We propose that the Big Bang does not have a singular start, but that it originates from gravitational collapse of a low density cloud that collapsed 25 Gyrs ago to form a Black Hole (BH) of mass M ≃ 6 × 1022M⊙. The collapse continued inside the BH for 11Gyrs until the density achieves neutron degeneracy and the collapse bounces into expansion like a core collapse supernova. From observations today, this model is very similar to the standard Big Bang cosmology but there is no need for Inflation or Dark Energy (DE). The observed cosmological constant Λ is not a new form of DE, but results from the dynamics of the Big Bang expansion inside the BH event horizon rS = 2GM = √3/Λ. Why our Universe has such a large mass M (or small Λ value)? If τO(≃ 10Gyr) is the astronomical timescale needed for observers like us to exist, we find a simple anthropic prediction, based only on gravitational collapse from Gaussian fluctuations, that the maximum probability for M is MO < M < 3MO where MO = τO/3G. This agrees well with the measured values for τO and M in our Universe.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

The cyclic universe theory is a model of cosmic evolution according to which the universe undergoes endless cycles of expansion and cooling, each beginning with a big bang and ending in a big crunch. In this paper, the researcher finds a unique property of Space-time, this particular and marvelous nature of space shows us that space can shrink, expand, and stretch. This property of space is caused the size of the Universe changing over time: growing or shrinking. The observed accelerated expansion, which relates the stretching of Shrunk space for the new theory, is derived. This model is based on three underlying notions: First, the Big Bang was not the beginning of Space-time, but rather at the very beginning fraction of a second there was an infinite force of infinite Shrunk space in the cosmic singularity, that force gave rise to the big bang and caused the rapidly growing of space-time, and all other forms of energy are transformed into matter and radiation and a new period of expansion and cooling begins. Second, there was a previous phase leading up to it, with multiple cycles of contraction and expansion that repeat indefinitely. Third, the two principal long-range forces are the gravitational force and the repulsive force generated by shrunk space. They are the two most fundamental quantities in the universe that govern cosmic evolution. They may provide the clockwork mechanism that operates our eternal cyclic universe. The space will not continue to expand forever, no need however, for dark energy and dark matter. This model of Space-time and its unique properties enables us to describe a sequence of events from the Big Bang to the Big Crunch.

Our understanding and theoretical interpretation of observations in astrophysics and cosmology depends on our knowledge of the fundamental constants and their possible dependence on time and space. Atomic spectroscopy and radio astronomy give important information on the validity and stability of the fundamental constants. The possible dependence of the fine structure constant alpha on time and spatial direction is an active topic of research.Period doubling is a universal property of nonlinear dynamical systems, and the doubling is exact in principle. The value of the elementary charge squared can be calculated by the period doubling process from the Planck charge and thereby the value of alpha.If ‘old’ and ‘new’ electrons are identical, then the Planck charge, i.e. a set of natural constants, has remained constant over time. In this article we show that the value of alpha calculated from the Planck charge is 0.007 % smaller than the current accepted value of alpha.

A new model of cosmology is proposed, where the state of high energy density commonly associated with the big bang is generated by the collapse of an antineutrino star that has exceeded its Chandrasekhar limit. To allow the first neutrino stars and antineutrino stars to form naturally from an initial quantum vacuum state, matter and antimatter are assumed to gravitationally repel. In this scenario, a degenerate antineutrino star in effective hydrostatic equilibrium has a density that is similar to the dark energy density of the ΛCDM model. When viewed from the core, such a star could today accelerate matter radially and emit the isothermal cosmic microwave background radiation, which addresses the horizon and flatness problems. This model and the ΛCDM model are in similar quantitative agreement with supernova distance measurements. The presented model is also in qualitative agreement with observed large-scale anisotropy and inhomogeneity, which distinguishes it from the ΛCDM model.

In this article, we assume that the beginning of the universe was before the Big Bang. In the beginning, all matter in the universe was combined in an infinitesimal spherical shape. This sphere was compressed to an incomprehensible value for a period, and then exploded and expanded time and space. We are referring to the negative time before the Big Bang. The evolution of the universe before the Big Bang, passing through the moment of the explosion to the end of the universe at the Big Rip, has been studied. In this article, we try to answer the questions; did the universe exist before the Big Bang? What is the origin of the universe and how did it arise? What are the stages of the evolution of the universe until the moment of the Big Rip? What is the length of time for the stages of this development?

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

The standard model of cosmology assumes that our Universe began 14 Gyrs (billion years) ago with a hot Big Bang expansion out of nothing. It can explain a vast range of different astrophysical data from a handful of free cosmological parameters. However successful this model is, we have no direct evidence or fundamental understanding of some key assumptions: low entropy start, Inflation, Dark Matter and Dark Energy. Here we present a simpler and more physical explanation for the same observations that do not require such assumptions or new laws of Physics. It is based on the evidence that we live inside a Black Hole (BH) of mass $ M \simeq 5 \times 10^{22} M_{\odot}$, which we observed as cosmic acceleration. How did the Big Bang end up inside such a BH? We propose that 25 Gyrs ago, a very low density dust cloud collapsed and form such BH. As there was no pressure support, the collapse continued inside until it reached neutron energy densities (GeV). Causal regions of solar mass size rebound by neutron degeneracy, like a supernovae, producing the Big Bang expansion and compact dark matter remnants. During collapse, perturbations exit the horizon to re-enter during expansion, given rise to the observed universe. We review the evidence for such BH Universe.

The BKL singularity inside a rotating black hole would be a neutrino star. Once its mass exceeds the limit that neutrino degeneracy pressure could support (larger than 3×10²² M_⊙), the neutrino star singularity would collapse, raising the temperature to the maximum and dropping the entropy to the minimum to initiate a Big Bang. From the cosmic microwave background, we find where the Big Bang occurred at about 0.66 times the radius of the surface of last scattering away from us and at Galactic coordinates (l, b) ≃ (286°,-43°). As it expands, the clockwise spinning universe has been veering towards the ellipsoidal. These findings are consistent with independent observations of cosmic inhomogeneities, spatial anisotropies, and time variations.

The first part of this paper contains a brief description of the beginnings of modern cosmology, which, the author will argue, was most likely born in the Year 1912. Some of the pieces of evidence presented here have emerged from recent research in the history of science, and are not usually shared with the general audiences in popular science books. In special, the issue of the correct formulation of the original Big Bang concept, according to the precise words of Fred Hoyle, is discussed. Too often, this point is very deficiently explained (when not just misleadingly) in most of the available generalist literature. Other frequent uses of the same words, Big Bang, as to name the initial singularity of the cosmos, and also whole cosmological models, are then addressed, as evolutions of its original meaning. Quantum and inflationary additions to the celebrated singularity theorems by Penrose, Geroch, Hawking and others led to subsequent results by Borde, Guth and Vilenkin. And corresponding corrections to the Einstein field equations have originated, in particular, R², f(R), and scalar-tensor gravities, giving rise to a plethora of new singularities. For completeness, an updated table with a classification of the same is given.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, suitable for complex vegetable field environments and resource-limited laser weeding devices.

Space-time evolution is briefly explained by using the 3-dimensional quantized space model (TQSM) based on the 4-dimensional (4-D) Euclidean space. The energy (mass), charges and absolute time are newly defined based on the 4-D Euclidean space. The photons and gravitons are understood as the 2-D space fluctuations along the space axes and 1-D time fluctuations along the time axis, respectively. This indicates that the electromagnetic (EM) and gravitational (G) waves can be unified into the so called gravityelectromagnetic (GEM) wave which has both space and time fluctuations. The photon with zero force fields is the 2EM wave because an EM wave corresponds to a charged fermion. The dark matter bosons, weak force bosons, strong force bosons are the 1-D time fluctuations which can be expressed by the G waves. This indicates that all five forces are unified by the GEM wave. It is called as the super unification theory in the present work. And the force carrying bosons and mesons are, for the first time, proposed as the possible candidates of the magnetic monopoles like the fermions and baryons are the electric monopoles. The signs of the magnetic charges quantized as q_m=cq are newly defined. The big bang is understood by the space-time evolution of the 4-D Euclidean space but not by the sudden 4-D Minkowski space-time creation. The big bang process created the matter universe with the positive energy and the partner anti-matter universe with the negative energy from the CPT symmetry.

Blockchain technology (BCT) has the potential for sustainable development, enhancing business efficiency, reducing costs, and strengthening data security. Despite its potential, adoption among small and medium-sized enterprises (SMEs) is low due to the need for significant changes in technology infrastructure and the challenge of interoperability. A cross-sectional survey was done with 226 Hong Kong SMEs. The PLS-SEM results highlighted favorable transformation conditions as the most critical factor for SMEs considering the adoption of BCT. These conditions address both internal and external readiness for adopting the disruptive strategy. Other contributing factors to BCT adoption include trust in BCT developers, perceived usefulness and ease of use, government support, and perceived security. The research results contributed to the literature on disruptive technology acceptance, adoption, transformation conditions, technology readiness, and SMEs’ technology adoption. Government-supported policies and initiatives can promote BCT adoption, including educational programs, case studies, and resources for readiness. Financial support and regulatory frameworks can further encourage adoption. BCT developers can demonstrate the value of their systems, effectively communicate potential ROI and cost savings, and provide user-friendly interfaces to increase SMEs’ intention to adopt BCT.

Background: In physiotherapy, a classic muscle-bone concept is used to translate basic and clinical anatomy. By defining the anatomical structures from superficial to deeper layers which frame the ArthroMyoFascial complex, our aim is to offer clinicians a comprehensive concept of fasciae within the muscle-bone concept. Method: Narrative review and ultrasound observation. Results: Based on literature and ultrasound skeletonization, the ArthroMyoFascial complex has been defined. This model clarifies fascial continuity at the joint level, describing anatomical structures from skin to deeper layers, including superficial fascia, deep fascia, myofascia including skeletal muscle fibers, and arthrofascia all connected via connective tissue linkages. This model enhances understanding of the muscle-bone concept within the larger ArthroMyoFascial complex. Conclusion: The ArthroMyofascial complex consists of multiple layers from superficial to deeper anatomical structures, namely the skin, superficial fascia, deep fascia, myofascia including muscle fibers, and arthrofascia, all linked within a connective tissue matrix. This model indicates that it is a force-transmitting system from the skin to the bone. This information is crucial for manual thera-pists, including physiotherapists, osteopaths, chiropractors, and massage therapists, as they all work with fasciae within the musculoskeletal domain. Understanding fascia within the mus-cle-bone concept enhances clinical practice, aiding in therapeutic testing, treatment, reporting, and multidisciplinary communication, which is vital for musculoskeletal and orthopedic rehabilitation.

