. In this paper, we introduce a series of definitions of generalized affine functions for vector-valued functions. We prove that our generalized affine functions have some similar properties with generalized convex functions. We present examples to show that our generalized affinenesses are different from one another, and also provide an example to show that our definition of presubaffinelikeness is non-trivial; presubaffinelikeness is the weakest generalized affineness introduced in this article. We work with optimization problems that are defined and taking values in linear topological spaces. We devote to the study of constraint qualifications, and derive some optimality conditions as well as a strong duality theorem. Our optimization problems have inequality constraints, equality constrains and abstract constraints; our inequality constraints are generalized convex functions and equality constraints are generalized affine functions.

Concerning the hippocampal mechanisms that simultaneously function to map environmental position and to generate episodic memories, a developing consensus emerged by the early 21st century. The hippocampus was recognized as embodying the primary location of personal narrative development, with the ability to create stable and coherent narrative largely representing the mental health of individuals. Determining recent advances regarding the relationship among the hippocampus, narrative, and mental health, a limited, six-database review was conducted for peer reviewed articles published since 2019. Of the 127 records located, 14 reports were included for study. The results support the necessity of creating and maintaining a stable and coherent narrative as fundamental to human mental health such that facts will be distorted in personal accounts, sleep will be disrupted, and DNA altered to retain one’s personal narratives. With narrative development fundamental to human mental health, a goal of interventions concerning hippocampal function and positive health outcomes is enhancing personal narratives in providing individuals with the ability to construct and recall those that are robust and effective. One such intervention that has proven successful in this regard, the Health Narratives Research Process, is outlined with its most recent results presented, offering opportunity for further such research.

In recent years, with the increase in maritime trade, the necessity of increasing the capacities of the ports has emerged. However, while it is planned to increase the capacities of the ports, it is important that the port continues to operate at the same time. In this respect, the old port structures should not be damaged during the capacity increase. In this study, the strengthening of a port in Guinea is discussed as a case study. In the study, the existing quay wall was evaluated, and geotechnical and structural alternatives of the new structure to be built for capacity increase were evaluated. A combined system was designed as a pile foundation and a reinforced foundation with plastic piles so as not to damage the existing quay wall. The pile capacities obtained as a result of the analyses were verified by loading tests.

The work presents a mathematical model of a thermogravimetric curve for the growth and simultaneous sublimation of scale on a metal or alloy surface. Reduction of the reaction area as a result of the formation of oxides of alloying elements (for obtaining the basic oxide) is considered for the alloy. For metals, the case where this area is increased as a result of powder metal crushing is also considered. The equations that were obtained are employed to explain the kinetic curves of the mass change of the samples.

Influenza viruses cause acute respiratory infections responsible for significant mortality and morbidity around the world. Various factors, such as antigenic drift, allow influenza strains to avoid being fully suppressed by seasonal vaccines. This has led to the increased scrutiny of antivirals as treatment and prophylaxis options for seasonal outbreaks and potential pandemics. Unfortunately, many influenza antivirals suffer from a lack of adequate clinical trials, as well as a lack of toxicity data. This is especially true of umifenovir (arbidol), a drug popularly used for the prevention and treatment of influenza strains in China and Russia. Neuraminidase inhibitors, though widely prescribed, display a potential for future resistance. Adamantanes, while proven to be effective in treating influenza A, are already encountering rapid and widespread cross-resistance and are effectively obsolete. Baloxavir marboxil, a newer antiviral, shows promise in treating acute uncomplicated influenza and may avoid the development of resistance when co-administered with other antiviral drugs. Indeed, the low genetic barrier to resistance associated with influenza antivirals could potentially be overcome by co-administration with other antivirals. This review explores the most widely prescribed antivirals for influenza treatment, their mechanisms of action, and the data currently available about their susceptibility to resistance and efficacy.

Weak contact metric structures on a smooth manifold, introduced by V. Rovenski and R. Wolak in 2022, have provided new insight into the theory of classical structures. In this paper, we define new structures of this kind (called weak nearly Sasakian and weak nearly cosymplectic and nearly K\"{a}hlerian structures) and study their geometry. We introduce weak nearly K\"{a}hlerian manifolds (generalizing nearly K\"{a}hlerian manifolds) and characterize weak nearly Sasakian and weak nearly cosymplectic hypersurfaces in such Riemannian manifolds.

We have found an infinite dimensional manifold of exact solutions of the Navier-Stokes loop equation for the Wilson loop in decaying Turbulence in arbitrary dimension $d >2$. This solution family is equivalent to a fractal curve in complex space $\mathbb C^d$ with random steps parametrized by $N$ Ising variables $\sigma_i=\pm 1$, in addition to a rational number $\frac{p}{q}$ and an integer winding number $r$, related by $\sum \sigma_i = q r$. This equivalence provides a \textbf{dual} theory describing a strong turbulent phase of the Navier-Stokes flow in $\mathbb R_d$ space as a random geometry in a different space, like ADS/CFT correspondence in gauge theory. From a mathematical point of view, this theory implements a stochastic solution of the unforced Navier-Stokes equations. For a theoretical physicist, this is a \textbf{quantum} statistical system with integer-valued parameters, satisfying some number theory constraints. Its long-range interaction leads to critical phenomena when its size $N \rightarrow \infty$ or its chemical potential $\mu \rightarrow 0$. The system with fixed $N$ has different asymptotics at odd and even $N\rightarrow \infty$, but the limit $\mu \rightarrow 0$ is well defined. The energy dissipation rate is analytically calculated as a function of $\mu$ using methods of number theory. It grows as $\nu/\mu^2$ in the continuum limit $\mu \rightarrow 0$, leading to anomalous dissipation at $\mu \propto \sqrt{\nu} \to 0$. The same method is used to compute all the local vorticity distribution, which has no continuum limit but is renormalizable in the sense that infinities can be absorbed into the redefinition of the parameters. The small perturbation of the fixed manifold satisfies the linear equation we solved in a general form. This perturbation decays as $t^{-\lambda}$, where the anomalous dimensions $\lambda$ satisfy the spectral equation \eqref{spectralEq}. The spectrum becomes a continuum in the statistical limit $N \to \infty$, presumably leading to multifractal phenomena.

The rapid growth of urban areas has led to a decline in the amount of green space available to residents. This has a number of negative consequences for human health and well-- being. Green spaces provide a number of benefits for human health, including improved physical, mental, and social health. They also have a number of economic benefits, such as increased property values and reduced crime rates. Given the many benefits of green spaces, it is clear that they are essential for the health and well-being of urban residents. Policymakers and planners should prioritize the creation and preservation of green spaces in urban areas.

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

There has been a revolution in crop breeding, the age-old technique of improving plant features for agricultural and nutritional purposes. The merging of Artificial Intelligence (AI) and genetics is the driving force behind these changes. The combination of AI-driven models, genomic data, and cutting-edge tools like CRISPR-Cas9 to speed up genetic improvements in crops is described in this abstract. From increased disease resistance and production potential to better nutritional content, AI plays a crucial role in the identification and improvement of crop features. The time it takes to review, select, and cross several generations of crops is reduced by AI's data-driven selection, precision editing, and predictive modeling. This innovative tool has the potential to transform farming by helping to combat issues like hunger, climate change, and malnutrition on a worldwide scale. Equal access, protecting genetic variety, and assessing risks are only some of the ethical and regulatory issues raised by AI-enhanced agricultural breeding. Responsible and equitable implementation of AI in agricultural breeding relies on successfully navigating these challenges. Finally, the use of artificial intelligence to improve crop breeding marks a revolutionary change in agriculture, speeding up genetic improvements to meet the needs of a growing global population while also addressing urgent environmental and nutritional concerns. This abstract provides a taste of the promise, difficulty, and ethical questions that characterize this innovative subject, where artificial intelligence and genetics join forces to grow a better future for agricultural production around the world.

ABSTRACT: This paper provides an overview of the complex relationship between culture, gender, and business. It highlights the importance of understanding how these factors intersect and influence various aspects of the business world, including organizational practices, leadership styles, workplace dynamics, and economic outcomes. It also explores how cultural factors shape gender roles, expectations, and opportunities within the business context. Additionally, it discusses the implications of cultural influences on women's participation, advancement, and overall experiences in the business world. Understanding the interplay between culture, gender, and business is essential for fostering inclusive and diverse workplaces. Organizations that embrace cultural diversity and gender equality tend to exhibit greater creativity, innovation, and adaptability. They also benefit from increased employee satisfaction, improved decision-making processes, and enhanced financial performance. The work also examined how the values, beliefs, socialization, and team spirit of some distinct ethnic groups such as Japanese, Chinese, Jews, Igbo, and Indians have helped in the economic growth of not the groups alone but the countries where they reside. By examining these dynamics and cultures, scholars, practitioners, and policymakers can gain insights to develop effective strategies that promote gender equality, cultural sensitivity, and inclusive practices in the business world, ultimately leading to more equitable and prosperous societies.

The prevalence of chronic diseases is dramatically increasing demand for emergency healthcare. Existing systems rely on patients self-identifying symptoms, causing dangerous delays. This study develops an AI and IoT-powered “digital twin” solution to enable continuous real-time monitoring and timely prediction of diverse medical emergencies. A digital twin is a virtual representation of an individual, modeled using multidimensional physiological data from wearable sensors. Machine learning techniques analyze patterns in this data to identify anomalies and predict emergencies like heart attacks or falls. A key contribution is an optimized ensemble algorithm combining gradient boosted trees, neural networks, and other techniques to accurately detect emergency events. Evaluation on a dataset of 9158 samples shows the digital twin identifies key emergencies with over 90% recall, enabling prevention and rapid response. It allows risk stratification and personalized interventions based on early warnings, circumventing over 2 million avoidable emergency room visits annually. This study demonstrates the feasibility of an integrated, predictive, patient-centric emergency response system enabled by digital twin technology.

The cities of the future are places of sustainable mobility and a continuous reduction in the amount of harmful substances entering the surrounding environment. Current emission models are mainly dedicated to fully combustion vehicles and do not fully reflect the actual emissions of vehicles in the fleet, which is becoming increasingly diverse. The increasing share of hybrid and electric vehicles is generating the need to measure emissions and energy consumption from these vehicles and to develop new emission models that are accurate. This problem mainly concerns low-emission zones in cities, where mobility planning should be based on simulation models that are based on a continuously updated database. This work concerns the proposal of a two-dimensional model of emissions from hybrid vehicles using artificial neural networks for low-emission zones. The results can be used to further develop such emission models.

Cotton is an important plant since it provides raw materials for various in-dustry bracnhes. Even though cotton is generally tolerant of drought, it is af-fected negatively by long-term drought stress. This experiment was conduct-ed according to a completed randomized plots trial design with 3 replications to determine the upland cotton genotype’s reactions against drought under controlled conditions in the 2022 year. All genotypes were watered with 80 mL-1 of water (100% irrigation, Field capacity) until three true leaves ap-peared. After this period water stress was applied at limited irrigation of 75% (60 mL-1), 50% (40 mL-1), and 25% (20 mL-1) of field capacity. After the trial terminated at 52. Days, the cv. G56, elit line G44, G5, and commercial variety G86 genotype, respectively resulted in the highest Root Length (RL), and the G76 genotype resulted in the lowest RL followed by G41 and G35 elite lines. G1 cultivar showed the highest Root Fresh Weight (RFW), followed by G56, G44, G86, G51, and G88, respectively. The lowest RFW was also obtained in elite lines G76 and G41. The third drought morphological marker, the Num-ber of Lateral Roots (NLRs), was the highest in G44, followed by G86, Cv. G56, elite lines G13 and G5 genotype. The lowest NLRs value was obtained in genotype G47 and G76. The highest RDW means was obtained in elite line G5, followed by Cv. G56, elite line G44, G75 and cv. G90, while the lowes RDW mean obtained in elite line G76, G35, and local varieties G20 and G61. In the SL marker, the highest averages were recorded for genotypes G35, G15, G26, G67, and G56, respectively. In the SFW marker, the highest averages were observed in genotypes G15, G52, G60, G31, and G68, respectively. The lowest SL values were observed in genotypes G91, G72, G32, and G22. Simi-larly, the lowest SFW values were found in genotypes G83, G2, G75, and G91. In another shoot drought marker, Shoot Dry Weight (SDW), the highest val-ues were obtained in genotypes G35, G52, G57, G41, and G60, respectively. Conversely, the lowest SDW values were observed in genotypes G65, G91, G4, G20, and G22. In conclusion, the commercial varieties with high averages in roots, namely G86, G56, G88, and G90, and the genotypes G67, G20, G60, and G57 showing tolerance in shoots, are suggested to be potential parent plants for developing cotton varieties resistant to drought. Using the culti-vars found tolerant in the current study as parents in a drought-tolerant vari-ety development Marker-assisted selection (MAS) plant breeding program will increase the chance of success in reaching the target after genetic diversi-ty analyses are performed. It is highly recommended to continue the plant breeding program with the G44, G30, G19, G1, G5, G75, G35, G15, G52, G29, and G76 genotypes which show high tolerance in both root and shoot sys-tems.

