In this paper, we present a full coverage path planning (CPP) algorithm for the marine surveys conducted in the convex polygon shaped search area. The survey is supposed to carry out by torpedo-type AUVs (autonomous underwater vehicles). Due to their nonholonomic mechanical characteristics, these vehicles have nonzero minimum turning radius. For any given polygon shaped search area, it can always be partitioned into one or more convex polygons. With this in mind, this paper proposes a novel search algorithm called CbSPSA (Calculation based Shortest Path Search Algorithm) for full coverage of any given convex polygon shaped search area. By aligning the search inter-tracks alongside the edge with the minimum height, we can guarantee the minimum number of the vehicle’s turns. In addition, the proposed method can guarantee the planned path is strictly located inside the polygon area without overlapped or crossed path lines, and also has the total path length as short as possible. Considering the vehicle’s nonzero minimum turning radius, we also propose a sort of smoothing algorithm to smooth the waypoint path searched by CbSPSA so as for the vehicle to exactly follow it. The smoothed path is also guaranteed to be strictly located inside the polygon. Numerical simulation analyses are also carried out to verify the effectiveness of the proposed schemes.

In the first part of this article, we discuss and generalize the complete convergence introduced by Hsu and Robbins (1947) to the r-complete convergence introduced by Tartakovsky (1998). We also establish its relation to the r-quick convergence first introduced by Strassen (1967) and extensively studied by Lai (1976). Our work is motivated by various statistical problems, mostly in sequential analysis. As we show in the second part, generalizing and studying these convergence modes is important not only in probability theory but also to solve challenging statistical problems in hypothesis testing and changepoint detection for general stochastic non-i.i.d. models.

This study presents an advanced data augmentation framework to enhance diagnostics in centrifugal pumps using vibration data, with a focus on practical industrial applications. Initially, Gaussian noise and signal stretching were employed to generate synthetic data, addressing the common issue of insufficient defect data in industrial settings. These methods simulate varied operating conditions and different rotating speeds. Recognizing the limitations of traditional approaches, we further integrated advanced models—Long Short-Term Memory (LSTM), Autoencoder (AE), and Generative Adversarial Networks (GANs) to augment the dataset comprehensively. This enhanced approach increases the robustness and accuracy of machine-learning models for fault detection and classification. Experimental results show significant improvements in diagnostic algorithm performance, reducing false positives and increasing fault detection rates. The study offers a complete framework for incorporating both traditional and advanced data augmentation techniques into predictive maintenance protocols, ensuring reliable operation of centrifugal pumps in diverse industrial environments.

Abstract: This study investigates the transition to sustainable shipping industry within the framework of the IMO's 2050 carbon neutrality objective, focusing on socio-technical systems analysis. The research delves into the sustainable paradigm shift towards alternative marine fuels such as LNG, methanol, ammonia, and hydrogen from a socio-technical transition perspective, analyzing the multi-level per-spective challenges and opportunities. Specifically, it scrutinizes the implications of adopting these al-ternative fuels for global shipping, identifying technical uncertainties, financial constraints, and infra-structure needs. Through a comprehensive review of existing literature on ship emissions and alterna-tive fuels, coupled with socio-technical transition theory, the study addresses the multifaceted chal-lenges and opportunities in meeting the IMO's environmental objectives. It emphasizes the necessity of collaborative governance, innovative niche strategies, and a holistic approach to leverage the current window of opportunity in facilitating the maritime industry's transition to sustainable fuel options. Fi-nally, this research enhances our understanding of the intricate interplay between technological, social, industrial, and cultural dynamics in the greening of shipping and offers insights into the strategic adop-tion of environmentally friendly marine fuels for a sustainable future.

Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods, first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, a non-linear mapping is a better choice. Our paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images into Type-I or Type-II. In the second phase, depending on the classification, the Deep Line Model (DLM) for Type-I images or the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, DML creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image’s characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio(PSNR) and root mean squares error (RMSE) affirm our method’s effectiveness in accurately restoring underwater images, outperforming existing techniques.

Recent advancements in communication technology have catalyzed the widespread adoption of realistic content, with augmented reality (AR) emerging as a pivotal tool for seamlessly inte-grating virtual elements into real-world environments. In construction, architecture, and urban design, the integration of mixed reality (MR) technology enables rapid interior spatial mapping, providing clients with immersive experiences to envision their desires. The rapid advancement of MR devices, or devices that integrate MR capabilities, offers users numerous opportunities for enhanced entertainment experiences. However, to support designers at a high level of expertise, it is crucial to ensure the accuracy and reliability of the data provided by these devices. This study explores the potential of utilizing spatial mapping within various methodologies for surveying architectural interiors. The objective is to identify optimized spatial mapping procedures and determine the most effective applications for their use. Experiments were conducted to evaluate the interior survey performance, using HoloLens 2, iPhone 13 Pro for spatial mapping, and photogrammetry. The findings indicate that HoloLens 2 is most well-suited for the tasks exam-ined in the scope of experiments. Nonetheless, based on the acquired parameters, the author also proposes approaches to apply the other technologies in specific real-world scenarios.

Recently, methods to detect DoS and spoofing attacks that occur on in-vehicle networks using CAN Protocol are being studied through deep learning models such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. In addition, significant results are being achieved through research on applying time series-based deep learning models such as LSTM to detect DoS attacks and replay attacks occurring in in-drone networks by expanding them to drones using the UAVCAN protocol. In this paper, we conducted an experiment to detect in-drone network attacks through non-time series analysis using machine learning models and deep learning models, and through appropriate learning for each attack type, it can also be analyzed through non-time series analysis. The results showed that it was possible to detect attacks.

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery which has direct impact on it’s longevity and profitability. Therefore, failure of a mechanical system or any of it component would be detrimental to production continuity and availability. Consequently,this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework involves analyzing the spectral characteristics of the vibration signals generated by Industrial Shot Blast. Additionally, a peak detection algorithms is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into 10 machine learning classifier, with Extreme gradient boosting (XGB) as the core classifier. Results show that the XGB classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 seconds. Other global assessment metrics were also implemented in the study to further validate the model.

Salvia miltiorrhiza (SM) contains the tanshinones, a compound with various pharmacological effects, and has been extensively studied as a pharmaceutical material. However, conventional methods for extracting tanshinones face challenges such as environmental hazards and high cost. In this study, we aimed to effectively extract tanshinones from SM using cloud point extraction (CPE) with lecithin, a natural surfactant. By optimizing various extraction conditions including the solid-to-liquid ratio, lecithin concentration, NaCl concentration, pH, and equilibrium temperature, the optimal extraction efficiency was achieved using 20 mL of solvent per 1 g of sample, 3% lecithin (w/v), 2% NaCl (w/v), pH 6, and room temperature (25 ± 2°C). The CPE method, which minimizes the use of organic solvent and is eco-friendly demonstrated improvements in extraction efficiency, with a 4.55% increased for dihydrotanshinone I, 8.32% for cryptotanshinone, 15.77% for tanshinone I, and 6.81% for tanshinone IIA compared to the conventional water extraction method. These results suggest that CPE is promising, environmentally friendly, and efficient approach for extracting hydrophobic components from pharmacologically active materials such as SM, with potential applications across various fields of natural product extraction.

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

We study L_2-approximation problems in the weighted Hilbert spaces in the worst case setting. Three interesting weighted Hilbert spaces appear in this paper, whose weights are equipped with two positive parameters $\ga_j$ and $\az_j$ for $j=1,\,2,\dots,\,d$. We consider the worst case error of algorithms that use finitely many arbitrary continuous linear functionals. We discuss the exponential convergence-(t,s)-weak tractability (EC-(t,s)-WT) of these L_2-approximation problems under the absolute or normalized error criterion. In particular, we obtain the sufficient and necessary conditions for EC-(1,1)-WT and EC-(t,1)-WT with t

In this study, we analyzed the microbial community of traditional fermented foods in Jeju Island to identify the distribution of useful microorganisms and confirm their anti-inflammatory and anti-melanogenic effects to determine their potential use as cosmetic ingredients. Firstly, we examined the microbial communities of Omphalius rusticus Jeotgal (OR), Spratelloides gracilis Jeotgal (SG), Chromis notata Jeotgal (CN), Turbo cornutus Jeotgal (TC), Trichiurus lepturus intestine Jeotgal (TL), Branchiostegus japonicus Sweet Rice Punch (BJ), Salted Anchovy Sauce (SA), Jeju Soy Sauce (JSS), and Jeju Soybean Paste (JSP). We found that Latilactobacillus sakei (87.2%), Tetragenococcus halophilus (37.7%), T. halophilus (96.8%), Bacillus subtilis (23.4%), T. halophilus (71.3%), L. sakei (53.7%), Lentibacillus sp. (42.9%), Enterococcus durans (14.6%), and E. durans (32.8%) were the dominant species. Secondly, to study the nine Jeju fermented foods’ anti-inflammatory and anti-melanogenic effects, we employed RAW 264.7 and B16F10 cells, classic cell models for inflammation and melanogenesis studies. Ethyl acetate extracts of the nine Jeju fermented foods all inhibited nitric oxide (NO) and melanin production in a concentration-dependent manner. Thirdly, to test the applicability of the nine Jeju fermented foods to human skin, we used the MTT assay to assess their cytotoxic effects on human keratinocytes (HaCaT cells). Finally, the topical applicability of the nine Jeju fermented foods was tested through primary skin irritation, and it was found that they did not cause any adverse effects. Therefore, extracts from the nine Jeju fermented foods have potential applications as ingredients in anti-inflammatory and anti-melanogenic products and can be used in the cosmetic industry.

The topic of predictive toxicology has been greatly influenced by recent progress in comprehending drug toxicity processes and enhancing medication development. The integration of omics technologies, such as transcriptomics, proteomics, and metabolomics, with traditional toxicological assessments has yielded extensive knowledge about the biological pathways implicated in drug-induced toxicity. The utilization of a multi-omics method amplifies the ability to identify biomarkers that can detect toxicity at an early stage, hence enhancing the safety profile of novel therapeutic medicines. Machine learning and in silico models, such as QSAR models and multi-task deep learning algorithms, have become essential tools. They have shown great accuracy in predicting toxicity endpoints and have helped in the identification of new biomarkers and therapeutic targets. The introduction of microphysiological systems and PBPK modeling has enhanced the transfer of preclinical discoveries to clinical results, providing more precise forecasts of human reactions to medications. Notwithstanding these progressions, obstacles such as the diversity of data and the complex nature of omics data require sophisticated computational techniques for efficient analysis. Continued cooperation and established procedures are crucial to fully utilize these technologies, guaranteeing the creation of safer and more efficient medicinal agents.

