The reduction of Ce cations in CeO2 can be enhanced by its partial substitution by Fe cations. The enhanced reduction of Ce cations results in a considerable increase in the reaction rates for thermal water splitting reaction when compared to CeO2 alone. This mixed oxide has a smaller crystallite size when compared to CeO2 in addition to a smaller lattice size. In this work, two Fe-substituted Ce oxides are studied (Ce0.95Fe0.05O2 and Ce0.75Fe0.25O2) by core and valence level spectroscopy in their as-prepared and Ar-ions sputtered states. Ar-ions sputtering substantially increases Ce4f lines at about 1.5 eV below the Fermi level. In addition, it is found that the XPS Ce5p/O2s ratio is sensitive to the degree of reduction, most likely due to a higher charge transfer from the oxygen to Ce ions upon reduction. Quantitatively it is also found that XPS Ce3d of the fraction of Ce3+ (uo, u’ and vo, v’) formed upon Ar-ions sputtering and the ratio Ce5p/O2s lines are higher for reduced Ce0.95Fe0.05O2 than for reduced Ce0.75Fe0.25O2. XPS Fe2p showed however no preferential increase for Fe3+ reduction to Fe0 with increasing time for both oxides. Since water splitting was higher on Ce0.95Fe0.05O2 when compared to Ce0.75Fe0.25O2, it is inferred that the reaction centers for the thermal water splitting to hydrogen are the reduced Ce cations and not the reduced Fe cations. These reduced Ce cations can be tracked by their XPS Ce5p/O2s ratio in addition to the common XPS Ce3d lines.

The current economic development paradigm, which is based on steadily rising resource consumption and pollution emissions, is no longer viable in a world with limited resources and ecological capacity. The "green economy" idea has presented this context with a chance to alter how society handles the interplay between the environmental and economic spheres. The related concept of "green nanotechnology" aims to use nano-innovations within the fields of materials science and engineering to generate products and processes that are economically and ecologically sustainable, enabling society to establish and preserve a green economy. Many different economic sectors are anticipated to be impacted by these applications, including those related to corrosion inhibitor nano fertilizers, nano remediation, biodegradation, heavy metal detection, biofuel, insecticides & pesticides, and catalytic CO2 reduction. These innovations might make it possible to use non-traditional water sources safely and to create construction materials that are enabled by nanotechnology, improving living and ecological conditions. Therefore, our aim is to highlight how nanotechnology is being used in the green economy and to present promises for nano applications in this domain. Additionally, we want to critically examine the practical difficulties these applications raise, especially in consideration of any possible implications for the health and safety of those employed within this innovative sector. In the end, it emphasizes how critical it is to attain a really sustainable advancement in nanotechnology.

Since the photocatalytic reaction is a surface reaction, enhancing the gas movement around photocatalyst would promote photocatalytic CO₂ reduction performance. A new approach to enhance the gas movement around the photocatalyst by the natural thermosiphon movement of gasses around photocatalyst using black body material was proposed and confirmed experimentally, but the heat transfer mechanism on the phenomena was not clarified yet. The purpose of this study is to clarify the heat transfer mechanism of it. This study calculated the temperature of mixed gases of CO₂ and NH₃ around P₄O₁₀/TiO₂ photocatalyst by the heat transfer formula. It is revealed that there was no difference between the increase temperature (T_g) from the temperature at the beginning of the CO₂ reduction experiment (T_ini) and the temperature of mixed gases of CO₂ and NH₃ measured by thermocouple in the experiment (T_e) under the illumination condition with a visible light (VIS) + an infrared light (IR) and IR only. The heat transfer model proposed by this study has predicted T_g well under the illumination condition with VIS + IR and IR only well. On the other hand, it is revealed that the difference between T_g and T_e was as large as 10 ℃ under the illumination condition with an ultra violet light (UV) + VIS + IR.

The solution of structural problems with nonlinear material behaviour in a model order reduction framework is investigated in this paper. In such a framework, greedy algorithms or adaptive strategies are interesting as they adjust the reduced order basis (ROB) to the problem of interest. However, these greedy strategies may lead to an excessive increase in the size of the reduced basis, i.e. the solution is no more represented in its optimal low-dimensional expansion. Here, an optimised strategy is proposed to maintain, at each step of the greedy algorithm, the lowest dimension of the PGD basis using a randomised SVD algorithm. Comparing to conventional approaches such as Gram-Schmidt orthonormalisation or deterministic SVD, it is shown to be very efficient both in terms of numerical cost and optimality of the reduced basis. Examples with different mesh densities are investigated to demonstrate the numerical efficiency of the presented method.

