This paper discusses approaches to the numerical integration of the coupled nonlinear Schrödinger equations system in case of few-mode wave propagation. The wave propagation assumes the propagation of up to nine modes of light in an optical fiber. In this case, the light propagation is described by the non-linear coupled Schrödinger equation system, where propagation of each mode is described by own Schrödinger equation with other modes interactions. In this case, the non-linear coupled Schrödinger equation system solving becomes increasingly complex, because each mode affects the propagation of other modes. The suggested solution is based on the direct numerical integration approach, which is based on a finite-difference integration scheme. The well-known explicit finite-difference integration scheme approach fails, due to the non-stability of the computing scheme. Due to this fact, the combined explicit/implicit finite-difference integration scheme, based on the implicit Crank–Nicolson finite-difference scheme, is used. It allows ensuring the stability of the computing scheme. Moreover, this approach allows separating the whole equation system on the independent equation system for each wave mode at each integration step. Additionally, the algorithm of numerical solution refining at each step and the integration method with automatic integration step selection are used. The suggested approach has performance gains (or resolutions) up to three or more orders of magnitude in comparison with the split-step Fourier method due to the fact that there is no need to produce direct and inverse Fourier transforms at each integration step. The main advantage of the proposed method is the ability to calculate the propagation of an arbitrary number of modes in the fiber.

This research aims to explore factors that influence sustainable music learning system from psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines nonlinear system and identifies relevant factors affecting music learning by establishing data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music learning system from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring music learning students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music learning system from emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on music learning system. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on music learning system are obtained. The comprehensive impact of emotional factors and practical learning on music learning system can be obtained by weighting them together.

Time evolution of the quantum correlation of the hybrid qubit-qutrit system in multilocal and global dephasing under the effect of a classical noises environment is presented. We considered the qutrit in z direction and the qubit x direction. In addition, our system presents two configurations: the different and the common environment and our discussion involves three types of noises: A Static Noise (SN), Random Telegraph Noise (RTN) and Ornstein- Uhlenbeck noise (OU). By using the density matrix approach to determine the evolution of the state of the system, the found that the static noise is fatal for the quantum entanglement, the quantum correlation and for RTN and OU noises the quantum entanglement, the quantum correlation decreases monotonically with a sufficiently long time. Therefore, we can say that, entanglement and the quantum correlation are more robust to the noise precisely those Random telegraph noise and Ornstein-Uhlenbeck noise.

This research aims to explore factors that influence sustainable music learning system from psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines nonlinear system and identifies relevant factors affecting music learning by establishing data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music learning system from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring music learning students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music learning system from emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on music learning system. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on music learning system are obtained. The comprehensive impact of emotional factors and practical learning on music learning system can be obtained by weighting them together.

In this paper, we propose a system of nonlinear mixed variational inequality problems, which consists of two elliptic mixed variational inequality problems on Banach spaces. Under suitable assumptions, using the Kakutani-Ky Fan fixed point theorem and Minty techniques, we prove the solution set to the system of nonlinear mixed variational inequality problem is nonempty, weakly compact and unique. Additionally, we suggest a stability result for the system of nonlinear mixed variational inequality problem by perturbing the duality mappings. Furthermore, we present an optimal control problem governed by the system of nonlinear mixed variational inequality problems and establish a solvability result.

Good Boussinesq equations will be considered in this work. First we apply three combined compact schemes to approximate spatial derivatives of good Boussinesq equations. Then three fully discrete schemes are developed based on symplectic scheme in time direction, which are sympletic-structure preserving.Meanwhile, the convergence and conservation of the fully discrete schemes are analyzed. Finally, we present numerical experiments to confirm our theoretical analysis. Both our analysis and numerical test indicate that the fully discrete schemes are efficient in solving the spatial derivative mixed equation.

This research aims to investigate psychological anxiety factors, particularly Music Performance Anxiety (MPA), that influence the sustainability of music learning systems. This study employs an innovative integration of qualitative and quantitative methodologies, marking the first use of a nonlinear system paired with a comprehensive data framework for analyzing questionnaire responses. This approach allows for a detailed examination of the effects of these factors on academic outcomes. Previous research has primarily focused on the fragmented music learning system from the perspective of teachers' instructional strategies, emphasizing phased teaching methods and learning objectives which focus often neglected a significant subset of music learners. Addressing this oversight, the current study specifically centers on the emotional aspects of anxiety within music learning systems, with a particular emphasis on students, who play a crucial role in music education. This perspective enables a deep exploration of how MPA and other related emotional factors affect music learning systems. A notable innovation of this research is the development of a stochastic nonlinear system designed to model the psychological factors impacting student music learning, complemented by a quantitative model that assesses actual learning outcomes. By analyzing this nonlinear system, the research identifies weight values for various negative emotions, particularly those related to MPA, that significantly influence the music learning process. The integration of MPA and other anxiety related emotional factors with practical learning outcomes provides a comprehensive understanding of their combined impact on the music learning system. These insights are invaluable for educators and policymakers aiming to enhance both the effectiveness and emotional well-being within music education. This comprehensive approach offers a novel perspective in understanding and improving the dynamics of music education through a more empathetic and scientifically grounded lens.

This research aims to explore factors that influence sustainable music education from a psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines stochastic nonlinear system of control theory and identifies relevant factors affecting sustainable music learning by establishing a data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music education from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring sustainable music education and students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music education from an emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on sustainable music learning. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of sustainable student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on sustainable music learning are obtained.

As an option to deal with the insulin-dependent disease, a recent commuted PD control strategy is designed and carefully analyzed for different clinic diabetic patients. This controller approach is mainly conceived to stabilize the glucose blood concentration in a diabetic patient around its basal value, hence avoiding extreme situations such as hypoglycemia and hyperglycemia. This control strategy receives two inputs carefully tuned to actuate when the measured variable is out of a prescribed healthy zone. Therefore, one of these variables is invoked to decrease the glucose concentration to insulin injection, and the other is employed to increase the glucose absorption, both by using a proper PD controller. According to our numerical experiments, our controller approach performs well, even when there is an external disturbance in the controlled system.

This study presents finite-time synchronization of chaotic fractional order systems with disturbance uncertainty and unknown time delay. First, a PID sliding surface is presented. Then, for the finite-time synchronization of the master and slave systems, a robust sliding-adaptive control method is provided. Update rules have been retrieved to estimate the system parameters by using the Lyapunov function while establishing the stability of the suggested mechanism and assuring the convergence of errors to zero. The proposed approach was used to solve the fractional order system of Gensou-Tsei with time-varying parameters, and the simulation results show that the presented approach performs well.

incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.

In this paper we study a bilinear optimal control problem related to a 2D parabolic-elliptic chemo-repulsion system consider a nonlinear chemical signal production term. We prove the existence of global optimal solutions with bilinear control, and applying a result on the existence of Lagrange multipliers in Banach spaces, we derive an optimality system for a local optimal solution.

Here we are concerning with two problems of a coupled system of random and stochastic nonlinear differential equations with two coupled systems of nonlinear nonlocal random and stochastic integral conditions. The existence of solutions will be studied. The sufficient condition for the uniqueness of the solution will be given. The continuous dependence of the unique solution on the nonlocal conditions will be proved.

The purpose of this paper is to find the time optimal control to a target set for semilinear stochastic functional differential equations involving time delays or memories under general conditions on a target set and nonlinear terms even though the equations contain unbounded principal operators. Our research approach is construct a fundamental solution for corresponding linear systems and establish variations of constant formula of solutions for given stochastic equations. The existence result of time optimal controls for one point target set governed by the given semilinear stochastic equation is also established.

In this paper, we employ fuzzy Sumudu transform for solving system of linear fuzzy differential equations with fuzzy constant coefficients. The system with fuzzy constant coefficients is interpreted under strongly generalized differentiability. For this purpose, new procedures for solving the system are proposed. A numerical example is carried out for solving system adapted from fuzzy radioactive decay model. Conclusion is drawn in the last section and some potential research directions are given.

The complexity of modern power grids, exacerbated by integrating diverse energy sources, espe-cially inverter-based resources (IBRs), presents a significant challenge to grid operation and plan-ning since linear models fail to capture the intricate IBR dynamics. This study employs the Sparse Identification of Nonlinear Dynamics (SINDy) method to bridge the gap between theoretical un-derstanding and practical implementation in power system analysis. It introduces the novel Volterra-based Nonlinearity Index (VNI) to examine system-level nonlinearity comprehensively. The distinction of dynamics into first-order linearizable terms, second-order nonlinear dynamics, and third-order noise elucidates the intricacy of power systems. The findings demonstrate a fundamental shift in system dynamics as power sources transit to IBRs, revealing system-level nonlinearity compared to module-level nonlinearity in conventional syn-chronous generators. The VNI quantifies nonlinear-to-linear relationships, enriching our comprehension of power system behavior and offering a versatile tool for distinguishing between different nonlinearities and visualizing their distinct patterns through the proposed VIN profile.

This paper presents a new analytical method for obtaining new deformations compatibility equations or, new Saint-Venant’s identities, into the relative coordinate system with projected deformations by applying the hypothesis of the lineal shell theory in general flexion state. The method proposed generalizes the compatibility conditions established by A.L. Goldenveizer for the shell theory. On the other hand, the new equations include the deformations compatibility equations by other authors: Flügge, Saint-Venant, Love-Kirchhoff, Timoshenko, Goldenveizer, and Reissner-Mindlin. The results showed an increase of knowledge in general shell theory, and provide inverse and semi-inverse solutions, whose systems solution correspond to the hyper-statics degrees of the physical model, and not to their degrees of freedom.

Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for a) describing the dynamics and b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and non-linearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of non-linearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

The main idea of this paper is to investigate the exact solutions and dynamic properties in optical nanofibers, which is modeled by space-time fractional perturbed nonlinear schr\"odinger equation involving Kerr law nonlinearity with conformal fractional derivative. Firstly, by the complex fractional traveling wave transformation, the traveling wave system of the original equation is obtained, then a conserved quantity, namely the Hamiltonian is constructed, and the qualitative analysis of this system is conducted via this quantity by classifying the equilibrium points. Moreover, the prior estimate of the existence of the soliton and periodic solution is established via the bifurcation method. Furthermore, all exact traveling wave solutions are constructed to illustrate our results explicitly by the complete discrimination system for polynomial method.

This paper presents a novel approach to power management techniques in energy storage system (ESS) interoperability with the energy power system (EPS) by inserting a SCADA controlled directional protection relay for improving the mode switching sequence. According with latest European standards, all necessary measures for re-synchronization and reconnection for health work of the system must be taken. The proposed approach intends to improve the “interface” between storage systems and electric power grid by using a directional relay which provides interoperability improvement by automatic recognizing and differentiating the switching sequence of the mode of operation from the fault mode. Simulated results from a small scale electrical generating system are provided. The results demonstrate that the proposed power management solution is technically ideal to achieve high quality energy while retaining the stability of the power grid network. The interoperability of PHS systems integrated with the electric power grid usually leads to perturbations in terms of frequency of the generated voltage, and for improving it we designed an automatic treating and re-connecting software module. This work regards the importance of producing electricity from green and renewable energy sources, while retaining the stability and reliability of the power grid. ESS systems are widely used to help balancing of intermittent forms of load demand on the electrical power grid at specific key moments. In this article, the pumped hydro storage (PHS) technologies are proposed as an economical way to utilize smoothly the off-peak energy or surplus energy, by pumping water to a reservoir at a higher level. During peak load periods, the stored water is discharged through the reversible pump-turbine to generate electricity to meet the peak demand controlled by designed SCADA application.

In graduate programs such as social work, field education is the signature pedagogy of education. As such, student placement with an appropriate field education agency is critical to ensure academic success and career readiness. A variety of Learning Management System (LMS) and Integrated Planning and Advising Service (IPAS) technologies have been developed to fully integrate technology into the educational system and streamline and improve the learning experience for students, educators, and administrators. Few (if any) of the existing solutions have capabilities to match students with field educators on the basis of an individual student’s completed coursework and area of specialization, as well as field educator needs and opportunities. This paper describes our experience developing a custom LMS/IPAS system—the School of Social Work information System (SSWiS)—that was designed specifically for student learning, faculty advising, and academic administration within our social work graduate program. We present the challenges that motivated the design of the SSWiS before describing the architecture and functionality of our solution. We then discuss our preliminary evaluation results. We conclude with a discussion of the benefits and limitations of our system in the context of today’s technical needs in graduate education in social work and other fields.

Complex diseases such as cancer are caused by changes in the Gene Regulatory Networks. Systems that model the complex dynamics of these networks along with adapting to real gene expression data are closer to reality and can help understand the creation and treatment of cancer. In this paper, for the first time, modelling of gene regulatory networks is performed using delayed nonlinear variable order fractional systems in the state space by a new tool called GENAVOS. This tool uses gene expression time series data to identify and optimize system parameters. This software has several tools for analyzing system dynamics. The results show that the nonlinear variable order fractional systems have very good flexibility in adapting to real data. We found that regulatory networks in cancer cells actually have a larger delay parameter than in normal cells. It is also possible to create chaos, periodic and quasi-periodic oscillations by changing the delay, degradation and synthesis rates. Our findings indicate a profound effect of time-varying order on these networks, which may be related to a type of cellular memory due to epigenetic and environmental factors. We showed that by changing the delay parameter and the variable order function for a normal cell system, its behavior changes and becomes quite similar to the behavior of a cancer cell. This work also confirms the effective role of the miR-17-92 cluster in the cancer cell cycle. GENAVOS is available at https://github.com/hanif-y/GENAVOS with its user guide and MATLAB codes.

As marine traffic is contributing to pollution and mostly have predictable route with repetitive load profiles, to reduce their impact on environment, hybrid system with proton exchange membrane fuel cells (PEMFC-s) and battery pack, is promising replacement. For this purpose, the numerical model of repowered existing vessel that have two diesel engines with 300 kWe is developed. The goal of this paper is to develop numerical model which analyzes and determine equivalent hybrid ship propulsion system for known traveling route. The developed numerical model consists of an interconnected system with PEMFC stack and battery pack as power sources. Numerical model is developed and optimized to meet minimal required power demand for successful route demand, which has variable loads and daily sails six-time same voyage- in total 54 nautical miles. The results have shown that equivalent hybrid power system consists of 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, hydrogen tank of 7200 l holding 284.7 kg at pressure of 700 bar is needed, compared to previous system that spend 1524 kg of diesel and generate 4886 kg of CO2.

Effective waste elimination is critical for the success of construction projects. Although several studies have focused on various aspects of construction waste, limited efforts have investigated the dynamic effect of Making-Do (MD) practices on productivity, rework, defects, and materials wastage. From a lean construction perspective, this study aims to address MD waste using the Last Planner System (LPS) and BIM. First, the casual structure that can cause MD in construction projects was expressed in a casual loop diagram (CLD), and thematic analysis uncovered the strategies of LPS-BIM to eliminate MD identified by reviewing the literature. Secondly, twenty-five strategies from the LPS and BIM strategies to address MD using Structural Equation Modeling (SEM) were assessed. Subsequently, a system dynamics model (SDM) for investigating LPS-BIM strategies on MD decisions in a construction project was formulated based on the underlying causal loop diagrams and the mathematical relations among the variables. Finally, the model was applied to three projects, and simulations for four LPS-BIM scenarios were carried out. The findings show that dynamic interactions among diverse production planning and control factors are critical in evaluating MD impacts on a construction project. The results demonstrate that the LPS-BIM approach resulted in an average 43.8% reduction in the tasks performed with MD, 45.3% of constraints, 66.5% of construction waste, increasing 13.7% completion rate, and 29.3% cost reduction, demonstrating that LPS-BIM as a more efficient solution for MD mitigation and construction planning.

Prolylcarboxypeptidase (PRCP, PCP, Lysosomal Pro-X-carboxypeptidase, Angiotensinase C) controls angiotensin - and kinin- induced cell signaling. Elevation of PRCP appear to be activated in chronic inflammatory diseases (cardiovascular disease (CVD), diabetes) in proportion to severity. Vascular endothelial cell senescence and mitochondrial dysfunction have consistently been shown in models of CVD in aging. The cellular senescence, a driver of age-related dysfunction, can differentially alter the expression of lysosomal enzymes due to lysosomal membrane permeability. There is a lack of data demostrating the effect of age-related dysfunction on the expression and function of PRCP. To explore the changes in PRCP, PRCP-dependent prekallikrein (PK) pathway was charcterized in early – and late-passage human pulmonary artery endothelial cells (HPAECs). Detailed kinetic analysis of cells treated with high molecular weight kininogen (HK), a precursor of bradykinin (BK), and PK revealed a mechansim by which senescent HPAECs activate the generation of kallikrein upon the assembly of the HK-PK complex on HPAECs in parallel with an upregulation of PRCP and endothelial nitric oxide (NO) synthase (eNOS) and NO formation. The NO production and expression of both PRCP and eNOS increased in early-passage HPAECs while decreased in late-passage HPAECs. Low activity of PRCP in late passage HPAECs was associated with rapid decreased telomerase reverse transcriptase mRNA levels. We also found that, with an increase in the passage number of HPAECs, reduced PRCP altered respiration rate. These results indicated that aging dysregulates PRCP protein expression, and further studies will shed light into the complexity of PRCP-dependent signling pathway in aging.

Large values and gradients of stresses and deformations, triggering concentrations of stresses and deformations, arise in the corner areas of a structure. The action of forced deformations, leading to the finite rupture of the contact between the elements of a structure, also triggers the concentration of stresses, while the rupture reaches an irregular point, a line on the area boundary. The theoretical analysis of the stress-strain state (SSS) of areas with angular cutouts in the boundary under the action of discontinuous forced deformations is reduced to the study of singular solutions to the homogeneous problem of the elasticity theory that has power-related features. The calculation of stress concentration coefficients in the domain of a singular solution to the elastic problem makes no sense. It is experimentally proven that the zone, that is close to the vertex of the angular cutout in the area boundary, has substantial deformations, rotations, and it corresponds to rising values of the first and second derivatives of displacements along the radius in cases of sufficiently small radii in the neighbourhood of the irregular point of the boundary. For such areas, it is necessary to consider the plane problem of the elasticity theory, taking into account the geometric nonlinearity under the action of forced deformations. This will allow analyzing the effect of relations between orders of values of deformations, rotations, and forced deformations on the form of the equation of equilibrium. The purpose of this work is to analyze the effect of relations of deformation orders, rotations, forced deformations on the form of the equilibrium equation in the polar coordinate system for a V-shaped area under the action of forced temperature-induced deformations with regard for the geometrical non-linearity and physical linearity.

Cancer remains one of the main causes of death in the world due to its increasing incidence and treatment difficulties. Although several progresses have been done in this field, innovative approaches are needed to reduce tumor incidence, progression and spreading. In particular, the development of cancer vaccines is currently ongoing like both preventive and therapeutic strategy. This concept is not new, but few vaccines have been approved in oncology. Antigen-based vaccination emerges as a promising strategy, leveraging specific tumor antigens to activate the immune system response. However, challenges persist in finding suitable delivery systems and antigen preparation methods. Exosomes (EXs) are highly heterogeneous bilayered vesicles which carry several molecule types in the extracellular space. The peculiarity is that they may be released from different cells and may be able to induce a direct or indirect stimulation of immune system. In particular, EX-based vaccines may cause anti-tumor immune attack or produce memory cells recognizing cancer antigens and inhibit disease appearance. This review delves into EXs composition, biogenesis, and immune-modulating properties exploring their role as tool for prevention and therapy in solid tumors. Finally, here we describe future research directions to optimize vaccine efficacy and realize the full potential of EX-based cancer immunotherapy.