A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to wake effect. The main engine of the algorithm is a gradient-descent method, in which an analogy is established between power deficit and repulsive electrostatic potential energy. To achieve a maximum dispersion in the initial solution, throughout the optimization process, new turbines are included in the centre of the concession area, and quickly expand outward looking for areas with less potential, in turn pushing the previous ones. When the optimization process ends, to avoid local maximums, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a potential map of the entire area is created and a greedy algorithm includes new turbines to complete the layout with the final number of turbines. To speed up the search process and the creation of the potential map, two simplifications have been validated and added: the spatial distribution of velocities has been converted into its equivalent with point symmetry; and, for turbines affected by multiple wakes, the power deficit has been calculated by using a linear aggregation of powers, instead of the usual linear (or quadratic) aggregation of speeds. Owing to this type of aggregation, the process is accelerated in a factor of 90.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

This hypothesis is the result of my conviction that science will oneday prove everything in space and time is part of a unification. In "A Brief History of Time", Stephen Hawking wrote, "If everything in the universe depends on everything else in a fundamental way, it might be impossible to get close to a full solution (of the universe's puzzles) by investigating parts of the problem (such as general relativity and quantum mechanics) in isolation." The goal: to establish a “proof of concept” to which equations can be added. It’s concluded the Steady State, Big Bang, Inflation and Multiverse theories all ultimately fail and a topological model including bits (binary digits), Mobius strips, figure-8 Klein bottles and Wick rotation works better. The failed cosmologies have impressive points leading to the idea that they’re all necessary stepping-stones. For example, the Big Bang is seen here as violation of the 1st Law of Thermodynamics but its supposed origin from quantum fluctuations is reminiscent of bits switching between 1 and 0. The topological hypothesis has potential to explain dark matter, dark energy, and electromagnetic-gravitational union. Finally, the article introduces what is called vector-tensor-scalar geometry - and extensions of Einstein's Gravity and Maxwell's Electromagnetism.

Space-time evolution is briefly explained by using the 3-dimensional quantized space model (TQSM) based on the 4-dimensional (4-D) Euclidean space. The energy (E=cDtDV), charges (|q|= cDt) and absolute time (ct) are newly defined based on the 4-D Euclidean space. The big bang is understood by the space-time evolution of the 4-D Euclidean space but not by the sudden 4-D Minkowski space-time creation. The big bang process created the matter universe with the positive energy and the partner anti-matter universe with the negative energy from the CPT symmetry. Our universe is the matter universe with the negative charges of electric charge (EC), lepton charge (LC) and color charge (CC). This first universe is made of three dark matter -, lepton -, and quark - primary black holes with the huge negative charges which cause the Coulomb repulsive forces much bigger than the gravitational forces. The huge Coulomb forces induce the inflation of the primary black holes, that decay to the super-massive black holes. The dark matter super-massive black holes surrounded by the normal matters and dark matters make the galaxies and galaxy clusters. The spiral arms of galaxies are closely related to the decay of the 3-D charged normal matter black holes to the 1-D charged normal matter black holes. The elementary leptons and quarks are created by the decay of the normal matter charged black holes, that is caused by the Coulomb forces much stronger than the gravitational forces. The Coulomb forces are very weak with the very small Coulomb constants (k₁(EC) = k_dd(EC) ) for the dark matters and very strong with the very big Coulomb constants (k₂(EC) = k_nn(EC)) for the normal matters because of the non-communication of the photons between the dark matters and normal matters. The photons are charge dependent and mass independent. But the dark matters and normal matters have the similar and very weak gravitational forces because of the communication of the gravitons between the dark matters and normal matters. The gravitons are charge independent and mass dependent. Note that the three kinds of charges (EC, LC and CC) and one kind of mass (m) exist in our matter universe. The dark matters, leptons and quarks have the charge configurations of (EC), (EC,LC) and (EC,LC,CC), respectively. Partial masses of elementary fermions are calculated, and the proton spin crisis is explained. The charged black holes are not the singularities.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

Fractional differential systems have received much attention in recent decades likely due to its powerful ability in modeling memory processes, which are mostly observed in real world. Fractional models have been widely investigated and applied in many fields such as physics, biochemistry, electrical engineering, continuum and statistical mechanics. It is shown by many researchers that fractional derivatives can provide more accurate models than integer order derivatives do. Obviously, bang-bang property is of a significant importance in optimal control theory. In this paper a Bang-Bang property and time-optimal control problem for time-Fractional Differential System (FDS) is under consideration. First we formulate our problem and we prove the existence theorem. We state and prove the bang-bang theorem. Finally we give the optimality conditions that characterized the optimal control. Some illustrate applications are given to clarify our results.

This article addresses the question why initiatives in the field of green business and sustainable development often fail. Therefore, it dismantles some typical patterns of failure and shows – as a case study – how these patterns can be challenged through an innovative educational concept: the green business and sustainable development school. The applied methodology is a real-life project that is designed through blended, interdisciplinary elements from business model canvas, Theory U, participation and design thinking. The results of the school initiative are discussed and evaluated by four distinctive stakeholder groups and outline the school’s supporting potential to overcome typical patterns of failure by the younger generation in the future. This article concludes with ideas to enhance the school concept reaching out to even more stakeholder-groups to increase its reliability and viability.

Recent advance in electric propulsion systems have demonstrated that these engines can be used for for long-duration interplanetary voyages. Constant specific impulse engine described as a thrust-limited engine is an example of this type of engine, processing the ability to operate at a constant level of impulse. The determination of minimum-fuel, planar heliocentric Earth-to-Mars low-thrust trajectories of spacecraft using a constant specific impulse is discussed considering the first-order necessary conditions derived from Lawden’s primer vector theory. The minimum-fuel low-thrust Earth-to-Mars optimization problem is then solved in two-dimensional, heliocentric frame using both indirect and direct methods. In the indirect method, two-point-boundary-value problems are derived to solve boundary value problems for ordinary differential equations. In the direct method, a general-purpose optimal control software called GPOPS-II is adopted to solve these optimal control problems. Numerical examples using two different optimization methods are presented to demonstrate the characteristics of minimum-fuel planar low-thrust trajectories with on-off-on thrust sequences at three chosen flight times and available maximum powers. The results are useful for broad trajectory search in the preliminary phase of mission designs.

Saltwater intrusion in the rapidly developing city of Da Nang in central Vietnam is currently causing various water-related challenges, including inadequate water supply and water pollution. An integrated SWAT-MODFLOW numerical model was used to investigate the origin and mechanism of saltwater in Holocene and Pleistocene aquifers. Geophysical and isotopic approaches were used to validate the SEAWAT model applied for simulating saltwater intrusion. The results suggest that the ebb and flow of tides, as well as water levels in rivers primarily impact coastal aquifers. However, effective water resource planning and management, along with maintaining the natural recharge of fresh water from local rain during the rainy season, could enable the rational and efficient utilization of groundwater, reducing saltwater intrusion in many areas. During the dry season, groundwater is recharged from higher altitude areas. The current saltwater intrusion mainly occurs along the rivers up to the hydraulic dam. Simulated models, using scenarios of stop abstracting groundwater, but changing to the use of surface water for drinking water, show that the area of saline water shrinks quickly after 30 years, reducing from 59.6 km2 to 39.5 km2 and from 40.2 km2 to 28.6 km2 in Holocene and Pleistocene, respectively.

(1) Background: Emotional resilience is defined as the capacity of individuals to cultivate positive emotions and effectively navigate through negative emotions in terms of stress or adversity. Recent research has focused on the significant impact of adolescents’ negative emotions, as opposed to positive emotions, on mental health. The examination of positive emotion provides a distinct viewpoint on the relationship between emotional resilience and mental health in adolescents. Consequently, the current research conducted two separate investigations to examine the impact of adolescent emotional resilience on mental health, as well as the mediating influence of positive emotion. (2) Methods: Study 1 comprised a cross-sectional analysis of 3086 Chinese adolescents, while Study 2 involved a longitudinal examination of 266 Chinese adolescents. (3) Results: Results indicated that emotional resilience was associated with enhanced levels of life satisfaction, self-esteem, and overall mental health, while also demonstrating a negative correlation with symptoms of depression and anxiety. Positive emotions were found to have a partial mediating effect on the relationship between emotional resilience and mental health. These results indicate that emotional resilience not only has a direct impact on mental health but also influences mental health indirectly through positive emotions. (4) Conclusions: The findings highlighted the role of emotional resilience in mitigating psychological problems and enhancing mental health in Chinese adolescents. The implications and limitations were discussed.

Clinicians are key in reclaiming the medical arts ceded to clinically irrelevant technology and thereby aligning patient with fast-changing biological realities. Narrowing the chasm between virtual and real perceptions of health hazards requires: 1) becoming acutely aware of the habitat loss aggravating the pervasive dissemination of chemicals via conventional food, air, and consumer products and the proliferation of non-ionizing radiation; and 2) making strategic use of slow, system 2 thinking so as to respond wisely to the rampant epidemics of chronic low-dose toxicity disregarded or misdiagnosed for half a century. To respond adaptively, take a moment during each patient encounter to add chronic ambient poisoning to the differential diagnosis and investigate subtle symptoms and signs of irritation in vulnerable organ systems. Enacting adaptive response across our profession could ease the suffering of millions, help avert the sixth extinction, and contribute to continuation of evolved life as we know it.

