The goal of VAR or BVAR is the characterization of the dynamics and endogenous relationships among time series. Also the VAR models are known for their applications to forecasting and policy analysis. This paper compare the performance of VAR and Sims-Zha Bayesian VAR models when the multiple time series are jointly influenced by different levels of collinearity and autocorrelation in the short term (T=16, 32, 64 and 128). Five levels (-0.9,-0.5, 0,+0.5,+0.9) of collinearity and autocorrelation were considered and the results from the simulation study revealed that VAR(2) model dominated for no and moderate levels of autocorrelation (-0.5, 0, +0.5) irrespective of the collinearity level except in few cases when T=16. While the BVAR models dominated for high autocorrelation levels (-0.9 and +0.9) irrespective of the collinearity level except in few cases when T=128. The performance of the models varies at different levels of the collinearity and autocorrelated error, and also varies with the short term periods. Furthermore, the values of the RMSE and MAE criteria decrease as a result of increase in the time series length. In conclusion, the performance of the forecasting models depend on the time series data structure and the time series length. It is therefore recommended that the data structure and series length should be considered in using an appropriate model for forecasting.

Black yeasts are a highly specified group of fungi, which are characterized by a high resistance against stress factors. There are several factors enabling the cells to survive harsh environmental conditions. One aspect is the pigmentation, besides the melanin black yeasts often display a highly diverse carotenoid spectrum. Determination and characterization of carotenoids depend on an efficient extraction and separation, therefore especially for black yeast, characterized by thick cell walls specific protocols are needed to ensure analyses regarding stress responses in these fungi. Here we present both, a method to extract and analyze carotenoids and the unusual carotenoid composition of the black yeast Knufia petriola A95. Mechanical treatment combined with an acetonitrile extraction gave us very good extraction rates with a high reproducibility. The presented extraction and elution protocol allows the separation of the main carotenoids (7) in K. petricola A95 and should be suitable for the detection of additional carotenoids in other species. K. petricola A95 displays an unusual carotenoid composition, with mainly didehydrolycopene, torulene and lycopene. The pigment composition varied in dependency to oxidative stress but remained relatively constant if the cells were cultivated under low temperature. Black yeasts are a promising source for carotenoid production and other substances. To unravel the potential of these fungi new methods and studies are needed. The established protocol allows the determination carotenoid composition in black yeasts. Oxidative stress results in an adaptation in pigment composition in K. petricola A95. Future experiments have to be carried out to determine if didehydrolycopene functions as a protective agent itself or if it serves as a precursor for antioxidative pigments like torulene and torularhodin, which could be produced after induction under stress conditions.

The plant-microbe-soil nexus is critical in maintaining biogeochemical balance of the biosphere. However, soil loss and land degradation are occurring at alarmingly high rates, with soil loss exceeding soil formation rates. This necessitates evaluating marginal soils for their capacity to support and sustain plant growth. In a greenhouse study, we evaluated the capacity of marginal incipient basaltic parent material to support native plant growth, and the associated variation in soil microbial community dynamics. Three plant species, native to the Southwestern Arizona-Sonora region were tested with three soil treatments including basaltic parent material, parent material amended with 20% compost, and potting soil. The parent material with and without compost supported germination and growth of all the plant species, though germination was lower than the potting soil. A 16S rRNA amplicon sequencing approach showed Proteobacteria to be the most abundant phyla in both parent material and potting soil, followed by Actinobacteria. Microbial community composition had strong correlations with soil characteristics but not plant attributes within a given soil material. Predictive functional potential capacity of the communities revealed chemoheterotrophy as the most abundant metabolism within the parent material, while photoheterotrophy and anoxygenic photoautotrophy were prevalent in the potting soil. These results show that marginal incipient basaltic soil has the ability to support native plant species growth, and non-linear associations may exist between plant-marginal soil-microbial interactions.

Mitochondrial dysfunction is relevant mechanism in cardiac aging. Here, we investigated the effects of late-life enalapril administration at non-antihypertensive dose on mitochondrial genomic stability, oxidative damage, and mitochondrial quality control (MQC) signaling in the heart of aged rats. The protein expression of selected mediators (i.e., mitochondrial antioxidant enzymes, energy metabolism, mitochondrial biogenesis, dynamics, and autophagy) was measured in old rats randomly assigned to receive enalapril (n=8) or placebo (n=8) from 24 to 27 months of age. We also assessed mitochondrial DNA (mtDNA) content, citrate synthase activity, oxidative lesions to protein and mtDNA (i.e., carbonyls and abundance of mtDNA4834 deletion), and mitochondrial transcription factor A (TFAM) binding to specific mtDNA regions. Enalapril attenuated cardiac hypertrophy and oxidative stress-derived damage (mtDNA oxidation, mtDNA4834 deletion, and protein carbonylation), while increasing mitochondrial antioxidant defenses. TFAM binding to mtDNA regions involved in replication and deletion generation was increased following enalapril administration. Increased mitochondrial mass as well as mitochondriogenesis and autophagy signaling was found in enalapril-treated rats. Late-life enalapril administration mitigates age-dependent cardiac hypertrophy and oxidative damage, while increasing mitochondrial mass and modulating MQC signaling. Further analyses are needed to conclusively establish whether enalapril may offer cardioprotection during aging.

β-hydroxy β-methylbutyrate (HMB) is a bioactive metabolite formed from breakdown of the branched-chain amino acid leucine. Given the popularity of HMB supplements among different athletes, specifically, those who engage in regular resistance training, this review was performed to summarize current literature on some aspects of HMB supplementation that have received less attention. Because of the small number of published studies, it has not been possible to conclude the exact effects of HMB on cardiovascular parameters, oxidative stress and inflammatory markers. Thus, the interpretation of outcomes should be taken cautiously. However, the data presented here suggest that acute HMB supplementation may attenuate pro-inflammatory response following an intense resistance exercise in athletes. Also, the available findings collectively indicate that chronic HMB consumption in conjunction with resistance training has no more adaptive advantages associated with decreasing cardiovascular risk factors and oxidative stress markers. Taken together, there is clearly a need for further well-designed, longer duration studies to support these findings and determine whether HMB supplementation affects the adaptations induced by resistance training associated with body’s inflammatory condition, antioxidative defense system, and cardiovascular risk factors in humans.

Though Moracin D derived from Morus alba was known to have anti-inflammatory and antioxidant activities, the underlying antitumor mechanism of Moracin D was never unveiled so far. Thus, in the recent study, the apoptotic mechanism of Moracin D was elucidated in breast cancer cells. Herein, Moracin D exerted significant cytotoxicity in MDA-MB231 and MCF7 cells. Also, Moracin D increased sub G1 population, cleaved poly (ADP-ribose) polymerase (PARP) and attenuated the expression of pro-cysteine aspartyl-specific protease (procaspase 3), c-Myc, cyclin D1, B-cell lymphoma 2 (Bcl-2),and X-linked inhibitor of apoptosis protein (XIAP) in MDA-MB231 cells. Of note, Moracin D reduced expression of Forkhead box M1 (FOXM1), β-catenin, Wnt3a, and upregulated glycogen synthase kinase 3 beta (GSK 3β) on Tyr216 along with disturbed binding of FOXM1 with β-catenin in MDA-MB-231 cells. Conversely, GSK3β inhibitor SB216763 reversed the apoptotic ability of Moracin D to reduce expression of FOXM1, β-catenin, pro-caspase3 and pro-PARP in MDA-MB-231 cells. Overall, these findings provide novel insight that Moracin D inhibits proliferation and induces apoptosis via suppression of Wnt3a/FOXM1/β-catenin signaling and activation of caspase and GSK3β

Ischemia stroke is one of a clinically common cerebrovascular disease. And Inhibition of brain tissue ischemia and reperfusion-induced damage, especially apoptosis, has an irreplaceable protective effect on ischemic nerves, and has special significance for the treatment of patients after treatment. However, the development of neuroprotective drugs still has certain challenges. Radix scrophulariae as a valuable medicine, has been discovered to has neuroprotective effects. Our researches initially proved that Radix scrophulariae aqueous extract (RSAE) exerting a neuroprotective effects on cerebral ischemia and reperfusion (I/R) injury in oxygen glucose deprivation and reperfusion (OGD/R)-induced PC12 cells and middle cerebral artery occlusion/reperfusion (MCAO/R) model mice, were associated with attenuation of infarct volume, brain water content, nitric oxide (NO) and malondialdehyde (MDA), inhibiting I/R-induced damage by reducing the levels of LDH release, improving anti-oxidant capacity by upregulating the SOD, GSH-Px and CAT activity, stabilizing mitochondrial membrane potential, reducing neuronal apoptosis, necrosis and neuronal loss by regulating the expression of anti-apoptotic BCL-2and pro-apoptotic protein Bax, and elucidating downregulate the phosphorylation levels of MAPK pathways. Our findings may elaborate the neuroprotective effects and potential mechanisms of RSAE on focal cerebral I/R injury in mice. Since, Radix scrophulariae, as a potential neuroprotective natural plant, has originally been identified to, our results may offer directions and clues for discovering new active compounds or drugs for treatment of ischemic stroke, which allows us to discover many new natural active chemicals by chemical separation and identification, and provide new insights into therapeutic targets in stroke patients.

Due to its devastating consequences, late life depression is an important public health problem. The aim of the study was the analysis of variables which may potentially influence risk of depression (GDS-SF). Furthermore, the aim was to study possible mediating effect of given variables on the relationship between the total protein concentration and risk of depression in older-adults with chronic diseases, and physical function impairment. The research sample included a total of 132 older adults with chronic conditions and physical function impairments, remaining under a long-term care in residential environment. Negative linear correlation was observed between patients’ physical functionality, total protein concentration, concentration of HDL cholesterol, arm circumference, and the risk of depression. Considerably stronger relationship was observed between total protein concentration, and GDS-SF, in elderly suffering from sensory dysfunction (b = −6.42, 95% CI = −11.27; −1.58). The effect of the mediation between depression risk is correlated to total protein concentration in blood serum, and the mediators are probably low function impairment and low levels of 25 (OH)D vitamin. Cohort control research is suggested to confirm the hypothesis.

This study presents an analysis of the spatial and temporal distribution of the two large destructive earthquakes that occurred in Lake Van area on October 23, and November 9, 2011, together with the azimuth-dependent distribution of the seismic activity and microseismicity clusters after the mainshocks, associated with the complex rupture processes of their aftershock sequence. The sequence began with the magnitude Mw 7.1 earthquake of 23 October and a second destructive earthquake of Mw 5.6. The aftershock sequences of the two mainshocks were linked to the local crustal faults beneath Lake Van area, followed successively and produced unusually intense activity and significant damage in the area. The main purposes of this study are to document the spatial and temporal distribution and evolution of the October 23, 2011 aftershock hypocenters and the azimuth-dependent distribution of seismic activity, and to understand the spatial and temporal character of the aftershock sequence using the distributional and evolutional patterns of the aftershock hypocenters. A total of 10,000 aftershocks were obtained from seismic data with a high signal-to-noise ratio over collected over three years from October 23, 2011 to March 2014. These aftershocks were plotted for the time periods from November 2011 through March 2012 to March 2014 and ≈ 5000 aftershocks were retained in the depth versus distance cross-sections to detect the clusters in the first step of study (November 2011–March 2012). The focal depth distribution of the aftershock clusters, the migration of hypocenter activity and microseismicity clusters were analyzed and the distributional patterns of the detected clusters were assessed using the geometric distribution of the aftershock hypocenters. The spatial and temporal distribution of aftershocks reveal interesting key features of the deep rupture complexity of the Van earthquake: (1) most prominent aftershocks have been located in the upper crust at depths shallower than 10 km beneath ruptured area, indicating that the upper crust is brittle and seismogenic; (2) two spatial clusters have been detected at 8-10 km depths and the upward extrapolation of these clusters intersects with faults; the main cluster (60 km wide) bounded by inferred reverse faults (f3 and f4) and the central cluster (25–30 km wide) bounded by faults (f1 and f2); (3) these spatial clusters form the largest volumetric pattern of the conical-shaped cluster at depths of about 25–30 km of the azimuth-dependent rotational projections, suggesting azimuthal distributions of deep rupture characteristics; and (4) the strongest temporal cluster of microseismicity derived from temporal distribution of aftershocks has been detected within an area of about 2.5–3.0 km² and it is spatially observed at 20 km depth within the central cluster, suggesting progressive failure of the adjacent patches of possible fault.