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

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

The article presents the case of a 27-year-old female patient with idiopathic congenital complete heart block who was referred by her primary care physician for cardiological evaluation. The conduction disturbance was recognized at the age of 6 years and was asymptomatic. The disqualification from pacemaker implantation included detailed history of a patient’s symptoms, echocardiographic assessment of the heart, exercise testing and ECG Holter monitoring. The aid of salbutamol administered orally was also useful.

The ubiquitous presence of microplastics (MPs) in the environment poses a threat to ecosystems and increases concerns about their exposure to the human body. However, despite being consumed for health improvement, limited research has been conducted on MPs in functional foods. This study aimed to investigate the occurrence of MPs and the relation of the source of raw material or the manufacture in various omega-3 oil in the Korean market. MPs in omega-3 capsule and raw oil, sourced from both plant-based (PB) and animal-based (AB) sources, were investigated. We developed the method by direct filtration with acetone washing and analyzed MPs greater than 5 μm using microscope-Raman spectroscopy. The average number of MPs was found to be 1.2 ± 1.7 MPs/g for PB raw oil, 2.2 ± 1.7 MPs/g for AB raw oil, 3.5 ± 3.9 MPs/g for PB capsule oil, and 10.6 ± 8.9 MPs/g for AB capsule oil. Polypropylene and polyethylene terephthalate were major specious (83-95 %) found in omega-3 oil. The ratio of size range remained consistent across all groups, with a trend of higher detection rates as the size diminished. The results reveal that the main reason of MPs contamination is not the source of raw material but the manufacturing and packaging process for omega-3 oil.

In the history, architects, engineers or artists traditionally visualized architectural, urban planning, urban design, or landscape design projects with physical models in a smaller scale to represent their ideas. The biggest problems of traditional materialistic models are the long production time and limitation of mobility. Thanks to advancements in digital technology, 3D models can now be generated easily from 2D blueprints using computer software. However, eventually the digitalized approach could not replace the whole industry of making physical models, because of a lack of stereoscopic 3D imaging of space and structures. In this study, we discuss two points: the normal procedure of visualizing 3D models in urbanism and architecture design projects; and proposal of the stereographic hologram visualization for 3D models created with CHIMERA hologram printer as a new approach to solve all the problems in spatial projects visualization of both digitalized and physical models. The experiment's results indicate that tabletop holograms' visualization quality can potentially replace traditional methods in the near future. Moreover, the input data for this method can be seamlessly integrated into current architectural and urban design workflows, making holograms a promising approach for 3D immersive content in this field.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in bio-technology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affin-ities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and en-abling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation, and in ad-dressing ethical concerns related to AI-driven protein design. This review provides a compre-hensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

AbstractEliadean analysis reveals the passive or the receiving aspect of the symbol of the presence of God, the Aron ha-Berit or the Ark of the Covenant of the ancient Hebrews. Consequently, we are able to establish the inner mode or the symbolic structure of Yahweh. The Sefirotic Tree of medieval Kabbalists replicates the symbolic structure of Yahweh as revealed by the Ark of the Covenant. The feminine aspect of the presence of God remains in the consciousness of the Jewish people and blossoms in the medieval Kabbalah. It is precisely the feminine aspect that is capable of transcendence or able to acquire an infinite character.The dynamic aspect of the Shekhinah is reflective of the changes it undergoes as a result of receiving the flow from the Sefiroth above. Looking at the Sefirotic Tree as a reflection of the mind of the Kabbalist, Shekhinah represents the human ability for acceptance. A narrow acceptance corresponds to a narrow mind, whereas the infinite ability to accept corresponds to the infinite mind. A particular description of the formative function of Shekhinah represents a different mode of cognition. Similar descriptions are found in the Tibetan Buddhist tradition related to the concept of Rigpa. In order to find out the equivalence of these descriptions we analyzed the latter and found that Shekhinah and Rigpa converge at both the foundational and transcendental levels.

Alzheimer’s disease (AD), the most common cause of dementia, is characterized by disruptions in memory, cognition, and personality, significantly impacting morbidity and mortality rates among older adults. However, the exact pathophysiological mechanism of AD remains unknown, and effective treatment options for AD are still lacking. Human induced pluripotent stem cells (iPSC) are emerging as promising platforms for disease research, offering the ability to model genetic mutations associated with various conditions. Patient-derived iPSCs are useful for modeling neurodegenerative and neurodevelopmental disorders. In this study, we generated AD iPSCs from peripheral blood mononuclear cells obtained from a 65-year-old patient with AD carrying the E682K mutation in the amyloid precursor protein (APP) gene. Cerebral organoids derived from AD iPSCs recapitulated the AD phenotype, exhibiting significantly increased levels of tau protein. Our analysis revealed that an iPSC disease model of AD is a valuable assessment tool for pathophysiological research and drug screening.

Fine dust causes various disorders, including cardiovascular, neurological, renal, reproductive, motor, systemic, respiratory, and cancerous diseases. Therefore, it is necessary to study functional materials to prevent these problems. In this study we used various methods, including quantitative PCR, flow cytometry, ELISA, and Alizarin O staining, to study the beneficial effects of erucic acid against fine dust. Erucic acid and erucic acid-induced exosomes (EIE) strongly suppress the attenuation of osteocytic differentiation and inflammation induced by fine dust. Erucic acid effectively suppresses inflammation in fibroblasts exposed to fine dust. Notably, despite exposure to fine dust, EIE promoted osteocytic differentiation in adipose-derived stem cells (ASC) and osteogenesis in macrophages, in addition to phagocytosis. Dramatic upregulation of RunX2 and BMP7 by EIE indicates that it is a strong activator of osteocytic differentiation in ASC and protects them from the effects of fine dust. Moreover, EIE induces immune activity and acts as an osteogenic trigger for macrophages. Therefore, this study suggests the potential use of erucic acid and erucic acid-induced exosomes (EIE) as biomaterials to prevent environment-borne diseases.

This article presents a new methodology for modeling and analyzing discrete dynamic systems through the construction of inverse algebraic models, giving rise to the Theory of Inverse Discrete Dynamical Systems. Key concepts such as inverse modeling, structural analysis in inverse algebraic trees, and the establishment of topological equivalences for the analytical transfer of properties between the canonical system and its inverted counterpart are introduced. Central theorems on homeomorphic invariance and topological transport are demonstrated, consolidating the validity of transferring cardinal attributes between both equivalent dynamic representations. As a concrete application, an alternative proof of the historic Collatz Conjecture is presented through the rigorous construction of the associated inverse model and the analytical transfer of properties exhibited in the inverted tree structure. Furthermore, evidence has been presented on Conway's Conjecture in the Game of Life through meticulous inverse modeling in bounded cases, overcoming initial limitations of the theory in combinatorial explosions. Although a complete proof of the conjecture is not achieved, this positively expands the modeling and analysis capacity of the methodology. The impact of properly applying this novel theory to expand understanding and solve open problems in discrete dynamical systems seems vast and profound.

We consider a Cauchy problem for differential equations in a Hilbert space $X$. The problem is stated in a time interval $I$, which can be finite or infinite. Weuse a fixed point argument for history-dependent operators to prove the unique solvability of the problem. Then, we state and prove convergence criteria for both a general fixed point problem and thecorresponding Cauchy problem. These criteria provide necessary and sufficient conditions on a sequence $\{u_n\}$ which guarantee its convergence to the solution of the corresponding problem, in the space of both continuous and continuously differentiable functions. We then specify our results in the study of a particular differential equation governed by two nonlinear operators. Finally, we provide an application in viscoelasticity and give a mechanical interpretation of the corresponding convergence result.

In this study, the microbial community during the barley yeast fermentation of stevia leaves and its correlation with antioxidant, antiobesity, and anti-inflammatory properties, as well as metabolites, were investigated using UPLC-Q-TOF-MS. Stevia was fermented using commercial TSB media (TSB 1, TSB 2, and TSB 3) and sucrose (Sucrose 1, Sucrose 3, and Sucrose 3) for 1, 3, and 5 days, respectively. Stevia ferments showed higher DPPH and ABTS radical scavenging capacity compared to samples incubated with sucrose for 5 days, and all six ferments inhibited nitric oxide production in a concentration-dependent manner in LPS-induced mouse macrophages. Furthermore, UPLC-QTOF-MS analysis identified 23 related substances, including 10 terpenoids, including rubusoside, steviolbioside, and rebadioside derivatives; dulcoside A; and phlomisoside II, which are indicators of stevia, as well as five flavonoids, four phenolic acids, and four fatty acids. We also identified the microbial community during fermentation via the next-generation sequencing of the 16S rRNA gene for bacteria and the internal transcribed spacer (ITS) gene for fungi. The results showed that TSB 1 and Sucrose 1 ferments were dominated by the pathogens Enterococcus hirae (58.93%) and Cronobacter sakazakii (80.92%), while samples fermented for more than 3 days were pathogen-free and dominated by lactic acid bacteria such as Pediococcus stilesii (73.37%). Microbial community analysis using the ITS region showed that Saccharomycopsis fibuligera, classified as a yeast rather than a mold, dominated the stevia fermentation regardless of the fermentation duration. In particular, the microbial community of the fermentation with a sugar solution was dominated by S. fibuligera by more than 99% throughout the fermentation periods of 1, 3, and 5 days. Finally, to apply the stevia ferments topically to human skin, skin irritation tests were performed on 30 volunteers. The results showed that the highly concentrated extracts (1 mg/mL) of all six stevia ferments were hypoallergenic. Taken together, these findings suggest that brewer's yeast fermented from stevia leaves with a sugar solution offers promise as a natural ingredient for use in functional foods and cosmetics.