We successfully designed an optimized plenum fan with a three-dimensional, smooth, curved blade. The optimized model revealed that the static pressure in the channel had been increased uniformly and stably, and the flow separation at the leading edge was significantly reduced. To conclude, the three-dimensional blade stabilized the fluid flow, and the flow friction was reduced by suppressing the flow separation as much as possible so that both the static pressure and the static efficiency were clearly improved in comparison with those of the original model. The static efficiency, as a result, was improved by 6% compared with that of the original model.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 215 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 14 April 2020, a cumulative total of 1,853,265 (118,854) infected (dead) COVID-19 cases were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify their applications in fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Summary Genome assembly projects have grown exponentially due to breakthroughs in sequencing technologies and assembly algorithms. Evaluating the quality of genome assemblies is critical to ensure the reliability of downstream analysis and interpretation. To fulfil this task, we have developed the AssemblyQC pipeline that performs file-format validation, contaminant checking, contiguity measurement, gene- and repeat-space completeness quantification, telomere inspection, taxonomic assignment, synteny alignment, scaffold examination through Hi-C contact-map visualisation, and assessments of completeness, consensus quality and phasing through K-mer analysis. It produces a comprehensive HTML report with method descriptions, tables, and visualisations. Availability and Implementation The pipeline uses NextFlow for workflow orchestration and adheres to the best-practice established by the nf-core community. This pipeline offers a reproducible, scalable, and portable method to assess the quality of genome assemblies – the code is available online.GitHub: https://github.com/Plant-Food-Research-Open/assemblyqc Supplementary information Pipeline usage documentation, parameter descriptions and example outputs are available on GitHub: https://github.com/Plant-Food-Research-Open/assemblyqc/tree/main/docs. A preview report is also hosted online: https://plant-food-research-open.github.io/assemblyqc

Hazelnuts are one of the most valuable nuts in the world due to their unique organoleptic properties and nutritional characteristics. This work was intended to analyze some physical and chemical properties of various hazelnut cultivars grown in Zakataly (Azerbaijan), namely Ata-baba, Elbari, Topqara, Firovan, Nasimi, Qalib, Mirzebeyli, Tala, Dash findiq, Barli, Azeri and Sachaqli. In general, the results showed statistically significant differences between the studied cultivars. The density of kernels, depending on the hazelnut cultivars, varied from 0.71 g/cm³ (Azeri) up to 1. 03 g/sm³ (Sachaqli). The peeled fruits of Dash findiq were heavier on average (1.64±0.34 g), and the peeled fruits of Nasimi, Qalib, Mirzebeyli and Sachaqli were lighter on average (0.8±0.23 g, 0.8±0.31 g, 0.8±0.20 g and 0.80±0.09 g, respectively. In the unpeeled form, Dash findiq nuts were the heaviest (3.70±0.34 g), and Nasimi and Qalib nuts were the lightest of all cultivars (2.0 ±0.48 g and 2.0 ±0.36 g, respectively). Ata-baba nuts account for 49 masses per kernel. %, in other studied cultivars - 33.3- 45.3 wt. %. Nasimi nuts are rounded (in the equatorial zone); Tala, Ata-baba, Firovan and Azeri - rounded-oblong; Topqara, Barli and Dash findiq – oblong; Elbari, Qalib, Mirzebeyli and Sachaqli - elongated. Firovan, Tala and Topqara have thicker shells. Ata-baba is significantly ahead of other varieties in the yield of kernels (490 kg / 1 ton of nuts), Tala is in second place in this indicator (453 kg / 1 ton of nuts), Topqara is in third (433 kg/ 1 ton of nuts). According to the maximum fat content, the cultivars Mirzebeyli (77.00±2.20 g/100 g of kernels) and Firovan (75.0±3.08 u/100 kernels), protein - Sachaqli (16.02±0.50 u/100 kernels) and Ata-baba (15.92±0.48 g/100 g of kernels) were distinguished. The results of this study help to better understand the differences between some varieties of hazelnuts grown in Azerbaijan, which provides important clues for all players involved in this sector.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

Glioblastoma (GBM) is a representative malignant brain tumor characterized by a dismal prognosis, with survival rates of less than 2 years and high recurrence rates. Despite surgical resection and several alternative treatments, GBM remains a refractory disease due to its aggressive invasiveness and resistance to anticancer therapy. In this report, we explore the role of fibronectin type III domain containing 3B (FNDC3B) and its potential as a prognostic and therapeutic biomarker in GBM. GBM exhibited a significantly higher cancer-to-normal ratio compared to other organs, and patients with high FNDC3B expression had a poor prognosis (p < 0.01). In vitro studies revealed that silencing FNDC3B significantly reduced the expression of Survivin, an apoptosis inhibitor, and also reduced cell migration, invasion, extracellular matrix adhesion ability, and stem cell properties in GBM cells. Furthermore, we identified that FNDC3B regulates PTEN/PI3K/Akt signaling in GBM cells using MetaCore integrated pathway bioinformatics analysis and a proteome profiler phospho-kinase array with sequential western blot analysis. Collectively, our findings suggest FNDC3B as a potential biomarker for predicting GBM patient survival and for the development of treatment strategies for GBM.

In the quest for more sustainable urban landscape development, the concept of ‘green infrastructure’ (GI) has become central in policy documents and as a multifunctional general planning tool. GI is not however a simple and unambiguous solution. While there in policy documents are claims for more and connected GI, actual urban development takes another direction. The densifying imperative is hard to combine with an increased and more connected GI. This paper argues for a critical and diversified approach to the concept of GI, to facilitate its implementation in urban planning and management. While GI most often is seen as a common asset and a public good, the actual land use negotiations and management responsibilities cannot be limited to a public service discourse, but should address more clearly a variety of actors. Linguistic as well as spatial definitions of the two relevant dichotomies of ‘green-grey’ and ‘public-private’ are crucial in GI location, design, construction and management, it is argued. Overarching representations of GI will be needed, but also – and linked to it – a spatial storm water plan and an overall plan for public space. The development over time will need an intersectorial implementation and management program. Thus some of the GI intentions may be implemented in planning processes, some through reorganisation and redesign of public space, and some by agreements with landowners.

Objective: Subclinical left ventricular disfunction (LVD) is common in asymptomatic patients with type 2 diabetes (T2D). Many patients with diabetes have an impaired diastolic and systolic function. With preserved ejection fraction LV (EFLV), the latter is defined as reduced global longitudinal strain (GLS), detected using the speckle-tracking echocardiography. This study is aimed to define the long-term structural and functional disorders of the LV myocardium in patients with T2D without CVD. Methods: Of the 120 patients with and without T2D of both sexes aged from 45 to 75 years (57.11±7.9 years), included in the study in 2012-2013, only 57 responded to the follow-up study. The patients were divided into two groups: one with T2D (n=29), the other without it, control (n=28). All patients underwent transthoracic two-dimensional echocardiography with assessment of standard indicators of systolic and diastolic cardiac function, as well as GLS. In addition, all participants underwent laboratory diagnostics of carbohydrate metabolism disorders markers, NT-proBNP and CRP. The median follow-up duration was 7.2 [7.0-7.8] years. Results: During the follow-up, a statistically significant increase in the incidence of diastolic dysfunction (DD) in the type 2 diabetes group was found from 53% to 61% (p = 0.004); no significant dynamics were noted in the control group (p = 0.48). The presence of T2D significantly increases the risk of developing DD (p=0.04). The presence of T2D, increased fasting glucose levels, and an increased HOMA index were independent predictors of the development of DD. Impairment of LVEF was not observed in both groups. At the same time, the proportion of patients with reduced GLS (< -18%) increased in the T2D group (p = 0.036). A significant difference in the frequency of decreased GLS depending on T2D was demonstrated. The presence of obesity, an increase in the HOMA index and an increase in CRP levels are independent predictors of impaired GLS. Conclusions: T2D is an independent risk factor for the worsening of subclinical left ventricular dysfunction in asymptomatic patients with T2D without CVD over 7-year follow-up. Diastolic dysfunction is represented by delayed relaxation of the LV myocardium; with preserved LVEF, reduced 2D GLS identifies systolic dysfunction. The tight control of glycemia, lipidemia, blood pressure and body weight is associated with the absence of poor clinical outcomes.

This study evaluated the antimicrobial activity of Leptospermum scoparium (Mānuka) and Cryptomeria japonica (Sugi) essential oils and assessed the effect of seasonal chemical variation on the oils’ antimicrobial efficacies. Plate based assays were conducted to elucidate the oils’ spectrum of in vitro antimicrobial activity and to determine the oils’ minimum inhibitory concentrations (MIC) as a measure of antimicrobial efficacy. Gas chromatography – mass spectrometry was adopted to chemically profile oils distilled in different seasons. The resultant compositional information in conjunction with MIC data was used to evaluate the effect of seasonal variation on the oils’ antimicrobial efficacy. Both Mānuka and Sugi essential oils were active against all classes of target microorganisms. However, limited activity was observed against Gram-negative bacteria. The oils displayed consistent chemotypic characteristics regardless of the time of distillation. Nonetheless, there were quantitative differences in compound abundance in both essential oils. Significant differences in the MIC of Sugi essential oil was observed against target microorganisms as a result of seasonal variation in constituent abundances while Mānuka essential oil’s antimicrobial efficacy was unaffected. This study demonstrates that seasonal chemical variation is an important quality assurance parameter to consider for future application of essential oils as antimicrobial agents in consumer products.

Scientific materialism is the largely unquestioned basis for modern science's understanding of life. It also holds enormous sway beyond science and thus has increasingly marginalized religious perspectives. Yet it is easy to find behavioral phenomena from the accepted literature that seriously challenge materialism. A number of these phenomena are very suggestive of reincarnation. The larger test for science's paradigm, though, as well as for any potential general import from reincarnation - is the DNA (or genetics)-based model of heredity. If that conception-beget, DNA (deoxyribonucleic acid)-carried model can be confirmed at the individual level then in a very substantial way we would be confirmed as material-only creatures. In particular, can behavioral genetics and personal genomics confirm their DNA-based presumptions? During the last decade enormous efforts have been made to find the DNA origins for a number of health and behavioral tendencies. These efforts have been an "absolutely beyond belief" failure and it is here that the scientific vision faces its biggest challenge. The common premodern reincarnation understanding, on the other hand, fits well on a number of specific conundrums and offers a broad coherence across this unfolding missing heritability mystery. For people trying to make sense of a religious perspective or simply questioning materialism, you should be looking at the missing heritability problem.

Water scarcity, particularly in arid and semi-arid regions, poses a significant challenge to crop production, with drought being a prevalent factor limiting grain production globally. Enhancing agricultural water use efficiency is crucial for managing water resources effectively in these areas. Water resource management involves planning, developing, distributing, and optimizing the use of water resources. Water use efficiency management is also essential for crop growth in arid regions. Various agronomic and irrigation strategies can improve crop water use efficiency and drought tolerance. The primary goal of these strategies is to maximize irrigation efficiencies by applying the precise amount of water needed to restore soil moisture to the desired level. These sustainable practices not only reduce the cost of water and labor for farmers through reduced irrigation but also optimize the use of soil moisture storage. This review paper explores practical techniques to improve water use efficiency, highlighting the role of nutrients in enhancing water use efficiency in crop plants.

