeking to enhance disaster preparedness and response capabilities on both national and global scales.

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.

The popularity of functional gummies has increased, which is evident from the growing line of functional gummies from almost every nutraceutical companies. Sensory evaluation serves the purpose of determining which brand of functional gummy would capture the largest market share. Texture profile analysis was used to determine the mechanical properties of functional gummies. The brands of functional gummies that came under the scope of this study were denoted as Brand A, B, C, D, E, F and G. Fourier Transform Infrared Spectroscopy was utilised to detect organic material and functional groups in the functional gummies. Texture profile analysis gave valuable insights into the gummies’ mechanical properties which are cohesiveness, springiness, hardness, gumminess, and chewiness. Amongst the gummies that were studied, Brand F gummy has the highest value of cohesiveness of 0.92. Brand A gummy has a high springiness value of 1.0. Brand B gummy possesses the highest value of hardness, gumminess and chewiness of 12 532.2 g, 7617.6 N, and 6256.8 J, respectively. Qualitative sensory evaluation reveals that Brand G gummy has the best aesthetic qualities in terms of colour and appearance. Brand B gummy tastes the best while brand A gummy claims the top spot for gumminess and chewiness. Overall, the respondents in this study preferred brand A gummy over other brands.

This study aimed to apply different complexity-based methods to surface electromyography (EMG) in order to detect neuromuscular changes after realistic warm-up and stretching procedures. Sixteen volunteers conducted two experimental sessions. They were tested before, after a standardized warm-up, and after a stretching exercise (static or neuromuscular nerve gliding technique). Tests included measurements of the knee flexion torque and EMG of biceps femoris (BF) and semitendinosus (ST) muscles. EMG was analyzed using the root mean square (RMS), sample entropy (SampEn), percentage of recurrence and determinism following a recurrence quantification analysis (%Rec and %Det) and a scaling parameter from a detrended fluctuation analysis. Torque was significantly greater after warm-up as compared to baseline and after stretching. RMS was not affected by the experimental procedure. In contrast, SampEn was significantly greater after warm-up and stretching as compared to baseline values. %Rec was not modified but %Det for BF muscle was significantly greater after stretching as compared to baseline. The a scaling parameter was significantly lower after warm-up as compared to baseline for ST muscle. From the present results, complexity-based methods applied to the EMG give additional information than linear-based methods. They appeared sensitive to detect EMG complexity increases following warm-up.

To address the problems of lining cracking and spalling of tunnel structures in coastal areas under the influence of special geologic conditions, environmental loading, and the coupling effect of chemical erosion effects, hybrid fibers were introduced to fly ash concrete in this study. The working performance, compressive strength, split tensile strength, and flexural strength of the hybrid fiber fly ash concrete were tested.The NEL chloride diffusion coefficient under steady-state conditions and the durability test for resistance to sulfate corrosion were carried out. Thus, in-depth analyses of the comprehensive performance of the hybrid fiber fly ash concrete used for tunnel lining were carried out and the damage mechanism were explored. The results showed that the hybrid fiber fly ash concrete exhibited higher strength compared to the control group concrete. However, when the fibers exceeded a certain dosage, the reduction in the working properties of the concrete structure led to the creation of larger pores in the matrix structure, which in turn affected the mechanical properties of the concrete. The most significant reduction in NEL chloride diffusion coefficient was observed when both steel fibers and coconut fibers were added at 1.0 % volumetric parameter, compared to the control group. The apparent state and compressive strength after sulfate corrosion were also minimally affected. This study ensured that the mechanical properties of the concrete were improved and also substantially improved the corrosion resistance of the matrix, providing a scientific basis for the performance improvement of tunnel lining concrete, confirming that steel-coconut fibers hybrid fiber fly ash concrete has a great potential to improve the structural load bearing capacity and durability, and may provide theoretical support for the continued use of tunneling projects and construction processes.

The spread of fake news poses a serious threat to public trust and informed decision-making. The emergence of AI-generated fake news exacerbates this issue, as it can be produced more rapidly and convincingly than human-generated content. Addressing this issue, our paper introduces a robust approach to detect AI-generated fake news that is applicable across major disinformation domains: Politics and Elections, Health and Medicine, Science and Technology, and Entertainment and Celebrities. Our models differentiate between AI-generated and human-generated news articles while assessing their truthfulness. Utilizing large language models such as ChatGPT, we curated datasets spanning both AI and human generated fake and genuine news. Following extensive data preprocessing and the application of diverse feature extraction techniques, our models were trained using multiple machine learning algorithms. Our results show the feasibility of accurately identifying AI-generated fake news, thereby adding a significant layer of defense in combating misinformation across various sectors. In addition, we have explored the potential utility of transferable machine learning models trained on data from one domain (e.g., politics), when tested on another domain (e.g., health).

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

Receptor–ligand interactions at cell interfaces initiate signaling cascades essential for cellular communication and effector functions. Specifically, T cell receptor (TCR) interactions with pathogen-derived peptides presented by the major histocompatibility complex (pMHC) molecules on antigen-presenting cells are crucial for T cell activation. The binding duration, or dwell time, of TCR–pMHC interactions correlates with downstream signaling efficacy, with strong agonists exhibiting longer lifetimes compared to weak agonists. Traditional surface plasmon resonance (SPR) methods quantify 3D affinity but lack cellular context and fail to account for factors like membrane fluctuations. In the recent years, single-molecule Förster resonance energy transfer (smFRET) has been applied to measure 2D binding kinetics of TCR–pMHC interactions in a cellular context. Here, we introduce a rigorous mathematical model based on survival analysis to determine exponentially distributed receptor–ligand interaction lifetimes, verified through simulated data. Additionally, we developed a comprehensive analysis pipeline to extract interaction lifetimes from raw microscopy images, demonstrating the model’s accuracy and robustness across multiple TCR–pMHC pairs. Our new software suite automates data processing to enhance throughput and reduce bias. This methodology provides a refined tool for investigating T cell activation mechanisms, offering insights into immune response modulation.

