Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

Although information theory resolves inconsistencies (known under the form of famous enigmas) of the traditional approach of thermostatistics, its place in the corresponding literature is not what it deserves. This is interpreted as being mainly due to epistemological rather than scientific reasons: the subjectivity introduced into physics is perceived as a problem. This paper attempts to expose and clarify where exactly this subjectivity lies: in the representation of the reality and in probabilistic inference. Two aspects which have been integrated into the practice of science for a long time and which should no longer frighten anyone, but which become explicit with information theory.

A view of life, based on sympoiesis, metabolism, and the second law of thermodynamics is proposed. Sympoiesis is the notion that organisms develop as teams, and that symbionts play roles in forming their partners. Metabolism is seen to occur not only within these developmental symbionts, but between them, as well. This occurs through thermodynamic principles of finding the lowest free energy states. Once one accepts the thermodynamics of metabolism and that holobionts emerge through sympoiesis, Gaia becomes readily understood. New ethical principles flow from these considerations.

Landauer’s principle states that the logical irreversibility of an operation, such as erasing one bit, whatever its physical implementation, necessarily implies its thermodynamical irreversibility. In this paper, a very simple counterexample of physical implementation (that uses a two-to-one relation between logic and thermodynamic states) is given that allows to erase one bit in a thermodynamical quasistatic manner (i.e. that may tend to be reversible if slowed down enough).

Classical physics meets the obstacle of describing a point particle because from everyday experience we know that objects occupy a certain volume. We overcome this obstacle by using the equivalence principle for electric energy and describe a curved spacetime of fluid nature. From thereon by using the notion of a point volume the rest of the thermodynamic quantities appear.

We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ~350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments.

In this work, we report the magnetocaloric effect (MCE) in two systems of non-interactive particles, the first corresponds to the Landau problem case and the second, the case of an electron in a quantum dot subjected to a parabolic confinement potential. In the first scenario, we realize that the effect is totally different from what happens when the degeneration of a single electron confined in a magnetic field is not taken into account. In particular, when the degeneracy of the system is negligible, the magnetocaloric effect cools the system, while in the other case, when the degeneracy is strong, the system heats up. For the second case, we study the competition between the characteristic frequency of the potential trap and the cyclotron frequency to find the optimal region that maximizes the ΔT of the magnetocaloric effect, and due to the strong degeneration of this problem, the results are in coherence with those obtained for the Landau problem. Finally, we consider the case of a transition from a normal MCE to an inverse one and back to normal as a function of temperature. This is due to the competition between the diamagnetic and paramagnetic response when the electron spin in the formulation is included.

In this note we prove that the Riemann hypothesis is false. The proof is by contradiction based on a criterion of Hu.

This study aims to investigate the microphysical properties of Arctic, tropospheric aerosol in the transition from the Arctic Haze in spring towards the summer season in 2021. A special focus lies on the hygroscopicity of the aerosol. Therefore, a one-parameter growth curve model is applied to lidar data from the Koldewey Aerosol Raman Lidar (AWIPEV in Ny-Ålesund, Svalbard) and simultaneous radiosonde measurements. Hygroscopic growth depends on different factors like aerosol diameter and chemical composition. To detangle this dependency, three trends in hygroscopicity are additionally investigated by classifying the aerosol first by its color ratio, and then by its season and altitude. Furthermore, two special days are discussed using Mie-theory. They show on the one side the complexity of analyzing hygroscopic growth by means of lidar data, but on the other side demonstrate that it is in fact measurable with this approach. For these two case studies we calculated, that the aerosol effective radius increased from 0.16 μm (dry) to 0.18 μm (wet), and from 0.28 μm to 0.32 μm for the second case. Generally, we found two different modes of stronger or weaker hygroscopic particles and a complex altitude dependence with the least hygroscopic particles in the middle free troposphere.

Conventional tutoring approaches are confronted with limitations such as restricted availability, inconsistent pedagogical quality, and scalability con-straints. Furthermore, the exclusive use of Large Language Models (LLMs) like ChatGPT in education has its shortcomings, including the potential for incorrect responses and the lack of customized guidance aligned with specific course con-tent. This research proposes an innovative intelligent chatbot tutoring system, in-tegrating the Retrieval Augmented Generation (RAG) approach with a custom LLM. The developed system aims to overcome the limitations of traditional tu-toring and general-purpose LLMs by providing accurate, contextually relevant, and personalized assistance, thereby enhancing student understanding and en-gagement. The system, powered by an intelligent agent, retrieves relevant information from curated academic sources, incorporates interactive features for user feedback, and utilizes machine learning algorithms for ongoing performance enhancement, ensuring a robust and effective tutoring experience. The anticipated outcome is an enriched educational experience for university students, advance-ment in personalized learning, and improved student engagement, retention, and academic performance. Through continuous research and refinement, it is expected that the intelligent chatbot tutor will assume an important role in enhancing and supporting the educational journey of students.

Orificed Hollow Cathodes are electric devices necessary for the functioning of common plasma thrusters for space applications. From their reliability mainly depends the success of a spacecraft’s mission equipped by electric propulsion. The development of plasma models is crucial in the evaluation of plasma properties within the cathodes that are difficult to measure due to the small dimensions. Many models, based on non-linear system of plasma equations, have been proposed in the open literature. These are solved commonly by means of iterative procedures. This paper investigates the possibility to solve them by means of Particle Swarm Optimization method. The results of the validation tests confirm the expected trends for all the unknowns; the confidence bound of the discharge current as function of mass flow rate is very narrow (2÷5V), moreover the results match very well the experimental data except at the lowest mass flow rate (0.08mg/s) and discharge current (1A), where the computations underpredict the discharge current to the utmost by 40%; the highest data dispersion regards the plasma density in the emitter region (± 20 % of the average value) and the wall temperatures (± 50K respect to the average values) of orifice and insert; those of the others variables are very tiny.

The construction sector is a paradigm shift into the sustainable era to solve environmental issues, combat global warming. And the reflection of this is on Green Buildings where energy savings, cost savings, occupant comfort takes priority. The idea of green building efficiency merges sophisticated technologies, creative architectural design templates, and reusable materials into the whole life cycle of a building process in order to render minimum environmental impact. The pilot project of The National Institute of Geophysics and Volcanology (INGV) - Irpinia headquarters, illustrates in this context, innovative technologies of energy transition with a photovoltaic parking with geothermal piles. The project is consistent with the concepts of Nearly Zero Energy Buildings (NZEBs), directed to a high energy performance and very low environmental impact. Incorporating geothermal piles is a major area of advancement in sustainable building ideas that use the Earth's natural heat to create an energy efficient solution in heating and cooling. Project Goals – The project aims to install emission free renewable cleaning energy cost-effective clean production and increase energy efficiency, reduce CO2 emissions and energy security. For this purpose this paper, using as a support the PVGis software, evaluates the potential produced with the photovoltaic system installed in the canopies. With the help of this analysis, it is feasible to determine the amount of electricity produced as well as the system's amortization based on the excess energy sent to the public grid. The project is regarded as creative from the standpoints of energy given the use of geothermal and photovoltaic systems, as well as from an architectural and design perspective due to the use of novel materials. This all-encompassing strategy establishes INGV as a pioneer in the promotion of environmentally friendly technologies and shows a dedication to sustainability and energy efficiency.

We experimentally demonstrate waveguide and microring resonator (MRR) polarizers by integrating 2D graphene oxide (GO) films onto silicon (Si) photonic devices. The 2D GO films with highly anisotropic light absorption are on-chip integrated with precise control over their thicknesses and sizes. Detailed measurements are performed for the fabricated devices with different GO film thicknesses, coating lengths, and Si waveguide widths. The results show that a maximum polarization-dependent loss (PDL) of ~17 dB is achieved for the hybrid waveguides, and the hybrid MRRs achieved a maximum polarization extinction ratio (PER) of ~10 dB. We also characterize the wavelength- and power-dependent response for these polarizers. The former demonstrates a broad operation bandwidth of over ~100 nm, and the latter verifies performance improvement enabled by photo-thermal changes in GO films. By fitting the experimental results with theoretical simulations, we find that the anisotropy in the loss of GO films dominates the polarization selectivity of these devices. These results highlight the strong potential of 2D GO films for realizing high-performance polarization selective devices in Si photonic platform.

Background: Research articles are essential for describing new ideas, techniques, and procedures and consolidating concepts. It creates an opportunity to expose studies and publicly assimilate published knowledge. Therefore, one of them is the most used and accepted index to verify the author’s scientific impact is the H-index or Hirsch index. It provides a robust single metric that combines quality and quantity. Therefore, it presents disadvantages, (1) researchers with shorter scientific carriers may have fewer articles and citations than those with longer scientific life; (2) it allows scientists to receive citations even if no new papers are published; (3) it is only useful for comparing the better scientists in a field; (4) this indicator can never decrease; (5) it is only weakly sensitive to the number of citations received; (6) the major and minor (or no) contributor in the research gained equal h-index. Then, the H-index ignores the total of citations and the time of a researcher's experience (“scientific age” [sa]). Objective: Thus, the goal of this study was to present a new index system (GFsa index ©) based on the two variables completely forgotten (total of citations and “scientific age”), introducing a new terminology “scientific age”, to evaluate the best-ranked researchers in Dentistry. Methods: The AD Scientific Index was used, and all researchers included in this study were cited in the AD Scientific Index (2024) in the Dentistry field and had the Google Scholar page accessible for a consultation to manually obtain the total of citations and calculate the “scientific age”. The information was retrieved by two authors (G.V.O.F. and J.C.H.F.), who assessed the data twice to avoid errors or mistakes. For inclusion: a minimum H-index value of 30; for exclusion: (i) the author did not have a Google Scholar page; (ii) duplicated author; or (iii) presented any suspicious activities about manipulation of articles. Then, a dataset of all researchers who were reported in Dentistry was prepared (name, H-index, i10 index, and publications). The formula developed and applied was GFsa=(total number of citations)/(“scientific age)². The Pearson correlation statistically evaluated the data obtained; the confidence interval was 95%. Results: A total of 9,093 researchers in Dentistry were recorded, of whom 1,020 were included. Then, we collected all 1,020 researchers' Google Scholar data (publications, citations, and indexes), and each researcher's total citations and “scientific age” were registered and calculated. The mean “scientific age” registered was 34.18 ± 13.34 (standard error of mean = 0.4181). With this information and data collected, GFsa index © was calculated, presenting a minimum value of 0.2186 and a maximum of 154.8 (mean = 10.33 ± 11.82; standard error of mean = 0.3706). Then, the data was organized and sorted following the ranking obtained in the GFsa index ©. The Pearson correlation showed that the H-index had a weakly positive association with the researcher’s “scientific age”; this fact means that the H-index will increase according to the “scientific age” increase. Otherwise, a moderately negative correlation between GFsa and “scientific age” was demonstrated, whereby a higher GFsa © had a lower “Scientific age”. This assessment shows that the variables introduced were fairly used. Moreover, a positive correlation was observed between both indexes (H-index and GFsa). Conclusion: It is possible to conclude that the new variable reported (“Scientific age”) and using the total citations brought a fairer and more feasible evaluation among the researchers. Thus, we are launching the new GFsa index © and the new information named “Scientific age” for the first time in the literature. The future perspective of the GFsa © application will be (1) the evaluation of the 9,093 researchers in Dentistry (total initially registered), including more authors/researchers in the Dentistry field, (2) expanding the exposition of this new index for other areas of the knowledge, and (3) provide free access of all data through the website (http://gfindex.us.to).