Suppressors of cytokine signaling (SOCS) influence cytokine and growth factor signaling by negatively regulating the JAK-STAT pathway. This maintains homeostasis during host immune response. However, functional characterization of SOCS family members in invertebrates is limited. In this study, we discovered the Type-I subfamily of the SOCS genes in the mealworm beetle, T. molitor. The full-length ORFs of TmSOCS5, TmSOCS6, and TmSOCS7 consisted of 1,389, 897 and 1,458 nucleotides, encoding polypeptides of 462, 297 and 485 amino acids, respectively, The C-terminal region of TmSOCS was highly conserved in the SH2 and SOCS box domains. Phylogenetic analysis revealed that the three SOCS genes clustered within the same sub-family and the highest amino acid identity was with the Tribolium castaneum SOCS genes (TcSOCS). While the expression of TmSOCS5 and TmSOCS6 was low in larval, pupal, and adult stages of the insect, TmSOCS7 showed higher expression. The expression of TmSOCS5 and TmSOCS6 was higher in larval hemocytes and adult ovary. The microbes expressed the three TmSOCS genes to varying degrees. C. albicans elicited the strongest response in the host with highest 15-fold expression in TmSOCS7 3 h post-inoculation. Collectively, these data suggest that the Type I TmSOCS could play a role in eliciting host immunity.

Big Data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia's healthcare system, IBM and Walmart's Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining Big Data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors.

This study presents structural analysis and optimization for the lightweight design of a buoyant rotor-type permanent magnet (BRPM) generator, which was first presented in Bang (2010), and compares its structural performances to a conventional generator with a spoke arm-type rotor and stator. The main benefit of a BRPM is that it can be realized as a bearing-less drive system, free from the mechanical failure of rotor bearings, by using a buoyant rotor. Also, the deformation of a generator by blade vibration can be effectively suppressed using joint couplings between the blades and the rotor. For the design optimization, the objective is set as the mass of the rotor and the stator, and the maximum deformation of the airgap clearance between the rotor and the stator by external forces is constrained below 10% of the gap width. The commercial software Optistruct is used for the analysis and optimization. In this investigation, the analysis and optimization are conducted for a 10 MW wind turbine generator. However, the proposed methods can be extended to larger generator designs without requiring considerable modification. The mass of the opti-mized 10MW BRPM generator is 160.7 tons (19.3 tons for the rotor and 141.4 tons for the stator) while that of an optimized conventional spoke arm-type generator is obtained as 325.6 tons.

A predictive approach to the phase behavior of four-component poly-mer-water-surfactant-electrolyte systems is formulated, by viewing the four-component system as a binary polymer-pseudosolvent system, with the pseudosolvent representing water, surfactant, and the electrolyte. The phase stability of this binary system is examined using the framework of the lattice fluid model of Sanchez and Lacombe. In the lattice fluid model, a pure component is represented by three equation of state parameters, the hard-core volume of a lattice site ( ), the number of lattice sites occupied by the component (r) and its characteristic energy ( ). We in-troduce the extra thermodynamic postulate that r and for the pseudosolvent are the same as for water and all surfactant-electrolyte composition dependent characteristics of the pseudosolvent can be represented solely through its characteristic energy parameter. The key implication of the postulate is that the phase behavior of polymer-pseudosolvent systems will be identical for all pseudosolvents with equal values of the characteristic energy, despite their varying real compo-sitions. Based on the pseudosolvent model, illustrative phase diagrams have been computed for several four-component systems containing alkyl sulfonate/sulfate surfactants, electrolytes and anionic or nonionic polymers. The pseudosolvent model is shown to describe all important trends in experimentally observed phase behavior, pertaining to polymer and surfactant molecular characteristics. Most importantly, the pseudosolvent model allows one to construct a priori phase diagrams for any polymer-surfactant-electrolyte system, knowing just one experimental com-position data for a system at the phase boundary, using available thermodynamic data on sur-factants and electrolytes and without requiring any information on the polymer.

In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted, and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x10^4 – 2x10^7 CFU per ml and has a sensitivity of S = 1.655x10^5 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10^-9 grams (g), which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

Regardless of the SARS-CoV-2 pandemic containment, it remains paramount to comprehensively understand its underlying mechanisms to mitigate potential future health and economic impacts, comparable to those experienced throughout the course of the pandemic. The angiotensin-converting enzyme 2 (ACE2) provide anchorage for SARS-CoV-2 binding, thus implicating that ACE and ACE2 might contribute to the variability in infection severity. This study aimed to elucidate predisposing factors influencing the disease course among people infected by SARS-CoV-2, focusing on angiotensin-converting enzyme (ACE) and ACE2 polymorphisms. Notably, despite similar demographics and comorbidities, COVID-19 patients exhibit substantial differences in prognosis. Genetic polymorphisms in ACE and ACE2 have been implicated in disease progression, prompting our investigation into their role in COVID-19 evolution. Using next-generation sequencing (NGS), we analyzed ACE and ACE2 genes in a sample group comprising six subjects infected by SARS-CoV-2. Our findings revealed a correlation between specific polymorphisms and COVID-19 outcomes. Specifically, ACE and ACE2 intronic deletions were observed in all deceased patients, suggesting a potential association with mortality. These results highlight the significance of genetic factors in shaping the clinical course of COVID-19, emphasizing the importance of further research into the impact of genetic variations on COVID-19 severity.

Antioxidants have multiple protective roles in cells and can be used as a supplement to protect cells against cryopreservation-induced detrimental effects, including protecting sperm fertility quality. The antioxidant resveratrol (3,5,4-trihydroxy-trans-stilbene; RSV) has been shown to be a protective supplement for the cryopreservation of animal sperm, including human sperm. In this study, we assessed the effect of RSV supplementation on canine sperm cryopreservation. Semen was collected from four dogs and the effect of different concentrations of RSV (0, 100, 200, and 400 µM) on post-thaw quality of sperm was examined. After thawing, sperm motility was assessed using computer aided sperm analysis, and the structural integrity of the plasma membrane, acrosome, and chromatin were examined, as well as mitochondrial activity and gene expression were assessed. Dog sperm cryopreserved with 200 µM RSV showed significant improvement in motility and viability following thawing compared with that of the control group (p &lt; 0.05). Moreover, RSV-supplemented samples showed significantly higher numbers of sperm with an intact plasma membrane, active mitochondria, and structural integrity of acrosomes and chromatin than that of control samples (p &lt; 0.05). Furthermore, gene expression showed that RSV supplemented samples showed lower expression of pro-apoptotic (BAX) oxidative stress-related (ROMO1) and oxidative induced DNA damage repair (OGG1) whereas higher expression levels of anti‐apoptotic (BCL2) protamine-2 (PRM2), protamine-3 (PRM3) and sperm acrosome‐associated (SPACA3) genes than control. Our results suggest that RSV, at its optimum concentration, can be efficiently used as an alternative antioxidant in the cryopreservation of dog sperm.

Kynurenic acid (KYNA), a tryptophan metabolite, is believed to exert neuromodulatory and neuroprotective effects in the brain. The study aimed to examine the KYNA’s capacity to modify gene expression and the activity of cellular antioxidant enzymes in specific structures of the sheep brain. Anestrous sheep were infused intracerebroventricularly with two KYNA doses: lower (4×5 μg/60 μL/30 min, KYNA20) and higher (4×25 μg/60 μL/30 min, KYNA100) at 30min intervals. The abundance of superoxide dismutase (SOD2), catalase (CAT) and glutathione peroxidase (GPx1) mRNA, as well as enzyme activities (µM/µg of protein), were determined in the hypothalamic medial-basal (MBH) and preoptic (POA) areas, and in the hippocampal CA1 field. Both doses of KYNA caused a decrease (P&lt;0.01) in the expression of SOD2 and CAT mRNA in all structures examined compared to the control group (except for CAT in the POA at the KYNA100 dose). Furthermore, lower levels of SOD2 mRNA (P&lt;0.05) and CAT mRNA (P&lt;0.01) were found in the MBH and POA and in the POA and CA, respectively, in sheep administered with the KYNA20 dose. Different stimulatory effects on GPx1 mRNA expression were observed for both doses (P&lt;0.05-P&lt;0.01). KYNA exerted stimulatory, but dose-dependent effects on SOD2, CAT and GPx1 activities (P&lt;0.05-P&lt;0.01) in all brain tissues examined. The results indicate that KYNA may influence the level of oxidative stress in individual brain structures in sheep by modulating the expression of genes and activity of at least SOD2, CAT and GPx1. The present findings also expand the general knowledge about the potential neuroprotective properties of KYNA in the central nervous system.

The paper presents the prediction of hydrodynamic characteristics of different rudder -plane configurations behind a submarine in deep water through the Reynolds-Averaged Navier-Stokes method in Ansys Fluent Solver. The hydrodynamic force and moment acting on the Cross-plane, X-plane, and Y-plane rudder configurations are analyzed for estimating hydrodynamic coefficientsfor predicting the advantages of each configuration to control the ship. The obtained results show that X-force of the X-plane rudder configuration is larger than the force acting on the Cross-plane rudder and Y-plane rudder configurations. However, Y-force, and Z-force of the X-plane rudder configuration are significantly greater than the Y-force of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y-axis and z-axis. The roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than the case of the X-plane rudder configuration.

The mechanisms underlying the establishment of asymmetric structures during development remain elusive. The wing of Drosophila is asymmetric along the Anterior-Posterior (AP) axis, despite that its growth signals that drive its growth are presumably symmetric. Here, we investigated the contribution of cell recruitment, a process that drives wing fate differentiation in Drosophila, to the asymmetric shape and pattern of the adult wing. Genetic impairment of cell recruitment results in a significant reduction in adult wing asymmetry, primarily affecting the region between longitudinal vein 5 and the wing margin (L5-M). Morphometric analysis reveals that the lack of cell recruitment results in a more symmetric wing with respect to the AP, suggesting a contribution of cell recruitment to the establishment of asymmetry in the adult wing. A close examination of the expression of the wing selector gene vestigial (vg) in wing disc does not support this hypothesis. However, the circularity of the Vg pattern significantly increase in recruitment-impaired wing disc, suggesting that while the decrease of the L5-M region in the adult wing may not be attributed to cell recruitment, it could contribute to the overall asymmetry of the adult wing. We conclude that cell recruitment, a widespread mechanism that participates in growth and patterning of several developing systems, could drive, at least partially, the asymmetric shape of the Drosophila wing.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Children are inherently curious about everything they encounter, making the early years a perfect time for STEAM (science, technology, engineering, arts, and math) investigations. An outdoor learning environment can influence STEAM concept formation of preschoolers with intentional design that offer STEAM learning affordances. Despite the rising interest in early STEAM education, there is still limited literature on how the outdoor environment may influence STEAM learning behaviors of preschoolers (3-5 years old). This scoping review intends to evaluate the existing knowledge regarding the physical factors that contribute to STEAM learning affordances in an outdoor environment for children aged 3 to 5. The review included studies from the last twenty years. This scoping review was conducted in accordance with the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). For this scoping review, 843 citations were discovered across four databases (JSTOR, Scopus, EBSCOhost, and Web of Science), ProQuest, and Google Scholar, and 31 articles were considered eligible for inclusion. Through the synthesis of information from those 31 studies, a list of STEAM learning behaviors of children and STEAM activity-supportive settings were identified that may positively influence preschoolers' STEAM concept development.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Big data technologies are disruptive technologies that affect every business, including those in the construction industry. The Thai government has also been affected, and attempted to use machine learning techniques with the analytics of big data technologies to predict which construction projects have a winning price over the project budget. However, this technology was never developed, and the government did not implement it because they had data obtained via a traditional data collection process. In this study, traditional data were processed to predict behavior in Thai government construction projects using a machine learning model. The data were collected from the government procurement system in 2019. There were seven input data, including project owner department, type of construction project, bidding method, project duration, project level, winning price over estimated price, and winning price over budget. A range of classification techniques, including an artificial neural network (ANN), a decision tree (DC), and K-nearest neighbor (KNN), were used in this study (ANN). According to the results, after hyperparameter tuning, ANN had the greatest prediction accuracy with 78.9 percent. This study confirms that data from the Thai government procurement system can be investigated using machine learning techniques from big data technologies.