Data regarding the pandemic's impact on tilapia culture remain limited, but it is known that there was a significant decline in production and marketing since 2020. The post-pandemic challenges confronting tilapia farming necessitate prompt solutions, encompassing the management of bacterial infections and the adoption of more advanced technologies by small-scale producers in developing nations. Probiotics, acknowledged as a viable alternative, are presently extensively employed in tilapia aquaculture. Multiple studies have suggested that the application of diverse probiotics in tilapia culture has yielded favorable outcomes. Nonetheless, only a limited number of studies have employed statistical methods to evaluate such findings. To address this gap, a regression analysis was carried out to investigate the existence of a linear relationship between the probiotic dosage added to the feed and two key dependent variables: the specific growth rate (SGR) and the feed conversion ratio (FCR). Additionally, a hierarchical regression analysis was undertaken to ascertain the extent to which the variance observed in these responses could be explained by the variable "probiotic dosage in feed," after accounting for covariates such as initial weight, test duration, water temperature, and number of replicate tanks. Finally, two Pearson correlation matrices were constructed since different studies were included for the SGR and FCR analyses.

Biosolids from stabilized sludge presents a high fertilization potential due to its rich content of nutrients and organic matter. Three formulas of organo-mineral fertilizers based on biosolids were manufactured having at least 2 % N, 2 % P2O5, and 2 % K2O and characterized by classical and advanced methods. The content of N and P in the final formula was measured using specific reactants combined with VIS-nearIR spectroscopy. Additionally, all three formulas were characterized by advanced 1H NMR relaxometry, double-quantum and FT-IR spectroscopy. Structural characterization was performed using SEM, EDX and X-ray diffraction. Four dynamical components were identified in the T2-distribution showing that the rigid component is into a percentage larger than 90 %. In order to evaluate the release properties of organo-mineral fertilizers dynamic measurements of electric conductivity and pH were performed, by placing 0.25 g of formulas (V1, V2 and V3) in 200 ml of distillate water. It was found that V3 present the smallest release velocity, but release the largest amount of fertilizers. Two release mechanisms were also observed.

E-waste management (EWM) refers to the operation-management of discarded or unproductive electronic devices and components, a challenge exacerbated due to overindulgent urbanization. This article presents a multidimensional cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties - which contemplate the 3 pillars of sustainability in an e-waste recycling plant, including the production-delivery-utilization process. The framework incorporates material recovery from a single e-waste facility provisioning for chemical and mechanical recycling. Each module is ranked using independent Machine Learning (ML) protocols: a) Analytical Hierarchical Process (AHP) and b) combined AHP and Principal Component Analysis (PCA). From a long list of possible contributors, the model identifies and ranks two key sustainability contributors to the EWM supply chain: overall energy consumption and volume of carbon dioxide generated. Another key finding is a precise time window for policy resurrection, which for the data considered, happens to be 400-600 days from the start of operation. Another interesting outcome is the quality of prediction using a combination of AHP and PCA, which consistently produced better results than any of these ML methods individually implemented. Model outcomes have been verified using a case study to outline a future E-waste sustained roadmap.

In the context of improving aircraft safety, this study focuses on the development and evaluation of a graphene-based ice detection system using an environmental chamber. The research is driven by the need for more accurate and efficient ice detection methods, crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors’ heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors, tested under varied and controlled atmospheric conditions, exhibit a remarkable potential in enhancing ice detection systems for aircraft. Their lightweight, efficient and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

The sympathetic restoration and conservation of built cultural heritage play a significant role in the management and preparedness for future climate scenarios by facilitating adaptive reuse, enhancing cultural resilience, preserving traditional knowledge, and boosting tourism. The importance of restoring damaged heritage sites after an earthquake drew international attention to Nepal after the 2015 Gorka Earthquake. UNESCO established an office in Kathmandu to promote the restoration of tangible and intangible heritage in the area. This included developing structural analyses of buildings with historical and cultural value that due to their nature cannot be intervened with the same methodology as modern buildings. In this paper, the case study of the earthquake-damaged Gopinath temple, is discussed. First, an initial visual inspection phase and the following diagnosis of the structure are discussed. Then, the results from a series of static and dynamic structural analyses performed to determine the safety level of the structure, together with a sensitivity analysis, are presented. A sympathetic intervention proposal capable of increasing the temple safety level and based on the addition of timber plates, has resulted in substantial improvements of the lateral behavior of the structure. The proposed intervention is deemed sustainable and able to increase the resilience of the temple in the face of future hazards.

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal-Organic Frame as Cu(BPDC), and 7 different polymer films including PVA, PEDOT: PSS, PFO, SB, SW, SG, PB were used for functionalizing of QCM sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models including Principal Component Analy-sis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

The attention to sustainable-related issues has grown fast in recent decades. The experience gained with these themes reveals the importance of considering this topic in the construction industry, which represents an important sector in the world. This work consists on conducting a multicriteria analysis of four cement powders, with the objective of calculating and analysing the environmental, human health and socio-economic effects of their production processes. The economic, technical, environmental and safety performances of the examined powders result from official, both internal and public, documents prepared by the producers. The Analytic Hierarchy Process permitted to consider several indicators (i.e. environmental, human health related and socio-economic parameters) and to conduct comprehensive and unbiased analyses which gave the best, most sustainable cement powder. As assumed in this study, the contribution of each considered parameter to the overall sustainability has a different incidence, therefore the procedure could be used to support on-going sustainability efforts under different conditions. The results also prove that it is incorrect to consider only one parameter to select the ‘best’ cement powder, but several impact categories should be considered and analysed if there is an interest for pursuing different, often conflicting interests.

Referring to the concept of inclusive growth, the authors analyse the transition economies of the Central and Eastern European countries, which are the current EU members (Bulgaria, Croatia, the Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Romania, Slovakia and Slovenia). That region was selected as the CEE countries characterized by comparable historic and economic background but now they seem to reach diversified stages of development. The objective of the study is to identify the level of inclusive growth among the CEE countries, taking into account indicators assigned to its seven pillars. The thesis is that the CEE countries represent socio and economic heterogeneity as well as different levels of sustainable development. The research methods involved the application of the principal components analysis and the multivariate analysis. For literature review, the bibliometric analysis was conducted with the visualization prepared by the VOSviewer software. The main findings suggest that Estonia, Slovenia and the Czech Republic seem to be the ones with the highest inclusive growth. On the other hand, Bulgaria and Romania represent the lowest level of inclusive growth indicators.

We report the experiences of the bike sharing system in Ningbo, China. To analyze the impact of the system, system log data are collected for the whole month of May 2014 (with more than 1.7 million usage records), supplemented by questionnaire data from 1,856 respondents. Our results show that public bike usage follows the same pattern as road traffic, with morning and evening peaks on week days. In terms of mode substitution, 16% of public bike trips would have been made by private cars. In average, each public bike is used 3.7 times per day, with each trip lasting around 23 minutes and covering around 3.5 kilometre. This translates into a reduction of car travel by more than 11 million kilometre per year, a significant contribution to the sustainability of urban transport in Ningbo.