Incobotulinumtoxin A and Yoga-like Isometric Exercise in Adolescent Idiopathic Lumbar Scoliosis – a randomized pilot study Abstract: Background: Approximately 90% of scoliosis is adolescent idiopathic (AIS). From its first appearance at 10-14 until age 18 it is most vulnerable to deterioration; young people are most susceptible to the condition worsening. An effective non-surgical means of remediation would be welcome. Design: Randomized control two-arm study, assessing the safety and efficacy of combining incobotulinum injections with yoga to reverse lumbar and thoracolumbar AIS. Methods: In a private clinic setting, non-pregnant healthy 12 – 18 year-olds were either taught a symmetrical “placebo” yoga pose (control sub-group 1), given the side plank (Vasisthasana) done thrice daily with placebo injection (control sub-group 2) or given the thrice-daily side-plank with botulinum injection (Intervention group 3). Injection: 33 IU of incobotulinum toxin type A (Xeomin) injected into concave-side lumbar paraspinals and quadratus lumborum at L2-3 and the psoas muscle at L3-4, or injected similarly with placebo. Randomization by Random.com. Objective: Assess whether muscular asymmetry treated with botulinum toxin injection and the side-plank are safe and effective in AIS.Results: Outcome: Twelve intervention and 12 placebo patients (Groups 1 + 2), 12 -18 years old completed the three-month study period. Mean daily side-plank time = 165 seconds. Mean initial lumbar curvature was 36.9 degrees (SD 14.36), (p<0.0001); mean curvature at 3 weeks was 29.5 degrees. (SD 14.23) (p<0.0001); mean curvature at 3 months was 26.0 degrees (SD 12.81) Onset vs 3-month value: p<0.0001. Two patients in Group 3 complained of shoulder pain, and 2 of wrist pain that resolved when the side-plank was done on the elbow. Conclusion: Muscle asymmetry appears relevant to AIS treatment. Incobotulinum injections combined with the side-plank done with the convex side downward may be safe and helpful in adolescent idiopathic lumbar scoliosis.

These three syndromes - long covid (LC), postural orthostatic tachycardia syndrome (POTS), and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) - have many symptoms in common. The common denominator remains elusive. The blood brain barrier (BBB) has been a barrier not only to microbes and toxins but also to understanding pathogenetic links. There are several areas within the brain that have no BBB. These are known as circumventricular organs (CVOs) and their location relative to CNS nuclei that direct autonomic and neuroendocrine functions is provocative in the quest for pathogenesis. In addition the majority afflicted with LC and ME/CFS appear to be those with two MTHFR polymorphisms, present in over 50% of Americans. These polymorphisms elevate homocysteine. When homocysteine is combined with CVOs, the fog of POTS and its paradox are lifted. POTS may represent the intersection of LC and ME/CFS in those with the MTHFR gene (hypermethylation or 677TT). The gut microbiomes of LC and ME/CFS, deficient in butyrates, GABA, and diversity, are then linked with MTHFR genotype 677TT. Reactivation of neurotropic EBV and VZV, due to loss of surveillance by CD4+/CD8+ T cells, is seen as secondary. The oxidative stress generated by homocysteine, loss of glutathione, low fiber diet, and persistent chronic inflammation exhaust available mitochondria and, assisted by BKN and estrogen, exacerbate all the elements of these post viral fatigue syndromes.

Purpose To alleviate the data imbalance problem caused by subjective and objective reasons, scholars have developed different data preprocessing algorithms, among which undersampling algorithms are widely used because of their fast and efficient performance. However, when the number of samples of some categories in a multi-classification dataset is too small to be processed by sampling, or the number of minority class samples is only 1 to 2, the traditional undersampling algorithms will be weakened. Methods This study selects 9 multi-classification time series datasets with extremely few samples as the objects, fully considers the characteristics of time series data, and uses a three-stage algorithm to alleviate the data imbalance problem. Stage one: Random oversampling with disturbance items increases the number of sample points; Stage two: On this basis, SMOTE (Synthetic Minority Oversampling Technique) oversampling; Stage three: Using dynamic time warping distance to calculate the distance between sample points, identify the sample points of Tomek Links at the boundary, and clean up the boundary noise.Results This study proposes a new sampling algorithm. In the 9 multi-classification time series datasets with extremely few samples, the new sampling algorithm is compared with four classic undersampling algorithms, ENN (Edited Nearest Neighbours), NCR (Neighborhood Cleaning Rule), OSS (One Side Selection) and RENN (Repeated Edited Nearest Neighbours), based on macro accuracy, recall rate and F1-score evaluation indicators. The results show that: In the 9 datasets selected, the dataset with the most categories and the least number of minority class samples, FiftyWords, the accuracy of the new sampling algorithm is 0.7156, far beyond ENN, RENN, OSS and NCR; its recall rate is also better than the four undersampling algorithms used for comparison, at 0.7261; its F1-score is increased by 200.71%, 188.74%, 155.29% and 85.61%, respectively, relative to ENN, RENN, OSS, and NCR; In the other 8 datasets, this new sampling algorithm also shows good indicator scores.Conclusion The new algorithm proposed in this study can effectively alleviate the data imbalance problem of multi-classification time series datasets with many categories and few minority class samples, and at the same time clean up the boundary noise data between classes.

In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated promising performance in various applications. However, its ability to incorporate feature information from nodes beyond the immediate neighborhood is limited, leading to degraded performance on heterophilic data. To address this limitation, this thesis proposes a novel attention-based model, namely the Directional Graph Attention Network (DGAT). This model combines the feature-based attention with the global directional information extracted from the graph topology, as inspired by the Directional Graph Network (DGN). A new class of Laplacian matrices is proposed and an existing theoretical result on DGN is extended. This extension bridges a gap in the literature. The experimental results presented in the thesis, based on nine real-world benchmarks and ten synthetic data sets, demonstrate the superiority of the proposed DGAT model compared to the GAT baseline model. Particularly on heterophilic data sets, DGAT showed a notable average increase of approximately 35% in node classification tasks across all heterophilic real-world data sets. In addition, DGAT outperforms GAT by an average margin of around 51% in all ten synthetic data sets with various levels of heterophily

The natural units of measure lauded by Max Planck more than 100 years ago are rarely used today. But many physical constants, including Planck's constant, the gravitational constant, the speed of light, vacuum permittivity, and vacuum permeability, consist of natural units in their unit dimensions. The natural units are present in all formulas containing these constants. The defining characteristic of the natural units is an alignment of unit values at the Planck scale. This alignment gives a computational basis of proportionality from which the correlated properties and dynamics of elementary particles, including wavelength, period, mass, momentum, and energy, manifest in equal or inversely proportional ratios of the Planck scale. These correlations explain many of the defining equations of quantum mechanics, classical gravity, and electromagnetism.

Climate changes challenges people’s life and sustainability. Environmental problems seem to derive from human behavior. Dentistry has a high environmental footprint that needs to be controlled worldwide. Thus, the change of behavior of dentists is a necessary condition to improve the environmental situation. In this study we use a system dynamics approach to analyze pro-environmental behavior in dentists. Systemic modelling in this case, allow us to make a clear prediction of how the population of non-eco dentists will change in the future if certain factors will be affected and how much time will be needed by the system of a group of dental professionals to become eco- dentists. The ECODENT model was designed with Vensim software. It was based on the influence of WOM (word of mouth) spread of information among colleagues and stakeholders (e.g. patients, auxiliary staff and community) and other factors such as dental income, state support, CaPex and OpEx, education hours, level of urbanization and time to react. By changing each one of them we can observe the changes in the groups of non-eco and eco dentists as well as the flow of the phenomenon. It is tested that by augmenting WOM parameter, state economic support or education hours, while diminishing CaPex and OpEx, changes in pro-environmental behaviors of dentist will be reported in a shorter time. The ECODENT model can be used to further study pro-environmentalism in different dental groups and associations and design relevant eco-friendly educational programs.

The atomic nucleus contains protons and neutrons surrounded by a structured electron cloud. Some combinations of protons and neutrons induce the nucleus to behave like a microscopic magnet, possessing a unique magnitude and orientation known as the nuclear magnetic moment (µ). When the numbers of protons and neutrons are both even then µ=0, but otherwise each nu-clide’s nuclear magnetic moment is unique. A second unique nuclear physical property relates to the nucleon binding energy (BE), which can be thought of as the energy between protons and neu-trons bound within the nucleus. The sequence of stable nuclides increases one nucleon at a time through 36Ar. The binding energy associated with the addition of each successive nucleon (∆BE) through this progression is unique. The hypothesis here is that the unique non-zero magnetic mo-ment and ∆ binding energy of each nuclide derive from its respective microscopic nuclear struc-ture, analogous to the way that the atomic physical properties of an element ultimately derive from its unique electron orbital structure. By extension, the identification of nuclear periodic pat-terns might eventually inform a theory of nuclear structure. The light, stable nuclides through 36Ar were arranged in ascending order according to atomic mass, and their nuclear magnetic mo-ments and binding energies were evaluated for periodic patterns. Fixed-period lengths from 4 through 18 nuclides per period were considered. These fixed periods, each of equal length, were stacked one upon the other and analyzed for vertical trends. The best evidence of periodicity in both µ and ∆BE converges precisely at the fixed 12-nuclide period. Implications for nuclear structure are discussed.

(1)Background: The prognosis of orthodontic treatment for a patient with impacted canine teeth can be affected by many factors and understanding some of the characteristics of impacted teeth can increase the effectiveness and reduce the duration of treatment. This study aims to explore the effects of positions and dentoalveolar morphological characteristics of impacted maxillary canines on the prognosis of orthodontic treatment. (2)Methods: In this study, a total of 46 adolescent individuals were divided into 3 groups: CC(no impaction), BC(unilateral maxillary buccal canine impaction), and PC(unilateral maxillary palatal canine impaction). The impacted canine and adjacent teeth were evaluated in terms of position, angulation, inclination, MD and BL widths, root length, dilaceration angle, root resorption. (3)Results: The mean values of U3Ang and U3/U2 angles were statistically significantly higher in the BC and PC groups(p&lt;0.001). The mean values of U2RL(U3root length) and U3RD (U3root dilaceration) angle were statistically significantly lower in the BC and PC groups. (4)Conclusions: The results of this study suggest that shorter lengths and reduced volumes of the maxillary lateral incisors may lead to the ectopic eruption of canines. The higher canine angulations in the PC group make treatment more difficult and increase the degree of root resorption.

