Photovoltaic(PV) power generation is highly nonlinear and stochastic. Accurate prediction of PV power generation plays a crucial role in grid connection as well as the operation and scheduling of power plants. To predict the PV power combination model, this paper suggests a method based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Auto-Regressive Neural Networks with Exogenous Input (NARXNN), Long Short Term Memory (LSTM) Neural Network, and Light Gradient Boosting Machine (LightGBM) algorithms. To attempt to reduce the non-smoothness of PV power, the weather variable features with the greatest effect on PV power are first identified by correlation analysis. Following this, the PV power modal decomposition is split and reorganized into a new feature matrix. Finally， a NARX is used to obtain preliminary PV power components and residual vector features， and the PV power is predicted by combining three models of LightGBM， LSTM, and NARX and then the final prediction results are obtained by combining the PV power prediction results using error inverse method weighted optimization. The prediction results demonstrate that the model put forth in this paper outperforms those of other models and validate the model's validity by utilizing real measurement data from Andre Agassi College in the United States.

This study investigates the power production and blade fatigue of a three-turbine array subjected to active yaw control (AYC) in full-wake and partial-wake configurations. A framework of two-way coupled large-eddy simulation (LES) and aeroelastic blade simulation is applied to simulate the atmospheric boundary-layer (ABL) flow through the turbine array and the structural responses of the turbine blades. Mean power outputs and blade fatigue loads are extracted from the simulation results. By exploring the feasible AYC decision space, we find that (a) in the full-wake configuration, the local power-optimal AYC strategy with positive yaw angles endures less flapwise blade fatigue and more edgewise blade fatigue than the global power-optimal strategy; (b) in the partial-wake configuration, applying positive AYC in certain inflow wind directions achieves higher optimal power gains than that in the full-wake scenario and reduces the blade fatigue from the non-yawed benchmark. Through a theoretical analysis based on the blade element momentum theory, we reveal that the aforementioned differences in flapwise blade fatigue between the positively and negatively yawed turbine are due to the differences in the azimuthal distributions of the local relative velocity on blade sections, resulting from the combined effects of vertical wind shear and blade rotation. Furthermore, the difference in the blade force between the positively and negatively yawed front-row turbine induces different wake velocity and turbulence distributions, causing different fatigue loads on the downwind turbine exposed to the wake.

After long years of development, the technology of analyzing the working condition of power units based on vibration signals has had relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the limitations of the current development of key technologies. With the development of big data and artificial intelligence technology, the involvement of new technologies will be an important boost to the development of this field. To support the subsequent research, bibliometrics is used as a tool to sort out the development of the technology in this field at the macro level; at the micro level, the classical and key literature is studied to grasp the development status at the technical level and prepare for the selection of entry points to continue in-depth innovation afterwards.

The scope of this work was part of our previous research on Pulsed Power Plasma Stimulation Technique. Electromagnetic fields generated during a pulsed power plasma discharge were simulated using a finite element method in the COMSOL RF module. The field distributions were calculated during and after the pulse. The current paper takes advantage of prior work measuring and modeling the current distribution in the plasma arc. Agreement with laboratory experimental measurements provides support for extension of the model to reservoir scale. The validated model was used to quantify the signal attenuation level in different medium environment.

The microgrid is a small-scale, autonomous decentralized power plant with its own distributed generation, storage capacity and multiple loads, with the capacity to function in grid interconnected and an island mode. The decentralized control of microgrids with parallel operated voltage source converters (VSCs) is proposed in this paper to improve power quality using a machine learning approach. The DNN based MPPT controller is proposed and its best performance is presented. The SRF-PLL is utilized for AC side synchronization in VSC control. The proposed microgrid involves two PV arrays fed to two voltage source converters along with their independent controls, connected in parallel through LC filters and line coupling transformers and serves the loads at PCC. The proposed model is simulated using MATLAB/Simulink. The dq-framed inner loop control is employed to individually regulate the real and reactive power at the point of common coupling. Furthermore, the proposed model is analyzed and compared by employing a mathematical model of AC system dynamics, inner loop control, output voltage quality, AC harmonic spectrum analysis, and total harmonic distortion (THD) in both grid interconnected and island mode. In island mode, AC harmonic spectrum and THD are accomplished within the permissible range.

In this article, there's an effort to make sense about the new versions of matroid. I believe that there's new idea on the background of. matroid. Two styles of matroid is defined in the background of fixed graphs and after that the attributes of these new notion on the graph and its parameters have been studied. The focus of this article is on the version of matroid which has the basis on the cycles as if there's gentle discussion on the results which are based on the set of independent vertices as matroid-x. The relation amid fundamental parameters and specific set like independent set and minimal set in the terminology of graph theory have been considered. Matroid is the word to use in the study on the parameters of graph theory as if set theory and its terminology are also recorded. The terms of word in various terminology have been relatively used. There are open ways to use hypergraphs or some serious relations amid these two types.

The incorporation of wind generation introduces challenges to the operation of the power system due to its uncertain characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal Kriging and analog approaches are used to forecast wind power generation and used as input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal Kriging takes into account the spatial and temporal information given by the database to enhance wind forecasts. We evaluate the performance of using the spatio-temporal Kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results show that the use of Kriging-based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

During the last twenty years around the world there has been a rapid increase in the number of people visiting long established religious shrines as well as the creation of new sites by those operating outside the boundaries of institutional religion. This increase is intimately associated with the revival of traditional routes, the creation of new ones and the invention of new rituals (religious, spiritual and secular). To examine this process I will focus on the European region and two contrasting destinations in particular – the Catholic shrine of Lourdes, France, and the pre-Christian shrine of Avebury, England – drawing on my personal involvement in travelling to both destinations and being involved in ritual activities along the route and at the two destinations. In the discussion section of the paper I will explore the relevance of these two case studies to the analysis of power, agency and performance and the ways in which they expose (a) the role of institutions and entrepreneurs in creating rituals and sacred places and (b) the relationship between people and the domesticated landscape.

Microgrids can be islanded to improve the reliability of the electrical energy supply during contingencies such as faults. It is still unclear what stability phenomena can occur in microgrids without inertia during and immediately after the fault-initiated islanding transient, and how the stability should be evaluated. To allow robust planning and operation, a stability analysis methodology should be able to take into account the inherent uncertainty of load and renewable energy sources. Therefore, this paper proposes a probabilistic stability analysis methodology for fault-initiated islanding of microgrids based on a thorough analysis of the root causes of instability during islanding transients. To perform stability analysis within reasonable time and allow control actions during operation, dynamical microgrid models in the dq reference frame are described and validated by comparing time-domain simulations results to previously validated component-based models. The stability analysis methodology is demonstrated in a case study of a generic microgrid. The instability phenomena and the dq microgrid models are successfully validated, and the results indicate that the stability analysis methodology can assist the microgrid operator to improve fault-initiated islanding capability in order to improve reliability of supply.

This paper aims to analyze the effects of Covid-19 on financial markets from the perspective of Complexity. The Covid-19 pandemic has caused turmoil in financial markets and is already one of the most important financial crises in history, causing a fall in several stock markets as well as great volatility. Unlike past crises, which in most cases were caused either by problems of fiscal deficits or in the financial system, this crisis has its origin in an epidemic disease, which occurred in Wuhan, China, and quickly spread across the globe affecting transport networks, commerce and finance, and will affect the public debt of many countries. This is a systemic situation in which there is a need to consider several interconnected systems, financial instability and high financial risk, something that econophysics and some complexity theorists already do. Therefore, this paper intends to show, theoretically, the systemic and complex character of the recent financial crisis caused by Covid-19.

This short communication makes use of the principle of singular perturbation to approximate the ordinary differential equation (ODE) of prompt neutron (in the point kinetics model) as an algebraic equation. This approximation is shown to yield a large gain in computational efficiency without compromising any significant accuracy in the numerical simulation of primary coolant system dynamics in a PWR nuclear power plant. The approximate (i.e., singularly perturbed) model has been validated with a numerical solution of the original set of neutron point-kinetic and thermal–hydraulic equations. Both models use variable-step Runge–Kutta numerical integration.

Microwave Power Modules (MPM) are high power devices largely used for radar applications. An MPM is a hybrid combination of a Solid-State Power Amplifier (SSPA) as a driver and a Traveling wave tube amplifier (TWT) as the final high power stage. Power equalizer is often used to flat the TWT output power gain owing to its big gain fluctuations in the operating frequency range. This paper deals with an optimal design and development of a fully-coaxial easy-tunable power equalizer for an X-band MPM. The structure utilizes a coaxial waveguide as the main transmission line and a coaxial cavity loaded with absorbing material as the resonant unit. A sensitivity analysis of the coaxial cavity length, the thickness of the absorbing disc and inserted depth of the probe on the attenuation amplitude and resonant frequency is carried out. The transmission characteristic curves and the voltage standing wave ratio curves show a good compromise in terms of transferred power and adaptive matching impedance at the input and output ports. The measured results show that the equalization curve meets the requirements well and prove that this structure is practical and effective.

This article presents an in-depth review of Virtual Power Plant (VPP), its organization in the energy system and its prospects in the face of the promising future of the increasing bidirectional complexity exposed in the current scenario. A new paradigm for the operations of modern power distribution and transmission systems requires greater grid flexibility that is accompanying an extensive change in the structure of electricity markets, the fruit of the development of the renewable industry and of the growing photovoltaic systems popularized, but in contrast due to the intermittent nature cause variable uncertainties in the power system. Innovative concepts like VPP are becoming a reality, establishing an efficient and effective mechanism. The objectives and components are described in a comprehensive way, and some of the most important are pointed out and presented in detail to contribute with a description of the energy systems and the implicit research needed for sustainability and resilience in the eminent energy scenario with this technology. In addition, the literature and studies of this technology already indicate a direction of this new tool as a promising solution to manage the uncertainties of the renewable energies.

In this article the methodology for the determination of the theoretical gamma spectrum originated by applying DGNAA (delayed gamma neutron analysis activation) techniques for the elemental analysis of ash from thermal power plants is exposed. This is a study must be prior to the experimentation in order to define the parameters of the test to obtain the optimal conditions for the detection of each element present in the sample.

In this paper, we investigate an analytical solution of a class of nonlinear fractional integro-differential equation base on a generalized fractional power series expansion. The fractional derivatives are described in the conformable's type. The new approach is a modified form of the well-known Taylor series expansion. The illustrative examples are presented to demonstrate the accuracy and effectiveness of the proposed method

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO₂ emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

Natural gas combined cycle power plants (CCPPs) are widely used to meet peak loads in electric energy production. Continuous monitoring of the output electrical power of CCPPs is a requirement for power performance. In this study, the role of ambient temperature change having the greatest effect on electric production is investigated for a natural gas CCPP. The plant has generated electricity for fourteen years and setup at 240 MW in Aliağa, İzmir, Turkey. Depending on the seasonal temperature changes, the study data were obtained from each gas turbine (GT), steam turbine (ST) and combined cycle blocks (CCBs) in the ambient temperature range of 8-23°C. It has been found that decreases of the electric energy in the GTs because of the temperature increase and indirectly diminishes of the electricity production in the STs. As a result, the efficiency of each GT, ST and CCB reduced, although the quantity of fuel consumed by the controllers in the plant was decreased. As a result of this data, it has been recommended and applied that additional precautions have been taken for the power plant to bring the air entering the combustion chamber to ideal conditions and necessary air cooling systems have been installed.

China combines green energy and industrial policy in its power market reform with various policy initiatives, including price support scheme for electricity from renewable sources and subsidies in the push for broader use of greener energy. This study focuses on the impacts of power market reform on the stock price volatility of listed power companies: 1) we use the Iterative Cumulative Sums of Squares (ICSS) algorithm to identify structural break points in stock prices; 2) we analyze the characteristics of stock price volatility based on the GARCH model; 3) we report the impact of power regulation on stock price fluctuations based on the Autoregressive Distributed Lag (ARDL) model. The result suggests three structural breaks in China’s power stock price volatility were related to the promulgation of power market reform policies. We find that industrial policies promote the reduction of power stock price fluctuations and its impact on power stock price volatility is consistent in the long run. However, our study also indicates the recent policies related to renewable energy do not have a very significant impact on the power stock market.

