This work explores state-of-the-art approaches for stabilizing an inverted pendulum on a cart system, which has been used for over a century to demonstrate ideas in nonlinear and linear control theory, owing to its simple practical implementation yet complex system dynamics. We introduce energy-based control methods and propose a simple linear controller to quickly force the system to the downward equilibrium before swing-up. By constraining the system to start from a stable equilibrium configuration, the problem of global stability is reduced to ensuring that the system can follow all heteroclinic orbits that start very close to the downward equilibrium. Multiple Lyapunov functions are employed to ensure non-zero tracking, increasing the system's domain of attraction than is achieved by traditional controllers based on a single Lyapunov function. We stabilize our inverted pendulum system in the upright configuration through pole placement using a linear quadratic regulator known for fast settling times and smooth system response and through sliding mode controllers known for their robustness to disturbances and model uncertainties. While sliding mode controllers exhibit faster stabilization of the pendulum angle, they fail to stabilize the cart position in a reasonable amount of time. The paper concludes by suggesting various controller techniques that can enhance the swing-up and stabilization controller architectures, leaving room for future exploration. To facilitate further research and education, an open-source MATLAB simulation code is provided, encapsulating the system dynamics and controller designs. This resource offers flexibility for customization, making it a valuable tool for both researchers and educators working with inverted pendulum systems.

Users of escalators and moving walkways with sufficient space to accommodate two lanes of users often follow an implied etiquette for the two lanes: one for walking and one for standing (in the US, China, and many countries, the convention is ``walk left, stand right"). When there is high volume, e.g., when exiting a subway or train station or at the conclusion of an athletic event, the escalators often experience bottleneck congestion that constitutes the primary source of delay. It has been suggested that during such high-congestion periods it would be more efficient if everyone just stood in both lanes, with empirical evidence used to support this counterintuitive finding. Simple deterministic queueing models are used to show under what conditions such results hold and also to provide further insights regarding tradeoffs between performance metrics as a function of the distribution of walkers and standers, which could inform practical implementation policies to increase efficiency.

In this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.

Steel damper columns (SDCs) are energy-dissipating members that are suitable for reinforced concrete (RC) moment-resisting frames (MRFs) and those often used for multistory housing. In a previous study, an energy-based prediction procedure for the peak and cumulative response of an RC frame building with SDCs was proposed. In this procedure, the accuracy of the equivalent velocity of the maximum momentary input energy (V_ΔE1^*)–peak equivalent displacement (D₁^*_max) relationship is essential for improved prediction. In this article, the seismic capacity curve (V_ΔE1^*–D₁^*_max relationship) of RC MRFs with and without SDCs is evaluated using incremental critical pseudo-multi impulse analysis (ICPMIA). In the ICPMIA, which is based on a study by Takewaki and coauthors, the structure is subjected to various intensities of critical pseudo-multi impulsive lateral force. An ICPMIA of four 8- and 16-story RC MRFs with and without SDCs is performed to obtain their structural behaviors under various intensities of pulsive input. Then, the seismic capacity curve obtained from the ICMPIA results are compared with the predicted results based on the simplified equations. The main findings of this article are as follows. (i) The seismic capacity curve of RC MRFs without SDCs strongly depends on the number of impulsive lateral forces (N_p). As N_p increases, the seismic capacity decreases. The predicted seismic capacity curve severely underestimates that obtained from the ICMPIA in the case of large N_p. This trend is notable when the level of pinching behavior in the RC members is severe. (ii) In the case of an RC MRF with SDCs, however, the influence of N_p on its seismic capacity curve is small. The predicted seismic capacity curve agrees very well with that obtained from the ICMPIA. The influence of the level of pinching behavior in RC members on the seismic capacity curve is small.

This paper explores the development of life support systems (LSS) for human space exploration, emphasizing the need for sustainable solutions to support long-term missions beyond Low Earth Orbit. As humanity seeks to explore and potentially colonize distant planets, maintaining a habitable environment in space poses significant challenges. Life support systems must manage air quality, water supply, temperature, humidity, and waste while ensuring crew safety in environments devoid of breathable air and exposed to harmful cosmic radiation. The International Space Station's (ISS) Environmental Control and Life Support System (ECLSS) represents a significant advancement, demonstrating that humans can live in space for extended periods with a combination of recycling and Earth-based resupply. However, future missions to the Moon, Mars, and beyond require more advanced, self-sustaining systems. This paper examines technological advancements such as closed-loop systems, bio-regenerative life support systems (BLSS), and In-Situ Resource Utilization (ISRU), focusing on their potential to reduce reliance on Earth-based resupply. Projects like NASA's Mars Oxygen In-Situ Resource Utilization Experiment (MOXIE) and ESA's MELiSSA initiative offer promising solutions for future deep space exploration. By enhancing recycling, integrating ISRU, and improving energy efficiency, future life support systems will support humanity's journey into the cosmos, paving the way for sustainable space exploration and eventual colonization.

The space exploration landscape has transformed dramatically over the past several decades, with a significant rise in the number of annual launches. Key factors driving this increase include technological advancements, the miniaturization of satellites, and the surge in commercial satellite deployments for communication, Earth observation, and remote sensing. This paper examines the evolution of space activities from the launch of Sputnik 1 in 1957 to recent developments in commercial spaceflight and international collaborations. The paper discusses notable milestones in space exploration history, including early space missions, Apollo Moon landings, and the development of space stations like the International Space Station. It highlights the shift from state-led space programs to a more diverse landscape, characterized by private sector involvement and international partnerships. The "Space 4.0" phase, beginning around 2006, has seen the entry of commercial players like SpaceX, leading to a significant upsurge in orbital launches. Statistical data reveals an upward trend in launch activity, with over 200 launches in 2023 alone, driven by large satellite constellations like SpaceX's Starlink and the steady growth of China's space program. The increasing launch rate poses challenges such as space debris, emphasizing the need for mitigation strategies. Looking ahead, the paper projects a continued increase in launch activity, with over 150 launches expected annually by 2026 and 200-300 launches by 2040. Commercial space ventures, deep space exploration, lunar activities, and space tourism drive this growth. The paper concludes by emphasizing the importance of addressing space debris and promoting the long-term sustainability of space activities, which is crucial as humanity's presence in space continues to expand.

The physics of Hubble's law, motivated by the anomalous acceleration of Pioneer 10/11, suggests that the Hubble constant corresponds to a gravitational field of the Universe. The Hubble constant inferred from Pioneer 10/11 data is $\sim 69$ km/s/Mpc. The anomalous acceleration of Pioneer 10/11 can be interpreted as an observational evidence for Mach's principle. The redshift is an apparent value. Its intrinsic value has a limit resulting to $v

Trade liberalization is commonly pursued to improve welfare of households in a country. However, the effects of liberalization are not evenly distributed. Certain households and sectors may benefit more than others. This study focuses on the East African Community Common External Tariff (EAC-CET), to analyze the distributional effects of trade liberalization in Kenya. The study utilizes two household budget surveys capturing pre- and post-trade liberalization scenarios. The paper examines changes in consumer prices, labor incomes, and consumption patterns to assess the effects of liberalization. A pass-through equation for prices and a negative compensation variation equation are estimated. The findings indicate that the EAC-CET had a significant pass-through effect on prices of manufactured goods, but incomplete pass-through for agricultural goods. This suggests a lack of competitiveness in domestic value chains. The distributional effects differ across worker categories, favoring skilled and formal workers over unskilled and informal workers. The income effect outweighs the negative expenditure effect for households in the middle- and upper-income deciles. However, the study reveals negative welfare effects for the poorest households in the lowest income deciles. Furthermore, the study identifies the EAC-CET to be pro-poor, with households in rural areas benefiting more than those in urban areas.

The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.

Traditional methodologies for measuring combat effectiveness rely on various contribution factors, where the main challenge lies in the complexity and subjectivity of these factors. Using frontline variation to measure combat effectiveness circumvents the limitations of many previous methods regarding complexity and subjectivity. Employing this methodology, we have quantitatively measured the combat effectiveness of both parties in the Russo-Ukrainian War across various temporal scales. Utilizing the time series data derived from our measurements, we constructed Daily, Weekly, Monthly, and Annual Candlestick Charts to illustrate the variation trends in combat effectiveness between the two sides across different temporal scales. This comprehensive approach facilitated a nuanced and all-encompassing analysis of the conflict, capturing the intricate dynamics of individual military engagements and the broader strategic operations' context. This case underscores the distinctive utility of our method in quantifying combat effectiveness, offering detailed and strategically significant insights.

The current study compared the goodness of fit and estimation precision of Bayes and maximum likelihood (ML) structural equation modeling across sample sizes. Data consisted of Markov Chain Monte Carlo generated samples of 50, 75, 100, 125, 150, 200, 250, 500, 750, 1000, and 1500 observations. The Bayes and ML methods were used to estimate a structural equation model with 40 parameters, including 12 continuous observed indicators, four latent variables, and three structural paths. Analyses were conducted with the Mplus 8.0 statistical software and were replicated 500 times with each of the 22 conditions (11 sample sizes x 2 estimation methods). Indices of model fit were the Bayes information criterion, the Tucker-Lewis index, the comparative fit index, the root mean square error of approximation, the posterior predictive p-value (for Bayes models), and the ꭓ2 test (for ML models). Results showed that as the sample size increased, both estimation methods exhibited improved model fit and enhanced estimation precision, and both methods performed well with samples of 200 or larger; however, ML demonstrated a marginally superior fit with smaller samples. These findings were not consistent with previous research and suggest that ML remains a better choice with continuous data and small samples.

A decade or so of research led to the conclusion, in 2022, that the Soil Realm is home to ~2.1 x 10²⁴ taxa and supports >99.9% of global species biodiversity, mostly Bacteria or other microbes based upon topographic field data. A subsequent 2023 soil biodiversity report tabulated a central value of just 1.04 × 10¹⁰ taxa and claimed soils had 59±15% (i.e., 44–74%) of total species on Earth, incidentally confirming upper values of ~90% for Bacteria. Incompatibility of these two studies is reviewed, supporting the prior global biodiversity data with a vast majority of species inhabiting soil, this despite excluding virus entities (now with >5 x 10³¹ virions and up to 10²⁶ species, 80–90% being in soils). The true status of Oligochaeta (earthworms) and other taxa marked “?” in the 2023 paper are clarified. Although soil biota totals are raised enormously, inordinate threats and the reality of species extinctions yet pertain, specifically to those biota – such as microbes and earthworms – essential for rich, healthy soils that house & nourish almost all the Tree of Life presently on Earth.