Building facade design plays an essential role in enhancing energy efficiency and reducing environmental impact in high-performance building design. Balancing the conflicts among various building facade design parameters to satisfy different optimization objectives constitutes a highly complex optimization problem. The rapidly increasing number of studies demonstrates a significant interest in implementing multi-objective optimization methods to tackle building facade optimization problems. This study conducts a systematic review of optimization methods for building facade optimization (BFO). The optimization objectives and design variables are categorized based on their characteristics. The efficiency and effectiveness of optimization algorithms in addressing BFO problems are compared. Building optimization techniques and tools are showcased, along with their functions and limitations. Key findings highlight the robust feasibility and effectiveness of optimization algorithms, methods, and techniques in resolving a diverse range of BFO challenges. The limitations, challenges, and future potential of these methods are summarized and proposed.

Flood is one of the most common disaster in Assam.The situation gets worse when pandemic hits the place during the flood time. The paper indicates about women’s flood related lived experiences during pandemic. It is thought that floods will increase the global burden of disease, morbidity, mortality, social and economic disruptions, and will place a continuing stress on health services, especially in low-resource countries. Natural disaster hampers both the men and women. Pandemic means the disease whose prevalence has been all over the world. Covid 19 has been the recently developed pandemic that touches each and every corner of the world. But within such obstruction, both the male and female’s lived experiences of health might be different from each other. This paper articulates the gendering lived experiences of pandemic during the time of flood. However here an attempt has been made to understand women’s negotiation with their everyday life during such time and whether different social, political and cultural capital have been significant factors as far as the negotiation is concerned that has also been discussed through this study. However, on the descriptive note, narrative analysis has been done to understand the women’s negotiation in their everyday life.The study is conducted at different places of Nalbari and Golaghat Districts of Assam. All interviews were audio-recorded, transcribed, translated into English and qualitative analysis was done . How disaster made adverse impact on women's everyday life that has been tried to understand through this paper. Keywords- Flood. Pandemic, Lived Experiences, Women, Everyday.

A model to rationalize the effects of test temperature and microstructural variables on the creep crack growth (CCG) and creep-fatigue crack growth (CFCG) rates in ferritic steels is described. The model predicts that as the average spacing between grain boundary particles that initiate creep cavities decrease, the CCG and CFCG rates increase. Further, CCG data at several temperatures collapse into a single trend when a temperature compensated CCG rate derived from the model is used. CCG and CFCG behavior measured at different temperatures is used to assess the effects of variables such as differences between basemetal (BM), weldmetal (WM) and the heat-affected zone (HAZ) regions. The model is demonstrated for Grade 22 and Grade 91 steels using data from literature. It is shown that differences between the CCG behavior of Grade 22 steel in new and ex-service conditions are negligible in the BM and WM regions but not in the HAZ region. The CCG behavior of Grade 91 steels can be separated into creep-ductile and creep-brittle regions. The creep-brittle tendency is linked to the presence of excess trace element concentrations in the material chemistry. Significant differences found in the CCG rates between BM and the WM and HAZ regions of Grade 91 steel were explained.

Ballasted railway tracks can be modelled using reduced/simplified models composed of several layers of discrete components. This paper deals with the two-layer model, which is very popular due to its computational efficiency. In order to provide some recommendations for track design, it is necessary to identify which set of parameters leads to some irregular/unexpected behavior. Such irregularity is investigated at three levels, such as: (i) critical velocity of moving constant force; (ii) instability of a single moving mass; (iii) instability of two moving masses. All results are presented in dimensionless form to cover a wide range of real parameters. Irregular cases are identified by sets of parameters leading to them and then general conclusions are drawn. Regarding the method, all results are obtained analytically or semianalytically, where “semi” refers to solving the roots of a given polynomial by predefined numerical procedures in symbolic software. No numerical integration is involved in all results presented. This means that the results are highly accurate and refer to exact values, so any kind of parametric or sensitivity analyses is readily possible.

The article aims to explore social aspects in the area of energy research by using bibliometrics methods and tools. The study is based on the data of Energy Research & Social Science journal publications exported from ScienceDirect database during 2019-2023. Methodologically, the work included clustering of the bibliometric records under consideration. This allowed to identify the main topics of publications and those in which the research of social problems prevails. This stage of the work was carried out using carrot2, an engine for clustering search results. Next, the analysis of publication topics related to social issues research was carried out using VOSviewer — a software tool for visualizing scientific landscapes. The topics of the four clusters obtained at this stage were described by key terms with the maximum total link strength. For the principal ones, a definition was made on the basis of the texts of publications that most fully disclose the meaning of these terms. The 1537 publications in the journal during the period under review deal with many aspects of energy transition research, renewable energy, energy policy, energy justice and energy systems, but only 78 of them are dominated by social issues. The paper reveals the main social issues in the field of energy research: sociology of energy, socio-technical system, community energy, energy democracy, sociology of the future, energy sovereignty, socio-technical imaginary and shows the insufficient attention to this topic, especially to the investment of such research.

Agricultural activities profoundly affect groundwater levels in Saudi Arabia. The Al Kharj and Wadi Sahba areas, central Saudi Arabia, produce groundwater mainly from the Jurassic Arab/Jubaila aquifer and the overlying Cretaceous Biyadh aquifer. A geographic information system analysis and linear regression analyses show that from 1978 to 2016, significant agricultural overexploitation of the area’s fossil groundwater was associated with groundwater level drops of ~104 m in Al Kharj and ~48 m in Wadi Sahba. The estimated total volume of groundwater withdrawal across both aquifers during this period of wheat and alfalfa irrigation was ~27 billion m3. Landsat image analysis shows that agricultural land use decreased by ~66% over the same period due to overexploitation. The 2016 phase-out of alfalfa farming was associated with increases in groundwater levels: ~26 m in the Arab/Jubaila aquifer and ~3 m in the Biyadh aquifer. This recovery was likely due to lateral flow from surrounding fossil water within the aquifer given the absence of significant surface recharge. Model-based evaluation of surface recharge of the aquifer reveals very low potential for surface infiltration, with almost no recharge into the deep aquifer. Assuming that recovery continues at the present rate, the average groundwater level for the Arab/Jubaila aquifer is expected to reach 98 m below ground level in 2025 and 74 m below ground level in 2030.

The main object of this work is to establish the existence and uniqueness of a solution to the 3D Navier-Stokes (NS) system for an incompressible fluid with viscosity. The nonlinearity of the NS system, as well as the need to estimate velocity and pressure for every value of the viscosity parameter [1] make them challenging to solve. In this regard, in the present work, we study the Navier-Stokes system, which describe the flow of a viscous incompressible fluid and the solution was obtained for velocity and pressure in an analytical form. In addition, the found pressure distribution law, which is described by a Poisson type equation and plays a fundamental role in the theory of Navier-Stokes systems in constructing analytic smooth (conditionally smooth) solutions.

Based on a literature review, a framework for digital organizational culture with a focus on innovation is proposed. This digital organizational culture becomes relevant today since digital transformation requires an appetite for risk and a willingness to undertake, aspects that are strengthened by an environment and atmosphere that motivate individuals to generate new ideas. The proposal begins with a diagnosis of the existing organizational culture, which allows identifying the differences between the components of the current organizational culture and the innovation-oriented digital organizational culture. Through this diagnosis, the gaps that need to be addressed to achieve a digital organizational culture with innovative characteristics are identified. In addition, specific recommendations are proposed to carry out this transformation. In the process, the assumptions underlying the current typology of the company's organizational culture are highlighted. The importance of integrating creativity, risk readiness, and entrepreneurial willingness into the digital organizational culture aimed at innovation is emphasized. These elements are essential to create an environment

Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

This article focuses on methods for designing heuristic models within the paradigm of systems theory and in the disciplinary context of intercultural communication. The main question arises from the striking observation that the common language is insufficient to develop knowledge about human communication, especially when many factors of complexity (such as ambiguity, paradoxes, or uncertainty) are involved in the composition of an abstract research object. This epistemological, theoretical, and methodological problematic is one of the main challenges to the scientificity of anthropological theories and concepts on culture. Moreover, these questions lie at the heart of research in intercultural communication. Authors and theorists in the complexity sciences have already stressed the need, in such case, to think in terms of models or semiotic representations, since these tools of thought can mediate much more effectively than unformalized language between the heterogeneous set of perceptions arising from the field of experience, on the one hand, and the philosophical principles that organize speculative thought, on the other. This sets the scene for a reflection on the need to master the theory of heuristic models when it comes to developing scientific knowledge in the field of intercultural communication. In this essay, my first aim is to make explicit the conditions likely to ensure the heuristic value of a model, while my second aim is to clarify the operational function and required level of abstraction of certain terms such as concepts, categories, headings, models, systems, or theories that are among the most commonly used by academics in their descriptive accounts or explanatory hypotheses. To achieve this second objective, I propose to create cognitive meta-categories to identify the three (nominal, cardinal or ordinal) roles of words in the reference grids we use to classify our ideas, and to specify how to use these meta-categories in the construction of our heuristic models. Alongside the theoretical presentation, examples of application are provided, almost all of which are drawn from my own research into the increased cultural vigilance of the majority population in Quebec since the reasonable accommodation crisis in this French-speaking province of Canada. The typology I propose will perhaps help to avoid the confusions regularly committed by authors who attribute only cosmetic functions to words that nevertheless have a highly heuristic value, and who forget to consider the logical leaps of their theoretical thinking in the construction of heuristic models.

Ebola virus is a zoonotic virus comprised of 6 different species designated within the family Filoviridae and genus Ebolavirus. The first recorded outbreak of an Ebola virus (EBOV) was in Yambuku, Zaire (ZEBOV) in 1976, followed by Sudan Ebola virus (SUBOV) later that year. Outbreaks have been increasing throughout the 21st century, and mortality rates can reach up to 90%. Such extraordinary virulence is evidenced with few pathogens, similarly with Marburg virus (MARV) that originated in Uganda and was first detected in Germany in 1967. The virulent nature of filovirus disease has established these related viruses as a formidable global concern. There are currently four types of Ebolaviridae species known to infect humans, with two more recently identified in other animals that are genomically different with respect to cellular pathogenesis or aetiology of disease. Recent advances into understanding the pathogenesis of filovirus disease infections have been remarkable, yet the immunological response to filovirus infection remains unknown. Scientific analysis of cellular mechanisms can provide insight into virulence factors utilised by other pathogenic viruses that also cause febrile illness with occasional haemorrhagic fever in humans. In this review, we aim to provide a brief summary of EBOV proteins and the role of innate and adaptive immune cells known since 2000. We will consider the relevance and implications of immunological proteins measured by CD marker, alongside cytokine, chemokine and other biologically relevant pathways, as well as genetic research. Thorough understanding of immunological correlates affecting host responses to Ebola viruses will facilitate both clinical and applied research knowledge, contributing towards protection against potential public health threats.

Escalation of commitment explains the choice for investing in an option for which great investments have previously been made even though better choices became available. The last 25 years of producing innumerous versions of Alzheimer Disease animal models with virtually no predictive validity should have exposed the fact that using animals for studying AD is clearly a failing course of action and as such should have been abandoned long ago, giving room to other alternatives to flourish. In this assay, I explore the idea that the investment in new animal models for studying Alzheimer (AD) represents a classic case of escalation of commitment, and sunk cost dilemma, in the biomedical research.

This comprehensive review examines the critical issue of antimicrobial resistance (AMR), a growing global concern with profound societal implications. This article presents an overview of the causes of AMR, including the misuse and overuse of antibiotics in healthcare, agriculture, and veterinary practices. Various conventional and advanced detection methodologies have been explored, highlighting the importance of the accurate and timely identification of resistant strains. The challenges associated with tackling AMR, encompassing scientific, economic, and regulatory aspects have been discussed in depth. In addition, this article sheds light on the far-reaching societal implications of AMR, including increased healthcare costs, treatment complexities, and potential disruptions to healthcare systems. This emphasizes the urgency of adopting proactive strategies to combat AMR and underscores the need for global cooperation, policy interventions and public awareness campaigns. By unraveling the multifaceted dimensions of AMR, this review aims to enhance our understanding, stimulate further research, and promote concerted efforts to mitigate the threat of antimicrobial resistance.