Lambertianic acid (LA) is a biologically active compound from the leaves of Pinus koraiensis. In the present study, apoptotic mechanisms of LA plus TNF-related apoptosis-inducing ligand (TRAIL) were elucidated in non-small cell lung cancer cells (NSCLCs). Cytotoxicity assay, flow cytometry, immunoprecipitation and Western blotting were performed. Here, combined treatment of LA and TRAIL increased cytotoxicity, sub-G1 population and cleaved poly (ADP-ribose) polymerase (PARP) and caspase3/8/9 in A549 and H1299 cells compared to LA or TRAIL alone. Furthermore, combined treatment of LA and TRAIL significantly decreased anti-apoptotic proteins such as B-cell lymphoma 2 (Bcl-2), Fas-like inhibitor protein (FLIP) and X-linked inhibitor of apoptosis protein (XIAP) and enhanced the activation of pro-apoptotic proteins Bid compared to LA or TRAIL alone. In addition, combined treatment of LA and TRAIL upregulated the expression of Death receptor 4 (DR4) and downregulated phosphorylation of nuclear factor kappa-light-chain-enhancer of activated B cells (p-NF-B), inhibitory protein of kB family (p-IB) and FLIP in A549 and H1299 cells along with disrupted binding of XIAP with caspase3 or NF-B. Overall, these findings suggest that lambertianic acid enhances TRAIL-induced apoptosis via inhibition of XIAP/NF-B in TRAIL resistant NSCLCs.

Sinulariolide, a natural product extracted from cultured-type soft coral Sinularia flexibilis, possesses bioactivity against the movement of several types of cancer cell. However, the molecular pathway of its effects on human bladder cancer remain poorly understood. Using a human bladder cancer cell line as an in vitro model, this study investigated the underlying mechanism of sinulariolide against cell migration/invasion in TSGH-8301 cells. We found that sinulariolide inhibited TSGH-8301 cell migration/invasion, and the effect was concentration-dependent. Furthermore, the protein expressions of matrix metalloproteinases (MMPs) MMP-2 and MMP-9, as well as urokinase, were significantly decreased after 24-h sinulariolide treatment. Meanwhile, the increased expressions of tissue inhibitors of metalloproteinases (TIMPs) TIMP-1 and TIMP-2 were in parallel with an increased concentration of sinulariolide. Finally, the expressions of several key phosphorylated proteins in the mTOR signaling pathway were also downregulated by sinulariolide treatment. Our results demonstrated that sinulariolide has significant effects against TSGH-8301 cell migration/invasion, and its effects were associated with decreased levels of MMP-2/-9 and urokinase expression, as well as increased TIMP-1/TIMP-2 expression. The inhibitory effects were mediated by reducing phosphorylation proteins of the PI3K, AKT and mTOR signaling pathway. The findings suggested that sinulariolide is a good candidate for advanced investigation with the aim of developing a new drug for the treatment of human bladder cancer.

The calculation of the standard enthalpies of vaporization, sublimation and solvation of organic molecules is presented using a common computer algorithm on the basis of a group-additivity method. The same algorithm is also shown to enable the calculation of their entropy of fusion as well as the total phase-change entropy of liquid crystals. The present method is based on the complete break-down of the molecules into their constituting atoms and their immediate neighbourhood; the respective calculations of the contribution of the atomic groups by means of the Gauss-Seidel fitting method is based on experimental data collected from literature. The feasibility of the calculations for each of the mentioned descriptors was verified by means of a 10-fold cross-validation procedure proving the good to high quality of the predicted values for the three mentioned enthalpies and for the entropy of fusion, whereas the predictive quality for the total phase-change entropy of liquid crystals was poor. The goodness of fit (Q²) and the standard deviation (σ) of the cross-validation calculations for the five descriptors was as follows: 0.9641 and 4.56 kJ/mol (N=3386 test molecules) for the enthalpy of vaporization, 0.8657 and 11.39 kJ/mol (N=1791) for the enthalpy of sublimation, 0.9546 and 4.34 kJ/mol (N=373) for the enthalpy of solvation, 0.8727 and 17.93 J/mol/K (N=2637) for the entropy of fusion and 0.5804 and 32.79 J/mol/K (N=2643) for the total phase-change entropy of liquid crystals. The large discrepancy between the results of the two closely related entropies is discussed in detail. Molecules, for which both the standard enthalpies of vaporization and sublimation were calculable, enabled the estimation of their standard enthalpy of fusion by simple subtraction of the former from the latter enthalpy. For 990 of them the experimental enthalpy-of-fusion values are also known, allowing their comparison with predictions, yielding a correlation coefficient R² of 0.6066.

This paper explores the geometric impossibility of angle trisection within Euclidean geometry, with a particular focus on the 90° angle. It presents a rigorous proof that highlights the fundamental limitations of various approaches to angle trisection, highlighting their failure to achieve universality. The paper critiques modern proofs that allow for the trisection of certain angles while dismissing others, arguing that such methods are inherently flawed due to their lack of geometric consistency. The paper introduces a new perspective through a new property named “inconsistent property”, a concept derived from geometric operations involving proportional magnitudes. This property reveals contradictions within the framework of Euclidean geometry, paralleling the logical challenges faced in proving the impossibility of trisecting an arbitrary angle. By drawing comparisons between traditional and contemporary proofs, the paper demonstrates that inconsistencies arise not only from specific cases but also from broader geometric principles. The findings reaffirm the robustness of Euclidean geometry in addressing the trisection problem and challenge the validity of alternative proofs that do not adhere to universal geometric principles. This paper contributes to a clearer understanding of the limitations of angle trisection methods and encourages further investigation into the technical implications and broader impact of the angle impossibility proof.

Artificial intelligence harbors significant, unrealized promise for educational reformers, adding benefit to low-resource settings, particularly in the Least Developed Nations where access to quality learning remains most acute. The purpose of the introduction is to summarize how AI can reduce the educational gap and increase the effectiveness of education in poor resource environments. This paper, in a critical way, reviews the existing literature on the integration of AI in the education systems of developing countries concerning the benefits and challenges. This is achieved by reviewing a cross-section of literature from MDPI, Taylor & Francis, Springer, Wiley Online Library, Science Direct, and IEEE Xplore databases. Much emphasis within the selection criteria is given to those studies that had either empirically or theoretically demonstrated the influence of AI on educational practices within developing countries. Key findings: AI has the potential to greatly improve learning outcomes; it somewhat increases access to education but bodes well for the personalization agenda. These, however, will be surmounted upon adoption if it is to be effective: there exist huge challenges such as limited access to the internet, infrastructural deficit, and socio-cultural barriers. This study can, therefore, give inference to the fact that AI can change education and further enhance new equity in developing countries if there are corresponding proper policy interventions, building the capacity of teachers, and involvement of the community. More research is required to inform the long-term implications of AI and new uses being developed for AI applications in many diverse educational contexts, on which to base strategies for more equitable and more inclusive global education.

Measurements of the properties of soft materials are important from the point of view of medical diagnostics of soft tissues as well as testing the quality of food products and many technical materials. One of the frequently used techniques for testing such materials, attractive due to its non-invasive nature, is the indentation technique, which does not puncture the material. The difficulty of testing soft materials, which affects the objectivity of its results, is related to the problems of stable positioning of the studied material in relation to the indentation apparatus, especially with a device held by the operator. This work concerns the comparison of test results using an indentation apparatus mounted in mobile and stationary handles. The tested materials are cylindrical samples of polyurethane foams with three different stiffnesses and the same samples with a 0.5- or 1-mm-thick silicone layer. The presented research uses an apparatus with a flat cylindrical indenter, with a surface area of 1 cm2, pressed to a depth of 10 mm (so-called deep tests). Based on the recorded force changes over time, five descriptors of the indentation test were determined and compared for both types of handles. The tests performed showed that the elastic properties of foam materials alone and with a silicone layer can be effectively characterized by the maximum forces during recessing and retraction, and the slopes of the recessing and retraction curves. In the case of two-layer materials, these descriptors reflect both the characteristics of the foams and the silicone layer. The results showed that the above property of the deep indentation method distinguishes it from the shallow indentation method. The repeatability of tests performed in the mobile and stationary holders were determined to be comparable.

During tensile testing of materials, strain measurement is conducted using either contact or non-contact methods. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions, whereas non-contact methods minimize damage to thin specimens and allow measurements in various environments, though they require longer prep-aration and calculation times. This paper proposes a circular grid marking pattern and a strain prediction algorithm using artificial intelligence (AI), which simplifies the preparation process and allows strain prediction without additional equipment. The circular grid pattern can be ar-ranged in various configurations from 1×5 to 5×7, and a laser marker, which requires minimal time, was used to engrave the pattern on the specimen to shorten the preparation time. The AI model, trained on image-based data, enables strain calculation regardless of the specimen's gauge length and size, and allows measurement of local strain as well as gauge-length strain. The reliability of this concept was verified by applying it to tensile testing.

Firefighter hoods are essential components of personal protective equipment (PPE), protecting the head, neck, and shoulders from extreme heat and hazardous particulates. This comprehensive review evaluates recent hood design and material innovations aimed at improving comfort and protective performance. Traditionally, firefighter hoods mainly focused on thermal insulation but lacked effective particulate filtration, which posed significant health risks. Recent updates to several standards set by the US National Fire Protection Association have led to advanced mul-ti-layered hoods incorporating innovative materials for improved performance. Nevertheless, challenges remain in achieving an optimal balance between comfort and protection. Firefighter hoods must not hinder movement or increase heat stress, and the process of donning and doffing must minimize contamination risks. Current standards primarily assess material properties but fail to address practical, real-world conditions and interactions with other PPE components. This review emphasizes that these knowledge gaps can only be closed through systematic performance evaluations that replicate actual firefighting scenarios and include both human trials and simu-lations. Future research should focus on refining hood designs and materials to enhance particulate filtration efficiency, improve ergonomic fit, and reduce complications associated with use. This will ensure that firefighter hoods offer comprehensive protection while maintaining high comfort levels in demanding environments.

This article considers the status of psychoanalytical archetypes as attitudinal bodies. It possesses the initiative to consider the prerogative that they are situated on, and to develop that into discussing the conditions for an archetype-like body that is derived from Jungian pretenses. As this article operates within the boundaries of the conjecture, it serves instead to cover the potentiality for developments outside of the simple basis of the archetype, which therefore means that it covers a form of guidance instead of specific results. Unlike Jung’s study, which serves in a multitude of sections and draws upon evidential factors of, however generally expressed, ancient civilisations, this article seeks to apply that logic to the purpose of explicating the meaning and potential extensions of the “archetypal dogma”.