The reaction between 5-acetylbarbituric acid and 4-dimethylthiosemicarbazide or 4-hexamethyleneiminyl thiosemicarbazide produces 5-acetylbarbituric-4-dimethylthiosemicarbazone (H2AcbDM) and 5-acetylbarbituric-4N-hexamethyleneiminyl thiosemicarbazone (H2Acbhexim). Eight new complexes with different cooper (II) salts have been prepared and characterized using elemental analysis, molar conductance, UV–vis, ESI-HRMS, FT-IR, magnetic moment, EPR and cyclic voltammetry. In addition, three-dimensional molecular structures of [Cu(HAcbDM)(H2O)2](NO3)·H2O (3a), [Cu(HAcbDM)(H2O)2]ClO4 (4), and [Cu(HAcbHexim)Cl] (6) were determined by single crystal X-ray crystallography, and was carried out an analysis of their supramolecular structure. The H-bonded assemblies were further studied energetically using DFT calculations, MEP surface and QTAIM analyses. In these complexes, the thiosemicarbazone coordinates to the metal ion in an ONS-tridentate manner, in the O-enolate/S-thione form. The electrochemical behavior of the thiosemicarbazones and their copper(II) complexes has been investigated at room temperature using the cyclic voltammetry technique in DMFA. The Cu(II)/Cu(I) redox system was found to be consistent with the quasi-reversible diffusion-controlled process.

In this paper, an improved system to efficiently utilize the low-temperature waste heat (WHUS) from the flue gas of coal-fired power plants is proposed based on heat cascade. The essence of the proposed system is that the waste heat of exhausted flue gas is not only used to preheat air for assisting coal combustion as usual but also to heat up feedwater and the low-pressure steam extraction. Preheated by both the exhaust flue gas in the boiler island and the low-pressure steam extraction in the turbine island, thereby part of the flue gas heat in the air preheater can be saved and introduced to heat the feedwater and the high-temperature condensed water. Consequently, part of the high-pressure steam is saved for further expansion in the steam turbine, which obtains additional net power output. Based on the design data of a typical 1000 MW ultra-supercritical coal-fired power plant in China, in-depth analysis of the energy-saving characteristics of the optimized WHUS and the conventional WHUS is conducted. When the optimized WHUS is adopted in a typical 1000 MW unit, net power output increases by 19.51 MW, exergy efficiency improves to 45.46%, and net annual revenue reaches 4.741 million USD. In terms of the conventional WHUS, these aforementioned performance parameters are only 5.83 MW, 44.80% and 1.244 million USD, respectively. The research of this paper can provide a feasible energy-saving option for coal-fired power plants.

This article examines the evolution of the most extensively researched subjects in E-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in E-mobility, can absorb in its efforts to embrace adoptions of electric vehicles (EVs). To strengthen the body of knowledge and deter-mine the most effective and efficient route to an "EV-ready" nation, the authors compiled data on the latest developments in the EV industry. A bibliometric analysis was performed on 3,962 articles utilizing the VOSviewer software, which identified six noteworthy clusters that warranted further attention. Additionally, we examine the sequential progression of these clusters as follows: 1. The environmental ramifications of electric mobility; 2. Advancements in electric vehicle technology, including range extension and soundless engines; and capital (CAPEX) and operational (OPEX) expenditures of purchasing and operating EVs. 3. Concerns regarding effectiveness and durability of EV batteries, 4. Availability of EV charging stations and grid integration, 5. Charging time, and finally 6. Origin and source of the energy utilized in the development of e-mobility. We are of the opinion that by delineating the critical aspects of e-mobility development, policymakers and decision-makers in Kuwait will be better equipped to formulate timely and economical choices pertaining to sustainable transportation.

Rapidly spreading disease, COVID-19 is classified as the human-to-human transmission-able disease and currently is a pandemic in the Globe. In this paper, we propose conceptual mathematical models for COVID-19 outbreak and it's control measurement; quarantine, hospitalization and the effect of panic and anxiety. In this situation, mathematical models are a important tool to employ an effective strategy in order to fight against this pandemic. We establish the positivity and boundedness of solutions, local and global stability analysis of equilibria to examine its epidemiological relevance. To validate the model and estimating the important model parameters and prediction about the disease, we consider the real cases of Italy from $15^{th}$ Feb to $13^{th} $ April 2020. In a series of graphical map, we have presented the comparative study to estimate the current scenarios and to predict the control measurement time boundary of the outbreak.

1) Background: Recently, illegal abuse of γ-hydroxybutyric acid (GHB) has increased in drug-facilitated crimes, but determination of GHB exposure and intoxication is difficult due to rapid metabolism of GHB. Its biochemical mechanism has not been completely investigated. And metabolomic study by polyamine profile and pattern analyses was not performed in rat urinefollowing intraperitoneal injection with GHB. 2) Methods: Polyamine profiling analysis by gas chromatography-mass spectrometry combined with star pattern recognition analysis was performed in this study. Multivariate statistical analysis was used to evaluate discrimination between control and GHB administration groups. 3) Results: Six polyamines were determined in control, single and multiple GHB administration groups. Star pattern showed distorted hexagonal shapes with characteristic and readily distinguishable patterns for each group. N¹-Acetylspermine (p < 0.001), putrescine (p <0.006), N¹-acetylspermidine (p <0.009), and spermine (p < 0.027) were significantly increased in single administration group but were significantly lower in the multiple administration group than in the control group. N¹-Acetylspermine was the main polyamine for discrimination between control, single and multiple administration groups. Spermine showed similar levels in single and multiple administration groups. 4) Conclusions: The polyamine metabolic pattern was monitored in GHB administration groups. N¹-Acetylspermine and spermine were evaluated as potential biomarkers of GHB exposure and addiction.

