Background: Several trace minerals have been shown to be associated with thyroid cancer. However, there is a paucity of data on the characteristics of thyroid nodules in different mineral rich regions of the Democratic Republic of Congo (DRC). The objective of this study is to establish the spectrum of thyroid nodules in different regions of the most mineral wealthy country in the world. Method: We conducted a cross-sectional study, performing descriptive statistics and logistic regression with a p value < 0.05 deemed of statistical significance. We enrolled 529 patients diagnosed with thyroid nodules between 2005 and 2019 in two of the richest provinces in minerals (Katanga and South Kivu) and the capital city, Kinshasa. Results: The mean age was 44.2±14.6 years with a female predominance, with a female to male ratio of 5.4. 66.5% of patients had a family history of thyroid disease. 74 patients had simple nodules, whereas 455 had multiple nodules. 87.7% of patients with nodules were euthyroid. The nodules mostly presented a solid structure (72.2%), hypoechogenicity (84.5%), a macronodule (59.8%), calcification (14.4%) and lymphadenopathy (15.5%). 22.3% of these nodules had a malignant character. The independent factors associated with this malignancy were age ≥60 years (aOR=2.81), Katanga as province of origin (aOR=8.19), solid echostructure (aOR=7.69), hypoechogenicity (aOR=14.19), macronodule (aOR=9.13), calcification (aOR=2.6) and the presence of adenopathy (aOR=6.94). Conclusion: By the way of this first of its kind study, we demonstrated that thyroid nodules were more likely to be malignant if found in patients originating from mineral rich region of Katanga. This high frequency of malignancy in mineral rich regions requires measures aimed at early detection in this population, lower threshold for suspicion of malignancy and adequate training of doctors involved in the management of these patients. This study paves the way for future, more extensive and well-funded studies to better understand the relationship of heavy metals and thyroid cancer incidence in the richest country in heavy metals in the world.

Malignant lymphomas, encompassing Hodgkin lymphomas (HL) and non-Hodgkin lymphomas (NHL), represent a significant category of hematological malignancies with diverse presentations and prognoses. This review synthesizes current understanding of malignant lymphomas, focusing on their classification, incidence, epidemiology, staging, histopathology, and evolving treatment strategies. Malignant lymphomas are broadly classified into HL and NHL, with NHL further subdivided into B-cell, T-cell, and NK-cell neoplasms. The emergence of grey zone lymphomas, which exhibit overlapping features and heightened aggressiveness, underscores the complexity of diagnosis and treatment. Epidemiologically, HL predominantly affects younger individuals, while NHL incidence increases with age, reflecting a global rise in both incidence and mortality rates. Advances in diagnostic techniques and treatments have led to improved outcomes, yet challenges persist, particularly in distinguishing between overlapping lymphoma types and managing increasing incidence rates. The management of HL and NHL involves a variety of treatment options tailored to the disease stage and individual patient characteristics. After diagnosis and staging, treatment regimens may include radiotherapy, chemotherapy (either alone or in combination), and immunotherapy. Monitoring patient response is crucial, with adjustments made in cases of therapeutic failure. Achieving remission—either partial or complete—depends on the normalization of biomarkers and imaging results, reduction of lymphoid organ size, and resolution of symptoms. Despite therapeutic advances, treatment complications, including short-term effects like myelosuppression and long-term issues such as cardiomyopathy and secondary cancers, remain significant. The future of HL and NHL treatment lies in refining these approaches, minimizing adverse effects, and enhancing efficacy through innovative therapies and personalized medicine strategies.

Based on our interest in properties of human movement, we investigated Japanese GPS data, and arrived at the following three observations: 1) there is a strong correlation between the area of polygons created by human movement trajectories and their perimeters; 2) short-distance movement trajectories less than 5 km tend to enclose a large area like a circle; 3) long-distance movement trajectories over 5 km tend to be straight. We also clarified the following four observations on individual attributes and external factors related to long-distance movements: 1) women tend to travel more linearly than men; 2) linearity is stronger on weekends and national holidays in areas with a large theme park; 3) linearity is weaker on weekends and holidays in areas with many historical tourist attractions; 4) these variations are created by people visiting such areas. These properties should be incorporated when modeling the movement trajectories of humans.