Conventional traceability systems in Cambodian agriculture, such as Rubber Supply Chain Management (RSCM), often struggle to provide timely data and fair market prices, leading to mistrust among stakeholders, exploitation by intermediaries, and hindering progress towards ethical sourcing and sustainable practices. This research aims to showcase the potential of integrating blockchain technology, the Internet of Things (IoT), the InterPlanetary File System (IPFS), and Differential Privacy (DP) methods to empower Cambodian rubber farmers. By enhancing transparency, facilitating fair pricing, and promoting sustainable methods in the rubber industry, this integrated approach leverages Hyperledger Fabric (HLF) to create a decentralized and secure system for tracking rubber from its source to the end consumer. Integration with IPFS ensures secure and easily accessible data storage, further enhancing the system’s reliability. Additionally, the implementation of differential privacy techniques safeguards confidential data during information sharing among farmers and supply chain stakeholders, minimizing potential privacy concerns. This comprehensive system encourages consumers to make ethical and sustainable alternative options by providing accessible information on harvesting methods, processing procedures, labor conditions, and environmental impacts. The potential benefits extend beyond Cambodia, serving as a model for other developing countries aiming to modernize and enhance the agricultural distribution networks. Our results show that transaction send rates (TPS) and throughput in systems with and without differential privacy grow with increased transaction volumes. However, the system without differential privacy achieves greater scalability and efficiency above a certain threshold. However, it offers a slightly reduced latency, reflecting a trade-off between privacy and performance efficiency in blockchain-based agricultural traceability systems.

Using a scaling symmetry, it is shown how to compute polynomial conservation laws, generalized symmetries, recursion operators, Lax pairs, and bilinear forms of polynomial nonlinear partial differential equations thereby establishing their complete integrability. The Gardner equation is chosen as the key example for it comprises both the Korteweg-de Vries and modified Korteweg de Vries (mKdV) equations. The Gardner and Miura transformations which connect these equations are also computed using the concept of scaling homogeneity. Exact solitary wave solutions and solitons of the Gardner equation are derived using Hirota’s method and other direct methods. The nature of these solutions depends on the sign of the cubic term in the Gardner equation and the underlying mKdV equation. It is shown that flat (table-top) waves of large amplitude only occur when the sign of the cubic nonlinearity is negative (defocusing case) whereas the focusing Gardner equation has the standard elastically colliding solitons. The paper’s aim is to provide a review of integrability properties and solutions of the Gardner equation and illustrate the applicability of the scaling symmetry approach. The methods and algorithms used in this paper have been implemented in Mathematica but can be adapted for major computer algebra systems.

A mathematical procedure enabling the transformation of an arbitrary tessellation of a surface into a bi-colored complete graph is introduced. Polygons constituting the tessellation are represented by vertices of the graphs. Vertices of the graphs are connected by two kinds of links/edges, namely, by a green link, when polygons have the same number of sides, and by a red link, when the polygons have a different number of sides. This procedure gives rise to a semi-transitive, complete, bi-colored Ramsey graph. The Ramsey number was established as R_trans (3,3)=5. Shannon entropies of the tessellation and graphs are introduced. Ramsey graphs emerging from random Voronoi and Poisson Line tessellations were investigated. The limits ζ=lim┬(N→∞)⁡〖N_g/N_r 〗, where N is the total number of green and red seeds, N_g and N_r, were found ζ=0.272±0.001 (Voronoi) and ζ= 0.47±0.02 (Poisson Line). The Shannon Entropy for the random Voronoi tessellation was calculated as S= 1.690±0.001 and for the Poisson line tessellation as S =1.265±0.015.

Synchronous electrical cardioversion is a relatively common procedure in the emergency department (ED), often performed for unstable supraventricular tachycardia (SVT) or unstable ventricular tachycardia (VT). However, it is also used for stable cases resistant to drug therapy, which carry a risk of deterioration. In addition to the inherent risks linked with procedural sedation, there is a possibility of malignant arrhythmias or bradycardia, which could potentially result in cardiac arrest following this procedure. Here, we present a case of complete heart block unresponsive to transcutaneous pacing and positive inotropic and chronotropic drugs for 90 minutes, resulting in multiple cardiac arrests. The repositioning of the transcutaneous cardio-stimulation electrodes, one of them placed in the left latero-sternal position and the other at the level of the apex, led to immediate stabilization of the patient. Extubation of the patient was performed the following day, while full recovery and discharge within 7 days, after insertion of a permanent pacemaker.

The Svenonius theorem establishes the correspondence between definability of relations in a countable structure and automorphism groups of these relations in the extension of the structure. This correspondence may help in finding the description of the definability lattice constituted by all definability spaces (reducts) of the original structure. However, the major difficulty here is the necessity to consider the extensions which generally are obscure and hardly amenable to classification. Because of that results on definability lattices were obtained only for $\omega$-categorical structures (i.\,e. those in which all elementary extensions are isomorphic to the structure itself). All known definability lattices for such structures proved to be finite. In this work we introduce the concept of upwards complete structure as such in which all its extensions are isomorphic. Further we define upwards completions structures. For such structures Galois correspondence between definability lattice and the lattice of closed supergroups of automorphism group of the structure is an anti-automorphism. These lattices could be infinite in general. We describe the natural class of structures which have upwards completion, that is discretely homogeneous graphs, present the explicit constrcution of their completion and automorphism groups of completions. We establish the general \emph{localness} property of discretely homogeneous graphs and present the examples of completable structures and their completions.

In this paper, we introduce and study the notion of rough I₂-lacunary statistical convergence of double sequences in normed linear spaces. We also introduce the notion of rough I₂-lacunary statistical limit set of a double sequence and discuss about some properties of this set.

The P versus NP problem is a fundamental question in computer science. It asks whether problems whose solutions can be quickly verified can also be quickly solved. Here, "quickly" refers to computational time that grows proportionally to the size of the input (polynomial time). While the problem's roots trace back to a 1955 letter from John Nash, its formalization is attributed to Stephen Cook and Leonid Levin. Despite extensive research, a definitive answer remains elusive. Closely tied to this is the concept of NP-completeness. If a single NP-complete problem could be solved efficiently, it would imply that all problems in NP can be solved efficiently, proving that P equals NP. This work posits that MONOTONE ONE-IN-THREE 3SAT, a notoriously difficult NP-complete problem, can be solved efficiently, thereby establishing the equivalence of P and NP.

A sparse language is a formal language such that the number of strings of length $n$ is bounded by a polynomial function of $n$. We create a class with the opposite definition, that is a class of languages that are dense instead of sparse. We define a dense language on $m$ as a formal language (a set of binary strings) where there exists a positive integer $n_{0}$ such that the counting of the number of strings of length $n \geq n_{0}$ in the language is greater than or equal to $2^{n - m}$ where $m$ is a real number and $0 < m \leq 1$. We call the complexity class of all dense languages on $m$ as $DENSE(m)$. We prove that there exists an $\textit{NP--complete}$ problem that belongs to $DENSE(m)$ for every possible value of $0 < m \leq 1$.

Garcinia indica (Thouars) Choisy (commonly known as Kokum) is an important medicinal plant in Ayurvedic medicine and belonging to the Clusiaceae family. Kokum is a highly traded medicinal plant species of India, dried rind of fruits and butter from seeds are traded in the markets. This study aimed to assemble the complete chloroplast (cp) genome of G. indica and compare it with the previously published cp genome within the Garcinia taxa to identify the potential species-specific barcode marker for Kokum authentication. The assembled cp genome, with a size of 1,56,891 bp, exhibits a typical quadripartite structure. The large single-copy (LSC) region spans 85,580 bp, and the small single-copy (SSC) region spans 17,181 bp, together comprising 64.5% of the genome. The pair of inverted repeats (IRA and IRB) are each 27,065 bp, covering the remaining 35.5% of the genome. A total of 126 unique genes were identified, including 86 protein-coding genes, 32 tRNA genes, and 8 rRNA genes. Phylogenetic analysis using complete cp genomes from 12 species in the Clusiaceae family indicated that 10 Garcinia species form a sister clade. Notably, the ycf1 gene in the LSC region was more divergent within the Garcinia species. This study presents the first report on the chloroplast genome of G. indica. This chloroplast genome resource provides a basis for identifying new DNA barcode marker as well as species-specific marker for herbal drug authentication of Kokum, and species delineation within the Garcinia genus.

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or ‘multi‐level convergent evolution’. To investigate multi‐level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long‐standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi‐level convergent evolution.

Recently, the properties of bouncing oil droplets, also known as `walkers', have attracted much attention because they are thought to offer a gateway to a better understanding of quantum behaviour. They indeed constitute a macroscopic realization of wave-particle duality, in the sense that their trajectories are guided by a self-generated surrounding wave. The aim of this paper is to try to describe walker phenomenology in terms of de Broglie-Bohm dynamics and of a stochastic version thereof. In particular, we first study how a stochastic modification of the de Broglie pilot-wave theory, à la Nelson, affects the process of relaxation to quantum equilibrium, and we prove an H-theorem for the relaxation to quantum equilibrium under Nelson-type dynamics. We then compare the onset of equilibrium in the stochastic and the de Broglie-Bohm approaches and we propose some simple experiments by which one can test the applicability of our theory to the context of bouncing oil droplets. Finally, we compare our theory to actual observations of walker behavior in a 2D harmonic potential well.

Balanced and coordinated economic development across regions is a critical goal of regional economic development and new-type urbanization in China. However, few studies have examined economic growth convergence clubs at the county level. To extend the research on convergence clubs, this research applies a log t convergence test and a dynamic spatial ordered Probit model (DSOP) to endogenously identify economic growth convergence clubs in counties and to examine the influence of initial states and structures on club convergence probability. The study sample covers 2286 counties of China from 1992 to 2010. The results show significant convergence club patterns at the county levels, resulting in the gradual formation of six convergence clubs. The DSOP estimation results show that per capita fixed assets, population density and industrialization have promoted convergence club formation to varying degrees.

A popular account of the mixing patterns for the three generations of quarks and leptons is through the characters $\kappa$ of a finite group $G$. Here we introduce a $d$-dimensional Hilbert space with $d=cc(G)$, the number of conjugacy classes of $G$. Groups under consideration should follow two rules, (a) the character table contains both two- and three-dimensional representations with at least one of them faithful and (b) there are minimal informationally complete measurements under the action of a $d$-dimensional Pauli group over the characters of these representations. Groups with small $d$ that satisfy these rules coincide in a large part with viable ones derived so far for reproducing simultaneously the CKM (quark) and PNMS (lepton) mixing matrices. Groups leading to physical $CP$ violation are singled out.