3D facial surface imaging is a useful tool in dentistry and in terms of diagnostics and treatment planning. Between-groups PCA (bgPCA) is a method that has been used to analyse shapes in biological morphometrics, although various “pathologies” of bgPCA have recently been proposed. Monte Carlo (MC) simulated datasets were created here in order to explore “pathologies” of multilevel PCA (mPCA), where mPCA with two levels is equivalent to bgPCA. The first set of MC experiments involved 300 uncorrelated normally distributed variables, whereas the second set of MC experiments used correlated multivariate MC data describing 3D facial shape. We confirmed previous results of other researchers that indicated that bgPCA (and so also mPCA) can give a false impression of strong differences in component scores between groups when there is none in reality. These spurious differences in component scores via mPCA reduced strongly as the sample sizes per group were increased. Eigenvalues via mPCA were also found to be strongly effected by imbalances in sample sizes per group, although this problem was removed by using weighted forms of covariance matrices suggested by the maximum likelihood solution of the two-level model. However, this did not solve problems of spurious differences between groups in these simulations, which was driven by very small sample sizes in one group here. As a “rule of thumb” only, all of our experiments indicate that reasonable results are obtained when sample sizes per group in all groups are at least equal to the number of variables. Interestingly, the sum of all eigenvalues over both levels via mPCA scaled approximately linearly with the inverse of the sample size per group in all experiments. Finally, between-group variation was added explicitly to the MC data generation model in two experiments considered here. Results for the sum of all eigenvalues via mPCA predicted the asymptotic amount for the total amount of variance correctly in this case, whereas standard “single-level” PCA underestimated this quantity.

Our recent analysis of empirical limit order flow data in financial markets reveals a power-law distribution in limit order cancellation times. These times are modeled using a discrete probability mass function derived from the Tsallis q-exponential distribution, closely aligned with the second form of the Pareto distribution. We elucidate this distinctive power-law statistical property through the lens of agent heterogeneity in trading activity and asset possession. Our study introduces a novel modeling approach that combines fractional Lévy stable motion for limit order inflow with this power-law distribution for cancellation times, significantly enhancing the prediction of order imbalances. This model not only addresses gaps in current financial market modeling but also extends to broader contexts such as opinion dynamics in social systems, capturing the finite lifespan of opinions. Characterized by stationary increments and a departure from self-similarity, our model provides a unique framework for exploring long-range dependencies in time series. This work paves the way for more precise financial market analyses and offers new insights into the dynamic nature of opinion formation in social systems.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into missing data problem formulation, assumption specification, and method development. In this paper, we highlight the gap particularly for observational data from healthcare facilities. We address this gap by identifying ten fundamental missingness scenarios arising during data measurement, recording, and pre-processing in observational health data, influenced by physicians, patients, healthcare facilities, and data scientists. We analyze the effect of scenarios on estimand formulation, missing data identification, estimation, and sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis (complete-case analysis, Missforest imputation, and inverse probability weighting estimation) for two estimands (variable mean estimation and classification accuracy). We advocate for our analysis approach as a reference for the analysis of observational health data. Furthermore, we posit that the proposed analysis framework is applicable to other medical domains, including medical wearable data analysis.

Fire is a common natural disturbance in forest ecosystems and plays an important role in subsequent vegetation patterns. Based on the spatial sequence method instead of the time successional sequence method, this study selected burned areas in different locations in the Anning River Basin, which contains typical dry valleys. Quadrat surveys and quantitative classification were used to identify the vegetation classification, distribution pattern, and environmental interpretation during the natural restoration process after forest fire. The results showed that: (1) the vegetation community in the early stage of natural recovery after forest fire disturbance could be divided into seven community types, and Quercus guyavaefolia H. Leveille (Qg) was the dominant species in the community; (2) vegetation samples could be divided into five ecological types, and the classification and distribution pattern of community types in this region changed most obviously with altitude; and (3) detrended correspondence analysis could clearly classify vegetation community types, and detrended canonical correspondence analysis could well reveal the relationships between species and environmental factors. This study provides a scientific basis guiding the restoration of ecosystem structural stability and biodiversity in burned areas.

The Drinking Water Directive (EU) 2020/2184 includes the parameter microcystin-LR, a cyanotoxin, which drinking water producers need to analyze if the water source has potential for cyanobacterial blooms. In light of the increasing occurrences of cyanobacterial blooms worldwide and given that more than 50 percent of the drinking water in Sweden is produced from surface water, both fresh and brackish, the need for improved knowledge about cyanotoxin occurrence and cyanobacterial diversity has increased. In this study a total of 98 cyanobacterial blooms were sampled in 2016-2017 and identified regarding their toxin production and taxonomical compositions. The surface water samples from freshwater lakes throughout Sweden including brackish water from 8 east coast locations along the Baltic Sea were analyzed for their toxin content with LC-MS/MS and taxonomic composition with 16S rRNA amplicon sequencing. Both the extracellular and the total toxin content was analyzed. Microcystin´s prevalence was highest with presence in 82% of blooms, of which as free toxins in 39% of blooms. Saxitoxins were found in 36% of blooms in which the congener decarbamoylsaxitoxin (dcSTX) was detected for the first time in Swedish surface waters at four sampling sites. Anatoxins were most rarely detected, followed by cylindrospermopsin, which were found in 6% and 10% of samples, respectively. As expected, nodularin was detected in samples collected from the Baltic Sea only. The cyanobacterial operational taxonomic unit (OTU) with the highest abundance and prevalence could be annotated to Aphanizomenon NIES-81 and the second most profuse cyanobacterial taxon to Microcystis PCC-7914. In addition, two correlations were found, one between Aphanizomenon NIES-81 and saxitoxins and another between Microcystis PCC-7914 and microcystins. This study is of value to drinking water management and scientists involved in recognizing and controlling toxic cyanobacteria blooms.

Magnesium (Mg) is a vital element, which involves in various key cellular processes in plants. Mg transporter (MGT) genes play an important role in magnesium distribution and ionic balance maintenance. Here, MGT family members were identified and characterized in three species of the plant family Malvaceae, including Theobroma cacao, Corchorus capsularis, and Gossypium hirsutum to improve our understanding of their structure, regulatory systems, functions, and possible interactions. We identified 18, 41, and 16 putative non-redundant MGT genes from the genome of T. cacao, G. hirsutum, and C. capsularis, respectively, which were clustered into five groups within maximum likelihood tree. Several segmental/tandem duplication events were determined between MGTs genes. It seems that MGTs were slowly evolving and have been evolved under a purifying selection. Analysis of gene promoter regions showed that MGTs have a high potential to respond to biotic/abiotic stresses as well as hormones. The expression patterns of MGT genes revealed their possible T. cacaorole in response to P. megakarya fungi in T. cacao, whereas MGT genes showed differential expression in various tissues and respond to several abiotic stresses, including cold, salt, drought, and heat stress in G. hirsutum. Besides, the co-expressions network of MGTs indicated that genes involved in auxin responsive, lipid metabolism, cell wall organization, and photoprotection can interact with MGTs.