The innate and adaptive immune systems collaborate to detect SARS-CoV-2 infection, minimize the viral spread, and kill infected cells, ultimately leading to the resolution of the infection. The adaptive immune system develops a memory of previous encounters with the virus, providing enhanced responses when rechallenged by the same pathogen. Such immunological memory is the basis of vaccine function. Here, we review the current knowledge on the immune response to SARS-CoV-2 infection and vaccination, focusing on the pivotal role of T cells in establishing protective immunity against the virus. After providing an overview of the immune response to SARS-CoV-2 infection, we describe the main features of SARS-CoV-2-specific CD4+ and CD8+ T cells, including cross-reactive T cells, generated in patients with different degrees of COVID-19 severity, and of Spike-specific CD4+ and CD8+ T cells induced by vaccines. Finally, we discuss T cell responses to SARS-CoV-2 variants and hybrid immunity and conclude by highlighting possible strategies to improve the efficacy of COVID-19 vaccination.

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2022, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. Utilizing stepwise regression, robust least squares, Fully Modified Least Squares (FMOLS), Canonical Cointegrating Regression (CCR), and various cointegration tests including the Johansen cointegration test, Engle-Granger cointegration test, and Vector Error Correction (VEC) model, the study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle-Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman's efforts towards sustainable development and decarbonization.

This review explores the critical difficulties confronting global agriculture in the face of changing climate patterns and limited resources, highlighting the importance of sustainable practices. With agriculture as the largest consumer of water worldwide, sustainable techniques are critical for long-term survival and ecological harmony. Even with advancements, rapid population increase and changing environment conditions demand immediate investment in cutting-edge technologies. Optimizing agricultural operations, increasing output, and reducing resource waste can be achieved through smart farming, which utilizes machine learning and Internet of Things tech-nologies. Particular difficulties like decreasing cultivator proportions and rising costs are empha-sized, with a focus on developing nations where agriculture plays a crucial role in the economy. Additionally, the anticipated rise in food demand highlights how urgent it is to find sustainable alternatives. But problems like inappropriate land and agricultural land fragmentation still exist, necessitating creative solutions. The paper explores how agriculture could be revolutionized by machine learning and IoT-based hydroponics, providing insights into how to improve resilience, productivity, and sustainability in the face of changing environmental and socioeconomic dy-namics. Through a thorough investigation, this paper advances the discussion on sustainable ag-riculture by demonstrating the important role that technology-driven strategies play in addressing concerns about environmental sustainability and global food security.

Peptide-based inhibitors represent a promising approach for the treatment of HIV-1, offering a range of potential advantages including specificity, low toxicity, and the ability to target various stages of the viral lifecycle. This review outlines the current state of research on peptide-based anti-HIV therapies, highlighting key advancements and identifying future research directions. Over the past few years, there has been significant progress in developing synthetic peptide-based drugs that target various stages of the viral life cycle, including entry and replication. These approaches aim to create effective anti-HIV therapies. Additionally, peptides have proven valuable in the development of anti-HIV vaccines. In the quest for effective HIV vaccines, discovering potent antigens and designing suitable vaccine strategies are crucial for overcoming challenges such as low immunogenicity, safety concerns, and increased viral load. Innovative strategies for vaccine development through peptide research are, therefore, a key focus area for achieving effective HIV prevention. This review aims to explore the strategies for designing peptides with anti-HIV activity and to highlight their role in advancing both therapeutic and preventive measures against HIV.

The electronics industry is a significant contributor to environmental challenges, generating approximately 50 million tons of electronic waste annually and high levels of carbon dioxide emissions throughout the production and disposal of devices. This paper investigates the interplay between reliability and sustainability by analyzing how degraded components affect the environmental footprint of electronic products. Using a board with 215 components as a baseline, we calculate the mean time between failures (MTBF) and compare it to scenarios involving degraded components. Our findings demonstrate that degraded components can reduce MTBF by up to 50%, resulting in higher failure rates, increased electronic waste, and up to 30% more CO2 emissions due to shortened product lifespans. This study underscores the crucial importance of component quality control in the production process to improve both reliability and sustainability. Additionally, the broader economic and environmental impacts of using unchecked components are highlighted, showing their contribution to elevated failure rates, shortened product lifespans, and an estimated 20% increase in e-waste. By adopting a 100% inspection protocol, leveraging advanced technologies, and enforcing stringent material quality controls, manufacturers can significantly enhance the reliability of electronic products while reducing their environmental impact, thus fostering a more sustainable electronics industry.

