Background: In our recent publications pertaining to 4G model of final unification and based on strong and electroweak interactions, we have proposed the existence of a weak fermion of rest energy 585 GeV. Objective: To confirm the physical existence of the proposed 585 GeV weak fermion by analyzing weak and strong interactions in a unified approach via 4G model of final unification, super symmetry and string theory. Method: Considering the proposed nuclear charge of 2.95e, proton, electron mass ratio, specific charge ratios of proton and electron, Fermi’s weak coupling constant, Reduced Planck’s constant, nucleon magnetic moments, nuclear stability, nuclear binding energy, nuclear mass and neutron lifetime, it is planned to confirm the physical existence of the proposed 585 GeV weak fermion. Results: All proposed logics and formulae clearly establish the physical existence of 585 GeV weak fermion directly and indirectly. Conclusion: Believing in the physical existence of the proposed 585 GeV weak fermion, there is a scope for observing galactic TeV radiation coming by virtue of annihilation of 585 GeV fermions and radiation associated with various astrophysical acceleration mechanisms of 585 GeV fermions. Appeal: As we are beginners of astrophysics domain, we appeal the science community to see the possibility of considering the proposed 585 GeV weak fermion with a charge of $\pm\left(e\right)$ in place of electron and proton.

The present studies are specifically aimed at investigating the sensitivity of different dynamic stall models when exposed to various excitation frequencies targeted at the blade edgewise vibrations. The work is done on a modified version of the IEA 15 MW reference wind turbine employing a wind turbine design tool Bladed. The state-of-the-art dynamic stall models for wind turbine applications such as the ye model, Beddoes-Leishman (BL) model and the newly developed IAG model are evaluated. The beginning of the research work starts by evaluating different dynamic stall model effects on rigid blade section forces against known airfoil datasets. Then, the blade flexibility is considered to enable systematic evaluations of the blade flexibility influences in comparison to the rigid blade cases. It is observed that the range of the angle of attack grows depending on the excitation frequency and the adopted dynamic stall model. The critical excitation frequency range and the effects of twist distribution are then identified from the studies, which can be useful as a rough guidance when designing wind turbine blades.

Robotics offers innovative possibilities at all levels of education, which should be considered when planning the teaching process for certain topics of engineering mathematics in higher education. In this paper, we present a new teaching-learning technique using STEAM-based methods to explore the properties of cycloidal curves for Computer Science Engineering BSc students. Frontal teaching, which is part of the tradition of higher education, has been complemented with teaching methods that meet the generational needs of students: problem-based learning, integration of STEAM-based methods and project-based learning. We have developed a new methodological model that combines the advantages of the traditional teaching method with new forms of teaching that have proven to be effective in teaching Generation Z. The four-stage methodological model uses a variety of didactical approaches because each stage applies a different type of learning technique to make the learning process interesting, motivating and effective. One of the main pillars of the model is the use of Desmos dynamic geometry software to create animations, and the other is the use of educational robotics to support visualisation. The implementation of educational robotics and its potential for teaching cycloidal curves is discussed in detail. The effectiveness of the method was investigated with first-year undergraduate students in computer science engineering. Quantitative studies were carried out to test the effectiveness of the new teaching technique. The experimental results strongly indicate the value of using the four-stage methodological model to implement active learning in the field of cycloidal curves.

In this paper, we propose a novel stochastic model to study prime distribution in almost-short intervals, inspired by a corollary of the Maynard-Guth theorem. The model uses the equation \[ x_{n+1} = x_n + \frac{y}{\log x_n} + \epsilon_n, \] where \(\epsilon_n\) follows a Laplace distribution, and demonstrates that our new bound surpasses both Walfisz and Maynard-Guth bounds, particularly for large \(x\): \[ \left| \pi(x + y) - \pi(x) - \frac{y}{\log x} \right| \leq C \cdot y \exp(-\sqrt[4]{\log x}). \] Additionally, we explore a modulated Hamiltonian \(\hat{H}(x)\), derived from a potential \(V(x)\) and a test function \(f(x)\), that exhibits behavior akin to the energy levels of the heavy nucleus \(H_{38}\). The gaps between these energy levels resemble the distribution of zeros of the Riemann zeta function near the critical line, suggesting that \(\zeta(0.5 + i \hat{H}(x))\) corresponds to the energy levels of \(\hat{H}(x)\), thus supporting a quantum mechanical interpretation of the Riemann hypothesis. Furthermore, assuming the Elliott-Halberstam conjecture, which implies that prime gaps can be as small as 12, we find that the transition rate \(\Gamma_{p \to p+12}\) between quantum states associated with prime numbers increases with larger primes. This observation indicates that the distribution of prime gaps reflects an underlying discrete structure in the "energy" landscape of primes, offering a novel perspective on the connection between number theory and quantum physics.

Background: Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets fre- quently encountered in life science domains gives rise to contextual dependence and numerical irregularities in the behavior of model selection criteria (such as MDL, Minimum Description Length) used in BN reconstruction. This renders model features, first and foremost dependency strengths, incomparable and diffi- cult to interpret. In this study, we derive and evaluate a model selection principle that addresses these problems. Results: The objective of the study is attained by (i) approaching model eval- uation as a classification problem, (ii) estimating the effect that sampling error has on the satisfiability of conditional independence criterion, as reflected by Mutual Information, and (iii) utilizing this error estimate to penalize uncertainty with the novel Minimum Uncertainty (MU) model selection principle. We vali- date our findings numerically and demonstrate the performance advantages of the MU criterion. Finally, we illustrate the advantages of the new model evaluation framework on real data examples. Conclusions: The new BN model selection principle successfully overcomes per- formance irregularities observed with MDL, offers a superior average convergence rate in BN reconstruction, and improves the interpretability and universality of resulting BNs, thus enabling direct inter-BN comparisons and evaluations.

This study provides a detailed numerical assessment of the productivity of single-slope solar stills across key cities in the Caspian region, including Aktau, Atyrau, Astrakhan, Makhachkala, Baku, Tehran, and Turkmenbashi. Employing a mathematical model based on heat balance equations and the 4th-order Runge-Kutta method, we simulated the distillation process under various climatic conditions. The results reveal that productivity is significantly influenced by geographic location and local meteorological conditions. Tehran demonstrated the highest productivity across all seasons, with values of 1.75 × 10³ kg/(m²·year) and an efficiency of 0.53, due to its optimal solar irradiation and ambient temperature. In contrast, Atyrau and Makhachkala exhibited lower productivity, particularly in colder months, highlighting the effect of ambient temperature on solar still efficiency. The analysis identified optimal water depth at 2 cm and insulation thickness between 4 and 9 cm for enhancing productivity in continental climates like Aktau. Additionally, the lowest cost of distilled water was $0.024 per kilogram in Baku. These findings align with existing literature, validating the numerical model's accuracy. Future research will explore integrating solar stills with other renewable and fossil fuel-based technologies.

This study introduces a novel predictive model for atomization droplet size, developed through comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability theory, has been meticulously parameterized using the Particle Swarm Optimization (PSO) algorithm. Through rigorous analysis, including analysis of variance (ANOVA), the model has demonstrated robust reliability and precision, with a maximum relative error of 19.3% and an average relative error of 6.8%. Compared to the classical atomization model by Rizkalla and Lefebvre, this model leverages theoretical insights and incorporates a range of interacting variables, enhancing its applicability and accuracy. Spearman correlation analysis reveals that air pressure and the air pressure drop ratio significantly negatively impact droplet size, whereas the fuel-air ratio (FAR) shows a positive correlation. Experimental validation at ambient conditions shows that the model is applicable with a reliability threshold of ≥0.13 and highlight the predominance of the pressure swirl mechanism over aerodynamic atomization at higher fuel flow rates (q > 1.25 kg/h). This research effectively bridges theoretical and practical perspectives, offering critical insights for the optimization of airblast nozzle design.

The Huaniushan lead-zinc-silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX). The mining area has complex geological conditions. The main tectonic structure in the area is the Huaheitan-Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan Lead-zinc-silver polymetallic deposit, there are diverse understandings of the deposit's genesis and mineralization law, making it challenging to meet the geological interpretation requirements of the Huaniushan Lead-zinc-silver polymetallic deposit. Moreover, a single geophysical exploration method utilizes limited rock physical parameters, making it difficult to effectively characterize underground structures. The integrated exploration of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of geophysical data inverse interpretation. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological-geophysical models based on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technology systems are the current development trends in mineral exploration. In light of this, this paper conducts research on the three-dimensional inversion interpretation of the heavy magnetoelectric exploration data of the Huaniushan sedimentary exhalative deposit lead-zinc-silver polymetallic deposit in the mining area and constructs a three-dimensional geological-geophysical model of the study area based on the obtained three-dimensional physical structure of the underground three-dimensional density, magnetization intensity, resistivity, and polarizability structure of the study area, in combination with the information of related geology and drilling holes. It comprehensively interprets the favorable mineralization parts in the study area.

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WT), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-time scale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ES, and distributed WT are analyzed. Based on this analysis, then a multi-time scale voltage optimization scheme for distribution networks using MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of proposed method, simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results that are more realistic compared to the day-ahead optimization method.

This paper aims to investigate appropriate mathematical models devoted to optimization of some cleaning processes related to pharmaceutical contaminants removal. In our previous recent works has been reported that a possibility for removal from water sources of this type of micropollutants is to rehabilitate the existing cleaning plants by introducing efficient techniques such as adsorption on granulated active carbon filters, micro-, nano- or ultrafiltration. To have such processes under a better control and to pass from small to large scale treatment stations, specific mathematical models are necessary. Starting from Navier-Stokes equations and imposing proper boundary conditions, some mathematical physics problems are obtained for which adequate solving methods via variational methods and surjectivity results are proposed. The importance of these solution characterizations consists in their continuation in a numerical method and the possibility to visualize the result by using CFD program.

Physics is imagined in the human brain which was formed in Darwin's Evolution by responding to certain threats for the survival of our evolutionary ancestors. The struggle to survive prevented us from wasting resources by paying attention to parts of reality that had no survival impact. These parts are of unknown scope and dimension. Can we negotiate this Darwin's barrier? As to reality within our reach, we suffer from imbalance between information absorption capacity and information analysis capacity which resulted in ignoring the overflow of information to match human capacity to make sense of it. Also James Clerk Maxwell suggested that reality has small scale phenomena beyond our human reach. Since we have no brain other than what Darwin equipped us with, this barrier looked unsurmountable. But lo and behold: much as we have built vehicles to carry us further than our biological legs, so we now build artificial intelligence machines that carry us beyond our Darwinist barrier. Physics may transform to building AI capability to ride on. This capability may require a more universal geometry and material representation to fit AI inference engines. Suggesting Depth Unbound Reality Architecture (DURA). AI connects raw data to its conclusion potential, by-passing a storied theory of nature. The objective of physics ahead may be to build theory-replacement AI machines.

Dengue fever remains one of the most significant public health challenges globally, particularly in tropical and subtropical regions. Traditional early warning systems (EWS) for dengue outbreaks, based on retrospective epidemiological data, suffer from delays and limited predictive accuracy. Modern systems, leveraging real-time data, artificial intelligence (AI), and machine learning (ML), improve accuracy but can be costly and difficult to implement in resource-constrained settings. This paper proposes a novel hybrid model that integrates the strengths of both traditional and modern EWS, with real-world examples from Brazil, and Malaysia. The hybrid model balances accuracy, scalability, and cost-effectiveness, making it a feasible option for low- and middle-income countries.

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is fundamental. This paper presents a decision-making structure for large consumers having flexibility to manage electricity or natural gas consume to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural-gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural-gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of U$ 3.98 million over a six-month analysis period.