This paper investigates the tank drainage problem of an isothermal, unsteady, incompressible electrically conducting Power law fluid. Analytic solution have been obtained from governing continuity and momentum equations subject to appropriate boundary conditions by using Perturbation method. The Power law fluid model solution without MHD is retrieved from this proposed model on substitution . Declaration on behalf of velocity profile, volume flux, average velocity, connection of time with respect to length of the tank and requirement of time for whole drainage of fluid are acquired. Special effects of numerous emerging parameter’s on velocity profile vz and depth of the fluid in the tank are graphically presented. Keywords: Tank drainage, Power law MHD fluid, Analytical solution.

In this paper, a method of charging lead acid battery with solar power by flyback converter is proposed. The basic system consists of a buffer circuit, auxiliary power circuit, control circuit, voltage feedback circuit and current feedback circuit. The system is capable of monitoring battery voltage and the current, charging the coupling inductor and automatic control of output duty cycle, overcharging and over-discharging protection. The experiment proves that the charging efficiency is improved by about 83% at full load.

In this article, an efficient die bonding technology based on silver sintering due to induction heating is presented. By using this technology, the heat for the sintering reaction is locally limited to the bonding area and the heating of the entire power module is avoided. Furthermore, the sintering reaction is promoted due to current flow between the silver particles and the sintering time is drastically reduced. Next to the experimental trials presented in this paper, FE-simulation methods were applied in order to develop a suitable induction coil geometry for the bonding of a diode to a direct bonded copper (DBC) substrate. Additional heating and sintering tests verified the reliability of the simulation model as well as the technological approach. Diodes were successfully bonded during the experiments and were analyzed by means of scanning electron microscopy (SEM) and function tests in order to qualify the inductive bonding technology. The results presented in this paper demonstrate, that induction heating has high potential for cost effective production in the field of die-attach and can drastically increase the output in power electronics production.

Due to its spatio-temporal variability, the mismatch between the weather and demand patterns challenges the design of highly renewable energy systems. A principal component analysis is applied to a simplified networked European electricity system with a high share of wind and solar power generation. It reveals a small number of important mismatch patterns, which explain most of the system's required backup and transmission infrastructure. Whereas the first principal component is already able to reproduce most of the temporal mismatch variability for a solar dominated system, a few more principal components are needed for a wind dominated system. Due to its monopole structure the first principal component causes most of the system's backup infrastructure. The next few principal components have a dipole structure and dominate the transmission infrastructure of the renewable electricity network.

Purpose: This study aimed to investigate useful preoperative clinical and hematologic variables in predicting malignancy in patients with the atypia of undetermined significance (AUS) nodules, and to suggest a machine learning-based prediction model. Methods: We enrolled 280 patients who underwent surgery for the AUS nodules at Wonju Severance Christian Hospital between 2018 and 2022. We evaluated preoperative hematologic indices, including Delta Neutrophil Index (DNI), Neutrophil-to-Lymphocyte Ratio (NLR), and Platelet-to-Lymphocyte Ratio (PLR), as well as preoperatively identifiable clinical variables such as age and sex at diagnosis, history of radiation exposure, familial history of thyroid cancer, primary tumor size, and the 2016 Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification. Results: A total of 116 patients (41.4%) were finally diagnosed with various types of thyroid malignancy, excluding 32 patients (11.4%) with thyroid cancer incidentally identified. Age at diagnosis (p = 0.012), primary tumor size (p = 0.048), and the K-TIRADS classification (p = 0.003) were independent risk factors to predict the diagnosis of malignancy in patients with AUS nodules younger than 55 years, not in those aged of 55 years or older. Adding the NLR to these risk factors significantly improved the predictability for malignancy in the same patient group (p < 0.001). Conclusions: The inclusion of NLR in the ma-lignancy prediction model enhances the predictive accuracy for malignancy in younger patients with AUS nodules. This finding suggests that incorporating NLR into preoperative assessment could refine management strategies and improve decision-making for surgical interventions.

In our study, we investigated the prognostic significance of hematological markers—NLR (Neutrophil-to-Lymphocyte Ratio), PLR (Platelet-to-Lymphocyte Ratio), and RDW-CV (Red Blood Cell Distribution Width - Coefficient of Variation)—in 117 glioblastoma patients. Data collected from January 2016 to December 2018 included demographics, clinical scores, and treatment regimens. Unlike previous research, which often examined these markers solely before surgery, our unique approach analyzed them at multiple stages: preoperative, postoperative, and before adjuvant therapies. We correlated these markers with overall survival (OS) and progression-free survival (PFS) using statistical tools, including ANOVA, Cox regression, and Kaplan-Meier survival analyses, employing SPSS version 29.0. Our findings revealed notable variations in NLR, PLR, and RDW-CV across different treatment stages. NLR and PLR decreased after surgery, with some stabilization post-STUPP phase (NLR: p=0.007, η2p=0.06; PLR: p=0.001, η2p=0.23), while RDW-CV increased post-surgery and during subsequent treatments (RDW-CV: p &lt; 0.001, η2p=0.67). Importantly, we observed significant differences between the preoperative phase and other treatment phases. Additionally, higher NLR and RDW-CV at the second-line treatment and disease progression were associated with an increased risk of death (NLR at 2nd line: HR=1.03, p=0.029; RDW-CV at progression: HR=1.14, p=0.004). We proposed specific marker cutoffs that demonstrated significant associations with survival outcomes when applied to Kaplan-Meier survival curves (NLR at 2nd line &lt; 5: p&lt;0.017; RDW-CV at progression &lt; 15: p=0.007). Elevated NLR and RDW-CV at later treatment stages correlated with poorer OS and PFS. No significant preoperative differences were detected. These biomarkers may serve as non-invasive tools for glioblastoma management.

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats.Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy.AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

Leveraging the liquid phase immiscibility effect and phase diagram calculations, a sequence of alloy powders with varying Fe content was designed and fabricated utilizing gas atomization method. Microstructural characterizations, employing SEM, EDS, and XRD analyses, revealed the successful formation of incomplete shell on the surfaces of Al-Bi-Fe powders, obviating the need for Sn doping. This study systematically investigated the microstructure, hydrolysis performance, and hydrolysis process of these alloys in deionized water. Notably, Al-10Bi-7Fe exhibited the highest hydrogen production, reaching 961.0 Nml/g, while Al-10Bi-10Fe demonstrated the peak conversion rate at 92.99%. The hydrolysis activation energy of each Al-Bi-Fe alloy powder was calculated using the Arrhenius equation, indicating a reduction in activation energy achieved through Fe doping.

The ever-growing concerns about global warming and the rapid depletion of fossil fuels have triggered a rising interest in research for cleaner, more efficient and cost-effective energy generation. Organic Rankine cycle systems have immense potential to become a strong alternative to conventional methods of energy generation. However, such systems’ efficiency is limited by the performance of the incorporated gas expanders or prime movers. Conventional gas expanders often utilize ports or cam-operated valves. Ported expanders offer limited efficiency and controllability, and produce high emissions causing a large amount of heated and compressed gas to be wasted. Cam-operated valves, in contrast, increase expansion performance compared to ported expanders, however, they have no variability and adaptability to changes in system parameters such as gas pressure, temperature, dryness fraction, and load variation to name a few. On a positive note, this issue can be addressed by adopting a variable timing and fast-operating, control valve with an accurate and adaptive control mechanism at the expander inlet. A properly designed and controlled valve can greatly improve the system performance of gas expanders and pave the way for an efficient low-cost alternative energy generation. This manuscript provides a comparative review of recent progress on the design, modeling, optimization, and control aspects of valves for gas expanders. A clear pathway on the scope of further development is also drawn based on the present state of the art.

Due to the adverse effect of prolonged drought stress on plants, accurate detection is essential for water use efficiency and maintaining productivity. Hyperspectral imaging is frequently used for non-invasive plant phenotyping, allowing for the long-term monitoring of crop health due to its sensitivity to subtle changes in leaf constituents. The broad spectrum of hyperspectral data enables the development of multiple vegetation indices (Vis) derived from the different spectral regions to estimate plant biophysical and biochemical traits. However, the known VIs often do not generalize well and perform poorly for multiple plant stresses. This study proposes new VIs combined with machine learning models to identify drought stress in wheat species under different nitrogen (N) levels. A wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high N and drought-stress low-N. In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) were taken regularly, serving as the ground truth data for this study. Sensitive spectral features were selected using a custom-designed ensemble modelling technique. New drought VIs are proposed using different combinations of the selected features. Three classification models (support vector machines, random forest and deep neural network) were developed and trained using four sets of data: known VIs, proposed VIs, combined VIs (from the known and proposed VIs) and PCA-transformed features (over the whole spectral data). From the results, the proposed VIs outperformed the known VIs, yielding &gt; 0.94 accuracies for all three models, and the performance improved when they were trained with the combined VIs. The combined VIs were used to train three regression models to predict the stomatal conductances and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat.

The subway station operation is susceptible to safety accidents when high-pressure gas pipeline overlaying, and analyzing the correlations between the safety influencing factors (SIFs) under this operation situation can provide paths to reduce safety accidents. Thus, this paper investigated the coupling correlations between the SIFs. We firstly identified the SIFs during subway station operation under high-pressure pipeline (SSOUHP) based on the literature review and experts’ discussion. Then, the analytic hierarchy process (AHP) and coupling degree analysis (CDA) were combined to assess the coupling correlations between the SIFs. And Y subway station was selected to test the proposed hybrid coupling analysis approach. Research results show that (a) 23 second-level SIFs were identified and these SIFs can be summed up into 5 first-level SIFs, namely, human-related SIFs, pipeline-related SIFs, station-related SIFs, environment-related SIFs, and management-related SIFs; (b) the proposed hybrid approach can be used to evaluate the coupling correlations between SIFs; (c) of the coupling situations during Y subway station operation, the internal coupling correlations among environment-related SIFs, the coupling correlations between pipe-related SIFs and environment-related SIFs, and the coupling correlations among human-related SIFs, pipe-related SIFs and environment-related SIFs are with higher safety risk; (d) the overall coupling correlation among all SIFs during Y subway station operation is with lower safety risk. The research can enrich the knowledge in the safety evaluation area, and provide reference for onsite safety management.

The planned closure of potash mines, achieved through the injection of highly mineralized brines into the worked-out area, is a complex and not fully studied process. A critical concern arises when brines obstruct the aerodynamic connections between the flooded mine's airspace and the atmosphere, potentially leading to the formation of closed cavities where explosive gases can accumulate. To address this hazard, it is imperative to develop systems capable of extracting the gas-air mixture from the unflooded domed portions of the worked-out area. This is the focus of this study, in which we analyze gas-dynamic processes within the potassium mine worked-out area during planned flooding. Two distinct scenarios are examined: the first involves controlled flooding with saturated brines, while the second contemplates flooding resulting from a hypothetical breakthrough of supra-salt strata, leading to the ingress of groundwater into the worked-out area. A novel mathematical model is introduced to predict the evolution of gas-air mixture parameters in the unflooded dome segment of the worked-out area. Utilizing this model, we assess the effectiveness of proposed measures designed to eliminate explosive gases from the worked-out area. Specifically, a pipeline system is proposed for the removal of gases, followed by their dilution below the maximum permissible concentrations in the effluent jet. The findings from this study contribute valuable insights into ensuring the safe and efficient closure of potash mines, shedding light on potential risks and effective mitigation strategies for gas-related hazards during planned flooding.

The nuclear reactor measurement system is a crucial component of the nuclear measurement system in a nuclear power plant. Its primary function is to measure the power distribution within the reactor core using detectors, calibrate, and provide data on core fuel consumption. Therefore, the study of the detectors' reliability is crucial for ensuring the safe operation of the nuclear power system. The article describes the operational mechanism of the gas ionization chamber detector utilized in the intermediate-range detector of the off-board nuclear measurement system. It also analyzes the movement of particles between the plates during the ionization process of the nuclear reaction taking place in the detector. The functional relationship between the current in the recombination region and the ionization region involved in the actual working condition, and the change with the increase of voltage between the plates, is deduced. A mathematical model is established for the number of pulses generated between the plates and the magnitude of the voltage between the plates. Subsequently, the change in the offset of the voltage corresponding to the inflection point of the ping curve and the change rule of the slope of the ping curve are investigated. Combined with the analysis of experimental data, the results show that the initial particle energy of Ar+, the primary sensitive particle on the detector surface, follows a normal distribution and is influenced by the energy loss associated with the initial velocity and the voltage between the plates. The trends of the inflection points and slopes of the ping curves can be utilized to evaluate the performance and operational status of the detector. Combined with the results of the experimental data analysis, the fit is good, and the inflection point currents in the normal state decrease by about 1.5% annually. The intelligent diagnostic system, based on the method studied in this paper, has been successfully implemented in the RPN digital retrofit and upgrade project at Qinshan No. 2 Plant.