Caffeine is the most popular and widely-consumed behaviourally-active substance in the world. This review focuses on the influence of caffeine on the cardiovascular system, with a special focus on blood platelets. For many years, caffeine was thought to have a negative effect on the cardiovascular system mainly due to increasing blood pressure. However, more recent data suggests that habitual caffeine consumption may reduce the risk of cardiovascular disease and hypertension. This could be a significant finding as cardiovascular disease is the leading cause of death worldwide. Caffeine is known to inhibit A1 adenosine receptors, through which it is believed to modulate inter alia coronary blood flow, total peripheral resistance, diuresis and heart rate. Although few publications have reported the effects of caffeine on platelet function, it seems reasonable to conclude that caffeine has antiplatelet activity. Platelet activation is known to play a crucial role in the development of arterial thrombotic diseases such as coronary heart disease and stroke, and hence there is a need to develop novel platelet inhibitors. One promising strategy to develop effective antiplatelet therapy involves targeting multiple pathways of platelet activation. Therefore, this review also examines the concept of dual antiplatelet therapy and the potential benefits associated with the therapeutic use of combined P2Y12 antagonists and adenosine receptor agonists in cardioprotection.

Twelve clinically healthy and sexually mature New Zealand White rabbits were studied. The non-contrast imaging included T1-weighted and T2-weighted spin echo and gradient echo sequences in transverse, sagittal and dorsal planes. Transverse MRI (Т2-weighted image) through L1 demonstrated only the right kidney. The transverse T2-weighted image through L2 showed both kidneys. The cranial part of the urinary bladder on T1-weighted transverse scans through L4 was flexed to the left. The T2-weighted sagittal image 30 mm to the right of the median plane showed the right kidney, the right ureter and the urinary bladder. The T2-weighted sagittal image 30 mm to the left of the median plane showed part of the left kidney, the left ureter and the urinary bladder. The T2-weighted sagittal image 45 mm to the left of the median plane presented the lateral part of the left kidney. The dorsal MRI image (Т2-weighted sequence) through the horizontal plane 30 mm ventral to the spine demonstrated the whole organs. The data will be useful in imaging anatomy and diagnostic studies of various pathologies of the excretory system in rabbits and other mammalian species.

Soil microorganisms plays an important role on plant development and the homogenization of soil microbiome is agri-environmental harmful. It is essential that agricultural practices are carried out taking soil microbiome preservation in consideration. Agroforestry systems are one of the most environmentally friendly agrosystems and its plant diversity influence directly the soil microbiome diversity. In addition, many technologies have been developed and used by applying inoculants in agriculture. However, most of the inoculants are developed with only one or a little number of microbial species to supply a specific plant nutritional demand. In this study we tested the efficacy of the microbial consortium (MC) obtained from compost and the cyanobacteria Arthrospira platensis (AP) comparing with the application of the vermicompost tea (VT) and bokashi (BK) in arugula, lettuce, beetroot, and carrot in an agroforestry system in two seasons. Soil changes on chemical properties and β-glucosidase and arylsulfatase were measured over time. The vegetables yield was measured and for arugula and lettuce the nutritional traits and plant root system were also evaluated. Greater plant yield, nutritional values, and plant root development was observed in the CM treated plants, AP and BK had similar results. Our results show that both CM and AP have potential to become a sustainable product for agricultural production

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

In recent years, the widespread introduction of bike-sharing systems in China has had a profound impact on the daily lives of Chinese citizens and the development of the urban transport system. This article attempts to analyse the impact of this phenomenon on sustainability. The gradual improvement of related monitoring measures has facilitated the maturation of the bike-sharing industry from the initial stage of uncontrolled growth to the current stage of standardised management. By tracing the global development of bike-sharing systems with a special focus on China, this study sheds light on the platformisation of bicycles and its multiple impacts on technical, environmental, cultural, economic and social sustainability. Furthermore, the study provides a comprehensive analysis of the transformation of bicycles in China and highlights the diverse impacts of platform-based bike sharing on various facets of Chinese society. The development of different bike-sharing systems in China is a unique and crucial case to interpret the current situation of bike-sharing and imagine future scenarios. In contrast to the prevailing and uniform approach derived from the experiences of Northern European countries, the massive and widespread experimentation with different bike-sharing schemes in China reveals not only potentials and aspects of sustainability, innovation and urban and social regeneration, but also some hidden shadows similar to those in small-scale contexts such as Northern Europe. Furthermore, the study emphasises the crucial role of sustainable development principles in addressing urban challenges specific to China.

Leishmaniasis, caused by Leishmania parasites transmitted through sandfly bites, encompasses various clinical forms with distinct immune responses. Mucocutaneous leishmaniasis (ML) exhibits a delicate balance between IFN-γ and IL-10, influencing disease severity. Conversely, cutaneous leishmaniasis (CL) presents varied IFN-γ/IL-10 ratios, affecting prognosis. Leishmania RNA virus 1 (LRV1) and extracellular vesicles modulate ML immunity. In visceral leishmaniasis (VL), severe immune defects necessitate combined host immunity and drug treatment for cure. Cytokines and chemokines serve as diagnostic and prognostic markers in CL. Neutrophils' dual role in leishmaniasis pathogenesis is extensively studied, alongside the NLRP3 inflammasome's regulation of Th1/Th2 responses. Live attenuated LdCen-/- parasites offer promising VL vaccine candidates, with biomarkers like miR-21 aiding efficacy assessment. Neutrophil-DC interactions play pivotal roles in vaccine-induced immunity. Genetically modified live attenuated vaccines and novel formulations elicit robust cellular responses against VL. Understanding cytokine networks is crucial for treatment strategy development. Ongoing research strives to overcome vaccine development challenges and ensure long-term protection against leishmaniasis.

Food composition data in the EMR countries are often lacking, obsolete or unreliable. The study aims to provide reliable and up-to-date nutrient data of selected food products consumed in Oman in order to evaluate consistency of nutrition labelling and update the current used food composition database. Contents of total fat, fatty acids, carbohydrates, total nitrogen, sugars and sodium were chemically analyzed in 221 foods and beverages purchased in 2022 and label and corresponding laboratory values were compared using the tolerance levels of the European Union. Food items were classified according their nutritional composition, the extent of processing and coded according FoodEx2 system. Results indicate that the nutrient values stated on a label align with the values obtained in the laboratory with the exception of a 6.3% discrepancy in TFAs content, where the reported values are higher than the appropriate reference values. The category with the highest frequency (71.5%) consisted of ultra-processed foods. In terms of inconsistencies in the nutrition claims, 5.1% of food products with claims did not comply with the statement “sugar free” or “low salt”. These findings underscore the significance of vigilant monitoring of nutritional labels as a means to implement measures that promote the well-being of consumers.

The sustainable development of an area is highly dependable on reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for the active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller's performance is evaluated in MATLAB environment and simulation results show the proposed scheme's superiority under different operating conditions.

This study presents an advanced Simulated Shearer Machine Cutting Experiment System enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to mirror the operational dynamics of actual mining cutters accurately. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time-domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal-rock identification research, offering significant insights into the nuances of shearer operation.

We present a partition of quantum observables in an open quantum system which is inherited from the division of the underlying Hilbert space or configuration space. It is shown that this partition leads to the definition of an inhomogeneous continuity equation for generic, non-local observables. This formalism is employed to describe the local evolution of the von Neumann entropy of a system of independent quantum particles out of equilibrium. Crucially, we find that all local fluctuations in the entropy are governed by an entropy current operator, implying that the production of entanglement entropy is not measured by this partitioned entropy. For systems linearly perturbed from equilibrium, it is shown that this entropy current is equivalent to a heat current, provided that the system-reservoir coupling is partitioned symmetrically. Finally, we show that any other partition of the coupling leads directly to a divergence of the von Neumann entropy. Thus, we conclude that Hilbert-space partitioning is the only partition of the von Neumann entropy which is consistent with the Laws of Thermodynamics.

Objectives: To assess the concordance of the preoperative application of the #Enzian classification (#ENZIANi) with the postoperative result (#ENZIANs) using surgical findings as the reference standard. Methods: This retrospective study included 282 consecutive patients with deep endometriosis undergoing surgical treatment. Preoperative assessment with transvaginal sonography and magnetic resonance imaging was compared with postoperative assessment. Concordance and diagnostic test evaluation were calculated. Results: The highest concordance was observed in the F (abdominal wall endometriosis) with k Cohen of 0.837, following the values for pelvic locations, with 0.795 for T left, 0.791 for T right, 0.776 for F (adenomyosis), 0.766 for C (rectum), and 0.75 and 0.72 for O right k and O left, respectively. The highest sensitivity was demonstrated for the P compartment *(98%), T compartment (both sides 97%), and A, B, C (94%- 96%), corresponding with deep endometriosis. Conclusions: Preoperative assessment using TVS/TAS+MRI with the ENZIANi score correlates well with the ENZIANs postoperative score and demonstrates good concordance in the detection and localization of deep endometriosis, thereby minimizing false negative results and ensuring accurate preoperative staging. The ENZIAN classification is well-suited to surgeons' needs and benefits from continuous development. Future improvements, such as adding the expanded C module, may be considered in the next edition.

In crystal structure prediction, Newton's second law can always be applied to particles (atoms or ions) in a cell to determine their positions. However the values of the period vectors (crystal cell edge vectors) should be determined as well. Here we applied the dynamical equations of period vectors derived recently based on Newton's laws (doi:/10.1139/cjp-2014-0518) for that purpose, where the period vectors are driven by the imbalance between the internal and external pressures/stresses. For equilibrium states, they became equations of state, which essentially turn out the equilibrium conditions of crystals from mechanical point of view. For external pressure, by ignoring some effects, they reduced to the Mie-Gruneisen equation. Since the internal stress has both a full kinetic energy term and a full interaction term, the influences of both external temperature and stress/pressure on crystal structures can be calculated, then thermodynamical properties and processes were presented. Contrary to usual ideas, the equations show that pure harmonic vibrating phonons can result in thermal expansion in crystals when the external temperature is changed. Finally, crystal system collapse due to temperature and/or stress/pressure change was discussed.