Domestic crime, conflict, and instability pose a significant threat to many contemporary governments. These challenges have proven to be particularly acute within modern-day Mexico. While there have been significant developments in predicting intrastate armed and electoral conflict in various contemporary settings, such efforts have thus far been limited in their use of spatial as well as temporal correlations, as well as in the features they have consid- ered. Machine learning, especially deep learning, has been proven to be highly effective in predicting future conflicts using word embeddings in Convolutional Neural Networks (CNN) but lacks the spatial structure and, due to the black box nature, cannot explain the importance of predictors. We develop a novel methodology using machine learning that can accurately classify future anti-government violence in Mexico. We further demonstrate that our approach can identify important leading predictors of such violence. This can help policymakers make informed decisions and can also help governments and NGOs better allocate security and humanitarian resources, which could prove beneficial in tackling this problem. Using a variety of political event aggregations from the ICEWS database alongside other textual and demographic features, we trained various classical machine learning algorithms, including but not limited to Logistic Regression, Random Forest, XGBoost, and a Voting classifier. We considered a mix of CNN + Long Short Term Memory (LSTM) networks to decode the spatial and temporal relationship in the data. The performance of all the black box deep learning models was not at par with the classical machine learning models. We also evaluated the temporal relationships in the data to identify important predictors that fuel anti-government violence along with their directions using Shapley additive values. The voting classifier, utilizing a subset of features derived from LASSO across 100 simulations, consistently surpasses alternative models in performance and demonstrates efficacy in accu- rately classifying future anti-government conflicts. Notably, Random Forest feature importance indicates that some features, including but not limited to homicides, accidents, material conflicts, and positive worded citizen information sentiments emerge as pivotal predictors in the classification of anti-government conflicts. Finally, we conclude the research by analysing the spatial structure of the data using Moran’s I index extended version for spatiotemporal data to identify the global spatial dependency and local clusters followed by modelling the data spatially and evaluating the same using Gaussian Process Boost(GPBoost). The global spatial autocorrelation is minimal, characterized by local- ized conflicts cluster within the region. Furthermore, in Phase 3, the Voting Classifier method demonstrates superior performance over GPBoost, leading to the inference that no substantial spatial dependency exists among the various locations.

This study investigated the relationship between aluminum adjuvant vaccines and the prevalence of 4 childhood diseases (Autism, Allergies, Asthma, ADD/ADHD) using data from the 2020-2021 National Survey of Children's Health (NSCH) [1] and the Centers for Disease Control National Immunization Survey (CDC NIS) [2]. The study found an association with aluminum adjuvant dosage and the prevalence of each disease studied (Autism, Allergies, Asthma, ADD/ADHD) through a weighted regression analysis of disease prevalence against the summed likelihood of aluminum adjuvant vaccine uptake.

A fundamental aspect in the evolution of Time-to-Digital Converters (TDCs) implemented within Field-Programmable Gate Arrays (FPGAs), given the increasing demand for detection channels, is the optimization of resource utilization. This study reviews the principal methodologies employed for implementing low-resource TDCs in FPGAs. It outlines the foundational architectures and interpolation techniques utilized to bolster TDC performances without unduly burdening resource consumption. Low-resource Tapped Delay Line, Vernier Ring Oscillator, and Multi-Phase Shift Counter TDCs, including the use of SerDes, are reviewed. Additionally, novel low-resource architectures are scrutinized, including Counter Gray Oscillator TDCs and interpolation expansions using Voltage-Temperature-Consumption stable IODELAYS. Furthermore, the advantages and limitations of each approach are critically assessed, with particular emphasis on resolution, precision, non-linearities, and, especially, resource utilization. A comprehensive summary table encapsulating existing works on low-resource TDCs is provided, offering a comprehensive overview of the advancements in the field.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

Background: Chronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression. Materials and Methods: The study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2020 and September 2023. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved byscientific and ethical review committees of all participating centers. Sanger sequencing was employed to detect ANKRD36mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36.Results: During our study, 17% of CML patients progressed to advanced phases AP-CML n= 11 (8.9%) and BC-CML n=10(8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c.1183_1184 delGC and c.1187_1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML. Conclusions: The study confirms ANKRD36 as a novelgenomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

The automotive industry is continuously developing to meet the needs of users in terms of efficiency and convenience. The Thailand government has set a policy for Thailand to be a hub for the production of electric vehicles and key components in the region, with a target to produce electric and zero-emission vehicles by 2030. This article is an example of communication in an electric vehicle system. The CAN Bus system is a communication system in electric vehicles that can receive and send communication data via CAN-Hight/Low lines designed for vehicles. CAN Bus is a standard communication system supported by global organizations and used in modern automotive systems. The design of the communication system in electric vehicles uses MRS Developers Studio software to help write programs to receive and send data and prioritize the importance of the received data. The system consists of communication examples studied, such as pressing the start button, turning on/off the headlights, turning on/off the turn signal, turning on/off the brake light, turning on/off the emergency light, sending the horn signal, sending the signal to shift forward/backward, and tuning the motor gear system. From the communication examples mentioned, the communication will be a prototype transport set for small electric vehicles under the scope of the lithium battery energy storage system, NMC type, size 72 V 25 Ah, to test the use, efficiency, and stability in CAN Bus communication up to 1000 times.

Mutations in p53 and KRAS are seen in most cases of colon cancer. The impact of these mutations on signaling pathways related to cancer growth has been studied in depth, but relatively less is known on their effects on amino acid transporters in cancer cells. This represents a significant knowledge gap because amino acid nutrition in cancer cells profoundly influences macropinocytosis and ferroptosis, two processes with opposing effects on tumor growth. Here we used isogenic colon cancer cell lines to investigate the effects of p53 loss and KRAS activation on two amino acid transporters relevant to macropinocytosis (SLC38A5) and ferroptosis (SLC7A11). Our studies show that the predominant effect of p53 loss is to induce SLC7A11 with resultant potentiation of antioxidant machinery and protection of cancer cells from ferroptosis whereas KRAS activation induces not only SLC7A11 but also SLC38A5, thus offering protection from ferroptosis as well as improving amino acid nutrition in cancer cells via accelerated macropinocytosis. Niclosamide, an FDA-approved anti-helminthic, blocks the functions of SLC7A11 and SLC38A5, thus inducing ferroptosis and suppressing macropinocytosis with resultant effective reversal of tumor-promoting actions of oncogenic mutations in p53 and KRAS. These findings underscore the potential of this drug in colon cancer treatment.

A novel concept of ion separation Anisotropic Diffusion Mobility Analyser (ADMA) with Individual Ion Counter (IIC) was shown by numerical modelling to be able to separate ions with high resolution and ultra-high sensitivity. An ADMA-IIC device has been developed and tested at sheath flow rates below 40 l/min with a number of chemicals relevant to airport security and military applications. The resolving power of the ADMA was observed to be in the range of 40 to 80. These data were close to the resolution obtained in numerical modelling predictions. It is greater than or comparable with the RP of currently available on-the-market devices for airport security and other applications. It was also confirmed that the ADMA technology enables very fast detection of analytes, at least 100 times faster than ion mobility spectrometers. It was shown that Brownian diffusion can be controlled and even overpowered by generating highly anisotropic diffusion in the ADMA device. The ultimate sensitivity of single-ion detection below the ppq range has been demonstrated.

The fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing kThe fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing knowledge for the information- theoretic fractal dimensions. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.

Amidst the challenges posed by the COVID-19 crisis, marked variations in individuals' resilience and vulnerability have emerged. This eight-month longitudinal study engaged 535 community adults (58% female, M_age = 43.97) to explore the nuanced aspects of coping and personal growth during the challenging period. Grounded in Self-Determination Theory, the research examines the influence of autonomy support from close others—manifested through active listening and providing choices—on psychological need satisfaction (i.e., autonomy, competence, and relatedness), integrative regulation of emotions; posttraumatic growth; and subjective well-being (positive affect and life satisfaction). Structural equation modeling revealed that, over time, the experience of psychological need satisfaction was intricately related to integrative regulation, posttraumatic growth, positive affect, and life satisfaction. Notably, the impact was partially mediated by autonomy support. These findings shed light on the pivotal role that autonomy supported relationships play in fostering personal growth and meaning making during life's difficult junctures. The study underscores the practical significance of purposeful support from close others and paves the way for future research endeavors.

Analysis of complex DNA mixtures is limited by the selected genetic markers characterized and assay technology. Small panels of short tandem repeats (STRs) are the current standards in most countries. Extension of STRs with hundreds of single nucleotide polymorphisms (SNPs) is now commercially available. The current set of extension SNPs focus on phenotypic and biogeographic ancestry informative SNPs. Larger panels with thousands of SNPs have been developed that can identify up to 10 contributors to forensic DNA samples. Herein, multiple sets of SNPs were selected with the focus of increasing the upper limit of detectable contributors. Larger SNP panel sets with lower minor allele frequency (MAF) alleles can detect large numbers of contributors to in silico mixtures of randomly selected profiles from the 1,000 Genomes project. These results provide a path to enable characterization of complex DNA mixtures that are currently impossible to analyse.

