Fission-track modelling rests on etching, counting and measuring the lattice damage trails from uranium fission. The tools for interpreting fission-track data are advanced but the results are never better than the data. Confined-track samples must be an adequate size for statistical analysis, representative of the track population, consistent with the model assumptions and with the calibration data. Geometrical and measurement biases are understood and can be dealt with up to a point. The interrelated issues of etching protocol and track selection are however more difficult to untangle. Our investigation favours a two-step protocol. The duration of the first step is inversely proportional to the apatite etch rate, so that different apatites etch to the same Dpar. A long immersion reveals many more confined tracks, terminated by basal and prism faces. This allows consistent length measurements and permits to orient each track relative to the c-axis. Long immersion times combined with deep ion irradiation reveal confined tracks deep inside the grains. Provided it is long enough, the precise immersion time is not important if the effective etch times of the selected tracks are calculated from their measured widths. Then, whether the sample is mono- or multi-compositional, we can, post hoc, select tracks with the desired properties. The second part of the protocol has to do with the fact that fossil tracks in geological samples appear to be under-etched compared to induced tracks etched under the same conditions. This should be assumed if the semi-axes (l_C, l_A) of a fitted ellipse plot above the induced-track line. In that case an additional etch can increase the track lengths to a point where they are consistent with the model based on lab-annealing of induced tracks, a condition for valid thermal histories. Here too, it is possible to select a subset with effective etch times consistent with the model if the widths of confined tracks are measured along with their lengths and orientations.

Energy consumption is a critical issue in the energy sector, and recent events such as the global energy crisis, costs, the need to reduce greenhouse emissions, and extreme weather conditions have increased the demand for energy efficiency. Accurately predicting energy consumption is one of the key steps to addressing inefficiency in energy consumption, and its optimization. In this regard, accurate predictions of energy consumption would not only help to minimize wastage, but also to save cost. In this article, we propose intelligent models using an ensemble of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural networks for predicting energy consumption in smart buildings. The proposed model outperforms other state-of-the-art deep learning models for predicting minute energy consumption, with a mean square error of 0.109. The proposed model also accurately captures latent trends in the data that other models struggle to capture. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Background: The commonly known assumption that combinations of several herbs in one formulation can have better efficacy due to additive or synergistic effects has not been systematically studied despite some evidence supporting the synergy concept. Study aim: The study aimed to reveal the molecular interactions in situ of host cells in response to the intervention of BHP and justify the benefits of implementing BHP in clinical practice. Results: This overview provides the results of recent clinical and network pharmacology studies of botanical herbal preparations (BHP) of Rhodiola with other plants, including Ashwagandha, Green Tea, Eleutherococcus, Schisandra, Eleutherococcus, Leuzea, Caffeine, Cordyceps, Gingko, Black Cohosh, saffron, and L-carnosine. Conclusions: The most important finding from network pharmacology studies of BHP was the evidence supporting the synergistic interaction of BHP ingredients, revealing unexpected new pharmacological activities unique and specific to the new BHP. Some studies show the superior efficacy of BHP compared with mono-drugs. At the same time, some a priori-designed combinations can fail, presumably due to antagonistic interactions and crosstalk between molecular targets within molecular networks involved in the cellular and overall response of organisms on the intervention. Network pharmacology studies help predict the results of studies to discover new indications and unpredicted adverse events.

Abstract. Graphene family (GF)-Calcium phosphate (CaP) composites, holds great potential as components of bone regeneration materials. GF nanomaterials can be modified physically as well as chemically, which are to be biomimetic, mechanically to be hard due to their capability to support exceptional mechanical, thermal and electronic properties. These biocompatible GF composites coated on the calcium phosphate can enhance and tolerate stem cell growth and differentiation, proliferation into various lineages or interact with bioorganisms. The development in CF-CaP materials and their physicochemical/biointeractions and strategies towards osteoblasts is a great concern to promote faster healing and reconstructions of large bone imperfections or defects. In this review, we explain the influence of the CF-CaP and summarize recent developments on designing CF-CaP architectures as multifunctional bone regeneration platforms. We have also explored the typical biological applications concerning these CF-CaP based bioactive nanocomposites. Furthermore, the future viewpoints and evolving challenges will also be emphasized. Due to the shortage of full-length reviews in this emerging research field, this review would be caught great attention and encourage various new chances across a wider range of disciplines.

In recent decade water pollution by micropollutants is an increasing environmental concern. Since 2019, due to COVID-19 pandemic, increased stormwater pollution by chlorine based disinfectants has been determined. Research aimed to treat of chlorine contaminated stormwater after outdoors spaces disinfection are very limited. Runoff from disinfected areas and residual chlorine present in stormwater are transported to surface water bodies. Residual chlorine reacts with dissolved organic compounds in water and results the formation of environmentally toxic disinfection by-products. Studies present event low residual chlorine concentrations may pose a risk to aquatic flora and fauna. In this study the efficiency of different filter materials, including peat, wood chips, sawdust (column tests) and ceramzites Leca, Pollytag, Polski, Ceski (batch tests), in retaining residual chlorine were tested in laboratory scale batch and flow through experiment. The best efficiency to retain chlorine presented sawdust (96 %) and ceramzite Leca (76 %). The plants abilities to reduce pollution by chlorine was analyzed in raised garden bed. Research results will contribute to future studies aimed to retain various micropollutants in stormwater using remediation technologies.

Childhood emotional abuse (CEA) has been linked to a range of unfavorable outcomes, including the development of unhealthy personality traits and maladaptive coping mechanisms and behaviors. In this study, our objective was to investigate whether neuroticism and perfectionism act as mediators in the relationship between CEA and workaholism, while also evaluating the gender invariance of the hypothesized model. A cohort of 1176 young workers, with a balanced gender representation (50% female), aged 18-25, participated in the study by completing self-report questionnaires. The results revealed significant connections between all direct and indirect links, indicating a complex interplay between childhood emotional abuse, neuroticism, perfectionism, and workaholism. Importantly, the mediation model exhibited no significant differences between genders, suggesting that the identified relationships are consistent across both male and female participants. This research underscores the critical importance of early identification of childhood emotional abuse and advocates for the implementation of trauma-informed care to address the negative consequences of emotional abuse, ultimately aiming to prevent the development of workaholism. Furthermore, the findings emphasize the necessity for tailored prevention strategies, focusing on mitigating neuroticism and perfectionism as potential pathways for effective therapeutic interventions.

Land use and cover change (LUCC) is an important method to investigate the causes of global environment change. We utilized the emergy ecological footprint (EEF) model to construct a land-use change model to be used as a systematic measuring tool for monitoring sustainable development trends. In particular, we estimated the eco-security of the Cing-jing region as a case study so that responsible agencies can use it to maintain a balance between ecological preservation and tourism development. The results indicated the following: First, the ecological environment of the Cing-jing region satisfied the safety standard in 2008–2014; however, the related indices increased considerably. Second, the grey model predicted a decrease in 2015–2024 ecological carrying capacities of Cing-jing and a large increase in capita EFs, resulting in a larger ecological deficit and higher EFI. The eco-security from 2015–2024 was higher compared to 7 years ago and is predicted to reach the Grade 2 intermediate level in 2022; thus the Cing-jing region is gradually becoming ecologically unsustainable. Strengths of our study included the use of EEF theory in a quantitative analysis of slope lands for the effective evaluation of ecological security. Finally, we expanded our research to include ecological security issues.

Soil quality is related to food security and human survival and development. In recent years, due to the acceleration of urbanization and the increase of abandoned land, land degradation occurs, poor topsoil quality. In this study, the minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics and influencing reasons of topsoil quality in Tieling County. The re-sults showed that MDS included total potassium (TK), Clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu). The MDS indicators can well replace all indicators to evaluate the topsoil quality in the study area. The overall soil quality of Tieling County showed a trend of low in the east and high in the west, and gradually increased from the hilly area to the plain area. The evaluation results are consistent with field research, which can provide reference for other topsoil quality evaluation, and it also provide a basis for the formulation of soil quality improvement measures.

Among magnetoelectric (ME) heterostructures, ME laminates of the type Metglas-like / PVDF (magnetostrictive+piezoelectric constituents) have shown the highest induced ME voltages, usually detected at the magnetoelastic resonance of the magnetostrictive constituent. This ME coupling happens because of the high cross-correlation coupling between magnetostrictive and piezoelectric material, and is usually associated with a promising application scenario for sensors or actuators. In this work we detail the basis of the operation of such devices, as well as some arising questions (as size effects) concerning their best performance. Also, some examples of their use as very sensitive magnetic fields sensors or innovative energy harvesting devices will be presented At the end, the challenges, future perspectives and technical difficulties that will determine the success of ME composites for sensor applications are discussed.

Citation: To be added by editorial staff during production.

Zingiber cassumunar Roxb. is a powerful medicinal plant that has been used as traditional medicine to cure respiratory problems, pain, and inflammation in China, Indonesia, Thailand and other Asian countries by using the crude extracts. The objective of this research is to identify phytochemical composition of Z. cassumunar Roxb. and to analyze antibacterial activity of crude extract, purified compounds, and their microencapsulated products of Rhizome Z. cassumunar Roxb. Identification of phytochemical composition in crude extract of rhizome Z. cassumunar Roxb. was achieved by chromatography-mass spectrophotometer. The major phytochemical composition in crude extract of Z. cassumunar Roxb. is essential oils, including terpinen4-ol (37.7%), β-pinene (20.8%), and (E)-1-(3,4-dimethoxyphenyl)but-1-ene (13.3%). Crude extract of Z. cassumunar Roxb. was purified with silica gel flash column chromatography, resulting two purified compounds. The antibacterial activity of crude extract, purified compounds, and their microencapsulated products of Rhizome Z. cassumunar Roxb. were evaluated against agricultural and foodborne pathogens by using disc agar diffusion and broth microdilution techniques. All of the samples studied (crude extracts, purified compounds, and microencapsulated of Z. cassumunar Roxb.) were effective against all the bacteria. Based on the results of the disc-diffusion assay suggested that amongst the samples studied, purified compounds (compound 1 and 2) and microencapsulated purified compounds (compound 1 and 2) exhibited more effective against all the bacteria compared to the crude extracts. Antibacterial activity of the rhizome of Z. cassumunar Roxb. was contributed mainly by the essential oils components as the active compounds. Gram-negative bacteria (X. oryzae, X. translucens, Pseudomonas spp, E. coli, and S. typhimurium) appeared to the most resistant to the crude extracts, purified compounds, and microencapsulated of Z. cassumunar Roxb. compared to the gram-positive bacteria (S. aureus, B. cereus, and L. monocytogenes). Microencapsulated of the tested samples (crude extract, purified compound 1, and purified compound 2) of the rhizome Z. cassumunar Roxb. exhibited high antibacterial activity with no significantly different with the tested samples without microencapsulation. These results suggest potential antibacterial properties of Z. cassumunar Roxb., which useful for agricultural plant health, food preservation, natural therapies, and pharmaceuticals.

Abstract For the past 70 years, dopamine hypothesis has been the key working model in schizophrenia. This has contributed to the development of numerous inhibitors of dopaminergic signaling, antipsychotic drugs, which led to rapid symptom resolution but only marginal outcome improvement. Over the past decades, there was limited research on the quantifiable pathological changes in schizophrenia, including premature cellular/neuronal, senescence, brain volume loss, attenuation of gamma oscillations on electroencephalogram and oxidation of lipids in plasma and mitochondrial membranes. We surmise that aberrant activation of aryl hydrocarbon receptor by toxins derived from the gut microbes or the environment drives premature cellular, including neuronal, senescence, a hallmark of schizophrenia. Early brain aging promotes secondary changes, including impairment and loss of mitochondria, gray matter depletion, decreased gamma oscillations, and a compensatory metabolic shift to lactate and lactylation. The aim of this narrative review is twofold: 1. To summarize what is known about premature cellular/neuronal senescence in schizophrenia or schizophrenia-like disorders. 2. To discuss novel strategies for improving long-term outcome in severe mental illness with natural senotherapeutics, membrane lipid replacement, mitochondrial transplantation, microbial phenazines, novel antioxidant phenothiazines, inhibitors of glycogen synthase kinase-3 beta, and aryl hydrocarbon receptor.