Accurate clinical diagnosis of patients presenting to primary care settings with acute sore throat remains challenging, often resulting in the over-prescribing of antibiotics. Using point-of-care tests (POCTs) to differentiate between respiratory infections is well-accepted, yet evidence on the application within primary care is sparse. We assessed the application of testing patients (n=160) from three family practices with suspected Streptococcal infections using rapid molecular tests (ID NOW Strep A2, Abbott). In addition to comparing clinical evaluation and prescription rates with either usual care or with testing, patients and staff completed a questionnaire about their experience of molecular POCT in primary care. The immediate availability of the result was important to patients (100%) and staff (≈90%) stated that molecular testing improved quality of care. Interestingly, only 22.73% of patients with a Centor score >2 tested positive for Strep A and overall, less than 50% of Centor scores 3 and 4 tested positive for Strep A with the ID NOW testing platform. The addition of rapid molecular POCTs to clinical assessment resulted in a 55- 65% reduction in immediate and deferred antibiotic prescriptions. The intervention was popular with patients and medical staff but was associated with increased cost and a longer appointment length.

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 (

In politics and society, organic farming is widely seen as the ideal way to address the many challenges facing agriculture and food security. In order to examine the extent to which this view is realistic, the impact of organic and conventional farming is compared on the basis of relevant criteria such as land requirements, greenhouse gas emissions, biodiversity, food quality, production and environmental costs, reduced meat consumption, the need for agricultural imports and the global expansion of arable land. Land is considered to be a central factor. Such a comprehensive analysis is necessary to assess the impact of different influencing factors and to provide important suggestions for targeted decision making. Although organic farming has a positive impact on land use in many ways, it quickly reaches its limits. The low yields and high production costs resulting from the limited choice of means of production make organic farming an unsuitable model for the future in a world with limited arable land, a high and growing demand for food, and low incomes for large sections of the population. It can, however, provide significant impetus and contribute important technological knowledge for the further development of agriculture as a whole to meet the complex objectives of sustainability, greenhouse gas mitigation, biodiversity and global food security by incorporating all available technologies. Based on this, a model for responsible, sustainable intensive agriculture is outlined and proposals for an eco-social framework to support this model are put forward.

Among the various types of gas sensors, the chemoresistive gas sensors based on metal oxides are considered the most attractive ones due to their simple operation, low costs and miniaturization. Thin films of V2O5 undoped and doped with TiO2 were studied as nitrogen dioxide, methane and hydrogen sensors. The experimental kinetics of the electrical conductivity changes due to introduction and removal of gas on metal oxide gas sensors were successfully explained in terms of the Langmuir adsorption theory.

The European Green Deal is the policy established by the EU against global environmental problems. However, it is closely concerned with the countries carrying import and export activities with the EU. Because it is planned to impose an obligation to declare greenhouse gas emission inventories of products imported into the EU, the EU will soon begin to assess the CBAM on imports from carbon-intensive industries. This regulation is expected to affect Turkey-EU trade relations. One of the industries that Turkey has export relations with the EU is the building materials industry. For this reason, it will have had necessary to provide sustainability criteria for the export of building materials. However, there is no database in Turkey where the inventory data of building materials is declared. In Turkey, within the scope of EU Green Deal action plans, it is aimed to create different structures for environmental information of building materials in the medium and long term. In light of such information, within the scope of this study it is aimed to create reference information for building material databases to be developed in Turkey. The study recommends weight and normalization reference values following the life cycle assessment methodology defined by ISO 14040 and ISO 14044 international standards. Twelve environmental impact categories accepted in the literature, including greenhouse gas emissions, have been considered as the environmental impacts of building materials. Semi-structured interviews were held with twenty-one industry stakeholders in Tur-key to determine the weight reference values. The results obtained from the semi-structured interviews were combined using the AHP method. An extensive research study was conducted on Turkey’s national inventory data to determine normalization reference values. Environmental impact calculations were carried out for different building materials in a case study to present the importance of regional adaptation of the determined reference values. The recommended reference information may be used in greenhouse gas emission declarations under the EU Green Deal and other potential environmental impact declarations from building materials.

As for their large surface area and pore volume, three-dimensional covalent organic frameworks (3D COFs) have emerged as competitive porous materials. However, due to the structural dynamic behavior particularly for imine-linked 3D COFs, potentially unlock their potential for gas storage application. Herein, we showed a pre-locked linker strategy introduces breaking dynamic behavior in 3D COFs. A predesigned planar linker based 3,8-diamino-6-phenylphenanthridine (DPP) was prepared to produce non-dynamic 3D JUC-595, as benzylideneamine moiety in DPP locked the linker flexibility and restrict the molecular-bond rotation of the imine linkages. Upon solvent inclusion and release, the PXRD profile of JUC-595 remained intake, while JUC-594 with flexible benzidine linker experienced crystal transformation due to framework contraction-expansion. As a result, the activated JUC-595 achieved higher surface areas (754 m² g-1) than that of JUC-594 (548 m² g-1). Furthermore, improved CO2 and CH4 storages were also seen in JUC-595 compared with JUC-594. Impressively, JUC-595 recorded high normalized H2 storage capacity that surpass other reported high surface area 3D COFs. This works shows important insight on manipulating structural properties of 3D COF to tune the gas storage performance.

To study the oil and gas distribution law in the slope area of the hydrocarbon-bearing basin, the distribution of the effective dominant path of sand bodies transporting oil and gas is a fundamental subject. In this paper, we introduced an effective way to predict such transport paths and applied the method to study the sand bodies of the lower first member of the Shahejie Formation (E3s1L) in the southern area of the Qikou Depression in the Bohai Bay Basin (Qinan). We first define the dominant hydrocarbon transport paths of the sand body as the superimposed area of the contiguously distributed area of the sand body and the paleotectonic ridges at the top of the formation where the sand body is located. Then we define the oil and gas supply area of the oil source fault as the overlapped area of the favorable transport path of oil source faults and the width of the associated fracture zone. Stacking the two areas, we can delineate the distribution of the effective dominant transport paths of the sand bodies. The results from our case study of the Qinan area show that the effective dominant paths of sand bodies transporting oil and gas in the E3s1L Formation in the Qinan area are distributed throughout the region with better development in the west than in the east. The paths in the eastern part extend from north to south, while the paths in the western part extend from northeast to southwest, which is favorable for the convergence of oil and gas generated from the source rocks of the third member of Shahejie Formation (Es3) in the Northeastern main depression of Qikou and the formation of oil and gas accumulations in the l E3s1L Formation. The results are consistent with the fact that oil and gas discovered in the E3s1L Formation in the Qinan region are mainly distributed in the central and western regions, with a small amount in the southeast. Therefore, the suggested method is feasible to predict the effective dominant path of sand bodies transporting oil and gas.

Transforming Small-Scale LNG Business, Enhancing Efficiency and Resilience in an Evolving Supply Chain In various industries, innovative systems have proven their value due to their efficiency and adaptability. A prime example of this lies in the rapid growth of the liquefied natural gas (LNG) market. LNG, a fuel derived from natural gas, has gained significant traction in recent years. In light of this, we aim to explore optimal strategies for small-scale LNG businesses, with a focus on revolutionizing their supply chain to ensure adaptability and sustainability. Our objective is to enhance efficiency and sturdiness in this evolving sector. By devising innovative methods and implementing cutting-edge technologies, we aim to revolutionize the small-scale LNG market, paving the way for a more optimized and resilient supply chain. Through this transformation, we strive to create a sustainable and adaptable framework that will drive the growth and success of a small-scale LNG businesses.LNG' ability to be transported and stored more cost-effectively in its reduced volume compared to natural gas in its original state is a significant advantage. The distribution network for LNG starts at numerous supply terminals and extends to a diverse range of consumption centers, each with varying tank capacities. In the broader economic context, the establishment of efficient routes for LNG Truck tank plays a critical role.To tackle the challenge of selecting a pickup location and determining optimal destinations with specific unloading volumes for liquefied natural gas (LNG), this research presents an innovative solution utilizing state-of-the-art machine learning techniques. The proposed approach combines the power of reinforcement learning, recurrent neural networks, online learning, and graph theory. By leveraging these cutting-edge techniques, we aim to develop an intelligent system that can effectively determine the most efficient pickup location and sequence of destinations for LNG delivery. This system will take into account various factors such as transportation costs tank capacity, and demand fluctuations. Through the integration of reinforcement learning, the system will continuously learn and improve its decision-making process in real-time. The recurrent neural networks will allow for the analysis of historical aim to model and optimize the complex network of pickup and delivery routes, considering factors such as distance, capacity constraints, and operational efficiencies. With this innovative approach, we strive to revolutionize the selection and planning process for LNG pickup and delivery, ultimately enhancing efficiency and reducing cost within the LNG supply chain.Improving the distribution network offers several financial benefits, including cost reduction, faster delivery times, more affordable distribution, and enhanced efficiency in the utilization of tanker trucks for LNG transportation. By optimizing the distribution network, transit costs associated with LNG transportation can be significantly lowered. This can be achieved through intelligent route planning, taking into account factors such as distance, traffic conditions, and fuel consumption. Minimizing transit costs contributes to overall savings for businesses operating in the LNG market. Faster delivery times are another advantage of an enhanced distribution network. By designing efficient routes and eliminating unnecessary detours or delays, the time taken for LNG to reach its intended destinations can be reduced. This not only improves customer satisfaction but also allows for more frequent deliveries and better coordination of supply and demand. Cheaper distribution is a direct outcome of an optimized distribution network. By strategically planning pickup locations, destinations, and intermediate stops, the costs associated with transferring LNG between terminals and consumption centres can be minimized. This leads to savings in transportation expenses, ultimately benefiting both businesses and end-users. Efficient utilization of tanker trucks is crucial for maximizing profitability in the LNG industry. An improved distribution network enables better allocation of resources, ensuring that tanker trucks are utilized optimally. This includes reducing empty or underutilized trips, maximizing load capacities, and minimizing idle time. Such efficiency improvements translate to cost savings and increased revenue for businesses involved in LNG transportation. Overall, the financial gains from improving the distribution network are substantial. Lower transit costs, faster delivery times, cheaper distribution, and more efficient use of tanker trucks contribute to increased profitability, competitiveness, and sustainability in the LNG market.

This project involves the development of Fire Suppression System (FSS) leveraging of Home automation system using Graphical System design (LabVIEW) software and Arduino microcontroller for real-time monitoring and control. The system integrates an Infrared (IR) flame sensor, an MQ2 gas sensor to detect and suppress fire hazards efficiently. The LabVIEW interface facilitates real-time monitoring of sensor data, allowing users to visualize and analyze the environmental conditions. Through this system, fires are automatically subdued, enhancing safety without requiring human intervention. This advances fire safety by swiftly containing threats and minimizing potential damage.

Gas turbine installations (GTIs) are widely used to generate electrical and thermal energy mainly by burning gaseous fuels. The use of GTIs to burn hydrogen as part of the development of hydrogen energy technology is currently of particular interest. In order to assess the prospects of using GTIs in such a way, it is necessary, among other things, to understand the carbon footprint of the gas turbine as part of the carbon footprint of the entire life cycle of hydrogen. The article provides an overview of the results of previously published studies on life cycle assessment (LCA) of technically complex devices associated with the production and consumption of fuel and energy, which, from the point of view of the complexity of LCA, are most similar to gas turbine installation. The characteristics of the considered stages and resources used in the assessment of the stages of the life cycle of technically complex equipment are presented, including an analysis of the amount of materials used in production and construction. Taking into account its specific application in Russia, an assessment of greenhouse gas emissions at the stages of the GTI life cycle per MWh of produced capacity was carried out.

The study of exogenous metabolites of algae-bacterial communities in the laboratory accumulative culture obtained from natural river water was done using gas chromatography-mass spectrometry. Exometabolites of the algae-bacterial community (including microalgae and cyanobacteria) in the culture medium were represented by saturated, unsaturated, and aromatic hydrocarbons, carboxylic acids, phenolic, and terpene compounds and their derivatives. Possible biological activities of the discovered exometabolites are considered. The study has demonstrated that an increase in the number of main groups of microorganisms, along with changes in the composition of algae and cyanobacteria, are responsible for the increase in the composition and concentration of metabolites in the microecosystem's culture medium after one month of cultivation. The presence of octacosane in high concentration (0.0603 mg/l; 23.78% of the total content of low molecular weight organic compounds) by the end of exposure accumulative culture is associated with the strong development of the cyanobacterium Gloeocapsa sp. in the presence of diatom algae of the genus Navicula and green algae of the genera Chlorella and Scenedesmus. Due to the need to comprehend the ecological and biochemical mechanisms of the formation and functioning of algae-bacterial communities, as well as to predict potential paths of transformation and evolution of aquatic ecosystems, the specificity of exometabolite complexes of algae and microorganisms, as well as their allelopathic and other biochemical interactions in freshwater ecosystems, requires further serious study.