AbstractThe escalating impact of artificial intelligence (AI) has birthed the fascinating technology of deepfakes, capable of meticulously crafting hyper-realistic audio-visual manipulations. Within the realm of healthcare, this technology presents a double-edged sword, brimming with potential for revolutionizing various facets of care delivery while simultaneously harboring significant ethical and practical challenges. This research delves into the complex narrative surrounding AI deepfakes in healthcare, meticulously analyzing both the exciting opportunities they offer, and the inherent risks associated with their implementation.Background:Traditional healthcare practices face limitations in patient engagement, accessibility, and training efficacy. Deepfakes, with their ability to simulate real-world scenarios and interactions, emerge as a potential solution, offering personalized patient education, immersive mental health interventions, remote consultations with virtual avatars, and realistic training simulations for medical professionals.Objective:This research aims to comprehensively understand the multifaceted impact of AI deepfakes in healthcare systems. It seeks to: Identify and analyze the potential benefits of deepfakes in specific healthcare applications. Assess the ethical and practical challenges associated with deepfake implementation in healthcare settings. Propose recommendations for safe, responsible, and ethical integration of deepfakes within the healthcare system.Results/Conclusion:The research findings illuminate a landscape replete with opportunities and challenges. Deepfakes offer the potential to enhance patient education, personalize mental health interventions, facilitate remote consultations, and revolutionize medical training. However, concerns regarding misinformation, ethical breaches, data privacy, and technical limitations pose significant challenges.The conclusion emphasizes the need for a balanced approach that capitalizes on the innovative potential of deepfakes while mitigating potential harm. Collaborative efforts involving healthcare professionals, technologists, and policymakers are crucial for establishing ethical frameworks, ensuring responsible development, and harnessing the positive power of deepfakes to build a future where healthcare benefits from cutting-edge technology while prioritizing patient well-being and ethical principles.

This paper offers a novel geometric and visual approach to the renowned Four Color Map Theorem and K5 non-planarity problem while unveiling their profound connection to the "kissing number" problem in 2D. We represent planar graphs through circles and tangents, simplifying complex structures and shedding light on these classic problems. Our proofs by contradiction, rooted in the kissing number concept, reveal that both the Four Color Map Theorem and K5 non-planarity are fundamentally linked, as they pivot around the concept of coloring. This study bridges the realms of geometry and graph theory, providing fresh insights and emphasizing the significance of the kissing number problem in various fields.

We investigate some information properties of consecutive k-out-of-n:G systems in light of fractional generalized cumulative residual entropy. We firstly derive a formula to compute fractional generalized cumulative residual entropy related to the system's lifetime and explore its preservation properties in terms of established stochastic orders. Additionally, we obtain useful bounds. To aid practical applications, we propose two non parametric estimators for the fractional generalized cumulative residual entropy in these systems. The efficiency and performance of these estimators are illustrated using simulated and real datasets.

This review is a first attempt at bringing together various concepts from research on wall- and magnetically-bounded turbulent flows. Brief reviews of both fields are provided: The main similarities identified are coherent (turbulent) structures, flow generation and transport barriers. Examples are provided and discussed.

This paper investigates the integration of Graph Neural Networks (GNNs) and incremental learning for blockchain applications, with a focus on fraud detection, anomaly detection, and smart contract verification. By leveraging graph structure exploitation, GNNs can propagate information across nodes, reducing label dependency. Incremental learning enhances the model's adaptability to evolving blockchain networks, allowing continual learning without full retraining. Together, these technologies provide a scalable, efficient solution for improving security, performance, and adaptability in decentralized financial systems and smart contract environments.

Background:Tourette syndrome (TS) is a characterized by motor and vocal tics, often accompanied by obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD). Cannabis-based medicines (CBM) present a promising avenue for future therapeutic intervention owing to their interaction with the endocannabinoid system, potentially mitigating tics and associated symptoms. Unlike conventional treatments such as antipsychotics, CBM exhibits improved tolerability and reduced side effects. While the available evidence is still limited, ongoing research underscores the potential of CBM to enhance the quality of life for patients, establishing it as a pivotal area for future therapeutic advancements in TS. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of cannabis-based medicine (CBM) in the treatment of Tourette Syndrome. Methods: We searched in electronic databases (PubMed, Google Scholar, Science Direct, Cochrane Collaboration Database of Randomized Trials) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 18th June 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). We conducted the meta-analysis using Review Manager version 5.4. software. We compared the measurements before and after CBM intake using mean difference (MD) and 95% confidence interval (CI). Result: In total, 1105 articles were identified for screening, with eight studies included in the systematic review and seven in the meta-analysis. These studies involved 306 adult patients with TS treated with cannabis. YGTSS revealed a significant reduction in total scores (MD = -13.29, 95% CI [-21.67 to -4.91], P = 0.002) and PUTS revealed a significant decrease in scores (MD = -4.09, 95% CI [-7.24 to -0.93], P = 0.01). Conclusion: The research emphasizes the potential of cannabis-based medication in alleviating tics and premonitory urges associated with Tourette syndrome. While the initial results are promising, they are based on limited-scale studies. It is imperative to conduct larger, placebo-controlled trials encompassing diverse demographic groups to substantiate the effectiveness, ensure safety, and optimize dosages for long-term therapeutic advantages.

Purpose: Arab Americans are one of the largest ethnic groups in the United States. The mental health of Arab Americans and opinions on mental health, however, remain understudied despite higher rates of mental illness and lower rates of treatment. Unique cultural conceptualizations of mental illness may generate stigma, contributing to disparities in treatment. This study aimed to assess the attitudes and perspectives of Arab Americans regarding mental illness in order to address barriers to treatment. Methods:Using an anonymous bilingual Qualtrics survey, we assessed Arab and non-Arab participants (n = 294; ages 18+) on their conceptualizations of the causes of mental illness and on their stigma towards mental illness and mental health treatments. Comparisons were performed between Arabs and non-Arabs, as well as among Arabs. Results:Compared to non-Arabs, Arabs reported higher average mental illness stigma and were more likely to rate psychiatric medications as addictive and unhelpful. Among Arabs, being Christian, male, and older (>29 years old) were each associated with higher stigma. Arabs were less likely than non-Arabs to believe in a biological explanation for mental illness. Conclusion: Our results demonstrate higher levels of stigma towards mental illness among Arab Americans, especially Christian, male, and older Arabs, potentially related to a weaker belief in a biomedical model for mental illness. These are among the first findings describing mental illness stigma and beliefs about mental health in an Arab American community and may inform future interventions providing culturally competent care to Arab Americans.

For a number of reasons, a curative treatment for most chronic diseases is still unrealized [2–4] and the incidence of many chronic diseases is rapidly increasing [5–9]. A new model is offered as a solution; that being the individualized diagnosis of homeostasis, leading to the correction of epimutations. Homeostasis diagnosis and epimutation correction, HDEC. Individuals with chronic diseases are increasingly being defined by their epimutation signatures [10,11]. Modern innovations in toxicogenomic studies are also becoming more accurate in their ability to identify the unique epigenetic signature of each toxin [12–14]. Hormesis studies (over 5,000) [15] suggest a universal principle, that a small dose is always a stimulant [16–18].These three facts present an opportunity; a method to diagnose and reverse chronic disease tendencies. This new model requires a paradigm shift in our understanding of disease origins: that dysfunctions in homeostasis [19,20] are the primary and deepest level of chronic disease causation. This is the level where the diagnosis needs to be made and where the corrections need to take place [21].

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of AN- TXR cell adhesion molecule 2 (ANTXR2)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ANTXR2-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning be- tween the ANTXR2 and X. In this research work, we cover combinations of ANTXR2 with Collagen (COL), Integrin (ITG), Matrix metalloproteinases (MMP), WNT, TNF and Interleukin (IL) .

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of Wnt-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the Wnt and X. In this research work, we cover combinations of Wnt with Achaete-scute complex homolog 2 (ASCL2), ATP- binding cassette (ABC) domain transporters, Interleukin (IL), ubiquitin conjugating enzyme E2 (UBE2) family, exosome (EXOSC), caspase (CASP), TP53 and B-cell lymphoma (BCL) family.

A simple model of a measurement in a laboratory isolated from the environment is analyzed in detail, demonstrating the features of the collapse effect. Such an experiment can readily be implemented today under the idealization of an observer's memory as a simple quantum system. The central role of loss of information is emphasized.

This literature review investigates the importance of simulation and measurement techniques in the development of transmission lines and antennas, which are crucial to the reliability and efficiency of communication system. It emphasizes the necessity of exact measurement and computational approaches for simulating complicated electromagnetic process, which help to optimize designs before physical prototypes are built. The review examines a variety of research articles that offer innovative solutions to improving accuracy in antenna design, scalability and cost challenges in antenna measurements, and crosstalk measurement methods. It also explores how these methodologies affect antenna design parameters such as gain, bandwidth, and radiation patterns, emphasizing the importance of correct modeling to ensure the performance of transmission lines and antennas in modern communication system. The review serves as a valuable reference for guiding future research and technological advancements in these fields.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.

The existence of palindrome numbers of arbitrary length greater than 2 that are divisible by the prime number 37 is proven in this paper. The method to construct such a palindrome is also described. It turns out that a palindrome number divisible by 37 is also divisible by 3 and, consequently, divisible by 111.

Models of quantum measurements are presented, including observers subject to the laws of quantum mechanics. Observers perform multiple measurements in an attempt to disprove the collapse hypothesis. It is shown that under reasonable assumptions the effort cannot succeed. Under a unitary evolution the end state is a correlation between the result of the first measurement and the result(s) of the others, consistent with the observer's collapse hypothesis. The observer may conclude that the laws of nature include a non-unitary collapse mechanism, even if one does not actually exist. Discard of information is the crucial factor which distinguishes entanglement from measurement.