We investigate if anyone is being left behind in accessing safely managed drinking water (SM) in achieving the 2030 agenda. We use 23 countries grouped globally as critically insecure water (CIWC). The results indicate none of the CIWC had over 50% of the population accessing SM, N. America and Europe had highest percentages while Sub Saharan Africa had the least. People left behind are found in rural areas, in countries vulnerable to climate change because of poor governance, low-income, and women’s low education. Institutional quality, equity and equality should be improved in using resources critical to leaving no one behind.

With the development of bioinspired green solutions for sustainable construction over the past two decades, bio-cementation, which exploits the naturally occurring phenomenon of calcium carbonate precipitation in different environments, has drawn a lot of attention in both building construction and soil stabilization. Various types of microorganisms, along with specific enzymes derived from these microorganisms, have been utilized to harness the benefits of bio-cementation. Different application methods for incorporating this mechanism into the production process of the construction material, as well as a variety of experimental techniques for characterizing the outcomes of bio-cementation, have been developed and tested. Despite the success of bio-cementation as a sustainable method to construction has been demonstrated in a significant body of literature at the laboratory scale, the expansion of this strategy to construction sites and field application remains a pending subject. The issue may be attributed to two primary challenges. Firstly, the complexity of the bio-cementation phenomenon is influenced by a variety of factors. Secondly, the extensive body of literature examines various types of microorganisms under different conditions, leading to a wide range of outcomes. Hence, this study aims to examine the recent advancements in utilizing the most commonly employed microorganism, Sporosarcina Pasteurii, to emphasize the significance of influential factors identified in the literature, discuss the findings that have been brought to light, and outline future research directions toward scaling up the process.

It is well known that the size of initial loans approved by private lending agencies (banks, credit unions etc.) corresponds with the median income of the location of the individual, it also corresponds with gender. We wanted to show if it was the case with the PPP. We use standard machine learning techniques such as Lasso and found statistically significant association between gender of business owner and size of initial loan approvals for the State of Arkansas, and Least Squares Regression and Decision Tree regression to find a somewhat tenuous relationship between median income and initial loan approval amount and finally a strong association with the size of business. This has important applications for the equity of access to credit for marginalized peoples.

Public health suffers from noxious gas of beached Sargassum algae. Floating barrages containing them at sea will be useful provided efficiently catch them and resist to hydrodynamic pressure. Nowadays design and installation of algae barrages are empiric. Structural break, anchor and mooring chain drifts can arise. We provide a mechanical model to evaluate stresses and loads on a structure made of nets and moorings. Hydrodynamic uncertainties occur through catches, fouling, and a range of sea currents appearing in lagoons or sheltered bays. We consider a nonlinear finite-element model of elastic membrane in large displacements and small strains. We study some forcings fixed a priori on a standard section of barrage. A section 50 m long, 1 m high, with two net-faces is examined at low and moderate current velocities, 0.05 and 0.35 m.s-1, while assuming specific vertical and horizontal catch pressures. A barrage installed in lagoon at Le François on Martinique Island and being observable by satellite imagery could benefit of the computed net and mooring tensions.

In this letter, we construct an effective field theory of gravity at large distances. We obtain to the leading order for large distances: a cosmological constant term and a logarithmic term in addition to the usual General Relativistic terms. The novel logarithmic term has important implications in explaining phenomena such as flat galactic rotation curves and stronger gravitational lensing than predicted that are generally attributed to the presence of dark matter.

The holographic principle states that the information about the volume of space is stored on its boundary. Assuming this holds, we can explain the aspects of gravity. The spacetime needs to be extended to 5D to explain gravity in this context. This naturally explains the four classical tests of gravity namely the gravitational redshift, the perihelion precession, the bending of light, and the gravitational time delay. We also show a novel way in which the universe expands in an accelerated manner at late times without the need for any form of dark energy.

This article aims to investigate the potential of artificial intelligence (AI) in revolutionizing the education sector and further advancing the vision of Education 6.0. This study explores how AI can foster personalized learning experiences and adaptive pedagogy. By analyzing recent developments and real-world applications of AI in education, this article offers insights into how AI can support educators in tailoring instruction to individual students' needs, promoting engagement, and optimizing learning outcomes. The article also discusses ethical considerations and challenges associated with the integration of AI in education, emphasizing the importance of responsible implementation and human supervision.

A recent research has identified an inverse amplitude link between obliquity damping and short eccentricity amplification during Mid-Late Pleistocene based on LR04 δ18O and equatorial Pacific Site 846 sea surface temperature records, which is associated with the Earth's long-term cooling. In the present study, new evidence of this anticorrelation is presented from Antarctic D-CO2-CH4 records, global benthic-planktic δ18O, and regional (Atlantic, Pacific, Mediterranean, and Indian) climate-related proxies. Based on a critical review of theoretical constrains (Earth's oblateness changes and ice-volume phase lag in the obliquity band <5.0 kyr), this widespread and symmetric (bipolar) obliquity response damping has been interpreted as an effect of the obliquity-oblateness feedback, which could be the latent physical mechanism at the origin of the Mid-Pleistocene Transition (MPT). Indeed, results and considerations of the present work suggest that fast and positive/negative net variation of the Earth's oblateness in the obliquity band was controlled by dominant glacio-eustatic water mass component and, assuming a rapid response of the ice-volume to surface temperature changes, the mean obliquity lag response is estimated to be <5.0 kyr over the past 800 kyr. These elements may explain the interglacial/glacial damping observed in the obliquity response. The consolidation of the Earth's long-term icy-state in the subtrend IV, culminated with the post-MPT obliquity damping, might have contributed to the strengthening of the short eccentricity response by mitigating the obliquity ‘ice-killing’ during obliquity maxima (interglacials), favouring the obliquity-cycle skipping and a feedback amplified ice-growth in the short eccentricity band (obliquity damping hypothesis). This could suggest a different impact of the climate friction than what is generally believed, which is presumably the latent physical mechanism that triggers transient ‘competitive’ interaction between obliquity and short eccentricity started early during the Piacenzian.

EGFR tyrosine kinase inhibitors (TKIs) are the preferred initial treatment for non-small cell lung cancer (NSCLC) patients harboring EGFR mutations. However, remission is transient, and no further effective treatment options are available for EGFR-TKI-advanced EGFR-mutant NSCLCs. Immunotherapy with immune checkpoint inhibitors (ICIs) induces sustained cancer remission in a subset of NSCLCs. However, ICI therapy exhibits limited activity in most EGFR-mutant NSCLCs. Mechanistically, the strong oncogenic EGFR signaling in EGFR-mutant NSCLCs contributes to the non-inflamed tumor immune microenvironment (TIME), characterized by a limited number of CD8+ T cell infiltration, a high number of regulatory CD4+ T cells and a high number of inactivated infiltrated T cells. Besides, EGFR-mutant NSCLC patients are generally non-smokers with low levels of PD-L1 expression and tumor mutation burden. However, current understanding only partially explains why a small population of EGFR-mutant NSCLCs durably respond to ICI therapy, resulting in many researchers actively working in this field. This review reviews the hope seen from pre-clinical studies and clinical trials which may be adopted to improve the outcome of ICI therapy in EGFR-mutant NSCLCs. Besides, the underlying mechanisms leading to the inferior clinical outcome of ICI therapy in EGFR-mutant NSCLCs are discussed.

A fascinating topic that has not been recently revisited by bioethics is the ethics of human experimentation within the military context, in keeping with the pace of modern technology development. Many research innovations stem from military research, where emerging technologies are first applied in the field then eventually repurposed for civilian contexts. This commentary presents an ethical framework for the usage of military personnel as research subjects, within the context of modern military research such as epigenetic technology development in soldiers. Tensions are raised between existing military versus civilian bioethical frameworks for human experimentation and compared to risk-benefit assessments within and beyond the military context. A harmonized ethical framework is proposed for the use of research subjects within the military. The pace of modern scientific research, particularly in genomics, poses new ethical considerations of genetic profiling, consent, risk, and data privacy that urges a timely revisit of military bioethics.

Among various processes, catalytic CO2 methanation has emerged as a promising method for carbon capture and utilization. Therefore, a CO2 methanation reaction studied with experimentally. The extent of coke formation on the catalyst after the reaction was quantitatively studied. Then, four different machine learning model were employed in order to model CO2 methanation process and to predict CO2, CO, CH4, H2 and N2 concentrations at outlet. The results of the machine learning models were consistent with the experimental data. Performances of the proposed regression methods are evaluated with 10-fold cross validation. Random forest and decision tree regression performed the best among other methods by achieving R >0.98 for the output concentrations and were able to outperform other modeling approaches. It was shown that the best result among the literature studies is reached in this study. The results have demonstrated the possibility of simulation of the methanation process via machine learning techniques. These techniques can be used control and optimization of carbon capture.

Recent global trends related to the increasing use of e-commerce are becoming a challenge for courier transport especially in the last-mile process of delivering products to the final retail recipient. One of the delivery methods is the personal collection of the parcel in an automated post box, available 24/7 for the customer. This research aims to define the most important attributes of the courier services quality, including the delivery of parcels through automated sending and receiving parcel lockers, leveraging advanced technologies, data, and connectivity to enhance the quality of life, sustainability, and efficiency for city residents. The research was based on a preliminary selection of the most important features of parcel lockers’ service quality, which were extracted from the analysis of scientific literature and previous research. The analysis was carried out by conducting a survey among city parcel locker users that provided data coded according to the dimensions of the Kano model. This allowed to conclude that the location of parcel stations, ensuring improvements for the disabled, adjusting the size of the parcel to the size of the box, proper placement of the parcel in the box and a properly functioning dedicated application are the most important features in the process of automatic delivery of parcels to recipients in urban areas. This paper enriches the literature on customer service quality of self-service technologies for last-mile delivery with the use of automated parcel lockers.

In this study, we investigate the behavior of randomly moving particles with a defined average energy, focusing on their biased motion around a specific point. These particles' velocity magnitudes follow a Maxwell distribution with a specific scale parameter. Our analysis derives the stochastic rotation rules for these particles and introduces the Ito equation conditions for biased stochastic rotation at a given curl value. We also consider the special relativistic-like effects in this context. The findings are validated through a representative example.

In contrast to some low molar-mass per- and polyfluoroalkyl substances (PFASs), well-established to be toxic, persistent, bioaccumulative and mobile, fluoropolymers (FPs) are water insoluble, safe, bio-inert, durable niche high performance polymers which fulfil the 13 polymer of low concern (PLC) criteria in their recommended conditions of use. In addition, more recent innovations (e.g., the use of non-fluorinated surfactants in aqueous radical (co)polymerization of fluoroalkenes) from industrial manufacturers of FPs are highlighted. This review also aims at showing how these specialty polymers endowed with outstanding properties are essential (even irreplaceable since hydrocarbon polymer alternatives used in similar conditions fail) for our daily life (electronics, Energy, optics, internet of things, transportation, etc) and constitute a special family a part from other “conventional” C1-C10 PFASs found everywhere on the Earth and Oceans. Furthermore, some information reports the recycling (e.g. the unzipping depolymerization of polytetrafluoroethylene, PTFE, into TFE), end of life of FPs, their risk assessment, circular economy and regulations. Various researches are devoted to Environment involving FPs, though they represent a niche volume (with a yearly production of 330,300 tonnes) compared to all plastics (with 460 million tonnes). Complementary to other reviews on PFASs, which lack of such above data, this review presents both fundamental and applied strategies as evidenced by major FP producers.