Melanoma is the most aggressive and deadly form of skin cancer, and its incidence has been steadily increasing over the past few decades, particularly in the Caucasian population [1]. Current treatment strategies, the combination of BRAF and MEK inhibitors (BRAF/MEKi) in BRAFV600 mutant melanoma, and immune checkpoint inhibitors (ICI), have changed the clinical evolution of this disease [2]. However, a significant percentage of patients do not benefit from these therapies. Therefore, to improve patient selection, it is imperative to look for novel biomarkers. Immune subsets, particularly the quantification of lymphoid populations, could contribute to the identification of ICI responders. The aim of this review is to thoroughly examine significant published data on this topic in melanoma patients and to evaluate the potential role of lymphoid subsets as prognostic and predictive response biomarkers.

The digital age has ushered in unprecedented connectivity and convenience, but it has also given rise to escalating cyber threats and privacy concerns. This study examines the intricate balance between technological advancement and personal privacy in the face of evolving cyber risks. A comprehensive literature review was conducted, analyzing peer-reviewed articles, government reports, and industry white papers from the past decade. Additionally, case studies of major data breaches and privacy violations were examined. Surveys and interviews with cybersecurity experts, policymakers, and privacy advocates were conducted to gather diverse perspectives. The research reveals a significant increase in both the frequency and sophistication of cyber-attacks, coupled with a growing public awareness of privacy issues. However, there is a notable disconnect between individuals' stated privacy concerns and their online behaviors. Regulatory efforts to protect privacy often lag behind technological advancements and cyber threats. Maintaining equilibrium between technological progress and privacy protection requires a multifaceted approach involving enhanced cybersecurity measures, updated regulations, and improved digital literacy. The study underscores the need for ongoing collaboration between government, industry, and academia to address this complex challenge.

Naomi Goldenberg’s model of religion as a “vestigial state” is an excellent interrogation of how religion interacts with the state. To Goldenberg, religions traditionally functioned in the same manner as nation-states before being conquered and delineated as semi-autonomous institutions under the larger secular apparatus. However, I argue in this paper that Goldenberg’s “vestigial state” fails to account for the dynamism that religions have in their relationships to the state. I propose seeing religions as “alternative authorities,” which can be subordinate, cooperative, or hostile to the secular state. These perspectives as an alternative authority are seen best in the evolution of American fundamentalism and Christian nationalism, whose adherents have historically distinguished themselves from the state while simultaneously engaging in the effort to reshape it. In looking at how these movements develop, we understand that religions are constantly evolving in how to achieve and maintain power.

Substance abuse is a significant public health issue both globally and nationally. Substance abuse involves the detrimental or risky consumption of psychoactive substances, posing a significant global issue, particularly among the youth. In India, cannabis abuse is a major concern. Cannabis is consumed legally in many states as Bhang, also known as marijuana or weed, while Charas and Ganja are illegal under both international drug conventions and the Indian NDPS Act of 1985. The Cannabis users are at great risk of developing Cannabis use disorder which is a pattern of problematic cannabis use leading to clinically significant impairment or distress. It is the most widely used illegal substance worldwide, showing a high prevalence in India.

This study explores the potential of digital technologies for preserving cultural heritage through the case study of the Raya Gopura, an incomplete monument in Melukote, India. Employing a combination of terrestrial photogrammetry, drone-based photogrammetry and terrestrial laser scanning, the research team captured highly detailed 3D models of the structure. This non-destructive approach offers a comprehensive digital record for future research and preservation. Furthermore, for the conjecture for the Raya Gopura two possible purposes: an entrance to a planned fort or a gateway to the temple are perceived through the collective intelligence (CI) approach. These insights inform a conjectural architectural drawing of the potentially completed monument. The 3D models hold significant potential for various applications, including tourism, education, and architectural research, with the potential for immersive experiences through VR and AR technologies. The study demonstrated the effectiveness of digital tools in heritage preservation, not only safeguarding the Raya Gopura but also offering valuable in-sights for future research and public engagements.

Background: The use of complementary therapies in the population is increasing, so it is necessary to understand the training that health professionals receive in this type of therapy in their training plans, as they are often the primary source of information for patients. Our objective was to investigate Spanish universities that offer subjects on complementary therapies in their Nursing degree programs. Methods: Observational descriptive cross-sectional study. For this purpose, we used the document published on the website of the Ministry of Universities as a working document. Additionally, a literature search was conducted up to September 2023 in the PubMed database, along with reverse searches. Results: Out of a total of 62 universities, only 16 (29%) offer a subject related to this type of therapy, with most of them being elective courses. Conclusions: The training content on complementary care in Nursing degree programs in Spanish universities is scarce, highlighting the potential benefit of expanding and promoting it, in line with the recommendations of the World Health Organization.

In genomics analysis, with the development of sequencing technology and the rapid growth of data volume, how to effectively identify and analyze important information in genomic data from massive data has become an important challenge. Identify key patterns and features in genomic data through advanced community testing methods. First, genomic data often contains a lot of noise and redundant information that needs to be processed through steps such as normalization, filtering, and dimensionality reduction. Normalization makes data at different scales comparable, filtering out data points with low quality or missing values, and dimensionality reduction reduces data dimensions through principal component analysis (PCA) and retains the main information. The gene co-expression network establishes the connection relationship between genes by calculating the expression similarity matrix between genes, and applies the Louvain algorithm to maximize the modularity of the network, aggregate nodes layer by layer, form a community structure, and identify the complex relationship and potential structure between genes. Finally, we utilize the cancer dataset to evaluate the proposed model. From our extensive experimental results, we can observe that Louvain's algorithm indicates outperformances and accuracy in the recognition and analysis of genomic data.

Coffee is one of the most valuable agricultural products worldwide, and it is crucial to have efficient tools to obtain reliable information about production. This study aims to estimate coffee plantation production using segmentation and deep learning techniques in RGB images. Photographs of coffee plants were taken in Tabaconas, San Ignacio-Cajamarca, to create a dataset of crops during the harvest stage. The images were segmented to detect coffee fruits. A deep learning method was developed with YOLOv5 to detect the fruits and OpenCV to count them. The results showed that YOLOv5 achieved an accuracy of 97.25%, a recall of 95.77%, and an F1-Score of 96.37%, demonstrating high reliability in detecting coffee fruits. The average detection time per image was 17.9 seconds. The metrics were evaluated using a confusion matrix, highlighting the model's good performance. In conclusion, segmentation and deep learning techniques, along with counting algorithms developed with OpenCV, proved effective for estimating coffee production. This approach provides a valuable tool for farmers, improving crop management and facilitating decision-making in precision agriculture.

In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case despite some fluctuations. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in financial fraud detection using deep learning methodologies.

Although long-term survival in Rett syndrome (RTT) has been observed, limited information on older people with RTT exists. We hypothesized that increased longevity in RTT would be associated with genetic variants in MECP2 associated with milder severity, and that clinical features would not be static in older individuals. To address these hypotheses, we compared the distribution of MECP2 variants and clinical severity between younger individuals with Classic RTT (under 30 years old) and older individuals (over 30 years old). Contrary to expectation, enrichment of a severe MECP2 variant (R106W) was observed in the older cohort. Overall severity was not different between the cohorts, but specific clinical features varied between the cohorts. Overall severity from first to last visit increased in the younger cohort but not in the older cohort. While some specific clinical features in the older cohort were stable from the first to the last visit, others showed improvement or worsening. These data do not support the hypothesis that mild MECP2 variants or less overall severity leads to increased longevity in RTT but demonstrate that clinical features change with increasing age in adults with RTT. Additional work is needed to understand disease progression in adults with RTT.

The integration of Artificial Intelligence (AI) in healthcare has transformed clinical practices by improving the accuracy of diagnosis, optimizing surgical procedures, improving the patient experience, and accelerating drug development. This article provides a comprehensive overview of the many applications of artificial intelligence (AI) across several industries, including fast diagnostics, where ML algorithms greatly enhance the accuracy and velocity of disease diagnosis. State-of-the-art robotic-assisted minimally invasive surgical procedures that shorten patients' hospitalization and improve their chances of recovery, cutting-edge AI applications in healthcare monitoring, and medication development. The article also looks at the primary challenges that AI in healthcare will inevitably encounter, such as differences in product quality, a shortage of skilled workers, privacy and ethical issues, and the need for improved regulatory frameworks. Despite the challenges, AI contributes significant advantages to the healthcare industry, providing novel and remarkable contributions to the efficiency of medical procedures and the progress of medical outcomes. The paper emphasizes the importance of cooperation in overcoming current challenges and enhancing the acceptability of AI technology in clinical settings. This will ensure that AI-driven innovations continue to enhance the standards of patient care.

This study explores the dynamics of transparency and accountability in supplier relationship management (SRM) within the e-commerce sector. It investigates how e-commerce platforms and their suppliers navigate complexities to foster trust, enhance collaboration, and ensure ethical practices in global supply chains. The research employs a qualitative approach, utilizing semi-structured interviews with industry stakeholders to gather insights into current practices, challenges, and strategies in SRM. Findings highlight transparency as pivotal for sharing critical information such as sales forecasts, inventory levels, and pricing strategies, enabling suppliers to align operations with market demands effectively. However, achieving transparency faces hurdles including data privacy concerns and competitive sensitivities, necessitating robust data security measures and clear communication protocols. Accountability mechanisms, such as formal agreements and governance frameworks, emerge as essential for mitigating risks, resolving disputes, and ensuring compliance with ethical and regulatory standards across diverse contexts. Technological innovations like AI, blockchain, and big data analytics are transforming SRM by enhancing supply chain visibility, operational efficiency, and decision-making capabilities. Cultural factors and regulatory compliance also significantly influence SRM practices, shaping communication styles, relationship dynamics, and legal considerations. Emerging trends such as sustainable sourcing practices and collaborative partnerships are reshaping SRM, emphasizing sustainability and responsible business practices. This study contributes to the understanding of effective SRM strategies, offering insights and recommendations for stakeholders navigating the complexities of e-commerce supply chains.

This study examines the balancing between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies such as blockchain, AI, and IoT to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains.

This qualitative research investigates sustainable procurement practices, focusing on the integration of environmental and social criteria in supplier evaluation processes. The study explores how organizations, ranging from large multinational corporations to smaller enterprises, navigate sustainability challenges and opportunities within their procurement strategies. Data were gathered through semi-structured interviews with procurement professionals and sustainability managers across diverse industries. Themes identified include varying organizational commitments to sustainability, from structured policies and resource allocation in larger firms to reactive approaches constrained by resource limitations in smaller entities. Effective supplier relationship management emerged as crucial, facilitating collaborative partnerships aimed at improving environmental and social performance across supply chains. Motivations for sustainable procurement practices encompass regulatory compliance and responding to consumer demands for eco-friendly products, shaping procurement decisions to enhance market competitiveness and stakeholder satisfaction. Environmental and social criteria integration in supplier evaluations highlighted strategies such as lifecycle assessments and sustainable sourcing practices, aimed at minimizing environmental footprints and promoting ethical labor practices. Benefits included cost savings and enhanced brand reputation, tempered by challenges such as higher procurement costs and supplier resistance. Leadership commitment and organizational culture were identified as critical enablers of sustainable procurement, influencing strategic alignment and fostering a culture of sustainability within organizations. The study underscores the transformative potential of sustainable procurement in achieving organizational resilience and contributing to global sustainability goals.

Dams significantly impact the environment, industry, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely within the framework of Integrated Water Resource Management (IWRM). Our research proposes that combining the physical model of evapotranspiration using the Penman–Monteith equation with data-driven models based on historical reservoir data, weather data, and consumption data is essential for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted a statistical test on the MAPE values. We achieved a remarkable accuracy in predicting the occupancy level one month ahead, with an error margin of just 1% using Extra Trees. This study represents a pioneering effort in providing guidance and proposing a hybrid model in this field.

Fusing data from many sources helps to achieve higher results of analysis. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle’s surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. We also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereovision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods.