Once considered relatively benign, superficial vein thrombosis (SVT) of the lower limbs is often linked to deep vein thrombosis (DVT) or pulmonary embolism (PE). Treatment goals include alleviating local symptoms and preventing SVT from recurring or extending into DVT or PE. Fondaparinux 2.5 mg once daily for 45 days is the treatment of choice for most patients with SVT. Potential alternatives include intermediate-dose low-molecular-weight heparin or the direct oral factor Xa inhibitor rivaroxaban. Despite these treatment options, significant gaps remain, including the role of systemic or topical anti-inflammatory agents alone or combined with anticoagulants, and the optimal duration of anticoagulation for patients at varying risk levels. Additionally, the efficacy and safety of factor Xa inhibitors other than rivaroxaban, management of upper extremity SVT, and optimal treatment for SVT near the sapheno-femoral or sapheno-popliteal junctions are not well understood. This narrative review aims to summarize current evidence on anticoagulant treatment for SVT, highlight key unmet needs in current approaches, and discuss how ongoing studies may address these gaps.

A new type of artificial block is proposed by modifying the traditional block. Based on the physical model experiment, the wave reflection and run-up characteristics of the new ecological blocks under regular waves were examined, and the influence of the height of the "planting trough" border on the hydrodynamic properties was analyzed. The results indicate that the wave reflection and run-up would decrease with a decrease in the breakwater slope. As wave height increases, wave run-up increases, and the reflection coefficient falls. Wave reflection and run-up increase with an increasing wave period. The relative depth of the water has a smaller impact on the wave reflection than the wave run-up. The reflection coefficient of the new ecological blocks is consistently lower than that of the conventional and continues to decrease as the border of the "planting trough" is raised. The raised borders prevent water loss in the trough, resulting in no significant improvement in the wave run-up performance of the new blocks. The reflection coefficient and the relative run-up R/H gradually increase with the average wave breaking parameter ξ increase. The crude permeability coefficients for the new blocks, New Heights 1 and New Heights 2, are 0.53, 0.53, and 0.54, respectively.

A complete understanding of groundwater dynamics and its interaction with surface water underthe impact of agricultural activities is vital for local agriculture, ecology, and residents of dry regions, which is not commonly recognized in arid areas. This research outlines the geochemical characteristics, recharge sources, and potential factors impacting groundwater quality in a new land reclamation located in the small basin of Abu Mina, which is part of the Western Nile Delta region.1 Thirty‑one groundwater samples and two surface water samples were collected in 2021 to represent the Pleistocene aquifer and were subjected to multivariate statistical, hydrochemical, and stable isotope analyses. Data analysis demonstrates that Na+ > Ca2+ > Mg2+ > K+ and SO4 2– > Cl– > HCO3 – > NO3 – are the predominant cations and anions, respectively. Groundwater salinity ranged from 465.60 to 6455.18 mg/l, with slightly alkaline. Most of the water samples fall into one of three types of facies: Ca–Cl, Na–Cl, and Mixed Ca–Mg–Cl, in decreasing order. The meteoric genesis index (r2) indicates that deep meteoric water percolation dominates the Pleistocene aquifer. The aquiline diagrams, correlation matrix, and different ionic ratios indicate that evaporation, reverse ion exchange reactions, and the dissolution of carbonate and silicate minerals are the main processes governing groundwater chemistry. Factor analysis (FA) indicated that three factors explain groundwater hydrochemistry, accounting for 71.98% of the total variance. According to the rotating components matrix (F1–F3), the chemistry of the Quaternary aquifer is principally affected by evaporation, ion exchange reactions, and anthropogenic influences. Additionally, salinity increases due to the return flow of irrigation activities and mixing between old and recent water. The stable isotopes (δ18O and δ2H) indicate that the Quaternary aquifer receives groundwater recharge through the return flow of excess irrigation and canal seepage. Under desert reclamation conditions, groundwater salinization processes should be given special consideration. All groundwater samples are appropriate for agricultural irrigation based on the Sodium Adsorption Ratio (SAR), Permeability Index (PI), Percent Sodium (%Na), and Residual Sodium Carbonate (RSC).

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

Traumatic brain injury (TBI) is an important global clinical issue, requiring not only prevention but also effective treatment. Following TBI, diverse parallel and intertwined pathological mechanisms affecting biochemical, neurochemical, and inflammatory pathways can have severe impact on the patient’s quality of life. The current review summarizes evidence for utility of amantadine in TBI in connection to its mechanism of action. Amantadine combining multiple mechanisms of action may offer both neuroprotective and neuroactivating effects in TBI patients. Indeed, use of amantadine in TBI has been encouraged by several clinical practice guidelines/recommendations. Amantadine is also available as infusion which may be of particular benefit in unconscious patients with TBI, due to immediate delivery to the central nervous system and the possibility of precise dosing. In other situations, orally administered amantadine may be used. There are several questions that remain to be addressed: Can amantadine be effective in disorders of consciousness requiring long-term treatment and in combination with drugs approved for treatment of TBI? Do the observed beneficial effects of amantadine extend to disorders of consciousness due to factors other than TBI? Well controlled clinical studies are warranted to ultimately confirm its utility in the TBI and provide answers these questions.

The study examines projected changes in dry- and wet-spell probabilities in West Africa using a Markov chain approach. Four simulations of regional climate models from the CORDEX-Africa program were used to analyze projected changes in intraseasonal variability. The results show an increase in the probability of having a dry day, a dry day preceding a wet day and a dry day preceding a dry day, and a decrease in the probability of wet days in the Sahel region under anthropogenic forcing scenarios RCP4.5 and RCP8.5. The decrease in wet days is stronger in the far future and under the RCP8.5 scenario (up to -30%). The study also finds that the probability of consecutive dry days (lasting at least 7 days and 10 days) is expected to increase in the Western Sahel, the Central Sahel, and the Sudanian Area under both scenarios, with stronger increases in the RCP8.5 scenario. In contrast, the decrease is expected over the Guinea Coast, with the changes more important under the RCP4.5. These changes in dry- and wet-spell probabilities are important for water management decisions and risk reduction in the energy and agricultural sectors. The study also highlights the need for decision-makers to implement mitigation and adaptation policies to minimize the adverse effects of climate change.

This paper focuses on environmental policies for mitigating the impacts of climate change on the Taj Mahal, an iconic structure built on the banks of the Yamuna River from 1632 to 1653 with a blend of Persian and Indian architectural styles. The Taj Mahal is threatened by damage caused by climate change, including air pollution resulting in acid rain and floods that lead to erosion and discoloration of the main marble material of the Taj Mahal. This study employs a Systematic Literature Review (SLR) of peer-reviewed journal articles with a primary focus on environmental policy, the impacts of climate change, and the preventive measures undertaken by the Indian government. Based on the analysis of 270 papers, it is concluded that out of the total, 30 papers specifically address objectives relevant to this research. The study period spans from 2014 to 2024. The research findings reveal that air pollution has a significantly adverse impact on the Taj Mahal. Furthermore, nine policies are generally identified from the journal literature review aimed at preserving and protecting the Taj Mahal from the negative effects caused by air pollution.

In Electrified Vehicles, the cost, efficiency, and durability of electrified vehicles are dependent on the energy storage system (ESS) components, configuration and its performance. This paper, pursuing a minimal size tactic, describes a methodology for quantitatively and qualitatively investigating the impacts of a full bandwidth load on the ESS in the HEV. However, the methodology can be extended to other electrified vehicles. The full bandwidth load, up to the operating frequency of the electric motor drive (20 kHz), is empirically measured which includes a frequency range beyond the usually covered frequency range by published standard drive cycles (up to 0.5 Hz). The higher frequency band is shown to be more efficiently covered by a Hybrid Energy Storage System (HESS) which in this paper is defined as combination of a high energy density battery, an Ultra-Capacitor (UC), an electrolytic capacitor, and a film capacitor. In this paper, the harmonic and dc currents and voltages are measured through two precision methods and then the results are used to discuss about overall HEV efficiency and durability. More importantly, the impact of the addition of high-band energy storage devices in reduction of power loss during transient events is disclosed through precision measurement based methodology.

Laser range-gated underwater imaging technology, by removing most of the back scattering noise, can effectively increase the image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47%. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios.

In this study, we introduce the dynamics of a Hepatitis B virus (HBV) model with the class of asymptomatic carriers and conduct a comprehensive analysis to explore its theoretical aspects and examine the crossover effect within the HBV model. To investigate the crossover behavior of the operators, we divide the study interval into two subintervals. In the first interval, the classical derivative is employed to study the qualitative properties of the proposed system, while in the second interval, we utilize the ABC fractional differential operator. Consequently, the study is initiated using the piecewise Atangana-Baleanu derivative framework for the systems. The HBV model is then analyzed to determine the existence, Hyers-Ulam (HU) stability, and disease-free equilibrium point of the model. Moreover, we showcase the application of an Adams-type predictor-corrector (PC) technique for Atangana-Baleanu derivatives and an extended Adams-Bashforth-Moulton (ABM) method for Caputo derivatives through numerical results. Subsequently, we employ computational methods to numerically solve the models and visually present the obtained outcomes using different fractional-order values. This network is designed to provide more precise information for disease modeling, considering that communities often interact with one another, and the rate of disease spread is influenced by this factor.

A small proportion of health care users are recognised to use a significantly higher proportion of health system resources, largely due to systemic, inequitable access and disproportionate health burdens. These high-resource health system users are routinely characterised as older, with multiple comorbidities, and reduced access to adequate health care. Geographic trends also emerge, with more rural and isolated regions demonstrating higher rates of high-resource use than others. Despite known geographical discrepancies in health care access and outcomes, health policy and research initiatives remain focused on urban population centers. To alleviate mounting health system pressure from high-resource users, their characteristics must be better understood within the context in which it arises. To examine this, a scoping review was conducted to provide an overview of characteristics of high-resource users in rural and remote communities in Canada and Australia. In total, 21 papers were included in the review. Using qualitative thematic coding, primary findings characterised rural high-resource users as those of an older age; with increased comorbid conditions and condition severity; lower socioeconomic status; and elevated risk behaviours.

Greenhouse gas (GHG) emissions from agriculture are significant contributors to global change. We experimentally manipulated biogeochemical control points of irrigation and nitrogen (N) to examine management strategies that could impact GHG flux, i.e., carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and soil physiochemical changes over a growing season in an arid New Mexico sorghum (Sorghum bicolor (L.) Moench) cropping system. Sorghum is water and N efficient and amenable to environmental stress. Interrogating how crop systems perform in intense heat, aridity and ultraviolet stress of the southwestern US climate can inform future management in areas that produce more food currently, but that will undergo these stresses in the near future. Water was applied at regionally typical rates, or at ~30% below those rates. Timing N to plant needs may reduce N loss and N2O emissions, and we tested this hypothesis by adding equal amounts of fertilizer to all plots, with half receiving all fertilizer at planting versus plots fertilized at 50:50 planting and 30 days post-planting. Gas flux from soil was analyzed via FTIR. More biomass was harvested from the fully irrigated plots; N timing did not significantly affect biomass. Soil pH fluctuated throughout the season in response to both treatments. Carbon dioxide emissions significantly increased in fully irrigated plots through time. Methane uptake was depressed by full irrigation. Nitrous oxide flux was lower in split N plots, but N2O emissions were not impacted by reduced irrigation. These results suggest that arid adapted crops can maintain economically feasible yield, and biogeochemical monitoring within a growing season can help manage for GHG flux.