Bacterial infection poses a serious threat to human life due to its rapidly growing resistance to antibacterial drugs, which is a significant public health issue. This study was focused on the design and synthesis of a new series of 25 analogues bearing 5-cyano-6-oxo-4-substituted phenyl-1,6-dihydropyrimidine scaffold hybridized with different substituted benzene-sulfonamides through thioacetamide linker M1-25 via the treatment of the key intermediates 2-mercapto-6-oxo-4-phenyl-1,6-dihydropyrimidine-5-carbonitriles 1a-c with different 2-chloro-N-substituted benzene-sulfonamide derivatives 4a-e overnight at room temperature. The spectroscopic data of all the new compounds (IR, 1H-NMR, 13C-NMR, and Mass) was studied in detail to confirm their molecular structures. The antimicrobial activity of the new molecules was studied against various Gram-positive, Gram-negative, and fungal strains. All the tested compounds showed promising broad spectrum antimicrobial efficacy, especially against K. pneumoniae and P. aeruginosa, with ZOI values ranging from 15 to 30 mm. Furthermore, the most promising compounds M6, M19,M 20, and M25 were subjected to minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays. In addition, the anti-virulence activity of the compounds was also examined using multiple biofilm assays. The new compounds revealed promising suppressing activity of microbial biofilm formation in the examined K. pneumoniae and P. aeruginosa microbial isolates. Additional in silico ADMET studies were conducted to represent their oral bioavailability, drug-likeness characteristics, and human toxicity risks. The obtained data suggest the newly prepared pyrimidine-benzene-sulfonamide derivatives may serve as model compounds amenable for further optimization and development of new antimicrobial and antisepsis candidates.

Quantum gravity is crucial for unifying general relativity and quantum mechanics, which are two of the fundamental pillars of physics. Despite the significant advances in theoretical models of quantum gravity, experimental validation of these theories remains sparse. This review paper aims to address this gap by examining the experimental proposal by Bose et al., that aims to show the non classicality of gravity, alongside a discussion of semiclassical and quantum models and their shortcomings. We explore the Double Stern-Gerlach experiment to observe gravitational entanglement, detailing the procedure and the quantum states involved. The minimization of Casimir-Polder forces, which could interfere with the experiment, is highlighted as essential for optimizing conditions for maximal entanglement, calculated using von Neumann entropy, and measured using an Entanglement Witness. We discuss the inferences drawn from this experiment and introduce semiclassical gravitational models, outlining the assumptions made by Bose et al. Furthermore, we delve into perturbative Quantum Gravity (pQG) using the path integral approach, explaining its non renormalizability and the extent of its applicability. The conclusions indicate that observing entanglement would demonstrate the non-classicality of gravity. While semiclassical models are experimentally inaccurate, they serve as useful calculational tools for black hole physics. Although pQG has proved to be non-renormalizable so far, it functions as an effective field theory (EFT) at energies below 10¹⁹ GeV.

In this study, Al-Al4C3 compounds were manufactured by mechanical milling followed by heat treatment. To analyze the microstructural evolution, the composites were sintered at 550°C during different sintering times of 2, 4 and 6 hours. The mechanical results suggest that dislocation density and crystallite size primary contribute to hardening before the sintering process, with a minimal contribution from particle dispersion in this condition. The compound exhibited a significant 75% increase in hardness after 2 hours of sintering, primarily attributed to the nucleation and growth of Al4C3 nanorods. The HRTEM analysis, combined with ge-ometric phase analysis (GPA) at and near the Al-Al4C3 interface of the nanorods, revealed strain field distributions primarily associated with partial screw dislocations and the presence of closely spaced dislocation dipoles. These findings are consistent with the microstructural parameters determined from X-ray diffraction pattern analysis using the convolutional multi-ple whole profile (CMWP) method. This analysis showed that the predominant dislocation character is primarily of the screw type, with the dislocation dipoles being closely correlated. Based on these results, it is suggested that samples with a lower weight percentage of rein-forcement and longer sintering times may experience reduced brittleness in Al/Al4C3 compo-sites. Strengthening contributions were calculated using the Langford–Cohen and Taylor equations.

This article considers the status of Jungian archetypes as potentially divergent bodies. It possesses the initiative to consider the prerogative that they are situated on, and to develop that into discussing the conditions for a differing subjective reality that is derived from Jungian pretenses. This article operates within the boundaries of the conjecture, it serves simply to cover the potentiality for developments outside of the simple basis of the archetype rather than to provide a metaphysical or ontological contribution to psychology. Unlike Jung’s study, which serves a multitude of ideas and draws upon evidential factors from - however generally expressed - ancient civilisations, this article instead seeks to apply that logic for the purpose of explicating the meaning and potential extensions of the “archetypal dogma”.