Training artificial intelligence (AI) for reasoning presents significant challenges due to the lack of clear objectives and reward functions. In structured environments such as chess and Go, objectives are well-defined, and the optimal moves can be determined through search algorithms that maximize the reward function. This clarity enables AI to learn and improve autonomously through random trials and recursive calculation of rewards, mirroring human learning processes that depend on prior experiences and state-dependent actions. However, in real-world applications, AI lacks access to direct interaction and feedback, making it difficult to define proper reward functions. This limitation is particularly evident in the training of large language models (LLMs), where the quality of the output is constrained by the training data and the absence of a dynamic reward system. Simulated environments offer some utility but are inherently limited by their design and scope. Achieving artificial general intelligence (AGI) requires AI to function and receive feedback in real-world settings, similar to human cognitive and strategic development through interactive experiences. This paper explores the critical role of clear objectives and reward functions in AI training, the limitations posed by the current lack of real-world interaction, and the implications for future advancements in AI reasoning capabilities.

This study examined the thermal reflective properties and mechanisms of composite inorganic coatings applied to various wall types. It evaluated the influence of these coatings on the thermal insulation and reflectivity of walls. The findings indicate that coating application markedly enhances the wall's thermal insulation capabilities and increases the heat flux reflection ratio. These results offer crucial theoretical backing and practical guidance for the future use of coating technologies in construction. By refining the coating formulations and application processes, the thermal insulation properties of walls can be further enhanced, thus making a significant contribution to building energy efficiency and environmental protection. Consequently, this research provides essential references and insights for advancing coating technology in the construction industry.

In marine ecosystems communication among microorganisms is crucial since the distance is significant if considered on a microbial scale. One of the ways used by microorganism cells to reduce this gap is the production of extracellular vesicles. Marine bacteria release extracellular vesicles (EVs), small membrane-bound structures of 50 to 250 nm diameter, into their surrounding environment. The vesicles contain various cellular compounds, including lipids, proteins, nucleic acids, and glycans. EVs may mediate the communication between microorganisms in aquatic environments thus influencing ecosystem function and determining the structure and composition of microbial populations. This review will focus on marine bacterial EVs analysing their structure, composition, functions, and applications.

This article examines illicit financial flows (IFFs) perpetuated in financial statements to develop a framework to curb IFFs. IFFs create opacity, impeding economic progress through investment deterrents and financial uncertainty. Through a comprehensive literature review and the synthesis of sets of qualitative propositions, the researchers previously developed a conceptual framework to address IFFs, and the purpose of the present article is to strengthen and validate the framework among stakeholders in the financial and audit sectors. Following a mixed inductive and deductive research approach and a qualitative methodological choice, the researchers conducted interviews among practitioners to enhance the framework, followed by a focus group to validate the framework. IFF challenges that emerged are tax evasion, for example, investments in untraceable offshore accounts, harming the economy, bitcoins not being subject to regulation everywhere in the world and being used by cryptocurrency criminals to transfer IFFs to nations with lax regulations. Internationally, IFF risks are also determined by geographical position, trade links and porous borders among countries emerged as further challenges, calling for entities to execute existing policies, improve tax enforcement methods, apply cross-border coordination, and practice financial reporting transparency aimed at combatting IFF practices. On the strength of these, the industry surveys significantly enhanced the conceptual framework.

Neurotrophins, particularly brain-derived neurotrophic factor (BDNF), act as key regulators of neuronal development, survival and plasticity. In this review, we focus on the role of BDNF for corticostriatal plasticity in movement disorders, including Parkinson’s Disease (PD) and dystonia. We discuss the mechanisms how dopaminergic input modulates BDNF/TrkB signaling at corticostriatal synapses and the involvement of these mechanisms in neuronal function and synaptic plasticity. Disruption of these pathways can lead to aberrant plasticity, ultimately affecting motor function. Advancing our understanding of these mechanisms could pave the way towards innovative therapeutic strategies aiming at restoring neuroplasticity and enhancing motor function in these diseases.

Keywords: Artificial Intelligence in Implantology; AI in Dental Implantology; Machine Learning in Dental Implantology; Deep Learning in Implant Dentistry; Robotics in Dental Implantology; AI-Based Implant Planning.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.

Over the last fifty years, management plans have become more descriptive with regard to potential sustainability of forest systems, raising questions about the feasibility of implementing management activities and ensuring sustainability of a wide variety of ecosystem services. To assess this issue, we conducted a survey among forest planning and operation research communities to understand their perceptions regarding the potential of a sample of sustainability statements currently used in forest plans to be incorporated into optimization models or other mathematical operations. The results revealed that only a few statements from the sample were deemed to have relatively mature or firm methodologies and data to enable inclusion in modern mathematical models for land use optimization. These statements were mostly related to economic sustainability, offering quantifiable information such as a non-declining flow of wood products over time and limits on the amount of timber harvested per decade. In contrast, sociocultural and, to some extent, ecological statements regarding sustainability were generally perceived to be more difficult to translate into mathematical modeling efforts. Particularly challenging were statements corresponding to sustaining natural or scenic characteristics of a forest. These findings may be attributed to various factors, including a lack of measurable indicators for sustainability and a potential lack of understanding about the modeling components and their interactions with planned management activities.

The number of connected IoT devices is increasing significantly due to their many benefits, including automation, improved efficiency and quality of life, and waste reducing. However, these devices have several vulnerabilities that have led to the rapid growth in the number of attacks. Therefore, several machine learning-based Intrusion Detection Systems (IDS) tools have been developed to detect intrusions and suspicious activity to and from a host (HIDS - Host IDS) or, in general, within the traffic of a network (NIDS - Network IDS). The proposed work performs a comparative analysis and an ablative study among recent machine learning-based NIDS to develop a benchmark of the different proposed strategies. The proposed work compares both shallow learning algorithms, such as Decision Trees, Random Forest, Naïve Bayes, Logistic Regression, XGBoost, and Support Vector Machine, and deep learning algorithms, such as DNN, CNN, and LSTM, whose approach is relatively new in the literature. The algorithms are evaluated on the KDD-99, NSL-KDD, UNSW-NB15, and IoT-23 datasets. The results show that the NIDS tools based on deep learning approaches achieve better performance in detecting network anomalies than shallow learning approaches.

Plant biomass could become a new world-class "currency" because almost everything can be made of it: fuels, foods, feeds, constructing materials, furniture, etc. Currently, growing biomass for energy needs is particularly relevant in so complicated socioeconomical situation. There are important its productivity and the lowest possible ecological footprint. Can increasing crop biodiversity address these challenges? A field experiment was started at the Vytautas Magnus University Agricultural Academy in 2020-2022. Maize, hemp and faba bean were grown as single, binary and ternary crops to determine its effect on soil properties. Studies have shown that most intercrops failed to stabilize soil aggregate composition, NPK levels and pH, and enzyme urease activity. Binary maize-hemp and the ternary crops reduced the amount of the main macroelements in the soil the most because of the highest yields, which were removed from the fields. Regardless of the ecological services provided by faba beans, biomass removal leads to degradation of most soil properties and making the soil vulnerable to erosion. In the future, the cultivation of such multicrops should be investigated under conditions of more abundant fertilization or the effect of green manure should be used by increasing the number of fast- and short-growing interior plant species in the cultivation.

Abstract: Chronic Obstructive Pulmonary Disease (COPD) is a continually advancing respiratory disorder, characterized by enduring airflow limitation along with long-lasting inflammation in the airways. An expanding pool of evidence is emphasizing how mitochondrial dysfunction directly impacts the development and progression of COPD. This comprehensive review delves deeper into the intricate cellular and molecular biology of mitochondria within the framework of COPD, spotlighting significant alterations in structure and function that accompany mitochondrial dysfunction. In this narrative, we delve into the observed changes in mitochondrial shape, behavior, and respiratory chain complex alterations seen in individuals afflicted with COPD. The discussion includes implications on cellular signaling pathways and how this affects the natural process of programmed cellular death (apoptosis) and cellular aging, particularly in reference to COPD. Additionally, we consolidate ongoing therapeutic strategies, honing in on the management of COPD through a focus on mitochondrial dysfunction. Prominent among these strategies is the potential role of antioxidants and mitochondrial biogenesis inducers, which are emerging as promising fields of intervention. Furthermore, we embark on an overview of the possible use of mitochondrial biomarkers as innovative tools in diagnosing COPD, evaluating disease progression, and monitoring the efficacy of COPD treatments. As we unravel the rather complex interplay between mitochondrial biology and COPD, this review highlights the critical importance of formulating treatments that target mitochondrial dysfunction. The ultimate goal of these strategic interventions is to slow down the rate of COPD progression and significantly improve patient outcomes. Lastly, though our understanding of the nexus between COPD and mitochondrial dysfunction has significantly enhanced, ample scope remains for further investigation. Improved insights into the mechanisms of mitochondrial dysfunction, discovered biomarkers, and targeted therapies could encompass a revolutionary step towards comprehensive disease management. The future of COPD treatment seems to lean towards a combination of strategies, prioritizing not only the management of symptoms but also focused on preserving and restoring lung function, improving physical capacity, and enhancing quality of life for COPD patients.

Future healthcare professionals can play a significant role in managing those with thoughts of suicide. This scoping review aims to determine healthcare students’ knowledge and attitude towards suicide. A systematic search was conducted across five databases: PubMed, CINAHL, EMBASE, PsycINFO, Cochrane, and DARE. Only primary studies written in English, from database inception until 31st December 2023 were included. A total of 44 studies were included. Overall, healthcare students demonstrated low to moderate confidence in managing patients with thoughts of suicide and low levels of relevant education despite acknowledging their importance in preventing suicide. Increased personal exposure to suicide was associated with increased suicide literacy, confidence in managing patients with thoughts of suicide, and reduced stigma. Demographics and culture were shown to affect knowledge and attitude as well. Further research is needed to better understand various contributing factors to healthcare students’ knowledge and attitude regarding suicide. Evaluating healthcare curricula should be considered to implement effective suicide training program.

Balance is important in lifesaving competitions. We aimed to investigate the relationship between dynamic balance and physical function in elite lifesavers by measuring the foot muscle cross-sectional area, ankle joint muscle strength, toe grasp strength, plantar superficial sensation, and dynamic balance. In this observational study, we measured the foot muscle cross-sectional area, ankle dorsiflexion, plantar flexion, external flexion, isometric muscle strength, toe grasp strength, and superficial foot sensation of 15 adult lifesavers (12 males). The results showed that toe grasp strength and ankle plantar flexion isometric muscle strength are particularly important for the dynamic balance function of elite lifesavers working on sandy surfaces. Sand training improves intrinsic muscle training and dynamic balance function. However, high training intensity may cause a decrease in plantar surface sensation, which needs to be verified with sand training interventions in the future.

This study explores the potential of digital technologies for preserving cultural heritage through the case study of the Raya Gopura, an incomplete monument in Melukote, India. Employing a combination of photogrammetry (terrestrial and drone-based) and terrestrial laser scanning, the research team captured highly detailed 3D models of the structure. This non-destructive approach offers a comprehensive digital record for future research and preservation. Furthermore, consultations with experts yielded two possible purposes for the Raya Gopura: an entrance to a planned fort or a gateway to a temple. These insights inform a conjectural architectural drawing of the potentially completed monument. The 3D models hold significant potential for various applications, including tourism, education, and architectural research, with the potential for immersive experiences through VR and AR technologies. In conclusion, this study demonstrates the effectiveness of digital tools in heritage preservation, not only safeguarding the Raya Gopura but also offering valuable insights for future research and public engagement.