Solar photovoltaic (PV) technology is a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. This paper explores the pivotal role of PV technology in re-ducing greenhouse gas emissions and combatting the pressing issue of climate change. At the heart of its efficacy lies the efficiency of PV materials, which dictates the extent to which sunlight is transformed into electricity. Over the last decade, substantial advancements in PV efficiency have propelled the widespread adoption of solar PV technology on a global scale. The efficiency of PV materials is a critical factor, determining how effectively sunlight is transformed into electricity. Enhanced efficiency, achieved through a decade of progress, has driven the global expansion of solar PV. Multi-junction photovoltaic materials have now exceeded 40% efficiency in lab tests. China leads the world in solar PV installations, boasting over 253 GW of installed capacity by the end of 2021. Other prominent countries in this sector are the United States, Japan, Germany, and India. Supportive policies like feed-in tariffs, net metering, tax incentives, and cost reductions in PV modules have made solar PV increasingly competitive against fossil fuel-based power generation. Solar PV technology holds immense potential for creating a cleaner, reliable, scalable, and cost-effective electricity system. To expedite its deployment and foster a more sustainable energy future, continued investment in research and development, along with supportive policies and market mechanisms, is essential. This paper underscores the pivotal role of solar PV technology in the global energy transition and advocates for a concerted effort to unlock its full potential in achieving a more sustainable and resilient energy future.

The whitefly leads extensive damage to plants through direct feeding, honeydew secretion, plant physiological disorders, and vectoring plant viruses. This study aimed to evaluate the physiological characteristics of susceptible and resistant soybean cultivars to B. tabaci. The experiments were conducted in a greenhouse. Eleven soybean cultivars were selected and infested with 100 adults of B. tabaci at the V3 stage. The evaluation of photosynthetic parameters, such as photosynthetic rate, leaf transpiration, stomatal conductance, and internal CO2 concentration, revealed that B. tabaci infestation influenced gas exchange in soybean plants. The photosynthetic rate was higher in cultivars AS3810 and M8349 during the V6 stage. Infestations led to alterations in photosynthetic parameters, suggesting increased energy demand to maintain photosynthetic activity. However, the response to infestation varied among different cultivars, indicating varying levels of resistance and tolerance to the whitefly's damage. Additionally, the impact of infestation was more significant during the vegetative phenological. In conclusion, B. tabaci infestation affects soybean plants' physiology, leading to changes in gas exchange parameters and water use efficiency. The response to infestation varied among soybean cultivars, suggesting potential differences in resistance to the pest. The study highlights the importance of evaluating the physiological impact of whitefly infestations on soybean.

Faced with difficulties stemming from the complex interactions between tight gas sand bodies and fractures when describing and identifying reservoirs, a composite reservoir model was established. By setting the supply boundary to characterize the superposition characteristics of sand bodies, a mathematical model of unstable seepage in fractured vertical wells in tight sandstone gas reservoirs was constructed considering the factors such as stress sensitivity, fracture density and fracture symmetry. The seepage law and pressure response characteristics of gas well in tight sandstone discontinuous reservoir with stress sensitivity, semi-permeable supply boundary and complex fracture topology were determined, and the reliability of the model was verified. The research results more accurately depict the pressure characteristic curve of the superposed sand body complex fracture vertical well, and provide a more comprehensive model for tight gas production dynamic analysis and well test data analysis, which can more accurately guide the dynamic inversion of reservoir and fracture parameters.

Agriculture (crop production, land use, and livestock) is the second most important greenhouse gas (GHG) emitting sector after the energy sector. Agriculture is also recognized as the source and sink of GHGs. Evidence demonstrates that the application of high amounts of nitrogen-rich fertilizers enhances methane (CH4) and nitrous oxide (N2O) emissions, which are potent GHGs with a high global warming potential (GWP). Considering its global contribution to the climate crisis, reducing GHG emissions in agriculture would considerably lower its share of the global GHG emission records, which may lead to enormous benefits for the environment and food production systems. Several diverging and controversial views questioning the actual role of plants in the current global GHG budget continue to nourish the debate globally. We must acknowledge that considering the beneficial roles of major GHGs to plants at a certain level of accumulation, implementing GHG mitigation measures from agriculture is indeed a complex task. This review seeks to provide key approaches for GHG mitigation in the literature (environmentally friendly crop cultivation and residue management practices, improvement of plants nutrients/fertilizer use efficiency, exploring the genetic diversity for low GHG emission, soil methane-producing bacteria, integrated soil fertility management, improved livestock feeding efficiency, and production, etc.). This work gathers key approaches from 275 peer-reviewed publications, including experimental research papers, review articles, and books, discussing greenhouse gas emissions mechanisms and mitigation to unravel effective strategies for GHG mitigation, proven to be effective or carry the potential to mitigate GHG generation from agriculture. This review also discusses in depth the significance and the dynamics of strategies regarded as game changers with a high potential to enhance, in a sustainable manner, the resilience of agricultural systems. Agricultural GHG mitigation approaches discussed in this work can serve as game changers in global efforts to reducing GHG emissions and alleviating the impact of climate change through sustainable agriculture and informed-decision making.

In recent years discrete element method (DEM) has gradually been applied to the traditional fluidization simulation of fine particles in micro fluidized bed (MFB). The application of DEM in simulating fast fluidization of fine particles in MFB has not yet received attention. This article presents a drag model that relies on the surrounding environment of particles, namely particle circumstance-dependent drag modle or PCDD model. Fast fluidization in MFB of fine particles is simulated using DEM based on the PCDD model. It is found that the gas-solid two-phase flow in MFB meets the common law of fast fluidization in circulating fluidized bed (CFB) to a certain extent, and also presents a special law that is significantly different from that in CFB. Simulations indicate that the local structure in MFB exhibits particle aggregation which is a natural property of fast fluidized, forming a structure where continuous dilute phase and dispersed concentrated phase coexist. The formation and fragmentation of particle clusters in different local regions of the bed have time synchronization. There exists strong effect of solid back-mixing in MFB, leading to relatively low outlet solid flux. The gas back-mixing effect is, however, not so distinct. The axial porosity shows a monotonically increasing distribution with the bed height, but does not strictly follow the single exponential distribution. The solid volume fraction at the bottom of the bed is significantly lower than the correlated value in CFB. The axial heterogeneous distribution of the cross-sectional average porosity in the lower half of the bed is also weakened. The radial porosity shows a distribution pattern of higher in the central region and lower in the sidewall region. Compared with the correlation results in CFB, the porosity near the central region in MFB is relatively low, while the porosity near the sidewall region is relatively high. The geometric size of the container, especially the radial size, is much smaller than that of a CFB. The relative area of contact between the MFB wall and particles is larger, and the wall friction factor becomes more significant. This may be the main reason for the time synchronization of particle agglomeration, severe particle back-mixing, low outlet solid flux, monotonically increasing axial porosity and weak core-annular structure of radial porosity.

The pre-chamber technologies can improve the ignition performance of IC engines by more than two orders in magnitude and thereby substantial economic benefits. Compared with the common pre-chamber, a plug-and-play passive scheme is suitable for quick retrofit, which is getting more attention from the automobile industry. Good scavenging is the precondition for improving turbulent jet ignition performance for a passive pre-chamber. Therefore, detailed evaluations of the scavenging process and turbulent jet ignition deserve investigations for new pre-chamber schemes. In this paper, the effects of design parameters on ignition processes of plug-and-play passive pre-chamber were numerically studied, allowing for the lateral angle, orifice diameter, and vertical angle design. Seven pre-chamber schemes were evaluated, and four optimal ones were selected for bench tests. The characteristics of the scavenging process, turbulent jet ignition, and main-chamber combustion were investigated and analyzed. The results show that allowing for the trade-off between ignition energy and scavenging efficiency, the volume ratio of the pre-chamber to clearance is recommended to be 0.2~0.7%, and the corresponding area-volume ratio is 0.003~0.006 mm-1. Compared with the original natural gas engine, the pre-chamber retrofit can save up to 13.2% fuel consumption, which presents a significant improvement in fuel economy.

This article provides a comprehensive analysis of the global gas and liquefied natural gas (LNG) markets. The article begins by discussing the increasing demand for gas and LNG, particularly in Asia, as countries aim to transition to cleaner sources of energy. It explores how market volatility has led to energy security interventions and lasting economic and emissions impacts. It then explores the global supply and demand dynamics, highlighting the structural change expected in the market and the competition between Europe and Asia for limited new LNG supply. The article also focuses on Europe's increased flexibility and dependence on LNG imports, which have risen by 60% to 121 million tonnes, offsetting lower Russian pipeline imports. Furthermore, the article delves into global supply and demand dynamics, how the market is expected to remain tight, and how record gas and LNG prices have led to demand reductions. Additionally, it analyzes the future outlook and investment needs, highlighting the continued uptake of gas in heavy-duty transport and the need for further investment to avoid a supply-demand gap. The article concludes with an analysis of the implications for the future of the global gas and LNG markets.

When present in specific amounts in the air, the colorless, odorless, and tasteless gases monoxide and carbon dioxide may either replace oxygen in red blood cells (CO) or increase the respiratory rate causing cardiac arrhythmias (CO2), leading to death. Commercial sensors take around 8 h to detect levels of CO (50 PPM), causing moderate poisoning. SnO2 presents controlled interactions with the atmosphere using conductance and vacancy adjustments to capture electrical properties. However, the selectivity of gas detection by SnO2 can still be improved, thus also increasing the application possibilities. The present study aimed to optimize the sensing of CO and CO2 in SnO2 using palladium functionalization. The vapor-liquid-solid method synthesized a network of SnO2 nanobelts decorated with palladium nanoparticles. The sensitivity of the sensors for CO and CO2 were evaluated, characterizing parameters such as response time, a wide range of CO and CO2 concentrations, and temperature. In the seventh measurement cycle, the sensor response for different concentrations of gases in consecutive cycles showed a sensitivity of up to 125% for CO in 60 s. Furthermore, we observed increased sensor sensitivity with material doping with nanoparticles from 130 ppm to 1360 ppm in 30 seconds to CO. Conclusion: The results provide a better understanding of the sensitivity of SnO2 in palladium-decorated nanoparticles, offering insights for detecting low CO concentrations quickly. The behavior of these doped nanosensors showed us the importance of considering them as a practical possibility for detecting these gases of importance to human health.

The power utility, Tanzania Electric Supply Company Limited (TANESCO) is faced with challenges of supplying adequate power to the ever-increasing demand. To this end, this paper explores the installed Ubungo Gas Power Plant (UGP II) located in Dar es Salaam to find means of utilizing it in co-generation method. The study embarked on detailed modelling of the system and run simulations which indicated that the co-generation can result to power generation efficiency increase between 10.42 to 50.92%. It is recommended to change the UGP II to exploit this co-generation method. Further, the study could be improved by including more parameters of the gas and steam plant and verify them experimentally.

CONTEXT– Energy is widely involved in human activity and corresponding emissions of SOX, NOX and CO2 from energy generation processes affect global climate change. Clean fuels are desired by society because of their reduced greenhouse gas emissions. Hydrogen is once such candidate fuel. Much hydrogen is produced from fossil fuel, with biomass being an alternative process. OBJECTIVE– The project compared the environmental impact of hydrogen production by natural gas steam reforming vs. biomass gasification. METHOD–Environmental impact was calculated from the input and output data from life cycle inventory analysis. The impact assessment was focused on greenhouse gas emission, acidification, and eutrophication. Models of the two processes were developed and analysed in OpenLCA. The agribalyse database was used to connect inventory flow data to environmental impacts. FINDINGS– For all three metrics, biomass gasification had lower impacts than natural gas steam reforming, sometimes by large margins. For biomass gasification the silica sand production contributes most to all three impact categories, whereas for natural gas steam reforming it is the LPG extraction.

We show that the van der Waals gas does not exhibit just the celebrated liquid-gas 1 transition but also a quantum-classical one. We study the issue with reference to the LMC statistical 2 complexity and in relation to the order-disorder contrast.