Background:In view of fact, the drawback of the armed conflict between Sudanese army and rapid support force was shocking, mass migration to neighboring countries, and many civilian were died, behind the war there were another factors for death, among these factors is brain tumors. The objectives of this study: To build up information and correlation between demographic data, brain tumors, surgery type and limitation of surgical resources, and to identify neurosurgical practice during armed conflict and how surgical resources can be utilized. Methods:This was a case report study, done at Almubarik hospital from April 2024 to July 2024.Ethical approval was taken from the National Center for Neurological Sciences.The operating room at Almubarik hospital includes X-ray viewing box, operating table, which is design for obstetrical and gynecology operation. very limited instruments for completing the surgery (forceps, straight dissector and scissors).Moreover, regarding anesthesia in this new situation there is no invasive BP, CVP, core temperature or even capnograph, all this made difficulties in monitoring the patient. In this conflict period, due to the lake of monitoring of the drugs and inappropriate storage, we used to give IV propofol , midasolam , morphine , lidocaine and nondepolrising muscle relaxant. ETT size 7 for all adult patients as it is the only big size available. Halothane MAC was used as volatile agent with propofol. pracetamol , magnisum sulphate ,are given as analgesia.Nevertheless,, for positing we manipulate our operating table by adding special head rest which was manually made from iron par and semi firm bellows are constructed on it tightly, so prone position was made. Conclusion: Despite of the infrastructural limitations, brain tumor surgery has special nature in the context of this conflict. Brain tumor surgery is challenges, and need not only skillful neurosurgeon but advance technology and well trained staff.

Significant global challenges, particularly in the areas of economics, society, and the environment, are posed by urbanization. In this researcher-led study, we look at how Jordan is trying to bring the Sustainable Development Goals (SDGs) to life in its capital, Amman, by bringing urban laws in line with these global goals. The paper finds legislative flaws that prevent SDG incorporation through a thorough analysis of the Amman Building and Regulation Act of 2019. Some of the main points covered are public participation, the use of renewable energy sources, sustainable construction practices, resilience in urban areas, and the management of natural resources. Although a lot of work has gone into it, the analysis shows that if Jordan wants to accomplish the 2030 Agenda and have sustainable urban growth in the future, it must overcome these institutional and legal barriers. As a model for other cities facing a similar challenge, Amman is being asked to lead the way in SDG localization through more public involvement, new laws, and innovative resource management.

In 2021, Japan's medical expenses will exceed 45 trillion yen, and the shortage of doctors, especially in remote and mountainous areas, is becoming serious, making it difficult to maintain the medical system. We have conducted a study of 800 health consultation text data. We developed an on-premise health counseling LLM system by constructing a dialogue flow based on 800 health counseling text data. We conducted a Turing test of this system using 200 test data and verified its effectiveness with three medical professionals. The Turing test was a comparison experiment between this system and a conventional LLM system. The health counseling LLM infrastructure focuses on exercise guidance and analyzes gender, height, weight, body fat percentage, and muscle mass. While the accuracy of the conventional LLM system was 87.5%, this system showed a higher accuracy of 93.1%. Although telemedicine has been slow to spread in Japan, the introduction of a health consultation system using Personal Health Record and Large Language Models has the potential to reduce the burden on physicians. In the future, we aim to improve the accuracy of the system by using Japanese language and medical-specific evaluation scales.

As computer networks develop in size and complexity, the need for equipment that can characterize, inspect, monitor, and summarize the features of a transmission in real time grows. Many transmission and packet inspection theories are required to examine network traffic. Unfortunately, loss the track or traceability of one traffic scenarios against others over real-time network. Deep packet inspection (DPI) approaches can be used to alleviate the problem but still face flexibility constraints in the sense that it will be difficult to keep track of prior occurrences. There have been significant theoretical improvements in packet inspection, but the problem of event tracking flexibility remains unsolved. A novel approach for “integrating blockchain with DPI” in summarizing computer network traffic flow events has emerged as a result of this research: This technique captured traffic flows in the network, DPI report, and the entire transmission session header and load them into a smart-contract based- Blockchain. Two experimental scenarios are used to test the validity of the system. The first scenarios involve analysis of DPI transmission without integrating blockchain, follow by the captured of transmission session involving DPI with blockchain. The finding indicates that DPI transmission with blockchain is more secured against "Tampering Incidents", "Unauthorized Access Attempts", “Auditability” and many other security concern. Whereas, the performance overhead introduced by blockchain-based DPI indicate an increase in "DPI Processing Time from 68ms to 150ms, a "CPU Usage increment by 20%, Network Latency increment of 15ms from 20ms to 35ms, and almost all other computational resources increase. Therefore, the finding can be summarized that the performance overhead introduced by integrating blockchain with DPI increases, but it provides a secure DPI reports for decision making.