Bonfire night is a worldwide phenomenon given to numerous annual celebrations characterised by bonfires and fireworks. Since Thailand has no national ambient air quality standards for metal particulates, it is important to investigate the impacts of particulate injections on elevations of air pollutants and ecological health impacts resulting from firework displays. In this investigation, Pb and Ba were considered potential firework tracers because their concentrations were significantly higher during the episode and lower than/comparable with minimum detection limits during other periods, indicating that their elevated concentrations were principally due to pyrotechnic displays. Pb/Ca, Pb/Al, Pb/Mg, and Pb/Cu can be used to pin-point emissions from firework displays. Air mass backward trajectories (72 h) from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated that areas east and north-east of the study site were the main sources for the air transportation. Although the combined risk associated with levels of Pb, Cr, Co, Ni, Zn, As, Cd, V, and Mn was far below the standards mentioned in international guidelines, the lifetime cancer risks associated with As and Cr levels exceeded US-EPA guidelines, and may expose inhabitants of surrounding areas of Bangkok to elevated cancer risk.

Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments based on simulations. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the performance of the methods using 18 metrics. The results indicate that stochastic and ML methods perform equally well.

Novel treatment strategies are emerging for rare, genetic diseases, resulting in clinical trials that require adequate biomarkers for the assessment of the treatment effect. For enzyme defects, biomarkers that can be assessed from patient serum, such as enzyme activity, are highly useful, but the activity assays need to be properly validated to ensure a precise, quantitative measurement. Aspartylglucosaminuria (AGU) is a lysosomal storage disorder caused by the deficiency of the lysosomal hydrolase aspartylglucosaminidase (AGA). We have here established and validated a fluorometric AGA activity assay for human serum samples from healthy volunteers and AGU patients. We show that the validated AGA activity assay is suitable for the assessment of AGA activity in the serum of healthy controls and AGU patients, and it can be used for diagnostics of AGU and, potentially, for following a treatment effect.

This paper presents the results of experiments on the study of a hydraulic fracture interaction with a preliminary created fracture that simulates a natural fracture. A distinctive feature of the conducted experiments is the ability to use an ultrasonic sounding to measure the fracture propagation and opening simultaneously with the fluid pore pressure measurements at the several points of the porous saturated sample. It allows to obtain the pressure distributions at various experiment stages and to establish a relation between the pore pressure distribution and the hydraulic fracture propagation and its interaction with macroscopic natural fracture. The possibilities of active ultrasonic monitoring have been expanded due to preliminary calibration experiments, which make it possible to estimate the fracture opening value by attenuation of ultrasonic pulses. The experiments show that the fracture propagation is limited by the natural fracture. This is caused by the hydraulic fracturing fluid leaks into the natural fracture, so both hydraulic fracture and natural fracture compose united hydraulic system.

For traffic management under sudden disaster in high-density areas, the first and foremost step is to prevent traffic congestion in the disaster-affected area by traffic flow control, as to provide enough and flexible traffic capacity for emergency evacuation and emergency rescue. Catastrophe border identification is the foundation and the key to traffic congestion prediction under sudden disaster. This paper uses a mathematical model to study the regional traffic flow in the high-density area under sudden fire disaster based on the Cusp Catastrophe Theory (CCT). The catastrophe border is identified by fitting the CCT-based regional traffic flow model to explore the stable traffic flow changing to the instable state, as to provide a theoretical basis for traffic flow manage and control in disaster-affected areas, and to prevent the traffic flow being caught into disorder and congestion. Based on VISSIM simulator data by building simulation scenarios with and without sudden fire disaster in a Sudoku traffic network, the catastrophe border is identified as 439pcu/lane/h, 529pcu/lane/h, 377pcu/lane/h at 5s, 10s, 15s data collection interval respectively. The corresponding relative precision, which compares to the method of Capacity Assessment Approach (CAA), is 89.1%, 92.7% and 76.5% respectively. It means that 10s data collection interval would be the suitable data collection interval in catastrophe border identification and regional traffic flow control in high-density area under sudden fire disaster.

Transition-metal-catalyzed amide bond formation from alcohols and amines is an atom-economic and eco-friendly route. Herein, we identified a highly active in situ N-heterocyclic carbene (NHC)/ruthenium (Ru) catalytic system for this amide synthesis. Various substrates, including sterically hindered ones, could be directly transformed into the corresponding amides with the catalyst loading as low as 0.25 mol%. In this system, we replaced the p-cymene ligand of the Ru source with a relatively labile cyclooctadiene (cod) ligand so as to more efficiently obtain the corresponding poly-carbene Ru species. Expectedly, the weaker cod ligand could be more easily substituted with multiple mono-NHC ligands. Further HR-MS analyses revealed that two tetra-carbene complexes were probably generated from the in situ catalytic system.

The relationship between Climate Change and Water is an obvious and key issue within the Sustainable Development Goals. This study aims to investigate the social representation created around this relationship in three different territorial contexts in order to evaluate the influence of the territory on the perception of the risk of Climate Change and its relationship with water. By means of a questionnaire completed by 1709 university students, the climatic literacy of the individual was evaluated in order to relate it to other dimensions on the relationship between Climate Change and Water (information, training previous on climate change and pro-environmental attitudes) in their different dimensions in three different territorial contexts. The results show that the socio-cultural context influences the social representation of Climate Change, but not from the climatological condition, so that it is reasonable to think that the social representation of this relationship is favoured by a common culture around this relationship.

Aerococcus urinae is an aerobic Gram-positive coccus that grows as tiny alpha-hemolytic colonies. Actinotignum schaalii is a slow-growing facultative anaerobic Gram-positive rod. These bacteria are part of the urogenital microbiota of healthy patients, but can also be involved in urinary tract infections (UTIs), particularly in elderly men and young children [1–3]. Because A. urinae and A. schaalii are fastidious and are difficult to identify with phenotypic methods, they are underestimated causes of UTIs. Their growth is slow and requires a blood-enriched medium and an incubation time of 48 hours under anaerobic or 5% CO2 atmosphere. Furthermore, accurate identification is only possible using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) or molecular-based methods. In rare cases, these bacteria can be responsible for invasive infections. We describe here the second case of bacteremic UTI caused by both A. schaalii and A. urinae in an 89-year-old woman who presented dyspnea and asymptomatic bacteriuria. This challenging clinical and microbiological diagnosis was made by our laboratory by Gram staining urine with a leucocyte count >50/μL and/or a bacterial count >14/μL, urinary culture on blood agar plate, and direct bacterial identification on positive blood culture bottles (BCBs) by MALDI-TOF MS. After 10 days of antimicrobial treatment consisting of 2g amoxicillin PO t.i.d the patient was discharged with complete clinical and biological recovery. A. schaalii and A. urinae are probably still underestimated causes of UTIs. Microbiologists should assess the presence of these two bacteria in urine using the appropriate culture and identification methods where direct examination is positive for small Gram-positive cocci or rods, and a urinary dipstick is negative for nitrites and associated with leukocyturia.

There is a long standing ‘two times problem’ in that a satisfactory reconciliation between the time of physics and that of psychology has not been realized. A partial solution to the past/present/future phenomenon has been successfully given by the Hartle information gathering and processing system (IGUS) view. That model IGUS robot is enhanced here for the entire ‘two times problem’ to deal with not only the temporal experiences of the flow of time but also those of manifest time. A dualistic robot is proposed which has a veridical system of temporal experiences that are compatible with various spacetime cosmologies. It also has an illusory system of corresponding temporal experiences. This dualistic IGUS robot was made possible by discovering temporal experience within the brain that correspond to those of physics. The dualistic theory suggests that the veridical system, as a result of evolution, begets the illusory system to enhance behavioral adaptation. Thus, there is just one fundamental physical time which the brain does, indeed, possess and then enhances with illusory counterparts. Therefore, there should no longer be a need to reify illusory temporal experiences as modern spacetime cosmologies tend to do. Physical time already resides within human time.

Summary: Hypertension is a public health problem with serious social, economic, and health consequences. The World Health Organisation (WHO) estimates that 1 in 3 adults worldwide suffers from hypertension. In Cameroon, the prevalence of hypertension is estimated at 35%, with nearly 17,000 deaths recorded each year. This study aimed to determine the factors associated with the occurrence and complications of hypertension at the Sangmélima Referral Hospital (HRS). A total of 528 patients treated in the cardiology department of the HRS were identified between January and December 2023. The data were analyzed using SPSS 28 software. Binary logistic regression determined the odds ratios and 95% confidence intervals associated with each variable. Differences were statistically significant for a p-value < 0.05. At the HRS, the annual incidence of hypertension was 16.13%. Hypertension was present in 78.8% of patients. The factors associated with the occurrence and complications of hypertension were age (OR=1.028; p=0.003 ), level of education (OR=15.49; p=0.023), marital status (OR=3.859; p=0.04), hypercholesterolemia (OR=2.856; p=0.01), monthly income (OR=0.882; p=0.026), number of dependents (OR=1.231; p=0.025), food security (OR=16.666; p<0.001), tobacco consumption (OR=8.592; p=0.041), fruit and vegetable consumption (OR=0.027; p=0.031), salt/sugar consumption (OR=8.129; p<0.001), place of residence (OR=4.794; p=0.005), access to essential technologies (OR=8.851; p=0.002), and use of traditional care (OR=3.137; np=0.032). HTA at the HRS is associated with several factors. In order to limit the impact of hypertension, it is crucial to emphasize improving socioeconomic and health conditions.

(1) Background: donor-derived circulating free DNA (dd-cfDNA) is an innovative biomarker with great potential for the early identification and prevention of graft damage; (2) Methods: Prospective study to analyze dd-cfDNA plasma levels in 30 kidney transplant patients during their post-transplant follow-up (15 days, 3, 6 and 9 months), to determine if the result could be of interest in the identification of possible adverse events, specially allograft rejection. In addition, the aim is also to verify the obtained data on sensitivity, specificity, NPV and PPV; (3) Results: Plasma levels of dd-cfDNA could be considered a novel biomarker of graft rejection after first term post-transplant up to several months before its clinical presentation, mainly in TCMR or ABMR. In this study, it appears to have no positive predictive value for borderline rejection or TCMR IA. Our results show a low NPV of the technique because other pathologies or biological post-transplant complications can also affect dd-cfDNA levels and they must be considered in the interpretation of the result.; (4) Conclusions: dd cfDNA could allow, together with other additional biomarkers in the context of a future score, avoid invasive biopsies in most cases, mainly in the case of suspected TCMR or ABMR.

Monitoring the quality of stored bulk grain is generally done using temperature cables hung from silo roofs. Little investigation has been done into the effects of number of sensors and their placement in terms of reliability of the monitoring system with regard to making stored grain quality management decisions. A previously developed 3D finite element simulation model was verified and used to investigate these aspects. In the first study, a silo was loaded with about 228.6 Mg (9000 bushels) of maize and six temperature cables were placed in the grain mass. The maize was aerated continuously for a period of two weeks, and the cable sensor temperatures were compared to the predicted temperatures which were in close agreement with the observed readings. The standard error of prediction ranged from 2.0 to 3.7°C. In the second study, 15 and 30 sensors were placed at manufacturer recommended depths and horizontal locations in the grain mass of three silo sizes (i.e., 11x11, 14.6x14.6 and 14.6x18.3 m diameter by eave height). The average grain temperatures predicted by the 15 and 30 sensors over a one-year simulation period were compared to the average grain temperatures predicted for the entire grain mass (1968, 3052, and 3204 mesh nodes). The number of sensors needed to monitor stored grain temperatures reliably in the three silo sizes investigated heavily depended on whether the aeration control strategy achieved a sufficiently low temperature by the time the aeration fans were turned off and sealed ahead of the non-aerated storage period. Fifteen or 30 sensors were sufficient to monitor grain temperatures during the aeration cooling period but for the two larger silo sizes more than 30 sensors would be needed during the storage period. As silo size increased, and surface-to-volume ratio decreased, grain temperatures remained lower during the storage period. Results support the best management practice recommendation of leaving cooled grain cold and not warming it up in the spring ahead of storage into the summer.