Butirosins are naturally occurring aminoglycoside (AG) antibiotics featuring a 4,5-disubstituted 2-deoxystreptamine (2-DOS) with a (2S)-4-amino-2-hydroxybutyrate (AHBA) side chain. This side chain has been shown to confer resistance against AG modifying enzymes, leading to ongoing studies on the butirosin biosynthetic pathway and the corresponding enzymes. Butirosin is produced by Niallia (formerly Bacillus) circulans and Bacillus vitellinus, with most research focused on the former strain. To date, no whole genome analysis has been performed on B. vitellinus. In this study, we sequenced the complete genome of B. vitellinus NBRC 13296 and performed a comparative analysis of different butirosin biosyntheric gene clusters (BGCs), including those from N. circulans. The complete genome of B. vitellinus NBRC 13296 comprises a 6,331,192 base circlular chromosome with a GC content of 52.68%. The annotation revealed the presence of 5,605 CDSs, 70 tRNA genes, 30 rRNA genes, and 3 ncRNA genes in NBRC 13296. The highest dDDH and ANI values between NBRC 13296 and the most closely related type strain, Paenibacillus chitinolyticus KCCM 41400, were 97.8% and 98.66%, respectively. Based on these genome-based comparative analyses, we propose reclassifying B. vitellinus NBRC 13296 as P. chitinolyticus. Genome mining revealed 18 gene clusters encoding the biosynthesis of diverse secondary metabolites in the genome of B. vitellinus NBRC 13296, indicating the enormous biosynthetic potential of this strain. The predicted structural diversity of secondary metabolites includes aminoglycoside, PKS, NRPS, PKS-NRPS hybrids, metallophores, phosphonates, terpenes, β-lactones, and RiPP peptides. We then comparatively characterized butorosin BGCs previously studied in several N. circulans strains. Additionally, comparative genome analysis revealed complete butirosin BGCs identified from P. chitinolyticus KCCM 41400, P. chitinolyticus NRRL B-23119, P. chitinolyticus NRRL B-23120, P. chitinolyticus B-14908, P. chitinolyticus YSY-3.1, P. chitinolyticus JMW06, Paenibacillus sp. GbtcB18, Paenibacillus sp. HGH0039, Paenibacillus sp. MZ04-78.2. Finally, we identified the core region consisting of BtrS, BtrN, BtrM, BtrL, BtrA, BtrB, BtrC, BtrD, BtrD, BtrE, BtrF, BtrG, BtrH, BtrI, BtrI, BtrJ, BtrK, BtrO, BtrP, and BtrV, followed by an upstream region organizing BtrQ, BtrW, BtrX, BtrY, and BtrZ in the same transcriptional direction and sequential genetic arrangement, and a downstream region organizing various proteins based on BtrT, BtrR2, BtrU, and BtrR1. Our study provided insights into the reclassification of B. vitellinus NBRC 13296 to P. chitinolyticus and suggested the need for continued studies on butirosin biosynthesis from an enzymatic perspective.

Background and Objectives: Renal cell carcinomas and upper tract urothelial carcinomas are types of malignancies that originate in the kidneys. Each of these examples shows an increasing trend in the frequency and the mortality rate. This study aims to comprehensively define carcinomas by analysing clinical, paraclinical, and histological aspects to predict aggressiveness and mortality. Materials and Methods: We conducted a retrospective investigation on a group of patients suspected with kidney cancers. Results: We identified 188 cases. We observed a higher mortality rate and older age in individuals with urothelial carcinomas. Anemia, acute kidney injury, hematuria and perineural invasion were the main risk factors that predicted their mortality. Tumour size in renal cell carcinomas correlates with the presence of necrosis and sarcomatoid areas. Factors that indicate a higher rate of death are older age, exceeding of the renal capsule, the lesion that includes the entire kidney, lymphovascular invasion, acute kidney injury, and anemia. Conclusions: Even if they originate at the renal level, and the clinical-paraclinical picture is similar, the histopathological characteristics make the difference. In addition, to these are added the previously mentioned common parameters that can represent important prognostic factors. In conclusion, the characteristics commonly identified in one type of cancer may act as risk factors for the other tumor. The detected data include threshold values and risk factors, making a significant contribution to the existing literature.

There is currently no effective treatment strategy for the recurrent/metastatic adenoid cystic carcinoma (R/M ACC). Furthermore, recent single-agent and combination immunotherapy trials have failed in unselected ACC cohorts unlike non-ACC salivary gland cancers. Genomic profiling revealed no actionable targets but NOTCH1 and KDM6A frameshift and CTCF splice site mutations (no MYB/L fusion) with a low TMB, MSS and negative PD-L1. We recommended anti-PD-1 plus anti-CTLA-4 combination based on TMB 2-fold greater than median TMB in ACC, tumor harboring multiple immunogenic frameshift or splice site mutations, and PD-L1 negativity. Accordingly, we achieved a complete response in a radiotherapy and chemotherapy-refractory patient with locally recurrent lacrimal gland ACC and lung metastasis following personalized immunotherapy in combination with integrative therapeutics. Therefore, it is crucial to assess not only conventional immune biomarkers but also patient-specific parameters.

COVID-19 pandemic in first seven months has led to more than 15 million confirmed infected cases and 600,000 deaths. SARS-CoV-2, the causative agent for COVID-19 has proved a great challenge for its ability to spread in asymptomatic stages and a diverse disease spectrum it has generated. This has created a challenge of unimaginable magnitude not only affecting human health and life but also potentially generating a long-lasting socioeconomic impact. Both medical sciences and biomedical research have also been challenged consequently leading to a large number of clinical trials and vaccine initiatives. While known proteins of pathobiological importance are targets for these therapeutic approaches, it is imperative to explore other factors of viral significance. Accessory proteins are one such trait that have diverse roles in coronavirus pathobiology. Here we analyze certain genomic characteristics of SARS-CoV-2 accessory protein ORF8, predict upon its protein features and review current available literature regarding its function. We have also undertaken review of ORF8 homolog ORF8ab from SARS-CoV with a purpose of developing holistic understanding of these proteins for reason that coronaviruses have been infecting humans repeatedly and might continue to do so. Despite low nucleotide and protein identity and differentiating genome level characteristics, there appears to be significant structural integrity and functional proximity between these proteins pointing towards their high significance. There is further need for comprehensive genomics and structural-functional studies to lead towards definitive conclusions regarding their criticality and that can eventually define their relevance to therapeutics development.

In quantum field theory (QFT), it is well known that when Feynman diagrams containing loops are evaluated to account for self interactions, probability amplitude comes out to be infinite which is physically not admissible. So, to make the QFT convergent, various renormalization methods are conventionally followed in which an additional (infinite) counter term is postulated which neutralizes the original infinity generated by diagram. The resulting finite values of amplitudes have agreed with experiments with surprising accuracy. However, proponents of renormalization methods acknowledged that this ad-hoc procedure of subtraction of infinity from infinity to reach at a finite value is not at all satisfactory and there is no physical basis for bringing in the counter term. So, it is desirable to establish a method in QFT which does not generate any infinite term (thus not requiring renormalization), but which predicts same results as conventional methods do. In this paper, we describe such a technique taking self interaction quantum electrodynamics diagram representing electron or photon self energy. In our method, no problem of infinity arises and hence renormalization is not necessary. Still, the dependence of calculated probability amplitude on physical variables in our technique comes out to be same as conventional methods. Using similar procedure, we hope, the problem of non-renormalizability of quantum gravity may be solved in future.

Generally, stochastic functional differential equations (SFDEs) pose a challenge as they often lack explicit exact solutions. Consequently, it becomes necessary to seek certain favorable conditions under which numerical solutions can converge towards the exact solutions. This article aims to delve into the convergence analysis of solutions for stochastic functional differential equations by employing the framework of G-Brownian motion. To establish the goal, we find a set of useful monotone type conditions and work within the space Cr((−∞,0];Rn). The investigation conducted in this article confirms the mean square boundedness of solutions. Furthermore, this study enables us to compute both LG2 and exponential estimates.

This study delves into the integration of Decentralized Finance (DeFi) within metaverses, probing its implications and potential. It elucidates DeFi protocols and the intricacies of metaverses before inspecting their intersection and the resulting economic prospects. The analysis further appraises inherent risks such as financial volatility, security, and regulatory issues. Case studies provide tangible insights into DeFi applications in metaverses. Future trajectories of DeFi in metaverses are projected, underlining the possible impact on the broader financial sector. This paper contributes to burgeoning research at the nexus of blockchain technology, finance, and virtual reality.

Sleep is still considered a mystery, despite intense scientific investigation. Here we present the first complete biological theory of sleep. The role of sleep is to restore the optimal homeostatic state, which is essential for tissue performance and health. Non-rapid eye movement sleep (NREMS) restores cortical and most other brain neurons, via relaxed global activity managed by thalamocortical circuits. The role of REM sleep is to restore acetylcholine (ACh) neurons, which support focused responses and hence cannot participate in global oscillations. Sleep enhances learning and memory via state restoration and ACh-affected paths. NREMS induces a lack of consciousness because global synchronous activity prevents focused responses, which are essential for consciousness. Dreams result from focused neural firing during sharp-wave ripples and REMS, and have a sense of reality because they involve the same neurons representing focused perceptual responses during wake. Anesthetics utilize a variety of mechanisms that prevent focused responses.

Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes are located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes are expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. Hox clusters are irreversibly elongated along the force direction. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can ‘explain’ this Empirical Rule. In a spontaneous mutation where the cluster fastening is dismantled, a weak pulling force automatically shifts the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of Temporal Collinearity in Drosophila.

Self-enhancement (SE) is often overlooked as a fundamental cognitive ability mediated via the Pre-Frontal Cortex (PFC). Here we present research that establishes the relationship between the PFC, SE, and the potential evolved beneficial mechanisms. Specifically, we believe there is now enough evidence to speculate that SE exists to provide significant benefits and should be considered a normal aspect of the self. Whatever the metabolic or social cost, the upside of SE is great enough that it is a core and fundamental psychological construct. Furthermore, though entirely theoretical, we suggest that a critical reason the PFC has evolved so significantly in Homo sapien is to, in part, sustain SE. We therefore elaborate as to its proximate and ultimate mechanisms.

The current study investigated the neuroprotective effects of rutin hydrate (RH) in an Alzheimer’s disease (AD)-like learning and memory impairments rat model. Since AD is an age-related degenerative brain disorder characterized by a progressive worsening in cognitive function and memory, RH may be a potential drug for the treatment of AD-like memory loss, and this work is aimed to evaluate whether RH has neuroprotective effects in memory deficits induced by scopolamine (SCO). In our study, RH demonstrated beneficial effects as a neuroprotector in the brain and improved learning, memory, and synaptic plasticity in a rat. Rats were administered RH (100 mg/kg) and SCO (1.5mg/kg), and performed behavioral tests such as Morris water navigation test, Y-maze test, and passive avoidance test to evaluate the ability of learning and memory. In accordance with these findings, RH behaviorally attenuated SCO-induced shortening of step-through latency in the PA test, increased the percent of alternation in the Y-maze, and increased the time spent in the target zone in the MWN. Moreover, the neuroprotective effect of RH on learning and memory was investigated through the long-term potential on organotypic hippocampal slice cultures. RH increased the total activity of field excitatory postsynaptic potential (fEPSP) following theta burst stimulation and attenuated SCO-induced blockade of fEPSP. This approach could potentially be used as a therapeutic strategy for the restoration of learning and memory function and the prevention of the progression of AD. SCO treatment decreased the levels of BDNF/TrkB/ERK/CREB/Bcl2 and increased the Bax protein level in rat hippocampus. RH ameliorated the expression of these proteins by attenuating SCO-induced up or downregulation in the hippocampus. Based on these findings, the results highlight the molecular and cellular mechanisms through which RH is proposed to exert its neuroprotective effects in the prevention and treatment of learning and memory deficit disorders.