The process of modelling energy systems is accompanied by challenges inherently connected with mathematical modelling. However, due to modern realities in the 21st century, existing challenges are gaining in magnitude and are supplemented with new ones. Modellers are confronted with a rising complexity of energy systems and high uncertainties on different levels. In addition, interdisciplinary modelling is necessary for getting insight in mechanisms of an integrated world. At the same time models need to meet scientific standards as public acceptance becomes increasingly important. In this intricate environment model application as well as result communication and interpretation is also getting more difficult. In this paper we present the open energy modelling framework (oemof) as a novel approach for energy system modelling and derive its contribution to existing challenges. Therefore, based on literature review, we outline challenges for energy system modelling as well as existing and emerging approaches. Based on a description of the philosophy and elementary structural elements of oemof, a qualitative analysis of the framework with regard to the challenges is undertaken. Inherent features of oemof such as the open source, open data, non-proprietary and collaborative modelling approach are preconditions to meet modern realities of energy modelling. Additionally, a generic basis with an object-oriented implementation allows to tackle challenges related to complexity of highly integrated future energy systems and sets the foundation to address uncertainty in the future. Experiences from the collaborative modelling approach can enrich interdisciplinary modelling activities. Our analysis concludes that there are remaining challenges that can neither be tackled by a model nor a modelling framework. Among these are problems connected to result communication and interpretation.

Bisphenol A production plant possesses considerable potential risks in the top of the methanol separation column, as pressurized acetone, methanol and water are processed at an elevated temperature, especially in the event of an abnormal pressure increase due to sudden power outage. This study assesses the potential risks in the methanol separation column through hazard and operability assessment and evaluates the damages in the case of fire and explosion accident scenarios. The study chooses three leakage scenarios: a 5-mm puncture on the methanol separation column, a 50-mm diameter fracture of a discharge pipe and a catastrophic rupture, and simulates using Phast (Ver. 6.531) the concentration distribution of scattered methanol, thermal radiation distribution of fires and overpressure distribution of vapor cloud explosions. Implementation of safety instrumented system equipped with two-out-of-three voting as a safety measure can detect overpressure at the top of the column and shut down the main control valve and the emergency shutoff valve simultaneously, all at the same time. By applying safety integrity level of three, the maximal release volume of the safety relief valve can be reduced and therefore, the design capacity of the flare stack can also be reduced. Such integration will lead to improved safety at a reduced cost.

In this article the problem of detection and isolation (FDI) in wheeled mobile robot is 1 treated. first of all we consider a master-slave robot system in which the master robot has to follow a 2 desired trajectory given by a well-determined control law, then the slave is controlled by the master. 3 However in real environment there is no ideality because faults can appear in the master or slave 4 or even the both and affect the system and may lead to unacceptable results so to ensure a well 5 functioning an extended kalman filter is tuned to diagnosis eventual faults and to generate residuals 6 in order to detect and isolate them. The performance of an estimator based on an EKF is simulated 7 based MATLAB Simulink simulations.

Building a new-type power system is the key core of the green and low-carbon development of energy, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy-energy-environment (3E) system. This paper constructs a "Dual-Triangle" theoretical framework consisting of the “Energy Trilemma” and the "Sustainable Development Triangle", by combining historical statistical analysis with scenario predictive analysis, analyzes the historical characteristics of the "Dual Triangle" and future development trends under different scenarios, and studies the coupled interaction relationship of the "Dual-Triangle" to explore the optimal transformation path of the power system that is most beneficial to the coordinated development of the 3E system. The conclusions are as follows: (1) The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, the level of coordinated development of the 3E system fluctuated upwards, and the operation status of the "Dual Triangle" was stable. (2) Positive coupling relationship between "Dual Triangle" of scenario S1 is close, which can be regarded as the preferred path for the reconstruction of the power system. Due to phased development has clear development priorities, scenario S2 will artificially break the balance of the “Energy Trilemma”, which is a development model that dynamically mitigates the challenges of the “Energy Trilemma”.(3)The contradiction between economy and cleanliness is continuously weakened, and security has become the core contradiction of the "Impossible Triangle". The combination of renewable energy and energy storage is the key to resolving this contradiction. This paper conducts an integrated and innovative application of the "Dual-Triangle" theory, and the results provide a case basis for how to achieve the coupled interaction of the "Dual-Triangle", promote high-quality energy development, and support the sustainable development of the economy and society.

The inherent capabilities of the K-means++ algorithm to approximate system dynamics within complex systems are subjected by constructing a network structure that captures interconnections among identified components, extending its original purpose as a data clustering method and transforming it into a tool for systems analysis. Leveraging advanced measurement technologies and sophisticated data collection systems, the K-means++ algorithm unveils hidden relationships among components and identifies critical elements. This study explored the algorithm's potential, facilitating the identification of critical components to enhance system operation, control, optimization, and decision-making and examining the practicality and resiliency of the method in real-world application with noisy and limited data. A case study conducted on power systems (IEEE 39-bus and IEEE 300-bus systems) exemplifies K-means++'s capacity to accurately identify critical components and approximate system dynamics, supported by performance metrics affirming its effectiveness and robustness in system analysis through measurements of bus similarity within clusters based on standard deviation and comparison of net tie-line flows in equivalent networks with the original network across scenarios. Performance metrics, including the Silhouette Score, Davies-Bouldin Index, and Variation of Information (VI) score, further validated K-means++'s performance, yielding reliable and consistent results.

Nanoparticles have been used as a novel drug delivery system. Drug-incorporated nanoparticles for local delivery might optimize the efficacy and minimize the side effects of drugs. The efficacy and safety of intratracheal administration of prostacyclin analog (beraprost)-incorporated nanoparticles and imatinib, a PDGF-receptor tyrosine kinase inhibitor, -incorporated nanoparticles in Sugen-hypoxia-normoxia or monocrotaline rat models of PAH and in human PAH-pulmonary arterial smooth muscle cells have been reported. The use of inhaled drug-incorporated nanoparticles might be a novel approach for treatment of PAH.

This work investigates the stability conditions for linear systems with time-varying delays via augmented Lyapunov-Krasovskii functional(LKF). Two types of augmented LKFs with cross terms in integral are suggested to improve the stability conditions for interval time-varying linear systems. Through this work, the compositions of the LKFs are considered to enhance the feasible region of stability criterion for linear systems. Mathematical tools such as Wirtinger-based integral inequality(WBII), zero equalities, reciprocally convex approach, and Finsler’s lemma are utilized to solve the problem of stability criteria. Two sufficient conditions are derived to guarantee the asymptotic stability of the systems using linear matrix inequality(LMI). First, asymptotic stability criteria are induced by constructing the new augmented LKFs in Theorem 1. And then simplified LKFs in Corollary 1 are proposed to show the effectiveness of Theorem 1. Second, asymmetric LKFs are shown to reduce the conservatism and the number of decision variables in Theorem 2. Finally, the advantages of the proposed criteria are verified by comparing maximum delay bounds in two examples. Two numerical examples show that the proposed Theorem 1 and 2 obtained less conservative results than existing outcomes. Also, Example 2 shows that the asymmetric LKF methods of Theorem 2 can provide larger delay bounds and fewer decision variables than Theorem 1’s in some specific systems.

Three Seas Initiative (3SI) is still an underresearched area and is particularly important due to historical circumstances and economic backwardness. A study was carried out to assess the impact of renewable energy and production generated by the agricultural sector on CO2 emissions in 3SI countries between 2008 and 2020. The study used panel data analysis based on the two-step system generalised method of moments (GMM) and the Dumitrescu-Hurlin panel causality test. The results show that a 1% increase in the value-added generated by agriculture increases CO2 emissions in the countries studied by 0.11%. In contrast, a 1% increase in GDP leads to a 0.29% increase in CO2 emissions. Conversely, when renewable energy consumption increases by 1%, CO2 emissions fall by 0.25% in the countries studied. One way to reduce CO2 emissions from agricultural production in the short term is to increase the share of renewables, which, incidentally, is in line with EU action.

This paper addresses the approach of fully automated intelligent control systems in production organizations, focusing on intelligent production systems research. The overview highlights a gap in control operations ahead of assembly as single-purpose control machines are frequently employed, which is precisely their drawback. Conversely, there are accurate multipurpose measurement centers that do not provide total quality control over the manufactured parts because of the cost and length of the measuring procedure. The study's primary objective was to create an intelligent modular control system, whose concept enables it to control various components. The purpose of the modular intelligent control system is to keep an eye on each module's quality during the pre-assembly phase. This system ensures exact control over the production process of modules by integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms. The technology makes it possible to monitor several parameters and important quality features. Integrated sensors and diagnostic technologies identify possible inaccuracies and disparities, guaranteeing prompt diagnosis of issues in specific modules. The system's intelligent control algorithms streamline the production process and guarantee synchronization between individual modules to achieve consistent quality and performance. One advantage of implementing the solution is that, on average, inspection time is reduced by 40 to 60% as compared to the prior manual inspection approach. In addition, full measurement data archiving was made available, and finally, the human element—which introduced a substantial error rate into this process—was removed. This system also helps to increase the project's overall efficiency, predictability, and safety while allowing for quick adjustments by standards and requirements.