Environmental, social, and governance (ESG) investment has become a common trend in capital markets, and many institutional investors have publicly declared their long-term ESG investment target (ESGIT). This study focuses on investors who conduct strategic asset allocation (SAA) using composite indices but establish their ESGIT outside the SAA process. A mismatch could arise between investors’ planned and actual portfolios because they must pursue their ESGIT by asset selection, which inevitably makes the constituents of their portfolios different from those of composite indices. To scrutinize the potential existence of a misalignment between their SAA and ESGIT, this study analyzes actual stock and bond indices over the past decade using traditional mean-variance optimization. The results reveal significant differences between the expected and realized performances, and the misalignment is more significant when the analysis period is divided into two sub-periods: stable and volatile. Specifically, the results indicate that the efficient frontier constructed using ESG indices significantly differs from that constructed using composite indices, and that the difference between these two efficient frontiers becomes more significant during volatile periods. Furthermore, the results show that investors cannot meet their targets if their ESGIT is established outside the SAA process.

Health-Related Quality of Life (HRQoL) encompasses the comprehensive evaluation of an indi-vidual's health and well-being across multiple dimensions. Mindfulness, recognized as a scientifi-cally supported strategy for anxiety management, plays a role in enhancing both physical and mental health. The Gut-Brain Axis (GBA) serves as the bidirectional communication link between the gastrointestinal system and the central nervous system. This study aims to explore the associa-tion between Mindfulness and factors influencing the GBA in both healthy individuals and those with Inflammatory Bowel Diseases (IBD). A cross-sectional investigation involved 338 adults, in-cluding 50 IBD patients, utilizing validated Greek scales for Mindfulness (MAAS-15), Mediterra-nean Diet (14-MEDAS), and HRQoL (EQ-5D-5L). The questionnaire gathered demographic, an-thropometric, and lifestyle data. Among healthy participants, EQ-5D-5L showed a moderate cor-relation with the MAAS-15 scale (r=0.389, p

The organic fraction of municipal solid waste (OFMSW) is a significant environmental threat, and an economic and social challenge to manage. As such, the efficient treatment of OFMSW is a significant key factor in achieving sustainable waste management. Decentralized Composting (DC) develops a new framework of waste management. The DC Analysis Model (DCAM), proposed in our previous study, provides a powerful tool for decision makers, based on the quantification of the DC project characteristics. In this paper, we focus on the qualitative analysis as a complementary tool to support decision making, in cases where quantitative analysis is unequivocal. The qualitative analysis identifies the main players in the field, the critical stakeholders and the potential conflicts between them. It also reveals the root problems and the core competencies for the project's implementation. DCAM qualitative analysis in Shefa-Amr (case study) indicates that unresolved root problems, such as the “lack of national regulation”, “clear ownership of the project” and “lack of ongoing budget” can result in an unsustainable composting system. Countering that, “the commitment of the municipality”, together with “economic viability” and securing “suitable areas for placing composters” are among the most important core competencies for the effective implementation of DC projects.

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 70% for known items and 62% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

Recently, we studied CR-slant warped products B1×fM⊥, where B1=MT×Mθ is the Riemannian product of holomorphic and proper slant submanifolds and M⊥ is a totally real submanifold in a nearly Kaehler manifold. In the continuation, in this paper, we study B2×fMθ, where B2=MT×M⊥ is a CR-product of a nearly Kaehler manifold and establish Chen’s inequality for the squared norm of the second fundamental form. Some special cases of Chen’s inequality are given.

Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system adjustments and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs. generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO₂ emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and the Rest of PJM region leading to scenario-varying changes in the plant-level release of several air pollutants. Due to air pollution chemistry and transport and socio-demographics, these changes in turn give rise to a reduction of 10 to 182 PM_2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Scenarios that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, vulnerable populations—in both PA and Rest of PJM—benefit most in scenarios that prioritize plant closures in PA near Environmental Justice Areas. These results demonstrate the importance of considering cross-regional linkages and socio-demographics in designing equitable retirement strategies.

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of various epithelial cancers. Despite sharing the same pathogenic variant (PV), Lynch syn-drome variant heterozygotes (LSVH) exhibit considerable phenotypic variability in cancer risk. The role of Human Leukocyte Antigen (HLA) in modifying cancer risk prompted our investiga-tion into whether HLA variations act as genetic modifiers influencing age at cancer diagnosis in a unique cohort of LSVH carrying a PV in the hMLH1 gene in South Africa. Within our extensive LS cohort, 426 individuals carried the same hMLH1 PV (MLH1:c.1528C>T). We selected 100 indi-viduals with the greatest diversity in age at cancer diagnosis and the oldest unaffected individu-als for high-throughput HLA genotyping of 12 HLA class I and II loci using next-generation se-quencing. Statistical analyses employed Kaplan-Meier survival analyses with Logrank tests and Cox proportional hazards. Following the robust application of statistical correction methods, six HLA alleles (3.2%) were significantly associated with a young age at cancer diagnosis. Notably, HLA-B*15:17 and HLA-DPB1*55:01 correlated significantly with very young colorectal cancer (CRC) diagnosis (Mean age: 21y [17-25]; HR = 71.59; q

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium that remains a prevalent clinical and environmental challenge. Quorum sensing (QS) molecules are effective biomarkers in pinpointing the presence of P. aeruginosa. This study aimed to develop a convenient-to-use whole-cell biosensor using P. aeruginosa reporters individually encapsulated within alginate-poly-L-lysine (alginate-PLL) microbeads to specifically detect the presence of bacterial autoinducers. The PLL-reinforced microbeads were prepared using a two-step method involving ionic crosslinking and subsequent coating with thin layers of PLL. The alginate-PLL beads showed good stability in the presence of a known cation scavenger (sodium citrate), which typically limits the widespread applications of calcium alginate. In media containing synthetic autoinducers, such as N-(3-oxo dodecanoyl) homoserine lactone (3-oxo-C12-HSL) and N-butanoyl-L-homoserine lactone (C4-HSL) or cell-free supernatants of planktonic or flow cell biofilm effluent of wild P. aeruginosa (PAO1), the encapsulated bacteria enabled a dose-dependent detection of the presence of these QS molecules. The prepared bioreporter beads remained stable during prolonged storage at 4 and -80 °C and were ready for on-the-spot sensing without the need for recovery. This whole-cell biosensor demonstrates the practicality of the encapsulated bioreporter for bacterial detection based on specific QS molecules.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

Tissue-like phantoms are widely used as a model for mimicking the optical properties of live tissue. This paper presents the results of a diffusion reflection method as well as fluorescence lifetime imaging microscopy measurements of fluorescein-conjugated gold nanorods in solution as well as inserted in solid tissue-imitating phantoms. A lack of consistency between the fluorescence lifetime results of the solutions and the phantoms raises a question about the ability of tissue-like phantoms to maintain the optical properties of inserted contrast agents.

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) has changed the paradigm associated with the production of Geospatial Information (GI), since due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature “waterway” that represents watercourses. The quality of OSM data characteristics is an issue that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in literature to studies that analyse the completeness of OSM watercourses. This study presents an analysis of the variation over time in completeness/coverage of the OSM "waterway" feature in the epochs of 2014 and 2023 in a mountainous region included in the Mondego River basin, located in Inland of Portugal. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of this period (nine years) and the fact that, nowadays, digital mobile devices enable access to satellite images, therefore allowing in an easy way the digitalization of entities or objects of the real world remotely. Regarding the indicator’s altitude, slope and location/proximity of settlements, it was not possible to observe their influence on the evolution of the completeness of this geographic entity (OSM waterway).

The ongoing SARS-CoV-2 pandemic has raised concerns surrounding immunological protection against the virus, particularly for people with inborn errors of immunity (IEI). While COVID-19 vaccination induces robust antibody, memory B-cell, and T-cell responses in healthy individuals, how well vaccination protects people with IEI against infection and severe disease remains unclear – especially in the context of new viral variants and vaccine formulations – leading to anxiety, ongoing self-isolation, and repeated vaccination with limited evidence of increased efficacy. Whilst most people with IEI generate some level of cellular and/or humoral immunity to COVID-19 vaccination, the level of protection and durability of the response are unclear. Alongside vaccination, antibody-based therapeutics are aimed at limiting infection and severe disease in this cohort. Immunoglobulin replacement therapy (IgRT) provides passive immunity in antibody-deficient individuals. However, to successfully prevent SARS-CoV-2 infection, a sufficient number of neutralizing antibodies must be present in product. While antibodies against circulating variants are eventually present in IgRT products (both from natural infection and vaccination of the donors), evidence of their capacity to recognize new variants is limited. Furthermore, while antibody-based pre- and post-exposure prophylaxis can be effective, they are susceptible to decreased efficacy in the context of variant evolution. This review provides an in-depth overview of current knowledge about COVID-19 vaccine efficacy in IEIs, the efficacy of SARS-CoV-2-specific antibody products, and knowledge and technological advances required for continued protection of people with IEI.