Cotton seeds as the main by-product of cotton are not only an important raw material for edible oil and feed but also a source of biofuel. The quality of cotton seeds directly affects cotton planting and is closely related to the yield and fiber quality. However, the molecular mechanism governing cotton seed size remains largely unexplored. This study investigates the regulatory mechanisms of cotton seed size by focusing on two cotton genotypes, N10 and N12, which exhibit notable phenotypic variations across multiple environments. Developing seeds were sampled at various stages (5, 20, 30, and 35 DAP) and subjected to RNA-seq. The temporal pattern clustering and WGCNA on differentially expressed genes identified 413 candidate genes, including these related to sugar metabolism that were significantly enriched in transcriptional regulation. A genetic transformation experiment indicated that the overexpression of the GhUXS5 gene encoding UDP-glucuronate decarboxylase 5 significantly increased seed size, suggesting an important role of GhUXS5 in regulating cotton seed size. This discovery provides crucial insights into the molecular mechanisms controlling cotton seed size, helping to unravel the complex regulatory network and offering new strategies and targets for cotton breeding to enhance the economic value of cotton seeds and overall cotton yield.

It is not plausible to predict disasters; however, still the least that may be done is to get ready for that. In disaster circumstances, nowadays, the existing internet of things (IoT) technology is rather developed and capable of being highly beneficial. For many countries, the early warning system for natural catastrophes is a crucial challenge, especially for high seismic hazard countries. Primary function of an early warning system is to assist individuals and authorities to save lives and reduce secondary damage once a disaster occurs. Even though such systems have been defined for the earthquake on the basis of IoT, the least attention has been focused on the post decisions for substructure monitoring systems which can control the gas and electric lifelines given the critical time. Combination of Fuzzy system with IoT can improve the decision making process after the disasters occur. This program minimizes costs and destruction in cities. Tehran is a high seismic hazard area which contains an expanded network of the gas transmission system. Hence, a major seismic event may cause serious loss directly and indirectly owing to the damage to the gas and electric transmission network. The proposed early warning system contains a grid of strong ground motion stations. Each station contains five sensors to collect accurate data and send the data to the cloud server, then the server executes relevant scenarios.

Recent trends concerning hydrogen as a fuel of the future and problems related to its production, storage and utilization increase the demand for efficient technologies related to these problems. A large part of such technologies is related to application of advanced materials based on various complex oxides which are used as efficient catalysts for fuel transformation into hydrogen and syngas, materials for membranes with selective permeability for hydrogen and syngas production, components of solid oxide fuel cells/electrolyzers as well as many other applications in energy, electrochemistry and catalysis. Such materials are typically based on oxides and solid solutions with perovskite and fluorite structures or their derivatives. Attention is also paid to the materials based on spinel, apatite, mayenite, sheelite, fergusonite structures. The above-mentioned materials are continuously studied in order to modify and optimize their functional properties. On the other hand, other classes of materials (weberites, monazites, etc.) are recently discovered and considered as candidate materials for the aforementioned applications due to their specific functional properties or a great potential of their improvement. In this review, some of these materials are considered.

Human Pose Estimation (HPE) is a critical technology in computer vision with diverse applications ranging from healthcare to sports analysis. This project presents a method for detecting the 2D stance of multiple persons in an image using a nonparametric representation known as Part Affinity Fields (PAFs). By leveraging the first 10 layers of the VGG-19 convolutional neural network and training on the COCO dataset, our model effectively identifies and associates key points of the human body.The architecture employs a two-branch system that jointly learns part locations and their associations through sequential prediction. This enables the model to maintain real-time performance while achieving high accuracy, regardless of the number of persons in the image. To enhance accessibility, we developed a mobile application using Flutter and TensorFlow Lite, allowing real-time pose estimation via a mobile device’s front camera. The app provides immediate feedback on physical exercises and yoga poses, making it an invaluable tool for fitness enthusiasts and healthcare professionals. Visual outputs such as heatmaps and PAFs confirm the model’s capability to accurately localize and connect key points. Despite potential challenges such as data quality and hyperparameter tuning, the results indicate that our approach is both reliable and practical for real-world deployment. This project not only advances the state-of-the-art in HPE but also opens up possibilities for future enhancements, including integrating 3D pose estimation and applying the technology in augmented and virtual reality applications.