Preventing or treating infections at implantation sites where the risk of bacterial contamination is high requires the development of intelligent drug delivery systems. In this work, honeycomb structures as a potential drug carrier were obtained for the first time in a fast, one-step anodization process of titanium grade 4, lasting only 60 seconds. Fourth generation oxide nanotube layers were obtained from an aqueous solution of 0.5% AgNO3 in the voltage range from 50 to 80 at 15(1) °C. The FE-SEM method showed the length of the oxide nanotubes from 1.02(2) µm at 50 V to 230(8) nm at 80 V. The EDS analysis revealed that during anodizing, silver particles are deposited on the Ti G4 up to 2.2(5) wt.%. Electrochemical measurements by open circuit potential and anodic polarization curves methods proved the improvement of in vitro corrosion resistance as a result of Ti G4 anodizing. Assessment of the anodized Ti G4 as gentamicin sulfate carrier carried out using ATR-FTIR and UV-VIS absorption spectroscopy revealed that the obtained oxide nanotube layers can be used for application of drugs directly to the tissue around the implant. This method of application allows full use of the therapeutic dose of the antibiotic used.

This article deals with the denoising of signals from sensors (current and voltage) in a photovoltaic conversion chain using wavelet theory. The idea is to reduce the noise as much as possible without distorting or losing the original signal information. To see the reliability of this proposed method, a comparative study between our proposed method (WDD) and the classic low-pass filter method was presented. The contribution in this work is the improvement of the DC-DC boost converter via the use of wavelet theory, however, that’s why we set two objectives: the first is to obtain reliable control of the DC-DC boost converter via the proposed WDD and the second to increase the overall system efficiency. The experimental results show a very great improvement in the quality of the signals of the proposed method compared to the classic method of filtering by low-pass filter.

Efficient processing of end-of-life lithium-ion batteries of electric vehicles is important and a pressing challenge for a circular economy. Regardless of whether the processing strategy is recycling, repurposing or remanufacturing, the first processing step would usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. The work was conducted as a case study to demonstrate the concept of autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported and future research directions are suggested.

Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

The Digital electronics collaborative enhanced learning (DECEL) project has recently developed an international collaborative education course. Its main objective is to enhance the digital electronics skills of international students by working on a complex, multidisciplinary applied problem using a mixed digital architecture. We have developed a logic level synthesis and dedicated software layers on the Red Pitaya FPGA platform. The diversity of digital concepts to be implemented, from hardware description language (HDL) to high-level languages such as Python or Matlab, forced the students to work together and rapidly improve their skills. Their motivation was fueled by the curiosity of controlling an ultrasound probe to obtain ultrasound signatures. This particular physics, little known to the students, was an additional source of curiosity. The goal of forming an image in a liquid medium was an additional motivating factor for them. The students reported that they learned a lot from the experiment. Thus, the technical parts and pedagogical results are documented in this work for reproducibility.

Household waste is the primary source of environmental pollution due to global population growth compared to other sources of waste. This research aims to develop a smart and integrated household waste management system framework. The resulting framework not only focuses on dimensions of information technology but also links it with other integrated dimensions. This research attempts to conduct a Systematic Literature Review (SLR) using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) protocol to produce a smart and integrated household waste management framework. The framework's design is carried out by identifying the type of household waste management process based on the Integrated Sustainable Waste Management (ISWM) framework, dimensions that support smart household waste management, and stakeholders involved. The SLR results, namely dimensions and subdimensions that support the framework of a smart and integrated household waste management system, were validated by a resource person from the Indonesian Ministry of Environment and Forestry. The smart and integrated household waste management framework that has been developed in this research contains five main dimensions: Information Technology, Operational Infrastructure, Governance, Economy, and Social-culture. This framework also addresses stakeholder engagement to support smart household waste management systems and identifies waste management processes based on the ISWM framework. This research uses the PRISMA technique to provide a framework for smart and integrated household waste management systems in the initial stage. The proposed framework has been validated and can be further developed as a smart and integrated household waste management system. This research also contributes to the involvement of various dimensions identified to deal with waste problems.

Protection suit is vital for fireman’ safety. Traditional protection suit just physically protects fireman from empyrosis, but can not locate the body injury position from the impact debris. Herein, we present a wearable impact debris positioning system for fireman protection suit alarming based on accelerometer array. The wearable piezoelectric accelerometers with distribute regularly on the suit to detect the vibration on different body parts, which is conducive to positioning injured body parts. In addition, the injured parts can be displayed on the dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11ms attributing to smart signal processing method. This work provides a reliable and smart method for locating and assessing the body injury position from the impact debris, which is significant for fire disaster commander to rescue injured fireman in time.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

In this work, we introduce the design a 16-channel in-pixel neural analog front-end which employs a current-based summing approach to establish a common-mode feedback loop. The primary aim of this novel structure is to enhance both the system common-mode rejection ratio (SCMRR) and the common-mode interference (CMI) range. Compared to more conventional designs, the hereby proposed front-end utilizes DC-coupled inverter-based main amplifiers, which significantly reduce the occupied on-chip area. Additionally, the current-based implementation of the CMFB loop obviates the need for voltage buffers, replacing them with simple common-gate transistors and in turn decreasing both area occupancy and power consumption. The proposed architecture is further examined from an analytical standpoint, providing a comprehensive evaluation through design equations of its performance in terms of gain, common-mode rejection and noise power. A 50μm×65μm compact layout of the pixel amplifiers that make up the recording channels of the front-end was designd in a 180 nm CMOS process. Simulations conducted in Cadence Virtuoso reveal an SCMRR of 80.5 dB and a PSRR of 72.58 dB, with a differential gain of 44 dB and a bandwidth that fully encompasses the frequency range of the bio-signals that can be theoretically captured by the neural probe. The noise integrated in the range between 1 Hz and 7.5 kHz results in an IRN of 4.04 μVrms. Power consumption is also tested, with a measured value of 3.77 μW per channel, corresponding to an overall consumpution of about 60 μW. To test its robustness with respect to PVT and mismatch variations, the front-end is evaluated by means of extensive parametric simulations and Monte Carlo simulations, revealing favorable results.

This study aims to assess the operational capabilities of disaster and emergency management re-sources (DEMRs), which are pivotal in mitigating risks and minimizing losses amid the increas-ing frequency of disasters. The research constructs an effective operational capability evaluation system for DEMRs, encompassing four key aspects: resource planning, organizational manage-ment capability, resource support capability, and information processing capability. It focuses on identifying the factors that influence the effective operational capability of DEMRs in China and Korea, comparing and analyzing the relative importance and priority of each evaluation domain and indicator within these countries to enhance DEMRs' operational effectiveness. The method-ology involves calculating and ranking the weight of the four domains, with findings indicating that organizational management capability is most significant in China, whereas resource sup-port capability is prioritized in Korea. A comparative analysis of the local weight of indicators within each domain revealed the largest discrepancy between China and Korea in the infor-mation processing capability domain. The study concludes by calculating global weights, identi-fying fast response capability and resource allocation capability as the most impactful factors on DEMRs' operational capability, highlighting their critical role in disaster and emergency man-agement.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

To assess the community health workers’ (CHWs) knowledge, attitude, and practices (KAP) in type-2 diabetes (T2D) and hypertension (HTN) management. We thus also sought to assess the potential health system barriers for incorporating CHWs in T2D and HTN management. Mixed-method study comprised (1) a survey among 153 active CHWs to assess their KAP of T2D and HTN, and (2) semi-structured interviews with key informants to understand challenges of the health system and proposed solutions in incorporating CHWs in T2D and HTN management. The majority of CHWs correctly answered general knowledge questions on NCDs, risk factors, and prevention; however, only one-quarter correctly answered questions on smoking, family history, or tobacco use as risk factors. Most respondents appeared to have positive attitudes and have been practicing some activities related to T2D and HTN. Both financial and non-financial resource constraints were cited as challenges of the health system; therefore, re-structuring the role definition of CHWs and task of responsibilities, and assessment the need and workload have been proposed as ways forwards to effectively incorporate CHWs in T2D and HTN care. CHWs in Cambodia have shown their potential in T2D and HTN management; however, a well-designed strategy including technical training, clearly defined roles and responsibilities, and strong supporting structure are important to maximize their potential in the health system.

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

As modern technologies, particularly home assistant devices, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information in 15-minute or even 5-minute intervals, including weather forecasts, outputs from Renewable Energy Sources (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the Local Energy Market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by Large Language Models (LLM), Scikit-llm and Zero-Shot Classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

Mechanical ventilation is a life-saving treatment for critically ill patients who are struggling to breathe independently due to injury or disease. Globally, large numbers of Individuals have always required mechanical ventilation per year. The Covid-19 pandemic elevated the significance of the mechanical ventilation which have played a significant role in sustaining Covid 19 infected critically ill patients who could not breathe on their own. The pandemic drew the attention of the world to the shortage of ventilators globally.This research work presents the formulation of a detailed Port Hamiltonian model of a mechanical ventilator integrated to the human respiratory system. The interconnection and coupling conditions for the various subsystem within the mechanical ventilator and the coupling between the mechanical ventilator and the human respiratory system is also presented.A structure preservation discretization is provided along side numerical simulations and results. The obtained results are found to be comparable to results presented in literature.The future work will include the design of suitable controllers for system.

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditional manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large numbers of drones expected to operate at low altitudes in complex urban environments. To address this critical issue, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include: i) Developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; ii) Designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; iii) Improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and iv) Investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Exploring for green and affordable protection of perishable fruit like cherry tomato is of significance. Here the protective efficacy and its underpinnings of coating with oleaster gum, alone or incorporated with cuminal, on cherry tomatoes stored at ambient temperature were investigated. The composite coating of oleaster gum with 0.1% cuminal reduced decay, respiration rate, weight loss, and softening of the fruit and decelerated their decreases of total soluble solids, titratable acidity, and soluble proteins, and therefore maintained their marketability. Furthermore, it reduced the accumulation of O•− 2and H2O2 in fruit, and mitigated cell membrane lipid oxidation and permeabilization, thereby retarding their senescence. Instrumentally, it elevated the activities of superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase and the levels of ascorbic acid and glutathione. This potentiation of the fruit antioxidant system makes the composite coating a promising approach to keeping the postharvest quality of perishable fruit.