This paper proposes a novel concept within Quantum Natural Language Processing (QNLP) to encode the interpersonal metafunction of Systemic Functional Linguistics (SFL), particularly tenor, into quantum circuits by treating Japanese honorifics seriously. Because of English language bias in the literature, the incorporation of nuanced aspects of interpersonal communication evident in languages, such as the aforesaid Japanese, remains under-developed and overlooked. Utilizing lambeq, monoidal categories, and string diagrams, this study extends lambeq’s quantum computing framework to capture not only grammar but also social context—specifically the roles and relationships that define interpersonal interactions in quantum circuits. This approach represents a significant step toward that end simply by defining the honorific type h in lambeq. This innovative strategy exposes the capability of quantum circuits to model the complex structures of language, moving beyond grammar and even semantics—the horizontal, textual dimension of language—to embrace the vertical, hierarchical dimension of human communication and social interaction. Through this lens, the paper underscores the potential of QNLP to transcend traditional linguistic analysis, advocating for a broader and more nuanced understanding of language that includes not only what is said but also the social persona of the interlocutors. An algorithm for parsing Japanese grammar into pregroup diagrams containing the h type is introduced along with a codebase for other researchers to use and to which to contribute. Lastly, a toy experiment is performed to demonstrate that the circuits generated from these pregroup diagrams are suitable for Quantum Machine Learning (QML) applications.

In the dynamic landscape of Sabah's socio-economic development, the integration of AI technologies holds both promise and challenge. While AI has the potential to drive economic growth, enhance productivity, and elevate quality of life, it also brings forth concerns regarding job displacement, skill gaps, and unequal distribution of benefits. The latest research problem in the realm of AI and employment in Sabah revolves around ensuring inclusive and sustainable development amidst this technological disruption. This encompasses two key dimensions: skill development and education, and labor market dynamics. Addressing the research questions necessitates a multifaceted approach. Firstly, in the realm of skill development and education, there is a critical need to assess the adequacy of existing programs in equipping the workforce with AI-driven skills. Strategies such as promoting STEM education, fostering digital literacy, and facilitating lifelong learning initiatives, particularly among marginalized communities, are essential. Additionally, fostering collaboration between stakeholders and implementing curriculum reforms are pivotal steps in enhancing the alignment between educational curricula and the demands of an AI-driven economy.

The Schwarzschild metric emerges independent of Einstein's field equations, offering a straightforward derivation solely reliant on Newtonian mechanics and Minkowskian proper acceleration. This approach provides a clear path to understanding the gravitational field around a spherically symmetric mass without the need for the complexities of Einstein's full theory of General Relativity. Transitioning to our exploration of the cosmic Friedmann equations, we adopt a novel perspective rooted in a Lagrangian formulation grounded in Newtonian mechanics and the first law of thermodynamics. Our investigation operates under the assumption that the universe is populated by either a perfect fluid or a scalar field. By elucidating the intricate interplay between the Lagrangian formulation and the cosmic Friedmann equations, we uncover the fundamental principles governing the universe's dynamics within the framework of these elemental constituents. In our concluding endeavor, we embark on the task of harmonizing the classical equations—namely, the conservation, Euler, and Poisson equations—with the principles of General Relativity. This undertaking seeks to extend these foundational equations to encompass the gravitational effects delineated by General Relativity, thus providing a comprehensive framework for understanding the behavior of matter and spacetime in the cosmic context.

Contrary to previous belief, mutation frequency also depends on how essential a gene is. Mutation frequency is substantially lower in the coding regions of essential genes and higher in less essential genes. Even though the mutation bias based on gene essentiality is incapable of invalidating Darwinism, this bias certainly added a rather unsubtle obstacle to it by contradicting a paradigm that was widely held: that mutations are random in the sense of being independent of their function for the organism. The reason this bias occurs is currently unknown. Here, I present the theoretical solution of why genetic mutations also depend on whether the gene is more or less essential. My theory is that each gene or epigene has its own level of resistance to entropic elevation, something directly proportional to the frequency of its expression. The more a gene is expressed, the higher is its resistance to entropic elevation and vice versa. For genetic information to be conserved instead of suffering mutation, a relevant factor is also the frequency of its expression. The energy expenditure directed to a gene of current utility engenders resistance to entropic elevation of the information encoded in that gene, as energy enables not only the existence of information but also its conservation. My theory—which is based on fundamental physics and contains predictions that can be empirically tested—solves a crucial problem in evolutionary biology and genetics; if I am right, I will have established a biological law. Finally, my aim here is also to provide a substantial advance in knowledge about the relationship between information, genetics, and thermodynamics.

The abstract summarizes the transformative impact of artificial intelligence (AI) on employment in Malaysia, highlighting the need for comprehensive strategies to ensure inclusivity and equitable access to AI-driven opportunities and digital skills training. It addresses the research problem of potential inequalities and marginalization among vulnerable populations due to uneven distribution of AI-related employment opportunities and training. The research objectives focus on developing decentralized strategies tailored to rural communities and marginalized groups, alongside evaluating policy interventions for equitable access. The literature review provides theoretical frameworks emphasizing skill development, understanding structural shifts, fostering inclusive innovation, and promoting collaborative governance. Future research directions include implementing and evaluating strategies in real-world contexts, exploring technological solutions, conducting qualitative research on individuals' experiences, and adopting interdisciplinary approaches. Overall, a multidimensional approach combining theoretical insights, empirical evidence, and stakeholder engagement is essential for developing inclusive and sustainable solutions to harness AI's potential for the benefit of all Malaysians.

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

This article encapsulates an exploration of seizure dynamics through the lens of computational modeling in epilepsy. Starting with a foundational understanding of the FitzHugh-Nagumo model, asimplified representation of neuronal activity, we try to understand the intricacies of epileptic seizures. We methodically demonstrated the transition from normal neuronal activity to a seizure-like state by altering key parameters in the model, such as external current and coupling strength. This was followed by an extension of the model to a network of neurons, simulating the complex interactions and synchronization patterns indicative of seizure propagation. Numerical simulations were conducted to visualize the impact of varying coupling strengths on network dynamics, offering insights into the mechanisms of seizure initiation and spread. The study was complemented by a discussion on the implications of these findings for understanding epilepsy, highlighting the bridging of theoretical models with clinical understanding. Our approach not only illuminates the potential of computational models in epilepsy research but also underscores the significance of interdisciplinary collaboration in advancing our comprehension of neurological disorders. Through this article, we aim to provide a nuanced perspective on the modeling of epileptic seizures, offering a valuable resource for researchers and clinicians in the field.

Myasthenia gravis (MG) is a disease characterized by typical clinical and immunopathogenic pe-culiarities, which correlate with age and sex/gender. Accordingly, MG patients show thymic al-terations and auto-antibodies productions, that can be the expression of a different pathogenetic mechanism related to age and gender. This determines, in relation to age at and sex/gender, a dif-ferent MG onset, prognosis, and outcome. Therefore, diverse MG forms can be identified, classi-fied, and characterized in relation to their different features, and precisely there are early onset and late-onset forms of MG. It has been hypothesized that the cause of appearance the two diverse forms of MG can be ascribed mainly to the diverse genetic and epigenetic factors. In this narrative review, we will analyze and discuss about the interplay of age- and sex-related factors (i.e. genet-ic and epigenetic factors) related to two different MG forms and how they contribute to their di-verse pathogenesis.

The complex dynamics of the neocortex, the outermost layer of the brain, have been a subject of intense research in neuroscience. Recent studies have suggested that magnetic fields generated by neuronal activity may play a significant role in neocortical dynamics. This paper presents a simplified mathematical model that explores the hypothesized interactions between magnetic dynamos, nondissipative systems, Hamiltonian equations, and the Lorenz force in the context of neocortical dynamics. The proposed model is based on a set of coupled Hamiltonian equations inspired by the Lorenz system, a well-known chaotic dynamical system. The generalized coordinates in the model represent the magnetic field components, while the generalized momenta are related to the electric field components or other relevant quantities. The Hamiltonian equations describe the conservative dynamics of the system, and the coupling terms in the equations are interpreted as representing the interactions between the magnetic and electric field components, analogous to the Lorenz force in electromagnetism. The model is implemented using Python, and the Hamiltonian equations are numerically solved using the odeint function from the scipy.integrate module. The resulting dynamics of the magnetic field components are visualized using matplotlib. While the model is highly simplified and does not capture the full complexity of neocortical dynamics, it serves as a starting point for investigating the potential role of magnetic dynamos and Lorenz force interactions in the brain. The paper discusses the limitations of the current model and highlights the need for further refinements, such as incorporating more realistic biological constraints, considering energy dissipation and compensation mechanisms, and validating the model against experimental data. Future work should focus on developing a more comprehensive and biologically plausible model that integrates the latest findings from neuroscience, physics, and computational modeling. By presenting this simplified Hamiltonian model, the paper aims to stimulate further research and discussion on the potential significance of magnetic dynamos and Lorenz force interactions in neocortical dynamics. The model provides a framework for exploring these hypothesized mechanisms and may guide future experimental and theoretical investigations in this emerging field of neuroscience

The endorsement from Christian circles in facilitating the rise of right-wing populism in Brazil, the United States, as well as the support and alliance of the Polish Catholic Church with the Law and Justice (PiS) government, is widely acknowledged. Embedded within the intricate fabric of multifaceted factors contributing to the surge of right-wing populism, the Culture War assumes a pivotal yet often underestimated role. Manifesting as a profound confrontation, the Culture War signifies an existential clash between contrasting perspectives concerning socio-cultural values grounded in religious scripture. Progressive endeavors strive to deconstruct longstanding norms, advocating for the ascendancy of post-materialistic ideals, while conservative domains portray an unwavering commitment to safeguarding national and religious socio-cultural values as sacrosanct. Both factions employ moral-ethical justifications in their respective pursuits, rendering the landscape of cultural discourse a nuanced tapestry of ideological contention. The influence of Christian doctrine on the conflict surrounding social justice, and the potential convergence between Christian doctrine and the positions of Populist right-wing movements on social justice issues, is underscored. By employing a singular case study focused on Portugal, owing to the nation's enduring relationship with Catholicism spanning from Monarchical times to the Republic and the Fascist Catholic regime, Portugal emerges as indisputably one of the most religious countries in the Western world, akin to Poland. Consequently, in theory, the support of the Portuguese Catholic Church for the nascent Populist right-wing and Christian-oriented party should be evident amidst the Culture War conflict, wherein conservative religious Christian values face existential challenge. However, the backing extended to CHEGA by both the Catholic Church and the Portuguese Episcopal Conference assumes an ambivalent and markedly diminished stance, diverging from the previously robust support observed in established center-right mainstream parties such as the PSD and CDS. Despite this reality and the absence of official support for CHEGA, numerous Christian politicians or activists have expressed endorsement or alignment with the right-wing party, departing from their former affiliations, primarily due to social justice concerns.