This paper presents a new methodology to evaluate the performance of an electric vehicle hybrid power system consisting of a fuel cell and a supercapacitor. The study compares the results with those obtained for a battery-electric vehicle. The methodology extends to three driving modes, ECO, NORMAL, and SPORT, corresponding to conservative, moderate, and aggressive acceleration, and three driving conditions, low, medium, and high energy demand. We develop a simulation process to evaluate the energy consumption and the energy rate of a specific electric vehicle used as a prototype for the study. The methodology applies to a driving route that includes acceleration, deceleration, braking, and constant speed segments reproducing standard driving conditions in urban journeys. The proposed method considers combined driving modes, ECO, NORMAL, and SPORT, in each acceleration process, with variable fractions, 0% to 100%, for each mode. This methodology optimizes the simulation results as it fits the current driving way in urban environments. The simulation results show an average reduction in energy consumption of 37% and 27.1% in vehicle weight, contributing to lower energy use. The study concludes that using a hybrid power system, fuel cell/supercapacitor, instead of a battery in electric vehicles is beneficial, especially in journeys with frequent acceleration processes.

Despite a global push in the development and implementation of widespread alternative energy use, significant disparities exist across given nation-states. These disparities reflect both technical and economic factors, as well as the social, political, and ecological gaps between how communities see energy development and national/global policy goals. Known as the “local-national gap,” many nations struggle with fostering meaningful conversations about the role of alternative energy technologies within communities. Mitigation of this problem first requires understanding the distribution of existing alternative energy technologies at the local level of policymaking. Using the State of Georgia, U.S.A. as a case study, we present a model for analyzing how existing adoption trends enable/limit conversation at the scale of local governance (i.e., county governments). Leveraging existing work on the Gini Coefficient as a metric for measuring energy inequity, we argue these tools can be applied to analyze where gaps exist in ongoing solar adoption trends. As we demonstrate, communities that adopt solar tend to be concentrated in a few counties, indicating existing conversations are limited to a circumscribed set of social networks. This information and the model we demonstrate can enable focused qualitative analyses of existing solar trends, not only amongst high-adoption areas but within communities where little to no adoption has occurred.

Diabetes mellitus is a chronic disease that affects millions of people worldwide. Article focuses on detecting the presence of diabetes in patients using microarray gene data obtained from the pancreas. Handle the high-dimensional nature of the data, four different dimensionality reduction techniques, namely Bessel Function, Discrete Cosine Transform (DCT), Least Square Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. After reduced the data, Meta-heuristic algorithms are applied like Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Ten classification techniques are using to classify the data in both the format like without feature selection method and with feature selection method. The classification techniques are Non-Linear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), Support Vector Machine (SVM) with linear kernel, Support Vector Machine (SVM) with polynomial kernel, and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. Results showed that the AAA with SVM(RBF) achieved an accuracy of 90% without feature selection. However, when feature selection was applied, with EHO of AAA with SVM(RBF) exhibited the highest accuracy of 95.714%, followed closely by with DOA of AAA with SVM (RBF) at 94.28%.

A scientific carbon accounting system can help enterprises reduce carbon emissions. This study took the enterprise in the Yangtze River basin as a case study. The accounting classification of carbon emissions in the life cycle of lime production was assessed, and the composition of the sources of carbon emission was analyzed, covering mining explosives, fuel (diesel, coal), electricity and high temperature limestone decomposition. Using the IPCC emission factor method, a carbon life cycle emission accounting model for lime production was established. We determined that a total of 354,200 tons of carbon emissions were generated from producing 321,000 tons of lime in a year. The decomposition of limestone at a high temperature was the largest carbon emission source, accounting for 62.34% of the total carbon emission. Coal combustion was also an important source of carbon emissions, accounting for 32.66% of total carbon emissions. The carbon emission for one ton of activated lime was 1.46kg, 1.49kg, 1100kg and 1102.95kg respectively for the open-pit, mining stage, crushing stage, and the calcination stage. Based upon the main sources of carbon emission for lime production, carbon emission reduction should focus on CO2 capture technology, fuel optimization and energy control. Using our analysis of carbon emissions, the carbon emission factor of producing unit quicklime of the lime enterprise in the Yangtze River basin was determined. Furthermore, the research into carbon emission reduction for lime production can provide a point of reference for the promotion of carbon neutrality in the same industry.