The integration of supply chain analytics and Customer Relationship Management (CRM) systems represents a critical strategy for organizations seeking to enhance operational efficiency and customer-centricity in today's competitive business environment. This qualitative study investigates the motivations, strategies, challenges, and outcomes associated with integrating supply chain analytics with CRM systems across diverse industries. Motivations for integration include gaining a unified view of customer data, improving decision-making capabilities, enhancing operational efficiency, and ensuring strategic alignment across organizational functions. Strategies employed by organizations include cross-functional collaboration, investment in scalable technology platforms, and leadership commitment to drive successful integration initiatives. Despite the potential benefits, the integration process presents challenges such as data integration complexities, technical interoperability issues, and organizational resistance to change. Addressing these challenges requires robust technological solutions, effective change management strategies, and a proactive approach to data governance and security. The study identifies significant outcomes from integration efforts, including improved operational efficiency through streamlined processes and optimized resource allocation, enhanced customer insights leading to personalized experiences, and strengthened strategic decision-making supported by real-time analytics.

Abstract: Background: The gut microbiota, a diverse community of microorganisms in the gastrointestinal tract, is essential for human health, primarily through nutrient metabolism and production of anti-inflammatory short-chain fatty acids. Dysbiosis, a disruption in microbial composition, is linked to diseases like inflammatory bowel disease, colorectal cancer, obesity, diabetes, asthma, and allergies. Dietary habits significantly influence gut microbiota diversity, with balanced diets crucial for maintaining microbial equilibrium. Culinary herbs and spices, known for their antioxidant and antimicrobial properties, may positively modulate the gut microbiota by inhibiting inflammation, stimulating beneficial bacteria, and exhibiting prebiotic effects. Methods: A comprehensive literature search was conducted using the PubMed database to identify relevant research articles on the impact of culinary spices on the gut microbiome. The search terms included "culinary spices," "gut microbiome," "gut modulation," "gut dysbiosis," "common spices," and "gut health." Thirty-five articles published between 2016 and 2024 were reviewed, with eight directly addressing the effects of culinary spices on gut microbiome modulation. Priority was given to peer-reviewed journals and reputable scientific sources. Results: Studies demonstrate that mixed spices and individual spices like ginger, turmeric, and chili can induce favorable changes in gut microflora, promoting beneficial probiotics and inhibiting pathogenic bacteria. Spices such as turmeric and curcumin exhibit potent anti-inflammatory properties, contributing to their protective effects against chronic diseases like colon cancer and metabolic disorders. Conclusions: Incorporating culinary spices into dietary interventions is important for overall well-being. Further research is needed to elucidate the mechanisms of action and optimize the therapeutic applications of spices in clinical settings.

Energy poverty in the Global South generally focuses on the lack of basic access to modern fuels and energy carriers. Impoverished people rely primarily on traditional biomass as a source of energy. This situation of basic resource deprivation, in which almost one third of the world's population lives, masks other relevant features of the problem. Current assessments of energy poverty in impoverished areas and the mitigation strategies being implemented derive from the development agenda and, with variations in detail and scope, highlight electricity connections and access to clean cooking fuels as guarantors of progress. However, a comprehensive understanding of energy poverty requires going beyond basic access, building on the interactions between the supply of energy sources and carriers, the provision of energy services and its impact on decent living conditions. Several studies in the last decade have attempted to construct an assessment framework centered on energy services to deal with these interactions. This work discusses the relevant dimensions in the framework (supply, services and impact in wellbeing), reviews the multidisciplinary work available in each aspect, presents a range of proposed taxonomies, and discusses the different issues. A detailed framework is proposed for the integrated assessment of the supply of energy carriers and energy equipment, the provision of relevant energy services, and the improvements obtained in living conditions.

Digital transformation is revolutionizing supply chain management, profoundly influencing marketing effectiveness across industries. This qualitative study explores the impact of digital transformation on supply chains and its implications for marketing strategies. Through in-depth interviews, case studies, and document analysis, the research elucidates how digital technologies such as IoT, blockchain, AI, and advanced analytics are reshaping supply chain dynamics. Key findings highlight enhanced supply chain visibility, improved decision-making capabilities, and strengthened collaboration among supply chain partners. These technological advancements empower organizations to optimize inventory management, anticipate market trends, and deliver personalized customer experiences, thereby enhancing operational efficiency and customer satisfaction. Furthermore, the study underscores the role of digital transformation in promoting sustainability and ethical practices within supply chains. Technologies like blockchain facilitate transparent and traceable supply chains, supporting initiatives for responsible sourcing and environmental stewardship. Despite the transformative benefits, the study identifies challenges including significant investment requirements, integration complexities, and data security concerns. Strategic approaches to technology adoption, robust cybersecurity measures, and employee training are essential for overcoming these challenges and maximizing the benefits of digital transformation. Overall, this study contributes to understanding how digital transformation drives innovation in supply chain management and enhances marketing effectiveness. By embracing digital technologies and aligning supply chain strategies with marketing objectives, organizations can navigate competitive landscapes, meet evolving consumer expectations, and achieve sustainable growth in the digital era.

Protein hydrolysates are plant biostimulants containing amino acids, oligopeptides, and peptides in their composition. When supplied to plants, protein hydrolisates (PH) have been identified to improve nitrogen metabolism, enhance the activity of antioxidant enzymes, boost plant defence response to stresses, and positively impact the quantity and quality of products. Soybean is a crucial global commodity, with nitrogen being the primary nutrient for crop development as it directly affects productivity. This study aimed to evaluate the effect of a HP-based biostimulant on the N metabolism in nodulated soybean plants and their productivity. A greenhouse experiment was conducted to test two modes of application of the 0.20% HP-based biostimulant. Soybean plants, growing in pots, were treated with HP 0.20% either via seed treatment or foliar application (at growth stages V3 and V5). Activities of enzymes and compounds related to N metabolism, gene expression, and productivity components were analysed. It was observed that the mode of application did not significantly influence the results. The application of HP increased the concentration of nitrate, amino acids, and ureides in soybean leaves. It also positively altered the expression of genes such as nitrate reductase, urease, and asparagine. Additionally, it enhanced productivity, resulting in plants with a greater number and weight of pods and grains. Therefore, it is possible to consider HP as a stimulator for increasing soybean productivity, even under non-stressing conditions.

Abiotic stress is a significant factor restricting the normal growth and development of plants. Under abiotic stress, plants maintain their life and continuous growth by reconfiguring transcriptional regulation and metabolic networks. In recent years, with the development of sequencing technology and mass spectrometry technology, transcriptomics and metabolomics have emerged as new disciplines following genomics and proteomics, which are conducive to identifying metabolites and regulatory genes in plants under abiotic stress. In this review, we mainly analyzes the technical characteristics, advantages, and disadvantages of transcriptomics and metabolomics, focusing on reviewing the research progress in the field of plant responses to abiotic stress (temperature stress, salt stress, water stress, and heavy metal stress) using transcriptomics and metabolomics both domestically and internationally in recent years. It also provides a prospective view on current issues, which helps accelerate the understanding of the mechanisms of plant responses to abiotic stress and provides new ideas for breeding stress-resistant varieties in the future.

This review investigates links between post-acute sequelae of SARS-CoV-2 infection (PASC), post-infection viral persistence, mitochondrial involvement and aberrant innate immune response and cellular metabolism during SARS-CoV-2 infection. Advancement of proteomic and metabolomic studies now allows deeper investigation of alterations to cellular metabolism, autophagic processes and mitochondrial dysfunction caused by SARS-CoV-2 infection while computational biology and machine learning have advanced methodologies of predicting virus-host gene and protein interactions. Particular focus is given to interaction between viral genes and proteins with mitochondrial function and that of the innate immune system. Finally, the authors hypothesise that viral persistence may be a function of mitochondrial involvement in sequestration of viral genetic material. While further work is necessary to understand the mechanisms definitively, a number of studies now point to resolution of questions regarding the pathogenesis of PASC.

Von Hippel-Lindau disease (VHL) is a multiple organ neoplastic syndrome with autosomal dominant transmission, complete penetrance, and variable expression caused by mutations in the VHL gene. Although VHL disease is hereditary in many cases, new mutations cause up to 20 % of the incidence. Mutations in VHL may cause the development of cysts and tumors in many organs, including brain and spinal cord hemangioblastoma, renal cell carcinoma (RCC), retinal heman-gioblastoma (RH), pheochromocytoma, epididymal and broad ligament cystadenomas, endo-lymphatic sac tumor, pancreatic neuroendocrine tumors, and renal and pancreatic cysts. VHL is a syndrome associated with functional inactivation of the Von Hippel–Lindau protein (pVHL). pVHL is a tumor suppressor mainly known for its role as a regulator of hypoxia-inducible factor (HIF) activity. The prevalence of VHL disease is between 1 in 39 000 and 1 in 91 000 individuals in different regional populations. Here, we review the etiology, epidemiology, pathophysiology, genetics, clinical manifestations, diagnosis, and current treatments, as well as the molecular aspects of this disease. We also discuss the animal models used to study this disease, VHL biomarkers, and current VHL clinical trials for VHL according to NIH.

This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learning, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services.

A three-dimensional vector field model is proposed, whose dimensions are the Hansen Solubility Parameters: dispersion parameter (δD), polarity parameter (δP), and hydrogen bonding parameter (δH). The vector space that defines the field has the peculiarity of having the dispersion vector with a magnitude of 2, as its base vector, while polarity and hydrogen bonding vectors have a magnitude of 1. A substance is characterised as a position vector, and the interaction between two substances is determined by calculating the vector difference of both, known as the interaction vector. Interaction among substances may involve solubility, swelling, cracking, surface tension, interface tension, and any physical phenomena where intermolecular energies of dispersion, polarity or hydrogen bonding come into play. This paper studies free surface energy (surface and interfacial tension). It has been found that free surface energy is directly proportional to the square of the magnitude of the interaction vector. The proportionality constant, τ, is expressed in length units, has a value of 0.025 nm, and does not depend on the chemical nature of the substance or state of matter (solid, liquid or gas). Constant value τ appears universal and aligns with the thickness of interfaces, thereby supporting Guggenheim's hypothesis. This hypothesis asserts that interfaces possess actual thickness and are not merely mathematical surfaces, as originally postulated by Gibbs. Moreover, it also has been found that interface thickness, τ, is approximately equal to half of the Bohr radius, a0, which is defined by universal constants. Because the solubility parameters of thousands of substances are known and can be easily determined from molecular structure, a good approximation of the surface and interfacial tension of any given substance can now be calculated. It has also been found that contact angles of sessile droplets in three-phased systems can be calculated from the interaction vectors of the implicated substances.

In this article, the sol-gel method was used as a synthesis method, which shows the physico –chemical nature of the synthesis of a new complex material ferrit Li0.5MnFe1.5O4. The synthesized structure and structure were determined by the method of X-ray phase analysis. According to the analysis indicators, it was found that our compound is single-phase, spinel-structured, and syngony - cubic type. The microstructure of the compound and the quantitative composition of the elements contained were analyzed under a scanning electron microscope (SEM). Through a scanning electron microscope, Microsystems were taken from different parts of l Li0.5MnFe1.5O4-type crystallite, the elemental composition of crystals was analyzed, and the general type of surface layer of complex ferrite was shown. As a result, the fact that the compound consists of a single phase, the clarity of its construction was determined by the topography and chemical composition of the compound. As a result, it was found that the newly synthesized complex ferrites correspond to the Formula Li0.5MnFe1.5O4. The particles of the formed compounds have a large size (between 50.0 μm, 20.0 μm and 10.0 μm). Electro physical measurements were carried out on the LCR - 800 unit at intervals of 293-483 K and at frequencies of 1.5 and 10 kHz. The increase in frequency to 10 kHz led to a decrease in the value ε in the range of the studied temperature (293-483 K).