Human papillomavirus (HPV), a DNA virus, causes one the most common sexually transmitted infections (STI), spreading through direct sexual contact to the anogenital and oropharyngeal regions. Although most infected individuals may be asymptomatic and naturally undergo viral clearance, in some individuals HPV causes carcinogenesis of the infected epithelia. HPV now accounts for over 70% of oropharyngeal carcinomas (OPC). More than 90% of these OPC are associated with HPV 16, one of the 14 high-risk subtypes. In recent years, HPV positive (HPV+) OPC has been one of the most rapidly rising cancers, by new cancer incidence rates, especially among men. The HPV driven OPCs present some unique infection driven cellular and molecular features, constituting distinct preventive, therapeutic and prognostic strategies, in contrast to HPV negative (HPV-) OPC. In this manuscript, we drive through the role of HPV, from a sexually transmitted infection to malignancy transformation landscape, while parsing through the significant immune response environment. In this comprehensive review we collate key literature around transmission and epidemiology of HPV, while addressing the current state of clinical management, diagnosis, prevention and screening strategies, that can help shed light on this critical area.

Recently, microfluidics deformability cytometry has emerged as a powerful tool for high throughput mechanical phenotyping of large populations of cells. These methods characterize cells by their mechanical fingerprints through exerting hydrodynamic forces and monitoring the resulting deformation. These devices have shown great promise for label free cytometry, yet there is a critical need to improve their accuracy and reconcile any discrepancies with other methods, such as atomic force microscopy. In this study, we employ computational fluid dynamics simulations and uncover how the elasticity of frequently used carrier fluids such as methyl-cellulose dissolved in phosphate-buffered saline is significantly influential to the resulting cellular deformation. We conducted CFD simulations that are conventionally used within the deformability cytometry field, which neglect fluid elasticity. Subsequently, we incorporated a more comprehensive model that simulates the viscoelastic nature of the carrier fluid. A comparison of the predicted stresses between these two approaches underscores the significance of the emerging elastic stresses in addition to the well-recognized viscous stresses along the channel. Furthermore, we utilize a two-phase flow model to predict the deformation of a promyelocyte (i.e. HL-60 cell type) within a hydrodynamic constriction channel. The obtained results highlight a substantial impact of the elasticity of carrier fluid on cellular deformation and raise questions about the accuracy of mechanical property estimates derived by neglecting elastic stresses.

Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. Assessment of plant growth and composition is vital for determining crop yield and overall quality, however, classical laboratory analyses are slow and costly. Therefore, new, less expensive, more rapid, and non-destructive approaches are being developed, including those based on (hyper)spectral reflectance. Additionally, it is also important to determine how plant phenotypes respond to fertilizer treatments and whether these differences in response can be detected from analyses of hyperspectral image data. In the current study, we demonstrate the suitability of hyperspectral imaging in combination with machine learning models to estimate the content of chlorophyll (SPAD), anthocyanins (ACI), glucose, fructose, sucrose, vitamin C, β-carotene, N, P, K, dry matter content, and plant fresh weight. The implemented five classification and regression machine learning models showed high accuracy in classifying the lettuces by the applied fertilizers treatments and estimating nutrient concentrations. To reduce the input (predictor data, i.e., hyperspectral data) dimension, 13 principal components were found and applied in models. The implemented artificial neural network models of the machine learning algorithm demonstrated high accuracy (r = 0.85 ... 0.99) in estimating fresh leaf weight, and contents of chlorophyll, anthocyanins, N, P, K, and β-carotene. The four applied classification models of machine learning demonstrated 100% accuracy in classifying the studied baby leaf lettuces by phenotype when certain fertilizer treatments were applied.

With the aging of China's population, the problem of pension security has become more and more prominent, and whether delayed retirement can effectively alleviate the pension fund gap and ensure the sustainability of the pension fund has become the focus of social concern. This study predicts the income and expenditure of urban workers' basic pension insurance fund from 2021 to 2050 by constructing an actuarial model of pension insurance fund income and expenditure, and simulates the effect of delayed retirement policy. The prediction results show that under the existing system, the basic pension insurance fund for urban workers will have a shortfall for the first time in 2027, and the shortfall will expand year by year. Compared with the non-implementation of delayed retirement policy, the simulation of the implementation of delayed retirementt program delayed the emergence of the fund gap until 2029, and the forecast period of the pension fund gap significantly narrowed, indicating that delayed retirement policy has a certain positive impact on alleviating the pressure of pension payments, but delayed retirementt can not completely eliminate the pension fund gap. In view of this, this paper suggests that a progressive and flexible delayed retirement policy should be introduced as soon as possible to better adapt to the needs of different groups. At the same time, differentiated policies should be formulated for different groups of people and a pension incentive mechanism for delayed retirement should be set up to improve public acceptance of delayed retirement policy. In addition, delayed retirement policy should be combined with other measures, such as lowering the corporate contribution rate and enhancing the value-added capacity of the pension fund, so as to ensure the sustainability of the pension fund.

The Myval is a balloon-expandable transcatheter heart valve (THV) devel-oped by Meril Life Sciences Pvt. Ltd. that has innovative operator-friendly de-sign which aids to improve deliverability and features precise deployment. Vari-ous clinical studies demonstrate its effectiveness and safety, making it a promis-ing choice in valvular interventions. Myval has been successfully utilized as a transcatheter aortic valve implanta-tion (TAVI) device in cases with conduction disturbances, bicuspid aortic valve anatomy, non-calcified aortic regurgitation, dysfunctional stenosed right ventric-ular outflow tract (RVOT) conduits, pulmonary valve replacement, mitral valve replacement, Vave-in-valve and Valve-in-ring implantation procedures. Myval’s diverse sizes are also of key importance in complex cases of large and complex annuli anatomy. Further long-term studies are needed to consolidate these results. Its introduction signifies a significant advancement in cardiology, aiming to enhance patient outcomes and quality of life. In the present review, we provide an update on new-generation Myval THVs and review the available clinical data published till date with an emphasis on diverse use in specific clinical scenarios.

As the primary secondary tree species in Northeast China, Quercus mongolica possesses significant ecological and economic value. This study employed the Biomod2 platform in conjunction with ArcGIS spatial analysis to assess the potential suitable habitat distribution area of Q. mongolica under current climatic conditions. Furthermore, it forecasted the distribution range and niche changes of potentially suitable habitats for Q. mongolica from 2022 to 2090, and pinpointed the key environmental factors influencing its distribution. The findings reveal that the total potential suitable area for Q. mongolica covers 74,994.792 km², predominantly spread across Inner Mongolia, Heilongjiang, Jilin, and other regions. The primary determinants of suitable area distribution were peak temperature of hottest month, lowest temperature of coldest month and altitude. Under future climate scenarios, the potentially suitable habitats of Q. mongolica are anticipated to diminish to varying extents, with the distribution center exhibiting a tendency towards northward migration. Concurrently, the overlap among different climate scenarios is predicted to expand over time. This investigation facilitates a comprehensive understanding of Q. mongolica's adaptation to climate change, enabling informed adjustments and serving as a valuable reference for the preservation and sustainable management of Q. mongolica populations.

African scientific research faces formidable challenges, particularly with limited access to state-of-the-art measurement instruments. The high cost associated with these devices presents a significant barrier for regional research laboratories, impeding their ability to conduct sophisticated experiments and gather precise data. This predicament not only hampers the individual laboratories but also has broader implications for the African scientific community and the advancement of knowledge in developing nations—the financial cost barrier considerably impacts the research quality of these laboratories. Reflection on technical and economical solutions needs to be quickly found to help these countries advance their research. In civil Engineering, the thermal conductivity property is the most important measurement for characterising heat transfer in construction materials. Existing devices (i.e., conductometers) in a laboratory are expensive (approximately 30,000 euros) and unavailable for some African laboratories. The study proposes a new and affordable device to evaluate thermal conductivity in construction materials. The method involves establishing a thermal flux between a heat source ( from the Joule effect provided by steel wool where a current is circulating) and a cold source (generated by ice cube) under steady-state conditions. The development of the cylindrical prototype is based on the comparative flux-meter method outlined in the measuring protocol of the ASTM E1225 standard document. Experiments were conducted on four distinct materials (polystyrene, wood, agglomerated wood, and rigid foam). The results indicate a correct correlation between the experimental values obtained from the newly developed prototype and the reference values found in the literature. For example, concerning the experimental polystyrene study, the detailed case analysis reveals a good correlation, with a deviation of only 4%. The percent error found falls within the acceptable range indicated by the standards recommendations of the ASTM E1225 standard, i.e., within 5% acceptable error.

Diabetes mellitus – type 2 (DM2) is a hypercoaguable state with enhanced platelets (PLTs) activation and increased clotting factor production. Simultaneously, the fibrinolytic cell system is inhibited due to formation of clots with high resistance to fibrinolysis. The stages of PLTs "activation" have been well-characterized microscopically, morphometrically and nanomechanically (by light microscope, transmisiion electron microscope - TEM, scanning electron microscope - SEM and atomic force microscope - AFM). Thrombocytes in "acivated" (pro-coagulant) state play central role in two main biological processes: hemostasis and repair of vascular vessels. Pro-coagulant PLTs participate in generation of thrombin and in development of fibrin fibers in the clot matrix. Enhanced PLTs reactivity in dibetic- (diabetes mellitus - DM2) patients is considered as a "pro-thrombotic" state. In the retrospective- and prospective- studies on the topic, the higher are the PLTs' hematometrical indices: PLTs (count), MPV (mean platelet volume), PDW (platelet distribution width), PCR (plateletcrit), as well as PLTs/Ly ratio. The PLТs parameters/indices are usefull biomarkers in early diagnosis and prognosis of DM2. The precise studies of PLTs' state of activation during DM2 might be useful for creation of new diabetes (DM2) treatment strategies and effective medicines. Using the amount of blood glucose, attached to hemoglobin (HbA1c values) as markers of glycemic control in diabetic patients, researchers have observed association between MPV and medications as Insulin, Metformin, sulphonyl ureas. Computational modellng of PLTs' activation in DM2 is also a controlling factor for thrombocyte distribution and margination in bood vessels, associated with micro- and macrovascular disease in DM2. PLTs-derived miRNAs are novel molecular biomarkers for diagnosis and prognosis of DM2, insulin resistance and diabetes complications. We have proposed (2010) oleic acid (OA) as a substance suppressing "PLT hyperactivation" and inhibiting “thrombocyte aggregation”. Antiplatelet agents, natural plant products including, could be effective in the prevention and the secondary treatment of micro- and macrovascular complications in the type 2 Diabetes mellitus. Our proposal on following up of a combination of hematological-, hemorheological- and hemostatic parameters (indices) as a new way in diagnosis, treatment, predicting and management of DM2 and its related vascular complications, probably could be more explored in future studies.

The research aims to present an innovative methodology to execute the Heritage conservation processes in a collaborative and interdisciplinary BIM project, with an effective management of the deterioration suffered over time, emphasizing the structures and coatings. The research begins with the architectural survey using scanning techniques (TLS) and terrestrial photogrammetry (SfM) and taking as a study the Duomo of Molfetta (Italy), a unique Romanesque architecture of Puglia (Italy). The methodological process is mainly aided by the precise semantic segmentation of global point clouds, a semi-automatic process assisted by classification algorithms implemented in the post-processing software, and which has allowed the classification of the unstructured information provided by the remote sensing equipment when identifying the architectural-structural systems of a building with high historical values. Subsequently, it has been possible to develop an efficient Scan-to-HBIM workflow, where the Heritage-BIM (HBIM) project has fulfilled the function of a database by incorporating and organizing all the information (graphic and non-graphic) to optimize the tasks of auscultation, identification, classification, quantification and, in turn, facilitating the parametric modeling of unique structures and architectural elements. The results have shown great effectiveness in the processes of characterization of architectural and artistic heritage, focusing on the deformations and deterioration of the masonry in columns, and making multidisciplinary conservation work in an HBIM project more flexible. But there are still some difficulties for the automatic interpretation of 3D point cloud data, related to specific systems of the historical architecture. In conclusion, human action and interpretation continues to be a fundamental pillar to achieve precise results in a heritage environment.