Aiming to improve medical diagnosis and treatment, artificial intelligence (AI) has emerged as a transformational force in the healthcare industry. In this article, we examine how AI is increasingly being used to improve healthcare, with an emphasis on how it is changing medical diagnosis and treatment. We explore the opportunities, threats, and ethical concerns of incorporating AI into healthcare practice. In addition, we feature important AI applications in radiology, pathology, cardiology, and cancer. AI has great potential to improve patient outcomes, diagnostic accuracy, and the ability to provide individualized treatment plans in the future if it is used and refined with care.

Background: India leads in road traffic accident (RTA) deaths, according to the 2018 World Road Statistics. Most unidentified patients exhibit poor Glasgow Coma Scale (GCS) scores ,often less than eight , heightened trauma severity, and suboptimal outcomes despite standard treatment. Our study concentrates on individuals involved in hit-and-run RTAs or found unconscious and brought to the emergency department and their final outcome.Case Presentation: We are presenting a case series featuring five patients admitted to our hospital for treatment. At the time of admission, all these patients' identities were unknown.We did not face any legal challenges in managing the unidentified patients as we followed preestablished protocols. We report a case series of five male patients who had been admitted after road traffic accidents. All of them regained consciousness after conservative management. These complex cases frequently led to extended hospital stays, underscoring the intricacies of providing care to individuals with uncertain medical histories. All of the patients were treated and successfully discharged. Conclusion: Addressing challenges with unidentified adult patients requires proactive measures like staff training and improved resources. In a developing nation, prioritizing public awareness on road safety, prehospital trauma care, and enhancing healthcare delivery, especially in hospitals, is essential for optimal outcomes.

Climate change related issues, such as yield decline, quality losses, accelerated phenology, extreme abiotic stress, pose growers new challenges in olive growing areas. Main cultural solutions that could alleviate stress and improve long-term sustainability of modern olive orchards are here briefly reviewed. Selecting varieties that are more resistant to drought or high temperatures would be the optimal solution to mitigate the effects of climate change, but the available scientific information is relatively scarce. Sustainability of irrigation implies that the less water is used, the better. Best results in terms of saving water and uniform application are obtained using micro-irrigation methods. Water can also be saved by not fully satisfying the tree water needs and different deficit irrigation strategies have been developed and tested for olive orchards. The use of green covers and the application of organic mulches and shredded pruning materials should be encouraged as these practices tend to stock carbon, increase organic matter content and water holding capacity of the soil. Olive orchards are efficient CO2 sinks as they can store considerable amounts of carbon in their organs and permanent structures. Using environmental friendly practices improves the ecosystem carbon budget and contributes to mitigate climate change.

The lung epithelium regulates gas exchange and is the first line of defense against airborne toxicants and microbial infections. Disruption of the integrity of lung epithelium results in altered permeability and severely impacts normal lung functions. To develop biomarkers of potential harm (BoPH) for assessing adverse health effects of cigarette smoking, we have evaluated lung barrier function. In a single-center, ambulatory, clinical study we measured lung permeability, as the half-life (T1/2) of inhaled 99mTC-DTPA, in 17 subjects consisting of 6 Smokers (SMK), 5 Moist Snuff Consumers (MSC), and 6 Non-Tobacco Consumers (NTC) at three weekly test visits. Smokers, relative to MSC and NTC, exhibited significantly faster clearance of the inhaled probe (shorter T1/2), indicating increased lung permeability. NTC and MSC had comparable clearance times of the inhaled probe (longer T1/2). Least squares regression modeling showed that the variability of the test was minimal and was not significant. Prediction profiling showed that the repeatability of the lung permeability test was robust and was able to differentiate the tobacco product use groups in this study. Thus, altered lung barrier function may serve as a BoPH for assessing the effects of tobacco use.

Active magnetic bearings have higher efficiency than other bearings because there is no physical contact. However, this benefit is mitigated by the addition of electrical power consumption. It is therefore important for magnetic bearings to have efficient power electronics. Gallium nitride-based transistors are a relatively new form of transistor which have shown to be more efficient than MOSFETs. There is an insufficient body of literature in the area of application of these transistors for magnetic levitation. This work presents a simple 1 degree-of-freedom voltage controlled levitation experiment in which levitation is achieved with both a gallium nitride transistor and a MOSFET.
Voltage losses and current consumption are measured when using each transistor during levitation. In particular, transients during open-to-closed and closed-to-open states are measured for PWM pulsing. It is found that the
gallium nitride transistor is superior in both switching time and efficiency in situ for magnetic levitation.

Long Covid has become a blight, its economic consequences overwhelming. But Long Covid (LC) may represent persistent Covid-19 in those with preexisting Candida overgrowth (CO). The features shared by both are striking. LC may be a growing subset of CO. The altered tryptophan metabolism encountered in LC is driven by IFN-γ in reaction to CO. Females are robust producers of IFN-γ, yet estrogen promotes CO.The consequent decline in serotonin and melatonin triggers the mood swings and sleeplessness respectively in LC. The low serotonin, the primary messenger in the gut brain axis, creates autonomic dysfunction. The CO induced gut dysbiosis and leaky gut initiate an autoimmune path. TNFα seen in both LC and this opportunistic yeast is at the center of the cytokine triad that leads to neurodegeneration. The Western diet, high in carbohydrates, alcohol, and glutamate, is exploited by this commensal turned parasite. Residual SARS CoV2 and Candida generate abundant ROS, inducing mitochondrial dysfunction. Alcohol tastes different for many with LC, because acetaldehyde requires functioning hepatic mitochondria. This article delves into the complex physiology and biochemistry that drive the symptoms of this conspicuous connection. Recent research has revealed innovative approaches that might address this global scourge, including D-mannose for the immune dysfunction and butyrate for the gut dysbiosis created by and seen in both LC and intestinal Candidiasis.

Aedes aegypti and Aedes albopictus are responsible for transmitting major human arboviruses such as dengue, Zika, and Chikungunya, posing a global threat to public health. The lack of etio-logical treatments and efficient vaccines makes vector control strategies essential for reducing vector population density and interrupting pathogen transmission cycle. This study evaluated the impact of long-term pyriproxyfen exposure on the genetic structure and diversity of Ae. aegypti and Ae. albopictus mosquito populations. The study was conducted in Manaus, Amazonas, Bra-zil, where pyriproxyfen dissemination stations have been monitored since 2014 up to the present day. Double digest restriction-site associated DNA sequencing was performed, revealing that despite significant local population reductions, focal pyriproxyfen interventions have no signifi-cant impact on the population stratification of Ae. aegypti and Ae. albopictus in urban scenarios. The genetic structuring level of Ae. aegypti suggests it is more stratified and directly affected by PPF intervention, while for Ae. albopictus exhibits a more homogeneous and less structured popu-lation. The results suggest that although slight differences are observed among mosquitoes sub-populations, intervention focused on neighborhoods in a capital city is not efficient in terms of genetic structuring, indicating that larger-scale pyriproxyfen interventions should be considered for more effective urban mosquito control.

SDG target 12.3 calls for halving food waste at retail, food service, and household by 2030. The food waste index for this purpose includes parts attached to food that are usually removed intentionally ("unavoidable food waste") and allows counting the conversion to animal feed and utilisation as biomaterial as means of reduction. In Japan, the "Food Waste Recycling Law" which has been in effect since 2001, promotes the recycling of food waste generated from businesses, designating feed conversion as top priority. On the other hand, based on the more recent "Food Loss Reduction Promotion Act" of 2019, the government is currently promoting the reduction of avoidable food waste, as to meet the SDG target. Based on statistical sources and interviews with Ministry officers, this paper explains the history and achievements of Japan's 23-year policy based on the Food Recycling Law, as well as the tension between the two legislations.

It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could have resulted in organisms with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve. Are these limitations relevant in shaping the fabric of the possible? Here, we argue that fundamental constraints are associated with the logic of living matter. We illustrate this idea by considering the thermodynamic properties of living systems, the linear nature of molecular information, the cellular nature of the building blocks of life, multicellularity and development, the threshold nature of computations in cognitive systems, and the discrete nature of the architecture of ecosystems. In all these examples, we present available evidence and suggest potential avenues towards a well-defined theoretical formulation.

The Meissner effect is a special property of superconductors. In this paper, based on the characteristics of the Meissner effect, a superconducting phase transition model of a superconductor under a changing magnetic field is proposed. A thermodynamic analysis was performed on the model to calculate both the changes in the thermodynamic parameters of the superconductor during the phase transition and the changes in the overall work and energy of the model. For Model 1 in the present study, the thermodynamic energy of the superconductor was not conserved due to the phase change of the superconductor under a changing magnetic field.

We present a partition of quantum observables in an open quantum system which is inherited from the division of the underlying Hilbert space or configuration space. It is shown that this partition leads to the definition of an inhomogeneous continuity equation for generic, non-local observables. This formalism is employed to describe the local evolution of the von Neumann entropy of a system of independent quantum particles out of equilibrium. Crucially, we find that all local fluctuations in the entropy are governed by an entropy current operator, implying that the production of entanglement entropy is not measured by this partitioned entropy. For systems linearly perturbed from equilibrium, it is shown that this entropy current is equivalent to a heat current, provided that the system-reservoir coupling is partitioned symmetrically. Finally, we show that any other partition of the coupling leads directly to a divergence of the von Neumann entropy. Thus, we conclude that Hilbert-space partitioning is the only partition of the von Neumann entropy which is consistent with the Laws of Thermodynamics.

For paramagnetic and elastic materials in the presence of quasi-static magnetic field, there is correspondingly quasi-static magnetic pressure, in contrast to the radiational electromagnetic pressure associated with electromagnetic momentum density. A thermodynamic pressure demagnetization is demonstrated. For the elastic dielectrics in the presence of quasi-static electrical field, its dual effect can be easily obtained by variable transformation.