Drowning is a leading cause of accidental death worldwide, with traditional drowning prevention strategies focused on buoyancy aids and protective equipment proving insufficient in mitigating the immediate risks of cold water shock and drowning. Swiftwater Breathing Apparatus (SWBA) has emerged as a potential game-changer, offering respiratory protection at the water's surface and a means of interrupting the drowning process. This report examines how SWBA can disrupt the drowning process, the benefits of using SWBA compared to existing protective equipment, the issue of water rescuer fatalities, and the potential implications for insurers and coronial inquests.

It is axiomatic to underscore that a well-managed education system is deemed globally as one of the crucial prerequisites for not only the growth and development of every society or geopolitical space but also one of the veritable hallmarks of a diversified and functional economy that will, in the long run, culminate in economic emancipation of the people. The education industry faces enormous challenges in several countries in Africa, and Nigeria is not an exception. Even though education remains the bedrock or superstructure on which other societal structures such as politics, culture, religion, economics, etc. are laid; it is quite disheartening that education in Nigeria is not effectively and efficiently managed for economic diversification and emancipation of the recipients of such education. Indeed, the industry faces innumerable challenges, especially in contemporary times than in the pre-colonial era when the education system was essentially functional. The hydra-headed nature of the challenges with Nigeria’s educational system has inevitably resulted in different dimensions of economic quagmires entangling the country and making most of its citizens find it extremely difficult to eke out and or survive. The level of dysfunctionalities in various sections of the education system has made it seemingly intractable to palliate the economic predicaments that have been confronting the country let alone emancipate it from the monoculture of its overdependence on oil. The author of this treatise is diagnostic in his approach to understanding the intricacies of the challenges that frontally battle the Nigerian education system and deprive it of being a veritable tool for a functional economy as well catalyst for the economic emancipation of its people. The paper, through the analyses of relevant literature, amplifies the conceptual and operational dimensions of the identified challenges, offers probable suggestions on the ways by which the challenges can be tackled and finally draws a conclusion.

Power quality (PQ) plays an important role in ensuring the smooth operation of power systems. PQ disturbances may cause significant consequences, impacting the reliability and efficiency of power networks. This paper presents a comprehensive review of PQ within smart grids and the integration of renewable energy sources. Methods of detecting, classifying, optimizing, and mitigating PQ events are reviewed with providing a brief theoretical explanation for each technique. Related work done in the past two decades, was conducted, presenting a clear view of the development of research in a chronological order serving as a guide for future investigations in this field of study.

This paper represents a groundbreaking paradigm shift in network optimization. Departing from traditional static methodologies, this innovative approach harnesses the power of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) to optimize cloud networks dynamically. By integrating advanced AI algorithms, this framework continuously adapts and evolves, ensuring optimal real-time performance. This dynamic optimization enhances efficiency and resilience, allowing cloud networks to adjust seamlessly to changing demands and conditions. Through the fusion of cutting-edge technology and adaptive intelligence, this approach heralds a new era in network optimization, empowering organizations to achieve unprecedent-ed levels of agility and scalability in their cloud infrastructures.

Somatosensory tactile experience is a key aspect of our interaction with the environment. It is involved in object manipulation, in the planning and control of actions and, in its affective components, in the relationships with other individuals. It is also a foundational component of body awareness. An intriguing aspect of sensory perception in general and tactile perception in particular is the way in which stimulation comes to consciousness. Indeed, although being aware of something seems a rather self-evident and monolithic aspect of our mental states, sensory awareness may be in fact modulated by many different processes that impact on the mere stimulation of the skin, including the way in which we perceive our bodies as belonging to us. In this review, we first took into consideration the pathological conditions of absence of phenomenal experience of touch, in the presence of implicit processing, as initial models for understanding the neural bases of conscious tactile experience. Subsequently, we discussed cases of tactile illusions both in normal subjects and in brain-damaged patients which help to understand which high order processes impact tactile awareness. Finally, we discussed the observations reported in the review in light of some influential models of touch and body representation.

The aim of this article is to present the course of the COVID-19 pandemic in Poland in 2020-2022, the formation of ad hoc health and anti-crisis policies, an assessment of crisis management and the perceived impact of the pandemic and anti-crisis measures.The paper draws on literature, official reports on the pandemic, data from Eurostat and other information from state institutions and public organisations.Particular attention was paid to the analysis and evaluation of the objectives, instruments and effects of the policies pursued by the state in a coordinated manner, i.e. the anti-pandemic policy and the anti-crisis policy. The anti-pandemic policy in Poland has failed to protect the country from significant personal losses and negative impacts on the health of the population and demographic processes.The anti-crisis policy of the state carried out with the mobilisation of businesses and disciplined society, in the form of anti-crisis shields supplemented and adjusted on an ongoing basis, allowed Poland to avoid a deep crisis and economic recession. Attention was drawn to the need to coordinate the management of health and economic crises nationally and internationally, and to the need to have a medium-term anti-crisis state strategy and a system for the early detection of crisis symptoms.

Emerging molecular and computational techniques may allow construction and computational treatment of dynamic digital twins on the scale of populations to individuals. This approach may pave the way for predictive, preventive, and personalized treatment. Because digital twins are independent of geographical location, this may also contribute to global equitable health.

Our study focused on investigating the impact of Instagram use on body satisfaction and self-esteem among participants aged 20 to 40 years (N = 95) through a cross-sectional survey design in which the Rosenberg Self-Esteem Scale, the Body Shape Questionnaire (BSQ), and ad hoc designed questions were administered. The results highlight the influence of Instagram on body dissatisfaction (p = .005) but not on self-esteem (p = .211). Gender was not found to be an influential factor (p = .173). However, it was found that greater body satisfaction correlates with higher self-esteem and vice versa (p < .001). Finally, we concluded that the number of hours users in the sample spend on Instagram is related to greater body dissatisfaction but not to lower self-esteem.

The global energy crisis is a significant issue, particularly in developing countries like Ethiopia, where traditional fuels such as wood and charcoal are predominantly used to meet energy demands in urban and rural areas. Despite Ethiopia's substantial charcoal production, the use of inefficient charcoal cookstoves exacerbates fuel consumption, forest degradation, and environmental damage. To address this, various improved charcoal cookstoves (ICS) have been introduced in urban and semi-urban communities in Wereta Town, located in the Amhara Regional State of Ethiopia. This study aims to evaluate household perceptions and identify barriers to the adoption of ICS in the Wereta district. The assessment involved a survey of 308 households using a structured cross-sectional method and analyzed five types of improved cookstoves: clay stoves, insulated clay cylinder stoves, non-insulated rocket stoves, insulated rocket stoves, and non-insulated cylindrical stoves. The study employed various socio-economic variables and utilized both cross-tabulation frequency factors and linear regression statistical analysis through SPSS 20 to identify factors influencing the adoption of ICS. The linear regression results indicated that marital status, income level, family size, and age significantly impact ICS adoption (p-value < 0.05), while the place of cooking, educational level, household decision-making, and the role of societal stakeholders showed no significant impact. This suggests that ICS adoption is primarily influenced by household income levels and family size.

Arbuscular mycorrhizal fungi (AMF) are soil microorganisms living in symbiosis with most terrestrial plants. They are known to improve plant tolerance to numerous abiotic and biotic stresses through the systemic induction of resistance mechanisms. With the aim of developing a more sustainable agriculture, reducing the use of chemical inputs is becoming of a major concern. After providing an overview on the AMF history, phylogeny, development cycle and the symbiosis benefits, the current review aims at exploring the potential of AMF as biostimulants and/or biocontrol agents. Nowadays, AMF inoculums are already increasingly used as biostimulants, improving mineral nutrient plant acquisition. However, their role as promising tool into the biocontrol market, as alternative to chemical phytosanitary products, is underexplored and discussed. Thus, in the current review, we will address the mechanisms of mycorrhized plant resistance to biotic stresses induced by AMF, and highlight the various factors in favor of inoculum application, but also the challenges that remain to be overcame.

Previous research demonstrates that the interruption of immersive experiences may lead to a bias in the results of questionnaires. Thus, the traditional way of presenting questionnaires, paper-based or web-based, may not be compatible with evaluating VR experiences. Recent research has shown the positive impact of embedding questionnaires contextually into the virtual environment. However, a comprehensive overview of the available VR questionnaire solutions is currently missing. Furthermore, no clear taxonomy exists for these different solutions in the literature. To address this, we present a literature review of VR questionnaire user interfaces (UI) following PRISMA guidelines. Our search returned 1.109 initial results, which were screened for eligibility, resulting in a corpus of 25 papers. This paper contributes to HCI and games research with a literature review of embedded questionnaires in VR, discussing the advantages and disadvantages and introducing a taxonomy of in-VR questionnaire UIs.

In this study, data analysis methods from ecology were applied to litter surveys to evaluate patterns among urban litter items found in two types of streets in England (High Streets and Central Business Districts). Results indicate that sites characterised as a High Street (predominantly leisure activities such as shopping and dining) contained lower densities, less variety, yet featured items with higher potential for environmental contamination than sites categorised as Central Business District (identified by high numbers of professional workers and transport links). Although litter were significantly different between sites, the litter community structure was not. Our results suggest that litter typologies and associated activities can lead to specific knowledge of key influential items in a site and inform future evidence based and sustainable mitigation systems.

We propose a novel framework for understanding quantum phenomena through the lens of a modified electromagnetic spacetime governed by Einstein field equations. By assuming the existence of an electromagnetic spacetime that adheres to these modified geodesic and Einstein field equations, provided the field equations satisfy the Coulomb force law in weak-field approximations, we explore the derivation of Maxwell’s equations and highlight the necessity of incorporating spin to validate these equations. We derive the wave equation for the normalized transverse metric and establish its equivalence with the de Broglie wave proposition, suggesting a relationship between this metric and the particle's wavefunction. This approach offers a classical explanation for several quantum phenomena, including the double-slit experiment, entanglement, tunneling, non-locality, and wavefunction collapse. Additionally, it posits a method to visualize atomic orbital structures in line with quantum mechanical predictions. These findings suggest that the foundational principles of quantum mechanics can be interpreted as linearized approximations of general relativity applied to electromagnetic spacetime. While this theory does not yet extend to the correlations between general relativity, quantum electrodynamics (QED), and quantum chromodynamics (QCD), it provides a valuable visualization tool for physicists and advances our understanding of the unresolved challenges linking quantum mechanics with general relativity.

Depression is the most common chronic mental illness and is characterized by low mood, insomnia, and affective disorders. However, the pathological mechanisms of depression remain unclear. Numerous studies have suggested that the ghrelin/GHSR system may be involved in the pathophysiologic process of depression. Ghrelin plays a dual role in the experimental animals, increasing depressed behavior and decreasing anxiety. By combining several neuropeptides and traditional neurotransmitter systems to construct neural networks, it modifies signals connected to depression. The present review emphasizes the role of ghrelin in neuritogenesis, astrocyte protection, inflammatory factor production, and endocrine disruption in depression. Furthermore, ghrelin/GHSR can activate multiple signaling pathways to produce antidepressant effects, including cAMP/CREB/BDNF, PI3K/Akt, Jak2/STAT3, and p38-MAPK. Given the antidepressant effects of ghrelin/GHSR1a system, it is expected to become a potential therapeutic target for the treatment of depression.