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process of developing the toolbox is demonstrated. Afterwards, a numerical example is illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

Using the example of a generator model with dedicated inertia, a theoretical study of two coupled self-oscillators with capacitive coupling, their sequential single-frequency synchronization, chaos and two-frequency synchronization with an adiabatic change in the magnitude of the coupling between partial self-oscillators was carried out. The parameters of self-oscillators and the values of the coupling coefficient at which the specified operating modes of coupled self-oscillators exist are determined. The results of numerical studies, illustrating the conditions for excitation of single-frequency, chaotic and dual-frequency oscillations in a system of coupled self-oscillators are presented.

Presynaptic Ca²⁺-influx through voltage-gated Ca²⁺ channels (VGCC) is a key signal for synaptic vesicle release. Synaptic neurexins can partially determine the strength of transmission by regulating VGCCs. However, it is unknown whether neurexins modulate Ca²⁺-influx via all VGCC subtypes similarly. Here, we performed live cell imaging of synaptic boutons from primary hippocampal neurons with a Ca²⁺-indicator. We used the expression of inactive and active Cre recombinase to compare control to conditional knockout neurons lacking either all or selected neurexin variants. We found that reduced total presynaptic Ca²⁺-transients caused by the deletion of all neurexins were primarily due to the reduced contribution of P/Q-type VGCCs. The deletion of neurexin1α alone also reduced the total presynaptic Ca²⁺-influx but increased Ca²⁺-influx via N-type VGCCs. Moreover, we tested whether the decrease of Ca²⁺-influx induced by activation of cannabinoid receptor 1 (CB1-receptor) is modulated by neurexins. Unlike earlier observations emphasizing a role for β-neurexins, we found that the decrease of presynaptic Ca²⁺-transients induced by CB1-receptor activation depended more strongly on the presence of α-neurexins in hippocampal neurons. Together, our results suggest that neurexins have unique roles in the modulation of presynaptic Ca²⁺-influx through VGCC subtypes and that different neurexin variants may affect specific VGCCs.

Finding a good MIMO system model also major issue in Wireless Communication system. It is facing with so many problem, one of the major problem is finding good system model in terms of capacity and transmitting antenna system. In this paper, we analyze the channel capacity of various MIMO system model with some constant SNR level and outage probability. We establish a novel idea for MIMO system models as consider as 2^N- MIMO system model and find-out change in channel capacity when different transmitting antennas with constant SNR and outage probability. The channel capacity ratio CC_R is presented here on the basis of 2^N- MIMO channel capacity model. Number of transmitted antenna presented in MIMO system is increases is well-known however paper shows change in capacity in simple form.

This paper explores Ghana's two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. The study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities as well as the availability of laws and policies to support analogue to digital conversion. Furthermore, a robust approach for protecting data, operating system and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. To sustain the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra's LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.

Switzerland, a wealthy country, has a cutting-edge healthcare system, yet per capita, it emits over one ton of CO2, ranking among the world's most polluting healthcare systems. To estimate the carbon footprint of the healthcare system of Geneva’s canton, we collected raw data on the activities of its stakeholders. Our analysis shows that when excluding medicines and medical devices hospitals are the main greenhouse gas emitter by far, accounting for 48% of the healthcare system’s emission, followed by nursing homes (20%), private practice (18%), analysis laboratories (7%), dispensing pharmacies (4%), the homecare institution (3%) and the ambulance services (

AbstractBackground: In digitalization era, health care system was conditioned with the health worker’s ability to adapt media of digital technology. Digital technology media was used to strengthening and fasted the health care for patient and community. Aims: This study aimed to explore the accurate information of health professional utilization digital technology during doing the job at the health facilities in Timor-Leste. Methods: This study was used correctional study with 56 respondents was participated in the study. Was collected the data from 1 to 30 February 2023. Was used questioner of google form based for data collection. Data collection was distributed via online with google form to social media as WhatsApp’s, email and face book and twitter to completed by respondents. The inclusive Criteria are: health professional living in Timor-Leste, to able response the questioner. Was used descriptive analysis in this study. Finding: This study was indicated that majority of health professional was participated in the study as 54,2% man then women have 45,8%. About 100% health professional used hand phone to facilitate their daily activities including related with their work activities. Other side indicated in digital technology tools use are able to use Microsoft word is 78,3%, able to use Microsoft Excel is 76,7% and able to use Power Point is 81,7%. E-mail use about 89,2%, WhatsApp’s use 81,7%, About 74,2% was used zoom/webinars/Google meet application, about 72,5% sad that they were used computer in the work place until now. Around 96.4% need to improve or deepen their knowledge and skills in using ICT in the future to facilitate they are activity. Conclusion: Majority of health professional in Timor-Leste was used the computer, internet, hand phone in daily activities. Majority need to gate the training to improve their knowledge, attitude and psychomotor in the future.

Acute respiratory distress syndrome (ARDS) is caused by infectious insults, such as pneumonia from various pathogens or related to other noninfectious events. Clinical and histopathologic characteristics are similar across severely affected patients, suggesting that a common mode of immune reaction may be involved in the immunopathogenesis of ARDS. There may be etiologic substances that have an affinity for respiratory cells and induce lung cell injury in cases of ARDS. These substances originate not only from pathogens, but also from injured host cells. At the molecular level, these substances have various sizes and biochemical characteristics, classifying them as protein substances and non-protein substances. Immune cells and immune proteins may recognize and act on these substances, including pathogenic proteins and peptides, depending upon the size and biochemical properties of the substances (this theory is known as the protein-homeostasis-system hypothesis). The severity or chronicity of ARDS depends on the amount of etiologic substances with corresponding immune reactions, the duration of the appearance of specific immune cells, or the repertoire of specific immune cells that control the substances. Therefore, treatment with early systemic immune modulators (corticosteroids and/or intravenous immunoglobulin) as soon as possible may reduce aberrant immune responses in the potential stage of ARDS.

Recent interest in intestinal hormones has risen with the idea that they modulate glucose tolerance and food intake through a variety of mechanisms, and such hormones like peptide YY (PYY), ghrelin, glucagon-like peptide (GLP)-1 and 2, and cholecystokinin (CKK) are therefore excellent therapeutic candidates for the treatment of diabetes and obesity. Furthermore, in the recent years, multiple studies suggest that the microbiota is critically important for normal host functions, while impaired host microbiota interactions contribute to the pathogenesis of numerous common metabolic disorders. In this study, we considered the nutraceutical effects of β-glucans added to pasta at the concentration of 6g\100g. Ten participants have been recruited and hematochemical analyses and intestinal hormones tests have been performed before and after 30 days of pasta intake. Stool specimens have been studied for Lactobacillus Fermentum, Lactobacillus acidophilus, Lactobacillus salivarius, Bifidobacterium longum, and Enterococcus faecium presence before and after 30 days of nutritional intervention. After 30 days of regular intake of pasta enriched by β-glucans results have been evaluated. In conclusion, pasta prepared from barley flour enriched with β-glucans at 6% exhibit promising responses on glucose metabolism, on intestinal hormones responses and on microbiota modification.

The interest in virtual fencing, as opposed to traditional physical barriers, has increased since it makes it easier to move the boundaries of animal enclosures for agricultural and rewilding purposes. Despite this, the implementation of virtual fence systems, such as the Nofence© system, requires large financial investments. The necessity of individual collars also makes it more difficult to implement this system on a larger scale. If a herd of cattle tend to maintain cohesive social groups around dominant individuals, it might be possible to use fewer collars thereby making the system more cost efficient. This study investigates the pattern in spatial distribution in a herd of 17 Angus cows on Fanø in Denmark with GPS locations, using a Nofence© system. The aim of this paper is to determine how individuals position themselves in a herd, spatially, and identify a pattern in ranks. The method used in this study examines the distances between an individual to the rest of the herd mates using nearest neighbour and distribution plots. Contrary to the expectations, this study did not show a distinct pattern in herd distribution. While some tendencies in spatial distribution patterns were observed, only a low concordance could be found (W=0.15,p=3.02e−16). Therefore, interactions among individuals should be considered in future studies, rather than only investigating the herd as one unit. A cumulative curves of the ranks estimated over the entire periods allowed however a rough estimation of the hierarchy and allowed the identification of the highest ranked cows. Therefore it might be possible to use fewer collars if the high ranked cows are discovered, but the estimation of the hierarchy will require a period of observation overall several days as the hierarchical positions are fluctuating too much from days to days. Therefore the cumulative rank curves seems to be a valid solution.

Sustainable development advocates for an environmentally friendly and resource-saving approach, fostering a new quality of productivity that is green and sustainable, and achieving a sustainable development where the economy and the environment are in harmony. The paper is based on clarifying the effects of coordinated development between the economy and the environment, and by establishing an indicator system, applying a remote sensing ecological index, entropy weight method, gray relational degree model, and a coupling coordination model, the relationship of coordinated development of Ordos in 2016-2022 is analyzed from a spatial-temporal perspective. The results are as follows: ① The years of coordinated development between the ecological environment and the economy are in the majority, while the uncoordinated years are fewer. ② The level of coordinated development between the ecological environment and the economy in the various regions of Ordos has been increasing year by year, but it still remains at the barely coordinated and primary coordinated levels. ③ The types of coordinated development between the ecological environment and the economy in Ordos exhibit significant spatial differentiation in terms of spatial distribution and clustering.Therefore, it is necessary to formulate coordinated development policies and measures according to the constraining factors of different regions.