The global steel industry is a major contributor to greenhouse gas emissions, and the adoption of green hydrogen in Direct Reduced Iron (DRI) production has the potential to significantly reduce CO2 emissions and support climate targets. This review explores the environmental, economic, technological, and social implications of using green hydrogen in DRI processes. The findings suggest that green hydrogen can reduce emissions by up to 90% compared to traditional methods, enhance energy security, and stimulate local economic development. However, high initial costs, energy efficiency concerns, and social acceptance issues remain significant barriers. The review also highlights the importance of integrating carbon capture and storage technologies, addressing logistical challenges, and considering the geopolitical implications of a shift towards green hydrogen. Green hydrogen presents a promising solution for decarbonising the steel industry but continued research, investment in infrastructure, and strategic planning are essential to overcome the challenges and drive widespread adoption.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Thermoregulation is important for maintaining homeostasis of our body. It can be easily broken under anesthesia. An appropriate method to measure the core body temperature is needed, especially for elderly patients because the efficiency of thermoregulation gradually decreases with age. The aim of this study was to examine the accuracy and feasibility of using of SpotOn® sensor in lower extremity orthopedic surgery in elderly patients aged over 80 years by comparing a SpotOn® sensor with two other reliable minimal-invasive methods: a tympanic membrane thermometer and a bladder thermometer. This study enrolled 45 patients aged over 80 years who were scheduled to undergo a lower extremity surgery. Body temperature was measured using a SpotOn® sensor, tympanic membrane thermometer and bladder thermometer. Agreements between the SpotOn® sensor and the other two methods were assessed using Bland and Altman plots for repeated measures adjusted for unequal numbers of measurements per patient. Compared with TempBladder, bias and limits of agreement for TempZHF were 0.07℃ ± 0.58℃. Compared with TempTympanic, bias and limits of agreement for TempZHF were -0.28℃ ± 0.61℃. 3M SpotOn® sensor using ZHF method for patients aged over 80 years undergoing lower extremity surgery shows feasible measurement value and sensitivity.

Vascular calcification (VC) is associated with increased cardiovascular risks in patients with chronic kidney disease (CKD). Sodium-glucose cotransporter 2 inhibitors, like empagliflozin, can improve cardiovascular and renal outcomes. We assessed the expression of Runt-related transcription factor 2 (Runx2), interleukin (IL)-1β, IL-6, AMP-activated protein kinase (AMPK), nuclear factor erythroid-2-related factor (Nrf2), and heme oxygenase 1 (HO-1) in inorganic phosphate–induced VC in mouse VSMCs to investigate the mechanisms underlying empagliflozin’s therapeutic effects. We evaluated biochemical parameters, mean artery pressure (MAP), pulse wave velocity (PWV), transcutaneous glomerular filtration rate (GFR), and histology in an in vivo mouse model with VC induced by an oral high-phosphorus diet following a 5/6 nephrectomy in ApoE−/− mice. Compared to the control group, empagliflozin-treated mice showed significant reductions in blood glucose, MAP, PWV, and calcification, as well as increased calcium and GFR levels. Empagliflozin inhibited osteogenic trans-differentiation by decreasing inflammatory cytokine expression and increasing AMPK, Nrf2, and HO-1 levels. Empagliflozin mitigates high phosphate–induced calcification in mouse VSMCs through the Nrf2/HO-1 anti-inflammatory pathway by activating AMPK. Animal experiments suggested that empagliflozin reduces VC in CKD ApoE−/− mice on a high-phosphate diet.

Undernutrition and micronutrient deficiencies such as anemia are considered significant public health challenges in Bangladesh, which enhancing fish consumption is a well-established food-based intervention to address these. This paper documents the establishment of community-based fish chutney production and reports the impact of its consumption on mid-upper arm circumference (MUAC) and hemoglobin (Hb) levels among targeted 150 pregnant and lactating women (PLW) in rural Bangladesh. A fish chutney was developed using locally available ingredients followed by a series of laboratory tests, including nutrient composition, shelf-life and food safety. A community-based fish chutney production process was designed to: 1) supply locally available ingredients for processing; 2) establish two fish drying sites; 3) initiate a community-based production site, and 4) distribute fish chutney to PLW by six women nutrition field facilitators for one year. Then a pre- and post-intervention study was designed for a selected 150 PLW to receive 30g of fish chutney daily for 12 months. Differences in mean mid-upper arm circumference (MUAC) and hemoglobin (Hb) levels pre- and post-consumption were analyzed using one-way analysis of variance. Consumption of 30g of fish-chutney resulted in significant increases of the mean values of Hb levels and MUAC among the targeted PLW.

Duke University was founded in 1930 primarily due to funds generated from James B. Duke’s tobacco business. Duke achieved great financial wealth primarily due to the early application of machine rolled cigarettes, as opposed to hand rolled. This early adoption of technology allowed Duke Tobacco to out-produce other companies still selling hand rolled cigarettes. By making smoking more inexpensive and easier than pipe smoking, the cigarette formed the foundation for nicotine addiction in the 1900s, generating huge profits for the tobacco industry. At the time Duke University was founded, little was known about the connection between nicotine, cigarettes, and respiratory diseases such as emphysema and lung cancer. Through James Duke’s philanthropy, the devastating harm from cigarettes has been mitigated in part through the founding of one of the world’s most prominent medical centers and research universities.

One of the main challenges of cultured meat is reducing the costs of production, often described as scaling. The main components of the cultivation process include: cells, media, scaffolds and bioreactors. This work presents an alternative to current stainless steel or glass bioreactors and uses food-grade plant-based scaffolds. Bioreactors must be sterile and leak-free to maintain a proper environment for cell growth. Thermoplastic films are a popular material for constructing these fluidic systems, but traditional methods of welding these film are expensive and do not allow rapid prototyping resulting in high research and development costs for companies. In this study, a laser welding method was developed to join thermoplastic films in a way that prevents contamination and leaks, affording effective macro-fluidics fabrication. This technique was tested using polyethylene (PET) films and a laser cutter operating with settings calibrated for the material to be welded or cut. The laser welding method was found to produce strong, leak-free seals in PET films with minimal heat-induced damage to the films. The ability to design fluidics, chambers and ports of various size. Using this method afforded the incorporation of plant-based scaffolds from food-grade plants (Rice). The laser welding method developed in this study provides a reliable, contamination-free method for the rapid fabrication of fluidic systems for cell cultivation. This technique has the potential to be compatible with a vast range of cell types and scaffolds and to be widely adopted in tissue engineering for regenerative medicine and food applications such as cultured meat.

In the contemporary accelerated pace of life, emphasizing the importance of sleep quality is essential for enhancing overall well-being and health. This study investigates the link between poor sleep quality and key factors affecting wellness, such as mental health and eating disorders. Through a cross-sectional analysis involving 407 participants, utilizing established measures including the Depression Anxiety Stress Scale (DASS-21), the Eating Disorder Examination Questionnaire Short (EDE-QS), and the single-item Sleep Quality Scale (SQS), data was collected and analyzed using SPSS v28 and R-Statistics. The findings reveal a significant correlation (p&lt;0.05) between DASS-21, EDE-QS, and SQS, indicating that individuals experiencing poor sleep quality exhibit higher levels of depression, anxiety, and stress. Furthermore, multinomial logistic regression analysis highlights low sleep quality as a risk factor for both mental health (OR: 1.071, 95% CI: 1.042, 1.102, p&lt;0.05, low vs. high sleep quality), and eating disorders (OR: 1.047, 95% CI: 1.004, 1.092, p&lt;0.05, low vs. high sleep quality). Overall, these results underscore the critical role of sleep quality in mental health and suggest insomnia as a predictive factor for both poor mental well-being and disordered eating habits. Further research through randomized controlled trials is warranted to validate the findings of this cross-sectional study.

Establishment of protected areas (PAs) is one of the key global conservation strategies that currently cover approximately 15% of the earth’s land surface. Globally, PA networks are designed to curb the growing anthropogenic pressures in areas with high biological diversity. Despite the importance of PAs in conserving the vanishing biodiversity and unique habitats, many of them are in critical condition due to poor governance thus functioning below the expected level. Moreover, in many developing countries, the PA coverage is below the global standard. Recognizing their contemporary role in conservation, governments have recently agreed to expand the global PA coverage to 17% by the year 2020 (Aichi target 11). This book with eight chapters from different regions of the world provides an overview of the PAs governance, institutional mechanisms, conservation benefits, limitations and challenges associated with their respective policy discourse, integrated management, and functional attributes. Protected areas expect to to play an important role in the long rn in conservation and protection of biodiversity and ecosystems particularly in countries where population pressure and habitat loss are high. Regular intervention, political commitment, and effective governance are essential for the sustainability of PAs across the world. Here, we also attempted to shed some light on future development clues for the sustainable management and monitoring of PAs worldwide.

The accuracy of cosmic ray observation by the Large High Altitude Air Shower Observatory Wide Field-of-view Cherenkov/fluorescence Telescope Array (LHAASO-WFCTA) is influenced by variations in aerosols in the atmosphere. The solar photometer (CE318-T) is extensively utilized within the Aerosol Robotic Network as a highly precise and reliable instrument for aerosol measurements. With this CE318-T 23, 254 sets of valid data samples in 394 days from October 2020 to October 2022 at the LHAASO site were obtained. Data analysis revealed that the baseline Aerosol Optical Depth (AOD) and A˚ngstro¨m Exponent (AE) at 440-870 nm (AE440−870nm) of the aerosols were calculated to be 0.03 and 1.07, respectively, suggesting that the LHAASO site is among the most pristine regions on Earth. The seasonality of the mean AOD are the order of spring &gt; summer &gt; autumn = winter. The monthly average maximum of AOD440nm occurred in April (0.11 ± 0.05) and the minimum was in December (0.03 ± 0.01). The monthly average of AE440−870nm exhibited slight variations. The seasonal characterization of aerosol types indicated that clean continental background aerosol predominated in autumn and winter, which is the optimal period for the absolute calibration of the WFCTA. Additionally, the diurnal daytime variations of AOD and AE across the four seasons are presented. Our analysis also indicates that the potential origins of aerosol over the LHAASO in four seasons were different and the atmospheric aerosols with higher AOD probably originate mainly from Northern Myanmar and Northeast India region. These results provide a baseline for comparison to the future on-site measurement of extinction coefficient profile and aerosol phase function by LHAASO-WFCTA, also enrich the valuable materials on aerosol observation in the Hengduan Mountains and Tibetan Plateau.

Background: Colorectal cancer (CRC) patients experienced multiple chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking. Objectives: We conducted a systematic review and meta-analysis to determine the chemotoxicity's prevalence and risk factors in CRC. Methods: A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis. Results: Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (β: -1.44) and GI toxicity (β:1.85) (Ps 65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (Ps

The purpose of this study was to systematically review forest therapy programs designed to decrease the level of depression among adults and subsequently identify the gaps in the literature. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors independently screened full-text articles from various databases using the following criteria: 1) intervention studies assessing the effects of forest therapy on depression in adults aged 18 years and over; 2) studies including at least one control group or condition; 3) been peer-reviewed; and 4) been published either in English or Korean before July 2016. The Scottish Intercollegiate Guideline Network (SIGN) measurement tool was used to assess the risk of bias in each trial. In the final sample, a total of 28 articles (English: 13, Korean: 15) were included in the present systematic review. This review concluded that forest therapy is one of the emerging and effective interventions for decreasing the level of depression in adults. However, the studies included in this review lacked methodological rigor. Future studies assessing the long-term effect of forest therapy on depression using rigorous study designs are needed.