We have now identified two thermodynamically distinct types (A and B) of energetic processes naturally occurring on Earth. Type A energy processes such as the classical heat engines, apparently well follow the second law of thermodynamics; Type B energy processes, such as the newly discovered thermotrophic function that isothermally utilizes environmental heat energy to do useful work in driving ATP synthesis, which follows the first law of thermodynamics (conservation of mass and energy) but does not have to be constrained by the second law, owing to their special asymmetric functions. Several Type-B energy processes such as asymmetric-function-gated isothermal electricity and epicatalysis have been created through human efforts. The innovative efforts on Type-B processes to enable isothermally utilizing endless environmental heat energy could help to liberate all peoples from their dependence on fossil fuel energy, thus helping to reduce greenhouse gas CO2 emissions and control climate change towards a sustainable future for the humanity on Earth.

The nucleation of voids in alpha iron and the influence of carbon were investigated through a combination of rate theory and nucleation theory calculations. The steady rates of void nucleation exhibit high sensitivity to carbon. Even a seemingly negligible carbon concentration, as low as a few atomic parts per million (appm), can dramatically reduce the nucleation rates and narrow the temperature window for nucleation. This study highlights the challenges in certifying and qualifying nuclear materials for use in reactor environments. It emphasizes the necessity for precise control of impurity levels during materials processing and stringent requirements for irradiation testing, which are susceptible to contamination. Furthermore, the study provides an explanation for the significant data scattering observed in void swelling behaviors as reported in the literature.

The problem of normal impact of a rigid sphere onto a Maxwell viscoelastic solid half-space is considered. The first-order asymptotic solution is constructed in the framework of Hunter’s model of viscoelastic impact [Hunter, S. C., 1957. Energy absorbed by elastic waves during impact. Journal of the Mechanics and Physics of Solids, 5(3), 162–171.]. In particular, simple analytical approximations have been derived for the maximum contact force and the time to achieve it. A linear regression method is suggested for evaluating the instantaneous elastic modulus and the mean relaxation time from a set of experimental data collected for different impactors and impact velocities.

Scattering of a probe laser pulse by an intense light spring in QED vacuum is considered. This new scattering configuration can be seen as the vacuum equivalent to the process originally associated with scattering of light by a rotating black hole, usually called Penrose superradiance. Here the rotating object is an intense laser beam containing two different components of orbital angular momentum. Due to these two components with slightly different frequencies, the energy profile of the intense laser beam rotates with a frequency that depends on the frequency difference. The nonlinear properties of quantum vacuum are described by first order Euler-Heisenberg Lagrangian. It is shown that, in such a configuration, nonlinear photon-photon coupling leads to scattered radiation, with frequency shift and angular dispersion. These two distinct properties, of frequency and propagation direction, could eventually be favourable for possible experimental observations. In principle, this new scattering configuration can also be reproduced in a nonlinear optical medium.

AFM nanoindentation showed variation in stiffness (Elastic modulus) due to heterogenous nature of the collagen fibres. The S values varied from 5 N/m to 11.11 n/m corresponding to an E change of 2.25 to 1.38 GPa. The variation in effective modulus (Eff) with respect to displacement indicated crossing the parabolic limit. A softening index n was proposed due to the heterogenous behaviour of the biomaterials

Electrochemical Impedance Spectroscopy (EIS) is a powerful non-invasive technique used to characterize Lithium-ion cells. Its application allows to identify and track the evolutions of cell degradation processes within a short testing time. This review collects all the available works which have used EIS spectra either to characterize Li-ion cell degradation or to develop Electric Circuit Models (ECMs). The objectives of this work are: (i) to highlight the influence of different aging test conditions on the EIS spectra; (ii) to find the correlations between EIS spectra variations and the underlying degradation mechanisms and (iii) to list the available options to formulate ECMs from EIS spectra of aged cells. After an exhaustive analysis of the state-of-the-art, a critical review is presented to discuss the existing links between degradation mechanisms and the most reliable solutions to model them.

The paper analyses the unused urban spaces in concert with the creative city idea. The research issues are developed as follows. 1. The unused urban places express the city's unsustainability since every urban space has the potential to become such a dead zone. 2. The concept of the creative city covers different creative activities in concert with sustainable development. 3. The unusable urban places result from a modernistic approach that neglects the cultural context, of the city's division into functional zones ignoring integral living intentions and industrial exploitation in the previous city's rims. 4. The Vilnius Sport Palace is an unused space not so much due to the lack of interest, but due to a knot of interests, which politicians cannot cut. The research raises the question of whether the problem of the VSP can be solved by using the idea of the creative city. In terms of the theoretical approach, a comparative analysis is used. Besides literature analyses, the case study is applied. Additionally, the semiotic approach is used. For this purpose, two of Greimas' traditional semiotic squares in combination with Ricoeur's idea of the interpretation's conflict are applied.

Slimy sculpin (Uranidea cognata) inhabitat coldwater streams in southeastern Minnesota, USA, many of which were subjected to probable 2000-year flood events in August 2007. Floods scoured streambeds, created new stream channels, and greatly reduced benthic invertebrate communities that serve as the primary food resource for sculpin. Diets and Fulton condition of sculpin in Gilmore Creek (with moderate flooding) and Garvin Brook (with very severe flooding) had been examined just prior to flooding, and were re-examined two weeks after flooding to assess possible diet and condition changes. Diets, condition, and reproductive fitness of sculpin were examined seven months post-flood in these same two streams, plus nearby Trout run (also experienced very severe flooding). Sculpin condition declined significantly post-flood in Garvin Brook, but improved in Gilmore Creek. Prior to spring spawning, condition of Garvin Brook sculpin had improved, but Gilmore Creek fish condition worsened. Sculpin diets were more diverse before and after flooding in Gilmore Creek than in Garvin Brook, although diets of fish from both streams were dominated (>55%) by midge (Diptera: Chironomidae) larvae. Diets differed significantly before versus after flooding in the more severely flooded Garvin Brook, but not in Gilmore Creek. Prey number per sculpin stomach declined post-flood in Gilmore Creek, but not in Garvin Brook, although dry mass of prey/fish wet mass declined post-flood in both streams. Pre-spawn sculpin displayed no patterns in reproductive fitness (gonadosomatic index, hepatosomatic index, oocyte number) among the three streams that may have been related to flooding severity the previous summer. Although sculpin diets and condition were altered significantly immediately post-flood in the more severely flooded stream, food resource recovery apparently was rapid enough to prevent longer-term impacts on sculpin condition and reproductive fitness.

This study investigates the impact of climate change on the efficiency of organic honey production in Türkiye using a machine learning approach. The eXtreme Gradient Boosting (XGBoost) algorithm is employed for the Machine Learning (ML) modelling. The analysis includes data on organic honey production performance from 2004 to 2023, spanning a period of 20 years, and 8 different climate variables (16 variables depending on daytime and nighttime values). The dataset created to investigate the impact of climate change on organic honey yield comprises 28 columns and 120,960 rows. The study aimed to determine the significance of different climatic variables on the efficiency of organic honey production. The study found that, in addition to daytime solar radiation and temperature, wind speed and direction were also important factors. The sensitivity analysis has shown that minor alterations in the variables have a significant impact on the model's predictions. Additionally, non-linear relationships between the variables and honey production were identified. These findings suggest that it may be beneficial to consider implementing climate change adaptation and mitigation strategies in agricultural policies and beekeeping practices. The study suggests that machine learning has the potential to provide valuable insights into the complex relationship between climate change and agricultural production. This could aid in the sustainability and economic optimization of the sector.

Introduction: It has long been accepted that trauma is one of the most important and frequent predisposing factors for onychomycoses . However, the role of direct trauma in the pathogenesis of fungal nail infections was only recently been elucidated in a series of 32 cases of post-traumatic single-digit onychomycosis. The importance of repeated trauma due to foot and toe abnormalities was rarely investigated.Aim of the study: This is a multicenter single-author observational study over a period of 6 years performed at a specialized nail clinic in three countries. All patient photographs taken by the author during this period were screened for toenail alterations and all toe onychomycosis cases were checked whether they contained enough information to evaluate potential foot and toe abnormalities. Particular attention was paid to the presence of hallux valgus, hallux valgus interphalangeus, hallux erectus, inward rotation of the big toe and outward rotation of the little toe, as well as splay foot. Only cases with unequivocal proof of fungal nail infection by either histopathology, mycologic culture or polymerase chain reaction (PCR) were accepted.Results: Of 1653 cases, 185 were onychomycoses proven by mycologic culture, PCR or histopathology. Of these, 179 involved at least one big toenail, 6 affected one or more lesser toenails. Three patients consulted us for another toenail disease and the onychomycosis was diagnosed as a second disease. Eight patients had a pronounced tinea pedum. Relatively few patients had a normal big toe position (n = 9). Most of the cases had a mild to marked hallux valgus (HV) (105) and a hallux valgus interphalangeus (HVI) (143), hallux erectus was observed in 43 patients, the combination of HV and HVI was observed 83 times. Discussion: The very high percentage of foot and toe deformations was surprising. It may be hypothesized that this is not only a pathogenetically important factor but may also play an important role for the localization of the fungal infection as no marked hallux deviation was noted in onychomycoses that affected the lesser toes only. As the management of onychomycoses is a complex procedure involving the exact diagnosis with determination of the pathogenic fungus, the nail growth rate, the type of onychomycosis, its duration and predisposing factors, anomalies of the toe position may be important. Among the most commonly mentioned predisposing factors are peripheral circulatory insufficiency, venous stasis, peripheral neuropathy, immune deficiency and iatrogenic immunosuppression whereas foot problems are not given enough attention. Unfortunately, many of these predisposing and aggravating factors are difficult to treat or to correct. Generally, when explaining the treatment of onychomycoses to the patients, the importance of these orthopedic alterations is not or only insufficiently discussed. In view of the problems encountered with the treatment of toenail mycoses, this attitude should be changed in order to make the patient understand why there is such a low cure rate despite excellent minimal inhibitory drug concentrations in the laboratory.

Throughout the COVID-19 pandemic, masks have been promoted as an important tool for reducing SARS-CoV-2 transmission. However, even though masks are plausible experimental interventions, their mechanistic effectiveness has not been validated by randomized clinical trials (RCTs), with reduction risks of just 1.1-fold (11%) or less. Several meta-analyses have suggested that masks effectively reduce viral transmission, but all of them rely heavily or exclusively on observational data. Indeed, there is a strong inverse correlation between the effectiveness of masks and the number of RCTs included in the meta-analysis. RCTs on alternative therapies often show similar low levels of effectiveness. Thus, masks can be compared with potential new drugs or therapies that failed clinical trials despite showing mechanistic evidence.