The present study utilizes a metal inert gas welding (MIG) to make a dissimilar weld joint of different stainless steel grades AISI 304, 314, 316L, 420 and a standard structural steel S355MC to estimate the correlation of a microstructure and the mechanical properties. The microstructure of the base metals (BM), the heat affected zone (HAZ), the fusion zone (FZ) and the weld seam were analyzed using optical microscopy. Optical microscopy did not reveal any presence of weld defects such as porosity or cracks. The analysis of microstructure showed that both the austenitic and martensitic stainless steel weld structures contain some retained delta ferrite and coarse Me23C6 carbides in the HAZ, while the FZ exhibits delta ferrite and some retained austenite. The hardness profiles revealed difference between austenitic and martensitic steel welds that the later showed extremely high values in the HAZ (~500 HV/0.1) which causes fracture in this zone. The welds of all austenitic steel grades withstood tensile test, showing the average tensile strength of 472 MPa with fracture observed in the base metal zone. It made clear that the use of a filler rod 308LSI is suitable only for the austenitic stainless and structural steel dissimilar welds, and not appropriate for martensitic-structural steel welds. The achieved results revealed that the higher hardness of the martensitic phase in the HAZ of AISI 420 is closely related with the formation of untempered coarse martensitic structure and higher carbon content.

For the past few decades, researchers have been intrigued with glassy polymers, which have applications ranging from gas separations to corrosion protection to drug delivery systems. The techniques employed to examine the sorption and diffusion of small molecules in glassy polymers are the subject of this review. Diffusion models in glassy polymers are regulated by Fickian and non-Fickian diffusion, with non-Fickian diffusion being more prevalent. The characteristics of glassy polymers are determined by sorption isotherms, and different models have been proposed in the literature to explain sorption in glassy polymers during the last few years. This review also includes the applications of glassy polymer. Despite having so many applications, current researchers still have difficulty in implementing coating challenges due to issues like as physical ageing, which is briefly discussed in the review.

In this paper we compare using computational fluid dynamics the aerothermal performance of two candidate casing manifolds for supplying an impingement-actuated active tip clearance control system for an aero-engine high-pressure turbine. The two geometries are (a) single-entry: an annular manifold fed at one circumferential location; (b) multiple-entry: a casing manifold split into four annular sectors, each sector supplied separately from an annular ring main. Both the single-entry and multiple-entry systems analysed in this paper are idealised version of active clearance control systems in current production engines. Aerothermal performance is quantitatively assessed on the basis of the heat transfer coefficient distribution, driving temperature difference for heat transfer between the jet and casing wall, and total pressure loss within the high-pressure turbine active clearance control system. We predict that the mean heat transfer coefficient (defined with respect to the inlet temperature and local wall temperature) of the single-entry active clearance control system is 77% greater than the multiple-entry system; primarily because the coolant in the multiple-entry case picks up approximately 40 K of temperature from the ring main walls, and secondarily because the average jet Reynolds number of impingement holes in the single entry system is 1.2 times greater than in the multiple entry system. The multiple-entry system exhibits many local hot and cold spots, depending on the position of the transfer boxes, while the single-entry case has a more predictable aerothermal field across the system. The multiple-entry feed system uses an average of 20% of the total available pressure drop, while the feed system for the single-entry geometry uses only 2% of the total available pressure drop. From the aerothermal results of this computational study, and in consideration of holistic aero-engine design factors, we conclude that a single-entry system is closer to an optimal solution than a multiple-entry system.

Introducing children to sustainable and healthy school meals can promote a long-term dietary shift to lower climate impact and improve population health. The aim of the OPTIMAT study was to optimize meals for minimum deviation from the current food supply while reducing greenhouse gases and ensuring nutritional adequacy without increasing cost. Optimized menus were tested in four primary schools in Sweden and effects on daily food consumption and waste evaluated. Pupils received their usual menu plan for three weeks and then the isocaloric optimized menu plan for another three weeks. Nutritional recommendations for a school lunch and a maximum of 500 grams of CO2eq/meal were applied as constraints during linear programming. Pulses, Cereals, Meat and Eggs increased, while Fats and Oils, Dairy, Sauces and Seasonings decreased. The amount of ruminant meat was reduced in favor of other meat products. The new menu was 28% lower in greenhouse gas emissions and slightly less costly than the original. No significant changes in mean food consumption or plate waste were found in interrupted time series analysis between the two periods. This pragmatic approach for combining linear optimization with meal planning could accelerate sustainable development of the meal sector in Sweden and abroad.

There are many pollution and environment problem in the human ecosystem. There are different methods are used to removal of sulfur from sour gases for example Basic Claus process and Modified Claus process . There are different chemical software are used for simulation and optimization of Claus process for example Aspen Plus and Chemcad ECT. The Gibbs free energy method is introduced and model of Claus process. There are new parameter are introduced in reaction furnace to reduce the error from 33% to 7 %. The waste heat boiler is installed at the reaction furnace in which high pressure stream is produced and study the decomposition the hydrogen sulphide. The new rate of reaction is introduced of the enhancement of H2 production in chemical process. The simulation of reaction furnace in Aspen plus software is the maximum utilization of process. Due to suitable operating condition of reaction furnace is caused the maximum destruction of ammonia gas in the reactor. When we are increasing the oxygen concentration and temperature of feed is causing decreasing the ammonia production in reaction furnace. It is below than acceptance value of ammonia is 150 ppm in the reaction furnace. The presence of oxygen components, Sulfur oxide, hydroxide components are effect on decreasing the amount of ammonia in furnace and temperature is about at 1350⁰C. It is noted that when the production of sulfur recovery is decrease in Claus process and the production of carbon monoxide is increase in the thermal section at the existence. Now we are work on parametric studies of furnace that could be causes the production of ammonia destruction and CO emission in the Claus process. Due to optimize the reaction furnace parameter are help to get large of sulfur production, ammonia gas destruction, increased the catalyst life and decreased of dangerous gases.

The development of sensors for monitoring Carbon Monoxide (CO) in a large range of temperature is of crucial importance in areas as monitoring of industrial processes or personal tracking using smart objects. Devices integrating GaN/Ga₂O₃ core/shell nanowires (NWs) is a promising solution allowing combining the high sensitivity of the electronic properties to the states of GaN-core surface; and the high sensitivity to CO of Ga₂O₃-shell. Because the performances of sensors primarily depend on the material properties composing the active layer of the device, it is essential to control these properties and in first time its synthesis. In this work, we investigate the synthesis of GaN/Ga₂O₃ core-shell NWs with a special focus on the formation of the shell. The GaN NWs grown by Plasma-assisted molecular beam epitaxy, are post-treated following thermal oxidation to form Ga₂O₃-shell surrounding the GaN-core. We establish that the Ga₂O₃-shell thickness can be modulated from 1 up to 14 nm by changing the oxidation conditions, and follows the diffuse-controlled reaction. By combining XRD-STEM and EDX analysis, we also demonstrate that the oxide shell formed by thermal oxidation is crystalline and presents the β-Ga₂O₃ crystalline phase, and is synthesized in epitaxial relationship with the GaN-core.

Hardy Cross originally proposed a method for analysis of flow in networks of conduits or conductors in 1936. His method was the first really useful engineering method in the field of pipe network calculation. Only electrical analogs of hydraulic networks were used before the Hardy Cross method. A problem with the flow resistance versus the electrical resistance makes these electrical analog methods obsolete. The method by Hardy Cross is taught extensively at faculties and it still remains an important tool for analysis of looped pipe systems. Engineers today mostly use a modified Hardy Cross method which threats the whole looped network of pipes simultaneously (use of these methods without computers is practically impossible). A method from the Russian practice published during 1930s, which is similar to the Hardy Cross method, is described, too. Some notes from the life of Hardy Cross are also shown. Finally, an improved version of the Hardy Cross method, which significantly reduces number of iterations, is presented and discussed. Also we tested multi-point iterative methods which can be used as substitution for the Newton-Raphson approach used by Hardy Cross, but this approach didn’t reduce number of required iterations to reach the final balanced solution. Although, many new models have been developed since the time of Hardy Cross, main purpose of this paper is to illustrate the very beginning of modeling of gas and water pipe networks or ventilation systems.

The present study was carried out at the north-eastern part of Bangladesh to investigate organochlorine pesticide (OCP) residues and microbiological quality of dried barb (Puntius sophore). Samples were collected from both producers and retailers from December 2016 to April 2017. A control sample was also prepared with the same raw fish used by the producers in the laboratory to compare the result. Gas Chromatography with Electron Capture Detector (GC-ECD) was used to detect and quantify OCP residues. Around 22 % (6) samples out of 27 were found contaminated with OCP residues. Among these six adulterated samples, four were from retailers and two from producers. Only Aldrin was detected in four samples and rest two samples were detected with both Aldrin + Dieldrin and Aldrin + Endrin. Aldrin was found between 0.332 to 0.967 ppm, Dieldrin 0.762 ppm, and Endrin 0.828 ppm. All these values were much higher than the Maximum Residual Limit (MRL) 0.1 ppm. Aerobic Plate Count (APC) of producer samples were ranged between log5.3 ± 0.02 to log5.4 ± 0.03, log6.2 ± 0.02 to log6.4 ± 0.02 for retailer samples and log5.0 ± 0.03 to log5.2 ± 0.04 for control samples. While fungal count was ranged between log3.2 ± 0.04 to log3.5 ± 0.04, log3.4 ± 0.04 to log3.6 ± 0.03 and log2.2 ± 0.05 to log2.5 ± 0.03 for producer, retailer and control samples respectively. All the producer and retailer samples and one-third of the control samples were found contaminated with Escherichia coli. Whereas, Salmonella sp. was detected as 13.3% in producer samples, 20% in retailer samples except for the control. In case of Vibrio sp., maximum count was found for retailer samples (13.3%), whereas, producer and control samples showed no detection. The finding of the present study revealed the presence of pesticides and poor microbiological quality of dried barb are alarming for the consumers of Bangladesh and which may cause chronic disease and potential long-term risk for human health.

The conversion of C1 gas feedstock, such as carbon monoxide (CO), into useful platform chemicals has attracted considerable interest in industrial biotechnology. One conversion method is electrode-based electron transfer to microorganisms using bioelectrochemical systems (BESs). In this BES system, acetate is the predominant component of various volatile fatty acids (VFAs). To appropriately separate and concentrate the produced acetate, a BES type electrodialysis cell with an anion exchange membrane was constructed and evaluated under various operational conditions, such as the applied external current. The higher acetate flux of 23.9 mmol/m2∙hr was observed under -15 mA current in an electrodialysis-based bioelectrochemical system. In addition, the initial acetate concentration affects the separation efficiency and transportation rate. The maximum flux appeared at 48.6 mmol/m2∙hr when the acetate concentration was 100mM, whereas the effect of the initial pH of the anolyte was negligible. The acetate flux was 14.9 mmol/m2∙hr when actual fermentation broth from BES based CO fermentation, was used as a catholyte. A comparison of the synthetic medium with the actual fermentation medium suggests that unknown substances and metabolites in the actual medium interfere with electrodialysis in the BES. These results provide information on the separation and optimal concentration for VFAs produced by C1 gas fermentation through electrodialysis, and a combination of a BES and electrodialysis.

Metal oxide p-n heterojunction nanofibers (NFs) are among the most promising approaches to enhancing the efficiency of gas sensors. In this paper, we report the preparation of a series of p-NiO-loaded n-ZnO NFs, namely (1 − x) ZnO-xNiO (x = 0.03, 0.05, and 0.1 wt%), for hydrogen gas sensing experiments. Samples were prepared through the electrospinning technique followed by a calcination process. The sensing experiments showed that the sample with 0.05 wt% NiO loading resulted in the highest sensing performance at an optimal sensing temperature of 200 °C. The sensing mechanism is discussed in detail and contributions of the p-n heterojunctions, metallization of ZnO and catalytic effect of NiO on the sensing enhancements of an optimized gas sensor are analyzed. This study demonstrates the possibility of fabricating high-performance H₂ sensors through the optimization of p-type metal oxide loading on the surfaces of n-type metal oxides.