There is a need to find innovative learning methods that enable accelerated learning of ‎a foreign language. This study examined the effect of computer-assisted language ‎learning (CALL) in acquiring a foreign language, which combines cognitive and ‎emotional stimuli in the background. ‎ The study explored two factors related to the acquisition of a foreign language: the ‎duration and scope of the learning process and the depth of internalization of the newly ‎acquired language. Another objective was to assess the learning method in two ‎learning environments, a multimedia setting and a virtual reality headset (VRH), to ‎determine if the learning environment affects the learning results and leads to better ‎vocabulary retention. ‎ One hundred native French speakers, with an average age of 47.5, participated in the ‎study and had no prior knowledge of a new native language. We randomly divided the ‎participants into two groups (multimedia and VRH). They studied 550 words in a new ‎native language for five days: 30 minutes each evening and 15 minutes in the morning.‎ The post-learning test pointed out that both groups improved their vocabulary scores ‎significantly. Approximately one month after the learning experience, we administered ‎a knowledge retention test to 32 participants and found that the level of knowledge had ‎been retained. Finally, background variables (e.g., gender, age, previous knowledge of ‎the new native language) did not affect the learning results. ‎ The findings indicate that CALL, which integrates background cognitive and ‎emotional stimuli in both learning environments, significantly accelerates learning ‎pace, broadens the scope of newly acquired words, and ensures retention. The level of ‎improvement observed in our study is notably higher than reported in the literature ‎that had previously evaluated CALL and in-class language acquisition.

Real-time simulation of toxic gas dispersion is critical for emergency management in high-sulphur gas fields. The CALPUFF (California Puff model) gas dispersion model, a Lagrangian Gaussian Puff model, is often used in mountainous environments due to its ability to handle complex terrain and weather conditions. Verifying the accuracy of toxic gas dispersion simulations under mountainous conditions is vital for emergency response and rescue. In this article, CALPUFF and Computational Fluid Dynamics (CFD) gas dispersion modeling are compared, studying emissions from a hypothetical pipeline breakout accident in a mountainous area, focusing on range of Semi-Lethal Concentration (LC$_{50}$), Immediate Danger to Life and Health Concentration(IDLH). Thirteen groups of simulation conditions are set up for the experiment, including static wind and winds from the east(E), south(S), west(W), and north(N) at speeds of 1, 2, and 3$m/s$. Comparative experiments between CALPUFF and CFD models show consistent results, confirming the feasibility of CALPUFF for near-field moutainous applications. The overall dispersion range deviation between two methods under calm or light wind of N, E and W is about 300$m$. However,the simulation results of the CALPUFF model and the CFD model show significant differences in S wind. In CALPUFF simulation that the toxic gases are channeled by the valleys, while in the CFD model the toxic gases climb over the mountain top and continue to spread and extend towards the northern valley. The evaluation of CALPUFF’s suitability for applications in near-field mountainous environments indicates its potential use for high-sulfur gas fields and gas dispersion simulations in emergency scenarios.

The COVID-19 pandemic has significantly impacted human health and daily life. Meteorological factors play a crucial role in virus transmission. Understanding these factors is critical for pandemic control, yet debates continue due to variations in research methods and data. This study uses a global dataset and focuses on administrative units smaller than 10,000 km². The relationship between daily COVID-19 incidence rates and meteorological factors—including temperature, relative humidity (RH), ultraviolet radiation (UVR), and diurnal temperature range (DTR)—was analyzed across 173 units in 62 countries. The results revealed significant nonlinear responses and lag effects between incidence rates and the meteorological factors. Key inflection points were observed at 0°C and 24°C for temperature, 63% for RH, 102 KJ/m² for UVR, and 15°C for DTR. Below 0°C, infection risk increased with rising temperatures; from 0°C to 24°C, risk decreased; and above 24°C, risk escalated rapidly. RH below 63% correlated inversely with infection risk, while above 63%, the correlation was positive. Infection risk decreased with UVR below 102 KJ/m² but increased beyond this threshold. These nonlinear responses are primarily driven by the differential effects of these factors on virus survival, transmission, and human immunity. Conditions around 24°C, 63% RH, 102 KJ/m² UVR, and DTR below 15°C appear optimal for human immunity, correlating with lower infection risk. High temperature and low UVR prompted immediate infection responses, while low temperatures, high UVR, and larger DTR showed delayed effects. These findings provide new, more reliable evidence for understanding the mechanisms driving COVID-19 pandemic dynamics and for preventing the future normalization of the epidemic.

The linear diblock copolymer polystyrene-b-poly(4-vinylpyridine) (PS-P4VP) is an important copolymer recently used in many applications such as optoelectronics, sensors, catalysis, membranes, energy conversion, energy storage devices, photolithography, and biomedical applications. (1) Background: The surface thermodynamic properties of PS-P4VP copolymer are of great importance in many chemical and industrial processes. (2) Methods: The inverse gas chromatography (IGC) at infinite dilution was used for the experimental determination of the retention volumes of organic solvents adsorbed on copolymer surfaces as a function of temperature. This led to the variations of the free energy of interaction necessary to the evaluation of the London dispersive and polar acid-base surface energies, the polar enthalpy and entropy, the Lewis acid-base constants, and the transition temperatures of PS-P4VP copolymer. (3) Results: The application of the thermal Hamieh model led to accurate determination of the London dispersive surface energy of the copolymer that showed non-linear variations versus the temperature highlighting the presence of two transition temperatures. It was observed that the Lewis’s acid-base parameters of the copolymer strongly depend on the temperature and the Lewis base constant of the solid surface was showed to be higher than the its acid constant. (4) Conclusions: Un important effect of the temperature on the surface thermodynamic properties of PS-P4VP was proved and new surface correlations were determined.

Abstract: Chitosan spherical scaffolds, derived from the deacetylation of chitin, are natural polymers with significant potential, though challenges remain in optimizing their properties. The use of chitosan as a carrier for the controlled release of iron chloride is not yet fully established, despite iron’s critical role in various essential biological processes, including as a component of metalloenzymes and in the maintenance of life. Methods: This study aimed to develop a novel protocol for synthesing chitosan scaffolds and to characterize their properties using analytical techniques. Additionally, the release profile of iron was modeled using two primary controlled-release mathematical models. Results: The results demonstrated successful synthesis and physicochemical characterization of the chitosan sphere scaffolds. These scaffolds exhibited appropriate topography for iron loading and cellular interaction, as confirmed by SEM analysis, and contained functional groups as identified by FTIR. The scaffolds were also semi-crystalline (XRD), showed controlled degradation, and were confirmed to be non-toxic biomaterials. Furthermore, the modeling of the controlled release of iron chloride adsorbed on the scaffold surface, as well as the modeling of iron chloride specimens using the MEDUZA software, was successfully conducted. A theoretical curve was generated from the experimental dose-response data collected over time, considering variations in pH and medium concentration. Conclusions: In conclusion, a novel protocol for chitosan-based carriers was successfully developed, enabling thorough physicochemical characterization and modeling of both absorption and controlled release.

This article develops duality principles applicable to originally non-convex primal variational formulations. More specifically, as a first application, we establish a convex dual approximate variational formulation for a non-linear Kirchhoff-Love plate model. The results are obtained through basic tools of functional analysis, calculus of variations, duality and optimization theory in infinite dimensional spaces. We emphasize such a convex dual approximate formulation obtained may be applied to a large class of similar models in the calculus of variations. Finally, in the last section, we present a duality principle and respective convex dual formulation for a Ginzburg-Landau type equation.

This research presents an ensemble model developed to enhance the safety of bedridden patients in healthcare settings. The model utilizes a unique dataset capturing six typical movements performed by patients laying in bed, such as rolling over, falling off the bed, etc. The aim is to automate the classification of these movements, thereby enabling real-time monitoring of patients without the constant presence of hospital personnel in the room, a common practice in current healthcare settings. The dataset is preprocessed and balanced using the Synthetic Minority Over-sampling Technique, and then split into training and test sets for model training and evaluation. Three different models are explored: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Bagging Regressor is an ensemble model that utilizes the Decision Tree Regressor as its base regressor. Each model’s performance is evaluated using accuracy, R2 score, and Mean Squared Error on both the training and test sets. The ensemble model demonstrates promising results, indicating the potential of machine learning to improve patient safety in healthcare settings. The study also employs K-Fold cross-validation and learning curves to validate the robustness of the ensemble model. This research contributes to the growing body of knowledge on the application of machine learning in healthcare and opens up new avenues for improving patient safety through real-time monitoring and classification of patient movements.

From ancient times to the present, mankind has struggled with infectious diseases that pose a serious threat to people's lives, COVID-19 pandemic has given a huge impact to the human society, which has triggered a lot of thinking. In the future, humans may encounter various unexpected disasters or awful events, therefore, should learn from COVID-19 pandemic incident that need to pay special attention to the emergency supply of manpower and materials, whether a major disaster or not. That is emergency medical management is very important in the event of a sudden outbreak. What’s more, the preparation and scheduling of emergency medical supplies well safeguards people’s lives, health, and safety. However, owing to the sudden onset of an epidemic and given the population trends, the scale of infections and the demand for supplies in the epidemic area are uncertain. The main influencing factors and mechanisms of emergency material demand under the development trend of emergencies can be studied, and materials can be allocated efficiently and effectively, so as to minimize damage. In this study, it improves upon the dynamics model of infectious diseases firstly, by establishing a demand forecasting model for a certain area, taking the Hubei province as an example. Furthermore, this dynamics model is used to predict demand for emergency supplies, such as masks, respirators, and food in the area where the disaster will occur. Experimental results demonstrate the effectiveness and reliability of this prediction model, which supports for the development of emergency material management systems, ulterior, this study provides a framework for the distribution of emergency supplies and a guideline for relief efforts.

Trichiurus japonicus is the most productive fish caught in our country. In order to understand the seasonal distribution of Trichiurus japonicus in the central and southern parts of the East China Sea and Yellow Sea, three species distribution models were adopted, namely Random Forest Model, K-nearest neighbor algorithm and gradient ascending decision Tree model, based on the data of trawling surveys in the central and southern parts of the East China Sea and Yellow Sea from 2008 to 2009. Combined with variance inflation factor and cross-check, the distribution model of Trichiurus japonicus was screened and constructed to analyze the influence of environmental factors on the distribution of Trichiurus japonicus in the central and southern parts of the East China Sea and the Yellow Sea. The results showed that the random forest model had the advantages of fitting effect and prediction ability among the three models. The analysis of this model showed that the water depth, bottom water temperature and surface salinity had a great influence on the habitat distribution of Trichiurus japonicus. The relative resources of Trichiurus japonicus increased with the increase of bottom water temperature and reached the maximum at 23.8℃, and first increased and then decreased with the increase of water depth and surface salinity, reaching the maximum at 72 m and 31.2‰, respectively. The random forest model was used to predict the spatial distribution of Trichiurus japonicus in the East China Sea and the central and southern Yellow Sea during 2008-2009, and the results showed that the predicted results were close to the actual situation. The research results can provide reference for the exploitation and protection of Trichiurus japonicus resources in the East China Sea and the central and southern Yellow Sea.

A previous research showed the 1755 tsunami would inundate the Marginal Avenue, the railway and the low ground area of Caxias, Oeiras municipality, Portugal. Thus, in this study several proposals for coastal protection are presented. A new Lidar data of local topography allowed the construction of a more accurate DEM, including the representation of the streets of Caxias downtown, to be used in the tsunami numerical model. The results were combined with field survey on Caxias and other coastal areas of Portugal and Japan. The new calculations show the low ground areas up to 6 m in height area inundated by the tsunami. However, with the new proposal DEM the tsunami inundation is restricted to the beaches. The proposal includes the construction of a new sea wall with 7 m in height at Caxias beach, the increase in height of the Barcarena Stream levees and the elevation of the sidewalk at São Bruno beach, with a gentle slope up to 7 m in height. It is also proposed the construction of a third bridge over the Barcarena Stream for pedestrian ranging from 5 to 7 m in height that will serve as a gate for the incoming tsunami waves.

Deep-learning-based convection schemes receive wide attention due to its impressive im-provement on precipitation distribution and tropical convections of earth system simulation. But they cannot represent the stochasticity of moist physics, which will degrade the simulation of large-scale circulations, climate mean, and variability. To solve this problem, a stochastic pa-rameterization scheme based on probabilistic diffusion model named DIFF-MP is developed. The cloud-resolving data from GRIST model is coarse-grained into resolved-scale variables and sub-grid contributions due to moist physics to form the training data. DIFF-MP’s performance is compared against generative adversarial network and variational autoencoder. Results show that DIFF-MP is consistently better than the other two models on prediction error, coverage ratio, and spread-skill correlation. The standard deviation, skewness, and kurtosis of subgrid contributions generated by DIFF-MP is also closer to the testing data than the others. Interpretability experiment shows that DIFF-MP’s parameterization of moist physics is physically reasonable.