Sample size using prevalence has been one of the most important medical research methodologies and has been used in original articles more often than any other statistical tool available. The simple formula consists of variables interdependent and it is as important to understand how they influence each other as it is to learn to use the formula to calculate the sample size. With the choice of different amounts of precision and levels of confidence available to a medical researcher, it should be of paramount importance to understand what a slight change in choosing the parameter would mean at the end of the research. Understanding of different Z values, Levels of precision, and ranging Prevalence is required to understand the effect on the sample size and subsequently on the research. Topological representation using parameter ranges can be used to demonstrate this relationship.

The following article analyzes Italian companies with more than 10 employees that use online sales tools. The data used were acquired from the ISTAT-BES database. The article first presents a static analysis of the data aimed at framing the phenomenon in the context of Italian regional disparities. Subsequently, a clustering with k-Means algorithm is proposed by comparing the Silhouette coefficient and the Elbow method. The investigation of the innovative and technological determinants of the observed variable is carried out through the application of a panel econometric model. Finally, different machine learning algorithms for prediction are compared. The results are critically discussed with economic policy suggestions.

Due to the high temperature limitation associated with silicon, SiC, Si₃N₄, TiC, TiN, TiB₂, cBN, CNx, SiO₂, Al₂O₃ and novel multicomponent nanocomposites polymer derived ceramics (PDC) based thin films like Si-C-N, Ti-Si-B-C-N and Si-O-CN that are compatible with silicon and have a wider range of operation in ambient conditions are being investigated. Apart from small scale electro-mechanical devices like N/MEMS, sensors, areas like metallurgical protection, water purification, energy storage, bio medical implants and aerospace have been benefited. The cross-sectional study involving the role of oxygen and nitrogen dissolution at different interfacial configurations has been presented.

Efficient heat management and control in optical devices, facilitated by advanced thermo-optic materials, is critical for many applications such as photovoltaics, thermal emitters, mode-locked lasers, and optical switches. Here, we investigate the thermo-optic properties of 2D graphene oxide (GO) films by precisely integrating them onto microring resonators (MRRs) with control over the film thicknesses and lengths. We characterize the refractive index, extinction coefficient, thermo-optic coefficient, and thermal conductivity of for the GO films with different layer numbers and degrees of reduction, including reversible reduction and enhanced optical bistability induced by photo-thermal effects. Experimental results show that the thermo-optic properties of 2D GO films vary widely with the degree of reduction, with significant polarization anisotropy, enabling efficient polarization sensitive devices. The versatile thermo-optic response of 2D GO substantially expands the scope of functionalities and devices that can be engineered, making it promising for a diverse range of thermo-optic applications.

This study develops a vitamin C controlled-release system trackable via color changes as a function of vitamin C release. The system is composed of coaxial microfibers prepared via coaxial electrospinning, with a core of poly(ethylene oxide) (PEO) incorporating vitamin C, and a shell composed of polycaprolactone (PCL) containing polydiacetylene (PDA) as the color-changing material. The shell thickness was controlled by adjusting the amount of PCL ejected during electrospinning, allowing the regulation of the release rate of vitamin C. When vitamin C added to PEO penetrated the PCL layer, the color of PDA changed from blue to red, indicating a color change. The results of this study can be applied to devices that require immediate detection of vitamin C release levels.

This work presents the mathematical framework of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-CASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters, which are considerably more than the number of features, being incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities (of any order) with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces. The application of the 2nd-CASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.

Polymerase I (Pol I) is at the epicenter of ribosomal RNA (rRNA) synthesis. Pol I is a target for treatment of cancer. Given the many cellular commonalities between cancer and neurodegeneration (i.e., different faces of the same coin) it seems rational to consider targeting of Pol I or, more generally, rRNA synthesis for treatment of disorders associated with the death of terminally differentiated neurons. Principally, ribosomes synthesize proteins and, accordingly, Pol I can be considered the starting point for protein synthesis. Given that cellular accumulation of abnormal proteins such as α-synuclein and tau is an essential feature of neurodegenerative disorders such as Parkinson disease and fronto-temporal dementia, reduction of protein production is now considered as a viable target for treatment of these and closely related neurodegenerative disorders. Abnormalities in polymerase I activity and rRNA production may also be associated with nuclear and nucleolar stress, DNA damage, and childhood onset neuronal death as is the case for the UBTF E210K neuroregression syndrome. Moreover, restraining the activity of Pol I may be a viable strategy to slow aging. Before starting down the road of Pol I inhibition for treating non-cancerous disorders of the nervous system, many questions must be answered. First, how much Pol I inhibition can neurons tolerate and for how long? Should inhibition of Pol I be continuous or pulsed? Will cells compensate for Pol I inhibition by upregulating the number of active rDNAs? At present, we have no effective and safe disease modulatory treatments for Alzheimer disease, α-synucleinopathies, or tauopathies, and novel therapeutic targets and approaches must be explored.

In the following article I analyze the determinants of regular internet users in the Italian regions. The data is analyzed both in terms of static analysis and also through the application of the k-Means algorithm optimized with the Elbow method. Subsequently, an econometric model is presented for estimating regular internet users in the Italian regions based on variables that reflect the state of technological innovation and digital culture. The results are analyzed and discussed in light of the implications that digitalisation has for triggering economic growth.

in the following article I analyze the trend of cultural and creative employment in the Italian regions between 2004 and 2022 through the use of ISTAT-BES data. After presenting a static analysis, I also present the results of the clustering analysis aimed at identifying groupings between Italian regions. Subsequently, an econometric model is proposed for estimating the value of cultural and creative employment in the Italian regions. Finally, I compare various machine learning models for predicting the value of cultural and creative employment. The results are critically discussed through an economic policy analysis.

Public housing stock, called Social Housing, in Italy developed between the 1950s and the 1980s. As of today, the first residential developments are almost nearing their end-of-life age, in need of urgent and intensive renovation. The European Commission, with the Renovation Wave, has set a goal of doubling the rate of building renovation over the next 10 years, reducing emissions, improving energy performance, and promoting decarburization. Renovation interventions, including structural, functional, energy, plant upgrading, etc., are to be preferred over integral demolition and reconstruction interventions, which have significant repercussions in terms of managerial and social discomfort. The case studies examined concern renovation interventions aimed at energy efficiency, functional adaptation of housing as well as facade restyling. The design variants analyzed were evaluated in terms of CO2e emissions, according to LCI (Life Cycle Inventory) and EPD (Environmental Product Declaration) approaches. This approach has a twofold purpose: to propose design guidelines, with low CO2e emissions, through hypotheses of variants to the case studies, and to propose, to the Economic Operators, economically advantageous bidding scenarios, in the procurement process.

The classical limit definition of a derivative is expressed in a more general form. The general form includes two arbitrary functions of the parameter for which the limit is calculated. A special case of the general form, which includes scaling and translational symmetry transformations of the limiting parameter, is also discussed. The errors in using the classical definition and the generalized form are calculated for small values of the limiting parameter. The derivatives of some known functions are proven using the new definition. For some well-known functions, a suitable selection of the generalized form may introduce simplicity in calculating the derivatives.

A selective permeable membrane between two different solutions can generate osmotic pressure, Donnan potential, and ion/pH gradients based on various factors such as membrane type, solute concentration and temperature. In this study, we introduce a novel method for extracting energy from ion gradients through an innovative cycle, presenting intriguing experimental findings. Additionally, by leveraging Raoult’s Law, which describes the relationship between vapor pressure and solute presence, in conjunction with osmosis within a closed system, we successfully engineer the simplest iteration of Maxwell’s demon device. This pioneering approach to sustainable energy generation harnesses ambient temperature marking a significant advancement in energy research. Finally this renewable energy source is available all the time and everywhere.

Contemporary global policy, driven by concern for the environment, imposes increasingly stringent goals for reducing CO2 emissions. Therefore, there is an urgent need to develop effective models that allow an accurate prediction of CO2 emissions from vehicles powered by alternative fuels such as CNG. This article presents the process of creating one of the first models of CO2 emission for a vehicle powered by CNG. Emission modelling is based on data obtained from chassis dynamometer tests and road tests using the portable emission measurement system (PEMS). CO2 emission modelling was conducted in Python programming language using the Optuna framework for the XGBoost technique. The models obtained were validated, with indicators R2 0.9 and RMSE 0.49 for data from chassis dynamometer tests, and R2 0.7, RMSE 0.71 for road test data. This work has the potential to be used by transportation decision makers involved in environmental analyses and policymaking for urban areas.

In the following article I take into consideration the role of knowledge workers in the Italian regions. The analysed data refers to the ISTAT-BES database. The metric analysis consists of an in-depth analysis of the trends of the regions and macro-regions, followed by clustering with the k-Means algorithm, the application of machine learning algorithms for prediction, and the presentation of an econometric model with panel methods date. The results are also critically discussed in light of the North-South divide and the economic policy implications.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

The study aimed to investigate the impact of incorporating deep learning strategies in mathematics instruction on students' problem-solving abilities, critical thinking skills, academic achievement, and mathematical skills development. The study population consisted of 280 students, with a sample size of 162 participants. Employing a purely quantitative approach, data collection utilized questionnaires and achievement tests as instruments. Data analysis involved simple linear regression, descriptive analysis, and Pearson correlation. The results of the study revealed that there was no significant enhancement observed in students' problem-solving abilities and critical thinking skills when deep learning strategies were incorporated into mathematics instruction. Additionally, students' attitudes towards deep learning activities in mathematics showed no effect on their academic achievement. Furthermore, students' engagement levels in deep learning activities did not impact their development of mathematical skills. Lastly, the frequency of deep learning activities in the mathematics classroom did not show a significant relationship with students' academic performance. These findings suggest that the implementation of deep learning strategies in mathematics instruction did not yield notable improvements in the specified areas based on the study outcomes.

: This study presents energy and exergy analyses conducted on a Gifford-McMahon cycle refriger-ator. Through detailed numerical simulations, the performance of the refrigerator is thoroughly assessed, with a particular focus on energy and exergy losses. The obtained results are compared with Sankey and Grassman diagrams, providing visual representations of energy and exergy flows within the system. The localization of exergy losses is carefully analysed to identify key areas for optimization aimed at enhancing efficiency and power output. By pinpointing sources of ex-ergy destruction, targeted improvements can be implemented to maximize the overall perfor-mance of the refrigerator. Overall, this research contributes to a deeper understanding of the thermodynamic behaviour of Gifford-McMahon cycle refrigerators and provides valuable insights for optimizing their performance in practical applications.