Neural network regularized learning has garnered significant attention in recent years. We give a systematic investigation on the performance of regularized regression associated zonal translation networks. We propose the concept of Marcinkiewicz-Zygmund inequality Setting (MZIS) for the scattered nodes collected from the unit sphere. We show that, under the MZIS, the corresponding convolutional zonal translation network has reproducing property. Based on these facts,we propose a kind of kernel regularized regression learning framework and provide upper bound estimate for the learning rate with the kernel approach. We also give proof for the density of the zonal translation network with spherical Fourier analysis.We provide the approximation error with a K-functional.

The vibrant landscape of Malaysia serves as a dynamic backdrop for the convergence of artificial intelligence (AI) and social science, reshaping knowledge and inquiry amidst tradition and modernity. With Malaysia leading a technological renaissance driven by digital transformation and innovation, the ethical implications of AI-driven decision-making in public policy and governance emerge as pressing research problems. Addressing concerns such as algorithmic bias and discrimination requires interdisciplinary collaboration to ensure transparency, accountability, and fairness. Exploring questions regarding the distribution of welfare benefits and strategies for enhancing transparency and accountability in urban planning underscores the need for contextually relevant frameworks and stakeholder engagement. Through comprehensive research objectives, including investigating algorithmic biases and analyzing their social implications, scholars aim to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy. As Malaysia navigates the challenges and opportunities presented by AI deployment, prioritizing interdisciplinary collaborations, longitudinal studies, and capacity-building efforts will be essential for shaping a future that upholds principles of social justice and collective well-being.

Fast convergence routing is an important issue for LEO constellation network, due to its dynamical topology changing and time varying transmission requests. Most of existing research focus on the OSPF routing algorithm, which cannot handle the frequently links state changing of network. In this paper, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, in which the satellite gets the network links status fast and adjusts its routing strategy. In FRL-SR, each satellite node is regarded as an agent. The agent selects the port for packet forwarding according to its own routing policy. When the satellite network state changes, agent would send ’hello’ packets to the neighbor node to update the neighbor node’s routing policy. Compared with traditional reinforcement learning, FRL-SR can perceive network information faster, and then converge faster. Also, FRL-SR can mask the dynamics of satellite network topology and adaptively adjust the forwarding strategy according to the link state. Various simulation is constructed, the results show that the proposed FRL-SR algorithm out performance the Dijkstra algorithm in performance of average delay, packet arriving ratio, network load balance.

Social recommendation is almost as the integration of the business platform and social platform, and gradually become a top in recommendation system. Social recommendation algorithm solves the problem of cold start and data sparseness for traditional commodity, while the internal structure of the relationship graph in social relations has not been fully excavated. This paper proposes two models of Micro Relation Transfer Model and Macro Relation Transfer Model of social relations, and applies the social relations transfer models into the social recommendation system. A relationship graph is built from the relationship between customers on the Internet. Micro Relation Transfer Model establishes the transfer activation function by calculating the relationship between the two customers using the similarity of interests set. Micro Relation Transfer Model spreads the relationship of friends by calculating the proportion of common neighbors held by the customer's social relations. In order to effectively control the transmission range and effect of social relations graph, we introduce pruning algorithm based on Monte Carlo Decision Tree convergence algorithm. The experimental results show that SRRTC algorithm enhances the success rate and stability significantly.

We present a first formal analysis of specific and complete local integration. Complete local integration was previously proposed as a criterion for detecting entities or wholes in distributed dynamical systems. Such entities in turn were conceived to form the basis of a theory of emergence of agents within dynamical systems. Here, we give a more thorough account of the underlying formal measures. The main contribution is the disintegration theorem which reveals a special role of completely locally integrated patterns (what we call ι-entities) within the trajectories they occur in. Apart from proving this theorem we introduce the disintegration hierarchy and its refinement-free version as a way to structure the patterns in a trajectory. Furthermore we construct the least upper bound and provide a candidate for the greatest lower bound of specific local integration. Finally, we calculate the i-entities in small example systems as a first sanity check and find that ι-entities largely fulfil simple expectations.

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

Wafer-scale blue micro-light-emitting diode (micro-LED) arrays are fabricated with a pixel size of 12 μm, a pixel pitch of 15 μm, and a pixel density of 1692 pixels per inch, achieved by optimizing the properties of e-beam-deposited and sputter-deposited indium tin oxide (ITO). Although the sputter-deposited ITO (S-ITO) films exhibit a densely packed morphology and lower resistivity compared to those of the e-beam-deposited ITO (E-ITO) films, the forward voltage (VF) values of a micro-LED with the S-ITO films are higher than those with the E-ITO films. The VF values for a single pixel and for four pixels with E-ITO films are 2.82 V and 2.83 V, respectively, while the corresponding values for S-ITO films are 3.50 V and 3.52 V. This was attributed to ion bombardment damage and nitrogen vacancies in the p-GaN layer. Surprisingly, the VF variations of a single pixel and of four pixels with the optimized E-ITO spreading layer from five different regions are only 0.09 V and 0.10 V, respectively. This extremely uniform VF variation is suitable for realizing micro-LED displays to be used in AR and VR applications, circumventing the bottleneck in the development of long-lifespan and high-brightness organic LED devices for industrial mass production.

In this paper we study the properties of a complete quadrangle in the Euclidean plane. Similarly, as it was done in our previous work the proofs are based on using rectangular coordinates symmetrically on four vertices and four parameters a,b,c,d. Here, we gather many properties of the complete quadrangle known from earlier but proved by the same method together with some new results given.

Robin criterion states that the Riemann Hypothesis is true if and only if the inequality $\sigma(n) < e^{\gamma } \times n \times \log \log n$ holds for all $n > 5040$, where $\sigma(n)$ is the sum-of-divisors function and $\gamma \approx 0.57721$ is the Euler-Mascheroni constant. We prove that the Robin inequality is true for all $n > 5040$ which are not divisible by any prime number between $2$ and $953$. Using this result, we show there is a contradiction just assuming the possible smallest counterexample $n > 5040$ of the Robin inequality. In this way, we prove that the Robin inequality is true for all $n > 5040$ and thus, the Riemann Hypothesis is true.

Background: The aim of this study was to investigate a series of complete blood cell count – based biomarkers of systemic inflammation as predictors of clinical outcomes in patients who underwent first-line chemoimmunotherapy for advanced NSCLC. Methods: Consecutive patients with pathologically diagnosed stage III/IV NSCLC who underwent first-line chemoimmunotherapy were retrospectively enrolled. The clinical outcomes used for biomarker evaluation were Objective Response Rate (ORR) and Overall Survival (OS). Results: Non-responders had significantly higher values of neutrophil to lymphocyte ratio (NLR, median: 5.36; IQR: 2.78–10.82 vs. 3.31; IQR: 2.15–4.12, p = 0.019), neutrophil to monocyte ratio (NMR, median: 14.00; IQR: 8.82–21.20 vs. 9.20; IQR: 7.45–11.20, p = 0.013), and systemic inflammation index (SII, median: 1,395; IQR: 929–3,334 vs. 945; IQR: 552–1,373, p = 0.025), but only NLR and NMR remained independently related with clinical response in multivariate logistic regression. In univariate analysis white blood cells (OR:1.2202; 95% CI: 1.0339 - 1.4400, p=0.019), neutrophils (OR:1.2916; 95% CI: 1.0692 - 1.5604, p=0.008), NLR (OR:1.3601: 95% CI: 1.0949 - 1.6896, p=0.005) and NMR (OR:1.2159; 95% CI: 1.00396 - 1.4221, p=0.015) were significantly associated with survival; Cox regression models confirmed that neutrophils, NLR and MLR were independently associated with survival; NLR at a cut-off value of 4.0 showed the better AUC (0.749) in predicting survival. Conclusions: Baseline complete blood cell count biomarkers, especially the NLR, can predict clinical outcomes in patients with advanced NSCLC treated with first-line chemoimmunotherapy.

Consider a uniquely ergodic $C^*$-dynamical system ba\-sed on a unital $*$-endomorphism $\Phi$ of a $C^*$-algebra. We prove the uniform convergence of Cesaro averages $\frac1{n}\sum_{k=0}^{n-1}\lambda^{-n}\Phi(a)$ for all values $\lambda$ in the unit circle which are not eigenvalues corresponding to "measurable non continuous" eigenfunctions. This result generalises the analogous one in commutative ergodic theory presented in [19], which turns out to be a combination of the Wiener-Wintner Theorem (cf. [22]) and the uniformly convergent ergodic theorem of Krylov and Bogolioubov (cf. [15]).

(1) Background: A growing number of communities are re-discovering the value of their streets as important public spaces for many aspects of daily life, thus creating the need for a transformation in the quality of streets. An emerging concept is to accommodate all users of the transportation system, a concept that has been labelled ‘complete streets’. (2) Methods: In this paper, we present sustainable complete streets design criteria that integrate complete streets by adding in socio-environmental design criteria related to aesthetics, environment, liveability, and safety. (3) Results: Proposed design criteria provide a street network which provides improvements in aesthetics, to recover historical urban character and realize historical area planning goals; environment, to increase permeable surfaces, reduce the heat island, and absorb traffic-related air pollution; liveability, to create a public space destination in the urban landscape; and safety, to improve the safety of all road users. and (4) Conclusions: The case study of the urban rehabilitation of the “Mostra d’Oltremare” area and of its cultural and architectural assets in Naples, Italy, highlights the practical application of the proposed criteria and the possibility of using these criteria in other urban contexts.