The rapid growth of urban areas is a major challenge facing cities around the world. This growth can have a significant impact on the local climate, leading to higher temperatures and other changes. In desert climates, the effects of urban expansion can be particularly pronounced. This study investigated the impact of urban expansion on land surface temperature (LST) in Baghdad, Iraq. Notably, this study employs a sophisticated artificial intelligence method known as Random Forest for Land Use Land Cover (LULC) classification, utilizing three Landsat images spanning the temporal spectrum from 1985 to 2021 to meticulously monitor land use transformations and associated LST variations. The results showed that vegetated areas declined by 46.8% during the study period, while built-up areas increased by 124.7%. This decline in vegetation was accompanied by an increase in LST, with bare soil recording the highest temperatures. The study also found that LST has a strong inverse relationship with vegetation and moisture. This means that areas with more vegetation and moisture tend to have lower LSTs. These findings suggest that urban expansion can lead to higher LSTs in desert climates, which can have implications for the health and wellbeing of residents. The study has important implications for urban planners and policymakers in Baghdad and other cities in desert climates. By identifying the main factors that control LST, the study provides insights into strategies for mitigating the effects of urban expansion on temperature.

Lipofuscin is indigestible garbage that accumulates in the autophagic vesicles and cytosol of post-mitotic cells with age. Drs. Brunk and Terman postulated that lipofuscin accumulation is the main or at least a major driving factor in aging. They even posited that the evolution of memory is the reason why we get lipofuscin at all, as stable synaptic connections must be maintained over time, meaning that the somas of neurons must also remain in the same locale. In other words, they cannot dilute out their garbage over time through cell division. Mechanistically, their position certainly makes sense given that rendering a large percentage of a post-mitotic cell’s lysosomes useless must almost certainly negatively affect that cell and the surrounding microenvironment. It may be the case that lipofuscin accumulation is the main issue with regard to current age-related disease. Degradation in situ may be an insurmountable task currently. However, a method of systemic lipofuscin removal is discussed herein.

We humans are natural-born engineers. As such, we model after machines not only isolated, naturally occurring systems, but also the basic laws of physics, sharing with machines a local-evolution-of-state `grammar'. However, previous work by the author casts doubt upon this mechanistic paradigm, suggesting that it is to blame for the stubbornness of many open problems in physics. Simple experiments are therefore proposed to identify `non-machines'. In one experiment, `non mechanistic correlations' in the spirit of Bell are sought in a pair of separated but previously coupled (macroscopic) chaotic systems. In another it is tested whether chaotic systems could fuzzily `remember their future' in the sense that a future binary perturbation applied to them could be inferred from their present behavior with probability>1/2. Chaotic systems are chosen as candidates because the long-time application of their (deterministic) mechanistic description is conceptually groundless: All scales are significant and mutually coupled in that regime, down to scales governed by QM, yet the quantum-classical transition remains inexplicable---if only due to the measurement problem. Similarly for non-deterministic chaotic systems, modeled as such purely for defying a short-time deterministic mechanistic description. Should a non mechanistic signature be found in such simple systems, the implications for science as a whole and life sciences in particular cannot be overstated.

The Collatz conjecture (or 3n+1 problem) has been explored for about 86 years. In this article, we prove the Collatz conjecture. We will show that this conjecture holds for all positive integers by applying the Collatz inverse operation to the numbers that satisfy the rules of the Collatz conjecture. Finally, we will prove that there are no positive integers that do not satisfy this conjecture.

Macrophages play a pivotal role in the process of healing burns. One of the major risks in the course of burns healing, in the absence of regenerating epidermis, is infections which greatly contribute to morbidity and mortality in such patients. Therefore, it is widely agreed that accelerating recruitment of macrophages into burns may contribute to faster regeneration of the epidermis and thus, decreasing the risk of infections. This review describes a unique method for rapid recruitment of macrophages into burns and activation of these macrophages to mediate accelerated regrowth of the epidermis and healing of burns. The method is based on application of bio-degradable “alpha-gal” nanoparticles to burns. These nanoparticles present multiple alpha-gal epitopes (Gal alpha1-3Gal beta1-4GlcNAc-R) which bind the abundant natural anti-Gal antibody that constitutes ~1% of immunoglobulins in humans. Anti-Gal/alpha-gal nanoparticles interaction activates the complement system, resulting in localized production of the complement cleavage-peptides C5a and C3a that are highly effective chemotactic factors for monocytes derived macrophages. The macrophages recruited into the alpha-gal nanoparticles treated burns are activated following interaction between the Fc portion of anti-Gal coating the nanoparticles and the multiple Fc receptors on macrophages cell membranes. The activated macrophages secrete a variety of cytokines/growth factors that accelerate the regrowth of the epidermis and regeneration of the injured skin, thereby cutting the healing time by half. Studies on healing of thermal injuries in the skin of anti-Gal producing mice, demonstrated a much faster recruitment of macrophages into burns treated with alpha-gal nanoparticles than in control burns treated with saline and healing of the burns within 6 days, whereas healing of control burns takes ~12 days. alpha-Gal nanoparticles are non-toxic, and do not cause chronic granulomas or keloids. These findings suggest that alpha-gal nanoparticles treatment may harness anti-Gal for inducing similar accelerated burn healing effects also in humans.

The structural identification (SI) problem of control objects has not been solved. The formalization and interpretation complexity of the structure concept is the main problem. In identification systems, the form of the model (its structure) choice is intuitive and bases on the experience and knowledge of the researcher in most cases. The task of parametric identification is often interpreted as SI. It introduces certain confusion in understanding of the task and decision-making. This is two different areas of research. The structural identification problem is multifaceted and includes many subtasks, and their solution gives the final result. Some tasks have been solved. The purpose of this work is to review existing approaches and methods to the structural identification problem of control objects from a system perspective. It is necessary to give the SI problem statement at the multiple-informational level to reflect the difficulties of formalizing SI. New directions to analyse that were not SI areas until now.

This work introduces an approach to investigate the existence of zeros for holomorphic complex functions, defined on open and simply connected subdomains of C, in a systematic and direct way. This is achieved by means of the theory of dynamical systems (Poincar\'e index) and the perception that a zero of a specific complex function is an equilibtium for the associated dynamical system. The logical basis of proof is to find the mathematical expression for the Poincaré index of the vector field associated to the function under study, assuming the existence of a zero, and investigating the consequences. Depending on the value of the index, an inconsistency may occur, establishing a contradiction.

Using the local bijectivity of Keller maps, we give a proof of two-dimensional Jacobian conjecture.

Demonstrating biosimilarity entails comprehensive analytical evaluations, clinical pharmacolo-gy profiling, and efficacy testing for at least one medical indication in patients. These require-ments are stipulated by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The costliest element—efficacy testing—can be waived if other compliance benchmarks are satisfied, including comparing functional pharmacodynamic (PD) biomarkers, even when they do not di-rectly correlate with clinical outcomes. Most biological drugs, such as monoclonal antibodies (mAbs), lack identifiable PD biomarkers. The FDA has employed various 'omics' technologies to identify potential PD biomarkers, including proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. Although these efforts provide a robust scientific basis for estab-lishing biosimilarity, they are neither practical nor necessarily superior to existing functional biomarkers, such as receptor binding and mode-of-action outcomes. As we report for the first time, these functional biomarkers can effectively serve as PD indicators for all FDA-licensed bio-logical drugs. We recommend that the FDA consider officially listing these functional biomarkers to expedite and reduce the cost of biosimilar development, thereby increasing the accessibility of biological drugs. PD surrogates, like the receptor binding and pharmacokinetic profiles, are more robust and offer a rational solution to finding PD markers to compare for establishing biosimi-larity.

Considering the aging society and budget constraints, a strategy for reforming public transportation welfare policies is essential for sustainability. Among the various transportation welfare policies, fare discount policies are provided to specific age groups, and most countries offer benefits to several public transportation. South Korea provides a fare-free policy limited to subways, and the need for policy modification is emerging due to sustainability problems caused by an aging society. Hence, the scope of the policy should be expanded to all travel modes, but the discount rate should be reduced, or the target group should be reduced in consideration of the budget. This study proposes a travel mode to focus on by city through the preference of the elderly. The latent class model is employed to classfy stated preference data collected from the survey. The estimation results show a significant preference heterogeneity depending on the level of subway supply by region, and a policy focused on subways would be more reasonable in cities with sufficient subway infrastructure. Hence, regarding sustainability, it may be more appropriate to focus on specific means than to provide the same discount policy for all public transport.

This work aims to introduce a novel concept: the Riesz-Dunkl fractional derivatives, within the context of Dunkl type operators. A particularly noteworthy revelation is that when a specific parameter $\kappa$ equals zero, the Riesz-Dunkl fractional derivative smoothly reduces to both the well-known Riesz fractional derivative and the fractional second-order derivative. Furthermore, we introduce a new concept: the fractional Sobolev space. This space is defined and characterized using the versatile framework of the Dunkl transform.

To handle the huge amount of data generated by IoT devices, Big Data processing tools make it easier. This paper discusses the Big Data concept and its main V’s characteristics. It further describes IoT-enabling technologies; nominally cloud computing such as SaaS and PaaS. The centralization and infrastructure of Big Data systems, and how Cloud Computing gives a platform access to the data from anywhere. The paper explores IoT with big data architectural solutions for various use cases across the healthcare and transportation sectors.

Background: There is a significant role for mind in health and disease. Subjective well-being (SWB) is an important indicator of a person’s psychological status beyond the traditional anxiety and depression. Shell and core (S&C) model of SWB is new modular framework has placeholders for various forms of positive feeling that can be adopted as an assessment tool in clinical settings.Method: 416 subjects recruited using social media platforms to complete an anonymous online survey probing into their demographic information, financial status, several well-being attributes, and personality traits. The obtained information was analyzed and the factors that most closely correlated with the sense of overall SWB were included in the model.Results: Overall, 73% of the subjects indicated that they are happy and the overall sense of SWB did not depend on gender or household income. Only those psychological traits with significant correlation with SWB were used to construct the proposed S&C model. The model comprises three main components—the shell of happiness that represents experiential happiness, i.e., positive feelings as a result of one’s experiences with their environment. The core of happiness represents inner happiness. The interface layer, between the shell and core of happiness consists of a set of coping and defense mechanisms that protects the core of happiness denoted as a happiness suit of armor (HSOA).Conclusion: The S&C model is a comprehensive model of SWB that is used as a framework to capture various aspects of happiness under a unifying umbrella. It can thus be used as an assessment tools at both personal and population level. It could also be adopted to identify the personality traits and coping mechanisms that are important for the overall sense of well-being, which could then be targeted as a part of psychological interventions aimed at individuals with different medical conditions.

In the wake of rapid advancements in artificial intelligence (AI) and sensor technologies, a new horizon of possibilities has emerged across diverse sectors. Livestock farming, a domain often sidelined in conventional AI discussions, stands at the cusp of this transformative wave. This paper delves into the profound potential of AI and sensor innovations in reshaping animal welfare in livestock farming, with a pronounced emphasis on a human-centric paradigm. Central to our discourse is the symbiotic interplay between cutting-edge technology and human expertise. While AI and sensor mechanisms offer real-time, comprehensive, and objective insights into animal welfare, it's the farmer's intrinsic knowledge of their livestock and environment that should steer these technological strides. We champion the notion of technology as an enhancer of farmers' innate capabilities, not a substitute. Our manuscript sheds light on: Objective Animal Welfare Indicators: An exhaustive exploration of health, behavioral, and physiological metrics, underscoring AI's prowess in delivering precise, timely, and objective evaluations. Farmer-Centric Approach: A focus on the pivotal role of farmers in the adept adoption and judicious utilization of AI and sensor technologies, coupled with discussions on crafting intuitive, pragmatic, and cost-effective solutions tailored to farmers' distinct needs. Ethical and Social Implications: A discerning scrutiny of the digital metamorphosis in farming, encompassing facets like animal privacy, data safeguarding, responsible AI deployment, and potential technological access disparities. Future Pathways: Advocacy for principled technology design, unambiguous responsible use guidelines, and fair technology access, all echoing the fundamental principles of human-centric computing and analytics. In essence, our paper furnishes pioneering insights at the crossroads of farming, animal welfare, technology, and ethics. It presents a rejuvenated perspective, bridging the chasm between technological advancements and their human beneficiaries, resonating seamlessly with the ethos of the Human-Centric Intelligent Systems journal. This comprehensive analysis thus marks a significant stride in the burgeoning domain of human-centric intelligent systems, especially within the digital livestock farming landscape, fostering a harmonious coexistence of technology, animals, and humans.