Weathering and erosion transport minerals and organics toward the sea or lake bottoms over geologic time. The finest solids are deposited in lower, less turbulent areas, such as lake bottoms and continental shelves. They sometimes stack up to thicknesses of kilometers, and begin compacting. These sediment sections are called shales, and as initially deposited in water, shales can have porosities up to 50-80{\%} water, As they are buried, many alteration products from oil to slate are produced due to overburden and temperature increasess, making them important to study. A preceeding paper \cite{1} showed that pressure solution is the primary mechanism for porosity reduction, with possible mechanical compaction at shallow depths. Without naming the mineral(s) involved, it postulated that the greater the product of the water and pore interfaces, the faster the reaction would proceed. This term is $ \varphi^{4m/3}(1-\varphi)^{4n/3}$ , where $\varphi$ is porosity and m and n are numbers close to unity. The large exponents, {\em 4/3}, recognize that the reaction occurs at the molecular scale at which the surfaces are rough. A second term, $\exp^{(-E/RT)}$ , indicates that the reaction is impeded by a quantum energy barrier, E, with diminished impeding power as increased available thermal energy, represented by the absolute temperature, T, becomes available at greater depths in the Earth. These two factors combine to allow porosity $\varphi$ to reduce with time, or equivalently for the fraction of solids, $(1-\varphi)$, to increase with time,$\left. \frac{\partial(ln(1-\varphi) ) }{\partial t } \right |_{\sigma} = (\varphi )^{4m/3}( 1-\varphi )^{4n/3}Ae^{-E/(RT)}$. { \em Here it is shown that this equation can cover quantitatively any actual deposition rates which may have been experienced by the six sections studied, the actual deposition rates being unknown for these cases. Hence a time-depth depositional history for any new shale section, known in detail, would allow determination of the parameters m, n, E and a lumped proportionality constant A. This was accomplished by showing that, for a wide range of depositional rates, r, the range of E for any of the studied sections is small compared to laboratory measurements of quartz solution\cite{2}, 24+-15kJ/mol. Results were obtained over this range of r's using the previously determined m and n, and porosity and temperature profiles. The presently existing porosity profiles necessarily incorporate any overpressure or underpressure conditions that may have existed in the past or currently, as the net difference between overburden and pore pressure is a primary driving force for pressure solution. $SiO_2$ usually comprises 20-50{\%} of shales. In conclusion, pressure solution of quartz can account for vertical compaction of shales quantitatively in the studied examples. Separately, a quasi-eqivalent method for discussing reduced porosities in Macron 2 versus Macron 1 as increased vertical stress, rather than additional horizontal stress, is illustrated. Experiments are suggested to clarify the pressure solution mechanism. The role of horizontal forces is discussed. From a global point of view, shale compaction by pressure solution is a quantified step in the cycling of continental rocks, beginning with rifting.

Citizens face online privacy threats from social media, organisations and governments. Privacy-enhancing tools (PETs) can help people to preserve their privacy, but the uptake of these is limited. We developed a conceptual framework for privacy self-protection, using a classification framework of four distinct privacy threats and our own novel staged model of PET adoption requisites. Through an expert survey (N = 12) and a lay user survey (N = 500), we identified suitable PETs for non-expert users and identified potential barriers to PET adoption. Based on the studies and our theoretical framework, we then developed and implemented a PET decision support tool called PEDRO, and conducted expert evaluations (N = 10).

Event detection provides a crucial foundation for intelligent text analysis and supports numerous downstream applications. However, existing detection models often fail to meet the high accuracy requirements and typically require a human–machine collaborative detection mode involving “machine detection followed by manual audit and correction.” Analysis reveals that the recall rate of machine detection is a major factor limiting the efficiency of human–machine collaboration. To address this at its root and ensure high recall, an event trigger recommendation task is introduced, and a span-based recommendation model for Chinese, SpETR (Span-based Event Trigger Recommender), is developed. This model integrates confidence information from both trigger identification and event classification stages to recommend candidate triggers and their event types for each event. To enhance recall, a semantic boundary smoothing method is proposed, which assigns soft labels to spans around gold triggers based on semantic completeness and part-of-speech, combined with a modified focal loss function to make the model predictions smoother. Experiments on DuEE and a self-built dataset show that SpETR is most effective when the number of recommendations is set to 3, achieving recall rates of 97.76% and 98.70%, respectively, comprising improvements of 4.30% and 5.22% over the optimal baseline models. In terms of human–machine collaborative detection, SpETR saved 30.6% and 25.8% of time compared to traditional detection models on two datasets, and the average F1 score of the annotation results was also improved by 5.35% and 3.48%, respectively.

This study explores the comparative performance of FinBERT, GPT-4, and Logistic Regression for sentiment analysis and stock index prediction using the NGX All-Share Index dataset. By leveraging advanced language models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment score, and predict market price movements. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed both FinBERT and GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was computationally demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of these approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 showing promise for future exploration.

Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is more harmful. Accurately detecting cancer in a patient's tissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, None of these have had a 100% accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have more harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with Squeeze-and-Excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100% accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy for around 0.35 million trainable parameters and around 6.4 million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

This paper presents a comprehensive exploration of Data Quality terminology, revealing a significant lack of standardisation in the field. We propose a novel approach to aggregating disparate Data Quality terms used to describe the multiple facets of Data Quality, under common umbrella terms, with a focus on the ISO 25012 standard. Our aim is to design a Data Quality Data Model that serves as a universally applicable framework for Data Quality assessment. We introduce four additional Data Quality dimensions: Governance, Usefulness, Quantity, and Semantics, enhancing specificity, complementing the framework established by the ISO 25012 standard, and understanding of Data Quality aspects. The ISO 25012 standard, while tailored for software development, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of software development across a multitude of domains. In contrast, frameworks like ALCOA+ that are specific to certain domains lack the ability to be generalised. The model we propose can be seen as a "Rosetta Stone" for Data Quality terminology, facilitating a seamless communication of Data Quality between different domains when collaboration is required to tackle cross-domain projects or challenges.

With the development of economy, maritime transportation has increased rapidly, which has had a significant impact on the marine environment and resources. In recent years, Synthetic Aperture Radar (SAR) has been widely used to observe maritime environmental incident. Through the large coverage area of SAR, marine environmental emergencies, especially oil pollution, can be better monitored and responded to. This study first proposed an improved Full-scale Aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas. The convolutional block attention module (CBAM) in FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model’s overconfidence in majority class samples while improving the model’s generalization ability. The detection performance of the improved FA-MobileUNet model reached 84.55% mIoU (mean Intersection over Union), which was 17.15% higher than that of the original U-Net model. The improved FA-MobileUNet model was then examined by some oil pollution incidents. According to the detection results, if the oil spill was suspected to be caused by a ship, the corresponding Automatic Identification System (AIS) data were collected based on the acquisition time and geo-location of the SAR image to search for the ship suspected of causing oil pollution. Finally, some oil pollution incidents in Taiwan marine area were collected to verify the effectiveness of the proposed model for marine oil pollution monitoring. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.

In the dynamic realm of agribusiness construction, firms are striving to bolster their operational efficiency and profitability amidst the global shift from traditional farming to commercial agriculture. This transition has intensified the demand for sophisticated infrastructure development, presenting new challenges for commercial managers. A critical hurdle is the accurate estimation of profitability for prospective contracts, a task often reliant on intuition rather than data-driven methods. To address this, we propose the development of a mathematical model utilising machine learning techniques to predict contract profitability and identify influential factors. This model aims to aid in bid decision-making, financial forecasting, and enhancing competitiveness in the marketplace. Furthermore, it would provide valuable insights into how altering specific contract attributes might affect predicted profitability. The implementation of such a system necessitates close collaboration between IT professionals and construction executives. This study delineates the development of this predictive model, outlining the data analysis process and the application of machine learning algorithms to tackle this complex commercial challenge. The ultimate goal is to bridge the gap between intuitive estimation and data-driven prediction, thereby enhancing the financial performance and strategic decision-making capabilities of firms in the agribusiness construction sector.

This research centres on advancing the utilisation of a novel Deep Ensemble model called RicEns-Net for predicting crop yields through sophisticated multi-modal data engineering. Initially, data for the study were acquired from Ernst \& Young (EY), a prominent global technology and management consulting firm, as part of their annual data science challenge (EY Open Science Challenge 2023). The predictive models leverage multi-modal data by integrating radar and optical-type remote sensing data alongside meteorological data to create supplementary features essential for predicting crop yield. The primary data features are derived from spectral indices extracted from Sentinel 2 satellites, while more advanced features are generated through combinations and interactions of these fundamental features. Furthermore, data from Sentinel 1 and 3, along with meteorological measurements such as surface temperature and rainfall, account for the intricate natural factors influencing crop yield, creating a comprehensive set of data features based on existing research. To tackle the complexity introduced by numerous data features, extensive data engineering was conducted to identify the most predictive features among 100+ and mitigate the "curse of dimensionality," resulting in 15 features from 5 different modalities. The proposed RicEns-Net comprises multiple machine learning algorithms in a deep ensemble approach to leverage the best features of each of these techniques, achieving optimal performance. The proposed model achieves an MAE value of 341 kg/Ha for prediction, surpassing the performance of the EY Open Science Challenge 2023 winner models and state-of-the-art deep/machine learning approaches.

The study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. The models were evaluated using multiple error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), and R-squared. RNN and LSTM were tested with both 30 and 60-day windows, with performance compared to SVR. LSTM delivered better R-squared values, with a 60-day LSTM achieving the best accuracy (R-squared = 0.993) when using a combination of endogenous market data and technical indicators. SVR showed reliable results in certain scenarios but struggled in fold 2 with sudden spike that shows high probability of not capturing the entire underlying NGX pattern in the dataset correctly as witnessed with the high validation loss during the period. Additionally, RNN faced the vanishing gradient problem that limits its long-term performance. Despite challenges, LSTM's ability to handle temporal dependencies, especially with the inclusion of On-Balance Volume, led to significant improvements in prediction accuracy. The use of the Optuna optimization framework further enhanced model training and hyperparameter tuning, contributing to the performance of the LSTM model.

Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities can remain undisclosed. Data informing an ongoing incident is often sparse, with a large proportion of relevant data only coming to light after the incident culminates or after police intervene - by which point it is too late to make use of the data to aid real-time decision making for the incident in question. Much of the data that is available to police to support real-time decision making is highly confidential so cannot be shared with academics, and is therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a model designed by an academic team to a model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness and tardiness seen in such a secure environment. The parallel development described by this protocol ensures that any sensitive information collected by police, and missing to academics, remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry - a suspected vehicle attack - can be embedded into such a police library of criminal plots.

The paper presents two procedures for creating 3D discrete models of apple using the finite element method (FEM). The study focused on a new Polish apple variety, Chopin at development, ripening, and senescence maturity stages. The analysis of the results allowed for the determination that, depending on the desired outcomes, the parenchymal tissue can be considered as a material with either elastic or elastoplastic properties. Model validation was performed using displacement-force relationships and changes in contact area as a function of loading force obtained from compression tests. Elastoplastic models reflect empirical studies much better. The fit of the elastic models, based on force vs. displacement curves, ranged from 79% to 83%, while the fit of the elastoplastic models to empirical data ranged from 91% to 96%. Elastic models, on the other hand, are well-suited to the modified force vs. displacement curves, which reflect the elastic behavior of apples under compression test. Contour plots of pressures for elastoplastic models align much better with the test results than those for elastic models. 4. Similarly, in the case of contact area values, the elastoplastic models outperformed the elastic models, achieving a fit between 92% and 96%, while the elastic models reached only 82% to 83% compliance.

This study examines the relationship between chondroitin sulphate, proteinoids, and computational neuron models, with a specific emphasis on the Izhikevich neuron model. We investigate the effect of chondroitin sulphate-proteinoid complexes on the behaviour and dynamics of simulated neurons. Through the use of computational simulations, we provide evidence that these biomolecular components have the power to regulate the responsiveness of neurons, the patterns of their firing, and the ability of their synapses to change within the Izhikevich architecture. The findings suggest that the interactions between chondroitin sulphate and proteinoid cause notable alterations in the dynamics of membrane potential and the timing of spikes. We detect adjustments in the features of neuronal responses, such as shifts in the thresholds for firing, alterations in spike frequency adaptation, and changes to bursting patterns. The findings indicate that chondroitin sulphate and proteinoids may have a role in precisely adjusting neuronal information processing and network behaviour. This study offers new and valuable information about the complex connection between the many components of the extracellular matrix, protein-based structures, and the functioning of neurons. In addition, our analysis of the proteinoid-chondroitine system using game theory uncovers a significant Prisoner’s Dilemma scenario. The system’s inclination towards defection, due to the appeal of cheating and the significant penalty for cooperation, with a mean voltage of −9.19 mV, indicates that defective behaviors may prevail in the long-term dynamics of these neuronal interactions.