The transformation of the states of polarization of electromagnetic waves that interact with polarimetrically linear media can be represented by means of the associated Mueller matrix. A global measure of the ability of a linear medium to modify the states of polarization of incident waves, due to any combination of enpolarizing, depolarizing and retarding properties, is introduced as a distance of the Mueller matrix to the identity matrix. This new descriptor, called the polarizing power, is applicable to any Mueller matrix and can be expressed as a function of the degree of polarimetric purity and the trace of the Mueller matrix. The graphical representation of the feasible values of the polarizing power provides a general view of its main peculiarities and features. The values of the polarizing power for several typical devices are analyzed.

The sheer number of gene variants and the extent of the observed clinical and molecular heterogeneity recorded in neuropsychiatric disorders (NPDs), could be due to the magnified downstream effects initiated by a smaller group of genomic higher order alterations in response to endogenous or environmental stress. Chromosomal common fragile sites (CFS) are functionally linked with microRNA’s, gene copy number variants (CNVs), sub-microscopic deletions and duplications of DNA, rare single-nucleotide variants (SNVs/SNPs) and small insertions/deletions (indels), as well as chromosomal translocations, gene duplications, altered methylation, microRNA and L1 transposon activity and 3-D chromosomal topology characteristics. These genomic structural features have been linked with various NPDs in mostly isolated reports, and have usually only been viewed as areas harboring potential candidate genes of interest. The suggestion to use a higher level entry point, (the ‘fragilome’ and associated features), activated by a central mechanism (‘stress’) for studying NPD genetics, has the potential to unify the existing vast number of different observations in this field. This approach may explain the continuum of gene findings distributed between affected and unaffected individuals, the clustering of NPD phenotypes and overlapping comorbidities, the extensive clinical and molecular heterogeneity and the association with certain other medical disorders.

Optical polarizers are essential components for the selection and manipulation of light polarization states in optical systems. Over the past decade, the rapid advancement of photonic technologies and devices has led to the development of a range of novel optical polarizers, opening avenues for many breakthroughs and expanding applications across diverse fields. Particularly, two-dimensional (2D) materials, known for their atomic thin film structures and unique optical properties, have become attractive for implementing optical polarizers with high performance and new features that were not achievable before. This paper reviews recent progress in 2D-material-based optical polarizers. First, an overview of key properties of various 2D materials for realizing optical polarizers is provided. Next, the state-of-the-art optical polarizers based on 2D materials, which are categorized into spatial-light devices, fiber devices, and integrated waveguide devices, are reviewed and compared. Finally, we discuss the current challenges of this field as well as the exciting opportunities for future technological advances.

: Considering that Einstein's relation between the diffusion coefficient and the drift mobility of the free randomly moving charge carriers in homogeneous materials including metals is always valid, it is shown that the effective electric force acting on free electrons in metal depends on the ratio between the kinetic free electron energy at the Fermi surface to the classical particle energy 3kT/2. The electrical resistivity of elemental metals dependence on very low temperatures has the quadratic term, which has been explained by electron-electron scattering. In this paper, it is shown that the quadratic term of the electrical resistivity at low temperatures is caused by scattering of the free RM electrons by electronic defects due to linear effective free electron scattering cross-section dependence on temperature, but not by electron-electron scattering.

Djibouti shares an exceptional geostrategic position by being close to one of the busiest maritime routes, which makes it possible to have a crucial position for the movement of international trade transported by sea. This study analyses the influence of port development on Djibouti’s economic growth in the case of Doraleh Container Terminal and identifies other significant variable factors. In this paper, we analyze the existence of an influence port development on economic growth and other variables, by applying the perspective of the multiple regression model in the analysis using Eviews software. the selected datasets in the analysis cover the period from 2010 to 2020. The result show that port development has a direct and strongly positive influence on the economic growth of Djibouti. In addition, this study also reveals that imports and exports trade is a mediating variable with a significant influence on economic growth in Djibouti. Furthermore, foreign direct investment has not directly and positive influences on Djibouti's economic growth. Therefore, we recommend that the Government of Djibouti attract and promote the benefits of foreign direct investment to enhance the development of various economic sectors and achieve significant economic growth in the coming years.

The reconstruction of traffic accidents has grown as an interdisciplinary field since the first accident occurred. Various methods have been developed over time, starting with primary data collected at the scene of the accident. In this work, I proposed a method by which the value of the force in the safety belt buckle can be determined when it is equipped with a pretensioning system with pyrotechnic trigger in the tube, PBP or PLP type. The anti-return system of the pretensioner mechanism, which prevents the passenger's body from moving forward, contains a set of balls that block the movement of the piston in the pretensioner tube after its activation. When limiting the movement, the force the human body exerts on the safety belt webbing is transformed into the deformation of the pretensioner tube by the balls of the anti-return system. Depending on the magnitude of the force, the marks left by the balls differ. I propose a relationship of dependence between the acting force and the deformation of the tube. An experimental test bench was created to perform shock traction tests on the pre-tensioning system, under similar conditions to those of the human body acting on the belt in case of a frontal collision. Following the tests, the force value of the seat belt buckle cable and the diameter of the spherical indentations that the balls left in the pretensioner tube were measured. A mathematical model was proposed to determine the normal force with which a ball from the anti-return system deforms the tube. The normal force was also determined by two other methods specific to contact mechanics and used in the specialized literature. By comparing the normal forces obtained by the three methods, a good correlation between the proposed mathematical model and the Komvopoulos-Ye method resulted, the "Hardness" method not being suitable for this study. The determined polynomial regression curve allows finding out the force in the cable of the closer depending on the size of the deformations produced by the balls in the tube. The forces in the belt webbing can be thus determined and biomechanics specialists can evaluate the injuries to the chest and abdomen of the occupants of a vehicle.

A method of geoengineering/solar radiation management is proposed as global warming countermeasure. The idea is to decrease surface temperature by increasing stratospheric ozone. One-dimensional (vertical) radiative-convective equilibrium temperature calculations show that surface temperature can be lowered by increasing the amount of ozone in the stratosphere. It was the increase in altitude above about 25 km that reduced surface temperatures. The method of increasing the amount of ozone (O3 injection), the rate of decrease of surface temperature after the ozone increase, and the rate of decrease of ozone concentration by photochemical reaction and diffusion/transport after the ozone increase are discussed.

The lexical analysis of seminal policy-to-diplomacy documents from the socio-environmental discourse of the last fifty year agendas allowed examining the evolution of five themes: resources/waste, pollution, social and economic development, justice, and health. Contextual affinities were strongest between resources, pollution, and health and are linked to societal trends and pressures. On those grounds, the central role of resource stewardship according to nature’s physical limits is highlighted, and dedicated concepts, analytical frameworks, and methodologies with different degrees of sustainability are analyzed. To reframe the identified social and economic problems, the work proposes criteria and choices that (1) allow to compare the dynamics of socioecological states across the planet, (2) help matching the pace of socioecological change by addressing path dependencies through participation and engagement of communities, and (3) enable stakeholders to make trade-offs and take decisions in specific social, economic, political, and cultural contexts. The prioritization of resource justice and responsibility becomes a societal project : from Wellfare State to commonfare communities.

This study aims to explore significant present and future trends in accounting and corporate sustainability by examining publications from the Web of Science database spanning from 2010 to 2023. With a particular focus on research related to the digital business environment, the study employs bibliometric analysis grounded in the resource-based view theory to investigate the evolution of auditing and digital accounting research. Utilizing the VOSviewer tool, the study analyzes keyword relationships and their geographical distribution based on a comprehensive examination of 700 publications from the Web of Science. The findings highlight the transformative impact of auditing and digital accounting on business operations in response to rapid digitization. Key clusters identified shed light on digital transformation and the intersection between information technology and corporate management, revealing the evolving research landscape. The study discusses both practical and theoretical implications arising from these findings.

Environment pollution is one of the major problem that faces by our civilization . Textile industry is one of the factor for pollute the environment. In this study various causes that occurs by textile industry and pollute the environment will be shown and described. In textile industry for energy to run the factory various kind of fuel are used and in the industry there are many chemicals and dyes are used for the textile products. Plastic is one of the main substance for making the synthetic fabric. They pollute the air, water, soil and also they pollute the ecosystem of the various animals and destroy the ozone surface of the earth. For various kind of pollution that causes by textile industry the life of normal life hampered. They got ill and many time men and women lost their organs and sometimes they got attacked by various life taking illness.

The two-parameter distribution introduced by Kumaraswamy (1980) is a very flexible alternative to the Beta distribution with the same (0,1) support. Originally proposed in the field of hydrology, it has subsequently received a good deal of positive attention in both the theoretical and applied statistics literatures. Interestingly, the problem of testing formally for the appropriateness of the Kumaraswamy distribution appears to have received little or no attention to date. To fill this gap, in this paper we apply the “biased transformation” methodology proposed by Raschke (2009) to several standard goodness-of-fit tests based on the empirical distribution function. A simulation study reveals that these (modified) tests perform well in the context of the Kumaraswamy distribution, in terms of both low size distortion, and respectable power. In particular, the “biased transformation” Anderson-Darling test dominates the other tests that are considered.

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

Adherence is vital for medicine to have an effect, yet adherence is considered to be low. However, research into adherence tends to focus on quantitative analysis of performance, something which fails to perceive how people are adherent in their many different environments. As a contribution to gaining a deeper understanding, interviews were held with 30 individuals in UK, Egypt, Kazakhstan and 6 countries in sub-Saharan Africa to understand their perceptions on adherence for a range of drugs, and these were compared with an existing well-regarded list. New reasons for non-adherence were discovered, and a new viewpoint on adherence derived which considers adherence to be a single act and therefore as an individual opportunity to be adherent, permitting greater focus on the enablers and inhibitors of adherence at a point in time.

SARS-CoV-2 Omicron variant spike RBD epitopes in complex with spike (S) -protein D614G mutations now predominate globally and increase infectivity by assembling more functional S proteins into the virion. On average, homeopathic medicine contains less than one molecule per dose on average. Single variable SphericalHarmonicY, LaguerreL, WhittakerM, SphericalBesselJ, LegendreP, LegendreQ, LaguerreL, ChebyshevT, and Hypergeometric1F1 Functions pFqarise in connection with the power series solution of the Schrodinger equation or in the summation of perturbation expansions in quantum mechanics. A quantum interpretation of the homeopathic method is presented here through a novel algebraic topology approach to supersymmetry and symmetry breaking in quantum fields and quantum gravity theory with the aim of developing a wide range of physical-based drug design applications in a Small Molecule Quantized Water Memory Network suggesting that the Hidden Black Hole Paradox of Information Loss might be solved under suitable conditions.