The work presented in this paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between November 9, 2021, and July 13, 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these tweets were positive. The results of gender-specific sentiment analysis indicate that for positive tweets, negative tweets, and neutral tweets, between males and females, males posted a higher percentage of the tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that for each subjectivity class, males posted a higher percentage of tweets as compared to females. Third, toxicity detection was performed on the tweets to detect different categories of toxic content - toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of tweets posted by each gender in each of these categories revealed several novel insights. For instance, for the sexually explicit category, females posted a higher percentage of tweets as compared to males. Fourth, gender-specific tweeting patterns for each of these categories of toxic content were analyzed to understand the trends of the same. The results unraveled multiple paradigms of tweeting behavior, for instance, the intensity of obscene content in tweets about online learning by males and females has decreased since May 2022. Fifth, the average activity of males and females per month was calculated. The findings indicate that the average activity of females has been higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India a higher percentage of the tweets about online learning during COVID-19 were posted by males as compared to females.

Background: The monocyte-phagocyte system (MPS), including monocytes/macrophages and dendritic cells (DCs), plays a key role in anti-viral immunity. We aimed to analyze the prognostic value of the MPS components on in-hospital mortality in a cohort of 58 patients (M/F ; mean ageSD years) with COVID-19 pneumonia and 22 age- and sex-matched healthy controls. Methods: We measured frequencies and absolute numbers of peripheral blood CD169+ monocytes, conventional CD1c+ and CD141+ (namely cDC2 and cDC1), and plasmacytoid CD303+ DCs by means of multi-parametric flow cytometry. A gene profile analysis of 770 immune-inflammatory related human genes and 20 SARS-CoV-2 genes was also performed. Results: Median frequencies and absolute counts of CD169-expressing monocytes were significantly higher in COVID-19 patients than in controls (p 0.04 and p 0.01, respectively). Conversely, percentages and absolute numbers of all DC subsets were markedly depleted in patients (p

Hydrodechlorination reaction of 3-(benzo-1,3-dioxol-5-yl)-3-chloro-2-methylacrylaldehyde in the presence of different low metal content heterogeneous mono- or bi-metallic catalysts was tested for the synthesis of the fragrance Helional® (3-[3,4-methylendioxyphenyl]-2-methyl-propionaldehyde). In particular, mono Pd/Al2O3, Rh/Al2O3 or bi-metallic Pd-Cu/Al2O3, Rh-Cu/Al2O3 catalysts were tested in different reaction conditions from which it emerged that mono-Rh/Al2O3 was the best performing catalyst allowing to achieve 100% substrate conversion and 99% selectivity towards Helional® in 24h at 80°C, p(H2) 1.0 MPa in the presence of a base. To establish correlations between atomic structure and catalytic activity, catalysts were characterized by Cu, Rh and Pd K-edge XANES, EXAFS analysis. These characterizations allowed to verify that the formation of Pd-Cu alloys and the presence of Cu oxide/hydroxide species on the surface of the Al2O3 support, are responsible for the very low catalytic efficiency of bimetallic species tested.

This study analyzed the changes and trends in twelve extreme precipitation-based climate indices obtained using daily data from 10 synoptic stations in the Chungcheong region of South Korea during the 1973-2020 period. The climate indices were used to assess the trends in the extreme precipitation characteristics of duration, frequency, and intensity using the innovative trend analysis (ITA) method. The results of the ITA were further compared with two other non-parametric test methods such as Mann-Kendall (MK) and Spearman’s rho (SR). The results showed that the most stations exhibited significant increasing trends in all the investigated climate indices at a 95% confidence level as indicated by the ITA method, with only a few stations indicating significant decreasing trends in R95p, R99p, Rx3day, and Rx5day. The sub-trend analysis further revealed the dominance of neutral behavior around the low-value cluster, especially for the extreme precipitation duration. At the same time, increasing trends dominate the high-value cluster in most stations. Meanwhile, only R10mm, R95p, and R99p exhibited monotonic trends in the Boeun, Seosan, and Boeun stations, respectively. Further, the ITA exhibited superior performance over the MK and SR methods by indicating the presence of more significant trends in the climate indices at most stations. The distribution of extreme precipitation indices for duration, frequency, and intensity indicate the pronounced risk of flood condition around the north-central and some parts of southern regions, while the western region indicate a potential drought risk, which could greatly impact the water resources and consequently agricultural activities in the study area. The results of this study provide essential information for addressing the climate-related problems of water resources management and agriculture in the study area and other related climatic regions.

Balancing the efficiency and robustness of food systems is a well-known trade-off process. Over-optimization of efficiency may lead to excessive use of resources. On the other hand, under-optimization of efficiency may lead to excessive waste of resources. Using non-parametric methods can help in developing actionable frameworks through which decision-makers and stakeholders can effectively balance and improve food systems' efficiency and robustness. Under this context, this paper presents a new structured approach integrating two well-suited non-parametric methods for analyzing and balancing the interconnection between the efficiency and robustness of food systems. This approach consists of three main steps. First, the slacks-based data envelopment analysis (SBM-DEA) is utilized to analyze the efficiency of five food robustness dimensions. Second, the weighted efficiency of these dimensions is computed and analyzed to provide an insight into their influence on the food system efficiency. Finally, two search methods were developed to identify opportunities for improving efficiency and robustness. The outcomes of these methods have been analyzed and validated to ensure their effectiveness and viability. The operational and mathematical properties of the proposed approach were evaluated using data from thirty-seven food-producing countries. The weights of the robustness dimensions in this study were determined using the analytic hierarchy process (AHP) method. The input matrix of the AHP was constructed using expert judgment and consultations with stakeholders. The results of this study have shown an appropriate level of applicability and effectiveness of the proposed approach. While the first search method provided valuable insights, the second method proved to be more effective in identifying the sources of inefficacy of robustness dimensions. Several conclusions have been drawn and reported with some recommendations for future research works.