With the development of human-computer interaction, the importance of emotion recognition is increasing. Emotion recognition technology provides practical benefits in various industries such as improving user experience, education, and organizational productivity. In education, emotion recognition can enable real-time understanding of students' emotional states and provide tailored feedback, and in the workplace, by monitoring employees' emotional states, it can improve work performance and job satisfaction. Therefore, multimodal-based research that combines text, speech, and video data for emotion recognition is being conducted in various industries. In this study, we propose an emotion recognition method that combines text and speech data, reflecting the characteristics of the Korean language. For text, KoELECTRA is used to perform embedding, and for speech, MFCC and pitch analysis are used to extract features. Finally, a multimodal transformer model is proposed that combines these two data types to perform emotion recognition. The multimodal transformer model processes text and speech data separately, and then learns the interaction between the two modalities through a Cross-Modal Attention mechanism. Through this, the complementary information from text and speech is effectively combined, improving emotion recognition performance. Experimental results show that the proposed model outperforms single-modality models, achieving a high accuracy of 73.13% and an F1-Score of 0.7344 in emotion classification. This study contributes to the advancement of emotion recognition technology by combining various language and modality data, and higher performance can be expected through the integration of additional modalities in the future.

The paper is dedicated to the numerical and experimental study of nonlinear oscillations exhibited by the Vilnius chaotic generator. The need for practical applicability of the mentioned generator in chaotic communication systems defined the necessity to study the operation of the circuit in a wide parameter range. The bifurcation maps and corresponding one-parameter diagrams, obtained and analysed utilising the Method of Complete Bifurcation Groups, reveal the complex nonlinear dynamics and regions of robust chaotic oscillations. Laboratory experiments allow the verification of the numerical results and identification of the most practically relevant phenomena.

We find that the degeneracies and many peculiarities of the DNA genetic code may be described thanks to two closely related (fivefold symmetric) finite groups. The first group has signature $G=\mathbb{Z}_5 \rtimes H$ where $H=\mathbb{Z}_2 . S_4\cong 2O$ is isomorphic to the binary octahedral group $2O$ and $S_4$ is the symmetric group on four letters/bases. The second group has signature $G=\mathbb{Z}_5 \rtimes GL(2,3)$ and points out a threefold symmetry of base pairings. For those groups, the representations for the $22$ conjugacy classes of $G$ are in one-to-one correspondence with the multiplets encoding the proteinogenic amino acids. Additionally, most of the $22$ characters of $G$ attached to those representations are informationally complete. The biological meaning of these coincidences is discussed.

Deep learning is widely used in image segmentation, effectively identifying crops and weeds and contributing to the reduction of herbicide use. Traditional methods of applying pesticides over large areas are inefficient in dealing with weeds as needed, leading to massive waste of pesticides, high-cost production, and serious environmental pollution. This affects crop yield and quality. Although weed recognition using conventional deep learning methods has evolved over time, there are still challenges in weed extraction, detection, and segmentation. Accurate recognition and detection of weeds are essential prerequisites for implementing variable spraying. This paper proposes a semantic segmentation method based on UNet++ for complex environments where accurate identification of plants and weeds is challenging, targeting weeds in sugar beets, peas, and rice. An attention module is integrated into the upsampling process of UNet++, and UNet++ is used as a backbone network to effectively integrate multi-scale information, efficiently suppressing external noise interference. The UNet++ model with an integrated attention mechanism module achieves higher IOU than the general UNet++ model used in medical image analysis. This method effectively detects crops and weeds in complex backgrounds, providing reference material for the accurate application of robotic herbicides.

In conventional Federated Learning (FL), clients work together to train a model managed by a central server, intending to speed up the learning process. However, this approach imposes significant computational and communication burdens on clients, particularly with complex models. Additionally, while FL strives to protect client privacy, the server's access to local and global models raises security concerns. To address these challenges, Split Learning (SL) separates the model into parts handled by the client and the server, though it suffers from inefficiencies due to sequential client participation. To overcome these issues, SplitFed Learning (SFL) was proposed, which combines the parallelism of FL with the model-splitting strategy of SL, enabling simultaneous training by multiple clients. Our main contribution is the theoretical analysis of SFL, which, for the first time, includes non-i.i.d. datasets, non-convex loss functions, and both full and partial client participation. We provide convergence proofs for a state-of-the-art SFL algorithm based on conventional convergence analysis assumptions for FL. Our results prove that we can recover the linear convergence rate of conventional FL for the SFL algorithm with the distinction that increasing the number of local steps or clients may not speed up the convergence in SFL.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. This study presents a comprehensive literature review on the convergence of affective computing, interactive installation art, multi-dimensional sensory stimulation, and artificial intelligence (AI) in measuring emotional responses. It addresses the problem of understanding emotional response and measurement in the context of interactive installation art under artificial intelligence (AI). The study aims to fill the existing research gaps by examining three key aspects: sensory stimulation, multi-dimensional interactions, and engagement, which have been identified as significant contributors to profound emotional responses in interactive installation art. The study proposed approach involves conducting a process analysis of emotional responses to interactive installation art, aiming to develop a conceptual framework that explores the variables influencing emotional responses. The study will formulate hypotheses that make specific predictions about the relationships between sensory stimulation, multi-dimensional interactions, engagement, and emotional responses. By employing the ASSURE model combined with experimental design, the research methodology ensures a systematic and comprehensive study implementation. The implications of this project lie in advancing the understanding of emotional experiences in interactive installation art under AI, providing insights into the underlying mechanisms that drive these experiences. The contributions of this research include bridging the identified research gaps, refining theoretical frameworks, and guiding the design of more impactful and emotionally resonant interactive artworks. Ultimately, this research aims to contribute to the broader field of human-computer interaction and enhance the development of immersive and emotionally engaging interactive installations.

This study used a two-stage network data envelopment analysis model to measure the water use efficiency of 108 cities in the Yangtze River Economic Belt in the initial water use and wastewater treatment phases from 2009 to 2019. We used the Dagum Gini coefficient to decompose the urban water use efficiency of six major urban clusters in the Yangtze River Economic Belt. We also used σ convergence and β convergence types to test the convergence characteristics of urban water use efficiency of six major urban clusters in the Yangtze River Economic Belt. This study found that the overall low level of water use efficiency in cities in the Yangtze River Economic Zone mainly stems from the low level of water use efficiency in the wastewater treatment stage. The 108 cities in the Yangtze River Economic Zone are divided into four types based on the average values of water use efficiency in the initial use and wastewater treatment phases; the highest number of cities are in the double-low category, with low average values of water use efficiency in the initial use and wastewater treatment phases. During the study period, spatial differences in urban water use efficiency in the Yangtze River Economic Zone narrowed, with the differences stemming mainly from hyperdensity, followed by intra- and inter-regional differences. Meanwhile, there is convergence in urban water use efficiency in the Yangtze River Economic Belt; significant β convergence in the urban agglomerations of the Yangtze River Delta, Jianghuai, middle reaches of the Yangtze River, Chengdu–Chongqing, and Central Yunnan; and insignificant β convergence in the Central Qian urban agglomeration. After considering control factors, such as industrial structure, financial development level, environmental regulation, economic development level, and science and education development level, the water use efficiency of the six major urban clusters in the Yangtze River Economic Belt converges faster, but the influence of these control factors on the water use efficiency of each urban cluster is heterogeneous. Research results have reference value for the development of improvement strategies on differentiated urban water use efficiency in the Yangtze River Economic Belt.

We construct a novel family of summation-integral type hybrid operators in terms of shape parameter $\alpha\in \lbrack 0,1]$ in this paper. Basic estimates, rate of convergence, and order of approximation are also studied using the Korovkin theorem and the modulus of smoothness. We investigate the local approximation findings for these sequences of positive linear operators utilising Peetre's K-functional, Lipschitz class, and second-order modulus of smoothness.

Autonomous vehicle technology, employing image sensors for road marking recognition, is largely dependent on the Lane Departure Warning System (LDWS) to prevent vehicular deviations from driving lanes. The effectiveness of image-based lane recognition, however, is susceptible to environmental influences like weather conditions and the surrounding road environment. This study investigates the impact of road marking retro-reflectivity on LDWS under varied environmental conditions. Conducted at the Yeoncheon SOC Demonstration Research Center, the experiments encompassed a spectrum of weather scenarios, incorporating rainfall and shifts between day and night lighting. Controlled wear was applied to white, yellow, and blue road markings, followed by measuring their retro-reflectivity at different stages of degradation. The LDWS's performance was evaluated by assessing the recognition rates of these markings under these diverse environmental settings. The study reveals that enhanced retro-reflectivity significantly boosts LDWS detection, especially in challenging weather conditions. Furthermore, the research led to the development of a simulation framework to analyze the cost-effectiveness of road marking maintenance, aligning it with the safety needs of autonomous vehicles. This framework suggests the necessity for updated road marking guidelines that cater to the advanced needs of driver assistance systems and autonomous vehicle technology.

In order to master the structure state of subway shield tunnel, reduce the diseases and avoid the potential safety hazards, the lateral convergence deformation of subway shield tunnels should be predicted. For the non-stationarity small sample size of subway shield tunnel lateral convergence deformation value, the existing prediction models have poor performance in terms of accuracy and stability. In this paper, a lateral convergence model of subway shield tunnel based on Kalman algorithm is constructed based on Kalman filtering theory. The model is efficient, adaptive and robust, and can accurately predict the lateral convergence deformation of subway shield tunnel. Taking the horizontal diameter of 200 rings shield segment in the interval section of a subway tunnel as an example, it is proved that the residuals of the Kalman prediction model are small, the residual distribution conforms to the normal distribution, and the prediction effect is great. The model is suitable for the prediction of more than 5 periods of data, and the prediction accuracy of the model is improved with the increase of the number of data periods. In addition, the Kalman model is compared with GM(1,1) model and GM-Markov model, and RMSE and MAPE are introduced as evaluation indexes. The results show that the Kalman model has higher prediction accuracy and is more suitable for predicting the lateral convergence deformation of subway shield tunnel.

We consider a stationary inclusion in a real Hilbert space X, governed by a set of constraints K, a nonlinear operator A and an element f∈X. Under appropriate assumptions on the data the inclusion has a unique solution, denoted by u. We state and prove a covergence criterion, i.e., we provide necessary and sufficient conditions on a sequence {un}⊂X which guarantee its convergence to the solution u. We then present several applications which provide the continuous dependence of the solution with respect to the data K, A and f, on one hand, and the convergence of an associate penalty problem, on the other hand. We use these abstract results in the study of a frictional contact problem with elastic materials which, in a weak formulation, leads to a stationary inclusion for the deformation field. Finally, we apply the abstract penalty method in the anlysis of two nonlinear elastic constitutive laws.