Polyphenols are the most prevalent naturally occurring phytochemicals in the human diet and range in complexity from simple molecules to high-molecular-weight polymers. They have a broad range of chemical structures and are generally categorized as "neuroprotective," "anti-inflammatory," and "antioxidant" given their main function of halting disease onset and promoting health. Research has shown that some polyphenols and their metabolites can penetrate the blood-brain barrier and hence increase neuroprotective signaling and neurohormonal effects to provide anti-inflammatory and antioxidant effects. Therefore, multi-targeted modulation of polyphenols may prevent the progression of neuropsychiatric disorders and provide a new practical therapeutic strategy for difficult-to-treat neuropsychiatric disorders. Therefore, multi-target modulation of polyphenols has the potential to prevent the progression of neuropsychiatric disorders and provide a new practical therapeutic strategy for such nervous system diseases. Herein, we review the therapeutic benefits of polyphenols on autism-spectrum disorders, anxiety disorders, depression, and sleep disorders, along with in vitro and ex vivo experimental and clinical trials. Although their methods of action are still under investigation, polyphenols are still seldom employed directly as therapeutic agents for nervous system disorders. Comprehensive mechanistic investigations and large-scale multicenter randomized controlled trials are required to properly evaluate the safety, effectiveness, and side effects of polyphenols.

The number of long-term cancer survivors increases continually. Understanding their needs is crucial to ensure an adequate follow-up. The aim of our study was to summarize the current literature concerning needs and what in-fluences these needs. A Scoping review of systematic reviews was conducted according to the recommendations of the Joanna Briggs Institute (JBI). Four electronic databases were searched. Of 414 retrieved papers, 11 met the eligi-bility criteria. Needs were aggregated into six domains (health-related information, health system, mental, practical, relationship and physical) and 15 categories. The lack of adequate information and the lack of access and/or conti-nuity of supportive care were the most prominent needs. Female gender, younger age, a low level of family and/or social support, and higher educational level were identified as risk factors. Employment and relationship status can affect the needs both in a positive or negative way. The weeks or months after the end of the treatments are partic-ularly critical and needs can be emphasized during this period. Cancer survivors could also leave positive changes. The variety of needs affects the quality of life of cancer survivors. The current Swiss healthcare system is not de-signed to support these people. Needs assessments should be systematically provided to ensure a better awareness on the part of health professionals and to allow an individual, holistic, and integrated follow-up.

Cancer cells are believed to be recognized and controlled by the immune system of the host, especially the adaptive immune system. Cancers may be initiated by a single gene defect in stem cells or regenerating cells during the replacement of old or injured tissue cells, and additional gene defect(s) then occur. To compensate for missing proteins intracellular homeostasis systems, such as proteostasis, may work with activation of new genes. The risk or predisposing factors in cancers may be related to the number of tissue cell renewals (the turnover time) and the probability of gene defect(s). Oncological symptoms may be associated with substances that are derived from antigens expressed on cancer cells and those derived from damaged cancer cells with corresponding immune reactions. In patients with cancers or other conditions, inadequate immune components to control these substances may result in chronic inflammatory or autoimmune diseases. Cancer cells communicate with host cells, including the immune cells such as T cells, for survival and proliferation and may need assistance from immune cells against intracellular infections or physicochemical insults. T cell-based immune therapeutics have been developed and clinically used in some cancers, especially in advanced cancers. We further discuss the oncogenesis, pathophysiology of cancers, and unresolved issues in tumor immunology and reinterpret the effectiveness of T cell-based therapies under the protein-homeostasis-system hypothesis.

The study of the tribological behavior of the braking system in auto vehicles requires knowing the characteristics of the material in contact and, in the work process, for the friction pair, brake disc-pads. Materials structural analysis is necessary because the wear process depends on the friction pair chemical composition, and brake disk-pads, respectively on the work process parameters (pressing force, rotational speed, traffic conditions, etc.). The material of the brake discs is generally the same gray cast iron, but the brake pads can be semi-metallic (particles of steel, copper, brass, and graphite, all united with a special resin), organic materials (particles of rubber, glass, and kevlar all joined with the help of a resin), composite materials that contain different constituents and ceramic materials (rarely have small copper particles). Therefore, the purpose of the paper is to know the crystalline structure and the mechanical properties of the brake pad materials, through specific studies and analyses, with important implications on the friction and wear behavior.

Curcumin, a hydrophobic polyphenol compound derived from the rhizome of the Curcuma genus, has a wide spectrum of biological and pharmacological applications. Previously, curcumin nanoparticles with different stabilizers had been produced successfully in order to enhance solubility and per oral absorption. In the present study, we tested the anti-inflammatory effect of D-α-Tocopheryl polyethylene glycol 1000 succinate (TPGS)-stabilized curcumin nanoparticles in vivo. Lambda-carrageenan (λ-carrageenan) was used to induce inflammation in rats; it was given by an intraplantar route and intrapelurally through surgery in the pleurisy test. In the λ-carrageenan-induced edema model, TPGS-stabilized curcumin nanoparticles were given orally one hour before induction and at 0.5, 4.5, and 8.5 h after induction with two different doses (1.8 and 0.9 mg/kg body weight (BW)). Sodium diclofenac with a dose of 4.5 mg/kg BW was used as a standard drug. A physical mixture of curcumin-TPGS was also used as a comparison with a higher dose of 60 mg/kg BW. The anti-inflammatory effect was assessed on the edema in the carrageenan-induced paw edema model and by the volume of exudate as well as the number of leukocytes reduced in the pleurisy test. TPGS-stabilized curcumin nanoparticles with lower doses showed better anti-inflammatory effects, indicating the greater absorption capability through the gastrointestinal tract.

Identification of drug targets and biochemical investigations on mechanisms of action is a are major issues in modern drug development. The present article reviews the classical “one drug” – “one target” paradigm. Novel methods for target deconvolution and for investigation of resistant strains based on protein mass spectrometry have shown that multiple gene products and adapta-tion mechanisms are involved in responses of pathogens to xenobiotics. This review is focused on protozoan pathogens. The conclusions can, however, be extended to chemotherapies against other pathogens or cancer. For this review we have searched Pubmed (pubmed.ncbi.nlm.nih.gov) and Web of Science (webofscience.com) using the key words listed below (status May 2024)

Ghana, blessed with abundant solar resources and has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one-year monitoring data of the climatic and AC energy output fed into the grid. This data was used to analyze PV performance on each month's sunniest and cloudiest days. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analysis showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days.

Integrating a grid-connected battery into a Renewable Energy Community boosts Collective-Self-Consumption of photovoltaic produced energy and allow to perform Energy Arbitrage in the day-ahead and intraday electricity market. However, is it a cost-effective investment? The proposed analysis relay on Net Present Value assessment. Ageing affects are integrated and sensitivity analyses are conducted: considering community customers number, electricity mar-ket prices, battery cost, size and the decision to engage in energy arbitrage. Each scenario under-goes a 20-year hourly simulation using an ageing aware rolling-horizon 24-hours-looking-ahead scheduling, optimized with Mixed-integer linear Programming. Forecast errors, provid-ing of ancillary services, real time control and grid constrains are not taken into account. Simulations conducted on the Italian market indicates that a battery dedicated solely to collec-tive self-consumption is not economically appealing. However, integrating Energy Arbitrage, despite impact on battery lifespan, halves the payback period and enhances the attractiveness of larger battery investments. The Net Present Value is contingent upon battery size, customer number and market prices. However, if battery cost does not exceed 200 €/kWh the investment become cost-effective across all scenarios.

Longitudinal cutting is a most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules.However, currently the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low.In this study,we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication.The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques,Pearson correlation coefficient for feature selection,and the Random Forest Regression(RFR) for prediction.Compared with the other three Machine Learning(ML) algorithms, the Random Forest algorithm has the best performance.The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy, so that it is suitable for man-hours prediction.

Suicide dynamics form a complex system deeply influenced by interrelated factors, necessitating advanced methodologies for comprehensive analysis. This study pioneers the application of Particle Markov Chain Monte Carlo (PMCMC) methods in suicide research, recognized for their enhanced sampling efficiency over traditional techniques in complex, high-dimensional models. PMCMC is particularly adept at handling non-Gaussian distributions and navigating parameter spaces efficiently, preventing the entrapment in local optima. Utilizing PMCMC, our research simulated a broad spectrum of potential outcomes, clarifying the probable scenarios and their likelihoods, thus enriching the predictive accuracy and understanding of suicide dynamics. These methods facilitated the exploration of dynamic interactions among key risk factors such as mental health issues, trauma, substance use, social isolation, and access to harmful means, whose complex interplays challenge predictive modeling. The study extends previous applications of PMCMC in complex systems like H1N1, opioid crises, and COVID-19 to the domain of suicide, suggesting its potential in enhancing decision-making and intervention strategies. However, limitations due to the model's simplified assumptions and the specificity of the data to certain populations underscore the necessity for broader application to validate findings across varied demographics. In summary, while PMCMC offers robust capabilities for the dynamic modeling of suicide, it requires careful parameter selection and consideration of computational demands. Future research should continue to leverage this approach in more complex settings, enhancing our ability to predict and mitigate suicide risks effectively.

Appropriate animal models of human diseases are a cornerstone in the advancement of science and medicine. To create animal models of neuropsychiatric and neurobehavioral diseases such as ASD necessitates the development of sufficient neurobehavioral measuring tools to translate human behavior to expected measurable behavioral features in animals. Indeed, at least in rodents, adequate neurobehavioral and neurological tests have been developed. Since ASD is characterized by a number of specific behavioral trends with significant severity, animal models of autistic-like behavior have to demonstrate the specific characteristic features, namely impaired social interactions, and communication, restricted, repetitive behavioral patterns with association to several additional impairments such as somatosensory, motor and memory impairments. Thus, an appropriate model must show behavioral impairment of a minimal number of neurobehavioral characteristics using an adequate number of behavioral tests. The proper animal models enable the study of ASD like-behavior from the etiologic, pathogenetic and therapeutic aspects. From the etiologic aspects, models have been developed by the use of immunogenic substances like polyinosinic-polycytidylic acid (PolyIC), Lipopolysaccharide (LPS) and propionic acid or other well-documented immunogens or pathogens, like mycobacterium tuberculosis. Another approach is the use of chemicals like valproic acid, Polychlorinated biphenyls (PCBs), organophosphate pesticides - chlorpyrifos (CPF) and others. These substances were administered either prenatally, generally after the period of major organogenesis, or, especially in rodents, during the early postnatal life. In addition, using modern genetic manipulation methods, genetic models have been created of almost all human genetic diseases that are manifested by autistic like behavior (i.e. fragile X, Rett syndrome, SHANK gene mutation Neuroligin genes and others). Ideally, we should not only evaluate the different behavioral modes affected by the ASD-like behavior but also assess the severity of the behavioral deviations by an appropriate scoring system, as applied to humans.