Despite the expansion of financial institutions and proliferation of mobile financial services, reaching the unbanked and bringing them under formal financial services has become a policy concern in many developing countries. Due to the lack of financial accounts, unbanked people prefer informal, risky, and inconvenient mechanisms for receiving, sending and transferring money. Previous studies rely much on common intervention like no account maintenance and opening fees, easy documentation processes and money subsidies for opening financial accounts. This study aims to examine the impact of the motivational workshop on opening savings accounts through causality among the unbanked people in a setting where the respondents are unbanked despite having all the requirements and many institutional offers for opening savings accounts. We encouraged the unbanked people through a one-hour-long motivational workshop to open saving account. Based on our cross-sectional data and randomized controlled trial experiment among 505 unbanked Village Defense Party (VDP) members at Dhubil Union under Sirajganj in Bangladesh, we have the evidence that motivational workshop positively impacts opening ac-counts by 32.33 percent. However, the account opening rate differs in terms of respondent’s pref-erence for financial institutions. Our study also finds that unbanked people have the highest pref-erence for mobile financial services for opening accounts resulting in 15.33 percent. The result of the study has some policy implications for adopting effective strategies of financial access in many developing countries.

Given the increase in consumers’ preferences for coffee, it is becoming important to understand their decision-making processes in the coffee chain context. To deepen the understanding of sustainable outcomes in this context, this study investigates the role of dedication- and constraint-based mechanisms in forming consumers’ repurchase and positive word-of-mouth (WOM) intentions, two critical sustainable outcomes. We examined the effects of coffee quality, the quality of the physical environment, and service quality in accelerating the formation of dedication-based factors. Moreover, this study offers an in-depth understanding of the enablers of perceived switching costs. Data collected from 238 university students that frequently visit coffee chains are empirically tested against the proposed theoretical framework by using structural equation modeling. The results confirm that both dedication- and constraint-based factors substantially predict consumers’ sustainable outcomes in the coffee chain context. Brand image and perceived switching costs play an important role in enhancing consumers’ repurchase and positive WOM intentions compared with customer satisfaction. Coffee quality is significantly associated with both customer satisfaction and brand image, whereas the quality of the physical environment and service quality are only significantly associated with brand image. Habit is found to be the key enabler of perceived switching costs, while loyalty programs have no significant impact on perceived switching costs.

The vulcanization of rubber compounds is an exothermal process. A carbon black-filled and natural rubber-based (NR) formulation was mixed with different levels of sulfur (0.5, 1.0, 2.0, 4.0 and 6.0 phr) and studied with Differential Scanning Calorimetry (DSC) for the determination of the vulcanization enthalpy. It was found that the vulcanization enthalpy is dependent from the amount of sulfur present in the compound and the vulcanization heat released was -18.4 kJ/mol S if referred to the entire rubber compound formulation or -46.0 kJ/mol S if the heat released is referred only to the NR present in the compound. The activation energy for the vulcanization of the rubber compounds was also determined by a DSC study at 49 kJ/mol and found quite independent from the sulfur content of the compounds under study. A simplified thermochemical model is proposed to explain the main reactions occurring during the vulcanization. The model correctly predicts that the vulcanization is an exothermal process although it gives an overestimation of the vulcanization enthalpy which is larger for the EV vulcanization package and smaller for the conventional vulcanization system. If the devulcanization is conducted mechanochemically in order to break selectively the sulfur-based crosslinks, then the natural rubber compounds recovered from used tires can be re-vulcanized again and the exothermicity of such process can be measured satisfactorily with DSC analysis. This paper not only proposes a simplified mechanism of vulcanization and devulcanization but also proposes an analytical method to check the devulcanization status of the recycled rubber compound in order to distinguish truly devulcanized rubber from reclaimed rubber.

Stress is an inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues like cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimate. However, in the case of more serious health issues such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable sensor computing platform with multiple channels. The platform aims to estimate stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor users' stress levels and integrate data on their blood pressure and blood glucose levels. Validation experiments were performed on patients with chronic diabetes, and the initial findings are presented in the study.

The combination of nanoemulgel and phytochemistry has resulted in several recent discoveries in the field of the topical delivery systems. The present study aimed to prepare turmeric (Curcuma longa) and neem (Azadirachta indica) based nanoemulgel against microbial infection as topical drug delivery. Olive oil (oil phase), Tween 80 (surfactant), and PEG600 (co-surfactant) were used for the preparation of nanoemulsion. Carbopol 934 was used as gelling agent to convert the nanoemulsion to nanoemulgel and promote to control the release of biological properties of turmeric and neem. The nanoemulsion were characterized based on particle size distribution, PDI values and compatibility using FTIR analysis. In contrast, the nanoemulgel was evaluated based on pH, viscosity, spreadability, plant extract and excipients compatibility or physical state, in-vitro study, ex-vivo mucoadhesive study, antimicrobial properties and stability. The resulting nanoemulsion was homogeneous, stable during the centrifugation process, with the smallest droplets and low PDI values. FTIR analysis also confirmed good compatibility and absence of phase separation between the oil substance, surfactant, co-surfactant with both plant extracts. The improved nanoemulgel also demonstrated a smooth texture, good consistency, pH, desired viscosity, ex-vivo mucoadhesive strength with highest spreadability, and an 18-hour in-vitro drug release study. Additionally, it exhibited better antimicrobial properties against different microbial strain. Stability studies also revealed that the product had good rheological properties and physicochemical state for a period of over 3 months. The present study has affirmed that turmeric and neem based nanoemulgel as a promising alternative for microbial infection particularly associated by microorganisms via topical application.

Seeing atomic configuration of single organic nanoparticles at sub-Å spatial resolution by transmission electron microscopy has been so far prevented by the high sensitivity of soft matter to radiation damage. The difficulty is related to the need to irradiate the particle with a total dose of few electrons/Å2, not compatible with the electron beam density necessary to search the low-contrast nanoparticle, to control its drift, to finely adjust the electro-optical conditions and particle orientation, and to finally acquire an effective atomic resolution image. On the other hand, the capability to study individual pristine nanoparticles, such as proteins, active pharmaceutical ingredients, and polymers, with peculiar sensitivity to the variation of the local structure, defects, and strain, would provide advancements in many fields, including materials science, medicine, biology, and pharmacology. Here we report direct sub-ångström resolution imaging at room temperature of pristine unstained crystalline polymer-based nanoparticles. This result is obtained by combining low-dose in-line electron holography and phase-contrast imaging on state-of-the-art equipment, providing an effective tool for the quantitative sub-angstrom imaging of soft matter.

The road traffic injuries risk factors such as driving offenses and average speed are concerns for health organizations to reduce the number of injuries. Without any comprehensive view of each road, one cannot decide about the effective policy. In this manner, the data-driven policy will help to improve and assess the decisions. The count data near the road of two airports is surveyed for investigating the time-varying speed zones. The descriptive statistics, ANOVA, and functional data analysis were used. The hourly data of traffic counts for four different locations at the entrance of the two airports, international and domestics, were collected for one the year 2018 to 2019.The hourly pattern of driving offenses for each road was assessed and the to and from airport roads had different peaks (<0.05). The hour, weekdays, type of airport, direction and their interactions were statistically significant (<0.05) for the chance of driving offenses. The speed average during the day was statistically different (<0.5) by the number of different types of vehicles. The traffic count data is a great resource for decision making in safe driving subjects such as driving offenses. With functional data analysis, we can analyze them to get the most of the characteristics of this data. The airports are public places with high traffic demand in all countries that yields the different pattern of traffic transportation, therefore we extract the factors that affect the driving offenses. Finally, we conclude that conducting a time-varying speed zone near the airports seems vital.

The elderly population is particularly susceptible to myocardial damage caused by heat stress (HS). With the continuous increase in global temperatures, this poses an undeniable challenge to human health. This study aimed to investigate the therapeutic effect of Shikimic Acid (SA) in treating HS-induced myocardial damage through network pharmacology, molecular docking, molecular dynamics (MD) simulations, and in vitro experiments. Network pharmacology analysis revealed that SA alleviates the inflammatory response in the heart muscle associated with HS by targeting 60 disease-related targets, such as TP53, TNF, and IL-6. The tumor proteoglycan, the PI3K-Akt signaling pathway, and hepatitis B were identified as the primary therapeutic pathways. Molecular docking and MD simulation results confirmed that SA can stably bind to both TNF (-6.642 kcal/mol) and IL-6 (-7.261 kcal/mol), and the complexes of Shikimic Acid-IL-6 and Shikimic Acid-TNF did not undergo significant changes within 100 ns. In addition, the in vitro experiment showed that 250 μM SA significantly increased the survival rate of HL-1 cells exposed to 43°C for 2 h by 71.1% and promoted the proliferation of HL-1 cells by 30.7%. Overall, these findings provide substantial evidence for the therapeutic potential of SA in HS-induced myocardial damage.

In the article, the results of research on a NiTi alloy with a high nickel content (51.7 at.%), produced using the additive technology SLM method and subjected to isothermal aging after so-lution annealing, are presented. The study involved the determination of the sequence of phase transformations occurring using differential scanning calorimetry (DSC), and the determination of the temperature range of these transformations. In parallel, the phase composition was determined using the XRD method; hardness and the Young’s modulus were also determined. The analysis of the DSC results obtained indicates the following characteristic features of the NiTi alloy, which change with ageing time: (1) during cooling (from +1500C to -500C), the type of transformation changes from a one-step transformation after solution annealing to a two-step transformation after the ageing process over 1, 20 and 100 hours at 500°C; (2) during heating (from -500C to +1500C) for all the samples, regardless of the ageing time, only a one-step transformation from martensite M(B19’) to austenite A(B2) is observed; (3) the temperature at which the transformation starts increases with the ageing time, (4) the width of the total temperature range of the transformation M(B19’)  A(B2) during heating changes from large (∆T=49,7°C), after solution annealing, to narrow (∆T=19.3°C and ∆T=17.9°C after 20 h and 100 h of ageing) and, most importantly, (5) a comparison with literature data shows that, irrespective of the composition of the NiTi alloy and the manufacturing technology of the alloy samples (regardless of the fact whether this was traditional or additive technology), a sufficiently long ageing process period leads to the occurrence of the martensite  austenite transformation in the same temperature range.

In this study, in order to develop the composition ceramics for the application of ultrasonic transducer device for temporomandibular joint disorder pain relief and facial skin massage, Pb0.88(La0.6Sm 0.4)0.08(Mn 1/3 Sb 2/3)0.02 Ti 0.98 O3 ceramics were manufactured using CuO as sintering aids, and their piezoelectric ,and resonant properties were investigated. At the specimen sintered at 1200 [℃], the excellent values of piezoelectric properties appeared, respectively: the dielectric constant (εr) of 202, piezoelectric constant (d33) of 56[pC/N], first and third overtone electromechanical coupling factor kt of 0.548, kt3 of 0.219, and then first and third overtone mechanical quality factor Qmt1 of 345, Qmt3 of 292 were suitable for the device application such as ultrasonic transducer, respectively. When the length of one side was 7.7 mm, first and third overtone electromechanical coupling factor kt1 of 0.555, kt3 of, 0.196, and then first ,third overtone mechanical quality factor Qmt1 of 381, Qmt3 of 393 , and the first dynamic ratio(D.R) and third overtone dynamic ratio(D.R) of 63.7(dB),and 37.7(dB) were suitable for the device application such as ultrasonic transducer ,respectively .

Beginning with navigation system design, this paper presents a comprehensive strategy for enhancing the search and rescue capabilities of agile mobile robots. Towards this, the autonomous ground vehicle (AGV) utilizes surface classification to determine and prioritize the terrain it is traversing. Our developed system design incorporates real-time terrain data with task objectives at a high level, ensuring that the robot can effectively navigate complex and ever-changing environments. This design, in conjunction with the introduction of a novel lightweight surface classification model, forms the basis of our adaptive terrain perception and decision-making systems, enabling robots such as Jackal to adapt rapidly and make the decisions necessary to complete the task. Subsequently, we exhaustively validated these systems through a series of extensive experiments in a variety of terrains, including normal and mixed terrains, demonstrating their robustness and efficacy in real-life situations.