Researchers are increasingly concerned about microplastics (MPs) since they have proven to be detrimental to the environment and humans. They not only contribute to the accumulation of plastics in the environment, but they can also help spread micropollutants through absorption. MPs have entered the environment through a variety of channels, including direct discharge through human activities, the textile industry, and wastewater treatment systems. Wastewater treatment plants (WWTPs) are seen as the final line of defense, forming a barrier between MPs and the environment. The most common MPs found in wastewater are polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), and polystyrene. The purpose of this study is to investigate and measure the efficacy of conventional and advanced technologies in removing microplastic. The fate of MPs in WWTPs and their impact on various wastewater treatment processes will be thoroughly examined. Both conventional and advanced methods of treatment will be discussed. Conventional methods include coagulation/flocculation, sedimentation, and filtration. Advanced treatment methods that will be discussed include membrane bioreactors, rapid sand filtration, electrocoagulation, and photocatalytic degradation. Despite the high removal of MPs, WWTPs remain an important entry point into aquatic and terrestrial systems due to the large volumes involved and the quantity of sludge reused through land application. Also, the complex interactions between MPs and other environmental pollutants may broaden MPs' effects on wastewater treatment processes, which have yet to be fully researched. Furthermore, potential issues with MP removal from treatment procedures and microplastic remediation techniques in drinking water was discussed.

Plants are subjected to abiotic stresses throughout their developmental period. Abiotic stresses include drought, salt, heat, cold, heavy metals, nutritional element and oxidative stresses. Improving plant response to various environmental stresses is critical for plant survival and perpetuation. The WRKY transcription factors have special structure (WRKY structural domains), which enable WRKY transcription factors to have different transcriptional regulatory functions. The WRKY transcription factors can not only regulate abiotic stresses response and plant growth and development by regulating phytohormone signalling pathways, but also promote or suppress the expression of downstream genes by binding to the W-box [TGACCA/TGACCT] in the promoters of their target genes. In addition, WRKY transcription factors not only interact with other families of transcription factors to regulate plant defence responses in abiotic stresses, but also self-regulate by recognizing and binding to W-boxes in their own target genes to regulate their defence responses to abiotic stresses. However, in recent years, research reviews on the regulatory roles of WRKY transcription factors in higher plants are scarce and shallow. In this review, we focus on the structure and classification of WRKY transcription factors, as well as the identification of their downstream target genes and molecular mechanisms involved in the response to abiotic stresses, which can improve the tolerance ability of plants in abiotic stress and we also look forward to their future research directions, with a view to providing theoretical support for the genetic improvement of crop abiotic stresses tolerance.

This study explores the intricate dynamics of customer-centric supply chains and the pivotal role of marketing innovations in enhancing efficiency and customer satisfaction. Employing a qualitative research methodology, semi-structured interviews were conducted with key stakeholders in supply chain management across various industries, providing rich insights into the practices and perceptions related to customer-centric strategies. The findings highlight the centrality of understanding and anticipating customer needs, which necessitates a cultural shift within organizations to prioritize customer satisfaction at all levels. Digital marketing tools, data analytics, e-commerce platforms, and social media emerged as critical enablers of these customer-centric supply chains, facilitating real-time insights, direct customer engagement, and personalized experiences. The integration of these technologies has led to more accurate demand forecasting, optimized inventory levels, and enhanced agility and responsiveness in operations. Furthermore, the study underscores the importance of collaboration and partnership within the supply chain network, emphasizing joint initiatives and strong relationships with stakeholders as key drivers of improved resource utilization and service levels. Sustainability, entrepreneurship, and emotional intelligence also play significant roles in shaping effective customer-centric supply chains. Sustainable practices meet growing consumer demands for environmental and social responsibility, while an entrepreneurial mindset fosters continuous innovation and competitiveness. Emotional intelligence among leaders and employees enhances trust, conflict resolution, and collaboration, contributing to a positive organizational culture and superior customer satisfaction. Overall, this study provides comprehensive insights and actionable recommendations for businesses seeking to navigate the complexities of customer-centric supply chain management and leverage marketing innovations to achieve sustainable competitive advantage.

Laser Powder Bed Fusion (LPBF), as a transformative technology in the field of additive manufacturing, offers unprecedented design flexibility and material efficiency. This review paper delves into the latest advancements in LPBF technology and the various alloys used in the process, including titanium alloys, aluminum alloys, nickel-based superalloys, steel alloys, copper alloys, and high-entropy alloys. For each alloy category, we discuss their specific applications, performance characteristics, and challenges faced in LPBF usage. The paper discusses important technological breakthroughs, including innovations in laser systems, scanning strategies, and in-situ monitoring technologies, which have enhanced the precision and reliability of LPBF.

This qualitative research explores the dynamics of collaborative marketing and supply chain strategies for enhanced product lifecycle management. Through in-depth interviews with industry stakeholders, key themes, challenges, and implications of collaboration emerge. Trust, communication, technology, strategic alignment, and sustainability are identified as critical factors driving successful collaboration. The study highlights the transformative potential of collaboration in driving innovation, efficiency, and customer satisfaction. Despite challenges such as aligning objectives and ensuring data security, collaborative strategies offer significant opportunities for value creation and differentiation. The findings underscore the importance of building trust, leveraging technology, and integrating sustainability considerations in collaborative efforts. Strategic alignment and cultural fit among partners are essential for overcoming differences and achieving common goals. The implications of the research extend to practitioners seeking to enhance collaboration in their organizations and researchers exploring collaborative practices in different industries and contexts. Future research could further explore the impact of collaboration on stakeholder perspectives and outcomes. By addressing challenges and leveraging opportunities, organizations can maximize the benefits of collaborative marketing and supply chain strategies to drive sustainable growth and competitive advantage in today's dynamic business landscape.

We use the Maximum Overlap Discrete Wavelet Transform (MODWT) to analyse the interconnectedness between the stock markets of continental Europe and the BRICS countries. More specifically, we examine the presence of financial contagion in the stock markets of Brazil, Russia, India, China, and South Africa subsequent to the EuroZone Sovereign Debt Crisis (EZDC). We find evidence of co-movement and volatility spillover at short time scales between the Eurozone equity market and the equity markets in South Africa and Russia, with the Eurozone stock market leading the BRICS markets. The presence of co-movement in short time scales is indicative of the presence of financial contagion. No financial contagion is observed in the Brazilian, Chinese, and Indian stock markets during the European sovereign debt crisis. The absence of financial contagion observed in these three BRICS countries during the European sovereign debt crisis suggests that policymakers in these countries should prioritise addressing idiosyncratic shock channels.

This work focuses on identifying the most effective machine learning techniques and supervised learning models to precisely estimate power output from Photovoltaic (PV) plants. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest, Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Linear Regressor (LR), Gradient Boosting, and k-Nearest Neighbors (KNN). The methodology applied starts with meticulous data preprocessing steps aimed at ensuring dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold, is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2). Among the array of models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048% and an R2 score of 0.968. Furthermore, the best-performing model is integrated into a MATLAB application for real-time predictions, thereby enhancing usability and accessibility for a wide range of applications in renewable energy.

The normative and safeguarding role of ethics and law in the development of biomedical science and technology, as well as their importance in promoting the progress of the industry, analyzed the key role of ethics and law in the field of biomedical science and technology, by restricting scientific research behavior, protecting the rights and safety of subjects, providing a solid guarantee for scientific research activities, and promoting the healthy development of biomedical science and technology. At the same time, the positive role of ethics and law in promoting international cooperation and exchange of biomedical science and technology is also discussed, which will inject strong impetus into the global development of biomedical science and technology. In addition, through case analysis, the paper demonstrates the key role of ethical review in the field of biomedical science and technology, as well as the importance of law in maintaining market order, protecting subjects' rights and interests, and fairness and justice. The successful practice of ethics and law in the development of biomedical science and technology is emphasized, which proves that following ethical principles and legal framework is the key factor to promote the progress of biomedical science and technology. This paper explores the important role of ethics and law in the development of biomedical science and technology, and provides valuable reference and guidance for practitioners in the industry. By following ethical principles and legal norms, we will jointly promote the prosperity and development of biomedical science and technology, and contribute to the common progress of human health.

Monoclonal antibodies (mAbs) have become an increasingly important therapeutic modality in recent years, with applications in oncology, hematology, and immunology. This review discusses the production of therapeutic mAbs, including the various types of mAbs (murine, chimeric, humanized, and fully human), their key applications, and the techniques used for their production, including hybridoma technology, single B cell antibody technology, and phage display technology. The review then proposes advanced techniques to overcome them. These include microfluidic sorting, alternative expression systems (including plants, bacteria, and yeast, and cell-free production systems), and the development of complex antibody formats like bi-specific monoclonal antibodies, antibody fragments, antibody-drug conjugates, and nanobody development. These advanced techniques promise to enhance the efficiency, cost-effectiveness, and scalability of mAb production, paving the way for more effective therapeutic applications. This review highlights the theoretical significance of integrating innovative technologies to overcome existing challenges in mAb production, ultimately advancing the field of precision medicine.

Aquaculture has rapidly emerged as one of the fastest growing industries, expanding both on global and on national fronts. With the ever-increasing demand for high biological value protein, the aquaculture industry has established itself as one of the most efficient forms of animal production, proving to be a vital component of global food production, by supplying nearly 50% of the planet’s seafood. As in classic animal production, the prevention of diseases constitutes an enduring challenge associated with severe economic and environmental repercussions. Nevertheless, remarkable strides in the development of aquaculture vaccines have been recently witnessed, offering sustainable solutions to persistent health-related issues challenging resilient aquaculture production. These advancements are characterized by breakthroughs in increased species-specific precision, improved vaccine delivery systems, and innovations in vaccine development, following the recent advent of nanotechnology, biotechnology, and artificial intelligence in the -omics era. The objective of this paper was to assess recent developments and milestones revolving around aquaculture vaccinology and provide an updated overview of strengths, weaknesses, opportunities, and threats of the sector, by incorporating and comparatively discussing various diffuse advances that span across a wide range of topics, including emerging vaccine technologies, innovative delivery methods, insights on novel adjuvants, and parasite vaccine development for the aquaculture sector.

The development of devices for In-Situ Resource Utilization (ISRU) of the lunar surface powder (regolith) by means of microwaves needs regolith simulants with electromagnetic properties similar to the lunar regolith. This document deals with the measurement of complex permittivity and dielectric loss tangent of the aforementioned simulants at ambient temperature from 400 MHz to 20 GHz. In addition, further studies are carried out taking into account humidity of the samples and their densities. The obtained results will be applicable for comparing the measured values of EAC-1A and JSC-2A among them and with other previously measured simulants and real samples. The measurements are carried out applying two different nonresonant techniques: Open- Ended Coaxial Probe (OECP) and Transmission Line. For this purpose, the DAK and EpsiMu commercial kits were respectively used.

Air pollution knows no boundaries, which means for a city or a region to attain clean air standards, must not only look at the emission sources within its own administrative boundary, but also at sources in the immediate vicinity and those originating from long-range transport. And there is a limit to how much area can be explored to evaluate, govern, and manage as designated airsheds for cities and larger regions. This paper discusses the need from an official airshed framework for India’s air quality management, urban airsheds designated for India’s 131 non-attainment cities under the national clean air programme, and proposes climatically and geographically appropriate regional airsheds to support long-term planning. Between 28 states, eight union territories, 36 meteorological sub-regional divisions, and six regional meteorological departments, establishing the proposed 15 regional airsheds for integrated and collaborative air quality management across India is a unique opportunity.

Intermittent fasting (IF) has been indicated as a valuable alternative to the classical caloric restriction dietary regimen for lowering body weight and preventing obesity-related complications such as metabolic syndrome and type II diabetes. However, is it effective? In this review article, we analyzed over 50 clinical studies in which IF, conducted by alternate day fasting (ADF) or time-restricted feeding (TRF), was compared with the caloric restriction approach. We evaluated the different roles of IF in treating and preventing human disorders such as metabolic syndrome, type II diabetes, and some types of cancer, as well as the usefulness of IF in reducing body weight and cardiovascular risk factors such as hypertension. Furthermore, we explored the cellular pathway targeted by IF to exert their beneficial effects by activating effector proteins that modulate cell functions and resistance to oxidative stress. In contrast, we investigated the concerns for human health related to the adoption of IF dietary regimen, highlighting the profound debate on weight loss regimens. We have examined and compared several clinical trials to formulate an updated conception regarding IF and its therapeutic potential.