E-commerce is the general concept of using new technologies to establish chain communication between producers, sellers, suppliers, generally providers of goods and services on the one hand and buyers, consumers or customers in general on the other hand, in order to make a decision. The best ways are to optimize goods and services, reduce costs and open new channels. This article tried to propose an e-commerce model for fresh agricultural products, which can not only maximize producer and consumer satisfaction, but also reduce waste, increase marketing efficiency, and provide environmental protection and sustainable development. The proposed model is based on a coherent and real-time monitoring system, designed by managers and clarification for producers and consumers. The components of this system are manufacturers, retailers, banks, sales and purchase brokers, logistics systems, supervisors and management of the e-commerce system. This system manages information flow, money flow and goods flow accurately and transparently. In this presented model, the duties of each member and how they work were explained, and its implementation requirements were also specified. This model paves the way for the future of sustainable food and preserving the environment with two functions, reducing waste and increasing consumer satisfaction through the supply of unprocessed and fresh products.

Abstract—Hydraulic switching actuators are high efficiency, fast response, and low cost solutions for hydraulic control systems. One of the challenging problems is throttling losses during valve transitions. Previously the authors proposed a zero-flowrate switching method to reduce throttling energy loss of the switching valve, where a hydraulic resonator is applied to make the flowrates through the lines approaching zero before the valves are switched off. The major challenge of this approach is fast switching valves whose transition times are less than 2 ms. In this paper, an improved zero-flowrate switching method is presented. It utilizes the capacity with independent inlet/outlet ports to regulate flowrates through the lines. Models of capacity applied in a simple line with different pressure signals are developed to explore characteristics of the capacity, based on which a complete actuation system is developed. In the complete model, resistance and inductance are optimized to achieve desired flowrate response. The improved zero-flowrate switching method reduces throttling energy losses by 99.945% compared to a hard switching system. The simulation results demonstrated that the improved zero-flowrate switching method performs as expected in the design condition. A capacity with proper volume is able to regulate flowrates through all the lines to zero, with the help of appropriate resistance and inductance. Compared to previous zero-flowrate switching method, the novel strategy allows slower switching valves applied in hydraulic actuation systems and achieves better efficiency performance. This research paves a new avenue for reducing throttling energy losses and improves system efficiency in hydraulic switching actuators, as well as most of the hydraulic switch-mode circuits.

The emotion of disgust protects individuals against pathogens, and it has been found that during pregnancy, it is elevated. Physiological mechanisms discussed in relation to these changes include immune markers and progesterone levels. The aim of this study is to assess the association between steroids and disgust in pregnancy. In a sample of 179 pregnant women, we have analyzed blood serum steroid concentrations and measured disgust sensitivity by text-based questionnaires in the first and third trimester. We found positive correlations between disgust sensitivity and the levels of C19 steroids, including testosterone, but also its precursors in the Δ5 pathway (androstenediol, DHEA, and their sulfates) and the Δ4 pathway (androstenedione). Furthermore, positive correlations between disgust sensitivity and 5α/β-reduced C19 steroid metabolites were consistently present in both trimesters. In the first trimester, we found a positive association between disgust sensitivity and 17-hydroxypregnanolone as well as some estrogens. In the third trimester, we have observed positive associations between disgust sensitivity and cortisol as well as immunoprotective Δ5 C19 7α/β-hydroxy-steroids. Our study has thus shown that disgust sensitivity is positively correlated with immunomodulatory steroids, and in the third trimester, steroids may reflect potential maternal anxiety-related symptoms.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

Recent studies have shown that the endogenous cannabinoid system (ECS) of the brain is essentially involved in the pathogenesis of Parkinson's disease (PD), influencing its symptoms by regulating the level of endogenous cannabinoids and altering the activation of cannabinoid receptors (CBR). Therefore, modulation of ECS with new drugs developed for this purpose may prove to be a promising strategy in the treatment of PD. However, fine regulation of ECS is quite a challenge due to the functional diversity of CBR in the basal ganglia. Our review analyses the effects of ECS modulators on experimental PD models and in patients with PD, as well as presents the outlooks for the development of new cannabinoid drugs for the treatment of motor and non-motor symptoms in PD.

The inclusion of various chemical substances in personal care products (PCPs) and cosmetic formulations can be associated with disruption and damage to the nervous system. Microplastics, benzophenones, parabens, phthalates and metals are among the most common chemical substances found in cosmetics that have been shown to induce neurotoxic mechanisms. Although cosmetic neurotoxin exposure is believed to be minimal, different exposure scenarios of cosmetics suggest that these neurotoxins remain a threat. Special attention should be paid to early exposure in the first gestation weeks, when critical processes, like the migration and proliferation of the neural crest derived cells start to form the ENS. Importantly, cosmetic’s neurotoxins can cross the placenta barrier and so affect the future embryo, but are also secreted in the breast milk, so that babies remain exposed for longer periods, even after birth. In this review we explore how neurotoxins contained in cosmetic and PCPs may have a role in the pathogenesis of various neurodevelopment disorders and neurodegenerative diseases, and therefore also in congenital enteric aganglionosis as well as in postnatal motility disorders. Understanding the mechanisms of these chemicals used in cosmetic formulations and their role in neurotoxicity is crucial to determining the safety of use for cosmetic products during pregnancy.

Hirschsprung disease (HSCR, incidence 1/5000 live births) is caused by failure of neural crest-derived precursors to migrate, survive, proliferate or differentiate during the embryonic development of the Enteric Nervous System (ENS), which could be disrupted by many factors, in-cluding inflammatory processes. The NF-κB family controls several biological processes includ-ing inflammation, neurogenesis and cell migration. With the aim of studying the potential role of NF-κB in HSCR, we have analyzed the expression of the NF-κB main subunits and other NF-κB-related genes by RT-qPCR in HSCR tissue samples (sub-divided in ganglionic and agan-glionic segments). We found a decreased gene expression of the NF-κB main subunit RelA/p65, but also of IkBα, TNF-α, TFGBR2 and ERBB3 in the pathologic distal aganglionic segments com-pared to the proximal ganglionic segments. Moreover, we could also confirm the lower protein expression of RelA/p65 in the aganglionic distal segments by immunofluorescence staining. Fur-ther, we show that the expression of RelA/p65 in the proximal segments concurs with lymphocyte infiltration in the bowel tissue, indicating a pro-inflammatory activation of p65 in the proximal ganglionic HSCR tissue in the patients analyzed. In all, our findings suggest that the modulation of NF-κB signaling in the neuro-enteric system does obviously contribute to the pathological ef-fects in HSCR.

The design complexity of the new generation of civil aero-engines results in higher demands on engines’ components, higher component temperature, higher heat generation, and, finally, critical thermal management issues. This paper will propose a methodological approach to create physics-based models for heat loads developed by sources as well as a systematic sensitivity analysis to identify the effects of design parameters on the thermal behaviour of civil aero-engines. The ranges and levels of heat loads generated by heat sources (e.g., accessory gearbox, bearing, pumps, etc.) and heat absorption capacity of heat sinks (e.g., engine fuel, oil, and air) are discussed systematically. The practical research challenges for thermal management system design and development for the new and next generation of turbofan engines will then be addressed through a sensitivity analysis of the heat load values as well as the heat sinks flow rates. The potential solutions for thermal performance enhancements of propulsion systems will be proposed and discussed accordingly.

Compressed carbon dioxide energy storage (CCES) offers several benefits over other existing energy storage systems, including ease of liquefaction, high energy storage density, and environmental friendliness. As a result, the research progress, economic and technological feasibility, and system operation of the CCES system are all discussed in depth in this study. The system evaluation method is summarized and the compressed carbon dioxide storage is analyzed, and the performance optimization direction of the compressed carbon dioxide energy storage technology is discussed. When the overall performance of a transcritical CCES system, a supercritical CCES system and a liquid CCES system are compared, it is discovered that the supercritical CCES system has better thermodynamic characteristics and a simpler system configuration, making it suitable for large-scale development and use. The goal of the CCES system's future development is to create a design with an optimum compression and expansion ratio, a more precise analysis and system model, and multi-field coupling. This review's discussion serves as a guide for the best design and use of the CCES system.

The onset of neurodegenerative diseases involves a complex interplay of pathological mecha-nisms, including protein aggregation, oxidative stress, impaired autophagy. This review focuses on the intricate connection between oxidative stress and autophagy in neurodegenerative disor-ders, highlighting autophagy as pivotal in disease pathogenesis. Reactive oxygen species (ROS) play dual roles in cellular homeostasis and autophagy regulation, with disruptions of redox sig-naling contributing to neurodegeneration. The activation of the Nrf2 pathway represents a criti-cal antioxidant mechanism, while autophagy maintains cellular homeostasis by degrading al-tered cell components. The interaction between p62/SQSTM1, Nrf2 and Keap1 forms a regulatory pathway essential for cellular stress response, whose dysregulation leads to impaired autophagy and aggregate accumulation. Targeting the Nrf2-p62/SQSTM1 pathway holds promise for thera-peutic intervention, mitigating oxidative stress and preserving cellular functions. Additionally, the review explores the potential synergy between the endocannabinoid system and Nrf2 signal-ing for neuroprotection. Further research is needed to elucidate the involved molecular mecha-nisms and develop effective therapeutic strategies against neurodegeneration.