Background: Parity and vaginal delivery may contribute to female urinary incontinence prev-alence. The aim of this study was to evaluate the impact of parity and type of child delivery on pelvic floor muscle strength (PFMS). Material and Methods: 140 healthy women were prospectively evaluated and distributed into 4 groups: G1 (n=34) 30-40 years old; G2 (n=38) 41-50; G3 (n=35) 51-60 and G4 (n=33) over 60. De-mographic data, and body mass index (BMI) were obtained using a clinical questionnaire. Sub-jective PFMS assessments were performed using transvaginal digital palpation (TDP), and ob-jective evaluation was obtained using a portable perineometer. Results: BMI was significantly higher in G4 compared to G1. There was a positive linear rela-tionship between age and BMI. The number of pregnancies was higher in G4 compared to G2 and vaginal delivery was more frequent in G4 compared to G1 and G3. There was no statistical correlation among different groups in PFMS assessed either by TDP and perineometer. Like-wise, we did not observe any negative linear relationship between PFMS and parity or number of vaginal deliveries. Conclusion: Our data demonstrates that both the number of pregnancies and of vaginal deliv-eries had no impact on PFMS in continent women.

IntroductionGlycogen synthase kinase 3 (GSK-3) has been associated with a lot of diseases, such as Alzheimer’s Disease and diabetes. Its role in depression is connected to stress and neuroinflammation. Lithium is an essential inhibitor of GSK-3. Some studies have suggested that the use of this psychiatric medication may lower suicide rates. Here, we outline the current understanding and discoveries regarding the link between GSK-3 and suicide. MethodWe used the PRISMA statement and defined the PECOS strategy, such as Population (P) = individuals; exposure (E) = GSK-3 alteration function; comparison (C) = without GSK-3 alteration function; outcome (O) = suicide; and study design(S) = all kinds of studies with an association between GSK-3 and suicide.ResultsTwelve studies were included in this review. GSK-3 appears to be related to suicidal behavior by activating inflammatory pathways, activating genes, altering neurotransmission, and increasing impulsivity. Clinical studies do not present robust results that allow defining an epidemiological profile of greater risk for suicidal behavior in association with changes in the functioning of GSK-3.ConclusionThe interference of GSK-3 functioning in suicidal behavior seems inevitable, with solid evidence from laboratory animal studies. However, there is a need for further clinical studies on this crucial topic to answer better some questions about the association between GSK-3 and suicide.

Clockwork fermions represent a novel theoretical construct in high-energy physics, addressing such fundamental issues as mass hierarchies and dark matter. This review provides an examination of the theoretical underpinnings of clockwork fermions, their implications for particle physics, and the current state of experimental validations. Clockwork fermions represent a paradigm in particle physics, elegantly addressing the hierarchy problem and offering insights into the generation of small neutrino masses. We provide an analysis of the clockwork mechanism, emphasizing its theoretical innovation and the unique mass hierarchies it produces. We examine recent experimental efforts, including collider searches and direct detection experiments, that place constraints on clockwork models, while also exploring their potential cosmological implications.

This article examines the biological characteristics and potential medicinal uses of the endemic plant species Scorzonera uzbekistanica, found only in Uzbekistan. The plant contains significant amounts of bio active compounds such as inulin, flavonoid, and oxyacetylene in its roots. These compounds are known for their antioxidant, anti-inflammatory, and prediction properties. Traditionally used to alleviate digestive issues, this plant's potential in modern medicine should be further supported by scientific research. The study calls upon the scientific community to explore the potential applications of this endemic plant.

Invasive Candida infections represent a significant cause of morbidity and mortality in the neonatal intensive care unit (NICU), particularly among preterm and low birth weight neonates. The nonspecific clinical presentation of invasive candidiasis, resembling that of bacterial sepsis with multiorgan involvement, makes the diagnosis challenging. Given the atypical clinical presentation and the potential detrimental effects of delayed treatment, empirical treatment is often initiated in cases with high clinical suspicion. This underscores the need to develop alternative laboratory methods other than cultures, which are known to have low sensitivity and a prolonged detection time, to optimize therapeutic strategies. Serum biomarkers, including mannan antigen/anti-mannan antibody and 1,3-β-D-glucan (BDG), both components of the yeast cell wall, a nano-diagnostic method utilizing T2 magnetic resonance and Candida DNA detection by PCR-based techniques have been investigated as adjuncts to body fluid cultures and have shown promising results in improving diagnostic efficacy and shortening detection time in neonatal populations. This review aims to provide an overview of the diagnostic tools and the current management strategies for invasive candidiasis in neonates. Timely and accurate diagnosis followed by targeted antifungal treatment can significantly improve the survival and outcome of neonates affected by Candida species.