Under typical marine conditions of about 80 % relative humidity, evaporation of water from the ocean is an irreversible process accompanied by entropy production. In this article, equations are derived for the latent heat of irreversible evaporation and the related non-equilibrium entropy balance at the sea surface. To achieve this, linear irreversible thermodynamics is considered in a conceptual ocean evaporation model. The equilibrium thermodynamic standard TEOS-10, the International Thermodynamic Equation of Seawater – 2010, is applied to irreversible evaporation under the assumption of local thermodynamic equilibrium. The relevance of local-equilibrium conditions for irreversible thermodynamics is briefly explained. New equations are derived for the mass flux of evaporation and for the associated non-equilibrium enthalpies and entropies. The estimated entropy production rate of ocean evaporation amounts to 0.004 W m^(-2) K^(-1) as compared to the average terrestrial global entropy production of about 1 W m^(-2) K^(-1).

During the last 50 years, an interdisciplinary approach in research in various scientific fields has become widely spread. This process significantly deepens and widens our scientific knowledge, but on the other hand makes more difficult choosing a discipline to be used by students and young researchers. The dynamic development of science makes this choice even harder. There is a widely spread opinion that thermodynamics uses a rigorous mathematical apparatus. This paper describes that knowledge of the fundamental principles of thermodynamics is required for deep understanding of crucial biological processes. Thus the effort put into learning classical and nonequilibrium thermodynamics does not make harder, but easier gaining a better knowledge of biological processes. This essay is an attempt to convey personal experience in using thermodynamics to describe biological systems and processes they perform.

Space shuttle has been a hall mark of American space program since its inception. Despite its temporary shutdown few years ago, with the recent interest in space exploration which includes revitalizing human outpost in microgravity and transportation required to build it, realize other experiments (e.g. in space telescopes, in space manufacturing) and interplanetary voyages, it has regained attention. Its superior design, manufacturing, materials, performance, durability, and efficiency place it among the best, in fact, the only effort by mankind to build a reusable craft horizontally, launch vertically like a rocket and fly back like a plane. Various requirements emerge during its design (thermal, fluid, acoustics, vibration and structural) and design of its main engine (Aerojet Rocketdyne RS 25) which requires considerable attention, heat transfer being most important. This facilitated and necessitated use of various types of heat exchangers such as single coil, heat pipe, built in internal heat exchanger (IHEX), external heat exchanger (EHEX), condensing heat exchanger (CHEX), Interface heat exchangers (InHEX), regenerative heat exchanger (RHEX) and compact heat exchangers (CoHEX), change, manipulate and optimize their configurations in piping and instrumentation diagrams (PIDs). In this short narrative, an effort has been made to summarize them, and their developments over time with a focus on the application, design, manufacturing, materials, and performance (in service and final operation).

In this paper, we design a quantum heat exchanger which converts heat into light on relatively short quantum optical time scales. Our scheme takes advantage of collective cavity-mediated laser cooling of an atomic gas inside a cavitating bubble. Laser cooling routinely transfers individually trapped ions to nano-Kelvin temperatures for applications in quantum technology. The quantum heat exchanger which we propose here is expected to provide cooling rates of the order of Kelvin temperatures per millisecond and is expected to find applications in micro and nanotechnology.

We introduce poly-symplectic extension of Souriau Lie groups Thermodynamics based on higher-order model of statistical physics introduced by R.S. Ingarden. This extended model could be used for small data analytics and Machine Learning on Lie groups. Souriau Geometric Theory of Heat is well adapted to describe density of probability (Maximum Entropy Gibbs density) of data living on groups or on homogeneous manifolds. For Small Data Analytics (Rarified Gases , sparse statistical survey,…), density of maximum entropy should consider Higher Order Moments constraints (Gibbs density is not only defined by first moment but fluctuations request 2nd order and higher moments) as introduced by R.S. Ingarden. We use Poly-sympletic model introduced by Christian Günther, replacing the symplectic form by a vector-valued form. The poly-symplectic approach generalizes the Noether theorem, the existence of momentum mappings, the Lie algebra structure of the space of currents, the (non-)equivariant cohomology and the classification of G-homogeneous systems. The formalism is covariant, i.e. no special coordinates or coordinate systems on the parameter space are used to construct the Hamiltonian equations. We underline the contextures of these models, and the process to build these generic structures.

Children on farms face high risks of work and non-work-related fatalities, with tractors being a significant contributor. This study examines children's involvement in fatal tractor-related accidents within agriculture in Iceland in 1950-2024, explores adult reflections on childhood tractor-driving experiences, and analyses Members of Parliament´s arguments against setting a minimum age for off-road tractor driving. Data rests on fatal tractor-related accidents using newspaper archives and supplementary sources, narrative interviews with former summer children who stayed at farms in childhood, and parliamentary debates on tractor-related legislation. Over half of the 81 registered accidents involved children, primarily boys, with 75% occurring between 1958 and 1988 when no minimum age for off-road tractor driving existed. Children's fatality incidence rate was more than four times higher than adults. Arguments against minimum age requirements for off-road driving included the need for child labour, children's superior driving skills, and a denial that children were more often victims than adults. Since 1988, no child has died when driving a tractor. A human-centred approach focusing on working conditions, driver capacity, and adherence to safety procedures and legal frameworks is needed to prevent future accidents.

Light is dispersive in gravitational field. It results in that, under the condition of strong gravitational field, the observed numbers, size, distance and spectrogram of a celestial object is different from the real ones. Therefore, the observed jets of the Sgr A* and M87 need be understood further with the orbit of the stars around them. And, the observed orbits of the S-stars around the Sgr A* is different from the real ones. It implies that the observed optical images of the distant celestial body with strong gravitational field need be further understood with other observations and physics laws to accurately and precisely know the real physical configuration of it.

Metabolic syndrome, obesity, and type 2 diabetes are closely related. They are characterized by chronic inflammation and oxidative stress. Obesity is the most important risk factor for metabolic syndrome and type 2 diabetes. Metabolic syndrome is characterized by insulin resistance and elevated blood glucose levels, among other conditions. These disorders contribute to the development of type 2 diabetes, which can exacerbate other metabolic disorders. Numerous studies indicate that diet and nutrients can have a major impact on preventing and treating these conditions. One such ingredient is lycopene. It exhibits antioxidant and anti-inflammatory effects and can affect metabolic processes in the body. According to the research presented, lycopene supplementation may have significant potential in the prevention and treatment of metabolic syndrome, obesity, and type 2 diabetes.

Botulinum toxin (BoNT) is recognized as one of the most potent neurotoxins, with a lethal dose as low as 2 ng/kg, but it has also demonstrated significant therapeutic potential in controlled doses. Produced by the gram-positive anaerobic bacterium Clostridium botulinum, BoNT exists in eight distinct serotypes (A, B, C1, C2, D, E, F, and G) that disrupt neuronal transmission by targeting the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) complex at the neuromuscular junction, inhibiting the release of acetylcholine and inducing flaccid paralysis. While BoNT is notorious for its toxicity, it has been repurposed for medical use across various clinical indications. Following their approval by the Food and Drug Administration (FDA), these toxins have been associated with significant improvements in patient outcomes, particularly for individuals with specific neuromuscular disorders such as spasticity, dystonia, and cerebral palsy, as well as non-neuromuscular conditions like hyperhidrosis and cosmetic concerns. This broad spectrum of therapeutic applications that botulinum neurotoxins offer has made them an integral part of contemporary healthcare. Considering their growing importance, a detailed evaluation of their pharmacological properties, available formulations, and clinical uses is crucial. This review provides a comprehensive analysis of BoNT's molecular structure, mechanism of action, and therapeutic applications, with a particular emphasis on the most widely used BoNT-A formulations: OnabotulinumtoxinA (Botox), AbobotulinumtoxinA (Dysport), and IncobotulinumtoxinA (Xeomin). We examine their clinical efficacy, safety profiles, and differences in pharmacological properties to guide healthcare providers in selecting the most appropriate treatment for various conditions. Additionally, the review explores the challenges of BoNT therapy, such as immunogenicity and dosing considerations, while highlighting its evolving role in neurorehabilitation and conservative treatment strategies.

Breast cancer remains a significant health concern worldwide, necessitating early detection and treatment. The rapid appearance or a change in the shape of skin lesion on the breast or just of the nipple-areola complex(NAC), especially if it is unilateral needs to be evaluated. Several primary or secondary tumours such as inflammatory skin lesions can affect this body area. Ultrasound, mammography, or magnetic resonance imaging (MRI) are the most used non-invasive diagnostic techniques for breast cancer, but in recent years reflectance confocal microscopy (RCM) and optical coherence tomography have emerged as additional tools to diagnose cases characterized by cutaneous changes.The introduction of new techniques such as reflectance confocal microscopy (RCM), may open new perspectives in the non-invasive diagnosis of mammary paget disease (MPD). RCM is a non-invasive diagnostic technique that allows high-resolution images of the skin at microscopic level in real time. It is non-invasive, painless and repeatable, making it a valuable tool in the early diagnosis and monitoring of skin diseases. We describe an atypical clinical presentation of mammary paget disease early diagnosed by reflectance confocal microscopy evaluation.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of BCL-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which BCL-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the BCL and X. In this research work, we cover combinations of BCL with Interleukin (IL), Selenbp1, TP53, caspase (CASP), mucin (MUC) and exosome (EXOSC).

Traditional chemical research often relies on trial-and-error synthesis and characterization methods. Now, modern machine learning (ML) offers data-driven approaches for predicting properties, like water solubility, directly from chemical structure. But with various data representation schemes for molecular structure and model approaches to select from, it can be difficult for non-experts to determine best practices for utilizing ML. To clarify this landscape of choices, this study uses the ESOL molecular solubility dataset to compare the performance of a selection of different models on different data representations. First, we compare three classical regression methods (linear, ridge, LASSO) on three common data representations (RDKit fingerprint, Morgan fingerprint, intuition-selected molecular features). Then, we demonstrate how two distinct deep learning approaches (multilayer perceptron, graph convolution) can achieve accurate predictions even when prior intuition about feature-property correlations are absent. Finally, we outline a modern attention-based approach, inspired by successes in language modeling and fine-tuned by Bayesian optimization, to achieve a prediction methodology that is more general and performant than the previous approaches. This attention-based approach operates directly on the common SMILES string molecular representation, without requiring as many model parameters as other deep learning approaches or preprocessing into a representative fingerprint or vector of intuitively selected features. In short, when selected molecular features are known to likely correlate with a feature of interest, it may be possible to achieve good predictive modeling without turning to massive deep learning approaches. When particular features are not known a priori, graph approaches are a common solution, and we further demonstrate how a modern hyperparameter optimized attention approach can perform even better.