Randomized block Kaczmarz Method and randomized extended block Kaczmarz Method are proposed for solving matrix equation AXB=C, where the matrix A and B may be full rank or rank deficient. These methods are iterative methods without matrix multiplication, and are especially suitable for solving large-scale matrix equations. It is theoretically proved these methods converge to the solution or least square solution of the matrix equation. The numerical results show that these methods are more efficient than the existing algorithms for high-dimensional matrix equations.

In this paper, the Phase-Scheduled-Command FXLMS algorithm with the phase error between the disturbance and command signal is analyzed in detail. The influence of the phase error on the convergence time constant, convergence rate, and performance of convergence is explained for both stationary and nonstationary disturbance signals case. For stationary disturbance, the phase error slightly increases the convergence rate but heavily increases the distance of the optimum vector from the initial value, leading to poor convergence time constant performance. For nonstationary disturbance, the existence of phase error leads to poor convergence performance in every step, resulting in poor sound profiling performance. And the estimation of the phase error influence is developed in the closed form. Simulations are performed to demonstrate the validity of the analysis results.

Crop breeding is as ancient as the invention of cultivation. In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. The applications of predictive breeding has been gaining momentum in agricultural industry and public breeding programs for the last decade, in the aftermath of genomic selection being recognized and widely applied for accelerating genetic gain in breeding programs. The massive amounts of data that has been generated by industry and farmers year after year through several decades has finally been recognized as an asset. A wide range of analytical methods such as machine learning, deep learning and artificial intelligence that were initially developed for diverse quantitative disciplines are now being adopted to crop breeding decision making processes. New technologies are currently being developed that would enable integration of data from various domains such as geospatial variables and a multitude of phenotypic responses as well as genetic information, in order to identify, develop and improve crop faster via partial or full automation of the decisions that pertain to variety development. Here we will discuss and summarize efforts from public and private domains for predictive analytics, and its applications to crop breeding and agricultural product development, and provide suggestions for future research.

In this paper, we demonstrate the Collatz conjecture using the mathematical complete induction method. We show that this conjecture is satisfied for the first values of natural numbers, and in analyzing the sequence generated by odd numbers, we can deduce a formula for the general term of the Collatz sequence for any odd natural number n after several iterations. This formula is used in one case that we analyze using the mathematical complete induction method in the process of demonstrating the conjecture.

Escherichia coli phage Eco_BIFF was isolated from several laboratory stocks of E. coli K-12 MG1655 derivatives. The source of the contamination is unknown. Eco_BIFF is a lytic phage that shows effective growth inhibition of E. coli K-12. Here, we announce the complete genome sequence of Eco_BIFF, and major findings from its genome annotation.

Our study aimed to identify predictors for the effectiveness of tumor regression in lung cancer patients undergoing neoadjuvant treatment and cancer resections. Patients admitted between 2016 and 2022 were included in the study. Based on the histology of the tumor, patients were categorized into lung adenocarcinoma group (LUAD), and squamous cell carcinoma group (SQCA). Ninety-five patients with non-small cell lung cancer were included in the study. Fifty-eight (61.1%) and 37 (38.9%) patients were included in the LUAD and SQCA groups, respectively. Nine (9.5%), 56 (58.9%), and 30 (31.6%) patients were categorized with a tumor regression score of I, II, and III respectively. In multivariable analyses, histology of the primary tumor (SQCA), lymph node size in the preoperative CT scan (>1.7cm) and absolute tumor size reduction after neoadjuvant treatment (>2.6 cm) independently predict effectiveness of tumor regression (OR, [95% Confidence interval, p-value] of 6.88 [2.40-19.77, p70 years, extended resection >one lobe, and tumor recurrence or metastasis were identified as significant independent predictors of reduced overall survival. Assessment of tumor size before and after neoadjuvant treatment might help to identify high-risk patients with decreased survival and to improve patient management and care.

A system of simultaneous multi-variable nonlinear equations can be solved by the Newton’s method with local q-quadratic convergence if the Jacobian is analytically available. If this is not the case, then quasi-Newton methods with local q-superlinear convergence give solutions by approximating the Jacobian in some way. Unfortunately, the quasi-Newton condition (secant equation) doesn’t completely specify the Jacobian approximate in multi-dimensional case, so its full-rank update is not possible with classic variants of methods. The suggested new iteration strategy (“T-Secant”) allows full-rank update of the Jacobian approximate in each iteration by determining two independent approximates for the solution. They are used to generate a set of new independent trial approximates, then the Jacobian approximate can fully be updated. It is shown, that the T-Secant approximate is in the vicinity of the classic quasi-Newton approximate, providing that the solution is evenly surrounded by the new trial approximates. The suggested procedure increases the super-linear convergence of the secant method (φS = 1.618...) to super-quadratic (φT = φS + 1 = 2.618...) and the quadratic convergence of the Newton-method (φN = 2) to cubic (φT = φN + 1 = 3) in one dimensional case. The Broyden-type efficiency (mean convergence rate) of the suggested method in multi-dimensional case is an order higher than the efficiency of other classic low-rank update quasi-Newton methods as shown by numerical examples on a Rosenbrock-type test-function with up to 1000 variables. The geometrical representation (hyperbolic approximation) in single variable case helps explaining the basic operations and a vector-space description is also given in multi-variable case.

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors, including abemaciclib, have been approved for the treatment of hormone receptor-positive, HER2-negative advanced, and metastatic breast cancer. Despite the high therapeutic efficacy of CDK4/6 inhibitors, they are associated with various adverse effects, including potentially fatal interstitial lung disease. Therefore, a combination of CDK4/6 inhibitors with letrozole or fulvestrant has been attempted but has demonstrated limitations in reducing adverse effects, highlighting the need to develop new combination therapies. This study proposes a combination strategy using CDK4/6 inhibitors and tricyclic antidepressants to enhance the therapeutic outcomes of these inhibitors while reducing their side effects. The therapeutic efficacies of abemaciclib and desipramine were tested in different cancer cell lines (H460, MCF7, and HCT-116). The antitumor effects of the combined abemaciclib and desipramine treatment were evaluated in a xenograft colon tumor model. In vitro cell studies have showed the synergistic anticancer effects of combination therapy in the HCT-116 cell line. The combination treatment significantly reduced tumor size compared with control or single treatment without causing apparent toxicity to normal tissues. The combination of abemaciclib and desipramine may be a promising therapeutic strategy as they could reduce side effects and enhance tumor suppression.

No system nor standardized analytical procedures at commercial laboratories exist to facilitate and accurately measure potential viable virus contamination in feed ingredients and complete feeds globally. As a result, there is high uncertainty of the extent of swine virus contamination in global feed supply chains. Many knowledge gaps need to be addressed to improve our ability to prevent virus contamination and transmission in swine feed. This review summarizes the current state of knowledge involving: 1) the need for biosecurity protocols to identify production, processing, storage, and transportation conditions that may cause virus contamination of feed ingredients and complete feed, 2) challenges of measuring virus inactivation, 3) virus survival in feed ingredients during transportation and storage, 4) minimum infectious doses, 5) differences between using a Food Safety Objective versus a Performance Objective as potential approaches for risk assessment in swine feed, 6) swine virus inactivation from thermal and irradiation processes, and chemical mitigants in feed ingredients and complete feed, 7) efficacy of virus decontamination strategies in feed mills, 9) benefits of functional ingredients, nutrients, and commercial feed additives in pig diets during a viral health challenge, and 10) considerations for improved risk assessment models of virus contamination in feed supply chains.

Goal-conditioned reinforcement learning (RL) holds promise for addressing intricate control challenges by enabling agents to learn and execute desired skills through separate decision modules. However, the irregular occurrence of required skills poses a significant challenge to effective learning. In this paper, we demonstrate the detrimental effects of this imbalanced skill(sub-goal) distribution and propose a novel training approach, Classified Experience Replay (CER), designed to mitigate this challenge. We demonstrate that adapting our method to conventional RL methods significantly enhances the performance of the RL agent. Considering the challenges inherent in tasks such as driving, characterized by biased occurrences of required sub-goals, our study demonstrates the improvement in trained outcomes facilitated by the proposed method. In addition, we introduce a specialized framework tailored for self-driving tasks on highways, integrating Model Predictive Control (MPC) into our RL trajectory optimization training paradigm. Our approach, utilizing CER with the suggested framework, yields remarkable advancements in trajectory optimization for RL agents operating in highway environments.

We firstly present two popular space discretization models of the Nonlinear Schrödinger Equation in dark solitons motion : the Direct-Discrete model and the Ablowitz-Ladik model. Applying the midpoint scheme to the space discretization models, we get two time-space discretization models: the Crank-Nicolson method and the New-Difference method. Secondly, we demonstrate that the solutions of the two space discretization models converge to the solution of the Nonlinear Schrödinger Equation. Also, we prove that the convergence order of the two time-space discretization models are O(h2+τ2) in discrete L2-norm error estimates. Finally, the numerical experiments agree well with the proven theoretical results.

Herein, we reported a case of complete excision of the jejunum for the treatment of esophageal cancer. A 62-year-old male was admitted with complaints of “difficulty in eating for one month and chest pain for 10 days". For complete gastrectomy and colonic polyposis, we chose the jejunum. We completely excised the jejunum and its vessels, anastomosed to the esophageal tumor resection, with no significant complications. This case showed that complete excision of the jejunum with bowel and vessels is an alternative surgical method for the treatment of esophageal cancer with free bowel shortage and no torsion of blood vessels.

Changes in hydrology from aridification in the U.S. Southwest have led to tension at the urban-rural divide about conservation and land management strategy. Strain on multi-generational agricultural livelihoods and a nearly 150-year-old Colorado River water adjudication system presents an opportunity for multidisciplinary solutions and symbiotic partnerships. In this study, producers in a small San Juan River headwater community in southwestern Colorado participated in a convergence research study developed collaboratively with community partners, prioritizing local objectives and policy needs. Acknowledging the historic and sometimes perceived role of academic institutions as representing urban interests, our goal was to highlight how research could support rural governance and identify rural interests. Respondents identified water availability and climate changes as the factor that most negatively affects operations. Respondents had interest in nature-based agroforestry methods like windbreak trees and drought-resistant crop species. Community members identified cost as the leading perceived barrier to implementing nature-based agricultural solutions, with over half of respondents indicating that programs to subsidize or eliminate cost would improve willingness to try nature-based methods. Analyses identified that satisfaction with community resources was positively correlated with scale of production, satisfaction with irrigation equipment, and familiarity with water rights. Further, our results outline barriers to perceptions about nature-based solutions on rural landscapes. We hope to contribute this model for wider applications in other regions to uncover place-based solutions to resource challenges and contribute to addressing stress at the urban-rural divide.