This study investigates the stochastic dynamics of hepatitis B virus (HBV) infection using a newly proposed stochastic model. Contrary to deterministic models that fail to encapsulate the inherent randomness and fluctuations in biological processes, our stochastic model provides a more realistic representation of HBV infection dynamics. It incorporates random variability, thereby acknowledging the changes in viral and cellular populations and uncertainties in parameters such as infection rates and immune responses. We examine the solution's existence, uniqueness, and positivity for the proposed model, followed by a comprehensive stability analysis. We provide the necessary and sufficient conditions for local and global stability, offering deep insight into the infection dynamics. Furthermore, we utilize numerical simulations to corroborate our theoretical results. This research provides a robust tool for understanding the complex behavior of HBV dynamics, contributing significantly to the ongoing quest for more effective HBV control and prevention strategies.

Artificial Intelligence (AI) stands as an emerging technology poised to reshape various facets of our daily lives, with a particularly noteworthy influence on the labor market. In Morocco, AI adoption has commenced in diverse sectors, including finance, insurance, and telecommunications, opening up fresh avenues for employment. Nevertheless, this technological shift also ushers in challenges for Moroccan workers, necessitating training and upskilling to effectively engage with AI-driven systems. Within this article, we delve into the burgeoning impact of AI on the Moroccan economy and job landscape, delving into both the opportunities and obstacles that accompany its proliferation. Furthermore, we explore strategies that authorities and businesses can employ to harness the benefits of AI while mitigating its potential adverse effects on Morocco's workforce.

In this research the possibility of using pH change to measure proton conductivity of proton exchange membranes (PEM) or else known as polymer electrolyte membranes are tested with using an experimental setup prepared by using pH meters. PEMs are an important part of the fuel cells and proton conductivity of the PEMs are one of the most important properties of the PEMs. Also, proton conductivity is the one of the biggest limiting factors of fuel cells thus it is important to measure the proton conductivity of the PEMs while trying new polymers or new types of membranes. But as current measurement methods do not have a standard and can be separated into mainly two different measurement methods which are Electron Impedance Spectroscopy (EIS) and Ion Exchange Capacity (IEC). EIS gives clearer results, but it needs an impedance analyzer machine, platinum electrodes and setup so this cannot be possible for every researcher due to its economical aspect. For IEC it is basically a titration method to see acid groups in the polymer or polymer matrix which are directly responsible for proton conductivity so this can show an indirect measurement of proton conductivity. But as for this one drawback are firstly this one is an indirect measurement and secondly needs overnight bath in solution can take long times. So, the possibility of using pH can be easier and both from time and economical aspect if it can be proven the give a direct relationship between pH change and proton conductivity.

Technology is in the way to reach in its climax but the basic understanding of science in many phenomena is still awaited even though the nature witnesses it. Scientific research reveals strong analogy between photon and electron. When an atom deals neutral state, it levitates electron of outer ring from the back surface while placing the bit-energy at front surface. Gravitation behavior of that electron starts at the centre of relaxation point by including the force of side pole where the pulling force of nearby unfilled state of that atom from the front surface results into depict forcing energy shape like Gaussian distribution symbol with both ends turned called unit photon. The inertia is being involved at each stage of changing direction of that electron by introducing the disappearances of forces of two poles against the appearances of forces of two opposite poles during rest to motion and motion to rest in the first half-cycle. The same is the case in the second half-cycle of that electron during rest to motion and motion to rest but it is under different introduction of the disappearances of forces against appearances of forces. However, at stage of levitating or gravitating period of electron, only one force is being involved at one time where the opposite force is disappeared. The uninterrupted confined inter-state electron-dynamics of atom under the availability of several bits of bit-energy results into generate forcing energy shape like a wave. Two bits of bit-energy where shape of bit for first half-cycle is like integral symbol and second half-cycle is like opposite integral symbol which are being placed along the configuring trajectory of inter-state electron-dynamics during forward-direction cycle and two bits of bit-energy shape in opposite order are being placed along the trajectory of inter-state electron-dynamics during back-direction cycle. Generating forcing energy of unit photon in each cycle is pushing to the rear side by remaining connected till the electron is not restoring the state of rest. Silicon atom is considered as a model system under neutral state. Uninterrupted confined inter-state electron-dynamics result into generate forcing energy that can travel immeasurable length and unavailability of necessitating bit-energy at any interrupted stage result into generate an overt photon. Inter-state electron-dynamics for at least two cycles generate an overt photon –a photon length twice to unit photon. Under certain interaction of unit photon, it divides equally into two bits of bit-energy instead of dividing into tits and bits of heat. The mechanism of generating photon characteristic current by the electron of neutral state atom validates that atoms are four-dimensional discs at centre of dealing no force. An isolated electron is being grounded under directed forcing energy to impinge a neutral state atom where the gained instantaneous velocity under merged energy resulting into distort atom at that point. Matter changes the role of energy and force under various sorts of interactions. Here, heat energy and photon energy explore matter at atomic and electron levels, thus, devise basis of science to describe.

An axial wind turbine's performance is analyzed numerically using geometrical and operational parameters. The performance of an axial wind turbine is simulated using three-dimensional computational fluid dynamics (CFD) and compared with experimental data, which confirms the validity of the assumptions and method used. A study of the effects of air in clean and sand particlesy conditions is then conducted. A study was also conducted on mechanical power and the impact of the blade tip on the extension of the vortex. It has been found that different sand particle sizes have a significant impact on wind performance numerically from 0.1 to 0.9, and the results indicate that mechanical power and thrust increase as wind velocity increases, but larger sand particle sizes reduce power, thrust, and torque as wind velocity increases. The turbulence intensity causes the pressure distribution on the walls to be irregular, thereby increasing mechanical power and propulsion force. Moreover, as the wind velocity increases, the tip speed ratio increases and the vortexes are subsequently extended.

Arguments have been made that violation of the CHSH and similar inequalities shows that reality at the quantum level must be non-local. The derivation of Bell inequality is re-examined and it is shown that violations of these inequalities merely demonstrates the existence of contextuality - they say nothing about the causal influences underlying such contextuality. It is argued that contextual systems do not possess enduring (propositional) properties, merely contingent properties. An example is presented of a classical situation, a two player co-operative game, whose random variables are consistently connected in the sense of Dzhafarov, and which is contextual, violating the CHSH inequality. In fact it also violates the Tsierl'son bound. The key is that this system is generated and its properties are disposed, not determined.

Addressing global environmental problem, water splitting to produce hydrogen fuel by solar energy is getting so much attention. In the water splitting, the essential problem to solve is the development of efficient　catalysts for oxygen production. In this paper, having the prospect for a practical application of photocatalysts to artificial photosynthesis, molecular mechanisms in the current literature are briefly reviewed. At first, recent progress in the function of Mn cluster at the natural photosystem II is briefly described. The kinds of devices in which oxygen evolution reaction (OER) catalysts are used were designated; water electrolyzers, photoelectrodes, and photocatalysts. Some methods for analyzing molecular mechanism in OER catalysis, emphasized by FTIR method, are shown briefly. After describing common OER mechanisms, the molecular mechanisms are discussed for TiO2 and BiVO4 photoelectrodes with our novel data, followed by presenting of co-catalysts IrO2, RuO2, NiO2, and other metal oxides for OER catalysts. Recent reports describing OER catalysts of perovskites, layered double hydroxides (LDH), metal-organic frameworks (MOF), single atom catalysts, as well as metal complexes are reviewed. Finally, by comparing with natural photosystem, the requiring factors to improve the activity of the catalysts for artificial photosynthesis will be discussed.

Fish and fish products are very appreciated in the human diet because of high quality proteins and other important nutrients, such as minerals and polyunsaturated fatty acids. Maintaining freshness, quality and safety is essential in the fish supply chain, starting from on board vessels where the product must be carefully checked and protected from any source of contamination. Safe fishing and handling of fish are very important to reduce the risk of foodborne diseases caused by pathogenic microorganisms such as Salmonella spp., Vibrio spp., Listeria monocytogenes, Clostridium botulinum type E, viruses, and parasites. The inspection of raw material, use of good quality ice and water, high standards of personnel hygiene and cleanliness of equipment and fa-cilities as well as proper storage temperature are the most effective measures to ensure safety of fish and fish products for consumers. This review describes the good hygiene practice to be ap-plied on ships to preserve both fish products and public health.

In the age of artificial intelligence (AI) and automation, understanding the factors that contribute to successful upskilling and reskilling is crucial for workforce adaptability. Research on AI and automation has shown that they affect the workforce, but there is still a gap in understanding what factors contribute to successful upskilling and reskilling initiatives, especially from an organizational perspective. This study employs fuzzy-set qualitative comparative analysis (fsQCA) to analyze these factors and identify effective combinations for promoting upskilling and reskilling. By examining complex causal relationships and incorporating case studies, this article aims to enhance the understanding of upskilling initiatives in the AI and automation era. The findings provide insights into key factors driving successful upskilling and reskilling and offer implications for organizations seeking to improve their initiatives, ultimately promoting workforce adaptability and competitiveness.

Recent years have seen organizations increasingly shift towards the development of a mentoring culture, as opposed to the imposition of formalized mentoring programs. This original research paper set out to explore the utility of fostering mentoring cultures within higher education settings with a particular emphasis placed on the ways in which a mentoring culture may support the nurturing of leadership potential and serve underrepresented minority faculty and staff members. Primary data was gathered using an open-ended qualitative survey, which was followed up by semi-structured interviews. One hundred faculty members in diverse academic and administrative leadership roles across the United States were sent online surveys, with 18 individuals expressing interest in participating in a semi-structured interview. Those who responded affirmatively were then contacted via telephone to follow up with further questions. Analysis of their insights, in conjunction with analysis of other literature, suggests that the cultivation of a mentoring culture offers diverse benefits for mentees advancing into leadership positions; for mentors; and for the institution as a whole. Overall, the development of a mentoring culture characterized by informal, spontaneous exchanges, and tailored to the needs of underrepresented minority faculty members, appears to be a useful tool in enhancing leadership potential in higher education contexts.