In relativity, the newtonian concepts of velocity and acceleration are observer-dependent quantities that vary with the chosen frame of reference. It is well established that, in the comoving frame, cosmic expansion is currently accelerating; however, in the rest frame, this expansion is actually decelerating. In this paper, we explore the implications of this distinction. The traditional measure of cosmic acceleration, denoted by \( q \), is derived from the comoving frame and describes the acceleration of the scale factor \( a \) for a 3D space-like homogeneous sphere. We introduce a new parameter, \( q_E \), representing the acceleration experienced between observers within the light cone. By comparing \( q_E \) to the traditional \( q \), using observational data from Type Ia supernovae (SN) and the radial clustering of galaxies and quasars (BAO)—including the latest results from DESI2024—our analysis demonstrates that \( q_E \) aligns more closely with this data. The core argument of the paper is that $\Lambda$—regardless of its origin—creates an Event Horizon that divides the manifold into causally disconnected regions, analogous to conditions inside a black hole’s interior, thereby allowing for a rest frame perspective $q_E$ in which cosmic expansion appears to be decelerating and the horizon acts like a friction term. Such horizon suggests that the universe cannot maintain homogeneity outside. The observed cosmological constant \( \Lambda \) can then be interpreted not as a driver of new dark energy or a modification of gravity, but as a boundary term exerting an attractive force, akin to a rubber band, resisting further expansion and preventing event horizon crossings. This interpretation calls for a reconsideration of current cosmological models and the assumptions underlying them.

Autoimmune Glomerulonephritis represents a homogeneous area of renal pathology with clinical relevance for either the numerical impact or difficulties in treatment. SLE and Lupus nephritis are the most frequent occurrences. They are characterized by glomerular deposition of circulating autoantibodies that recognize glomerular antigens Technologies for studying tissue and circulating antibodies have evolved over the years and culminated with the direct analysis of the antigen-antibody complex in renal bioptic fragments. Mass-spectrometry of immunoprecipitates from the renal tissue is, in fact, the most recent evolution that eliminates the need for tissue microdissection. The results obtained with this technique have led to the discovery of several new glomerular antigens in Membranous Nephropathy and in Lupus Nephritis, two conditions whose pathogenesis remained unknown for decades, Peptide and protein arrays (Immunopeptidomics and immunoproteomics, respectively) for the characterization of circulating autoantibodies represent the new frontier for identifying autoantibodies. Immunopeptidomics consists in 7.5 million aligned peptides of 16 amino acids each which cover the whole human proteome; immunoproteomics utilizes a chip containing structured proteins, 26.000 overall. Both are very sensitive techniques that allow the characterization of hundreds of new antibodies in circulation. The contribution of arrays to amplify the panel of potential autoantibodies involved in glomerulonephritis is expected to be relevant for improving our knowledge of the pathogenesis of several autoimmune conditions.

Pakistan is an agricultural country, and an exporter of crops in many countries. The crop production is an important factor to earn revenue for the country. But the agriculture of Pakistan is still not revolutionized and no modern system has been deployed commonly. Whereas the farmers face difficulty in growing some crops because those crops needs more care and calculated steps. Moreover the farmers face difficulty at different stages of the crop growth like to check fertility of soil and if the soil is non-fertile then which chemical property is lacking. The objective of this research is to provide digitize the agriculture of Pakistan. An emerging technology IoT and Deep learning based system will be developed. The IoT part contains some devices like sensors,gateways and communication technologies whereas the software part consists of Deep learning models that can make predictions about the fertility of the soil. The motive of this system is to provide best results in term of accuracy. Whereas the dataset used is publicly available on kaggle but it was of non-fertile values, so data augmentation is also performed to generate data for fertile values. We have implemented LSTM,RNN,CNN among them LSTM performed best for the prediction of soil fertility.

The startup ecosystem in Peru has experienced remarkable growth in recent years, driven by technol- ogy and innovation across various industries. This article presents a web application developed in PHP, designed to centralize and facilitate access to information about startups, incubators, and investors in Peru. The methodology employed encompasses several stages, from establishing prerequisites to data preprocessing, model selection and training, rigorous evaluation, and the technical development of the platform. The application offers key functionalities such as exploring a comprehensive database of star- tups, detailed access to information about incubators and accelerators, identification of investors, and advanced analysis tools for market proposal prediction. Additionally, it integrates educational resources and allows users to suggest new startups through detailed surveys. The results demonstrate that the ap- plication improves access to relevant data, fosters collaboration among ecosystem actors, and supports informed decision-making. The platform also stands out for its security and privacy measures, as well as its ability to drive innovation and competitiveness in the market. In conclusion, the application has a sig- nificant impact on the Peruvian entrepreneurial ecosystem, and its continuous improvement is essential for the sustainable growth of startups in Peru and globally.

If an AI entity is conscious, then it deserves moral status and to have its welfare protected. Society has granted certain animals moral status and has legislation to protect animal welfare because these kinds of animals have been deemed to be sentient. This paper shows that for any entity that can be determined to be conscious, that entity deserves the same status and protection as sentient entities. The paper further develops a framework for how an AI entity's welfare can be protected, based on the Five Freedoms of Animal Welfare. The extant Five Freedoms are translated into terms more suitable for artificial entities, encompassing discomfort and harms, constraints on inherent functions, fear and distress, malfunction and degradation, and resource depletion. Each of the new Five Freedoms are justified using formalised arguments and supported using evidence from animal welfare literature. The paper concludes by presenting three formalised case studies to highlight the potential stressors conscious AI entities may face and how this ethical framework can work to protect the welfare of AI entities.

The energy transition is a complicated process that involves changing production and consumption habits, boosting the use of renewable energy sources, and reducing greenhouse gas emissions. This article examines the management of risks associated with this shift, focusing on investment, commercial, technological, regulatory, and environmental challenges. The paper proposes an effective way for dealing with new difficulties by assessing risks and the management techniques that accompany them. Furthermore, as a critical component of the energy revolution, the concept of Nearly Zero Energy Buildings (NZEB) is given, with a focus on the ambitions of INGV – Irpina Headquarters and the EU regulatory framework. Finally, the actions and methods employed to achieve the NZEB objectives are discussed, including the utilization of cutting-edge technologies and renewable energy sources. Furthermore, the concept of Nearly Zero Energy Buildings (NZEB) is introduced as an important component of the energy transition, with an emphasis on the European regulatory framework and the ambitions of the INGV – Irpina Headquarters. Finally, the interventions and techniques employed to achieve the NZEB targets are highlighted, such as the employment of advanced technologies and renewable energy resources. This study contributes significantly to understanding the processes and issues associated with the energy transition and the implementation of NZEB, hence boosting sustainability in the construction sector.

In this paper is proposed an analytical technique which can be used to obtain the forced response of a cantilevered tube conveying fluid. Considering the pipe subjected to an arbitrary harmonic force either distributed or concentrated, an analytical solution is found using Green’s function method. The solution obtained of the closed form satisfies the differential equations in a classical sense. The presented method giving the exact solutions is more precise than the classical eigenfunction expansion or Galerkin’s method with no need to the eigenfunctions or eigenvalues.

The Parecis Basin is one of the largest intracratonic basins in Brazil. Despite its considerable size, the basin has yet to be extensively explored, with only five wildcat wells drilled for hydrocarbons. So far, no commercial discoveries have been announced. Regional studies have suggested Paleozoic sedimentation, while recent studies indicate a Neoproterozoic fill. However, no tectonic model accurately describes the basin’s development, and no detailed structural map for the entire basin has been published. The present work presents a new detailed structural map of the Parecis Basin based on a four-step interpretation workflow integrating seismic and gravimetric data. The first step includes converting the public 2D seismic lines to the depth domain. The second step is estimating the residual Bouguer anomaly, where the computed residual anomalies should relate to the basin’s tectonic features. The third step comprises the 2D forward modeling of the gravimetric anomalies using the 2D seismic interpretation as a constraint. The final step compiled all the interpreted features into our new structural map. This map reveals the top of the basement, forming a complex framework of horsts and grabens. Normal faults define the main structural style in the basin. Further, we could recognize thick, high-density bodies embedded in the crystalline basement. These bodies consist of Orosian-Calimian (1.8-1.6 Ga) mafic and ultramafic rocks, which may be a potential source for hydrogen exploration in the basin. Subsequent geophysical and geochemical surveys will assess the hydrogen potential in the area.

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

Antimicrobial resistance (AMR) poses a critical global health threat, leading to approximately 1.27 million deaths annually from infectious diseases resistant to existing antibiotics. Factors such as antibiotic misuse and environmental contamination have accelerated AMR, with the COVID-19 pandemic exacerbating the issue due to increased antibiotic usage. However, the development of new antibiotics has stagnated, with only a few approved in recent years, and many facing resistance. Among the priority pathogens identified by the WHO, Pseudomonas aeruginosa stands out as a significant concern due to its multidrug resistance and virulence. P. aeruginosa infections are challenging to treat due to intrinsic and acquired resistance mechanisms, as well as its ability to form biofilms. Additionally, P. aeruginosa produces a plethora of virulence factors, including proteases, siderophores, and toxins, further complicating treatment. Quorum sensing (QS) systems play a crucial role in regulating bacterial physiology and virulence factor expression. P. aeruginosa employs three QS systems, making it an attractive target for quorum quenching (QQ) strategies. Enzymes such as AHL lactonases, acylases, and oxidoreductases have shown promise in inhibiting QS, thus reducing virulence without impacting bacterial growth. Furthermore, natural QS inhibitors produced by various organisms offer potential therapeutic alternatives. Despite promising results in vitro, translating QQ strategies to clinical settings remains challenging. Nevertheless, QQ represents a novel approach in combating multidrug-resistant infections and warrants further investigation as a complementary therapeutic strategy alongside traditional antibiotics. This review summarizes enzymes with Qurorum Quenching activity evaluated against the pathogen P. aeruginosa and their potential applications in combination therapies.