Background/Objectives: Asthma is a chronic inflammatory disorder of the airways affecting over 10% of the global population. It is characterized by airway inflammation, mucus hypersecretion, and bronchial hyperresponsiveness, driven predominantly by type 2 helper T cells (Th2) and type 2 innate lymphoid cells (ILC2s) in a subset of patients. However, a significant portion of asthmatic individuals present with "type 2-low" asthma, often refractory to standard inhaled corticosteroid (ICS) therapy. Therefore, developing innovative therapeutic strategies has become essential. Recent studies have highlighted cannabidiol (CBD) as a promising anti-inflammatory agent capable of modulating immune responses. This study investigates the therapeutic potential of a high-CBD extract (CBD-X) in asthma; Methods: We evaluated the effects of CBD-X on cells involved in asthma pathogenesis using primary human Th2 cells, neutrophils and asthma mouse model; Results: Our findings indicate that CBD-X extract inhibits Th2 differentiation and reduces the secretion of IL-5 and IL-13, which are crucial cytokines in asthma. Additionally, CBD-X significantly reduces pro-inflammatory cytokines IL-8 and IL-6 in neutrophils and impairs their migration, a critical step in airway inflammation. In a murine asthma model, CBD-X administration led to marked downregulation of IgE and pro-asthmatic cytokines, along with reduced leukocyte, eosinophil, and neutrophil infiltration in lung tissues; Conclusions: These results suggest that CBD-X extract could offer a novel, complementary approach to managing both type 2-high and type 2-low asthma by targeting key inflammatory pathways and modulating immune cell behavior.

The electric network frequency (ENF), often referred to as the industrial heartbeat, plays a crucial role in the power system. In recent years, it has found applications in multimedia evidence identification for court proceedings and audio-visual temporal source identification. This paper introduces a ENF region classification model named UniTS-SinSpec within the UniTS framework. The model integrates the si-nusoidal activation function and spectral attention mechanism while also redesigning the model frame-work. Training is conducted using a public dataset on the open science framework platform (OSF), with final experimental results demonstrating that after parameter optimization, the UniTS-SinSpec model achieves an average validation accuracy of 97.47%, surpassing current state-of-the-art and baseline models. Accurate classification can significantly aid in ENF temporal source identification. Future re-search will focus on expanding dataset coverage and diversity to verify the model's generality and ro-bustness across different regions, time spans, and data sources. Additionally, it aims to explore extensive application potential of ENF region classification in preventing crimes such as telecommunications fraud, terrorism, and child pornography.

Most available studies on the mechanical properties of apples lack the comprehensive results needed for the construction and validation of FEM models. Researchers typically focus on either the flesh and epidermis or the whole fruit, often overlooking the maturity stage of the examined apples. They usually report only firmness or the starch index as indicators of maturity. Furthermore, many studies use store-bought apples, which is impractical for industrial applications since this fruit have already undergone various treatments before reaching the shelf. This article aims to determine the mechanical properties of apples necessary for constructing FEM models that are both adequate and useful for the industry. The new Polish apple variety, Chopin, was selected as the research material. The study was conducted for three stages of apple maturity: development, ripening, and senescence. Mechanical properties of the flesh and skin were determined as material data for FEM models. Force-displacement curves and pressure-force functions were examined for future model validation. Using micro-computed tomography, the bruise volumes of fruit subjected to 20%, 50%, and 80% of the destructive force were determined. Significant differences were found between apples in the senescence stage and those in the development and ripening stages. Results of Micro-Ct and the results of modified and real compression tests of whole fruit have allowed us to formulate the research hypothesis regarding the influence of flesh cracking (characterized by local drops of force), influencing the bruise visibility and detection.

GR4J (Genie Rural a 4 parame`tres Journalier) is a rainfall-runoff model widely used to modelling rainfall into discharge. In this model, the input data used are daily rainfall and evapotranspiration data, calibrated using daily observed discharge data. This model seeks the optimum parameter value that produces the minimum deviation or error. The Nash-Sutcliffe Coefficient and Relative Volume Error (RVE) are used to estimate the errors that occur. One of the modelling parameters, namely X4 (peak time), has a value that does not match the observed hydrograph peak time (Tp). For this reason, it is necessary to adjust the modelling equation so that the unit hydrograph of the model appropriately in terms of value and shape with observed unit hydrograph. This study examines ten watersheds in Java Island so that it is expected to represent catchment conditions in tropical areas and developing countries. A relationship was obtained between the difference peak time from modelling (X4) and observed Tp. Adjustments Formula can be made to the general GR4J modelling equation for unit hydrograph in Java Island watersheds.

In physics we construct idealized mathematical models in order to explain various phenomena which we observe or create in our laboratories. In this article, I recall how sophisticated mathematical models evolved from the concept of a number created thousands years ago and I discuss some challenges and open questions in quantum foundations and in the standard model. We liberated nuclear energy, landed on the Moon and built ‘quantum computers’. Encouraged by these successes many believe, that when we reconcile the general relativity with the quantum theory then we will have the correct theory of everything. Perhaps, we should be much more humble. Our perceptions of reality are biased by our senses and by our brain bending them to meet our priors and expectations. Our abstract mathematical models describe only in an approximate way different layers of physical reality. To describe the motion of a meteorite we can use a concept of a material point but the point-like approximation breaks completely, when the meteorite hits the Earth. Similarly, thermodynamic, chemical, molecular, atomic, nuclear, elementary particle layers of physical reality are described using specific abstract mathematical models and approximations. In my opinion, the theory of everything does not exist.

The spread of false information can significantly harm public opinion, underscoring the importance of accurately identifying untrustworthy news. This paper presents an innovative machine learning (ML) tool, TMining, designed to evaluate news credibility and facilitate various text-mining tasks. By examining a range of ML methodologies alongside preprocessing techniques, we aim to boost the system's effectiveness. Our research meticulously assesses different datasets, highlights the impact of applying stemming techniques, and employs Local Interpretable Model-Agnostic Explanations (LIMEs) to shed light on the rationale behind model predictions. The outcomes reveal a notable enhancement in both the precision and clarity of the news verification process. The ultimate version of the model has been made available as an Application Program Interface (API), and its source code has been shared openly, encouraging further exploration and collaboration within the scientific community. This initiative advances our ability to discern manipulative context from fictitious content and promotes transparency and understanding in the domain of ML applications.

In this paper, we propose "Synergized Efficiency Optimization for Large Language Models" (SEO-LLM), a groundbreaking approach that integrates advanced data utilization and model compression techniques to significantly enhance the performance, efficiency, and scalability of large language models (LLMs). Our method synergistically combines Adaptive Data Augmentation (ADA), Transfer- Active Learning (TAL), Adaptive Iterative Pruning (AIP), and Synergistic Quantization and Distillation (SQD). These components work together to reduce the training data requirement by 30%, compress model size by 67.6%, and improve inference speed by up to 50%, while preserving or even enhancing model accuracy across various NLP tasks. ADA dynamically adjusts augmentation strategies to optimize model generalization, while TAL leverages pre-trained models to focus learning on the most informative data samples. AIP intelligently prunes less significant weights, and SQD harmonizes quantization with knowledge distillation to achieve high compression rates without significant performance loss. The synergy between these techniques makes SEO-LLM a robust solution for deploying LLMs in resource-constrained environments, maintaining state-of-the-art performance with a fraction of the computational and data resources.

Utilising the metabolic processes (urease activity) of certain microorganisms that result in the precipitation of calcium carbonate (CaCO₃) as a sustainable method for manufacturing self-healing cementitious materials for use in the construction, soil stabilization, and wind-induced erosion industries has garnered considerable attention over the last twenty years. Despite extensive efforts for experimental characterization of the effect of numerous influential factors such as the bacteria type, nutrition type and quantity, and environmental conditions governing this phenomenon, computational modeling of this biochemical process has not advanced significantly due to its complexity and intertwined involved parameters. Among these parameters, pH is of special significance since the initial pH level has an immediate effect on the bacteria's urease activity, which then influences the rate at which calcium carbonate precipitates. Furthermore, during the CaCO₃ precipitation process, pH changes due to the generation of byproducts such as ammonium, which alters the velocity of the bacteria's urease activity continuously.The present study proposes a computational model for calcium carbonate precipitation by urease activity of Sporosarcina pasteurii using COMSOL Multiphysics^®. The theoretical background on governing parameters and chemical reactions involved in the process are discussed. The model takes into account the impact of calcium and urea concentrations, as well as the initial pH level and pH variations caused by the production of by-products during the process. The capability of the model to foresee CaCO3 concentration and ultimate pH level is evaluated by comparing the computational outcomes with empirical data obtained from established literature sources. The influence of the initial pH of the environment on the pH variation of the system during the precipitation process was simulated and compared to actual data available in the literature, suggesting that the model can accurately predict the kinetic. Finally, a parametric analysis is performed to identify the ideal initial environmental pH for calcium carbonate precipitation by the S. pasteurii bacterium.

Streamflow forecasting is of great importance in water resources management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time series data on river flows, weather patterns, and other relevant factors, machine-learning models can learn patterns and relationships to present predictions about future river flows. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray, in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time series data were used for the model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt-Winters' method. The model's forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resources management and flood warnings.

Molten salt electrolysis stands as an indispensable technique for the production of rare earth metals, where the judicious distribution of the electric field within the electrolytic cell is paramount for ensuring the seamless operation of the electrolysis process. This study introduces a pioneering approach by integrating the Nernst-Planck equation with the Butler-Volmer equation, thereby devising a holographic electric field model tailored for the electrolysis of the NdF3-LiF molten salt system. The paper presents pioneering experimental measurements concerning the potential distribution and voltage drop characteristics within a 6kA rare earth electrolytic cell, elucidating the interplay between decomposition voltage and current density, which substantiates the model's precision. Our findings unveil that, under operational conditions of 4010 amperes, the 6kA electrolytic cell incurs a total voltage drop of 6.3 volts, delineating a melt voltage drop of 4.43 volts, an ohmic voltage drop of 0.248 volts, and an interfacial voltage drop of 1.622 volts at the electrode-electrolyte junction. The discrepancy in error is confined to within 0.3% of the actual measured voltage of 6.28 volts.The simulation outcomes from the holographic electric field model have been confirmed to precisely and comprehensively reflect the characteristics of the electric field distribution during the rare earth molten salt electrolysis process, offering valuable theoretical insights for practical manufacturing applications.

The advent of modern satellite technology has transformed observational astronomy and astrophysics, offering unprecedented insights into the large-scale behavior of gravitation and challenging established cosmological models. This technological progress has reinvigorated the study of relativistic cosmology, leading to a critical reassessment of foundational assumptions, particularly the cosmological principle, which posits that the universe is homogeneous and isotropic on large scales. While this principle underpins the Standard Cosmological Model and the Friedmann-Lemaitre-Robertson-Walker metric, emerging data has increasingly been challenging its validity. Central to this investigation are the redshift-distance and light intensity-distance relations, essential for testing cosmological models. The integration of both parametric and nonparametric redshift models provides a more comprehensive analysis, addressing discrepancies in our understanding of the universe's structure and evolution. However, unresolved mysteries, particularly concerning dark matter and dark energy, complicate these models. This research critically examines the cosmological principle using the latest observational data and scrutinizes the Friedmann model's assumptions. The study shows that galaxy formation was most rapid in the early universe, especially within the redshift range of , with notable peaks around at and These two characteristic redshift values, predicted by our standard Friedmann redshift model used in this work, are confirmed by the observational data utilized in this research. The research also highlights that dark matter plays a significantly more critical role than dark energy in the process of galaxy formation. While dark energy primarily affects the large-scale expansion of the universe, dark matter seems to go beyond the domination of local galaxy formation and evolution of cosmic structures. These findings underscore the limitations of current models and contribute to the ongoing refinement of cosmological theories, offering a clearer understanding of the universe’s evolution.