With an explosion of data in the nearly every domain, from physical and environmental sciences to econometrics and even social science, we have entered a golden era of time-domain data science. In addition to ever increasing data volumes and varieties of time-series, there's a plethora of techniques available to analyses them. Unsurprisingly, machine learning approaches are very popular. In environmental sciences (ranging from climate studies to space weather/solar physics), machine learning (ML )and deep learning (DL) approaches complement the traditional data assimilation approaches. In solar and astrophysics, multiwavelength time-series or lightcurves along with observations of other messengers like gravitational waves provide a treasure trove of time-domain data for ML/DL approaches for modeling and forecasting. In this regard, deep learning algorithms such as recurrent neural networks (RNNs) should prove extremely powerful for forecasting as it has in several other domains. Such methods agnostic of the physics can only predict transient flares that are represented in the original distribution trained on (eg. coloured noise fluctuations) and not special, isolated episodes (eg. a one-off outburst like a major coronal mass ejection) potentially distinguishing them. Therefore it is vital to test robustness of these techniques ; here we attempt to make a small contribution to this by exploring sensitivity of RNNs with some rudimentary experiments. We test the performance of a very successful class of RNNs, the Long Short Term Memory (LSTM) algorithms with simulated lightcurves. As an example, we focus on univariate forecasting of time-series (or lightcurves) characteristic certain variable astrophysical objects (eg. active galactic nuclei or AGN). Specifically, we explore the sensitivity of training and test losses to key parameters of the LSTM network and data characteristics namely gaps and complexity measured in terms of number of Fourier components. We find that typically, the performances of LSTMs are better for pink or flicker noise type sources. The key parameters on which performance is dependent are batch size for LSTM and the gap percentage of the lightcurves. While a batch size of $10-30$ seems optimal, the most optimal test and train losses are under $10 \%$ of missing data for both periodic and random gaps in pink noise. The performance is far worse for red noise. This compromises detectability of transients. Thus, we show that generative models (time-series simulations) are excellent guides for use of RNN-LSTMs in building forecasting systems

Elliptic curve cryptography (ECC) over prime fields relies on scalar point multiplication realized by point addition and point doubling. Point addition and point doubling operations consist of many modular multiplications of large operands (256 bits for example), especially in projective and Jacobian coordinates which eliminate the modular inversion required in affine coordinates for every point addition or point doubling operation. Accelerating modular multiplication is therefore important for high-performance ECC. This paper presents the hardware implementations of modular multiplication algorithms, including 1) Interleaved modular multiplication (IMM), 2) Montgomery modular multiplication (MMM), 3) Shift-sub modular multiplication (SSMM), 4) SSMM with advance preparation (SSMMPRE), and 5) SSMM with CSAs and sign detection (SSMMCSA) algorithms, and evaluates their execution time (the number of clock cycles and clock frequency) and required hardware resources (ALMs and registers). Experimental results show that SSMM is 1.76 times faster than IMM, and SSMMCSA is 3.21 times faster than IMM. We also present the ECC hardware implementations based on the Secp256k1 protocol in affine, projective, and Jacobian coordinates using the IMM, SSMM, SSMMPRE, and SSMMCSA algorithms, and investigate their cost and performance. Our ECC implementations can be applied to the design of hardware security module systems.

Australia has recently declared its commitment to a circular economy. Policy and initiatives to date have focused on recycling and waste management and research to date has highlighted the need for more ambitious policy, clearer definitions, collaboration, and consensus on goals. There are also calls from some government and non-business sectors for more inclusive circular models, including Doughnut Economics. In a context of competing mainstream circular economy and inclusive circular society discourses, circular intermediary organizations and their representatives are key to achieving change. Compared to the green growth business narrative of policy and industry media, intermediary representatives are aware of the diversity of challenges and solutions for Australia. Based on semi-structured interviews with twenty representatives of circular intermediaries in Australia and thematic discourse analysis, this study finds circular economy, circular society and de-growth discourses informing themes about government, business, growth, consumers, society, and policy present and future. The study concludes with recommendations for a more holistic policy and practice beyond the current circularity for circularity’s sake.

Abstract: In recent years, low frequency (LF) electroencephalogram (EEG) signals have been decoded to obtain kinematic trajectories with the aim of achieving closed loop natural control. However, decoded trajectories suffer variability and low correlation against the measured movement. This paper suggests that if EEG features of motor intents are directly associated with fixed points along a given range of motion, then the natural kinematic parameters can be quantified. A novel classification method is proposed to evaluate this hypothesis along with equations for obtaining the movement parameters (velocity, acceleration). Furthermore, this implies that the force of an end-effector and work done in moving the end-effector may be computed, leading to natural control of EEG-based robotic arms and neuroprostheses. Finally, expectations are that combining the proposed method with existing classification methods will increase the accuracy and scalability of brain-computer interface (BCI) applications.

This study focuses on the recently emerged Internet of Vehicles (IoV) concept to provide an integrated agricultural vehicle/machinery tracking system through two leading low power wide area network (LPWAN) technologies, namely LoRa and NB-IoT. The main aim is to investigate the theoretical coverage limits by considering the urban, suburban, and rural environments. Two vehicle tracking units (VTUs) have been designed for LoRa and NB-IoT connectivity technologies that can be used as reference hardware in coverage analysis. On this basis, the closed-form explicit analytical expressions of the maximum transmission range have been derived using the Hata path loss model. Besides, the computer simulation results have been validated via the maps from XIRIO online radio planning tool. In light of the obtained findings, several evaluations have been made to enhance the LPWAN-based agricultural vehicle tracking feasibility in smart farms.

Entrepreneurship education in South Africa holds significant promise in empowering the youth and fostering economic growth. This article explores the impact of entrepreneurship education on youth empowerment, focusing on case studies of successful young entrepreneurs, evaluation of entrepreneurial skills development, and analysis of employment and economic outcomes. While effective models of entrepreneurship education emphasize experiential learning and collaboration with local businesses, resource constraints and infrastructure gaps pose challenges. Moreover, socio-cultural perceptions and gender disparities influence youth's willingness to engage in entrepreneurship, hindering inclusivity. Evaluating the long-term impact remains challenging, requiring robust monitoring and evaluation mechanisms. The article concludes with recommendations to enhance the impact of entrepreneurship education, including increasing funding, challenging stereotypes, fostering collaboration, and developing evaluation mechanisms to support South Africa's youth in realizing their entrepreneurial potential.

The current recommendation to avoid non-steroidal anti-inflammatory drugs (NSAIDs) in management of dengue virus disease is, as it was adopted earlier in COVID-19, scientifically considered of very low to low certainty. In this perspective I argue, for the first time globally, basing on real-life practice that NSAIDs might be lifesaving in early management of dengue virus disease as I originally argued and proved rightful in COVID-19. Moreover, I also argue that the Egyptian immune-modulatory Kelleni’s protocol, including nitazoxanide as an integral component, can be safely and effectively used to early manage COVID-19, human metapneumovirus, dengue virus disease, Zika virus separate or co-infections similar to what has already been experienced and published with separate or conjoined infections with SARS CoV-2, influenza, RSV and Norwalk viruses without the need of prior serological or molecular investigations to determine the causative agent.

A novel theoem is presented about a circle with four antipodal points, arbitrarily ordered counterclockwise (or clockwise), in a Euclidean plane.

Spinors are used for the computation of probability in quantum mechanics. They are treated as elements of a complex vector space in contrast to a real vector space. Spinor theory is abstract mathematics with an ambiguous interpretation. The overall phase of spinors does not affect the computation of probability in quantum mechanics. If two spinors have an overall phase of imaginary number i, then they can be treated like vectors in a real vector space. The square of the magnitude of the number, which is the sum of dot product and the term having basis vector i of cross product of two such vectors, is equal to the probability computed using the corresponding spinors. Therefore, the geometry of such spinors can be easily depicted in a three-dimensional space like vectors in a real vector space. Spinors are not isotropic vectors in Hilbert space. The sum of dot product and cross product of two complex numbers is equivalent to the quotient of the division of one complex number by another. Similarly, the sum of dot product and cross product of two vectors is the quotient of the division of one vector by another. Using the rules of division of vectors, we can find the rules of multiplication of vectors. The rules of multiplication and division of basis vector i match the rules of multiplication and division of imaginary number i. Therefore, basis vector i is nothing but imaginary number i. Multiplication of dot and cross product of two vectors to the second vector will give us the components of the first vector that are parallel and orthogonal to the second vector. Vectors are also made up of complex numbers like spinors. The reason for finding dot product of complex numbers in the process of computing the probability is to ignore the overall phase in contrast to the phase difference. This is misconstrued as complex vector space in quantum mechanics. 3-D number which is nothing but a spinor with new notation is an extension of vector algebra. It can have a real number as a term in addition to the terms i, j and k. In all other respects, it is a vector. 3-D numbers are part of the number system like real numbers and complex numbers. The real numbers are one dimensional numbers, the complex numbers are two-dimensional numbers and the 3-D numbers as well as vectors and spinors are three-dimensional numbers. As polarisation and spin are one and the same, we can straight away apply 3-D numbers to polarisation of light. 3-D numbers will greatly simplify the study of areas of science where three-dimensional space is involved.

New ideas on the framework of Neutrosophic SuperHyperGraph for different styles of Neutrosophic SuperHyper-Bipartite and Neutrosophic SuperHyper-Path are introduced. More instances and more clarifications alongside sufficient references.

Background: Pancreatic cancer (PC) is a disease with poor prognosis and survival rate. There is a pertinent need to identify the risk factors of this disease. The purpose of this study is to identify a subset of factors (a.k.a. features) as predictors of PC from the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer dataset consisting of responses to 65 questions about demographics, cancer and health history, medication usage, and smoking habits from 154,897 participants. Method: There are two challenges to selecting the subset of features that predict PC with highest probability: the problem is computationally intractable, and the PLCO dataset is highly imbalanced. We use an innovative method to use the dataset in a balanced way, without involving up- or down-sampling. We use nine feature selection methods to select the optimal subset of features from the preprocessed and balanced dataset. Results: Our preprocessed dataset consists of 32 risk factors (8 demographics, 5 cancer history, 13 health history, 2 medication usage, 4 smoking habits). Risk factors belonging to cancer and health history, followed by smoking habits, were consistently chosen by the feature selection methods. We also discuss findings in the medical sciences literature that corroborate our findings. Conclusions: The study found that risk factors belonging to cancer and health history are the most prominent ones for PC. In particular, previously diagnosed with PC is chosen as the most prominent risk factor by majority of methods. While most of our findings are consistent with the literature, some of our findings shed light on novel factors that may not have received their due attention by the research community.

In recent years, there has been an increasing demand for personalized experiences in various domains, including the cultural and educational sectors. Museums, as custodians of art, history, and scientific knowledge, are seeking innovative ways to engage their visitors and provide tailored content that enhances their understanding and appreciation of the exhibits. This article presents ExhibitXplorer, a distributed architecture service that leverages geofencing, artificial intelligence, and microservices to enable personalized content delivery in museums. By combining implicit and explicit segmentation of museum visitors and utilizing the GPT API for content generation, ExhibitXplorer offers a dynamic experience to different visitor segments, including researchers, students, casual visitors, and children. The system utilizes push notifications triggered by visitor location changes, allowing seamless delivery of personalized information both indoors and outdoors. Tests were conducted to evaluate the user experience of visitors to an outdoor ethnographic museum. The results showed that 55% of the test participants were satisfied and 45% very satisfied with the way personalized content was delivered.

Delineating the epidemic of vaccine injury from the coterminous condition long covid is a challenging prospect, but one with many implications not just for treatment, but also has important legal considerations for settlements of vaccine injury. The shared etiological factor of the spike protein in both vaccine injury and long covid make differentiation difficult, and while treatment is largely similar between vaccine injury and long covid, there are important distinctions. Furthermore, diagnostics are important for monitoring treatment progress and assessing the extent of subclinical vaccine injury in population having received a covid-19 vaccine. The development of rigorous diagnostics is an important step towards the recognition of both long covid and vaccine injury, as those suffering these conditions have faced immense challenges in having their conditions recognized, treated, and compensated by insurance companies or national health services.

Induction heating (IH) technology is widely recognized and utilized in residential applications due to its high efficiency and safe operating characteristics. Resonant inverter circuits are widely used in IH systems because of their high efficiency and ability to perform soft-switching. Among the various resonant inverters used in IH systems, the single-switch quasi-resonant (SSQR) inverter topology is typically preferred for low-cost and low-output-power applications. Despite its cost advantage, the SSQR topology has a relatively narrow soft-switching range, which can be unstable depending on the electrical parameters of the load and the resonant converter circuit. Accurately determining the capacitance value of the resonant capacitor and the inductance value of the induction coil, which are the key circuit elements of the SSQR induction cooker, is crucial for designing a reliable, efficient, and durable cooking system. In other words, there exists a critical relationship between the resonant converter circuit parameters, load characteristics, and safe operating conditions. Additionally, when considering closed-loop control methods used for power control and safety, selecting appropriate resonant circuit elements becomes vital in ensuring both reliable and efficient operation. This paper focuses on a novel and simplified design method for the SSQR inverter utilized in household appliances. The proposed method and its advantages in terms of the safe operating area of the switch are theoretically investigated and verified through simulations and prototype circuits.