This study employs Principal Component Analysis to examine cranial measurements obtained from both extant and fossil specimens spanning 38 species and comprising over 300 individuals within the subfamily Cervinae. Our findings elucidate craniometric distinctions among cervids characterized by varying body sizes and certain evolutionary adaptations. While our results generally corroborate previous assertions regarding the limited craniometric variability among plesiometacarpal deer, they also unveil specific cranial adaptations within certain genera and species. The Principal Component Analysis of craniometric data revealed that giant and large-sized deer display significantly broader ecomorphological diversity in cranial shape compared to small-sized deer. Secondly, small-sized deer exhibit greater uniformity in their cranial shape, appearing densely clustered on the factorial map. Thus, we conclude that body size imposes ecological constraints, limiting the available niches due to eco-physiological factors. The study demonstrates that endemic insular deer do not evolve consistent craniometric features attributable to insular isolation, while the cranial proportions of medium-to-small sized deer delineate a ubiquitous morphological archetype characteristic of numerous cervid taxa spanning diverse phylogenetic lineages and sharing comparable body sizes. This group of "Dama-like" deer, characterized by similar body size, metabolic rates, ecological needs, and cranial morphometry, is referred to here as the fundamental eco-physiological type, typical of warm regions within the Palearctic and Oriental biogeographic realms..

Anthropogenic activities such as the damming of rivers have resulted in the formation of depositional systems for sediments that are naturally transported along river channels all over the world. The steady accumulation of sediments has profound effects on how reservoirs function. As the reservoir ages, the impact of sedimentation becomes more pronounced. In an alluvial channel, the dynamics of sediment movement, scour, and deposition are exceedingly complicated. The quantitative and qualitative management of river engineering heavily relies on sediment motion. One of the key concerns affecting reservoir storage capacity is reservoir sedimentation. Understanding the form and behavior of the river or reservoir is necessary for a scientific approach to sedimentation. An overview of reservoir sedimentation processes, and models of reservoir and shallow water bodies is provided in this article. More research in the developing fields is advised for continuing studies on reservoir sedimentation due to climate change effects

Abstract Background In tumors, somatic mutagenesis presumably drives DNA damage response (DDR) via altered regulatory pathways increasing genomic instability and proliferative activity. These considerations led to the standard therapeutic strategy against cancer: the disruption of mutation activated DNA repair pathways of tumors. Purpose Justifying that cancer cells are not enemies to be killed, but rather they are ill human cells having the remnants of physiologic regulatory pathways. Results 1. Genomic instability and cancer development may be originated from the flaw of estrogen signal rather than excessive estrogen signaling. 2. Healthy cells with genomic instability exhibit somatic mutations helping DNA restitution. 3. Somatic mutations in tumor cells aim the restoration of DNA damage rather than further genomic derangement. 4. In tumors, estrogen signal drives the pathways of DNA stabilization leading to apoptotic death. 5. In the peritumoral cellular infiltration, the genomic damage of tumor induces inflammatory cytokine secretion and increased estrogen synthesis. In the inflammatory cells, increased growth factor receptor (GFR) signal confers unliganded activation of estrogen receptors (ERs). 6. In tumor cells responsive to genotoxic therapy, constitutive mutations help the upregulation of estrogen signal and consequential apoptosis. In tumors non responsive to genotoxic therapy, the possibilities for ER activation via either liganded or unliganded pathway, are exhausted leading to farther genomic instability and unrestrained proliferation. Conclusion Understanding the real character and behavior of human tumors at molecular level suggests that we should learn the genome repairing methods of tumors and follow them by supportive therapy rather than provoking additional genomic damages.

The longleaf pine (Pinus palustris P. Mill.) forest type occupied a much greater area in the United States prior to the arrival of Europeans, estimated to be around 37.2 million hectares. This area has been greatly reduced, and these ecosystems now occupy only about 1.2 to 1.6 million hectares. However, there has been great interest in the restoration of this forest type, and existing stands are actively managed. Thus, managers need tools to estimate site quality and ultimately productivity. Here, 16 existing equations to estimate site index and dominant height in naturally regenerated longleaf pine stands are examined for their behavior across a range of site quality and age conditions. Others can use this information as a reference to determine which equation may be best for their situation.

During the last decade, the demand for lithium has been growing exponentially with its widespread uses in manufacturing, especially with the worldwide deployment of electric vehicles. Thus, lithium is considered integral to the U.S. economy, technological advancement, and the pursuit of sustainable and clean energy solutions. The produced water is known to be rich in several minerals and valuable elements, such as potassium and magnesium, and a trace amount of precious elements including lithium. However, the existence of metals in the produced water is one of the challenges to extract lithium, especially magnesium which is located in diagonal positions within the periodic table that exhibit similarities. The produced water was first purified, resulting in the complete removal of magnesium, and then lithium was precipitated by phosphate. The effects of operating conditions on the Li₃PO₄ precipitation behaviors were evaluated. The effect of different precipitating reagents was evaluated on the percentage of lithium extracted, Na₃PO4 (TSP) was found to be a promising Li precipitating reagent. The highest percentage of lithium extracted was reached when the Li concentration of produced water was enriched and increased up to 350 mg/l, then Li removal was 53% at 7 grams of TSP and 200 rpm for 2 hours. The results indicated that temperature is a more important factor than stirring speed. The novelty of the current work is not only by the results obtained that may create additional financial benefits to oil-producing areas but on that the sustainable disposal of produced water may encourage the recycling and reuse practice, ultimately reducing the use of freshwater for hydraulic fracturing.

This study presents a dynamic modeling approach to investigate the impact of saturation dynamics on HIV/AIDS transmission in the presence of vertical transmission and treatment strategies. The proposed model incorporates disease-free and endemic equilibria, with a focus on analyzing the basic reproduction number and conducting stability analysis. Results indicate that the disease-free equilibrium is locally asymptotically stable under certain conditions. The findings suggest that a combination of treatment interventions and awareness campaigns, along with other control measures, may play a crucial role in curbing the spread of the HIV/AIDS virus. This research contributes to understanding the complex dynamics of HIV/AIDS epidemics and highlights the importance of considering saturation effects in epidemic modeling.

Evolutionary algorithms and swarm intelligence algorithms find applicability in reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using these algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. In Part1, 2 and 3, the author previously reported experimental evaluations on Evolution Strategy, Genetic Algorithm, and Differential Evolution for reinforcement learning of neural networks, utilizing the Acrobot control task. This article constitutes Part4 of the series of comparative research. In this study, Particle Swarm Optimization is adopted as an instance of major swarm intelligence algorithms. The experimental result shows that PSO performed worse than all of DE, GA and ES. The difference between PSO and DE was statistically significant (p<.01). In addition, PSO exhibited lower capability in exploring solutions in high-dimensional search spaces than DE, GA, and ES did. A larger swarm size compensated for the weakness of PSO in global exploration, thus making itself more beneficial than a larger number of swarm search iterations.

Open science, accessibility and knowledge sharing, especially of articles and monographs stemming from a publicly funded research, seem to be quite a positive direction in the development of science and have received almost unanimous approval from a scientific community. However, when it comes to data sharing, the data obtained by qualitative methodology deserve special attention and treatment. Although FAIR principles provide ways to anonymise the data and interlocoutors (as explained in Celjak et al 2020) individuals coming from smaller communities or even communities of practice can sometimes be easily regcognized by members of the same communitiy, especially if the data refers to audio files! The researcher is obliged to thoroughly describe the ways in which the data will be managed and used, and if it will state that the data will be managed in line with FAIR principles, this will inevitably impact the narratives collected. The prerogative to make all the data open will inevitably lead to autocesorship i.e. in creating a kind of FAIRy Tales for the future, interlocutors will share with researchers. Having all that in mind I argue for a more vigilant approach when embracing the idea that all the data should be made FAIR. Not all the data should be made FAIR because this could, in the end, compromise the research process itself. Some data must remain in the space of trust created between main actors in the research process. These data should be treated with CARE principles.

The question of whether the early visual cortex (EVC) is involved in visual mental imagery remains a topic of debate. In this paper, I propose that the inconsistency in findings can be explained by the unique challenges associated with investigating EVC activity during imagery. During perception, the EVC processes low-level features which means that activity is highly sensitive to variation in visual details. If the EVC has the same role during visual mental imagery, any change in the visual details of the mental image would lead to corresponding changes in EVC activity. Within this context, the question should not be whether EVC is ‘active’ during imagery but how its activity relates to specific imagery properties. Studies using methods that are sensitive to variation in low-level features reveal that imagery can recruit the EVC in similar ways as perception. However, not all mental images contain a high level of visual details. Therefore, I end by considering a more nuanced view which states that imagery can recruit the EVC, but that does not mean that it always does so.

Environment pollution is the major problem for all the sectors. In environment pollution there are different types of pollution is included such as air pollution, water pollution, soil pollution etc. In these types of pollution most of it is air pollution. There are many reasons for pollution of air. Human advance in science but one of the major draw backs of science is the pollution of air rapidly. Air pollution is injurious to health. Every year a large number of people died for the effects of air pollution. It can be improved in many ways among these ways technology upgradation plays a major role here. There are various method that prevent air pollution. Many researchers tried and is trying to prevent the air pollution in many ways. In this review paper I summarise the existing research on different ways for preventing air pollution in addition I will discuss the gaps and future aspect on this topic.

In 2022, the new WHO Classification of Endocrine and Neuroendocrine Tumors, Fifth Edition (beta version) (WHO 5th) was published. The importance of understanding the molecular genetics of thyroid tumors as revealed by large-scale genomic analyses such as The Cancer Genome Atlas (TCGA). Consequently, the WHO 5th was fundamentally revised, resulting in a systematic classification based on tumor cell-of-origin and clinical risk. This paper outlines following critical points of the WHO 5th. 1. Genetic mutations in follicular cell-derived neoplasms (FDN) highlight the role of mutations in the MAP kinase pathway, including RET, RAS, and BRAF, as drivers of carcinogenesis. Differentiated thyroid cancers such as follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC) have specific genetic alterations that correlate with morphological classifications: RAS-like tumors (RLTs) and BRAF p.V600E-like tumors (BLTs), respectively. 2. The Framework for benign lesions have revised. The WHO 5th introduces new categories such as "developmental abnormalities" and "thyroid follicular nodular disease" in benign FDN. 3. Low-Risk Tumors include NIFTP, TT-UMP, and HTT. 4. PTC histological variants are reclassified as "subtypes" in the WHO 5th. 5. The concept of high-grade carcinomas is introduced, encompassing poorly differentiated thyroid carcinoma (PDTC), differentiated high-grade thyroid carcinoma (DHGTC), and high-grade medullary thyroid carcinoma (MTC). 6. Squamous cell carcinoma is included in anaplastic thyroid carcinoma (ATC) in the WHO 5th due to shared genetic and prognostic features. 7. Hürthle cell adenoma/carcinoma is renamed oncocytic thyroid adenoma/carcinoma. 8. Other miscellaneous tumors are classified into salivary gland-type carcinomas of the thyroid, thyroid tumours of uncertain histogenesis, thymic tumours within the thyroid, and embryonal thyroid neoplasms. The WHO 5th thus emphasizes the importance of classifying tumors based on genetic abnormalities together with histomorphology, aiding in accurate pathological diagnosis and treatment selection, including molecular-targeted therapies.