Modeling dense nonaqueous phase liquids (DNAPLs) contamination in groundwater is challenging because of its multiphase distribution. To understand the research trends of DNAPL modeling in groundwater, a bibliometric analysis was conducted using CiteSpace based on 614 publications from the WoS Core Collection database (1993-2023). The publications were statistically analyzed, and the research hotspots and trends were summarized. The statistical analysis of the publications indicates that: the United States is leading the international research on DNAPL models, followed by China and Canada; collaboration between countries and disciplines in this field needs to be strengthened. The summary of keyword clustering and burst detection reveals that: the current research hotspots focus on multiphase flow models, mass transfer models, back diffusion, and practical applications of the models; the research trends are centered on back diffusion mechanisms, characterization of contamination source zones and prediction of contaminant distribution in real-world sites, as well as the optimization of remediation strategies.

In an era of rapid technological advancements, complex global challenges, and intense market competition, the ability to generate and scale innovative solutions has become a critical imperative for organizations, policymakers, and societies worldwide. However, the existing academic landscape has lacked a cohesive, multidisciplinary framework for comprehensively understanding the multifaceted nature of innovation. Innovationology, a newly established scientific discipline, aims to address this gap by providing a unifying, transdisciplinary approach to the study and practice of transformative innovation. This comprehensive article introduces Innovationology as a cutting-edge science that integrates insights from diverse fields, including management, psychology, sociology, economics, and technology studies. Innovationology posits that innovation is a multilayered, context-dependent phenomenon, shaped by the intricate interplay of individual, team, organizational, and ecosystem-level factors. By synthesizing the latest theoretical advancements and empirical evidence, this article presents a holistic model of Innovationology that illuminates the key determinants of radical, game-changing innovations capable of disrupting existing industries and creating new market spaces. The article delves deep into the individual cognitive, behavioral, and motivational drivers of innovativeness, the team dynamics and organizational structures that foster collaborative innovation, and the ecosystem-level characteristics that catalyze the emergence and scaling of transformative innovations. Importantly, the article explores the crucial role of contextual factors, such as socio-cultural norms, institutional support, and resource availability, in shaping innovation outcomes. This article also establishes the epistemological foundations of Innovationology, grounding it in a transdisciplinary, holistic, and pragmatic approach to knowledge generation. Innovationology embraces a pluralistic epistemology that acknowledges the complexity and context-dependence of innovation, drawing on diverse methodological approaches to capture the multifaceted nature of this phenomenon. Furthermore, the article outlines the object of Innovationology, which is to provide a comprehensive, evidence-based understanding of the drivers, processes, and outcomes of transformative innovation. Innovationology seeks to elucidate the multilevel determinants of innovation, the dynamic interplay between various factors, and the contextual influences that shape innovation trajectories. By establishing a unifying, transdisciplinary framework, Innovationology aims to bridge the gap between innovation theory and practice, empowering a wide range of stakeholders to unlock the transformative potential of innovation. Importantly, this article outlines the practical applications of Innovationology, providing comprehensive strategies and evidence-based interventions for cultivating innovative mindsets, designing innovation-conducive organizational systems, and navigating the challenges of innovative ecosystems. The implications of Innovationology for entrepreneurs, corporate leaders, policymakers, and innovation scholars are discussed in detail. By establishing Innovationology as a distinct, authoritative scientific discipline, this article sets the foundation for a more holistic, context-sensitive understanding of innovation and its multifaceted drivers. The insights generated by this new science can empower global organizations, institutions, and policymakers to address the complex, interconnected challenges of the 21st century through the strategic deployment of transformative innovations.

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two-parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

This is a methodological contribution to reference mode development in system dynamics model conceptualization. Existing behavior mode taxonomies-developed for various modeling phases-provide no comprehensive method for mode classification based on an exhaustive set of mutually exclusive modes, and derivation of questions the simulation model should answer. Novice modelers need such guidance. The article proposes a conceptual framework for systemic behavior with a hierarchical taxonomy of elementary, composite, and overlaid modes. A structured method identifies the intervals of each respective mode, orders them into episodes and then condenses the variables’ episodes to systemic episodes. Each transition then gives rise to a fundamental question for the model under development. Three classical cases illustrate the application and contrast the proposed taxonomy to others in the extant literature. The article then discusses some validation criteria referring to reference modes and their link to the dynamic hypothesis, current limitations, and questions for further research.

Background: Patients with mutations in the monocarboxylate transporter 8 (MCT8) suffer from Allan-Herndon-Dudley syndrome (AHDS), characterized by developmental delay and a highly disabling movement disorder. Despite the potential of thyroid hormone derivatives to overcome the transporter defect, current trials did not achieve patient-oriented therapeutic goals. Objectives: Since most neurological symptoms are related to the dopaminergic system, we investigated the role of dopamine and its metabolites in MCT8 deficiency with regard to pathophysiology and potential therapeutic strategies in an observational cohort study. Methods: We present longitudinal data from the DEEPTYPE registry of ten patients with video-documentation, standardized phenotyping, cerebrospinal fluid analysis, treatment response to levodopa/carbidopa supplementation, and neuroimaging data. To establish a cell-based model for pathophysiological studies, we differentiated healthy human induced pluripotent stem cells (hiPSCs) into dopaminergic neurons. Results: Children presented with signs of parkinsonism in childhood, including hypokinesia, hypomimia, inability to sit or stand, rigidity, dystonia, and autonomic dysfunction along the classification of Leuzzi and colleagues. Cerebrospinal fluid homovanillic acid concentrations were decreased (n=11), suggesting isolated dopamine pathway impairment. Six out of seven patients responded favorably to levodopa/carbidopa supplementation and we did not see any adverse drug reactions. Our cell-based studies showed that hiPSC-derived dopaminergic neurons expressed MCT8 and produced quantifiable levels of biogenic amines. Conclusions: Parkinsonism is part of AHDS's clinical presentation and may be amenable to treatment. The precise impact of MCT8 deficiency on the dopamine metabolism needs to be further elucidated, e.g. by using patient iPSC-derived dopaminergic neurons in future studies.

The increase in greenhouse gas emissions fosters the transport sector towards new technological perspectives on personal mobility. Addressing sustainable mobility through electric micromobility requires interdisciplinary design research methods and approaches. In the context of the LEONARDO project, funded under the Horizon 2020 framework, the paper addresses a critical literature review on the design thinking, design research models, tools, and mixed methods to be undertaken for driving product mobility innovation in a cross-disciplinary context. Following the “research through design” research strategy, the authors applied the Double-Diamond design thinking model to frame the design research process in 4 phases, aligning with 3 general objectives, 12 specific research objectives, and 24 tasks, supported by a total of 71 mixed methods and tools. As a result, the transdisciplinary process provides a co-designed energy-efficient stand-alone microvehicle and a scalable interdisciplinary design model for urban transport product innovation. In conclusion, this case study suggests the value of the Double-Diamond design thinking model as a design research instrument capable of addressing sustainable mobility and guiding interdisciplinary design research, design practice, and education in the Industrial Engineering and Design disciplinary sectors.

The amount of solar energy produced is directly proportional to the surface area of solar panels exposed to sun rays. To increase the productivity of solar panels, a number of techniques are proposed by curving the surface or protruding the panel surface to maximise the surface area, e.g. Protruding panel surfaces with shapes such as pyramidal, cone, spherical cap, sinusoidal wavy curve, etc. These techniques are analysed to determine whether the panel surface area increases. The geometrical design analysis showed that, depending on the chosen surface curving method, the panel can achieve a remarkable increase in surface area.The system advisor model was used to assess a sample of solar cells with standard measurements of 6 by 6 inches that were organised in a zigzag pattern with different angles of inclination (10°, 15°, 20°, 25°, 30°, and 35°). Overall, the maximum power pump and annual AC energy performance increase by 1.4%, 4.2%, 6.8%, 10.8%, 14.9%, and 21.6%, respectively, over a one-year period of productivity. However, by applying the concept of solar panel efficiency degradation due to tilting solar panels at an angle by Mamun et al. (2022), for each 5° increase in tilt angle beyond the ideal 10°, the efficiency decreases by approximately 0.76%. In conclusion, the overall efficiency is greater than that of standard flat solar panels, provided the tilt angle is within a reasonable range.

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances. To address these issues, a robust UKF-MPC-based longitudinal control algorithm for the platoon is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state-space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction compensation is added to the MPC algorithm to design the robust UKF-MPC longitudinal controller. Finally, The effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. Simulation results show that the robust UKF-MPC longitudinal controller performs better compared to the MPC and UKF-MPC longitudinal controllers. When facing the communication delay and packet loss problems, the robust UKF-MPC longitudinal controller has higher control accuracy and less performance degradation. It has strong robustness and stability.

The Kennedy model offers a robust framework for modeling forward rates, leveraging Gaussian random fields to accommodate emerging phenomena such as negative rates. In our study, we employ maximum likelihood estimations to determine the parameters of the Kennedy field, utilizing Radon-Nikodym derivatives for enhanced accuracy. We introduce an efficient simulation method for the Kennedy field and develop a Black-Scholes-like analytical pricing formula for diverse financial assets. Additionally, we present a novel parameter estimation algorithm grounded in numerical extreme value optimization, enabling the recalibration of parameters based on observed financial product prices. To validate the efficacy of our approach, we assess its performance using real-world par swap rates in the latter part of this article.

As a critical component of electric vehicles, the powertrain has a significant impact on the overall performance of the vehicle. In addressing the challenge of lengthy testing cycles, this study develops a para model of the powertrain utilizing digital twin (DT) technology, thereby establishing a framework for simulation testing of multi-controller intermodulation. The research conducts functional definition coverage testing through the design of specific functional requirement use cases, and it validates the failure mechanism via fault injection use cases. The results indicate that the DT testing platform can effectively simulate the operational interactions among various controllers within the powertrain system. In comparison to traditional field testing, the digital twin-based testing methodology offers enhanced operational efficiency and allows for the examination of testing conditions that are impractical to implement in real vehicles, particularly in the context of fault injection testing, thus facilitating the early detection of potential safety risks within the system. The advancement of this technical solution holds significant practical implications for the future mass production and development of electric vehicles.

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model could be an appropriate econometric framework for characterizing the dynamics of the U.S. public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable in capturing the main stylized facts of the U.S. public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with several theoretical frameworks that seek to explain the behavior of the U.S. public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, the paper provides some arguments on why the previous studies that use the exponential smooth threshold autoregressive (ESTAR) models, or SETAR-type models for the first differences of the U.S. public debt/GDP ratio, are potentially mis-specified, both on econometric and economic grounds.

Objectives: To develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aimed at aiding community healthcare workers in the early identification of individuals at high risk of Mild Cognitive Impairment (MCI); Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively; Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). Calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801); Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

Short-circuit blowing is an important technical means to ensure the rapid surfacing of submersible. In order to study the impact of seven multiple manipulation factors of three levels on blowing, a proportional short-circuit blowing model test bench had been built and L18(37) orthogonal experiments were carried out; then, using Back propagation neural network and Pearson correlation analysis, the experimental data were trained and predicted, correlation between individual factor and blowing was further studied as supplement of orthogonal experiments. It had been proved that both multi-factor combination and individual including blowing duration, sea tank back pressure, the gas blowing pressure of the cylinder group, and sea valve flowing area had larger influence, whose correlation coefficients were 0.6535, 0.8105, 0.5569, 0.5373, of which the F-ratio of blowing duration is over critical value 3.24. And statistical evaluation indicators of Back propagation neural network were between 10e-2 and 10e-13, relative error was less than 3%, and prediction error accuracy was 100%. Based on the results above, a reasonable prediction method for submersible short-circuit blowing had been formed and suggestions on engineering design and operations were proposed, including advantage of short-circuit blowing method, initial condition settings and manipulation strategy.