Objective: To investigate fundamental movement skills (FMS) and physical activity differences among Hispanic and non-Hispanic White elementary school children. Materials and Methods: This study included 194 children aged between 4 and 11 in kindergarten, 2nd, and 5th grades. For evaluable participants, t-tests were used to test potential differences in fitness or movement skills. The characteristics of Hispanic and non-Hispanic White were compared using the p-value to detect statistical differences in anthropometric, FMS, and fitness characteristics. To identify any noticeable differences, the means and standard deviations of each group were compared. Results: There were significant group differences across the participants in age, height, Body Mass Index (BMI), BMI percentile, arm circumference, and waist circumference. In addition, there were significant differences between Hispanic and Non-Hispanic White in catch, run, dodge, punt, and two-hand strike (all p’s <0.05). Lastly, there were significant differences between Hispanic and Non-Hispanic White in pull-up, push-up, and sit-up (all p’s <0.05). Conclusion: Future studies should focus on decreasing obesity, increasing fitness, and FMS in elementary school children. Particularly, with Hispanic children whose FMS is negatively affected at such a young age, leading to eventual implications for their physical fitness and health in the future.

The genus Musa spp. contains commercially important fleshy fruit producing plants including plantains and bananas with a strong potential of providing food security and source of revenue to farmers. Occasionally, plantlets are often not available to fulfill the supply demand of farmers particularly in Caribbean region. Concerns with the quality of vegetative tissues along with the possibility of the transmission of phytopathogens makes availability of plantlets limited to farm-ers. Micropropagation of plantains offers an alternative to producing large number of in-vitro plantlets. However, conventional methods of micropropagation techniques require high produc-tion costs and are labor-intensive. Recently, Temporary Immersion Bioreactor (TIB) has emerged as an alternative to conventional micropropagation methods. Our work utilized SEM (Scanning Electron Microscope), molecular and biochemical tools (qRT-PCR and ICP-OES) to characterize and compare the morphological, elemental composition, and photosynthetic gene expression of plantains cultured on TIB. Additionally, morphological features of growth and propagation rates were analyzed for comparing outputs obtained from temporary immersion bioreactor with conventional micropropagation (CM) techniques. Results showed higher growth and multiplication rates for plantlets cultivated in TIB. Gene expression analysis of selected photosynthetic genes demonstrated high transcript abundance of phosphoenol pyruvate carboxylase (PEPC) in plantain tissues obtained by TIB. Elemental composition analysis showed higher content of iron in plantains grown in TIB suggesting a potential correlation with PEPC expression. These results demonstrate the potential of TIB to be an efficient method to produce healthy in-vitro plantains.

Background: Glioblastoma remains a challenging brain tumor to treat due to its infiltrative nature. Accurately identifying tumor boundaries during surgery is crucial for successful tumor resection. This study introduces an innovative intraoperative visualization method utilizing a surgical fluorescence microscope to precisely locate tumor cells. The focus of the study is on IR-Glint, a novel preparation comprising Cy7.5-labeled aptamers Gli-233nt and Gli-55_3L specific to human glioblastoma. Methods: The aptamers were modified using molecular modeling and quantum chemical techniques. The effectiveness of the preparation was assessed using flow cytometry and microscopy on primary cultures of human glioblastoma. In vivo studies were conducted on mouse and rabbit models, employing orthotopic xenotransplantation of human brain glioblastoma with various imaging techniques, including PET/CT, in vivo fluorescence visualization, confocal laser scanning, and surgical microscopy. Results: The experiments validated the potential of IR-Glint for intraoperative visualization of glioblastoma using infrared imaging. Surface application of the aptamer-based formulation on the brain allowed clear visualization of the tumor, aiding surgeons in tumor resection. This approach also reduces the dosage required and mitigates potential toxic effects on the patient. Conclusions: This study demonstrates the promising potential of using in-frared dye-labeled aptamers for intraoperative visualization of glioblastoma. By improving surgical treatment outcomes in neurosurgery, this novel approach may pave the way for future advancements in the field.

To better understand farmers’ refusal and corresponding negative emotions to pay agricultural water fee under current policy in rural China, this paper applies mental accounting, a behavioral economics framework, to explore how the governmental policies of reform of rural taxes and fees, direct agricultural subsidy programs and agricultural water fee individually influence farmers’ decisions in paying agricultural water fee. Using fieldwork data from 577 farmers and 20 water managers in Sichuan, we explore farmers’ information processing regarding paying agricultural water fee via three sequential mental accounting processes, with the associated underlying principles and measures behind each process. We find that the information processing in three mental accounting scenarios related to the agricultural water fee elucidates farmers’ observed behaviors in rural China. Generally, in the three mental accounting scenarios, two conditional intuitive expectations and nine conditional intuitive preferences are formed, however, the conditions of those expectations or preferences cannot be matched with the facts due to the reform of rural taxes and fees, the direct agricultural subsidy programs and the internal attributes of agricultural water fee, which interpret those negative behaviors in rural China. Additionally, this paper offers a view into how previous policies create negative psychological externalities (such as farmers’ psychological dependence on the government) through mental accounting to negatively influence agents’ subsequent decision making; it highlights the significance of underlying mental factors and information processing of negative behaviors in policymaking for managing or conserving common pool resources.

Dysbiosis, as reflected by the altered composition of the microbiome favoring pathogenic species, is a major contributor to colorectal cancer (CRC) development, which could lead to bacterial translocation into the bloodstream. This study aimed to evaluate the presence of circulated bacterial DNA (cbDNA) in CRC patients, with or without surgical removal of the primary tumor. In total, 100 participants enrolled in this prospective clinicolaboratory study, equally divided into four groups; healthy controls (Group 1), patients with non-metastatic CRC with surgical removal of the primary tumor (Group 2), patients with metastatic CRC with surgically excised (Group 3) or non-excised (Group 4) primary tumor. DNA extracted from peripheral blood was analyzed using PCR with specific primers targeting 16S rRNA, Escherichia coli (E. coli), and Fusobacterium nucleatum (F. nucleatum). Only the detection of F. nucleatum in the blood was significantly higher in Group 4 compared to Group 1 (p < 0.001), Group 2 (p = 0.023) and Group 3 (p = 0.023). The association of cbDNA with other clinical parameters or co-morbidities was also evaluated. These results highlighted the importance of bacterial translocation in CRC patients and the role of F. nucleatum as an intratumoral oncomicrobe associated with metastatic CRC.

There is much interest in effective copper chelators to correct copper dyshomeostasis in neurodegenerative and oncological diseases. In this study, a recombinant fusion protein for expression in E. coli cells was constructed from glutathione-S-transferase (GST) and the N-terminal domain (ectodomain) of human high affinity copper transporter CTR1 (hNdCTR1), which has three metal-bound motifs. Several biological properties of the GST-hNdCTR1 fusion protein were assessed. It was demonstrated that in cells, the protein was prone to oligomerization, formed inclusion bodies and displayed no toxicity. Treatment of E. coli cells with copper and silver ions reduced cell viability in a dose- and time-dependent manner. Cells expressing GST-hNdCTR1 protein demonstrated resistance to the metal treatments. These cells accumulated silver ions and formed nanoparticles that contained AgCl and Ag0. In this bacterial population, filamentous bacteria with length about 10 μm were often observed. The possibility for the fusion protein carrying extracellular metal binding motifs to integrate into the cell’s copper metabolism and its chelating properties are discussed.

Recent studies have shown correlations between the microbiota's composition and various health conditions. Machine learning (ML) techniques are essential for analyzing complex biological data, particularly in microbiome research. ML methods help analyze large datasets to uncover microbiota patterns and understand how these patterns affect human health. This study introduces a novel approach combining statistical physics with the Monte Carlo (MC) methods to characterize bacterial species in the human microbiota. We assess the significance of bacterial species in different age groups by using notions of statistical distances to evaluate species prevalence and abundance across age groups and employing MC simulations based on statistical mechanics principles. Our findings show that the microbiota composition experiences a significant transition from early childhood to adulthood. Species such as Bifidobacterium breve and Veillonella parvula decrease with age, while others like Agathobaculum butyriciproducens and Eubacterium rectale increase. Additionally, low-prevalence species may hold significant importance in characterizing age groups. Finally, we propose an overall species ranking by integrating the methods proposed here in a multicriteria classification strategy. Our research provides a comprehensive tool for microbiota analysis using statistical notions, ML techniques, and MC simulations.

Isothermal interrupted hot compression tests of 5754 aluminum were conducted on Gleeble-3500 thermo-mechanical simulator at temperature 350 °C and 450 °C, strain rate 0.1 s-1 and 1s−1. These tests simulated flat rolling to investigate how softening behaviors respond to controlled parameters, such as deformation temperature, strain rate and delay times. This data allowed the parameters for the hot rolling process to be optimized. The delay times during interrupted compression vary between 5s and 60s. The dynamic softening at each pass and metadynamic recrystallization at the intervals of deformation passes were analyzed in detail. 0.2% offset yield strength is applied to calculate the softening fraction undergoing metadynamic recrystallization. A kinetic model was developed to describe metadynamic recrystallization behaviors of the hot deformed 5754 aluminum alloy. Furthermore, the time constant for 50% recrystallization was expressed as functions related to the temperature and the strain rate. The experimental and calculated results were found to be in close agreement, which verified the developed model.

The world is currently facing an energy crisis. To solve the problem, many countries have been exploring renewable energy sources. However, the existing renewable energy sources are highly dependent on weather changes, necessitating the utilization of significantly expensive and large storage batteries.As a solution, we propose a system for generating renewable energy. The proposed system comprises a current source and a voltage source connected in series. With appropriate adjustments, the current source supplies current without voltage and the voltage source provides voltage without current. Considering that electric power is a product of current and voltage, electric power is generated from the interaction between the current source and voltage source. In our previous study, the same result was obtained; however, the output voltage was low, which resulted in a low amount of generated energy. In this study, by introducing some ingenuities, we successfully increased the output voltage. Consequently, we succeeded in powering a light-emitting diode and charging a super-condenser to the standard voltage. Using this system, we generated a relatively large amount of energy in the right place and at the right time, validating our systems’ robustness to changes in weather. The large-scale adoption of our system could contribute to solving the global energy crisis.

In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Current models have achieved high accuracy results on public datasets. Despite this success, they require significant computational resources for training. Given that transfer learning based techniques allow reusing what other models have already learned and training models with less computational resources, in this work we propose using a transfer learning based approach for action recognition in videos. We describe a methodology for human action recognition using transfer learning techniques in a custom dataset. The proposed method consists of four stages: 1) human detection and tracking, 2) video preprocessing, 3) feature extraction (using pretrained models with ImageNet), and 4) action recognition using a two-stream model consisting of TCNs, LSTMs, and CNNs layers. The custom dataset is imbalanced with 189, 390, 490, 854, and 890 videos per class, respectively. For feature extraction, we analyzed the performance of seven pretrained models: Inception-v3, MobileNet-v2, MobileNet-v3-L, VGG-16, VGG-19, Xception, and ConvNeXt-L. We show that the best results were obtained with the last one. Finally, using pretrained models for feature extraction allowed training in a PC with a single GPU with an accuracy of 94.9%.

Drug-target binding is an essential parameter in drug discovery and design to ensure drug efficacy and specificity. In 2022, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined: $Kd = f(molecules, envPara)$. Technically, GIBAC represents a set of in silico approaches for structural and biophysical data generation towards a paradigm shift in precise drug discovery and design, providing a comprehensive framework for intermolecular binding affinity calculation with adequate accuracy, precision and efficiency. For the first time, this study reports a prototype of GIBAC (semaGIBAC), i.e., a one-dimensional semaglutide-GLP-1R-based mini static GIBAC, based on an experimental complex structure of semaglutide and GLP-1R. Semaglutide is a potent GLP-1 receptor agonist used to treat type 2 diabetes mellitus by regulating blood glucose levels and promoting weight loss. In 2021, a structural modification involving a Val27-Arg28 exchange was manually introduced to enhance semaglutide-GLP-1R binding affinity. This study employs a comprehensive structural and biophysical analysis aimed at thoroughly exploring the sequence space of semaglutide-GLP-1R to design analogues with improved binding affinity, leading to the identification of a promising semaglutide analogue, which binds to GLP-1R with an affinity that is more than two orders of magnitude (113.3 times) higher than native semaglutide. To sum up, this article puts forward a promising structural biophysical approach for developing GLP-1 receptor agonists with enhanced efficacy, and with a GIBAC prototype (semaGIBAC), this article argues again that the time is now ripe for the construction of a real GIBAC to be listed on the agenda of the drug discovery and design community.