Transient analysis of gas flow in pipeline was studied. Finite Element Method based on ANSYS FLOTRAN was used to account for changes in pressure, temperature and flow rate. Compressibility factor function of temperature and pressure was considered. For non-isothermal transient results, the pressure and the flowrate gave wave propagation as a result of slow transients created by demand condition at the outlet end of custody transfer. Results obtained were in agreement with the demand restrictions at the outlet end of custody transfer indicating that the predictions are accurate and reliable. The results demonstrated that the Finite Element Method gave accurate prediction of pressure, temperature and flowrate in transient gas studies. For steady state non-isothermal model, results showed that the magnitude of the average pressure drop was higher when pressure was predicted with constant compressibility factor, but the same average pressure drop was reduced when the pressure was predicted with variable compressibility factor, z. Since compressibility factor is a function of temperature and pressure, the above findings signifies that in the case when gas temperature does not stabilize, the prediction of pressure with isothermal model and constant compressibility factor will lead to significant errors.

This paper consists of the design and implementation of a simple conditioning circuit to optimize the electronic nose performance, where a temperature modulation method was applied to the heating resistor to study the sensor’s response and confirm whether they are able to make the discrimination when exposed to different volatile organic compounds (VOC’s). This study was based on determining the efficiency of the gas sensors with the aim to perform an electronic nose, improving the sensitivity, selectivity and repeatability of the measuring system, selecting the type of modulation (e.g., pulse width modulation) for the analytes detection (i.e., Moscatel wine samples (2% of alcohol) and ethyl alcohol (70%)). The results demonstrated that by using temperature modulation technique to the heating resistors, it is possible to realize the discrimination of VOC’s in fast and easy way through a chemical sensors array. Therefore, a discrimination model based on principal component analysis (PCA) was implemented to each sensor, with data responses obtaining a variance of 94.5% and accuracy of 100%.

Basaltic activity is the most common class of volcanism on Earth, characterized by magmas of sufficiently low viscosities such that bubbles can move independently of the melt. Following exsolution, spherical bubbles can then expand and/or coalesce to generate larger bubbles of spherical-cap or Taylor bubble (slug) morphologies. Puffing and strombolian explosive activity are driven by bursting of these larger bubbles at the surface. Here, we present the first combined model classification of spherical-cap and Taylor bubble driven puffing and strombolian activity modes on volcanoes. We furthermore incorporate the possibility that neighboring bubbles might coalesce, leading to elevated strombolian explosivity. The model categorizes behavior in terms of the temporal separation between the arrival of successive bubbles at the surface and bubble gas volume or length, with the output presented on visually intuitive two-dimensional plots. The categorized behavior is grouped into the following regimes: puffing from a) cap bubbles; and b) non-overpressurised Taylor bubbles; and c) Taylor bubble driven strombolian explosions; each of these regimes is further subdivided into scenarios whereby inter-bubble interaction does/doesn't occur. The model performance is corroborated using field data from Stromboli (Aeolian Islands, Italy), Etna (Sicily, Italy) and Yasur (Vanuatu), representing one of the very first studies, focused on combining high temporal resolution degassing data with fluid dynamics, as a means of deepening our understanding of the processes which drive basaltic volcanism.

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is to evaluate posterior probabilities of observing symbolic sequences and most probable state sequences they may locate. Hence an estimating based model and a decoding based model are used to identify anomalies in two different ways. Experimental results indicates that these two models have both ideal performance overall, and estimating based model has a strong ability in robustness, while decoding based model has a strong ability in accuracy, particularly in a certain range of length of sequence. Therefore, the proposed method can well facilitate existing symbolic dynamic analysis based anomaly detection methods especially in gas turbine domain.

Natural Gas (NG) processing is a prominent industrial separation process. Among the available techniques, the innovative membrane process shows potential for efficient removal of impurities, including carbon dioxide (CO₂). This study focuses on utilizing breakthroughs in membrane separation to drive sustainable CO₂ extraction from natural gas. Through comprehensive research and analysis, we explore the effectiveness and feasibility of membrane-based systems in removing CO₂ impurities from NG, thus promoting greener and more sustainable industrial practices. Our findings underscore the transformative nature of membrane separation technology, presenting new possibilities for a more environmentally-friendly and sustainable approach to CO₂ extraction from natural gas.Natural Gas (NG) processing utilizes various techniques for impurity removal, with the membrane process emerging as a promising option for efficient carbon dioxide (CO₂) extraction. This research proposes the integration of a simple mathematical model into ASPEN HYSYS to design a membrane system for CO₂/CH₄ separation. The study also investigates parameter sensitivities by altering operating conditions, such as feed composition and pressure, as well as membrane properties, including selectivity. By analyzing these variables, we aim to optimize the performance and efficiency of the membrane system, facilitating the sustainable extraction of CO₂ from NG. The findings contribute to advancing the design and operation of membrane-based processes for CO₂ separation, paving the way for greener and more sustainable industrial practices. In addition, this study explores various configurations for optimizing the design of the membrane system, including single-stage with and without recycling as well as double-stage configurations. The investigation demonstrates that methane recovery can be enhanced through the recycling of the permeate stream and by implementing a double stage membrane system. These findings highlight the potential for improving the efficiency and performance of the membrane system, enabling higher methane recovery rates. By considering different configurations, this research contributes to the development of more effective and sustainable CO₂ extraction processes from natural gas.

Agriculture is the largest sector of Nepalese economy among the south-Asian developing countries that contributed 26.98 % share of Gross Domestic Product (GDP) in the year 2018/19. The capacity to derive maximum output by per unit of resource is resource use efficiency. Resource use efficiency of a producer explains the comparison between inputs and output value. Potato is staple vegetable in every parts of Nepal. Cobb-Douglas production function is used to predict the significance of inputs in income from cash crops. Due to lack of optimum utilization of available resources in cultivation of vegetable like lack of technical knowledge, low education level, and extensive service the productivity and production is low in Nepalese economy. It ensures vegetable security in Nepal. Though, there are extensive researches on the resource use efficiency and productivity issues on agriculture, very few papers explored in the case of Sudurpaschim province of Nepal OBJECTIVE: This study had the specific purposes: (1) To identify the factors affecting in resource use efficiency and productivity of potato production (2) to utilize the resource use efficiency and productivity in potato production and (3) to show the type of returns to scale in potato production. METHODS: Applying post-positivist paradigm and quantitative causal survey research design, it explored the resource use efficiency and productivity through the semi-structured questionnaire from 140 households by random sampling. Data analysis was carried out by multiple regression model using Cobb-Douglas production function. SPSS software was used to find quantitative results. From the analysis of data using mean, and SD, t-test, analysis of variance, Durbin-Waston test, Marginal Value Product and marginal Factor cost, findings of this research was emerged. RESULTS AND DISCUSSION: The major findings: education level, extension service, farm size and pesticide cost were significant variables in potato production; seed, hired labor, Family Yard Manure and pesticide cost were overused and needed to decrease their cost for optimum allocation and there was decreasing returns to scale in production. This study emphasized on increasing education level, extensive service and farm size for optimum utilization of resources and increasing returns to scale. SIGNIFICANCE: Barriers to Indian potatoes and increasing returns to scale are only possible through protection policy of government. For optimum use of resources and increasing returns to scale, government can introduce capital intensive technique. This study can be implied in policy making and improving existing situation of agriculture, and allocating budget.

: Based on fairness perspective, this paper combines the social vulnerability assessment of natural hazards with multi-objective site selection model to propose a new site selection planning model for central refuges. To begin with, the social vulnerability assessment system of regional natural disasters is founded through literature analysis, and the social vulnerability index of natural disasters is constructed; and based on the exponential differentiation of index to depict regional refuge demand, design vulnerability coverage function quantitive site selection fairness; then taking the shortest weighted evacuation distance, maximum vulnerability coverage and construction cost, the coverage population and refuge quantity as constraint conditions to construct multi-objective optimization model; finally, taking Zigong City, Sichuan Province as the object of empirical research, adapting NSGA-II algorithm to get the Pareto optimal solution set, and using TOPSIS method to get the optimal location scheme. The research results can provide a new method for the location layout of central refuge.

Chili peppers (Capsicum annuum L.) are economically important crop worldwide. Pepper yellow leaf cure Thailand virus (PepYLCTHV), a Begomovirus causing yellow leaf mosaic disease of chili pepper has been reported to damage 95% economic loss under favorable epidemic conditions. Thirty-one chili genotypes were screened for resistance to PepYLCTHV disease through challenge inoculation using 10-15 viruliferous whiteflies per plant. We purified two resistant lines (PEP6 and PEP12) through four generations of selfing and selection through challenged inoculations. At the 28 days after inoculations two chili genotypes showed symptoms less based on disease severity and percentage of disease incidence of 30 plants PEP6 showed a disease severity 3.64 with 69.33 % DI and PEP12 showed a disease severity 3.83 with 77.67 % DI. PEP6 and PEP12 were selected for pure-line development for four cycles.Both varieties showed an increase in percentage of the resistant plant 12.78 % and 16.77 %, respectively. These lines have potential for release as resistant lines for improvement chili pepper resistance to PepYLCTHV and for developing maker associated with the resistant trait.

A number of studies suggested that influenza vaccination can provide protection against COVID-19 but the underlying mechanisms that could explain this association is still unclear. In this study, the effect of the 2021/2022 seasonal influenza vaccination on the immune response to the booster dose of anti-SARS-CoV-2 vaccination was evaluated in a cohort of healthy individuals. One hundred and thirteen participants were enrolled, and 74 of them with no prior COVID-19 diagnosis or significant co-morbidities were considered for the analysis. Participants received the anti-influenza tetravalent vaccine and the booster dose of the anti-SARS-CoV-2 vaccine or the anti-SARS-CoV-2 vaccine alone. Blood was collected before and 4 weeks after each vaccination and 12 weeks after SARSCoV2 vaccination and analyzed for anti-flu and anti-spike-specific antibody titers and for in vitro influenza and SARS-CoV-2 neutralization capacity. Results indicated an increased reactivity in subjects who received both influenza and SARSCoV2 vaccinations, as compared to those who received only the SARS-CoV-2 vaccine, with sustained anti-spike antibody titers up to 12 weeks post-vaccination. Immune response to influenza vaccine was evaluated and individuals were stratified as high or low responders. High responders showed increased antibody titers against SARSCoV2 vaccine both after 4- and 12-weeks post-vaccination. On the contrary, the individuals classified as low responders were less responsive to the SARS-CoV-2 vaccine. These data indicate that both external stimuli, such as influenza vaccination, and the host's intrinsic ability to respond to stimuli play a role in the response to vaccine.

Background: Currently, a Dolutegravir (DTG)-based regimen is administered to women on option B plus to prevent mother-to-child transmission (MTCT) of the virus. However, its effect on reducing MTCT of human immunodeficiency virus (HIV) among exposed infants over the previously used Efavirenz (EFV)-based regimen is unknown. Objective: This study aimed to compare the effects of DTG-based and EFV-based regimens on the MTCT of HIV among exposed infants in southern Ethiopia. Methods: A retrospective before-after study design was employed from March to May 2023 among 958 mother-infant pairs (479 on EFV-based and 479 on DTG-based regimens) enrolled in the prevent mother-to-child transmission (PMTCT) care from September 2015 to February 2023. The outcome variable was the HIV infection status among the exposed infants. A log-binomial model was employed and the proportion was computed to compare the incidence of MTCT of HIV in both groups. The risk ratio (RR) with 95% confidence interval (CI) was calculated to assess the predictor variables. Results: Mothers on a DTG-based regimen were approximately 44% (adjusted risk ratio (aRR): 0.56; 95% CI: 0.44, 0.70) less likely to transmit HIV to their infants than those on an EFV-based regimen. In addition, poor/fair adherence to antiretroviral therapy (ART) (aRR: 5.82; 95% CI: 3.41, 9.93), home delivery (aRR: 3.61; 95% CI: 2.32, 5.62), mixed feeding practice (aRR: 1.83; 95% CI: 1.45, 2.3), and not receiving antiretroviral prophylaxis (aRR: 3.26; 95% CI: 1.6, 6.64) were found to increase the risk of MTCT of HIV infection, whereas older maternal age (aRR: 0.93; 95% CI: 0.9, 0.96) was a protective factor.Conclusion: The Dolutegravir-based regimen significantly reduced MTCT of HIV among exposed infants compared to the EFV-based regimen. Thus, DTG-based first-line ART regimen supplementation should be sustained to achieve global and national targets for zero new infections in HIV-exposed infants.