Chemical plants are characterized by the management and operation of various hazardous materials; thus, accident risk is inherently high. Nevertheless, the incidence of chemical accidents in Korea has effectively stabilized, with no marked increase attributable to the implementation of systematic safety management procedures based on process safety management system principles. In June 2023, the Ministry of Employment and Labor in Korea unveiled the Serious Injury and Fatality (SIF) report, meticulously cataloging 4,432 instances of grave accidents spanning six years (2016–2021). The compilation comprises 1,834 and 2,574 cases within the manufacturing and affiliated sectors and the construction sector, respectively. This study provides an overview of each accident, and elucidates the causes, and recommends preventative strategies. The objective was to galvanize companies across various industrial landscapes and fortify their intrinsic safety management protocols by using accident data delineated per sector. Sixteen specific accident cases from the SIF report, all of which occurred in facilities regulated by process safety management (PSM), were investigated. Technical analysis was conducted, focusing on explosion accidents within the organic catalyst packaging process, notable for their unique patterns of occurrence. This analysis extended to an examination of the reproducibility of such accidents. Employing two system analysis methodologies, system theoretic accident model and process and barrier-based systemic cause analysis technique, this study provides insights into the causative factors of the accidents and formulates comprehensive strategies for the prevention of future accidents. The findings will contribute significantly to reducing the risk of accidents within the catalyst packaging domain and assist in the development of robust recurrence prevention measures.

The article is focused mainly on verifying the suitability of data from experimental milling heat-treated beech wood and on investigation of the effect of technical and technological parameters of milling on the energy consumption of this process. The independent parameters of machining process are cutting speed, feed speed, rake angle, and hydrothermal modification of experimental wood material. Based on analysis of variance it can be argued that the greatest statistically significant effect on energy consumption have cutting speed and rake angle of the tool while the feed speed had the least influence. The measured data of cutting power during milling were used to build a regression model and validate it, while the most suitable type of model with correlation of 87 % is Classification and Regression Tree followed by model created by Random Forests method.

As the primary secondary tree species in Northeast China, Quercus mongolica possesses significant ecological and economic value. This study employed the Biomod2 platform in conjunction with ArcGIS spatial analysis to assess the potential suitable habitat distribution area of Q. mongolica under current climatic conditions. Furthermore, it forecasted the distribution range and niche changes of potentially suitable habitats for Q. mongolica from 2022 to 2090, and pinpointed the key environmental factors influencing its distribution. The findings reveal that the total potential suitable area for Q. mongolica covers 74,994.792 km², predominantly spread across Inner Mongolia, Heilongjiang, Jilin, and other regions. The primary determinants of suitable area distribution were peak temperature of hottest month, lowest temperature of coldest month and altitude. Under future climate scenarios, the potentially suitable habitats of Q. mongolica are anticipated to diminish to varying extents, with the distribution center exhibiting a tendency towards northward migration. Concurrently, the overlap among different climate scenarios is predicted to expand over time. This investigation facilitates a comprehensive understanding of Q. mongolica's adaptation to climate change, enabling informed adjustments and serving as a valuable reference for the preservation and sustainable management of Q. mongolica populations.

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMS) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMS often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMS can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMS significantly boosts energy efficiency and safety in smart buildings. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management.

Background: The hub-and-spoke model (HSM), offers a framework for efficient healthcare service delivery. This scoping review seeks to establish the implementation and effectiveness of the HSM in Antimicrobial Stewardship (AMS) programs. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guideline was followed. A systematic search was conducted in four electronic databases (PubMed, Medline, Cochrane library and Google scholar) from inception until January 2024. Studies where the HSM was used for delivering any AMS activity, intervention or action, were included. No study specific filters were applied and all populations, study designs and health settings were included. Data screening and selection was achieved using Rayyan. Three authors independently screened studies, with conflicts resolved by a fourth author. Data were narratively synthesized. Standard appraisal tools were impractical; however, critically evaluation of data collection and outcome reporting was ensured. Results: Out of 1,438 articles, three were included in the scoping review. The primary interventions utilising the HSM in AMS involved reducing antibiotic misuse, training healthcare professionals, case-based learning, establishing AMS programs, developing antibiograms, and formulating policies or guidelines pertinent to AMS. The studies demonstrated significant clinical improvements in AMS. Clinical outcomes from the studies include a significant reduction in antimicrobial usage and improved antibiotic management, with a notable decrease in days on antimicrobial therapy and increased antibiotic de-escalation. Key facilitators for AMS program success: tailored education, collaborative learning, strong leadership, strategic practices, and data-driven decisions. Key barriers: leadership challenges, change resistance, knowledge gaps, inadequate data systems, resource limitations, and technological constraints. Conclusion: The review identified a literature gap in HSM use for AMS programs. Further studies are needed to assess HSM's effectiveness, feasibility, and cost-effectiveness in AMS contexts.

This article introduces a new method for modelling and verifying the execution of timed security protocols (TSP) and their time-dependent security properties. The method, which is both novel and reliable, uses an extension of interpreted systems, accessible semantics in multi-agent systems, and timed interpreted systems (TIS) with dense time semantics to model TSP executions. We enhance the models of TSPs by incorporating delays and varying lifetimes to capture real-life aspects of protocol executions. To illustrate the method, we model a timed version of the Needham-Schroeder Public Key Authentication Protocol. We have also developed a new SMT-based bounded model checking reachability algorithm for the proposed structures and implemented it with the tool. The method comprises a new procedure for modelling TSP executions, a translation of TSP into TIS, and a translation of TIS's reachability problem into the SMT problem. The paper also includes thorough experimental results for nine protocols modelled by TIS and discusses the findings in detail.

There is a pressing need for tools that can rapidly predict PM2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of PM2.5 concentrations and related health effects. The U-Net model was trained with 2019 South Korean PM2.5 data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA’s BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required PM2.5 emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce PM2.5-related health risks and estimate economic benefits.

This case study examined performance of rural cassava SMEs managed by small scale farmers in Busia and Kisumu Counties in the lake region of Kenya. Formal survey, focus group discussions and key informant interviews were used to collect data from farmers and stakeholders in the region. The data were analysed descriptively using proportions (percentages and proportions), means and standard deviation and inferentially, using statistical tests of significance. The findings revealed gross inefficiencies in the management of cassava SMEs. Grass-root SMEs lack professional managers and exhibit inadequate organizational, quantity and quality management, high operational costs, lack of trust from farmers, inadequate financing, weak technical, agribusiness skills and weak linkages with markets. The findings challenge promotion of farmer-led organizations in rural areas to perform additional roles such as processing and marketing functions beyond their core business which is production. To remain relevant in a competitive value chain, grass-root farmer-led SMEs need to be re structured into viable economic entities with clear market-orientation. Ordinary smallholder farmers should specialize in producing sufficient quantity and quality of agricultural commodities, while processing and marketing functions are taken up by professional entrepreneurs. This study recommends an entrepreneur model rather than farmer-led SMEs as potentially best suited in creating efficiency in the cassava value chain in the lake region of Kenya.

This paper offers an integrated approach for correlated storage assignment strategy (CSAS) to optimize travel distance by considering item correlation, picking frequency, zoning, and slot constraints to improve operational efficiency in distribution centers. We simplify the mixed integer non-linear programming (MINLP) model and incorporate integrated heuristic procedures for faster convergence. We introduce positive and negative centroid deviations as techniques to guide the model convergence and explore different scenarios. In the second stage of item assignment, we prioritize items within zones using a ranking formula that is optimal for both single-item and multiple-item orders. By analyzing the distribution of correlated item across zones and the impact of their picking frequency on optimizing travel distance, we propose an improved CSAS that minimize travel distance by strategically placing items based on their proximity to the depot. Our model reduces trips in distant zones by prioritizing larger average order sizes, while maximizing trips for frequently ordered smaller lists in closer zones. This method significantly reduces travel distance by minimizing the need for pickers in distant zones to frequently travel to the depot. Simulation results show reductions of up to 36.75% for fitted data and 33.59% for recent data.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in some sections we present concerning numerical examples and the respective softwares.

In today’s data-driven marketing landscape, accurately predicting customer responses to marketing campaigns is critical for optimizing engagement and return on investment (ROI). This study utilizes a Decision Tree (DT) model to identify key factors influencing customer behaviour. Initially, the model achieved a high accuracy of 87.3% but struggled with precision and recall due to class imbalance. By applying a resampling technique, the model’s performance improved significantly, with a recall increase from 44% to 83.1% and an F1-score improvement from 49% to 74.2%. Key influential features identified include how recently a customer made a purchase, the number of days they have been a customer, and the number of previous campaigns they responded to. The study highlights the DT model’s interpretability, making it a practical tool for marketing professionals to improve campaign effectiveness and customer targeting.

Bipolar disorder (BD) is a complex mental health condition characterized by alternating periods of mania and depression. This review provides a comprehensive overview of BD, focusing on its genetics, causal hypotheses, clinical signs and evolution, potential biomarkers with an emphasis on electroencephalography (EEG), and treatment options. Genetic studies have identified several susceptibility genes and polygenic risk scores, highlighting the heritable nature of BD. Various causal hypotheses, including monoamine dysregulation, kindling, circadian rhythm disruptions, and neuroinflammation, have been proposed to explain its pathophysiology. The clinical course of BD is characterized by recurrent episodes of mania/hypomania and depression, with significant heterogeneity among individuals. EEG and other biomarkers hold promise for improving diagnosis, predicting treatment response, and elucidating the neurobiological underpinnings of BD. Current treatment options include pharmacotherapy, psychotherapy, and neuromodulation techniques, which can be tailored to individual needs based on clinical presentation and response to treatment. Future research should focus on identifying more specific biomarkers, developing novel treatments, and optimizing existing therapies to improve outcomes for individuals with BD.

Clarifying the distribution of temperature and pressure in the wellbore and cavern during hydrogen injection and extraction is crucial for quantitatively assessing cavern stability and wellbore integrity. This paper establishes an integrated flow and heat transfer model for the cavern and wellbore during hydrogen injection and production, analyzing the variations in temperature and pressure in both the wellbore and the cavern. The specific conclusions are as follows. (1) The temperature and pressure parameters of hydrogen and natural gas within the chamber and wellbore were compared. Under identical injection and production conditions, the temperature of hydrogen in the chamber was 10°C higher than that of natural gas, and 16°C higher in the wellbore. The pressure of hydrogen in the chamber was 2.9 MPa greater than that of natural gas, and 2.6 MPa higher in the wellbore. (2) A comparative analysis was conducted on the impact of surrounding rock’s horizontal and numerical distance on temperature during hydrogen and natural gas injection processes. As the distance from the cavity increases, from 5 to 15 meters, the temperature fluctuation in the surrounding rock diminishes progressively, with the temperature effect in the hydrogen storage chamber extending at least 10 meters. (3) The influence of rock thermal conductivity parameters on temperature during the processes of hydrogen injection and natural gas extraction is compared. The better the thermal conductivity, the deeper the thermal effects penetrate the rock layers, with the specific heat capacity having the most significant impact.

In the present research, it has been tried to use the applied research bases in the field of business management with marketing orientation. Since there were wide and diverse spectrums in the field of economic activities in Isfahan province and Isfahan city, the model of economic sectors was used to categorize these activities. In this regard, the specific goals of the research in all five sectors of the model of economic sectors, drawing and prioritizing the components (indices) of the 7P marketing mix model in each of these economic sectors by the related individuals and members of subgroups of guilds and non-governmental organizations in Isfahan province, have been formed, specified and determined. Collecting data for processing and answer the question of this research, after designing a questionnaire in accordance with the AHP model standard, by spending a period of 2 months from September 2022 and ended in November 2022 , from the statistical population has been done.

In this paper, we integrate the Finite Element Method (FEM) with orthogonal polynomial approximation in high-dimensional spaces to innovatively model the Moon’s surface gravity anomaly. Our aim is to approximate solutions to Laplace’s classical differential equations of gravity, employing classical Chebyshev polynomials as basis functions. Using classical Chebyshev polynomials as basis functions, the least-squares approximation was used to approximate discrete samples of the approximation function. These test functions provide an understanding of errors in approximation and corresponding errors due to differentiation and integration. These test functions provide an understanding of errors in approximation and corresponding errors due to differentiation and integration. The first application of this project is to substitute the globally-valid classical spherical harmonic series of approximations with locally-valid series of orthogonal polynomial approximations (i.e. using the FEM approach). With an error tolerance set at 10−9ms−2, we use this method to adapt the gravity model radially upwards from the lunar surface. The results showcase a need for a higher degree of approximation on and near the lunar surface, with the necessity decreasing as the radius increases. Notably, this method achieves a computational speedup of five orders of magnitude when applying the method to radial adaptation. More intrinsically, our second application involves using the methodology as an effective tool in solving boundary value problems. Specifically, we implement this approach to solve classical differential equations involved with high precision, long-term orbit propagation. This application provides a four-order of magnitude speedup in computational time while maintaining an error within the 10−10ms−2 error range for various orbit propagation tests. Alongside the advancements in orthogonal approximation theory, the FEM enables revolutionary speedups in orbit propagation without compromising accuracy.