A 68-year-old man with malignant metastatic melanoma in complete remission after Pembrolizumab treatment, developed 10 months after Pembrolizumab cessation due a grade 3 diarrhea, confusion, altered mental status, progressive memory loss, gait disturbance. He was admitted to the neurologic department and comprehensive neurological workup, brain magnetic resonance imaging, cerebral fluid analysis, EEG, blood test allowed the diagnosis of autoimmune encephalitis. The patient was treated with plasmapheresis, high dose of intravenous steroids and intravenous immunoglobulins. The patient improved and he is now well with a performance status 1. This case is interesting since the AE developed approximately 10 months after the cessation of immunotherapy, the underlying cancer was in complete remission and the AE showed a good response to the treatment done.

In this paper, we generalize the concept of I-statistical convergence, which is a recently introduced summability method, with an infinite matrix of complex numbers and a modulus function. Lacunary sequences will also be included in our definitions. The name of our new method will be A^{I}-lacunary statistical convergence with respect to a sequence of modulus functions. We also define strongly A^{I}-lacunary convergence and we give some inclusion relations between these methods.

Infant electrocardiograms (ECG) and heart rates (HR) are very useful biosignals for psychological research and clinical work, but can be hard to analyze properly, particularly long form (≥5 minutes) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open-source ECG approaches are tested on infant datasets. The best performing open-source method is then modified to maximize its performance on infant data (e.g., including a 15Hz high pass filter, adding local peak correction). The HR signal is then subsequently analyzed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HR. A Signal Quality Index (SQI) for HR is also developed, providing insight into where a signal is recoverable and where it is not, allowing for more confidence in analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for future analysis of infant ECG and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world large datasets.

Tug-of-war (TOW) is an internationally played activity including professional and amateur athletes and defined as early (4000 years ago as a rope less version) in the artwork on Egyptian tomb engravings and is played as per the rules laid out by TWIF, which has 73 member countries and administrative headquarters in the USA. Typically, two teams of “pullers” participate and apply enormous contra directional forces on the pulling rope. Originally, two types of competition are used: knockout and points. This narrative review describes the scientific state of the art about of TOW. For the best of the author’s knowledge no previous information has been published. Anthropometric parameters are near 83.6, lean body mass 69.4, and body fat 16. The VO2MAX is 55.8 ml/kg/min. Relative strength, the dynamic leg power was 4659.8 N. Endurance TOW elicits minimal muscle damage. The injured strains and sprains comprised over half of all injuries: back (42%), shoulder–upper limb (23%) and knee (17%). Pulling movement in TOW contests can be divided into three phases: namely "Drop", "Hold" and "Drive" phase. The maximal pulling forces was 1041.6 ± 123.9 N. The percentage of dynamic pulling force in static maximal pulling force was 75.5 ± 14.4% and the dynamic ranged from 106.4 to 182.5%. There are two gripping styles, indoor and outdoor. The friction characteristics between surface and shoe in TOW is important to determine a suitable shoe for indoor TOW. Waist Belt might be a useful piece of equipment for TOW sport. The EMG technique in Tow described a high activity of dorsal muscle during the pulling. The factor of force vanishing was the coordination among athletes. The force vanishing percentage goes from 8.82±5.59 for 2 contenders to 19.74±2.22 for 8 athletes, 6.4 % in the sum of 2 pullers. However, in the drop phase, for female elite TOW team, only the 0.5 % of them pulling force was wasted. Future studies are need in order to understand better this historical sport activity.

Cleomaceae is an important group that is medicinally and economically important. However, its systematic position has been controversial. In this study, we have obtained three chloroplast genomes sequencing results of Cleomaceae species. The genome sizes ranged from 157,073 bp (Sieruela rutidosperma) to 158,152 bp (Gynandropsis gynandra). These chloroplast genomes have a typical quadripartite formation with a large single-copy (LSC) region (86,422-87,242bp), a small single-copy (SSC) region (18,485-18,548bp), and a pair of inverted repeat (IR) regions (26,083-26,184bp). The chloroplast genomes encoded 131-132 genes, comprising 8 rRNA genes, 37 tRNA genes, and 79-84 protein-coding genes. The overall GC contents of the three chloroplast genomes are almost identical (35.81%-36.02%). The phylogenetic trees concluding 8 Cleomaceae plants were constructed based on ML and BI methods. The results showed that Gynandropsis gynandra and Sieruela rutidosperma were in the same branch, with Corynandra viscosa as their sister. Six hypervariable areas were identified, containing clpP, matK, ndhF, atpH-atpI, atpF-atpH and petN-psbM which could be used in species identification and population genetics studies. This study provides valuable information for further species identification, evolutionary and genetic studies of Cleomaceae in the future.

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

One of the most important challenges in the Machine and Deep Learning areas today is to build good models using small datasets, because sometimes it is not possible to have large ones. Several techniques have been proposed in the literature to address this challenge. This paper aims at studying the different available Deep Learning techniques and performing a thorough experimentation to analyze which technique or combination thereof improves the performance and effectiveness of the models. A complete comparison with classical Machine Learning techniques was carried out, to contrast the results obtained using both techniques when working with small datasets. Thirteen algorithms were implemented and trained using three different small datasets (MNIST, Fashion MNIST, and CIFAR-10). Each experiment was evaluated using a well-established set of metrics (Accuracy, Precision, Recall, F1, and the Matthews correlation coefficient). The experimentation allowed concluding that it is possible to find a technique or combination of them to mitigate a lack of data, but this depends on the nature of the dataset, the amount of data, and the metrics used to evaluate them.

Genus Clematis is one of the largest within Ranunculaceae. Here we report the chloroplast genome of two Clematis species, C. brachyura and C. trichotoma endemic to Korea. The chloroplast genome lengths of C. brachyura and C. trichotoma are 159,532 bp and 159,170 bp, respectively. Gene contents in the complete chloroplast genomes of these two Clematis species are identical to that of most Ranunculaceae and other angiosperms. However, our data results demonstrated that genus Clematis has inversion and rearrangement events concerning gene rps4 gene, rps16 to trnH region, and trnL to ndhC region, and IR regions expansion. Comparison of IR regions among Ranunculaceae species revealed that Clematis species contained six protein coding genes (infA, rps8, rpl14, rpl16, rps3, and rpl22) usually found in the long single copy (LSC) region of other species. Phylogenetic analysis demonstrated that genus Clematis is closely related to genus Ranunculus. Differences in repeat structure, substitution rates, and IR expansion in genera Clematis and Ranunculus, explained their relationship. Clematis species showed slightly higher tandem repeats content than Ranunculus species. The six protein-coding genes showed lower synonymous substitution rates in the IR of Clematis species than in the LSC of Ranunculus species. Overall, the chloroplast genomes and results presented here provide important information on the evolution of Ranunculaceae.

In humans, age-associated degrading changes are observed in molecular and cellular processes underly the time-dependent decline in spatial navigation, time perception, cognitive and psy-chological abilities, and memory. Cross talk of biological, cognitive, and psychological clocks provides an integrative contribution to healthy and advanced aging. At the molecular level, ge-nome, proteome, and lipidome instability are widely recognized as the primary causal factors in aging. We narrow attention to the roles of protein aging linked to prevalent amino acids chirali-ty, enzymatic and spontaneous (non-enzymatic) post-translational modifications (PTMs SP), and non-equilibrium phase transitions. The homochirality of protein synthesis, resulting in the steady-state non-equilibrium condition of protein structure, makes them prone to multiple types of enzymatic and spontaneous PTMs, including racemization and isomerization. Spontaneous racemization leads to the loss of the balanced prevalent chirality. Advanced biological aging re-lated to irreversible PTMs SP has been associated with the nontrivial interplay between poor so-matic and mental health conditions. Through stress response systems (SRS), the environmental and psychological stressors contribute to the age-associated “collapse” of protein homochirality. The role of prevalent protein chirality and entropy of protein folding in biological aging is mainly overlooked. In a more generalized context, the time-dependent shift from enzymatic to the non-enzymatic transformation of biochirality might represent an important and yet un-der-appreciated hallmark of aging.

Although Lov\'{a}sz introduced the graph exponential $G^K$ in 1967, this product has received little attention. This paper focuses on the number of components in $G^K$ when exponent $K$ is a complete graph $K_n$. For a connected bipartite $G$, when $K$ is $K_2$, $G^{K_2}$ has three components; and when $K$ is $K_n$ with $n>2$, $G^{K_n}$ produces $\lceil\frac{n+1}{2}\rceil$ components. A connected $G$ with an odd cycle produces one component in $G^{K_n}$.

Streptococcus pneumoniae is an pathogenic and opportunistic Gram-positive bacteria that is the leading cause of community acquired respiratory diseases, varying from mild- to deathly- infections. Appearance of antibiotic resistant isolates has prompted the search for novel targets. One of the most promising approaches is the structure-based knowledge of possible targets in conjunction to rational design and docking of inhibitors of the chosen targets. A useful technique to help solving protein structures is to label them with a heavy atom, like selenium, that facilitates tracing of the some of the amino acid residues. We have chosen two pneumococcal DNA-binding proteins, namely the relaxase domain of MobM protein from plasmid pMV158, and the RelB-RelE antitoxin-toxin protein complex. Through the update of a previous protocol [1] that uses seleno-L-methionine, we could achieve 100% labelling of the proteins. Furthermore, the labelled proteins retained full activity as judged from relaxation of supercoiled plasmid DNA and from gel-retardation assays.

Background: Selectively targeting dopamine receptors has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases evolving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D1-like and D2-like receptors). Fifteen structurally diverse ligands were docked to these models. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for DR sub-type specificity. Results: We showed that the number of conformations for a receptor:ligand complex was associated to unspecific interactions > 2.5 Å and hydrophobic contacts, while the decoys binding energy was influenced by specific electrostatic interactions. Known residues such as 3.32Asp, the serine microdomain and the aromatic microdomain were found interacting in a variety of modes (HB, SB, π-stacking). Purposed TM2-TM3-TM7 microdomain was found to form a hydrophobic network involving Orthosteric Binding Pocket (OBP) and Secondary Binding Pocket (SBP). T-stacking interactions revealed as especially relevant for some large ligands such as apomorphine, risperidone or aripiprazole. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, key for the design of more specific ligands.