Over the last few decades, there is a remarkable increase in air traffic worldwide; however, despite of rapid technological development and improved airport infrastructure, there is still an increasing number of aviation accidents and fatalities. In order to meet safety demands of Air Traffic Control (ATC), Machine Learning (ML) based aircraft conflict detection techniques are focused to predict events before they come to reality. This research presents a systematic literature review (SLR) about the applications of ML techniques in safety prediction of ATC system. Initially, the existing aircraft conflict prediction models are classified into three broad categories; logical, geometric and probabilistic. A formal process of SLR is then conducted which is initiated with preprocessing and effective searching. A series of filtering called title, abstract and objective-based are then applied to extract the most relevant articles. Detailed and critical reviews of selected articles are performed to identify challenges which are segregated into dataset, model and evaluation techniques, furthermore, the optimal solutions for each are proposed. Finally the matrices to map the identified challenged with their proposed solutions are constructed. This research serves as a foundation for future research and towards integration of ML based safety prediction techniques into modern ATC system.

ABSTRACT Objectives: To investigate the effect of individual-level, neighborhood, and environmental variables on uterine fibroid (UF) prevalence in a Chicago-based cohort. Methods: Data from the Chicago Multiethnic Prevention and Surveillance Study (COMPASS) were analyzed. Individual-level variables were obtained from questionnaires, neighborhood variables from the Chicago Health Atlas, and environmental variables from NASA satellite ambient air exposure levels. The Shapiro-Wilks test, logistic regression models, and Spearman’s correlations were used to evaluate the association of variables to UF diagnosis. Results: We analyzed 602 participants (mean age: 50.3±12.3) who responded to a question about UF diagnosis. More Black than White participants had a UF diagnosis (OR, 1.32; 95% CI, 0.62–2.79). We observed non-significant trends between individual-level and neighborhood variables and UF diagnosis. Ambient air pollutants, PM2.5 and DSLPM were protective against UF diagnosis (OR 0.20, CI: 0.04–0.97: OR 0.33, CI: 0.13–0.87). Conclusions: Associations observed within a sample in a specific geographic area may not be generalizable and must be interpreted cautiously.

Due to the great threat of torpedo against surface warships, an efficient hydroacoustic countermeasure system must output realtime strategies that can accommodate varied antagonizing scenarios. Aiming at the decision-making problem in the anti-torpedo hydroacoustic countermeasure cases, this paper proposes an intelligent adversarial strategy based on the game theory. By discretizing the strategy space of both sides, a matrix game model is established with the payoff characterized by the capture probability of attacking torpedo. An improved simplex algorithm is then developed to get the mixed-strategy Nash equilibrium of the game model, with which preferred avoiding strategies can be obtained to minimize the risk of being captured by the attacking torpedo. Based on the probabilistic view of preferred adversarial strategy, the launching strategy of floating acoustic decoy can be achieved, and the evasion trajectory of warship can be confirmed simultaneously. Numerical simulation demonstrates that the proposed method can generate realtime intelligent antagonizing strategy, and consequently, can improve the survival rate of surface warships in the of anti-torpedo countermeasure scenarios.

Shape Memory Alloy (SMA) have emerged as a revolutionary material that holds the potential to change the design and construction methods of buildings and other structures. One of the most promising applications of SMAs in civil engineering is their use as reinforcements for steel structures. This paper presents the latest research progress, opportunities and challenges of SMA in the field of steel structural reinforcement, both in terms of basic components and applications. In terms of components, the construction forms and working mechanisms of Fe-SMA strips, SMA/CFRP composite patches and SMA dampers are introduced. On this basis, the application of SMA in steel structures reinforcement is introduced, and its effect is analyzed from three aspects: crack restoration, seismic retrofitting and structural strengthening. Finally, the results of the current research are summarized and the shortcomings are analyzed, hoping to provide a reference for the research of SMA in the field of steel structures reinforcement.

Accurate fuel mapping plays a crucial role in fire detection and management strategies. This paper presents a method for discriminating between wildfire fuel types by exploiting together remote sensing data and Convolutional Neural Networks (CNN). Specially, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (conifers, broadleaf, shrubs, grass, bare soil, urban areas, and water bodies) with an high accuracy of 0.99$\%$. To further refine the fuel mapping results, subclasses were generated from the seven principals by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Scott/Burgan fuel classification system. This refinement step allows for a more detailed and comprehensive assessment of fuel types, enhancing the accuracy and effectiveness of fire management efforts, which can be utilized by fire management agencies, policymakers, and researchers for improved fire behavior prediction and mitigation practices. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts.

Due to the influence of background conditions, lighting conditions, occlusion issues and the image resolution, how to extract robust person features is one of the difficulties in ReID research. Vision in Transformers (ViT) has achieved significant results in the field of computer vision. However, the existing problems still limit its application in ReID due to slow extraction of person features and difficulty in utilizing local features of people. To solve the mentioned problems, we utilize Pyramid Vision Transformer (PVT) as the backbone of feature extraction and propose a PVT-based ReID method in conjunction with other studies. Firstly, some improvements suitable for ReID are used on the PVT backbone, and we establish a basic model by using powerful methods verified on CNN-based ReID. Secondly, in an effort to further promote the robustness of the person features extracted by the PVT backbone, two new modules are designed. (1) The local feature clustering (LFC) is recommend to enhance the robustness of person features by calculating the distance between local features and global feature to select the most discrete local features and clustering them. (2) The side information embeddings (SIE) are used to encode non-visual information and send it into the network for training to reduce its impact on person features. Finally, the experiments show that PVTReID has achieved excellent results in ReID datasets and are 20% faster on average than CNN-based ReID methods.

In the realm of modern drug discovery, the integration of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structural-activity relationships (SARs), play a pivotal role in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. By embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

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

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

Introduction: The rapid emergence of COVID-19 as a global crisis has led to the approval of many vaccinations, which were unfortunately associated with high complication rates due to a lack of sufficient safety studies. Objectives: The following manuscript focuses on discussing the demyelinating disorders that were noticed after COVID vaccine administration. Methods: We conducted a retrospective study using anonymous medical records from the US vaccine adverse events reporting system, complications retrieved included Acute disseminated encephalomyelitis (ADEM), Guillain Barre syndrome (GBS), and Multiple sclerosis (MS), outcome parameters were age, sex and the dose after which this complication was observed. Patients younger than 18 years-old were excluded as some of the vaccines, namely Janssen (JNJ-78436735) is not yet approved below this age. Results: Our analysis showed that demyelinating disorders were more likely to occur in patients over the age of 50 compared to other age groups, regardless of the type of vaccination, except for MS and ADEM occurrences after the Jansen vaccine. In addition, demyelinating complications were more likely to occur after the first dose of vaccination. Conclusion: Further research and observation of demyelinating diseases in different vaccinations, as well as additional in vitro studies, are recommended to further explain the pathogenesis of demyelinating disorder occurrence.

The need for miniaturized shear force sensors is expanding, particularly for biomedical applications. Examples include measuring interfacial shear stresses between a human and an external device (e.g., footwear or a prosthesis). However, there are considerable challenges in designing a shear sensor for these applications due to the need for a small package, low power requirements, and resistance to interference from motion artifact and electromagnetic fields. This paper presents the design, fabrication, and characterization sensor that measures two-axis shear force by detecting displacement between a color panel and a red, green, and blue light-sensing photodiode. The sensor response to applied displacements and forces was characterized under benchtop testing conditions. We also present the design of a prototype wireless version of the sensor for integration into footwear. The sensor exhibited strong agreement with gold standard measurements for two axis shear displacements (R2>0.99, RMSE≤5.0 µm) and forces (R2>0.99, RMSE≤0.94 N). This performance, along with the sensor’s scalability, miniaturized form, and low power requirements make it well-suited a variety of biomedical applications.

Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows to make inferences about a population. Mistakes are usually committed when generalizing the results and anomalies, therefore, appear in unprocessed sets. However, there are some efforts to address these limitations using process mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (Dotted Chart, Trace Alignment, Fuzzy Miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sector. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.

Geopolymers are ecologically-friendly inorganic materials which can be produced at low temperatures from industrial wastes such as fly ash, blast furnace slags or mining residues. Although to date their principal applications have been as alternatives to Portland cement building materials, their properties make them suitable for a number of more advanced applications, including as photocatalytic nanocomposites for removal of hazardous pollutants from waste water or the atmosphere. For this purpose, they can be combined with photocatalytic moieties such as metal oxides with suitable bandgaps to couple with UV or visible radiation, or with carbon nanotubes or graphene. In these composites the geopolymers act as supports for the photoactive components, but geopolymers formed from wastes containing oxides such as Fe2O3 show intrinsic photoactive behaviour. This review discusses the structure and formation chemistry of geopolymers and the principles required for their utilisation as photocatalysts. The literature on existing photocatalytic geopolymers is reviewed, suggesting that these materials have a promising potential as inexpensive, efficient and ecologically-friendly candidates for the remediation of toxic environmental pollutants and would repay further development.

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

The recent outbreak and spread of the COVID-19 virus infection across the world has seen a massive global system-wide shutdown of human social and economic activity. Both developed and developing nations have been forced to contain and isolate their citizens as much as possible. However, the continuous rising cases in both categories of nations, especially those with poor or nonexistent testing facilities and healthcare systems pose a hidden danger. The seemingly lack of and access to a truly global concerted research effort in both temporary but effective symptom mitigation may lead to more deaths in infected cases. We propose that a fusion of both technological and home-grown solutions can be utilized effectively to manage symptoms. This would add to the preventive methods of social distancing, isolation, quarantine and frequent handwashing to halt the impact of the disease. We also hope to spur further research in such drug/non-drug combination therapy-based methods with emphasis on effectiveness based on quality of nutrient sources.

Bio-devices are designed to allow biological matter to perform in vitro almost the same functions it performs in vivo. Therefore, they can benefit from the specificities of such materials and are expected to perform better than traditional devices. On the other hand, the integration of biological material with electronic/electrochemical instrumentation requires careful attention and can produce unconventional results. In this paper, we describe the impedance response of an electrochemical cell that converts sunlight into electrical power. It uses the photosynthetic system known as Reaction Center, which is the core of photosynthesis in several living beings. Under illumination, an abrupt transformation drives the cell electrical response from insulator to conductor and a photocurrent is observed. The impedance spectrum shows a peculiar shape which significantly modifies with the protein activation. It has been analyzed by means of a graphical/analytical/ numerical procedure. The modelling identifies an analogue electrical circuit, whose parameters give quantitative information about the underlying process. Finally, an appropriate normalization of data is proposed which validates data in dark and light and can be useful as a fast screening of measurements.

Semantic Web technologies are being increasingly adopted by the geospatial community during last decade through the utilization of open standards for expressing and serving geospatial data. This was also dramatically assisted by an ever increasing access and usage of geographic mapping and location-based services via smart devices in people’s daily activities. In this paper we explore the developmental framework of a pure Javascript client-side GIS platform exclusively based on invocable geospatial Web services. We also extend Javascript utilization on the server side by deploying a node server acting as a bridge between open source WPS libraries and popular geoprocessing engines. The vehicle for such an exploration is a cross platform Web browser capable of interpreting Javascript commands to achieve interaction with geospatial providers. The tool is a generic Web interface providing capabilities of acquiring spatial datasets, composing layouts and applying geospatial processes. In an ideal form the end-user will have to identify those services, which satisfy a geo-related need and put them in the appropriate row. The final output may act as a potential collector of freely available geospatial web services. Its server-side components may exploit geospatial processing suppliers composing that way a light-weight fully transparent open Web GIS platform.