Contrasting results have suggested that the prevalence and intensity of post abortion emotional distress (PAD) either diminishes rapidly or persists over time. Few studies have followed women for longer than five years. This study examines retrospective population data on 226 ever aborting U.S. women aged 41-45, at an average of 20 years following the abortion, with extensive measures of PAD. Most post abortive women (55.2%) experienced little or no distress from their abortion(s) and 74.6% were confident it had been the right decision, but 44.8% reported moderate (20.7%) to high (24.1%) distress related to their abortion(s), including 37.7% who reported negative emotions; 31.2% “frequent feelings of loss, grief or sadness;” 24.6% “frequent thoughts, dreams, or flashbacks;” 23.2% for whom thoughts and feelings about their abortion “negatively interfered with life, work, or relationships.” Time since the abortion (mean 20 years, range 0-31 years) was not correlated with PAD (r = -.02, p .71). For the minority of women who experienced it, PAD did not diminish but persisted for decades. An estimated 7.5 million U.S. women currently experience persistent high PAD. Further research to specify risk factors and possible interventions for postabortion distress would be of value for this group of women.

SOC stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal-spatial and vertical distribution characteristics of SOCS on the Western Sichuan Plateau (WSP) using the sparrow search algorithm-random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal-spatial varia-tions in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS on the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed that the SOCS in the study area has increased significantly since 2010, with an increase of 27.88 Tg C compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forest and grassland are the main sources of SOCS, the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total SOC pool in 2020, respectively. Expansion of the wetlands, forest, and grassland areas could increase SOCS in the study area.

Reconstructing the Tree of Life remains a central goal in biology. Early methods, relying on small numbers of morphological or genetic characters, often yielded conflicting evolutionary histories, undermining confidence in the results. Investigations based on phylogenomics, which use hundreds to thousands of loci for phylogenetic inquiry, have provided a clearer picture of life's history, but certain branches remain problematic. To resolve difficult nodes on the Tree of Life, two recent studies tested the utility of synteny, the conserved collinearity of orthologous genetic loci in two or more organisms, for phylogenetics. Synteny exhibits compelling phylogenomic potential while also raising new challenges. We identify and discuss specific opportunities and challenges that bear on the value of synteny data and other rare genomic changes for phylogenomic studies. Synteny-based analyses of highly contiguous genome assemblies mark a new chapter in the phylogenomic era and the quest to reconstruct the Tree of Life.

The protection and regulation of international trade of Australia’s native species is legislated by its Environment Protection and Biodiversity Conservation Act of 1999. In response to overwhelming evidence that the Act is ineffective at protecting biodiversity, the Australian Government is currently reforming its environmental laws. One problematic component of the Act is in cases where threatened species interact with multiple regulatory and management frameworks, such as commercially fished marine species that are traded as seafood. Here, we analyzed international seafood trade data and found that Australia exports four threatened fish species listed under the Act and additional species acknowledged as globally threatened. In light of our analysis, we have three recommendations for Australia’s new environmental legislation: 1) apply the same precautions to commercially harvested species as to other threatened species; 2) review the status of all threatened species annually; and 3) assess species listed on global conservation conventions. These recommendations will help ensure that Australia’s marine biodiversity is protected, despite its commercial value.

Understanding the dynamics of land use and land cover (LULC) change in today's context of rapid urbanization is critical for sustainable urban planning and environmental conservation. Therefore, the research aim is to understand the LULC changes in Chattogram Metropolitan Area and to assess the policy implications of these changes for sustainable urban development. Through a comparative analysis of LULC maps for two periods (1997–2007; 2007–2017), we investigated the transformation of a detailed planning zone before and after detailed area plan (DAP) approval. Using a mixed-method approach integrating quantitative data with policy analysis, we elucidated the impacts of urbanization trends on local ecosystems—specifically the conversion of forest cover and waterscape use. The findings reveal a significant conversion of forested and waterscape areas to urban and peri-urban landscapes, highlighting the urgent need for sustainable planning interventions that focus on these peri-urban areas. The study further critiques the DAP's effectiveness, revealing a disconnect between regional policy implementation and local outcomes. This research proposes a validated, scalable framework for urban master plans that adapts to the complex socio-ecological contexts of rapidly urbanizing regions.

Semi-Interlocking Masonry (SIM) represents an innovative building system developed at the Centre for Infrastructure Performance and Reliability at The University of Newcastle, Australia. This system employs a unique approach to interlocking mortar-less engineered masonry panels constructed from SIM units. These units are designed to offer substantial energy dissipation capacity, primarily attributed to the friction occurring on the sliding bed joints between the units within the panel during seismic events. The primary aim of this study is to evaluate the viability of incorporating semi-interlocking masonry as infill panels in the construction of multi-storey buildings across diverse geographical locations with varying seismic conditions in Australia. To assess the feasibility of SIM panels in different conditions (according to Australian Standard AS1170) in Australia, a comprehensive analysis using Strand7 is conducted on a three-storey structure incorporating traditional masonry infill panels and SIM panels. The comparison of displacement and base shear capacities is carried out in response to seismic events across various scenarios in different locations within Australia. The insights gained from this comparative analysis contribute valuable information regarding the viability of implementing SIM as a contemporary construction material.

CO2 emission activity of xylotrophic fungi response to an increase in temperature in the range of 10-30 °C on the example of pure dikaryotic cultures of Fomes fomentarius s. str., F. inzengae, Fomitopsis betulina, F. pinicola, Phellinus igniarius was analyzed. Emission activity was assessed by the difference in CO2 concentration in 0.5-liter exposure chambers with Petri dishes with mycelium growing on agar at the beginning of exposure and an hour later using a Gasmet DX-4030 FTIR spectrometer (Gasmet Technologies Oy, Finland), error measurements ±50 ppm. Specific (μg CO2/cm2/h) and total (μg CO2/h) emission activity and its relationship with temperature and size (area) of the mycelium were assessed. It has been shown that in the range of 10-30 °C, the specific and total CO2 emission activity of the mycelium is closely and positively related to temperature. Specific emission, which is an indicator of the respiratory activity of the mycelium, does not depend on its size; its only driver is temperature, the relationship with which is linear: an increase in temperature by 10 °C causes an increase in the specific emission activity of the mycelium by 1.7 times. The total CO2 emission activity, which is an indicator of the total amount of CO2 emitted, is directly proportional to the specific emission activity and the size of the mycelium. In the range of 10-30 °C, an increase in temperature causes an almost equal increase in both the specific emission activity of the mycelium (Q10 1.7) and its growth (Q10 1.5) and causes an exponential increase in the total emission of CO2. This must be taken into account when predicting CO2 emissions from woody debris under climate change, as it could potentially contribute to accelerating climate change.

Greek roots: "phos" (φῶς) - means "light" and "dynamis" (δύναμις) - means "force" or "power". Photodynamic therapy (PDT) is an innovative treatment method based on the ability of photosensitizers to produce reactive oxygen species after exposure to light of a specific wavelength range, either in the visible or near infrared range. This process results in damage to pathological cells, such as cancer cells, while minimizing the impact on healthy tissues. PDT is a promising direction in the treatment of many diseases, with particular emphasis on the fight against cancer and other diseases asso-ciated with excessive cell growth. The power of light contributed to the creation of phototherapy, whose history dates back to ancient times. It was then noticed that some substances exposed to the sun have a negative effect on the body, while others have a therapeutic effect. This work provides a detailed review of photodynamic therapy, from its origins to the present day. It is surprising how a seemingly simple beam of light can have such a powerful healing effect, which is used not only in dermatology, but also in oncology, surgery, microbiology, virology, and even dentistry. However, despite promising re-sults, photodynamic therapy still faces many challenges. The availability of this thera-py is limited due to the small number of centers offering this type of treatment and the high costs, which may be unaffordable for many patients. Moreover, photodynamic therapy requires further research and improvement.

In this study, the olive pomace was considered because of its importance in the environment. So, this study carried out to estimate the total nitrogen content and the fatty acids profile of olive pomace. The olive pomace samples were collected from Irbid, Jarash, Ajloun and Mafraq during the harvesting season 2014 to study the biochemical characteristics of fatty acid composition and the total nitrogen (TN) content. The total nitrogen content was determined by standard Kjeldahl method and the fatty acid profile was detected by gas chromatography (GC). The results show that the total nitrogen content ranged from (0.39 ± 0.0) to (0.62 ± 0.02) with statistically significant difference suggesting that the composition of matrices and their percentage may be responsible for composition of amendments. The calculated total protein percentage ranged between (2.43 ± 0.00) to (3.87 ± 0.17). Also, 13 different fatty acids were quantitatively profiled and quantitated. Oleic acid (C 18:1) was found to be the highest percentage of all other fatty acids and ranged between (59.03%) and (63.81%), moreover the C 18:1/C 18:2 (oil quality) was calculated and C.V% showed variation meaning that nutritional implication could affect the oxidative stability of oils. In conclusion, OP by-product could give a sustainable and alternative-cheap source for fertilizers, pharmaceutical industries, cosmetics and other industries.

Recent studies have elucidated the diversity of genes encoding venom in sea anemones. However, most of those genes are yet to be explored in an evolutionary context. Insulin is a common peptide across metazoans and has been coopted into a predatory venom in many venomous lineages. In this study, we focus on the diversity of insulin-derived venoms in sea anemones and on elucidating their evolutionary history. We sourced data for 34 species of sea anemones and found sequences belonging to two venom families that have Insulin PFAM annotations (Cono-Insulin, VP302). Our findings show that both families have undergone duplication events. Members of each of the independently evolving clades have consistent predicted protein structures and distinct dN/dS values. Our work also shows that VP302 is part of a multidomain venom contig and has experienced a secondary gain into the venom system of cuticulate sea anemones.

This paper is based on the concept of absolute airspace and its local absolute stationary reference system proposed by the author, on the premise of abandoning space concept and the relativity principle of relativity theory, It is proposed that general relativity and special relativity can be unified into Simple and practical a space-time transformation theory which fully includes other basic physical principles of relativity. And the unified and simple expression of the time transformation effect is proved and deduced. By comparing with the theoretical derivation and checking calculation of Hafele-Keating experiment in an ideal state, It is proved that the expression of time transformation effect proposed in this paper is completely correct. Compared with the theory of relativity, the calculation is simpler and more accurate. The paper also puts forward two checking prediction of physical cases about the time transformation effect, which are left for the capable competent unit or individual to verify in practice.