We describe a forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to operationally forecast conditions in Northern and Central California. It was established in 2014 and has been periodically improved. In the current configuration, WRF forecasts are routinely performed multiple times each day on a 2-km grid, and the results are used as input to wildfire fuel moisture, fire probability, and wildfire spread models. Components of the system include a stochastically perturbed ensemble; an annually updated reanalysis covering more than 30 years; and validation against PG&E’s extensive mesonet and other observing networks. We describe the development and validation of the next operational edition. Configurations tested include irrigation triggered by crop growing season and an ensemble-based approach with intelligently sub-selected Global Ensemble Forecast System (GEFS) members. The latter approach allows for enhanced uncertainty sampling in WRF forecasts and can greatly improve WRF forecasts when calibrated by observations.

Currently, urban renewal activities in China are booming. And promoting the renovation of public buildings is the key due to its large scale, high cost and significant impact to the nature and social environment. To reduce the ambiguity and uncertainty in evaluating the potential for renewal of existing public buildings, a renewal potential evaluation model combining game theory combination weighting method and cloud model theory is proposed. The paper constructs a comprehensive evaluation index system based on relevant standards and literature. Game theory is used to optimize the weights obtained by AHP and entropy weight methods to obtain a combined weight. MATLAB programming is used to calculate the comprehensive cloud parameters of the evaluation index for the potential renewal of existing public buildings and therefore generate cloud Graphs. Through case study in Nanjing of China, it has been demonstrated that the combination empowerment cloud model can objectively reflect the relationship between the fuzziness and randomness of evaluation indicators for public building renewal potential. The expression of cloud Graphs can intuitively reflect the magnitude of renewal and renovation potential and the degree of uncertainty in evaluation results. The research result provides useful references for the sustainable utilization of building resources in the era of building.

The samples from lunar farside have great significance for the study of the Moon, and even the solar system. Chang’e-6 landed successfully on the southern mare of the Apollo basin and returned ~2 kg samples from lunar farside. To provide a better understanding for the background of the returned samples, we conducted detailed crater size-frequency distribution (CSFD) measurements in the Chang’e-6 landing region, the southern mare of the Apollo basin. The southern mare is divided into western mare (W region) and eastern mare (E region), and then subdivided into five sub-units (W1, W2, W3, W4, W5) and three units (E1, E2, E3), respectively, according to the elevation, Ti and Fe abundances. Within the W2 and W5 region, more detailed sub-units were divided. The results show that the southern mare generally have two epochs, one of the Imbrian period and the other of the Eratothenian period. The basalt eruption lasted for ~1.7 Ga, from 3.28 Ga of the eastern mare to 1.54 Ga of the western mare. W region is younger than E region, while the three units of E region has an age of ~3.2 Ga. The ages of the western mare basalts range from 2.98 Ga to 1.54 Ga, lasting for 1.4 Ga. It’s worth noting that the age of the basalt at the Chang’e-6 sampling site is ~1.68 Ga, indicating the samples returned may include components with this very young age.

Ballastless track has high smoothness, but the laying requirements are strict. Therefore, the maximum span of cable-stayed bridges laying ballastless tracks is 392m. For laying ballastless track structures over larger spans, the deformation characteristics of long-span cable-stayed Bridges under complex loads are incomplete, and the interaction between long-span bridge-track structures is unclear. The influence of wavelength of cosine wave on the track- bridge mapping of different orbital structures was explored. The wavelength characteristics of vertical deformation under complex loads were investigated. The track-bridge integrated model for the cable-stayed bridge was establish to analyze the mapping relationship between rail and bridge and wavelength characteristics of deformation. Based on mapping relationships and wavelength characteristics of deformation, the Train-Track-Bridge dynamic simulation model was simplified. The results show that when the minimum wavelength of bridge deformation surpasses 6 m, 10 m, and 16 m, the rail deformation in the ballasted track, the longitudinal-connected track, and the unit slab-type ballastless track accurately mirrors the deformation of the bridge. For the span of bridge ranging from 200 m to 600 m, the wavelength of vertical deformation ranges from 21 to 1270 m under complex loads. During local loads, the vertical deformation below the 200 m wavelength constitutes a significant proportion near the pie. Considering the influence of the deformation on train vibration response, the Train-Bridge dynamic coupling model can be employed to treat the track structure as a load to reduce complexity of model and enhance the calculation efficiency.

Art's unique style and creativity are essential in defining a work's identity, conveying emotions, and shaping audience perception. Recent advancements in diffusion models have revolutionized art design, animation, and gaming, particularly in generating original artwork and visual identities. However, traditional creative processes face challenges such as slow innovation, high costs, and limited scalability. Consequently, deep learning has emerged as a promising solution for enhancing painting-style creative design. In this paper, we present the Painting-Style Design Assistant Network (PDANet), a groundbreaking network architecture designed for advanced style transformation. Our work is supported by the Painting-42 dataset, a meticulously curated collection of 4,055 artworks from 42 illustrious Chinese painters, capturing the aesthetic nuances of Chinese painting and offering invaluable design references. Additionally, we introduce a lightweight Identity-Net, designed to enhance large-scale text-to-image (T2I) models by aligning internal knowledge with external control signals. This innovative Identity-Net seamlessly integrates image prompts into the U-Net encoder, enabling the generation of diverse and consistent images. Through extensive quantitative and qualitative evaluations, our approach has demonstrated superior performance compared to existing methods, producing high-quality, versatile content with broad applicability across various creative domains. Our work not only advances the field of AI-driven art but also offers a new paradigm for the future of creative design.

In this study, we investigate different control scenarios through theoretical analysis and numerical simulations. To account for two important types of control for an early ascending stage of an outbreak, social distancing and treatment with antiviral medications, a compartmental model is considered with one control, $u_1(t)$, aimed to lower the disease transmission rate and the other control, $u_2(t)$, aimed to lower the period of infectiousness. In all experiments, the implementation of control strategies reduces the daily cumulative number of cases and successfully "flattens the curve". The reduction in the cumulative cases is achieved by eliminating or delaying new cases. This delay is incredibly valuable as it provides public health organizations with more time to advance antiviral treatments and devise alternative preventive measures. The main theoretical result of the paper, Theorem 3.1, concludes that the optimal control functions, $u_i(t)$, $i=1,2,$ may be increasing until some moment $\tau \in [0, T)$. However, for all $t \in [\tau, T]$, both controls, $u_i(t)$, decline as $t$ approaches $T$ (possibly causing the number of newly infected people to grow). Numerical simulations presented in Section 4 confirm theoretical findings, which indicates that, ideally, around the time $t=\tau$, the control strategy has to be upgraded and a vaccination campaign needs to start to ensure the epidemic wave does not rebound.

This study systematically addresses the challenge of accurately modeling memristors, focusing on four distinct types doped with tungsten, tin, chromium, and carbon, fabricated by Known Inc. A comprehensive characterization was performed by subjecting the devices to sinusoidal excitations with varying frequencies and amplitudes, followed by data averaging and high-frequency filtering. The resulting measurements were fitted using three prominent memristor models: VTEAM, MMS, and Yakopcic, with additional bespoke modifications assessed. These models, typically formulated as coupled algebraic-differential equations integrating electrical quantities (voltage and current) with internal state variables governing device dynamics, were optimized using two robust approaches: (1) interior-point optimization with gradient-based search, and (2) Nelder-Mead gradient-free optimization, both with box constraints applied. A thorough comparison and discussion of the optimized model parameters ensued, accompanied by an examination of sensitivity to diverse frequency and amplitude ranges. The findings inform conclusions and provide a foundation for future refinements, underscoring the importance of multi-model evaluation and advanced optimization strategies in precise memristor modeling. The presented methodology offers a valuable framework for elucidating optimal modeling paradigms tailored to specific memristor architectures and operating regimes, ultimately enhancing their integration in emerging neuromorphic and computational applications.

To achieve real-time identification of soybean seedlings and weeds in soybean seedling fields on agricultural intelligent terminal devices, this article constructs a real-time evolutionary identification system ESWIM driven by computational complexity under few-shot training. Firstly, based on the identification task, high-quality image samples are collected and annotated. By analyzing the terrain, the weather during the growth period, image acquisition methods, and the status of equipment fieldwork, we obtained a suitable enhancement algorithm. This algorithm not only effectively simulates the diversity of actual images, but also solves the problem of significant imbalance in the number of targets. Then, using the computational complexity driven approach and YOLOv5n as the foundation model, a soybean seedling and weed recognition model SWIM is constructed. The model's computational space and time complexity are reduced by 45.19% and 42.86%, respectively, while mAP (0.5) increased by 0.1%. Finally, by utilizing human-computer interaction to enable real-time and effective evolution of the dataset, an evolutionary strategy is designed to rapidly iterate and update model parameters, resulting in the development of the evolutionary system ESWIM. Laboratory experiments have shown that the ESWIM system can quickly adjust the recognition system during device operation to achieve target identification tasks in different environments.

Floods in various regions cause irreparable damage to infrastructure, agricultural lands, and industrial centers. In recent years, due to the effects of climate change and, in particular, the increased activity of the monsoon system in the Balochistan region located in southern Sistan and Baluchestan province, local rivers have overflowed, causing widespread flooding and significant damage. Accurate flood routing not only provides decision-makers with precise information about flood volume and flow behavior but also facilitates planning for effective preventive measures. In this study, the Muskingum-Cunge model was used to simulate floods in eight major rivers of Balochistan, including Kajou, Sarbaz, Nik Shahr, Kehir, Bahu, Sia Jangal, Rapch, and Kamb. The model parameters (K and X) were optimized using two machine learning algorithms: Particle Swarm Optimization (PSO) and Harmony Search (HS). The results showed that in most stations, the PSO algorithm outperformed HS. The algorithms were evaluated using the coefficient of residual mass (CRM), normalized root mean square error (NRMSE), efficiency (EF), and agreement index (d). For instance, at the Kajou station, PSO improved CRM by 0.017, Ef by 0.921, d by 0.982, and NRMSE by 0.099 compared to HS. At the Kamb station, both algorithms yielded comparable results. Considering the overall better performance of the PSO algorithm in most evaluation indices, it can be concluded that this algorithm is a more suitable option for optimizing the parameters of the Muskingum-Cunge model and simulating flood hydrographs with higher accuracy at most of the studied stations.

Groundwater flow and transport models are essential tools for assessing and quantifying the mi-gration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol’s High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analysed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity and hydraulic head calibration metrics. Input parameters in-clude aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductiv-ities, and boundary inflows and conductances. VARS, HDMR and graphical methods agree to identify the most influential parameters which for most of the outputs are the hydraulic conduc-tivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations.

Climate variability influences plant growth. With the variation in atmospheric CO2 concentration leading to global warming, it is urgent to monitor the performance of plant ecosystems for optimal carbon sequestration. Net primary productivity (NPP) is the perfect measurement tool as it measures the net carbon flux between the atmosphere and green plants and the factors that affect it. This study applied remote sensing techniques, specifically one of the radiation use efficiency models; the CASA model (Carnegie-Ames-Stanford approach) to assess the spatio-temporal dy-namics of NPP in Togo from 1987 to 2022 and the climatic parameters that influence it. The annual average NPP over the 36 years of the study is 4565.31 Kg C ha⁻¹. Variability is observed over the years with extremes in 2017 (6312.26 Kg C ha⁻¹) and 1996 (3394.29 Kg C ha⁻¹). Natural formations identified as high-production areas saw their productivity increase between 2000 and 2022. The interaction between climate change and land use changes negatively influences the variation of Total Production (PT) between 2000 and 2022, while individually, these parameters positively influence the variation of NPP (58.28% and 188.63%). The correlation result is positive and higher between NPP and light use efficiency (LUE) (r² = 0.75). Actual evapotranspiration also shows a positive correlation with NPP (r² = 0.43). A positive but weak correlation is observed between NPP and precipitation, potential evapotranspiration (r² = 0.20; 0.10 respectively). Temperatures have almost no correlation with NPP (r² = 0.5). Climatic parameters as a whole under the LUE banner influence NPP more. This study helps understand ecosystem performance in the context of To-go's commitments to reduce greenhouse gas emissions and combat climate change.