Xyleborus beetles, a diverse group of ambrosia beetles, present significant challenges to forestry and agriculture due to their damaging burrowing behavior and symbiotic relationships with fungi. This review synthesizes current knowledge on the biology, ecology, and management of Xyleborus. We explore the beetles' life cycle, reproductive strategies, habitat preferences, and feeding habits, emphasizing their ecological and economic impacts. Control and management strategies, including preventive measures, chemical and biological control, and integrated pest management (IPM), are critically evaluated. Recent advances in molecular genetics and behavioral studies offer insights into genetic diversity, population structure, and host selection mechanisms. Despite progress, managing Xyleborus effectively remains challenging. This review identifies future research needs and highlights innovative control methods, such as biopesticides and pheromone-based trapping systems.

Gaussian mixture models (GMMs) are powerful tools specifically suited for problems where data distributions are multi-modal. Inspired by Fourier expansion, we propose a concept called GMM expansion to approximate arbitrary densities. A simple learning method is introduced under this framework. Theoretical and numerical analyses are provided to show that densities under GMM expansion are able to approximate arbitrary densities with certain accuracy. The proposed learning algorithm demonstrates better accuracy and time efficiency compared to classic density approximation methods such as the Expectation Maximization (EM) algorithm and Bayesian variational inference (BVI) for Gaussian mixture models. The proposed framework also shows better accommodation of neural networks. Three neural network applications are built to demonstrate its usability. The accuracy of density estimation in neural networks is reported to be significantly improved in one of the applications. Another application shows that latent variables can be easily turned into random variables. This user-friendly method opens up more possibilities in terms of latent manipulation, embedding techniques, and many other potential uses.

Comparative analysis of mortality rates between public and private healthcare systems remains a critical yet complex area of study. This article synthesizes findings from extensive academic literature and empirical studies to examine the relationship between healthcare system type and patient outcomes across various countries. By focusing on mortality rates as a key indicator, we elucidate the nuanced impacts of public versus private healthcare delivery. Our analysis reveals the multifaceted challenges inherent in both systems and their implications for patient survival. We conclude by identifying crucial gaps in current research and proposing policy recommendations to optimize healthcare outcomes in diverse global contexts.This revised abstract:Starts with a strong opening statement that immediately captures the topic's importance. Clarifies the article's methodology (synthesizing existing literature and studies).Specifies the focus on mortality rates as the primary outcome measure.Hints at the complexity of the findings, avoiding oversimplification.Ends with a clear statement about the article's contributions to both research and policy.

Recurrent Neural Networks (RNNs) have significantly advanced the field of machine learning by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures such as Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Bidirectional LSTM (BiLSTM), and stacked LSTM. The study examines the application of RNNs in different domains, including natural language processing (NLP), speech recognition, financial time series forecasting, bioinformatics, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide machine learning researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.

The α-Klotho protein (hereafter Klotho) is an obligate coreceptor for fibroblast growth factor 23 (FGF23). It is produced in the kidneys, brain and other sites. Klotho insufficiency causes hyperphosphatemia and other anomalies. Importantly, it is associated with chronic pathologies (often age-related) that have an inflammatory component. This includes atherosclerosis, diabetes and Alzheimer’s disease. Its mode of action in these diseases is not well understood, but it inhibits or regulates multiple major pathways. Klotho has a membrane form, and a soluble form (s-Klotho). Cytosolic Klotho is postulated but not well characterized. s-Klotho has endocrine properties that are incompletely elucidated. It binds to the FGF receptor 1c (FGFR1c) that is widely expressed (including endothelial cells). It also attaches to soluble FGF23, and FGF23/Klotho binds to FGFRs. Thus, s-Klotho might be a roaming FGF23 coreceptor, but it has other functions. Notably, Klotho (cell-bound or soluble) counteracts inflammation, and appears to mitigate related aging (inflammaging). It inhibits NF-κB and the NLRP3 inflammasome. This inflammasome requires priming by NF-κB, and produces active IL-1β, membrane pores and cell death (pyroptosis). In accord, Klotho countered inflammation and cell injury induced by toxins, damage-associated molecular patterns (DAMPs), cytokines, and reactive oxygen species (ROS). s-Klotho also blocks the TGF-β receptor and Wnt ligands, which lessens fibrotic disease. Low Klotho is associated with loss of muscle mass (sarcopenia), as occurs in aging and chronic diseases. s-Klotho counters the inhibitory effects of myostatin and TGF-β on muscle, reduces inflammation, and improves muscle repair following injury. Inhibition of TGF-β and other factors may also be protective in diabetic retinopathy and age-related macular degeneration (AMD). This review examines Klotho functions especially as related to inflammation, and potential applications.

Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of MS, particularly when coupled with liquid chromatography (LC-MS), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized medicine advancement. MS-based technologies have transformed precision medicine, enabling comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing detection of low-abundance molecules and elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress towards precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies.

The fundamental goal of modern medicine is to increase human life expectancy. Currently, most of the patients in hospitals with different clinical profiles are elderly and senile subjects, with a high incidence of comorbidities, as well as concomitant age-related changes in peripheral tissues. One such change is sarcopenia, a progressive loss of muscle mass, strength, and muscle function that is associated with a high risk of adverse outcomes, leading to an increased risk of disability and death among older adults. The mechanisms of development of sarcopenia are currently being actively studied. This narrative review presents the issues of epidemiology, pathophysiology, and diagnostic criteria for sarcopenia. The relationships between sarcopenia and metabolic dysfunction-associated steatotic liver disease (MASLD) with possible promising directions for the treatment of this comorbid pathology are considered.

The article presents the issues related to the operation and modelling of the ship main propulsion steam boiler. Such objects are used widely in seagoing ships especially the very large crude oil carriers as a main propulsion turbine feed. The advantages of such along with simple construction thus great reliability make oil feed boilers an excellent source of power for chosen type of the large ships. It must be pointed out that a lot of research work was done in the previous years regarding automatic control of boiler automation but since then this technology was not in the focus of the researchers. In the future the fossil fuels will become less important than they are now, but the control techniques of boilers would eventually change due to different fuel parameters therefore, development of such is necessary. The article presents the results of research collected in real ship boilers during their operation and then, based on the results, a digital model of such an object was created. This model was implemented into Hardware-In-Loop which is the initial step for preparing the full model (including sensors etc.) of a steam heated boiler. Theoretical background along with chosen simulation and experimental results of control implemented into HIL were presented to validate assumptions...

The implementation of morphing wing mechanisms holds promise in enhancing aircraft perfor-mance, as indicated in current literature. The Clean Sky 2 AirGreen 2 European project is currently conducting ground and wind tunnel tests to validate innovative studies on morphing wing structures. The project aims to showcase the effectiveness of these morphing architectures on a potentially flying demonstrator at true scale. This article describes design methodology and struc-tural testing of a scaled morphing flap structure capable of 3 different morphing modes required for low-speed (take-off/landing) and high-speed (cruise) conditions. A significant scale factor of 1:3 was selected to enhance the importance and relevance of the wind tunnel test campaign. Geometrical scaling to the real flap prototype was deemed impractical due to limitations associated with scaling the embedded mechanisms and actuators responsible for shape changing. Static analyses were performed using the finite element method to address the most critical loads determined through three-dimensional CFD analysis. The Finite Element (FE) analysis results were briefly outlined, and comparisons were drawn between the obtained results and the empirical data collected during the structural test. Good correlations were observed between the results and numerical predictions, encompassing static deflections under applied loads and elastic deformations. Consequently, the modeling approaches utilized during the design and testing phases were considered highly suc-cessful. Therefore, based on the results extrapolated from simulations under ultimate load condi-tions relevant to the wind tunnel test campaign, the scaled flap prototype was deemed suitable for testing.

Sudden sensorineural hearing loss (SSNHL) is a rapidly developing acquired idiopathic disorder. Differential expression of microRNAs (miRNAs) have been identified in the acute serum of SSNHL patients. miRNAs are transmitted in both serum and plasma but it is unknown which better reflects changes associated with inner ear disease. Therefore, we sought to compare the serum and plasma miRNA expression levels in adult SSNHL patients. We extracted and reverse transcribed total RNA from serum and plasma, and analyzed the product with quantitative re-al-time PCR. hsa-miR-191-5p was used for normalization, and miRNA expression levels were calculated using the delta Ct method. Paired t-tests comparing serum and plasma samples from 17 SSNHL patients (mean age 51.9 years, Std. deviation 13.9 years) showed no significant differences in miR-128-3p, miR-132-3p, miR-375-3p, miR-590-5p, miR-30a-3p, miR-140-3p, miR-186-5p, and miR-195-5p expression levels. We conclude that, plasma and serum are equally suitable for investigating potential miRNA SSNHL disease markers.

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.

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.

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.