This review deals with the functionalization of double bonds carried out in the presence of a chiral catalyst exploiting the intramolecular attack to haliranium ions by nucleophilic nitrogen of amides or carbamates prepared from achiral aminoalkenes, and the C-N bonds formation leads to highly enantioenriched non-aromatic heterocycles. A range of protocols are reported, emphasizing the synthesis of many natural and biologically active products of pharmacological interest prepared according to this methodology.

The application of adaptive antennas in wireless communication systems is examined in this review of the literature, with particular attention to how well they can transmit and receive signals by dynamically adjusting radiation patterns. The review covers a range of artificial intelligence (AI) approaches that have been used to enhance adaptive antenna performance in beamforming, channel estimation, interference mitigation, and spectrum utilization. Although adaptive antennas have the potential to improve wireless communication systems, the paper also discusses their limitations, including computational complexity, interference sensitivity, and the significance of precise channel state information (CSI) estimates.

In this paper, we introduce a new causal formula to distinguish Gliosarcoma (GSM) from Glioblastoma (GBM). Our formula combines Probabilistic Easy Variational Causal Effect (PEACE) with XGBoost, or eXtreme Gradient Boosting algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. It offers a spectrum of both positive and negative causal effects of the events causal effect values based on the degree ?, reflecting the rarity and frequency of the events. By using PEACE with XGBoost, we achieve a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM.

Colpodella species are free-living opportunistic pathogens that cause human and animal infections and use ticks and flies as vectors. Trophozoite and cyst stages of Colpodella species can be distinguished from stages of the prey Parabodo caudatus using Sam-Yellowe’s trichrome staining. Colpodella species obtain nutrients by attaching to their prey, aspirating the prey’s cytoplasmic contents into a posterior food vacuole and encysting. It is unclear whether both trophozoite and cyst stages are present during infection. Molecular techniques identified Colpodella species in all reported infections. However, no morphological information was reported to aid life cycle stage identification in hosts. This review discusses the increased incidence of Colpodella infection in animals and in vectors and the need to identify stages used for transmission and pathogenesis. The potential for zoonotic infections through tick and fly bites is a public health concern. We previously used Sam-Yellowe’s trichrome staining to identify life cycle stages of Colpodella sp. In order to gain a better understanding of transmission and pathogenesis, identifying Colpodella life cycle stages in infected tissue and in vectors will provide important insights regarding nutrient uptake in hosts by determining whether attachment of trophozoites to host cells occurs, and identifying trophozoites and cysts in infected hosts.

High-density polyethylene (HDPE) waste poses a significant environmental challenge due to its non-biodegradable nature and the vast quantities generated annually. However, conventional recycling methods are energy-intensive and often yield low-quality products. Herein, HDPE waste is upcycled into anti-aging superhydrophobic thin films suitable for outdoor applications. A two-layer spin-casting method combined with heating-induced crosslinking is utilized to produce an exceptionally rough superhydrophobic surface, featuring a root mean square (RMS) roughness of 50 nm, an average crest height of 222 nm, an average trough depth of –264 nm, and a contact angle (CA) of 148°. To assess durability, weathering tests were conducted, revealing the films' susceptibility to degradation under harsh conditions. The films' resistance to environmental factors is improved by incorporating a UV absorber, maintaining their hydrophobic properties and mechanical strength. Our research demonstrates a sustainable method for upcycling waste into high-performance, weather-resistant superhydrophobic films.

While there are software tools available for assisting to conduct the Life Cycle Assessment (LCA), such as OpenLCA, these tools lack integration with process design, simulation and optimization software. As LCA has critical role in sustainable product design, it is presented in this work a platform called EMSO_OLCA, which integrates the LCA provided by OpenLCA into the Environment for Modeling, Simulation, and Optimization (EMSO). EMSO_OLCA incorporates a database of environmental impact assessment methodologies from OpenLCA and aligns to the principles of LCA outlined in ISO 14040 and ISO 14044. Validation tests were conducted to compare the results obtained by the LCA of sugarcane ethanol using OpenLCA and EMSO_OLCA, revealing a high level of agreement. The average relative error was 0.045%, indicating a negligible discrepancy between the tools. Besides, it took only 0.3 seconds for calculation, which is desirable for using with process systems engineering tools. A second case study was applied to a steam and bioelectricity production from the combustion of sugarcane bagasse and straw in a combined heat and power system. The results show the integration of LCA with simulation and sensitivity analysis tools, thus contributing for supporting sustainable decision-making processes. EMSO_OLCA bridges the gap between LCA and process engineering, enabling a holistic approach to sustainability, design and implementation of environmentally friendly solutions.

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.

The Southern European countries of Spain and Portugal both show a high degree of export spe-cialization in agri-food products compared to the EU-27 as a whole. This sector plays a funda-mental role in their economies, determining the management of natural resources, with the food and beverage industry being the most important manufacturing activity in both countries. Drawing on evidence obtained from Eurostat's database on foreign trade, this study analyzes whether the geographical concentration of agri-food exports from the Iberian Peninsula coun-tries to the EU-27 increases between 2008 and 2022, or, conversely, if there is a process of spatial diversification in favor of non-EU markets. Expansion towards new trading partners, as indicat-ed by estimated concentration indices, is significant in Spain but does not occur in Portugal. However, in both economies, the regional market with European partners, characterized by strong competition and low development expectations, remains decisive in the evolution of their exports in the short and medium term. Consequently, institutions and companies in this sector must adopt a more dynamic approach and implement measures that allow for increased sales orientation towards higher growth markets, thus enhancing the global competitiveness of the national agri-food sector.

Several series of new polymers were synthesized: binary copolyesters of vanillic (VA) and hydroxybiphenylcarboxylic (HBCA) acids, as well as ternary copolyesters additionally containing hydroxybenzoic acid (HBA) and obtained via three different ways (in solution, in melt and in solid state). The high values of logarithmic intrinsic viscosities and insolubility of several samples proved its high molecular weights. It has been shown that the use of vanillic acid leads to the production of copolyesters with a relatively high glass transition temperature (about 130°C). Thermogravimetric analysis revealed onset of weight loss temperatures of ternary copolyesters occurred at 330-350 °C, the temperature of 5% mass loss is in the range of 390-410 °C. Two-stage thermal destruction is observed for all aromatic copolyester of vanillic acid: decomposition begins with VA units at 420-480 °C, and then decomposition of more heat-resistant units demonstrated above 520 °C. The copolyesters are thermotropic and exhibit a typical nematic type of liquid crystalline order. Mechanical characteristics of copolyesters are close to those for semi-aromatic copolyesters, but are much lower than typical values for fully aromatic thermotropic polymers. Thus, vanillic acid is a mesogenic monomer suitable for the synthesis of thermotropic fully aromatic and semi-aromatic copolyester, but resulting processing temperature must not exceed 280°C.

To analyze the combined response of ocular cyclotorsion and head tilt during body tilt and to interpret the physiologic role of those movements, each of the three subjects completed six different experiments of body tilt not fixating their head movements using a flight simulator, which consisted of combinations of three tilt angles (30, 45, and 60) and two visual conditions. Head tilt and ocular cyclotorsion of the right eye before, during, and after the tilt maneuver were recorded and analyzed by time sequence. In the active tilting phase, the right eye showed a stepladder pattern of multiple repeats of fast and slow cyclotorsion along with head tilt to obtain maximum visual information. During the stationary tilting phase, there occurred a small amount (mean, 2.3) of ocular cyclotorsion which was reciprocal to that of head tilt. The visual cue of body tilt through the central visual field induced a larger amount of head tilt but affected little ocular cyclotorsion. The maximum degree of ocular cyclotorsion during each experiment was 10.2 on average. In conclusion, the combined response of ocular cyclotorsion and head tilt develops during body tilt and it compromises the conflicting needs between binocular vision and gravitational orientation.

In order to understand the defense response of Sanchezia nobilis to biotic and abiotic stresses, this study investigated the phenomenon of S. nobilis under biotic of feeding by Achatina fulica, and measured the temporal changes of malondialdehyde content, catalase and peroxidase activities under biotic of feeding by Achatina fulica and abiotic stress of garden pruning conditions on S. nobilis. The results showed that: 1) S. nobilis was seriously damaged by A. fulica and the average hourly food intake of A. fulica is 0.22 ± 0.01 g. 2) Leaf cutting stimulation and snail feeding stress significantly affected the content of malondialdehyde, the activities of catalase and peroxidase in the leaves of S. nobilis. 3) Compared with the control group, the snail feeding and leaf cutting stimulated stress could induce the leaf of S. nobilis to synthesize more malondialdehyde under varying durations, and the activities of catalase and peroxidase were significantly enhanced in a short time, and the induction response of snail feeding biological stress was faster than that of leaf cutting stimulated abiotic stress. 4) After the defense enzyme was strengthened, the activities of catalase and peroxidase in the feeding stress group of snails showed a gradual downward trend, while the activities of catalase and peroxidase in the leaf cutting stimulation stress group showed a first upward and then downward trend, indicating that abiotic stress had more lasting effects on plants than biological stress. It can be seen that biotic stress and abiotic stress significantly affect the defense response of plants, and the defense-related substances malondialdehyde, catalase and peroxidase play an important role in plants' response to biotic and abiotic stress. This study has important production guidance significance for carrying out stress resistance breeding or cultivation management of garden plants.

Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study two separate sets of strawberry images, along with their associated diseases, were collected and subjected to, resizing, and augmentation. Subsequently, a combined dataset consisting of 9 classes was utilized to fine-tune three distinct pre-trained models: Vision Transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first 5 and last 5 layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the Vision Transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.

Background: Nowadays vital signs in clinics are electronically monitored excepted for urine output which still requires, manual recording, which is subject to human errors. We, in-vitro, assessed in this study the ability a novel electronic device to measure by a combination of two different methods, the real time flow-output, like urine output. Methods: Flow or urine output was measured by a combination technique of gravimetry using a dynamometer and capacitive sensing to improve precision. Data were obtained in 3 different situations, low, medium, and high flow. Results: Three different situations were tested: full or empty dead space and Pasteur’s chamber. The differences between the injected and measured volume varies from 0 to 5 ml representing a maximal error of 3 % regardless of the volume injected and the flow in ml/hour. The capacitive sensors behave as expected with a parallel curve of the expected and measured flow. Conclusion: We have shown in this in-vitro study that the combination of gravimetry and capacitive sensors analysis allow accurate measurement of volume and flow-output. Clinical trials should be conducted to confirm its usefulness in urine output measurement in the real life.

Millions of children in developing countries face preventable deaths due to inadequate vaccination and malnutrition, in part due to insufficient monitoring and the absence of official identification. A reliable fingerprint recognition system can be a practical solution to address this issue. However, the scarcity of longitudinal fingerprint datasets for young children leads to unresolved questions regarding the earliest age for fingerprint biometric use, the frequency of enrollment required for reliable recognition, and the methods to accommodate agerelated changes. A few recent studies introduced high-resolution fingerprint scanners and showed promising recognition performance for young children. However, these studies were conducted on a small dataset over a shorter period with limited diversity; further evaluation of their finding is essential. This study assessed the effectiveness of a high-resolution contactless scanner in a controlled, diverse longitudinal dataset of children (0-15 years). Our results indicate that infants can be enrolled at five days old and reliably recognized after two months with a TAR= 100% @ FAR = 0.1%, and children aged 4-15 years can be recognized after one year with a TAR= 98.72% @ FAR = 0.1%.