Pigeon paramyxovirus serotype 1 (PPMV-1), primarily originating from racing pigeons, has become a global panzootic, spreading to poultry and wild birds, thus posing a threat to the poultry industry. Egypt uses both inactivated pigeon paramyxovirus (PPMV-1) and conventional Newcastle disease virus (NDV) vaccines to protect pigeons. However, the impact of prevalent strains and the effectiveness of available vaccines in pigeons in Egypt are unclear. This study investigates the virulence of PPMV-1 isolated from vaccinated pigeons in Sharkia, Egypt (Pigeon/ Egypt/Sharkia-19/2015/KX580988). Ten-day-old specific-pathogen-free embryonated chicken eggs infected with this strain exhibited a mean death time (MDT) of 86.4±5.88 hours. The intracerebral pathogenicity index (ICPI) in day-old chicks was 0.8, while pigeons experienced an ICPI of 0.96 and an intravenous pathogenicity index (IVPI) of 2.11. These findings classify the strain as virulent and velogenic. Experimental infection with this PPMV-1 strain at 106 EID50/0.1 ml resulted in a 62.5% mortality rate in pigeons, displaying nervous and enteric distress. The virus caused extensive lesions in visceral organs, with strong immunohistochemistry signals in all examined organs, indicating the systemic spread of the virus concurrent to its neurotropic and viscerotropic tropism. Furthermore, vaccination using inactivated PPMV-1 and live NDV LaSota vaccine regimen protected 100% of pigeons against mortality, while a single NDV LaSota vaccine provided 62.5% protection. The PPMV alone or combined with NDV LaSota induced protective levels of hemagglutination inhibition antibody titers and reduced virus shedding from buccal and cloacal cavities. These findings suggest that using both inactivated PPMV-1 (G-VI) and live attenuated NDV (LaSota) vaccines is an effective prophylactic regimen for preventing and controlling PPMV-1 and NDV in pigeons, thereby reducing the risk of interspecies transmission.

The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of Particulate Matter with an aerodynamic diameter of 2.5μm (PM2.5) and 10μm (PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) needs constant monitoring, as they consist the main cause for many diseases. Based on the existence of statutory limits, by the World Health Organization (WHO), for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that will have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict the air pollutants concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural networks (ANNs) models. In the frame of the specific work, a significant number of ANNs was developed. For this purpose, an open-access meteorological and air pollution database was used. Finally, a statistical evaluation of the developed prognostic models was carried out. Results showed that ANNs present a re-markable prognostic ability in order to forecast the air pollution levels in an urban environment.

Segment Anything Model 2 (SAM 2) is a state-of-the-art development by Meta AI Research, designed to address the limitations of its predecessor, SAM, particularly in the realm of video segmentation. SAM 2 employs a transformer-based architecture enhanced with streaming memory, enabling real-time processing for both images and videos. This advancement is important given the exponential growth of multimedia content and the subsequent demand for efficient video analysis. Utilizing the SA-V dataset, SAM 2 excels in handling the intricate spatio-temporal dynamics inherent in video data, ensuring accurate and efficient segmentation. Key features of SAM 2 include its ability to provide real-time segmentation with minimal user interaction, maintaining robust performance even in dynamic and cluttered visual environments. This study provides a comprehensive overview of SAM 2, detailing its architecture, functionality, and diverse applications. It further explores the model's potential in improving practical implementations across various domains, emphasizing its significance in advancing real-time video analysis.

The author previously investigated the thermodynamic properties of the one-dimensional Potts-SOS model on the lattice of positive natural numbers N in [Akin H, Phys. Scr. 99 (2024), 055231]. In this present paper, we extend that research by determining the Gibbs measures for the same mixed-type model using partition functions constructed on a second-order semi-infinite Cayley tree. By leveraging the self-similarity of the Cayley tree, we analyze the behavior of these partition functions and identify the regions where phase transitions occur. We pinpoint these phase transition regions through a stability analysis of the dynamical system associated with the model at specific fixed points.

Education is generally considered to be a great equalizer as well as a tool the young generation can use in becoming the nation’s future leaders. It is imperative that today’s young people are well equipped with good quality education to better lead a country into the future. As a consequence, it is also important that we find out the factors that affect the learning success of the youth, since knowing these factors, either positive or negative will help the government in designing programs that can help propagate the positives and rectify the negatives. This study utilized data from the elementary and high school students of a private educational institution in the Philippines. Multiple linear regression using lin-lin model was used to analyze the data. Results indicated both the over-all significance of the model and the absence of violations regarding the basic assumptions of CLRM. Three variables English proficiency, Math proficiency, and time spent in playing video games turned out to be significant. This study adds to the existing body of knowledge by showing that time spent in commuting between house and school can also at times to be used to study resulting and impacting Cumulative Grade Point Average positively.

This paper deals with the formulation of a Cobot kinematic model by developing both the direct and inverse kinematics and choosing the noa transformation matrix for defining the end-effector position and orientation with respect to the base frame. These algorithms have been validated via software in Matlab environment and by means of suitable experimental tests, which have been carried out by using a UR5e Cobot. Moreover, a specific industrial application regarding the robotized assembling of a multi-cylinder IC engine, has been designed in terms of layout and corresponding automatic cycle that is intended as a sequence of the robot elementary actions. In particular, the proposed robotized cell is composed by the UR5e Cobot along with its controller and a suitable electropneumatic circuit that includes a PLC with its user interface, which are both governed by means of a specific electronic board and a switch control box. The IC engine in scale of 1:5 and specific parts of the end-effector, which takes the role of vacuum gripper and torque limiting screwdriver, have been designed and manufactured by using a 3D printer.

There are many features of homeostasis worth investigating such as how it works (information pathways, set point processing), where its command and control processors are located in the cell, the language used and how homeostasis interacts with all aspects of an organism's functional maintenance and development. All are necessary and valid inquiries, but this paper examines the implications of ignoring it as a factor of disease causation. Alternatively when properly understood the homeostasis profile becomes a rational explanation for any individual’s range of health; where some areas are very resistant to disease (resilient) and other areas of homeostasis are dysfunctional leading to minor, moderate or very susceptible tendencies to disease (lack of resilience in homeostasis). An understanding of this phenomenon, its interactions with gene expression (epigenetics) is necessary in order to evaluate the solutions for acute and chronic diseases. By this method diseases can be defined more precisely as specific susceptibilities in homeostasis, leading to a method to restore specific homeostasis functions.

This paper contains a comparative review of existing Zitterbewegung models. It attempts to synthesize them by offering a toy model containing an extra hypothesis of electromagnetic self-oscillation at the Fermi scale (10−15m). Inspired by Bell violation experiments, Zitterbewegung models and hydrodynamic walker studies, we revisit standing waves in quantum mechanics, echoing de Broglie-Bohm pilot wave theories. Starting with the electromagnetic self-oscillation hypothesis at the Fermi scale we propose an emergent and deductive hierarchical electron structure across multiple scales. The model spans Fermi, Compton (10−13m), Bohr (10−11m) and finally de Broglie (>10−10m). At the Fermi scale, the hypothesis is one of "source-free chiral light", known as the "zilch", emerging as a self-oscillating electromagnetic field at the Schwinger limit. This self-interaction of retarded potentials due to real relativistic delays is a generic self-oscillation of Liénard-Wiechert potentials. This primary Fermi oscillation accounts for charge and mass at the Fermi scale. It generates secondary stable "auto-orbits" dynamics at the Compton scale, manifesting as observable spin and other quantum properties of the usual Dirac zitterbewegung models. These zitter models account for known electron characteristics, including mass, charge, spin and g/2 anomalous factors up to 9 digits precision. We explain the de Broglie wavelength through Doppler-shifted Compton-scale standing waves, in the tradition of de Broglie. The framework extends to electron-positron pair production at the Schwinger limit, described as a result of natural light focusing in non-homogeneous media, utilizing René Thom’s catastrophe theory. This offers a new perspective on baryogenesis and matter emergence from electromagnetic fields. We dub this toy model the "sub-standard" model of the electron.

The astro-structures with the shape of comet and semi-circle, are found in the electronegative inductively coupled plasmas (ICPs), e.g., Ar/O2 and Ar/SF6. They are formed by means of the self-coagulation theory. This theory is built and based on two-dimensional fluid model simulation dynamic data of the above ICP sources. Concretely, quasi-Helmholtz equation is constructed through the free diffusion transport component and a novel negative chemical source term. The absolute value of recombination rate is higher than the attachment rate and so the net source of simulated anion is negative. This is quite a new chemical phenomenon in the electronegative and gaseous discharging plasmas when compared to the previously reported normal positive chemical source that will generate ambi-polar diffusion potential. In electronegative plasma, the self-coagulation is ambi-polar to sustain the electrical neutrality. Besides, when the anions are accelerated by ambi-polar potential before self-coagulated, the self-coagulation is again advective which leads to the formation of blue sheath. At the ambi-polar self-coagulation of high electronegativity, the electrons dynamics are relatively decoupled from ions and meanwhile they have their own relatively weak, spontaneous, and monopolar self-coagulation in the periphery of astro-structure. At the ambi-polar self-coagulation, both the anion and cation are individually treated as mass point models that carry charge (point charge models, more precisely). When the charge amounts of two individual astr-structures, e.g., semi-circle SF3+ and Ar+, are equal, the coagulated profile of heavy species SF3+ collapses by the expelling effect of Coulomb force that exists between the two point-charges. This is an anti-collective behavior of astro-structure, instead of the collective interaction of plasma, since the collapse of coagulated heavy species creates spatially dispersed charge density that cannot be shielded. The simulation shows that the lighter the species, the easier it self-coagulates, determined by the inertial effect of density quantity. The discharge picture of electronegative plasma is combined by a dynamic continuum transport (e.g., forming the ellipse background) and the mechanical balance contained in the static self-coagulation process (proven by dimensional analysis of continuity equation). The popular astro-structure of electronegative ICP source awaits urgently for the validation of experiment.

Identification the parameters of triple-diode electrical circuit structure of PV-module is a challenging issue and has been emphasized as an important research area. Accordingly, a hybrid evolutionary optimization algorithm is presented in this paper. Differential evolution algorithm (DEA) is hybridized with electromagnetism-like algorithm (EMA) in the mutation stage to enhance the reliability and efficiency of DEA. The presented algorithm is called differential evolution with integrated mutation per iteration (DEIMA). A new formula is presented to adapt the control parameters (mutation factor and crossover rate) of DEA and is based on a sigmoid function in terms of the current and previous fitness function values. Seven different experimental data sets are used to assess the performance of the proposed DEIMA. The results of the proposed PV modeling method are evaluated with other approaches in literature. According to different statistical criteria, DEIMA offered superiority in terms of root mean square error and main bias error by at least 5.4% and 10%, respectively, as compared to other methods. Furthermore, DEIMA needs 27.69 sec. as an average execution time less than other compared methods.

Large-scale deep learning models have produced significant results in underwater visual image target detection. However, deploying these models on underwater embedded devices poses a series of issues and challenges. Firstly, underwater devices have limited communication capabilities and carry relatively short supply of energy, resulting in constrained computing power for edge devices. Secondly, the large scale of deep neural networks creates a conflict in power demands with unmanned underwater equipment. Thirdly, insufficient underwater light and turbid water quality will lead to degradation problems in the images acquired by the device. In this paper, we build an offline system to complete the task of underwater scene target recognition. This paper introduces several innovations: 1) The new YOLO-TN model is developed by compressing the target recognition model based on YOLO-V5. The model use a model compression algorithm replace the YOLO-V5’s backbone network with a lightweight network, then perform parameter pruning on the model detection head, while reduce the size of the input image. The model uses a Teacher-guided Neural Architecture Search method based on YOLO-V5, named as YOLO-TN. 2)A method for constructing and processing real underwater environment data sets is designed. The datasets are all captured using underwater unmanned vehicles in real underwater environments, addressing issues present in existing underwater datasets, such as unbalanced target volume and single-image environment. Experimental results demonstrate that the YOLO-TN model significantly reduces the model size while maintaining the original recognition accuracy. Furthermore, it achieves 28.6 FPS on embedded devices, which is 12 times that of the original model.

Billions of euros are invested every year by professional football clubs for the recruitment of players. This article presents the latest advances in the statistical modelling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain almost 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices seems highly relevant.