(1) Background: Alzheimer’s Disease (AD) accounts for 70% of dementia cases and with no effective pharmacological treatments, new rehabilitation methods are needed. Motor activities and transcranial direct current stimulation (tDCS) have shown promise in stabilizing and enhancing cognitive functions. Objective: We want to investigate the effects of combined motor or cognitive activity with tDCS on cognitive functions in AD patients. (2) Methods: Patients with mild or moderate AD were randomized into four groups: motor activity or cognitive activity associated with anodic or sham tDCS (MotA, MotS, CogA, CogS). They received two weeks of treatment (45 minutes, 5 days/week), with the first 15 minutes using 2mA tDCS on the dorsolateral prefrontal cortex. Cognitive assessments were conducted pre-treatment (T0), post-treatment (T1), and one week after (T2). (3) Results: Twenty patients were included. Mann-Whitney test showed significant improvements in anodic groups (MotA+CogA) in the global cognitive status (p=.042), in selective attention (p=.012), and sustained attention (p=.012) at T1, but not at T2. In addition, MotA improved in the Picture Recognition Test (p=.027) and in sustained attention (p=.047) at T2. (4) Conclusion: Combined motor or cognitive activity with anodic tDCS could slow cognitive decline in AD patients, with notable improvements in global cognitive state and attention at T1.

Metallo-β-lactamases (MBLs) are members of the structurally conserved but functionally diverse MBL-fold superfamily of metallohydrolases. MBLs are a major concern for global health care as they efficiently inactivate β-lactam antibiotics, including the “last-resort” carbapenems, and no clinically suitable inhibitors are currently available. Increasingly, promiscuous β-lactamase activity is also observed in other members of the superfamily, including from viruses, which represents an underexplored reservoir for future pathways to antibiotic resistance. Here, two such MBL-fold enzymes from Bacillus phages, the cyclic mononucleotide-degrading proteins ApycGoe3 and ApycGrass, are shown to efficiently degrade β-lactam substrates in vitro. In particular, ApycGrass displays a distinct preference for carbapenem substrates with a catalytic efficiency that is within one order of magnitude of that of the clinically relevant MBL NDM-1. Mutagenesis experiments also demonstrate that the loss of a metal-bridging aspartate residue reduces nuclease activity up to 35-fold, but improves carbapenemase activity. In addition, we propose that the oligomeric state significantly influences β-lactamase activity by modifying access to the active site pocket. Together, these observations hint at a possible new avenue of resistance via the spread of phage-borne MBL-fold enzymes with β-lactamase activity.

To mitigate pyrethroid resistance in mosquito vectors of emerging and re-emerging human pathogens, there is an urgent need to discover insecticides with novel modes of action. Natural alternatives, such as extracts derived from plants, may serve as substitutes for traditional synthetic insecticides if they prove to be sustainable, cost-effective, and safe for non-target organisms. Hemp (Cannabis sativa) is a sustainable plant known to produce various secondary metabolites with insecticidal properties, including terpenoids and flavonoids. The goal of this study was to assess the larvicidal activity of hemp leaf extract on 1st instar mosquito larvae from both pyrethroid-susceptible (PS) and pyrethroid-resistant (PR) strains of Aedes aegypti Another goal was to identify which components of the extracts were responsible for any observed larvicidal activity. We found that methanol extract of hemp leaves induced similar concentration-dependent larvicidal activity against PS (LC50: 4.4 ppm) and PR (LC50: 4.3 ppm) strains within 48 hours. Partitioning of the dried leaf extract between methanol and revealed that full larvicidal activity was restricted to the methanol fraction. Analysis of this fraction by gas chromatography-mass spectrometry and nuclear magnetic resonance showed it to be dominated by cannabidiol (CBD). Larvicidal assays using authentic CBD confirmed this compound was primarily responsible for the toxicity of hemp leaf extract against both strains. We conclude that hemp leaf extracts and CBD have the potential to serve as viable sources for the development of novel mosquito larvicides.

Hasselmann and coauthors proposed the discrete interaction approximation (DIA) as the best tool replacing the nonlinear evolution term in a numerical wind-wave model. Much later, Polnikov and Farina radically improved the original DIA by means of location all the interacting four wave vectors, used in the DIA configuration, exactly at the nodes of the numerical frequency-angular grid. This provides nearly two-times enhancing the speed of numerical calculation for the nonlinear evolution term in a wind-wave model. For this reason, the proposed version of the DIA was called as the fast DIA (FDIA). In this paper we demonstrate all details of the FDIA concept for several frequency-angular numerical grids of high resolution, with the aim of active implementation the FDIA in modern versions of world-wide used wind-wave models.

The proliferation of fake news on social media platforms poses significant challenges to information authenticity and public trust. In this study, the impact of various word embedding techniques on the performance of both machine learning and deep learning models for fake news detection is systematically evaluated. The comprehensive TruthSeeker dataset, which spans over a decade of labeled news articles and social media posts, is utilized. Three popular word embedding techniques are analyzed: TF-IDF, Word2Vec, and FastText. These embeddings were tested with multiple machine learning classifiers, including logistic regression, naive Bayes, K-nearest neighbors, support vector machines, multilayer perceptrons, decision trees, and random forests, as well as deep learning models, specifically convolutional neural networks (CNNs). Among machine learning models, the Support Vector Machine (SVM) with TF-IDF achieved the highest accuracy of 99.03%, precision of 98.92%, recall of 99.15%, and F1 score of 99.03%. The Multilayer Perceptron (MLP) also demonstrated strong performance with Word2Vec, reaching an accuracy of 95.24%, precision of 94.95%, recall of 95.50%, and F1 score of 95.07%. For deep learning models, CNNs combined with TF-IDF embeddings showed outstanding results, achieving an accuracy of 98.99%, precision of 98.56%, recall of 99.48%, and F1 score of 99.02%. These results highlight the critical role of word embeddings in enhancing model accuracy and reliability in misinformation detection.

Australia’s avian species are unique and diverse. Due to the dramatic changed in distribution, physiology, and behaviour caused by both changing climatic conditions and human activities, Australian bird species becomes highly vulnerable to health-related factors such as impact injury and vehicle trauma, as well as environmental-related stressors such as animal attack and abnormal behaviours. This is reflected in increasing number of avian patients received by wildlife hospitals. The most common outcome of avian species in wildlife hospitals was euthanasia, only few of them were released back into their ecosystem. During the process of bird rescue and rehabilitation, stress can be caused by various stressors such as injury, infection, trauma, shock and stressful environment in wildlife hospitals and rehabilitation centres. Therefore, in order to better understand the causes of the high rates of mortality and improve the management and conservation strategies, it is important to study the species responses to environmental changes. Similar with other species, in birds, the HPA axis plays an essential role in maintaining homeostasis by producing glucocorticoids (corticosterone in birds). In this study, we sampled a total of 30 individuals humanely euthanised at the Adelaide Koala and Wildlife Centre (AKWC), and noted their clinical information. Feather samples were collected from all 30 individuals, and then were processed and analysed for corticosterone levels using enzyme-immunoassay (EIA). The biological relevance of feather CORT assay was evaluated by comparing the absolute levels of CORT between birds. We also analysed the effects of different stressors by grouped the diagnosis into acute and chronic categories. Although results suggest that stress levels were variable between the diagnosis, no statistically significant difference was found between these two categories in this study due to the limited sample size. We also obtained patient records from the 2022-2023 period from AKWC for retrospective analysis. Records were categorized and analysed based on the diagnoses, species and admission dates. Retrospective analysis suggests that in 2022, the Rainbow Lorikeet had the highest admission rate (192/544, 35.29%), followed by Magpie (124/544, 22.79%). Most admissions occurred in Summer. Musculoskeletal injury was the most prevalent diagnosis across all species with Magpies having the highest rates (73.39%) across all diagnoses and Rainbow Lorikeets the second highest (36.46%). Beak and Feather disease (BFD) was another common diagnosis for lorikeets. Data collected at 2023 followed the similar trend. This is the first study that uses non-invasive method to determine stress levels of rescued Australian native avian species, aims at improving and implementing native avian species management and conservation initiatives. This research also provides a basis for future studies which allow for more detailed analyses of wild populations and a broader range of species.

Stimulation of the alpha 7 nicotinic acetylcholine receptor (α7nAChR) has shown beneficial effects in several acute inflammatory disease models. This study aims to examine whether treatment with the selective α7nAChR agonist PHA 568487 can dampen inflammation and thereby improve cardiac function after myocardial infarction in mice. The possible anti-inflammatory properties of α7nAChR agonist PHA 568487 were tested in vivo using the air pouch model and in a permanent occlusion model of acute myocardial infarction in mice. Hematology parameters and cytokine levels were determined. Infarct size and cardiac function were assessed via echocardiography 24 h and one week after the infarction. Treatment with α7nAChR agonist PHA 568487 decreased 12 (CCL27, CXCL5, IL6, CXCL10, CXCL11, CXCL1, CCL2, MIP1a, MIP2, CXCL16, CXCL12 and CCL25) out of 33 cytokines in the air pouch model of acute inflammation. However, α7nAChR agonist PHA 568487 did not alter infarct size, ejection fraction, cardiac output or stroke volume at 24 h nor at 7 days after the myocardial infarction compared with control mice. In conclusion, despite promising immunomodulatory effects in the acute inflammatory air pouch model, α7nAChR agonist PHA 568487 did not affect infarct size or cardiac function after a permanent occlusion model of acute myocardial infarction in mice. Consequently, this study does not strengthen the hypothesis that stimulation of the α7nAChR is a future treatment strategy for acute myocardial infarction when reperfusion is lacking. However, whether other agonists of the α7nAChR can have different effects remains to be investigated.

Opossums, native to the Americas, are unique marsupials with over 100 species. The Common Opossum and Virginia Opossum are notable species. They are nocturnal, omnivorous, and adapt to various habitats. Their reproduction involves a short gestation and marsupial pouch development. Despite their ecological benefits like seed dispersal and insect control, their proximity to human habitats exposes them to pathogens, posing public health risks. These include protozoa, helminths, and arthropods that can spread zoonotic parasites and vector-borne diseases. The marsupial immune system, including opossums, is complex, rivaling eutherian mammals. Key immune genes were identified in the grey short-tailed opossum genome. CD1 protein and VpreB surrogate light chain were also found. Marsupials are proposed as ideal models for studying developmental immunology. A West Indian opossum (Didelphis marsupialis) was found and examined. Blood tests revealed abnormalities, including a low white blood cell count and signs of anemia. The results contribute to understanding the species’ health and adaptability in the wild.