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

Several plant species produce mixtures of odorous and volatile compounds known as essential oils (EOs). These mixtures play important roles in nature and have been utilized by man for pharmaceutical and agrochemical purposes. There are more than 3000 EOs reported in the literature with approximately 300 having commercial use, including the oils from Eucalyptus species. Such oils are rich in monoterpenes and have found applications as pharmaceuticals, agrochemicals, food flavorants and in the perfume industry. Such applications are related to their diverse biological and organoleptic properties. In view of their importance, we review in this article up to date information concerning chemical composition and biological activities of essential oils from different species of Eucalyptus. Among the 900 species and subspecies of the Eucalyptus genus¸ we examined 68 species. The studies associated with these species were conducted in 27 countries. We have focused on the antimicrobial, acaricide, insecticide and herbicide activities, hoping that such information will contribute in the advances of the research in this field. It is also intended that the information herein described can be useful in the rationalization of the use of Eucalyptus EOs as components for new pharmaceutical and agrochemical applications as well as food preservatives and flavorants.

Aedes aegypti (L.) is the primary vector of chikungunya, dengue, yellow fever and Zika viruses. The leucine-rich repeats (LRR)-containing domain is evolutionarily conserved in many proteins associated with innate immunity in invertebrates and vertebrates, as well as plants. We focused on the AaeLRIM1 and AaeAPL1 gene expressions in response to Zika virus (ZIKV) and Chikungunya virus (CHIKV) infection using a time course study, as well as the developmental expressions in the eggs, larvae, pupae, and adults. RNA-seq analysis data provided 60 leucine-rich repeat related transcriptions in Ae. aegypti in response to Zika virus (Accession number: GSE118858, https://www.ncbi.nlm.nih.gov/gds/?term=GSE118858). RNA-seq analysis data showed that AaeLRIM1 (AAEL012086-RA) and AaeAPL1 (AAEL009520-RA) were significantly upregulated 2.5 and 3-fold during infection by ZIKV 7-days post infection (dpi) of an Ae. aegypti Key West strain compared to an Orlando strain. The qPCR data showed that LRR-containing proteins AaeLRIM1, AaeAPL1 and five paralogues were expressed 100-fold lower than other nuclear genes, such as defensin, during all developmental stages examined. Together, these data provide insights into transcription profiles of LRR proteins of Ae. aegypti during its development and in response to infection with emergent arboviruses.

The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAV operating autonomously beyond visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagships UAVs of our industrial partner. Results show that this UAV doesn’t have all the technologies and methodologies necessary to achieve all the identified SA goals identified for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

Weaning is a critical period in the pig’s life. Piglets are confronted with abrupt changes to their physical and social environment, as well as management and nutritional changes. Weaning was always associated with a growth check and was frequently accompanied by post-weaning diarrhea in piglets. However, rapid increases in litter size, in the last decade, has increased within-litter piglet weight variation, with piglets now generally lighter at weaning, making the challenges associated with weaning even greater. Many interventions can be employed during the suckling period to ease the weaning transition for piglets. Pre-weaning strategies such as supervised farrowing (assistance with suckling, oxytocin provision), provision of pain relief to sows around farrowing, split-suckling, early oral supplementation with glucose, bovine colostrum, fecal microbiota transplantation, feed additives, solid and liquid creep feeding (milk and liquid feed) have all been investigated. The objective of these strategies is to stimulate earlier maturation of the digestive tract, improve immunity, reduce latency to the first feed post-weaning and increase early post-weaning feed intake and growth. This review focuses in particular on: 1) pain relief provision to sows around farrowing, 2) split-suckling of piglets, 3) pre-weaning provision of supplementary milk and/or liquid feed, 4) other strategies to stimulate earlier enzyme production (e.g. enzyme supplementation) and 5) other nutritional strategies to promote improved gut structure and function (e.g. L-glutamine supplementation). Correctly implementing these strategies can not only increase post-weaning growth and reduce mortality but also maximize lifetime growth in pigs.

Laminate structures composed of stiff plates and thin soft interlayers are widely used in aerospace, automotive and civil engineering encouraging the development of reliable non-destructive strategies for their condition assessment. In the paper, elastodynamic behaviour of such laminate structures is investigated with emphasis on its application in ultrasonic based NDT and SHM for the identification of interlayer mechanical and interfacial contact properties. A particular attention is given to the practically important frequency range, in which the wavelength considerably exceeds the thickness of the film. Three layer model with spring-type boundary conditions employed for imperfect contact simulation is used for numerical investigation. Novel effective boundary conditions are derived via asymptotic expansion technique and used for analysis of the peculiar properties of elastic guided waves in considered laminates. It is revealed that the thin and soft film influences the behaviour of the laminate mainly via the effective stiffnesses being a combination of the elastic moduli of the film, its thickness and interface stiffnesses. To evaluate each of these parameters separately (or to figure out that the available experimental data are insufficient), a step-wise procedure employing the effective boundary conditions is proposed and tested versus the laser Doppler vibrometry data for Lamb waves in Aluminium/Polymer film/Alumunium structure. The possibility of using film-related thickness resonance frequencies to estimate the film properties and contact quality is also demonstrated. Additionally, the rich family of edge waves is also investigated, and the splitting of fundamental edge waves into pairs is revealed.

The prim objective of commenced article is to determine q-sumudu transforms of a product of unified family of q-polynomials with basic (or q-) analogue of fox’s H-function and q-analog of I-functions. Specialized cases of the leading outcome are further evaluated as q-sumudu transform of general class of q-polynomials and q-sumudu transforms of the basic analogues of Fox’s H-function and I-functions.

This mini-review discusses the development and applications of naturally derived bioconjugates from hydroxycinnamic acids, such as coumaric, sinapic, ferulic, and caffeic acids, combined with various biomaterials. These bioconjugates offer a range of benefits including antioxidant properties, UV protection, customized hydrophilic-lipophilic balance, improved safety, solubility, emolliency, biocompatibility, biodegradability, and targeted delivery for biomedical, cosmetic, and food applications. The increasing demand for sustainable and natural products in the biomedical, cosmetic, and food industries has led to the exploration of these hydroxy cinnamic acids and their derivatives. We discuss the synthesis and modification of hydroxy cinnamic acids with biomaterials such as ω-hydroxy fatty acids, castor and lesquerella oils, glycerol, isosorbides, and synthetic polyethylene glycol to form functional phenolipids for biomedical, sunscreen and skincare applications. Techniques such as encapsulation with β-cyclodextrins and modification of biopolymeric supports like polysaccharides and starch are discussed for enhancing bioavailability and solubility. The fine-tuned development of bioconjugates from hydroxycinnamic acids using glycerol to modify the hydrophilic-lipophilic balance, substitution by water-soluble carboxylic acid groups, vegetable oil-based phenolipids, polysaccharides, PEGylation provide enhanced functionalities and offers a promising avenue for creating sustainable and effective products across various applications.

Developing prolonged antigen delivery systems that mimick the long-term exposure to pathogens appears as a promising, still poorly explored approach to reach durable immunities. In this study, we have used a simple technology by which his-tagged proteins can be assembled, assisted by divalent cations, as supramolecular complexes with progressive complexity, namely protein-only nanoparticles and microparticles. Microparticles produced out of nanoparticles are biomimetics of secretory granules from the mammalian hormonal system. Upon subcutaneous administration, they slowly disintegrate, acting as an endocrine-like secretory system, and rendering the building block nanoparticles progressively bioavailable. The performance of such materials, previously validated for drug delivery in oncology, has been tested here regarding the potential for time-prolonged antigen release. This has been done by taking, as building blocks, a nanostructured version of p30, a main structural immunogen from the African swine fever virus (ASFV). By challenging the system in both mice and pigs, we have observed unusually potent pro-inflammatory activity in porcine macrophages, and long-lasting humoral and cellular responses in vivo, which might overcome the need for an adjuvant. The robustness of both innate and adaptive responses tag, for the first time, these dynamic depot materials as a novel and valuable instrument with transversal applicability in immune stimulation and vaccinology.

Human activities and natural events alter the earth’s surface and pose a constant threat to the environment. Thus, accurately monitoring and predicting these changes in a timely manner is crucial to provide solutions and mitigate the environmental consequences beforehand. This research introduces a novel framework, called HCD-Net, for change detection using bi-temporal hyperspectral images. The framework is based on double-stream deep feature extraction and an attention mechanism. The first stream focuses on deep feature extraction through 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks. The second stream focuses on deep feature extraction through 2D convolution and 2D SE blocks. The extracted deep features are then concatenated and fed to dense layers for decision-making. The efficiency of HCD-Net is compared to the state-of-the-art change detection methods. Additionally, the bi-temporal Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) hyperspectral dataset was used for further evaluation of the change detection results. The results show that HCD-Net has a higher accuracy and the lowest false alarm rate, where the overall classification accuracy is above 96%, and the kappa coefficient is over 0.9.

With the engineering system becoming more and more complex, the uncertainty factors have a more and more profound influence on the reliability and security of the engineering system. In recent years, Reliability-Based Design Optimization (RBDO) has been studied extensively in complex engineering system design. The research on reliability optimization design considering stochastic uncertainty has been comprehensive and widely used. However, the reliability optimization design considering mixed uncertainty has the disadvantages of large computation and imprecise convergence. This study proposes an RBDO method based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to overcome this challenge. In the method, PSO is used to solve the most probable point, while SA has excellent global optimization capability to acquire the global optimal solution. Finally, three examples are given to illustrate the advantages of the proposed method.

Ready-to-eat fresh fruit have an increasing presence in international markets due to its convenience and health benefits. However, these produces are highly perishable and efficient technologies to increase their shelf life are needed. In the present research, different citrus fruit species and cultivars from organic farming were assayed to obtain enzymatically citrus segments. The best results in terms of segment quality were observed for ‘Hernandina’ clementine which was chosen to make a new ready-to-eat produce based on peeled citrus segments packaged in glass jars with slight syrup made on citrus juice and organic sugar cane. The most appreciated syrup was those containing 50-50 (v/v) of ‘Fino’ lemon juice and ‘Hernandina’ clementine based on the sensory scores given by the panellists. In addition, different pasteurization treatments were assayed to preserve the new produce safety and nutritional and sensory quality properties during storage at cold temperature. Results showed that pasteurization treatment at 50 °C for 45 min could be enough to avoid microbial contamination with mesophilic and psychrophilic aerobic bacteria or yeast and mould and to maintain sensory properties until five weeks of storage at 4 °C. In addition, only a 10 % reduction of vitamin C was observed from fresh segments or syrup until the end of storage period, showing that the new ready-to-eat produce would conserve high bioactive compound content and health benefits after pasteurization and prolonged cold storage.

Machinery degradation assessment can offer meaningful prognosis and health management in-formation. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain dis-criminator to extract invariant features from different machine operating conditions. The case studies with the FEMTO-ST bearing dataset and the Monte Carlo method for RUL prediction during the degradation process are conducted. The experiment results fully exhibited the signif-icant advantages of the proposed method compared to other state-of-the-art techniques.

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network LBFNet is proposed in this paper.First, a lightweight convolutional neural network LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning makes the model accuracy reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers.

Data could be in many forms like text, image, audio, video and many others. Amongst these data, processing image is of important concern even with sophisticated technologies. Generally, images consume more storage space and processing time. This work aims in attaining a novel image encryption strategy that consumes very less computational time with maximum security. The idea is to split the image into blocks and rearrange the pixels in diagonal fashion to achieve confusion. Next, the blocks are combined to form sub images and one block of the sub image is subjected to Secure Hash Algorithm (SHA 512 bits). The SHA bits are XORed with the remaining blocks of the sub image. Finally, the entire image is encrypted using Advanced Encryption Standard (AES). Decryption process is the exact reverse of encryption process. The computational overhead is very low, and security is efficient. The results are compared with existing encryption techniques.