Keywords: SAR; interferometry; bistatic

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

Forage fish are crucial to human nutrition, especially in developing countries. However, in these countries, most forage fish catches are used to make aquafeeds for aquaculture, which leaves only a small quantity for direct human consumption. This practice increases fish prices in local markets and undermines people's nutrition. Overfishing forage fish for industrial purposes harms the ecosystem, reduces the catch of larger fish, and marginalizes local fishermen. Although previous discussions have touched qualitatively on the ethics and injustice of this practice, this article provides quantitative scales to measure the impact of the practice on the finances and forage fish consumption of low-income consumers using reasonable assumptions and literature values. Increasing the annual supply of forage fish by 25% to the 5 million MT of fresh fish markets globally could save consumers several billion US dollars annually. Families could consume a higher percentage of nutritious fish for less money. Furthermore, leaving forage fish in the sea could benefit wildlife and increase the capture of larger fish, such as mackerel, which are in demand in local markets and fished by local fishermen. The unintentional and involuntary contributions of low-income societies to enable the growth of high-value fish and shrimp for affluent societies seem unjust and warrant further investigation.

This paper focuses on the paradigm that explains the link between energy transitions and financial transformations. In particular, it attempts to shed some light on the following question: How did the transition from wood to coal affect financial development in late 18^th and early 19^th century Britain? The results demonstrate that the energy transition in Britain during the 1^st Industrial Revolution constituted an important moment in the country's financial development and can be used as an example of how an energy innovation can foster the financial development of particular countries or regions.

The hard open problem of finding the lower bound for the Fokker Planck Kolmogorov’s time-dependent controller parameter probability density function is addressed for the first time in literature in this work. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to multi-interdisciplinary fields of human knowledge, and beyond. So, this by default adds more taste and credibility to this study. Concluding thoughts are included at the end of the work along with a difficult outstanding problem and the direction of future work.

Recent literature has seen a rapid expansion in the reference to front-end forensics as an indication of the untapped value of forensic science. While some of these contributions have centered on development of forensic intelligence from a single area of investigation, others call for a more fundamental change in the relationship between crime laboratories and policing, particularly relating early laboratory analysis with big datasets to provide leads to investigators. We highlight several recently implemented tactical strategies of crime laboratories that contribute to the body of forensic intelligence. Beyond the scientific gains from these tactical applications, the corresponding details on associated efficiencies, costs, time savings, and quality improvements offer insights towards patterns of success for the community of crime laboratories. Further details expand an interpretation of what constitutes success with an eye on the contributions of the crime laboratory towards public health, safety, and protection of the innocent in addition to societal gains from conviction of the guilty. The economic interpretation of the value provided by the forensic laboratory assists in the cost-benefit review of strategic and tactical decisions and supports the justification for laboratory public funding with measures of the return on investment from public support of the forensic laboratory

Prokaryotic genomes are dynamic tapestries that are strongly influenced by mobile genetic elements (MGEs), including transposons (Tn’s), plasmids, and bacteriophages. Of these, miniature inverted-repeat transposable elements (MITEs) are undoubtedly the least studied MGEs in bacteria and archaea. This review explores the diversity and distribution of MITEs in prokaryotes and describes what is known about their functional roles in the host and involvement in genomic plasticity and evolution.

A persistent idea in cosmology is that the universe originated and then continued to evolve as a black hole. While the notion of a black hole universe has generally been framed within the standard cosmological model, the latter has had numerous problems related to dark matter, dark energy and other issues. To avoid such problems an alternative is proposed which omits cosmic expansion, dark matter and dark energy. The observable universe is cast instead as primarily a thin spherical shell of cold (~29 K) baryonic matter situated near the Hubble radius. This shell of plasma holds 95% of the observable universe’s mass, the remaining 5% existing in the interior galaxies and gas clouds. A key premise of the model is that spacetime is fundamentally photonic in nature. This allows photon energy to be transferred to spacetime in the Hubble redshift and to be transferred back to photons in a novel blueshift. These exchanges together drive a cosmic energy cycle for gravity and the cosmological constant, Λ. Photons of the cosmic microwave background originating in the plasma shell lose energy to ‘cooler’ regions of spacetime in interior zones via the Hubble redshift. This gives rise to gravity through the optical gravity approach. The depleted photons moving back towards the shell are then reenergized in ‘hotter’ spacetime regions via the Hubble blueshift. These photons eventually exert outward pressures on the shell which perfectly balance the inward forces of gravity, in this manner functioning as Einstein’s cosmological constant. The observable universe behaves as a closed system in thermodynamic equilibrium with constant energy and entropy and indeterminate age. Black holes are suggested to have analogous plasma shell structures and gravity/Λ cycles.

Advances in gastric cancer screening and endoscopic technology have made it possible to detect and treat gastric cancer at an early stage. As a result, about 40% of all gastric cancers have now been resected by endoscopic treatment in Japan. In endoscopic diagnosis, magnifying endoscopic observation using band-limited light such as narrow band imaging (NBI) has significantly improved the accuracy of gastric cancer diagnosis. At the same time, in endoscopic treatment, endoscopic submucosal dissection (ESD) has contributed to increase explosively the number of gastric cancers which could be resected endoscopically. In recent years, the construction of algorism for endoscopic diagnosis and treatment with a view to personalized medicine has been explored, and endoscopic use for gastric cancer managements is expected to develop further in the future. Keywords (5 words):

Numerous scientific fields employ entropy, the Shannonian measure of disorder and inaccessible information. Current paper examines educational uses of entropy. The major objectives of this survey are to create a coherent perspective of the applicability of classical entropies and to provide a concise detailed explanation that blends technical pertinence with conceptual extraction from the most recent literature. What's more intriguing are the closing thoughts mixed with a few unsolved issues and the creation of a future work schedule in these areas.

Deep learning machines are computational models composed of multiple processing layers of adaptive weights to learn representations of data with multiple levels of abstraction. Their structure is mainly reflecting the intuitive plausibility of decomposing a problem into multiple levels of computation and representation since it is believed that higher layers of representation allow a system to learn complex functions. Surprisingly, after decades of research, from learning and design perspectives these models are still deployed in a heuristic manner. In this paper, deep learning feed-forward machines are modeled from a statistical mechanics point of view as disordered physical systems where its macroscopic behavior is determined in terms of the interactions defined between the basic constituent of these models, namely, the artificial neuron. They are viewed as the equilibrium states of a theoretical body that is subject to the law of increase of the entropy. The study of the changes in energy of the body when passing from one equilibrium state to another is used to understand the structure and role of the phase space of the system, the stability of the equilibrium states, and the resulting degree of disorder. It is shown that the topology of these models is strongly linked to their stability and resulting level of disorder. Furthermore, the proposed theoretical characterization permit to assess the thermodynamic efficiency with which information can be processed by these models, and to provide a practical methodology to quantitatively estimate and compare their expected learning and generalization capabilities. These theoretical results provides new insights to the theory of deep learning and their implications are shown to be consistent through a set of benchmarks designed to experimentally assess their validity.

One of the most important objectives of the studies worldwide is to improve the performance of automotive engines to reduce fuel consumption and environmental pollution. Accordingly, the principal motivation of this research study is improving the tribological behavior of the piston ring/cylinder liner interfaces as a promising and straightforward approach in automotive fuel economy and increasing engine durability using Al₂O₃ and TiO₂ nanomaterials as smart nano-lubricant additives that adapted to different operating conditions by replenishing mechanisms anti-friction and anti-wear in automotive engines.

We show the tensor properties of joint probabilities on a Riemannian parametric manifold. Initially we develop a binary data matrix for parameter measurements of a large number of particles confining in a closed system in order to retrieve the joint probability densities of related parameters. By introducing a new generalized inner product as a multilinear operation on dual basis of parametric space, we extract the set of joint probabilities and prove them to meet contravariant tensor properties in a general Riemannian parametric space. We show these contravariant tensors reduce to classical ordinary partial derivatives definition in ordinary Euclidean parametric space. Finally we prove by a theorem that symmetrized iterative contravariant derivative of cumulative probability function on Riemannian manifold gives the set of joint probabilities in those manifold. We bring some examples for compatibility with physical tensors.

: The activated carbons (ACs) derived from okara powder waste with high surface areas were modified with ammonia aqueous solution impregnation in an autoclave to enhance their CO2 adsorption properties toward CO2. The impregnated ACs were characterized in which surface chemical composition and properties of the ACs were analyzed by SEM-EDX, FTIR. Activated carbons were functionalized with ammonia aqueous solution (25%) through a hydrothermal process within 24; 48, and 72 hours. The adsorption performance of CO2 onto carbon samples was experimentally evaluated through a TPD CO2 measurement. FTIR spectra confirm the N-containing in N-modified activated carbons and the presence of the –C = O stretch and N-H groups. CO2 uptakes of N-modified activated carbons are 0.24; 1.78; 2.24 and 1.26 mmol/g, which are relatively comparable with those of activated carbons studied in in the literature.

CD44 regulates cell adhesion, proliferation, survival, and stemness and has been considered a tumor therapy target. CD44 possesses the shortest CD44 standard (CD44s) and a variety of CD44 variant (CD44v) isoforms. Since the expression of CD44v is restricted in epithelial cells and carcinomas compared to CD44s, CD44v has been considered a promising target for monoclonal antibody (mAb) therapy. We previously developed an anti‐CD44v10 mAb, C44Mab-18 (IgM, kappa) to recognize the variant exon 10-encoded region. In the present study, a mouse IgG2a version of C44Mab-18 (C44Mab-18-mG2a) was generated to evaluate the antitumor activities against CD44-positive cells compared with previously established anti-pan CD44 mAb, C44Mab-46-mG2a. C44Mab-18-mG2a exhibited higher reactivity compared with C44Mab-46-mG2a to CD44v3–10-overexpressed CHO-K1 (CHO/CD44v3–10) and oral squamous cell carcinoma cell lines (HSC-2 and SAS) in flow cytometry. C44Mab-18-mG2a exerted a superior antibody‐dependent cellular cytotoxicity (ADCC) against CD44v3–10. In contrast, C44Mab-46-mG2a showed a superior complement‐dependent cytotoxicity (CDC) against CD44v3–10. A similar tendency was observed in ADCC and CDC against HSC-2 and SAS. Furthermore, administering C44Mab-18-mG2a or C44Mab-46-mG2a significantly suppressed CD44v3–10, HSC-2, and SAS xenograft tumor growth compared with the control mouse IgG2a. These results indicate that C44Mab-18-mG2a could be a promising therapeutic regimen for CD44v10-positive tumors.