: Inflammation may mediate the association of mitral valve prolapse (MVP) with ventricular scarring and arrhythmia. The purpose of this study is to investigate the association of neutrophil- and platelet-to-lymphocyte ratios (NLR, PLR) with ventricular arrhythmia in MVP. Clinical and demographic data were extracted from electronic medical records for all patients aged 18-90 years with MVP on echocardiography between 2016-19 at our institution. We studied 65 patients who had a differential blood count within six months prior to ambulatory ECG (A- ECG) (medi-an age [IQR] 70 [61-77] years; 62% female; 12% African American). Patients with surgery or in-fection within six months prior to CBC were excluded. Complex or frequent ventricular ectopy (cfVE) was defined as the presence of two or more premature ventricular contractions (PVCs) on a 10-second ECG tracing or greater than 1% PVC burden or NSVT on ambulatory ECG monitoring. Logistic and bivariate regression was used to analyze clinical, electrocardiographic and echo-cardiographic factors associated with NLR and PLR. Factors associated with higher NLR were congestive heart failure (p=0.005), male sex (p=0.01), age (p=0.003), and bileaflet MVP (p=0.04). NLR was associated with higher absolute PVC burden (rs=0.28, p=0.03) and cfVE (median NLR [IQR] 3.0 [1.6 – 6.3]) with cfVE versus (2.4 [1.4-3.4], p=0.04) without cfVE. An NLR of 5.6 yielded 50% sensitivity and 90% specificity for PVC burden >1% (AUC 0.69). No significant association was found between PLR and PVC burden or cfVE. NLR is associated with PVC burden and com-plex ventricular ectopy in MVP, suggesting that inflammation is a significant factor in the patho-genesis of ventricular arrhythmia in this condition.

In the last ten years, plasma waste treatment has gained increasing prominence as a technology in response to growing challenges in waste disposal and the recognition of opportunities for generating valuable by-products. The efficiency and outcomes of this process are intricately linked to the characteristics of the plasma involved, such as plasma enthalpy and heat flux. We employed an experimental radio frequency inductively coupled plasma (RF-ICP) torch operating at atmospheric pressure to generate plasma. Utilizing optical emission spectroscopy (OES), we measured plasma parameters, specifically the effective electron temperature (Teff) and plasma density (ne), while varying argon gas flow rates and RF powers. Our investigations revealed that both flow rate and RF power exert significant control over plasma parameters, and we observed an unusual phenomenon within this range of discharge pressures. Consequently, both the plasma's enthalpy and the heat flux it generates are influenced by variations in both flow rate and RF power.

Addressing the increasing nursing demand in the face of a limited nursing workforce, understanding job satisfaction and retention becomes critical. Our cross-sectional study, conducted among 2,572 nurses in Bavaria, Germany, uncovers a dichotomy where nurses have strong empathy for their profession yet yearn for significant improvements. The top areas identified for enhancement include (i) ensuring sufficient time for patient care, (ii) providing a reliable service organization with minimal unplanned shift changes, and (iii) streamlining nursing documentation. Moreover, the largest disparity between expectations and job satisfaction was measured in patient care time. Multivariable regression analysis further investigates factors influencing intentions to stay in the profession, including (i) demographic aspects (age, living conditions), (ii) working area, (iii) working hours, (iv) career choice satisfaction, (v) career opportunities, (vi) payment, (vii) working and rest times, and (viii) satisfaction derived from aiding individuals in challenging life situations. The findings uncover a need to change nursing conditions, and measures for younger nurses and elderly care nursing. Although there are limitations, and a potential bias due to convenience sampling, our study provides key insights into nursing conditions. Future research should broaden its scope to investigate nurse job satisfaction and retention, emphasizing potential relief tools such as digitization.

In the realm of artificial intelligence (AI), generative systems, most notably Midjourney, have tremendous power to generate creative images of buildings and sites of Islamic architectural heritage through text-to-image generation based on the internet. The AI-generated representations have significant potential for architects, specialists, and common users. However, the system has considerable limitations when generating images for some buildings and sites where the representations appear too far from their original represented structures. This research article attempts to answer the question: What are the limitations of using the AI system of Midjourney in producing images similar to the original buildings and sites of the Islamic architectural heritage? The research employs prompt engineering techniques based on historical sources as inputs to examine the accuracy of the output of the AI-generated images of selected examples of structures of the Islamic tradition. It compares the output with the original look by employing direct observation and critical analysis of human intelligence (HI). It categorizes these limitations into four groups: (1) limits of the prompt, (2) limits of fame, (3) limits of regionality and historical styles, and (4) limits of architectural elements and details. It concludes that while Midjourney has great capability to represent high-end AI-generated images inspired by the Islamic tradition, it currently falls short of presenting the actual appearance of some of their original structures.

This work focuses on an inland area of North Sardinia (Italy), which is currently subject to depopulation and economic crisis because it is not affected by the summer tourism typical of the region. Despite this, the area shows an interesting architectural and natural heritage, as of today insufficiently known and valued. Therefore, the purpose of this paper is to illustrate the application of the Conjoint Analysis methodology on this case study aimed at supporting processes of valorisation, diversification of the tourist offer and promotion of the through the detection of the preferences of a sample of residents and visitors on a set of 9 cards (itinerary profiles) which also promote the peculiarities of small rural centres. Thus, by valorising the material and immaterial heritage and their intrinsic characteristics, it is shown that inland areas can generate an economy by offering new types of tourism, not just the typically invasive summer and maritime tourism. The research, therefore, seeks to estimate the economic value of the area from a public point of view through the WTP measured by a questionnaire of 600 interviews between inhabitants (300) and tourists (299).

Food Neophobia (FN), defined as the reluctance to eat new or unfamiliar foods, mainly concerns fruit, vegetables, and legumes, typical of the Mediterranean Diet (MD). Considering these premises, this study aimed to clarify the relationship between FN and AMD in a sample of Italian children and their association with some socio-demographic factors and children's nutritional status. A sample of 288 children aged 3-11 years, participated to an assessment carried out with a questionnaire evaluating FN and AMD, respectively with Child Food Neophobia Scale (CFNS) and the KIDMED Test. Most of the sample showed an intermediate (67.3%) or high level of FN (18.1%) with high rates among 6-11 years old children (63.9%) and especially in only-child (50%). The AMD was mostly low (29.5%) or medium (54.8%) and reached lower levels among higher neophobic children (51.9%; p value &lt; 0.05). Present results confirm the study hypothesis that FN is a driver of MD abandonment and show the positive effects on children's eating habits of having siblings. Finally, this study proves the relevance of adopting effective feeding strategies against FN to avoid its maintenance in adulthood and detrimental effects on future overall health and well-being.

Canine mammary tumors represent one of the leading malignant pathology in female dogs, displaying the importance of efficient therapeutic findings, beside the golden standard surgery, able to limit the development of the disease. Studies in human cancers demonstrated that Doxorubicin presents a good effect in different biological processes like apoptosis, autophagy, cell cycle, cell invasion and epithelial to mesenchymal transition. Our study following the effects of Doxorubicin on two canine mammary cancer cell lines P114 and CMT-U27, showing an inhibitory effect in cell proliferation and an expression alteration of the EMT related genes.

Ustekinumab, a new anti-interleukin-12/23 antibody, is an effective treatment for ulcerative colitis; however, data regarding predictive factors of its efficacy are limited. This study aimed to identify biomarkers that can predict the long-term outcome of ustekinumab treatment. We retrospectively reviewed the records of patients with active ulcerative colitis treated with ustekinumab at our hospital from June 2020 to January 2023. We divided patients into non-remission and remission groups, and examined whether baseline biomarkers, including C-reactive protein-to-lymphocyte ratio, and early treatment response could predict clinical remission at week 48 of ustekinumab treatment. Of the 33 patients included in the study, 21 (63.6%) were in clinical remission at week 48 of ustekinumab treatment. The baseline C-reactive protein-to-lymphocyte ratio value was identified as an independent prognostic factor for clinical remission at week 48 (odds ratio: 10, 95% confidence interval: 1.6–62.4, p=0.014), with the cutoff value of 3.353 showing excellent prognostic performance (sensitivity: 71.4%, specificity: 83.3%). Furthermore, the clinical response at week 4 (p=0.009) and that at week 8 (p=0.005) were significantly associated with clinical remission at week 48. The baseline C-reactive protein-to-lymphocyte ratio value and early treatment response are useful biomarkers to predict the long-term efficacy of ustekinumab treatment.

The cutting of difficult-to-machine materials, including their preconditioning, often requires specialised tools and machinery. An alternative processing technology can be cutting materials with a water jet. From the initial stage, the selection of appropriate technological parameters will influence what further finishing treatments will be applied. Vibrations occurring during this process can negatively affect the quality of the cutting edges and surfaces. Therefore, this study analysed vibrations during water-jet cutting with variable technological parameters (speed vfi and pressure pi). To measure the vibrations, a three-axis accelerometer from SEQUOIA was used in water-jet cutting of three materials: aluminum alloy, titanium alloy, and steel. Based on the study, it was found that the maximum value of vibration amplitude reaches the lowest value of vibration acceleration for aluminum alloy (not exceeding 5 m/s2), regardless of the value of pressure during the cutting process. This, taking into account the material properties, indicates that aluminum alloy is the material most susceptible to the cutting process while maintaining a high quality of the cutting surface. Significantly higher values of vibration acceleration amplitude (reaching up to 60 m/s2) during cutting were registered for steel and titanium alloy in all zones and phases of the process (entry zone, cutting zone, and exit zones).

Graphene is a two-dimensional nanostructured material with intrinsic properties that show promising performance in various applications, including electronics, renewable energy, medicine, mechanical enforcement and others. This study reports a bottom-up approach for the conversion of cyclohexane into graphene nanoflakes, which were then deposited onto fiberglass using a non-thermal generator. The composite was characterized using transmission electron microscopy, which revealed the formation of stacked few-layer graphene with a partially disordered structure and a d-spacing of 0.358 nm between the layers. X-ray diffraction confirmed the observations from the TEM images. SEM images showed the agglomeration of carbonaceous material onto the fiberglass, which experienced some delamination due to the synthesis method. Raman spectroscopy indicated that the obtained graphene exhibited a predominance of defects in its structure. Additionally, Atomic Force Microscopy (AFM) analyses revealed the formation of graphene layers with varying levels of porosity.

In this paper, a novel strategy is adopted in a degradation model to affect the implied lifetime distribution. The multiplicative degradation model is utilized as a postulate in the model. It will be established that the implied lifetime distribution forms a classical mixture model. In this mixture model, time-to-failure is lying with some probabilities between two first passage times of the degradation process to reach two specified levels. Stochastic comparisons in the model under a change in the probabilities are studied. Several examples are provided to highlight the applicability of the results in the cases when typical degradation models are candidate.

Epithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy, largely due to metastasis and drug resistant recurrences. Fifteen percent of ovarian tumors carry mutations in BRCA1 or BRCA2, rendering them vulnerable to treatment with PARP inhibitors such as olaparib. Recent studies have shown that TGFβ can induce “BRCAness” in BRCA wild-type cancer cells. Given that TGFβ is a known driver of epithelial to mesenchymal transition (EMT), and the con-nection between EMT and metastatic spread in EOC and other cancers, we asked if TGFβ and EMT alter susceptibility of EOC to PARP inhibition. Epithelial EOC cells were transiently treated with soluble TGFβ and their clonogenic potential, expression and function of EMT and DNA repair genes, and response to PARP inhibitors compared with untreated controls. A second epithelial cell line was compared to its mesenchymal derivative for EMT and DNA repair gene expression and drug responses. We found that TGFβ and EMT resulted in downregulation of genes responsible for homologous recombination (HR) and sensitized cells to olaparib. HR efficiency was reduced in a dose-dependent manner. Furthermore, mesenchymal cells displayed sensitivity to olaparib, cis-platin, and the DNA-PK inhibitor Nu-7441. Therefore, treatment of disseminated, mesenchymal tumors may represent an opportunity to expand clinical utility of PARP inhibitors and similar agents.

Since the beginning of 2020, tetracycline (TC), such as minocycline (MINO), has been used to inhibit coronavirus disease 2019 (COVID-19). Traditional Japanese Kampo medicine, such as Saiko-keishi-to (SKT), has recently received a lot of attention for its anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) effects. We describe a COVID-19 patient treated with MINO and SKT in anticipation of their anti-SARS-CoV-2 properties. A 90-year-old male patient with Alzheimer’s disease was referred to a medical clinic due to fever, appetite loss, and general malaise. Based on positive SARS-CoV-2 rapid antigen tests, he was diagnosed with COVID-19. He did not have COVID-19 pneumonia, but he had difficulty in eating on his own. As a result, he was referred to a hospital for admission. After admission, he was treated with molnupiravir to avoid aggravation. Seven days after his admission, he was discharged. After his discharge, he was referred to our hospital as an outpatient due to a prolonged mild fever and general malaise. As a precaution, a real-time reverse transcription-polymerase chain reaction (RT-PCR) test was performed, which yielded a positive result. Therefore, the aforementioned symptoms were thought to be caused by prolonged COVID-19. He was treated with MINO and SKT due to the anti-viral properties of these two drugs. The RT-PCR test became negative 7 days after receiving this treatment, and the fever and general malaise subsided.