The present study provides a detailed analysis of the chloroplast genome of Microula sikkimensis. The genome was consisted of altogether 149,428bp and distinct regions, including large single-copy (81329bp), small single-copy (17261bp), and inverted repeat (25419bp). The genome contained 112 genes, including 78 protein-coding genes, 30 tRNA genes, and 4 rRNA genes, some exhibited duplication in the IR region. The chloroplast genome displayed different GC content across regions, with the IR region exhibiting the highest. Codon usage analysis and the identification of simple sequence repeats (SSRs) offer valuable genetic markers. Comparative analysis with other Boraginaceae species highlighted conservation and diversity in coding and noncoding regions. Phylogenetic analysis placed M. sikkimensis within the Boraginaceae family, revealing its distinct relationship with specific species.

When designing the battery of an electric vehicle, different parameters must be considered to obtain the safest arrangement of the battery/modules/cells from the mechanical and thermal point of view. In this study, the thermal runaway propagation mechanism of lithium-ion cells is analyzed as a function of their arrangement within a battery pack in case of a fire propagation of a battery pack in which a thermal runaway has occurred. The objective is to identify which cell/module arrangement is most critical within the battery pack by microscopic analysis of the structure and chemical composition of the most damaged cells, both horizontally and vertically, of a battery belonging to a burnt vehicle. And their final condition has been compared with the condition of new cells of the same type. In this way the structure and chemical composition of the anode, cathode and separator after Thermal Runaway has been compared. This research has been carried out to obtain information to understand the mechanical properties of lithium-ion cells and their behavior after thermal runaway heating leading to the propagation of a fire. Through the analysis carried out, it is concluded that cells placed in a vertical arrangement have worse behavior than cells in a horizontal arrangement. Regarding the safety of the battery, the results of this study will allow to determine which arrangement and structure of the cells within the battery pack is safer against thermal runaway due to thermal failure.

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.

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

Ocean big data is the scientific practice of using big data technology in the marine field. Data from satellites, manned spacecraft, space stations, airship, unmanned aerial vehicles, shore-based radar and observation stations, exploration platforms, buoys, underwater gliders, submersibles, and submarine observation networks are seamlessly combined into the ocean’s big data. Increasing numbers of scholars have tried to fully analyze the ocean’s big data. To explore the key research technology knowledge graphs related to ocean big data, articles between 1990 and 2020 were collected from the “Web of Science”. By comparing bibliometric software and using the visualization software Cite-Space, the pivotal literature related to ocean big data, as well as countries, institutions, categories, and keywords, were visualized and recognized. Journal co-citation analysis networks can help determine the national distribution of core journals. Co-citation analysis networks for documents show authors who are influential at key technical levels. Key co-occurrence analysis network keywords can determine research hot spots and research frontiers. The three supporting elements of marine big data research are shown in the co-citation network. These elements are author, institution, and country. By examining the co-occurrence of keywords, the key technology research directions for future marine big data were determined.

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM2.5). We propose a data-fusion method to study wildfire contribution to PM2.5 using satellite-derived smoke plume indicators and PM2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy.

The ground consists of many layers of soil with different properties. The propagation speed and path of seismic waves are affected by different soil layers. It is necessary to understand that layered soil exhibits different dynamic behaviors and responses under the action of seismic waves. This study utilized weathered soil and silica sand as materials to create multi-layered soil conditions with varying degrees of compaction. By conducting 1 g shaking table test on multi-layered soil, the interaction and influence between different soil layers under different earthquake conditions were observed. The approach of numerical analysis aims to validate the experimental results and provide an in-depth understanding of the dynamic behaviors of multi-layered soil grounds during seismic events. The results of this study show that the amplification effect of seismic waves is related to the number of soil layers and the degree of compaction of the soil layers. This indicates that the multi-layered soil ground and the behavior of the soil layers play an important role in the propagation and impact of seismic waves, and this amplification effect is of great significance in actual seismic disaster risk assessment and design.

A new approach based on input-output (IO) analysis has emerged to estimate the carbon footprints of companies and their products from cradle to gate. While they rely on the same principles as the GHG Protocol, they use a distributed iterative framework to improve the footprint estimations and reduce their uncertainty. While optimal estimations would result if all the world’s companies would enter such a system, this paper shows how such a distributed system could apply to the real world where many enterprises would stay out of the system. We show how it would increase the quality of the estimations with respect the GHG Protocol by integrating scope 1 and scope 2 data of the value chains in the footprint estimations and reducing the part of remaining scope 3 data. To help analyzing uncertainty, we show how to use the scope 1/2/3 decomposition to estimate the biases and the standard deviations of the computed production intensities. We illustrate the model on macroeconomic data for 44 sectors and two regions (Europe and Rest of World), using the Inter-Country Input-Output database (ICIO). Such a system would necessarily rely on Information and Communication Technology since the companies would be permanently interconnected in a large-scale meshed network, using an application protocol for data exchange.

Over the years, network analysis became a promising strategy to analyze complex system, i.e. systems composed by a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists to uncover complex relationships and identify key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we model omics, diseases and drugs data together through the multilayer network representation. Then, we mined the multilayer network with a community detection algorithm by obtaining top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected form the selected genes. The results show that the genes forming the top community have the multiple roles through different pathways.

Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity states, has tracked the transition patterns of these states, and has demonstrated meaningful alterations in these transition and spatial patterns in neurological disorders, psychiatric disorders, and over the course of development. Recently, researchers have begun to analyze this data from the perspective of dynamic system and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Consciousness, in particular, appears to be strongly linked to entropy production in the brain. Little attention has been paid to the effects of psychiatric disease on entropy production in the human brain, however. Even disorders characterized by substantial changes in conscious experience have not been widely analyzed from this perspective. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls, and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients’ brain function.

This study addresses the enhancement of material efficiency and reduction of brittleness in timber-to-concrete adhesive connections for beam-type timber and timber-concrete composite panels. The research explores the potential benefits of adding longitudinal timber ribs to Cross-Laminated Timber (CLT) beam-type panels. Three groups of flexure-tested specimens were analysed: (1) timber panels (1400 mm × 400 mm) with two 100 mm thick CLT panels and two 60 mm thick CLT panels reinforced with 150x80 mm timber ribs; (2) eight specimens (600 mm × 100 mm × 150 mm) with CLT members (600 mm × 100 mm × 100 mm) connected to a 50 mm concrete layer using granite chips and Sikadur-31 (AB) epoxy adhesive; (3) six CLT panels (1400 mm × 400 mm × 50 mm) bonded to a 50 mm concrete layer, with two panels containing polypropylene microfibers and two panels incorporating polyethene dowels for mechanical connection. Specimens were subjected to three-point bending tests and analysed using the transformed section method, γ-method, and finite element method with ANSYS 2023R2 software. Results indicated a 53% increase in load-carrying capacity for ribbed CLT panels with no additional material consumption, a 24.8%-41.1% increase for CLT panels strengthened with a concrete layer, and improved ductility and prevention of disintegration in timber-concrete composites with polypropylene microfibers.