Flares, sometimes accompanied by coronal mass ejections (CMEs), are the result of sudden changes in the magnetic field of stars with high energy release through magnetic reconnection, which can be observed across a wide range of the electromagnetic spectrum from radio waves to the optical range to X-rays. In our review, we attempt to collect some fundamental new results, which can largely be linked to the Big data era that has arrived due to the expansion of space photometric observations of the last two decades. We list the different types of stars showing flare activity, their observation strategies, and discuss how their main stellar properties relate to the characteristics of the flares (or even CMEs) they emit. Our goal is to focus, without claiming to be complete, on those results that may in one way or another challenge the "standard" flare model based on the solar paradigm.

Simple Summary: The distribution of typical bacterial redox enzymes such as pyruvate-ferredoxin oxidoreductase (PFOR) in protozoa remains interestingly puzzling. Previous studies have demonstrated diverse cellular localizations of PFOR in some amitochondriate anaerobic protozoa. PFOR is of particular pharmacological importance because it catalyzes the reductive bio-activation of nitro-based prodrugs to cytotoxic radical metabolites. Metronidazole was developed primarily as an antiprotozoal agent against infections caused by Trichomonas vaginalis. However, its antimicrobial spectrum was subsequently expanded to cover anaerobic bacterial infections. It has been shown that mutations in the genes encoding PFOR result in inherent resistance of PFOR-possessing anaerobic protozoa and bacteria to nitro-based prodrugs. Deciphering the evolutionary history of PFOR is crucial in deepening our understanding of the evolution of anaerobic pathogens and unfold new approaches for drug discovery and targeting in pathogen chemotherapy. Abstract: The present frontrunners in the chemotherapy of infections caused by protozoa are nitro-based prodrugs that are selectively activated by PFOR-mediated redox reactions. This study seeks to analyze the distribution of PFOR in selected protozoa and bacteria by applying comparative bioinformatics approaches to test the hypothesis that PFOR was most likely acquired through horizontal gene transfer (HGT) from bacteria. Furthermore, to identify other genes that were putatively acquired using proteome-wide and gene expression analyses. A plausible explanation for the patchy occurrence of PFOR in protozoa is based on the hypothesis that bacteria are potential sources of genes that enhance optimal adaptation of protozoa in hostile environments. The exclusively expressed proteins obtained from Entamoeba histolytica and the putative gene donor, Desulfovibrio vulgaris, showed an over-expression of eleven genes involved in intermediary metabolism. If these results can be reproduced in other PFOR-possessing protozoa, it would provide a more validated evidence to support the horizontal transfer of pfor from bacteria.

Background: this interventional pilot study aimed to evaluate the short-term (3 years) efficacy of focal laser ablation (FLA) in treating the index lesion of low-intermediate risk prostate cancer, along with assessing the safety of the procedure (ClinicalTrials.gov ID NCT04045756). Methods: forty patients aged between 46 and 86 with histologically proven organ-confined prostate cancer and low to intermediate progression risk were included. FLA was performed under percutaneous fusion magnetic resonance/ultrasound guidance in a Day Hospital setting under local anesthesia. Patients underwent regular clinical assessments, PSA measurements, post-procedure MRI scans, and biopsies at 36 months or if positive findings were detected earlier. Statistical analyses were conducted to assess trends in PSA levels and cavity dimensions over time. Results: forty patients were initially included, with 15 lost to follow-up. At 36 months, a mean PSA reduction of 60% was observed, and 80% of MRI scans showed no signs of in-field clinically significant residual/recurrent cancer. Biopsies at 36 months revealed no malignant findings in 20 patients. No deterioration in sexual function or urinary symptoms was recorded. Conclusions: FLA appears to be safe, feasible and effective in the index lesion treatment of low-intermediate risk prostate cancer, with a high rate of tumor eradication and preservation of quality of life.

We are interested in studying alterations in the regulatory circuitry that controls mRNA stability and function, and more specifically in the interactions among miRNAs and mRNAs. We have previously reported transcriptome-wide changes in mRNA expression in an animal model of atherosclerosis (ATS) progression, and reversion upon treatment with an αCD40 specific siRNA. In this work, we have used our data on mRNAs downregulated during these processes to study length dynamics as an adaptive mechanism to thwart miRNA activity. We show that ATS progression was characterized by the lengthening of the 5’UTR and 3’UTR regions of these mRNAs. Furthermore, 3’UTR lengthening (but not that of the 5’UTR) was shown to be partially reversed by the treatment with the αCD40 specific siRNA. Study of the exonic composition of the transcriptome of these mRNAs suggested a role for alternative/cryptic/latent splicing in the generation of length variants. This was confirmed by the detection, during ATS progression, of a transcript variant switching process in which specific isoforms were replaced by others that differed in their pattern of alternative splicing, mainly at their 5’ or 3’UTRs. This work highlights alternative splicing as a mechanism contributing to the generation of 5’/3’UTR length and sequence variability during ATS progression and calls attention on factors regulating alternative splicing as possible targets of therapeutical intervention.

As the adoption of deep learning models continues to surge across various applications, the need for efficient deployment architectures becomes increasingly critical. This paper presents a novel approach to enhance the deployment of deep learning models by leveraging serverless architecture. Serverless computing has been popular for its auto-scaling, cost-effectiveness, and simplified management characteristics. However, the intense resource demands of deep learning models pose challenges in maintaining low response times and effective load balancing within serverless environments. The proposed architecture addresses these challenges by integrating principles from both deep learning model optimization and serverless computing. Through systematic experimentation and analysis, we demonstrate that by appropriately designing and tuning the deployment architecture, significant improvements in response time, performance, resource utilization, and load distribution can be achieved.

The study of the morphology, thermal, and chemical stability of the fluorinated compounds was performed using Becke’s three-parameter hybrid functional approach with the correlation provided by Lee, Yang, and Parr and the cc-pVTZ basis set aiming to design low sensitivity, toxicity, instability, and proneness to decomposition or degradation over a short time high-energy materials. The most stable conformers of the compounds under study were selected based on their total energy. Their thermal and chemical stability was evaluated based on the binding energy per atom, chemical hardness, and softness. The oxygen-fluorine balance is assessed to evaluate the sensitivity of these new materials. The density, detonation pressure, and velocity of the selected conformers were theoretically obtained to reveal the influence of -CF3, -OCF3, and cyclic -O(CF2)nO- fragments on the energetic properties of nitroaromatics as well as their stability and resistance to shock stimuli. The results allow us to predict new multipurpose energetic materials with a good balance between power and stability. Referring to the results obtained, we recommend CF3N2, OCF3N2, C2F6N2, 1CF2N2/O2CF2N2, and 2CF4N2/O2C2F4N2 for practical usage because these compounds possess greater stability compared to tetryl and better explosive properties than TNT.

Nanoporous graphene structures have high accessible surface area for lithium-ion adsorption or intercalation. Here, we present theoretical investigations on lithium-ion adsorption mechanism in a graphene nanopore. Ab initio electronic structure calculations are performed based on density functional theory. We also study different lithium-ion distributions in graphene nanopores to determination multilayer lithium-nanoporous graphene structures for lithium-ion batteries. We believe, these multilayer nanoporous graphene nanostructures with more pores opening can offer improved capacity and better rate performance for energy storage applications.

The scientific community has explored new packaging materials due to environmental challenges and pollution from plastic waste. Bacterial cellulose (BC), produced by bacteria like Gluconaceto-bacter xylinus, shows high potential for food preservation due to its exceptional mechanical strength, high crystallinity, and effective barrier properties against gases and moisture, making it a promising alternative to conventional plastics. This review highlights recent advances in BC production, particularly agro-industrial residues, which reduce costs and enhance environmental sustainability. Incorporating antimicrobial agents into BC matrices has also led to active packaging solutions that extend food shelf life and improve safety. A bibliometric analysis reveals a significant increase in research on BC over the last decade, reflecting growing global interest. Key research themes include the development of BC-based composites and the exploration of their antimicrobial properties. Critical areas for future research include improving BC production's scalability and economic via-bility and its integration with other biopolymers. These developments emphasize BC's potential as a sustainable packaging material and its role in the circular economy through waste valorization.

The importance of maintaining a healthy indoor climate has recently increased, as has the durability of building structures and for this issue we need prediction of mould growth. The prepare this model under real conditions is challenging, and this work aimed to contribute data to this model. This article presents the findings of a laboratory study investigating the effects of fluctuation relative humidity and temperature conditions on mould growth on pine and spruce. The study compared the results to a previous steady-state experiment, demonstrating that fluctuations in relative humidity and temperature prolonged the onset of mould growth. The mould growth observed depended on the type of wood, with pine or spruce wood exhibiting different growth patterns compared to heartwood or sapwood. In sapwood, mould growth was found to be almost independent of the direction of the fibre. The first microscopic indications of mould growth on pine sapwood were observed around day 76th, with the first macroscopic indications observed around day 90th. On the contrary, spruce sapwood demonstrated a limit for mould growth. The mould growth was only visible under the microscope, with the first indications observed between the 72nd and 80th day. Furthermore, heartwood was found to be unsuitable for mould growth under fluctuating conditions.

Shape memory alloys (SMA) are becoming a more important factor in actuation technology. Due to their unique features, they bring the potential to safe weight, installation space and reduce energy consumption. The system integration of the generally small diameter NiTi wires is an important cornerstone for the emerging technology. Crimping, a common method for the mechanical and electrical connection of SMA wires, has several drawbacks when it comes to miniaturization and high force outputs. For high force applications for example, multiple SMA wires in parallel are needed to keep actuation frequencies high, while scaling up the actuation force. To meet these challenges, the proposed study deals with the development of a resistance welding process for manufacturing NiTi wire bundles. The wires are welded to sheet metal substrate, resulting in promising functional properties and high joint strengths. The welding process benefits from low cost, easy to control parameters and good automation potential. A method for evaluating the resistance welding process parameters is presented. With these parameters in place, a manufacturing process for bundled wire actuators is discussed and implemented. The welded joints are examined by peel tests, microscopy, and fatigue experiments. The performance of the manufactured bundle actuators is demonstrated by comparison to a single wire with the same accumulated cross-sectional area.