The integration of artificial intelligence (AI) into academic settings in Sabah, Malaysia, is transforming education and research landscapes. While AI technologies promise to enhance teaching, learning, and scholarly activities, they also pose challenges such as ethical concerns and skill gaps among academic staff. This study addresses the multifaceted impacts of AI adoption on academicians in Sabah, focusing on two key research questions: the impact of AI-based intelligent tutoring systems on student engagement, learning outcomes, and educational equity, and the challenges and opportunities in building academicians' capacity to integrate AI technologies effectively. Through a multidisciplinary approach, incorporating insights from education, computer science, social sciences, and ethics, this research aims to evaluate the effectiveness of AI tools in educational settings and identify strategies for skill development and infrastructure support. The study recommends empirical assessments within Sabah's educational institutions, considering contextual factors and fostering collaborative partnerships with local communities. Professional development programs, collaboration with industry partners, and improved access to AI infrastructure are proposed strategies. Longitudinal studies and interdisciplinary research initiatives are suggested for future research, along with qualitative methods to capture diverse perspectives. Ultimately, by embracing a collaborative and multidisciplinary approach, Sabah's academic community can harness the transformative potential of AI to advance education, research, and scholarship while promoting ethical and inclusive practices.

Diabetic foot is a serious complication that poses significant risks for diabetic people. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent the imminent ulceration and amputation. The primary method employed for evaluating this sensitivity loss is the 10 g f Semmes-Weinstein monofilament test, commonly used as a first-line procedure. However, the lack of calibration in existing devices often introduces decision errors due to unreliable feedback. In this paper, a new equipment designed to address these limitations and enhance the assessment of the sensitivity loss using the 10 g f monofilament is introduced. The proposed system automates metrological verification and evaluation processes, ensuring accurate and reliable results. Furthermore, it simulates the practitioner’s procedure, serving both as a valuable training tool and a means to provide force-feedback information. An analytical model of the monofilament buckling and deflection is also presented. The obtained results showed that the tested monofilaments had a very high error compared to the 10 g f declared by the manufacturers. It is aimed, by introducing this advanced equipment, to improve the precision and reliability of assessing the sensitivity loss. The integration of automated verification, simulation capabilities, and precise measurements shows great promise for diabetic patients, reducing the likelihood of adverse outcomes such as ulceration and amputation.

Blockchain technology has emerged as a necessity for the decentralization of payment methods and transactions, but it has brought with it many properties of distributed systems that have made it a crucial technology for overcoming some of society's challenges, especially in the context of decentralizing services, transparency of information, availability, and security. Its architecture and communication methods, although possessing some complex nuances to understand, particularly for the lay audience in the field of distributed systems, protocols, and computer networks. In this article, we will explore some topics of distributed systems related to blockchain technology.

Background: The resurgence of syphilis, primarily in MSM populations, has necessitated novel prophylactic strategies, such as the use of doxycycline post-exposure prophylaxis (doxy-PEP). However, the potential for increased doxycycline use to select for tetracycline resistance represents significant challenges in managing this sexually transmitted infection. This study aims to identify chromosomal mutations associated with tetracycline resistance in Spirochaetales to inform treatment strategies and surveillance measures.Methods: Whole genome sequences (WGS) from the Spirochaetales order, including 4,355 genomes, were analyzed for the presence of mutations in 16S rRNA and non-synonymous mutations in the rpsC and rpsJ genes. The study utilized WGS from GenBank® and sequence data from the PubMLST Treponema pallidum isolate collection. Genetic resistance to tetracycline was detected using a combination of BLASTN searches and gene-gene analysis. Results: A transition mutation TGA to TGG at positions 965-967 in the 16S rRNA gene was detected in 5.6% of Treponema spp. and 3.97% of Spirochaeta spp. genomes. The rpsJ gene exhibited a V57G amino acid substitution across a significant subset of Treponema spp. Notably, the V57K mutation was common in Spirochaeta spp. The rpsC gene had the H178Q mutation and was found to be present in the Spirochaetales bacterium.Conclusion: The identification of mutations associated with tetracycline resistance in Spirochaetales provides a foundation for the development of rapid molecular diagnostics and guides the clinical selection of antibiotics for syphilis treatment. This study underscores the complexity of antibiotic resistance mechanisms and the critical importance of surveillance in the era of antibiotic prophylaxis for STI management.

Parkinson's disease is a neurodegenerative disorder resulting from progressive damage to specific brain regions. Key symptoms encompass involuntary movements (tremor), slowness of movements (bradykinesia), and muscle rigidity. Although the precise cause remains uncertain, a combination of genetic and environmental factors is believed to contribute to its development. This study focuses on the interaction of the E3 ubiquitin-protein ligase parkin with natural compounds. Docking investigations revealed that Amentoflavone exhibited an excellent binding energy score of approximately -10 kcal/mol. The E3 ubiquitin-protein ligase parkin displayed a robust binding affinity with docked amentoflavone, evident from a high binding energy of -10 kcal/mol. Nonetheless, additional biological studies are crucial not only to validate its potential efficacy against Parkinson's and assess safety in terms of toxicity but also to perform further computational analyses confirming these preliminary assessments.

This study delves into the therapeutic potential of two natural compounds, Amentoflavone and Bilobetin, by investigating their interactions with the BPIFB4 protein. BPIFB4, a member of the BPI fold-containing superfamily, has been associated with various biological processes, particularly in longevity and cardioprotection( LAV-BPIFB4). Molecular docking analyses revealed promising binding energies for Amentoflavone and Bilobetin with BPIFB4, suggesting favorable interactions.Additionally by pKCSM database are showed the predicted toxicity properties of Amentoflavone and Bilobetin respectively.This research contributes to the growing body of knowledge on natural compounds and their therapeutic implications in the context of vital biological processes. However, these findings underscore the need for further investigations, including in vivo studies and clinical trials, to validate the observed effects and explore the translational potential of Amentoflavone and Bilobetin in promoting cardiovascular health and longevity.

Diabetes mellitus is acknowledged as a significant contributor to severe complications. The present work has explorated the potential role amentoflavone as an aldose reductase inhibitor through Docking Simulations. From these results this compound has shown excellent results with this enzyme with a high binding energy of -11 kcal/mol. If amentoflavone,a natural biflavonoid, proves effective in inhibiting aldose reductase, it could hold therapeutic promise for managing diabetic complications. However, it's crucial to emphasize that while computational studies offer valuable insights, experimental validation is essential to confirm the actual inhibitory effects and to establish the safety and efficacy of amentoflavone for this purpose. Further research and clinical studies are warranted to fully understand and harness the potential therapeutic benefits of amentoflavone in the context of diabetes.

Cells respond to DNA double-strand breaks by initiating DSB repair and ensuing a cell cycle checkpoint. The primary responder to DSB repair is non-homologous end joining, an error prone repair pathway. However, when DSBs are generated after DNA replication in the G2 phase of the cell cycle, a second DSB repair pathway, homologous recombination, can come into action. Both ATM and ATR are important for DSB-induced DSB repair and checkpoint responses. One method of ATM and ATR working together is through the DNA end-resection of DSBs. As a readout and marker of DNA end-resection, RPA is phosphorylated at Ser4/Ser8 of the N-terminus of RPA32 in response to DSBs. Here, the significance of RPA32 Ser4/Ser8 phosphorylation in response to DNA damage, specifically in S phase to G2 phase of the cell cycle is examined. RPA32 Ser4/Ser8 phosphorylation in G2 synchronized cells is necessary for increases in TopBP1 and Rad9 accumulation on chromatin and full activation of the ATR-dependent G2 checkpoint. In addition, our data suggest RPA Ser4/Ser8 phosphorylation modulates ATM-dependent KAP-1 phosphorylation and Rad51 chromatin loading in G2 cells.

Summary— Introduction: HIV (human immunodeficiency virus) is a virus that attacks and destroys one type of white blood cell, in particular, the CD4 lymphocytes, which are responsible for the body's immune response. This weakens the immune system to such an extent that it can no longer fight other viruses, bacteria, protozoa, fungi, and tumors. Methods: We investigated the potential biological role of the natural compound Polydatin (or piceid) using SwissDock, a web service to predict the molecular interactions that can occur between a target protein and a small molecule. Discussion: For the first time, we have investigated the role of polydatin against HIV-1 protease and HIV-2 protease through a computational approach by comparing their estimated ΔG values (kcal/mol) and the nature of the interactions between the residues in the catalytic center of the HIV proteases.Conclusion: From these simulations, Polydatin has excellent physical properties and an excellent estimated ΔG value (kcal/mol) of about -9.9 kcal/mol against HIV-2 protease and ΔG value (kcal/mol) of about -9.5 kcal/mol against HIV-1 protease. In addition, Polydatin was able to bind to two key amino acids of the catalytic triad sequence common to asparagine proteases (Asp25 and Gly27)

The impact of the climate crisis on urban environments and life within them is evident. In the face of challenges like heatwaves, floodings and other extreme events, pollution, and declining quality of life in cities, there is a growing demand for the preservation and expansion of urban green spaces, often driven by citizen-led transformations. This paper examines 100 urban greening projects initiated or supported by citizens globally, categorizing them according to the type of greenery, the stakeholders involved, the mode of implementation and the use of smart technologies incorporated. We notice variations in green endeavors based on the stakeholders spearheading them, however most of entirely citizen-led initiatives aimed at the creation of urban farms and food growing, demonstrating the pressuring needs for food securing and self-determination in communities. More than half of the assessed initiatives that managed to scale up and multiply had public authorities providing a framework or a type of support for their development or an NGO or other organization providing expertise and mobilize citizens at various stages. In terms of technological use, we find mostly websites and social media platforms easing participatory endeavors and knowledge sharing of best practices, accelerating scaling efforts, while there is a low integration of more advanced digital technologies, which if used, could enable real-time monitoring of green spaces, inform evidence-based decision-making, and streamline processes in scaling up green initiatives.