Groundnut is an important cash crop and a diet component for rural households in sub-Saharan Africa. However, Aflatoxin contamination remains a barrier to promoting groundnut-based food products as food levels above maximum limit (ML) can adversely affect human health. Three objectives were set, namely, (i) to characterize the post-harvest operations and risk factors affecting aflatoxin contamination, (ii) to assess aflatoxin incidence in on-farm stores and, iii) to assess agronomic practices that can be integrated to improve yield and reduce aflatoxin contamination. The average aflatoxin levels in on-farm stores were 11.3-16.4μg/kg, in which the proportions of ML were 25.7% at 4μg/kg, 56.3% at 15μg/kg, and 80% at 20μg/kg (~5-7 months after harvest). The application of Aflasafe® resulted in safe or no aflatoxin contamination across the locations. Significant hazards identified during postharvest operations include intermittent rain at harvest and drying (6.5), insect pests attacks after harvest (6), prolonged harvest to stripping interval (6.6), drying on bare ground (6.2), storing on bare floor (6.9), use of poorly efficient shelling machines (6.5), and re-use of rejected nut (6.1). This study provides solutions to minimize aflatoxin risk if appropriate technologies (e.g. improved variety, fertilizer, rhizobia inoculant, and Aflasafe®) and policies are deployed.

Recently, many independent research project focus on the study of the molecular basis of aging processes. I parallel, the different progression of many diseaes between sex is a hot topic research area. These studies require many data, models and tools for inferring aging and sex specific molecular machinaries. Among the others, the Genotype-Tissue Expression (GTEx) database is one of the preminent resources for the analysis of expression data related to tissues, sex and age. The current version of the database has a lot of querying interface that enable many analysis centered on the expression of genes on tissues. Despite this the database lacks on the analysis at sex/age level, thus the researcher has to download data and then write queries by hand. Nevertheless it lacks on the integration with existing protein interaction data. Therefore, the need for the introduction of tools enabling easy access and powerful analysis capabilities (i.e. state of the art network based analysis and integration), arises. We here present NOMA-DB a framework for the analysis of age related genes based on the GTEx database that enable easy querying at sex/age level, network based analysis. The framework is based on wrapping the GTEx database and on building an application logic level on top of existing data. The current version enables the analysis of genes by tissue, gene and age, thus it may be used in potentially future directions of analysis towards better comprehension of aging/sex-related molecular machineries based on the analysis of expression dat

Sentiment analysis is a technique used to understand the publics’ opinion towards an event, product, or organization. For example, positive or negative opinion or attitude towards electric vehicle (EV) brands. This provides companies with valuable insight about the public's opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performances over the traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming signif-icant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 [1]. In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach and generalized autoregressive pretraining for language un-derstanding using Lucid motors and Tesla motors YouTube datasets. Results evidenced LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically, when fi-ne-tuned. Furthermore, our findings presents the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with a F1 score of at least 92%.

The article explores Qualitative Comparative Analysis (QCA), an approach for establishing causal relationships between conditions and outcomes. QCA, applied in case studies and empirical analyses, facilitates generalizations for future research and addresses causal complexity by identifying various causal "recipes" leading to the same outcome. Three widely used QCA variants are Crisp-Set QCA (csQCA), Multi-value QCA (mvQCA), and Fuzzy-Set QCA (fsQCA), each with unique approaches. The article emphasizes the significance of bibliometric analysis in mapping a research field's intellectual structure, revealing key contributions and trends. In QCA, bibliometric analysis identifies leading researchers, affiliations, and influential countries. Results show QCA's widespread adoption across disciplines, with continuous growth in article production since 2013. The favored variant is fsQCA, followed by csQCA, and China notably contributes to QCA-related research. In conclusion, QCA proves a potent tool for discerning complex causal relationships in empirical studies, with its increasing interdisciplinary use indicating substantial potential for advancing scientific knowledge.

The potency and challenges of sorghum development in tidal swamplands in Indonesia have yet to be well studied. Our research was the first to evaluate land suitability, economic performance, and strategies for developing sorghum in tidal swamplands in Central Kalimantan. The assessment methods for the potency and utilization of sorghum used the land suitability evaluation method, gross margin and profit analysis, break-even, and competitive analysis. As a tool for decision-making, we used SWOT followed by the quantitative strategic planning matrix (QSPM) analysis. The results showed that the potency of arable land suitable for sorghum development was 578,511 ha. Economically, sorghum farming can generate an IDR 12,894,000 per ha with a revenue-cost ratio of 1.72; the break-even price was IDR 2,447 per kg, around 42% lower than the current market price. Sorghum was also more competitive than cassava, sweet potato, and soybeans (Q values of 0.76, 0.58, and 0.61, respectively) and less competitive than maize (Q = 1.33). Based on the QSPM, there were five alternative strategies for developing sorghum in tidal swamplands, i.e., (1) Optimization of productivity, (2) Improving the quality of human resources of farmers, (3) Facilitation of partnership cooperation, (4) Application of site-specific technology, and (5) Optimization of waste utilization. Those strategies show the potential expansion of sorghum planting in the tidal swamplands and the economic value enhancement for the community.

Objectives: Accumulating evidence suggests that physical activity (PA) is an efficient intervention to maintain functional capabilities and mitigate physiological changes in the older population. However, an attempt has yet to be made to comprehensively investigate the published landscape on the subject. Methods: This study had two aims. The first aim was to perform a bibliometric analysis for two keywords, “aging” and “PA”, to analyze the research trend. Since ‘frailty’ is the most noticeable co-occurring keyword with the two keywords, the second aim was to investigate the effects of PA, particularly resistance training (RT), on frailty via meta-analysis to provide a summary of the current evidence base. Results: Bibliometric analysis revealed that the number of publications on this research topic has gradually increased, highlighting the importance of understanding the role of PA in aging. A meta-analysis found that RT had significant beneficial effects on physical frailty factors, including handgrip strength, lower limb strength, balance, gait speed, and stair climbing ability. Conclusion: These findings demonstrate that RT is an effective intervention for improving physical function in frail populations; thus it has important implications for the development of PA programs for older adults with frailty. Future research is warranted to explore the optimal dose, frequency, and duration of RT programs for older adults, as well as the potential benefits of combining RT with other forms of PA, such as aerobic or balance exercises.