In this article, we discuss problems that exist in modern SCADA systems and present a Controls-kt (formerly DataForge-controls) software development kit, that allows both to create a control system from scratch and provide integration with existing systems via Magix specification and connector.

Nanoparticles are of great importance for various scientific and technological applications, drug discovery, ecology, molecular biology, etc. Size is a basic characteristic of any particle, and dynamic light scattering is beyond competition when measuring particle size in dispersions ranging from nanometers to micrometers. Unfortunately, this method has a serious problem in processing and interpreting experimental data. Instrument noise and polydispersity of the sample lead to the situation when one initial autocorrelation function corresponds to several different particle size distributions. Sometimes these different solutions of the inverse task can be found by changing parameters in the software. Sometimes the scientist cannot be suspicious of possible other solutions, having one result from the program. In this article I demonstrate the problem on model and experimental data using three known programs: CONTIN, the Malvern Zetasizer and DynaLS. I also present my own free program Autocor, which allows to test our own hypotheses about the modality and shape of the particle size distribution by optimizing the parameters of our own models with a small number of degrees of freedom.

Youth is the essential entrepreneurial force for every country's social, political, and economic development. Urban youth make up about 34.27 percent of the population in Ethiopia. The youth unemployment crisis is one of the serious challenges that Ethiopia is facing nowadays, and it should be mentioned in the nation's public discourse. Therefore, the main objective of this study is to specify the socioeconomic and demographic factors that influence urban youth unemployment in the Somali Regional State, specifically in Jigjiga, Degahbour, Kebridahar, Gode, and Dollo Ado towns. Both primary and secondary data sources were employed. A multi-stage sampling technique has been used to select 385 sample respondents from the total population of the study. This study found that almost 63% of youth in the study area were unemployed, and a large number of unemployed youths live in Jigjiga and Dollo Ado Towns as compared to other city administrations. The econometric model's findings indicate that factors like gender, education level, work experience, access to credit, access to information, time applied for job vacancy, active job seeker, social network (network with clan elders, government officials, businessmen, and other friends), and residence in Jigjiga and Gode town have a significant association with the likelihood of youth unemployment in the Somali regional state. Therefore, this study recommends that the regional government and local city administration should develop effective policies and strategies to address the underlying causes of gender inequality, improving access and quality of education, supporting freshman graduates to find jobs, providing access to credit and job vacancies, ensuring fair and free job competition, and managing youth migration for job seeking.

The overexploitation of fossil fuels and their negative environmental impact has attracted the attention of researchers worldwide to propose alternatives to produce bioenergy. Microbial fuel cells (MFCs) systems are sustainable biotechnologies that use bacterial activity to break down organic matter while generating bioelectricity. MFCs have bioelectricity from domestic wastewater (DWW), municipal wastewater (MWW), and potato and fruit waste, reducing environmental contamination and decreasing energy consumption and treatment cost. This review focuses on the recent advancements regarding the designs and configurations, the operation mode of MFCs, and their capacity to produce bioelectricity (e.g., 2203 mW/m2) and fuels (i.e., H2: 438.7 mL/g and CH4: 358.7 mL/g, respectively). Besides, this review highlights practical applications, challenges, techno-economic, and life cycle assessments (LCA) of MFCs. Despite MFC's promising biotechnology, great efforts should be made to implement it in real-time and commercialization.

In the struggle for survival, all organisms have evolved adaptations for protection from predation. Angiosperms are among the most abundant producers on the planet, but they are trapped in a precarious symbiosis with animals for pollination and fruit dispersal, while also having to deter herbivory. About 10% of all angiosperms produce the deterrent latex from ubiquitous isoprene precursors. Latex is an aqueous suspension of rubber particles and other compounds which works as an antifeedant by clogging chewing mouthparts and by coagulating into a solid in stomach acid. A survey of stomach pHs from various animals supports the hypothesis that foregut-fermenting ruminants have evolved the capacity to evade latex coagulation due to the presence of anterior digestive chambers with near-neutral to alkaline pH before it enters the acidic abomasum. Hindgut-fermenting animals lack this adaptation and have to eat forage which is low in latex, such as grasses. The latex-adaptive foregut browsers vs. latex-maladapted hindgut grazer hypothesis has implications for both livestock and zoo animals which may be provided with forage that is not optimal for their gut health. Further, ruminants and herbivorous tadpoles are at risk for damage by popular bioinsecticides such as Bt which are activated by alkaline gut pH.

The metabolism of lipoproteins, which regulates the transit of the lipid to and from tissues, is crucial to maintaining cholesterol homeostasis. Cardiac remodeling is referred to as a set of molecular, cellular, and interstitial changes that, following injury, affect the size, shape, function, mass, and geometry of the heart. Acetyl coenzyme A (acetyl CoA), which can be made from glucose, amino acids, or fatty acids, is the precursor for the synthesis of cholesterol. In this article, authors explain concepts behind cardiac remodeling, its clinical ramifications, and the pathophysiological roles played by numerous various components, such as cell death, neurohormonal activation, oxidative stress, contractile proteins, energy metabolism, collagen, calcium transport, inflammation, and geometry. The levels of cholesterol are traditionally regulated by two biological mechanisms at the transcriptional stage. First, the SREBP transcription factor family regulates the transcription of crucial rate-limiting cholesterogenic and lipogenic proteins, which in turn limits cholesterol production. Immune cells become activated, differentiated, and divided, during an immune response with the objective of eradicating the danger signal. In addition to creating ATP, which is used as energy, this process relies on metabolic reprogramming of both catabolic and anabolic pathways to create metabolites that play a crucial role in regulating the response. Because of changes in signal transduction, malfunction of the sarcoplasmic reticulum and sarcolemma, impairment of calcium handling, increases in cardiac fibrosis, and progressive loss of cardiomyocytes, oxidative stress appears to be the primary mechanism that causes the transition from cardiac hypertrophy to heart failure. De novo cholesterol production, intestinal cholesterol absorption, and biliary cholesterol output are consequently crucial processes in cholesterol homeostasis. In the article's final section, the pharmacological management of cardiac remodeling is explored. The route of treatment is explained into different steps: including, promising, and potential strategies. This chapter offers a brief overview of the history of the study of cholesterol absorption as well as the different potential therapeutic targets.

This study evaluates the accessibility of primary care physicians (PCPs) in Eastern Quebec using an enhanced two-step floating catchment area (E2SFCA) methodology. The E2SFCA methodology considers both the population supply of PCPs and the travel time between PCPs and dissemination areas, and was found to be effective in identifying possibly underserved areas. The availability of primary care services is contingent upon the presence of adequate road infrastructure, and populations residing in areas with limited access to main road networks may experience compromised access to primary care. The study concludes that the E2SFCA methodology can facilitate the identification of areas experiencing a shortage of PCPs, and this information can be used to inform the development of regional medical workforce programs and support the establishment of rural residency initiatives.

The growing concerns on sustainability urge insurance companies to incorporate Environmental, Social, and Governance (ESG) policies in order to remain competitive. As all dimensions of sustainability involve taxation, it is important to establish if this association reflects on financial performance. Our analysis of worldwide property and casualty (P&C) insurers during 2013-2022 reveals that high ESG insurers pay more taxes, while are less profitable compared to low ESG insurers. This pattern is confirmed using instrumental variable regressions and simultaneous equations systems. We argue that sustainable insurers are less tempted to avoid taxes, and don't shift their tax burdens on policyholders and investors. However, the interplay between taxes and sustainability seems to harm insurers' profitability, potentially sorting negative consequences on investment and economic growth. This is an important insight for tax authorities and insurance managers.

Acquisition traits are those that depend for their evolutionary (fitness) value on the acquisition of something that gradually accumulates during an organism’s life but is not transmitted genetically to descendants. This situation causes these traits including language, immunity, intelligence, and social status to represent a special need for evolvability and programmed aging. Programmed aging refers to the idea that mammals and some other multiparous sexually reproducing organisms evolved complex biological mechanisms that cause gradually increasing fitness deterioration and internally limit individual organism lifetimes. The rationale is that this process enhances the survival (non-extinction) of a species population. Evolvability theories propose that mammals have evolved design characteristics that aid their ability to evolve, that is, reduce the time required for a particular increment of evolutionary adaptation and/or increase the precision with which the adaptation can be performed. Because an increase in evolvability aids a population in escaping extinction, evolution selects the associated design characteristics. Both concepts conflict with traditional (Darwinian) theory regarding details of the nature of evolution. However, more recent genetics discoveries have exposed rich detail regarding the nature of the evolution process including the fact that biological inheritance involves the transmission of organism design information from parent to descendant in digital form. These discoveries have acted to support both programmed aging and evolvability theories.

This study updates previous research showing that 22 all-cause mortality in 31 European countries increased over time the higher the 21 COVID-19 full vaccination uptake. The update illuminates that a one percentage point increase in 21 full vaccination uptake initially decreased all-cause mortality from Jan to Mar 22 by –0.423 percent (95% CI –0.577, –0.270), but the following 14 months, a one percentage point increase in 21 booster vaccination uptake oppositely increased mortality by 0.366 percent (95% CI 0.250, 0.482). The findings indicate that full vaccination initially prevented mortality, but subsequently, booster vaccination, in particular, detrimentally and consistently induced higher mortality. The effects remained robust when controlling for alternative explanations. Studies have argued that heat waves caused mortality in the 22 summer and energy prices caused mortality in the 22-23 winter. However, the update shows that booster vaccination consistently induced higher mortality when neither heat waves nor energy prices were likely explanations.

Nutrients are the driving force of plant and herbivore interactions. Different elements and fertilization levels in soil, or artificially fertilized substrates can have a direct impact on plant defense responses. Meanwhile, nutrient levels mediated by root associated microbes can indirectly affect plant-herbivore interactions. Nutrient uptake promoted by beneficial mycorrhizal fungi or rhizobacteria interacting with roots can stimulate plant tolerance to herbivore attack, as evidenced by herbivore performance, plant nutrient status, plant biomass allocation and compensatory growth. In return, herbivory affects nutrient fluxes between plants and their associated beneficial microbes. Furthermore, aboveground and belowground herbivory can affect the nutrient exchange between plants and soil microbial communities, mainly for carbon-and nitrogen-based resources supporting herbivores´ life cycles. These ecological interactions are governed by jasmonic acid (JA) and key enzymes on the molecular level. This review summarizes the current scientific literature on the role of nutrients in plant-herbivore interactions. This will support efforts to optimize plant defense strategy via managing nutrient variance.

Maxwell equations governing electromagnetic effects are being shown to be equivalent to the compressible inviscid Navier-Stokes equations applicable in fluid dynamics and representing conservation of mass and linear momentum. The latter applies subject to a generalized Beltrami condition to be satisfied by the magnetic field. This equivalence indicates that the compressible inviscid Navier-Stokes equations are Lorentz invariant as they derive directly from the Lorentz invariant Maxwell equations subject to the same Beltrami condition. In addition, the derivation results provide support for the claim that electromagnetic potentials have physical significance as demonstrated by Aharonov-Bohm effect, and are not only a convenient mathematical formulation.

In this paper, we derive Jeffreys divergence, generalized Fisher divergence and corresponding De Bruijn identities on space-time random field. First, we determine the relation between Fisher information on the space-time random field in one of the space-time points and the ratio of Jeffreys divergence on a space-time random field at distinct space-time positions to the square of coordinate difference. In addition, we also find identities between the partial derivative of the Jeffreys divergence and the generalized Fisher divergence with respect to space-time variables, i.e. the De Bruijn identities, between two space-time random fields obtained by different parameters under the same Fokker-Planck equations. At the end of this paper, we present three examples of the Fokker-Planck equations on space-time random fields, identify their density functions, and derive the Jeffreys divergence, generalized Fisher information, generalized Fisher divergence, and accompanying De Bruijn identities.