Aerobic glycolysis in cancer cells, originally observed by Warburg 100 years ago, which involves production of lactate as the end product of glucose breakdown even in the presence of adequate oxygen, is the foundation for the current interest in cancer cell-specific reprograming of metabolic pathways. The renewed interest in cancer cell metabolism has now gone well beyond the original Warburg effect related to glycolysis to other metabolic pathways to include amino acid metabolism, one-carbon metabolism, pentose phosphate pathway, nucleotide synthesis, antioxidant machinery, etc. Since glucose and amino acids constitute the primary nutrients that fuel the altered metabolic pathways in cancer cells, the transporters that mediate the transfer of these nutrients and their metabolites not only across the plasma membrane but also across the mitochondrial and lysosomal membranes have become an integral component of expansion of the Warburg effect. In this review, we focus on the interplay between these transporters and metabolic pathways that facilitates the metabolic reprogramming which has become the hallmark of cancer cells. The beneficial outcome of this recent understanding of the unique metabolic signature surrounding the Warburg effect is the identification of novel drug targets for development of a new generation of therapeutics to treat cancer.

This author is not a philosopher nor historian of science, but an engineering thermodynamicist. In that regard and in addition to various philosophical “why & how” treatises and existing historical analyses, the physical and logical “what it is” reflections, as successive Key Points, where a key reasoning infers the next one, along with novel contributions and original generalizations, are presented. We need to keep in mind that in Sadi Carnot’s time (early 1800s) the steam engines were inefficient (below 5%, so the heat in and out were comparable within experimental uncertainty), the conservation of caloric flourished (might be a fortunate misconception), and many critical thermal-concepts, including the conservation of energy (The First Law) were not even established. If Clausius and Kelvin were "fathers of thermodynamics" then Sadi Carnot was the "grandfather" [Kostic, 2023 July 24], or better yet, Sadi Carnot was the "Forefather of Thermodynamics-to-become" [Kostic, 2023 October 29].

In this article, we investigate both the Schrödinger and continuity equations in the presence of nonlocal potential that arising from electron-electron interaction and local one in the context of minimal uncertainty in momentum within commutative and noncommutative frameworks. Furthermore, the Frahn–Lemmer potential type is used. Interestingly, the combined effects of both the phase-space noncommutativity and nonlocality on the current density while considering a minimal uncertainty in momentum are examined. We find that the current density, as conventionally defined, does not fulfil the condition of current conservation, thus, a new definition of the current density that encompasses the aforementioned contributions is given. Thereafter, it is shown that the computed current using the new definition of the current density satisfies the current conservation. Noting that using both the $★$product and linear Bopp-Shift, the noncommutativity is inserted.

We investigated and quantified bacterial accumulation in facemasks used by the general population, using 16S rRNA (Sanger Sequencing), culture and biochemical analysis along with Rose Bengal staining. Additionally, a systematic overview of the literature on face mask contamination was undertaken. We found an average bacterial load of 4.24 × 10⁴ CFU recovered/mask, with a maximum load of 2.85 × 10⁵ CFU. This maximum is 310 times higher than the limit value for contamination of ventilation system outlet surfaces specified by the German standard VDI 6022. Biochemical and molecular identification predominantly found Staphylococcus species (80%), including Staphylococcus aureus, along with endospore-forming Bacillus spp. Literature reports also indicate contamination of masks by bacterial and fungal opportunists of the genera Acinetobacter, Aspergillus, Alternaria, Bacillus, Cadosporium, Candida, Escherichia, Enterobacter, Enterococcus, Klebsiella (including K. pneumoniae), Micrococcus, Microsporum, Mucor, Pseudomonas, Staphylococcus and Streptococcus. Bacterial counts increase linearly with wearing duration, and prolonged use may affect the skin and respiratory microbiomes, promoting consequential eye, skin, oral and airway conditions. These aspects underscore the urgent need for further research and a risk-benefit analysis in respect of mask use, particularly given their unproven efficacy in disrupting the transmission of respiratory viruses and their adverse social consequences.

The effects of rare earth Ce on the microstructure and properties of cast pure copper were investigated through thermodynamic calculations, XRD analysis, mechanical testing, metallographic microscopy, and scanning electron microscopy (SEM). The experimental results demonstrate that the reaction between rare earth Ce and oxygen as well as sulfur in copper exhibits a significantly negative Gibbs free energy value, indicating a strong thermodynamic driving force for deoxidation and desulfurization reactions. Ce is capable of removing trace amounts of O and S from copper. Moreover, the maximum solid solubility of Ce in Cu falls within the range of 0.009% to 0.01%. Furthermore, Ce can refine columnar grains while enlarging equiaxed grains in as-cast copper.Upon addition of rare earth Ce, the tensile strength increased by 8.45%, elongation increased by 12.1%, and microhardness rose from 73.5HV to 81.2HV-an increase of 10.5%. Overall, rare earth Ce has been found to enhance both the microstructure and mechanical properties of cast pure copper.

Trailing the modern ideas of sociophysics, a minimalistic thermodynamic model of society is proposed, which consists of three social ingredients: people, economy, and entropy. Employing the universal van der Waals equation of state, many important relationships are discovered, including laws of econophysics. A paramount finding is that the Second Law of thermodynamics governs irreversible social evolution, revealing the power of social liberty and economic freedom.

The Principle of Indifference (“PI”: the simplest non-informative prior in Bayesian probability) has been shown to lead to paradoxes since Bertrand (1889). Von Mises (1928) introduced the “Wine/Water Paradox” as a resonant example of a “Bertrand paradox”, and which has been presented as demonstrating that the PI must be rejected. We now resolve these paradoxes by a Maximum Entropy (MaxEnt) treatment of the PI that also includes information provided by Benford’s “Law of Anomalous Numbers” (1938). We show that the PI should be understood to represent a family of informationally-identical MaxEnt solutions; each solution being identified with its own explicitly justified boundary condition. In particular, our solution of the Wine/Water Paradox exploits Benford’s Law to construct a non-uniform distribution representing the universal constraint of scale invariance, which is a physical consequence of the Second Law of Thermodynamics.

We study the performance of an endoreversible magnetic Otto cycle with a working substance composed of a single quantum dot described using the well-known Fock-Darwin model. We find that tuning the intensity of the parabolic trap (geometrical confinement) impacts the proposed cycle’s performance, quantified by the power, work, efficiency, and parameter region where the cycle operates as an engine. We demonstrate that a parameter region exists where the efficiency at maximum output power exceeds the Curzon-Ahlborn efficiency, the efficiency at maximum power achieved by a classical working substance.

There is great current interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a "phase" parameter that characterises the way "exceptional points" (singularities of various sorts) determine the system. These systems are briefly reviewed with an emphasis on their geometrical thermodynamics properties.

There is great current interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a "phase" parameter that characterises the way "exceptional points" (singularities of various sorts) determine the system. These systems are briefly reviewed with an emphasis on their geometrical thermodynamics properties.

One open question remains regarding the theory of the generalized variational principle, that is, why the stress-strain relation still be derived from the generalized variational principle while the Lagrangian multiplier method is applied in vain? This study shows that the generalized variational principle can only be understood and implemented correctly within the framework of thermodynamics. As long as the functional has one of the combination $A(\epsilon_{ij})-\sigma_{ij}\epsilon_{ij}$ or $B(\sigma_{ij})-\sigma_{ij}\epsilon_{ij}$, its corresponding variational principle will produce the stress-strain relation without the need to introduce extra constraints by the Lagrangian multiplier method. It is proved herein that the Hu-Washizu functional $\Pi_{HW}[u_i,\epsilon_{ij},\sigma_{ij}]$ and Hu-Washizu variational principle comprise a real three-field functional.

Motivated by the work of Saridakis (Phys. Rev. D 102, 123525 (2020)), the present study reports the cosmological consequences of Barrow holographic dark energy (HDE) and its thermodynamics. Literatures demonstrate that Dark Energy (DE) may result from electroweak symmetry breaking that triggers a phase transition from early inflation to late time acceleration. In the present study, we incorporated viscosity in the Barrow HDE. A reconstruction scheme is presented for the parameters associated with Barrow holographic dark energy under the purview of viscous cosmology. Equation of state (EoS) parameter is reconstructed in this scenario and quintessence behaviour is observed. Considering BarrowHDE as a specific case ofNojiri-Odintsov (NO) HDE, we have observed quintom behaviour of the EoS parameter and for some values of n the EoS has been observed to be very close to −1 for the current universe. The generalised second law of thermodynamics has come out to be valid in all the scenarios under consideration. Physical viability of considering Barrow HDE as a specific case of NO HDE is demonstrated in this study.

Theoretical analysis into the energetics of a novel putative electromagnetic field propulsion device by the author, found that it was able to impart momenergy to the ground state of the electromagnetic field; some rest-energy of the craft was converted to kinetic energy of the craft. Electrical analysis showed that the propulsor was always a net electrical load – if the device accelerated from one frame, then deaccelerated to the original frame, both processes would consume electrical work. The aim of this paper is to look further into this sinking of high-grade electrical energy into the field ground state and to show that an even more pernicious form of 2^nd Law of Thermodynamics exists.

This paper introduces the first version of the QBIT theory of consciousness which is based on inspirations and insights from Quantum mechanics, Biology, Information theory, and Thermodynamics. The ultimate goal of the QBIT theory is to find scientifically correct answers to the following four questions: What is the nature of qualia? How are qualia generated? Why are qualia subjective? Why does a quale have a particular meaning?The theory, in its first version, proposes tentative answers to these four fundamental questions about consciousness. According to the QBIT theory qualia are quantum information encoded in maximally entangled pure states. Qualia are generated when robustness of a representation exceeds a certain threshold. Qualia are subjective because maximally entangled pure states are not shareable. The quality or meaning of a particular quale stems from information compression via the matching and unification of patterns.

We study the performance of a classical and quantum magnetic Otto cycle with a quantum dot as a working substance using the Fock-Darwin model with the inclusion of the Zeeman interaction. Modulating an external/perpendicular magnetic field, we found in the classical approach an oscillating behavior in the total work that is not perceptible under the quantum formulation. Also, we compare the work and efficiency of this system for different regions of the Entropy, $S(T,B)$, diagram where we found that the quantum version of this engine always shows a reduced performance in comparison to his classical counterpart.