Gwadar is essential for Pakistan's financial stability. The third deep-water port in Pakistan, Gwadar, plays a significant part in trade between the Gulf countries, Africa, China, United Arab Emirates, and Cordillera Administrative Region. However, the load Shedding of 12-16 hours in Gwadar is the most concerning issue. Pakistan imports 70 MW of electricity from Iran. The wind and solar system are already installed but for a limited residential area. In Gwadar, there are enough renewable energy resources that can be utilized for electricity generation. In this context, a Techno and Economic Analysis is performed using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) Pro. Two models are considered for this study. Model-1 includes PV/Wind Turbine/ and Battery while Model-2 consists of PV/Wind Turbine/Converter and Grid. The yearly energy generated by Model-1 and Model-2 is 57.37 GWh and 81.5 GWh, respectively. The levelized cost of electricity (LCOE) for Model-1 and Model-2 is respectively $0.401/kWh and $0.0347/kWh. It is shown that the simple payback of Model-1 is of 6.70 years, and the simple payback of Model-2 is 7.77 years. Due to the high LCOE of Model-1, its payback year is lesser than model-2. All of these facts indicate that Model 2 is the most optimal solution.

This paper is intended at highlighting the main mechanical parameters controlling the behavior of the so-called "acceleration-sensitive" Non-Structural Components (NSCs). In the first part a short review of the current state of knowledge and the critical issues related to the prediction of the seismic response of NSCs is reported. Then, the paper presents the results of a numerical parametric analysis intended to capture the key features of the dynamic response of a two-degree-of-freedom (2DOF) system which is supposed to be representative of both the main structure and the "non-structural" component (NSC). Particularly, it allows to simulate the coupled behaviour of both main structure and NSC and evaluating their response. The main parameters controlling the dynamic response of NSCs emerge from this study, which could pave the way towards formulating more mechanically consistent relationships for evaluating the maximum accelerations induced by seismic shakings on NSCs.

Abstract: The SARS-CoV-2 pandemic has reemphasized the urgent need for broad-spectrum antiviral therapies. We developed a computational workflow using scRNA-Seq data to assess cellular metabolism during viral infection. With this workflow we predicted the capacity of cells to sustain SARS-CoV-2 virion production in patients and found a tissue-wide induction of metabolic pathways that support viral replication. Expanding our analysis to influenza A and dengue viruses, we identified metabolic targets and inhibitors for potential broad-45 spectrum antiviral treatment. These targets were highly enriched for known interaction partners of all analyzed viruses. Indeed, phenformin, an NADH:ubiquinone oxidoreductase inhibitor, suppressed SARS-CoV-2 and dengue virus replication. Atpenin A5, blocking succinate dehydrogenase, inhibited SARS-CoV-2, dengue virus, respiratory syncytial virus, and influenza A with high selectivity indices. In vivo, phenformin showed antiviral activity against SARS-CoV-2 in a Syrian hamster model. Our work establishes host metabolism as druggable for broad-spectrum antiviral strategies, providing invaluable tools for pandemic preparedness.

Phospholipases are among the important elements involved in lipid-dependent cell signaling that lead to the induction of downstream pathways. In the current study, phospholipases D (PLDs) gene family was characterized and compared in two important oilseeds crops, Brassica napus and Camelina sativa. The results revealed that PLD has 33 members in Camelina sativa (CsPLD) and 41 members in Brassica napus (BnPLD). All studied PLDs showed a negative GRAVY value, indicating that PLDs are probably hydrophilic proteins. Phylogenetic analysis separated PLDs into five subfamilies; gamma, delta, beta, alpha, and zeta. According to evolution analysis, a different evolution process was observed between CsPLD and BnPLD. In addition, the results disclosed that the most of the PLD genes have been segmentally duplicated under purifying selection. Cis-regulatory elements related to ABA and auxin responsiveness were more found in upstream region of CsPLDs, while elements linked with MeJA responsiveness were more dis-tributed in promoter region of BnPLDs. Analysis of the expression data showed that PLD alpha genes have a wide expression and are expressed in most tissues. Quantitative expression analysis (qPCR) of CsPLD genes under salt stress, 200 mM of NaCl, was done in different time series. The results revealed that the CsPLD genes are involved in the response to salinity stress and their expression levels enhance with the increase of salinity stress time. The outcomes of this research will be useful for future molecular work related to lipid signaling in oilseed plants.

The availability rating of any system is the probability that it will be able to operate for a given time in other words, to restore the system in the case of a system failure, or during the failure it is a measure of the availability index of resource system. We aimed to complete the study for the reasons as below. The rating and upgrading of underground mine radio communication system availability is to ensure operation reliability of the underground mine and to establish reliable radio communication system for miner’s safety during failure. Therefore, our study is contributed to reveal system availability rating of single resource (with two parallel connections) system and two resources (with three parallel connections) system. The study is related to not only practical contribution but also reliability engineering theory. To achieve this result, we have used Exponential, Weibull, and Poisson distributions. The readiness rating of single resource (with two parallel connections) and two resources (with three parallel connections) systems were calculated using Matlab software and the simulation results were introduced. This result confirms that any system has a resource system, which increases its readiness rating. The study was in examples of single resource radio communication system on the two shafts of the underground mine and two resource radio communication system for three shafts. During the initial system reliability enhancement study, the readiness of the dual resource system was assessed. The rating shows a 99 percent result, which is very meaningless for systems with more than two n-resources and we have revealed that it can show more than 100 percent result. In the process of the study on system reliability improvement the rating availability of system which has two resources was 99 % result. It is very meaningless for systems with more than two n-resources or more than 100% result.

A novel kind of power spectrum is constructed, the inter-spike spectrum, which transforms any signal into its spike-frequency domain. This method clearly shows the apparent cycles in the data and overcomes the problems for spike-train-like signals when using the obvious idea of Fourier-transforming it. We invent this instructive approach with the idea of transforming the τ-recurrence rate of a recurrence plot (RP), which often has a spiky appearance. The τ-recurrence rate is the density of recurrence points along diagonals of the RP, which are parallel to the main diagonal with a distance of τ. In this context the inter-spike spectrum can be interpreted as a nonlinear power spectrum of a potentially high dimensional system which constitutes the RP. The proposed measure is robust to noise and is able to detect and analyze bifurcations.