The gauge field equation for fluid mechanics established in Part 1 is developed into a first-order scattering theory in the simplified case of a two-dimensional incompressible flow over a flat plate. This is used to present a model for the origin of Tollmien-Schlichting (TS) waves based on scattering between fluid particles. As a result, analytical formulae for the maximum amplification factor and the transition point from laminar to turbulent flow in the boundary layer are obtained. The mathematical transformations from the stationary field equations in Part 1 to a scattering theory with time evolution along the flow axis using Wick rotation are elaborated in detail.

Food waste is one of the greatest challenge to sustainability in developed and developing countries. In the former, food waste is concentrated in the final part of the chain. Too Good To Go is a digital platform aimed to prevent food waste, where consumers can buy magic boxes from different outlets containing food products that are approaching their end-of-life. However, the mechanism of the platform enable the outlets to publish their offering much in advance of the pick-up time, raising the question whether the food sold on the platform is effectively a leftover or a planned production. Our empirical study gathers data from over 1200 outlets in Rome on the Too Good To Go platform.

Deep neural networks have been extensively researched in the field of document image classification to improve classification performance and have shown excellent results. However, there is little research in this area that addresses the question of how well these models would perform in a real-world environment, where the data the models are confronted with often exhibits various types of noise or distortion. In this work, we present two separate benchmark datasets, namely RVL-CDIP-D and Tobacco3482-D, to evaluate the robustness of existing state-of-the-art document image classifiers to different types of data distortions that are commonly encountered in the real world. The proposed benchmarks are generated by inserting 21 different types of data distortions with varying severity levels into the well-known document datasets RVL-CDIP and Tobacco3482, respectively, which are then used to quantitatively evaluate the impact of the different distortion types on the performance of latest document image classifiers. In doing so, we show that while the higher accuracy models also exhibit relatively higher robustness, they still severely underperform on some specific distortions, with their classification accuracies dropping from ~90% to as low as ~40% in some cases. We also show that some of these high accuracy models perform even worse than the baseline AlexNet model in the presence of distortions, with the relative decline in their accuracy sometimes reaching as high as 300-450% that of AlexNet. The proposed robustness benchmarks are made available to the community and may aid future research in this area.

Previous studies have quantified repolarization variability using time-domain, frequency-domain and non-linear analysis in mouse hearts. Here, we investigated the relationship between these parameters and ventricular arrhythmogenicity in a hypokalaemia model of acquired long QT syndrome. Methods: Left ventricular monophasic action potentials (MAPs) were recorded during right ventricular regular 8 Hz pacing during normokalaemia (5.2 mM [K+]), hypokalaemia modelling LQTS (3 mM [K+]) or hypokalaemia with 0.1 mM heptanol in Langendorff-perfused mouse hearts. Results: During normokalaemia, mean APD was 33.5±3.7 ms. Standard deviation (SD) of APDs was 0.63±0.33 ms, coefficient of variation was 1.9±1.0% and the root mean square (RMS) of successive differences in APDs was 0.3±0.1 ms. Low- and high-frequency peaks were 0.6±0.5 and 2.3±0.7 Hz, respectively, with percentage powers of 38±22 and 61±23%. Poincaré plots of APDn+1 against APDn revealed ellipsoid morphologies with SD along the line-of-identity (SD2) to SD perpendicular to the line-of-identity (SD1) ratio of 4.6±1.1. Approximate and sample entropy were 0.49±0.12 and 0.64±0.29, respectively. Detrended fluctuation analysis revealed short- and long-term fluctuation slopes of 1.62±0.27 and 0.60±0.18, respectively. Hypokalaemia provoked ventricular tachycardia in six of seven hearts, prolonged APDs (51.2±7.9 ms), decreased SD2/SD1 ratio (3.1±1.0), increased approximate and sample entropy (0.68±0.08 and 1.02±0.33) and decreased short-term fluctuation slope (1.23 ± 0.20) (ANOVA, P<0.05). Heptanol prevented VT in all hearts studied without further altering the above repolarization parameters observed during hypokalaemia. Conclusion: Reduced SD2/SD1, increased entropy and decreased short-term fluctuation slope are associated with ventricular arrhythmogenesis in hypokalaemia. Heptanol exerts anti-arrhythmic effects without affecting repolarization variability.

Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) are constantly threatening global public health. With no end date, the pandemic still persists with the emergence of novel variants that threaten the effectiveness of diagnostic tests and vaccines. Mutations in the Spike surface protein of the virus are regularly observed in the new variants, potentializing the emergence of novel viruses with different tropism from the current ones, which may change the severity and symptoms of the disease. Growing evidence has shown that mutations are being selected in favor of variants that are more capable of evading the action of neutralizing antibodies. In this context, the most important factor guiding the evolution of SARS-CoV-2 is its interaction with the host’s immune system. Thus, as current vaccines cannot block the transmission of the virus, measures complementary to vaccination, such as the use of masks, hand hygiene, and keeping environments ventilated remain essential to delay the emergence of new variants. Importantly, in addition to the involvement of the immune system in the evolution of the virus, we highlight several chemical parameters that influence the molecular interactions between viruses and host cells during invasion and are also critical tools making novel variants more transmissible. In this review, we dissect the impacts of the Spike mutations on biological parameters such as (1) increase of Spike binding affinity to hACE2; (2) bound time for the receptor to be cleaved by the proteases; (3) how mutations associate with increase of RBD up-conformation state in the Spike ectodomain; (4) expansion of uncleaved Spike protein in the virion particles; (5) increment of Spike concentration per virion particles; and (6) evasion of the immune system. These factors play key roles in the fast spreading of SARS-CoV-2 variants of concern, including the Omicron.

This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.

The ratio of males to females with ASD is generally quoted as 4:1 though it is believed that there are biases preventing females being diagnosed and that the true ratio is lower. These biases have not been clearly identified or quantified. Starting with a clinical dataset of 1711 children <18 years old four different methods were employed in an inductive study to identify and quantify the biases and calculate the proportion of females missed. A mathematical model was constructed to compare the findings with current published data. The true male to female ratio appears to be 3:4. Eighty per cent of females remain undiagnosed at age 18 which has serious consequences for the mental health of young women.

As V2X technology develops, acute problems related to reliable and secure information exchange between network objects in real time appear. The article aims to solve the scientific problem of building a network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The authors present a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels, and an energy-efficient algorithm of traffic offloading to the MEC server. The paper presents the results of application of blockchain technologies and mobile edge computing in the developed system, their description, evaluation of advantages and disadvantages of the implementation.

Sustainability is very important in business. The residential high-rise building all firms wanted to successful and sustainability in business. There is need the construction to completed low time, low cost and high quality in construction work. Therefore, real estate developers must find various strategies to use in construction projects to achieve goals and success of the firm. The purpose of this study was to indicate the very importance of knowledge management can lead to sustainability in business. Analyze 14 critical success to sustainability in residential high-rise building projects, collected questions from literature review, and survey in deep interview senior managers of 15 projects real estate development firms listed on the stock market of Thailand. Budget building more than 1000 million baht per project. From conceptual frame work five foundations integrated to knowledge management of critical success (KMITL PROCESS) used made tool survey in deep interview the critical success to sustainability in residential high-rise building project from project managers. Used IBM SPSS evaluated the critical success to sustainability. Found was one very important of critical success to sustainability, which was quality of products x̄ = 4.83. All 14 critical success to sustainability was found can applied made the knowledge management of critical success to sustainability in residential high-rise building project in firm. Especially in the field of construction managements project can used improve of all construction projects.

We show that an exponential income distribution will emerge spontaneously in a peer-to-peer economic network that shares the publicly available technology. Based on this finding, we identify the exponential income distribution as the benchmark structure of the well-functioning market economy. However, a real market economy may deviate from the well-functioning market economy. We show that the deviation is partly reflected as the invalidity of exponential distribution in describing the super-low income class that involves unemployment. In this regard, we find, theoretically, that the lower-bound u of exponential income distribution has a linear relationship with (per capita) unemployment compensation. In this paper, we test this relationship for the United Kingdom from 2001 to 2015. Our empirical investigation confirms that the income structure of low and middle classes (about 90% of populations) in the United Kingdom exactly obeys exponential distribution, in which the lower-bound u is exactly in line with the evolution of unemployment compensation.

Indonesia is an interesting case study for students of sustainable development and sustainable energy due to its ability to connect the multiple “worlds” it has become part of. Indonesia is an important bridge to Muslim countries, the voice of the Global South in the G20 and a main pillar of the 134-country-strong G77. Indonesia’s development trajectory is also key to the achievement of the Paris Climate Agreement as well as of the 2030 Agenda. Students can learn from how Indo-nesia address contradictions that would have been unsurmountable for other countries. Indone-sia’s energy transition offers helpful lessons, because of its aspiration to become a developed country by 2045. This goal is only possible when a country is able to effectively address barriers and caveats to sustainable energy. It is interesting how Indonesia focuses on silver linings and come up with pragmatic solutions to energy-related issues. This is followed by the “teaching guide,” which provides recommendations how the lessons from Indonesia can be embedded into a learning experience. The “learning activation approach” is introduced, which encourages stu-dents to systematically reflect on the complexity of selected contexts and understand this com-plexity by looking at the technical issues and processes that allow decision-making.

Peer-to-peer (P2P) energy trading is a new data-driven business model currently being trialed within the energy sector. Introducing P2P transactions to an essential service such as energy could have far-reaching implications for individuals and the grid. This paper raises considerations and questions from social, economic/markets and regulatory points of view, that should be understood and addressed by societies and policymakers. It does this by considering under what circumstances it is reasonable to conceptualize P2P energy trading as part of the sharing economy, and drawing parallels to the sharing economy experience in other sectors. In order to reap the full societal benefits, while avoiding considerable risks to infrastructure and individuals, a policy approach promoting dialogue and innovation is necessary. We suggest the regulatory sandbox is the most appropriate tool to achieve this and would help avoid the breakdown of trust between policymakers and platform companies observed in other sectors.

Paraphrase generation is an important yet challenging task in NLP. Neural network-based approaches have achieved remarkable success in sequence-to-sequence(seq2seq) learning. Previous paraphrase generation work generally ignores syntactic information regardless of its availability, with the assumption that neural nets could learn such linguistic knowledge implicitly. In this work we make an endeavor to probe into the efficacy of explicit syntactic information for the task of paraphrase generation. Syntactic information can appear in the form of dependency trees which could be easily acquired from off-the-shelf syntactic parsers. Such tree structures could be conveniently encoded via graph convolutional networks(GCNs) to obtain more meaningful sentence representations, which could improve generated paraphrases. Through extensive experiments on four paraphrase datasets with different sizes and genres, we demonstrate the utility of syntactic information in neural paraphrase generation under the framework of seq2seq modeling. Specifically, our GCN-enhanced models consistently outperform their syntax-agnostic counterparts in multiple evaluation metrics.

Why are pieces of spaghetti generally broken into three to ten segments instead of two as one thinks? How can one obtain the desired number of fracture segments? For these problems, in this paper a strand of spaghetti is considered an elastic rod, and the finite-element software ABAQUS is used to simulate the detailed fracture dynamics of the elastic rod. By changing the size (length and diameter) of the rod, the relevant data on the fracture limit curvature and the number of fractured segments of the elastic rod are obtained. The ABAQUS simulation results confirm the scientific judgment of B. Audoly and S. Neukirch (Fragmentation of rods by cascading cracks: Why spaghetti does not break in half. Phys Rev Lett 95: 095505). Using dimensional analysis to fit the finite-element data, two relations of the elastic rod fracture dynamics are obtained: (1) the relationship between the fracture limit curvature and the diameter, and (2) the relationship between the number of fracture segments and the diameter-length ratio. Results reveal that when the length is constant, the larger the diameter (the smaller the diameter-length ratio D/L), the smaller the limit curvature; and the larger the diameter-length ratio D/L, the fewer the number of fractured segments. The relevant formulations can be used to obtain the desired number of broken segments of spaghetti by changing the diameter-to-length ratio.