Manual interpretation of these huge amounts of image volumes are susceptible to inter-reader variability and human error. Thus, accurate automated CAD scheme is highly desirable in clinical pathological diagnosis. In this research, plethora of machine learning paradigms (e.g. feature extraction, dimensionality reduction and supervised classification methods) were explored, evaluated, compared and analyzed to identify the optimal pathway for brain MR images (normal vs neoplastic) binary classification task. External validation dataset was used to test the generalizability of the optimal predictive models implemented. Relevant and informative features were selected to construct cross-validated decision tree and eventually simple rule set was built based on the decision tree. The experimental results show that almost all pattern recognition paradigms achieve high accuracy with careful selection of number of attributes. LDA+ELM with 55 features are the optimal pipelines which achieve perfect classification when training and test data are of same source; and achieving (accuracy=97.5%, AUC=0.989, sensitivity=95% and specificity=100%) under balanced test dataset; (accuracy=99.5%, AUC=0.988, sensitivity=95% and specificity=100%). Cross-validated decision tree model also shows comparable performance: accuracy=98.8%, AUC=99.1%, sensitivity=99.6% and specificity=98.2%. Three highly relevant and robust attributes are visualized and selected for construction of decision tree models and finally a rule sets are read directly off the decision tree. This rule sets can potentially serve as fast and accurate classification algorithm.

To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade. Microgrid systems have a number of advantages over the conventional utility grid systems, however, it faces severe instability issues due to continually increasing constant power loads. To improve the stability of the entire system, load side compensation technique is chosen because of its robustness and cost effectiveness. In this particular occasion, a sliding mode controller is developed for microgrid system in the presence of CPL to assure certain control objective of keeping the output voltage constant at 480V. After that, the robustness analysis of the sliding mode controller against parametric uncertainties is presented. The sliding mode controller robustness against parametric uncertainties, frequency variations, and additive white Gaussian noise (AWGN) are illustrated in this paper. Later, the performance of the PID and sliding Mode controller is compared in case of nonlinearity, parameter uncertainties, and noise rejection to justify the selection of Sliding Mode controller over PID controller. All the necessary calculations are reckoned mathematically and results are verified in the virtual platform such as MATLAB/Simulink with the appreciable outcome.

Delay in data transmission is one of key performance indicators (KPIs) of a network. The planning and project value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in an Long Term Evolution (LTE) network using three ML techniques - Neural Networks (NN), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that, in all three optimization approaches, the k-NN model is selected as the best solution. For the RReliefF optimization algorithm, the best model has 6 inputs and minimum relative error (RE), RE=0.109; for the Backward selection via the recursive feature elimination algorithm, the best model has 4 inputs and RE=0.041; and for the Pareto 80/20 rule, the best model has 11 inputs and RE= 0.049. The comparative analysis of the results concludes that according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

The aim of this study was to assess and contrast the effects on the vertical marginal fit of full contour CAD/CAM generated monolithic zirconia crowns pre- and post-cementation of employing chamfer finishing line designs with various occlusal reduction schemes (planar and flat) and cements. Material and Method: Forty sound human maxillary first premolars were selected for this study. Samples will be divided into two main groups of twenty samples in each group according to the occlusal reduction scheme: Group A: chamfer finishing line design with planar occlusal reduction scheme. Group B: chamfer finishing line design with flat occlusal reduction scheme. Each main group will be subsequently separated into two smaller groups based on the kind of cement that was utilized (n=10) Subgroup A1: crowns will be cemented by using resin-modified Glass Ionomer cement (Fuji Plus). Subgroup A2: crowns will be cemented using a Universal adhesive system (Duo Estecem II). Subgroup B1: crowns will be cemented by using Fuji Plus. Subgroup B2: crowns will be cemented using a Duo Estecem II. Result: Marginal gaps were measured at 4 indentations, using a Dino light-stereomicroscope (230X). Paired T-tests and student t-tests analyzed the data. Prior to cementation, subgroup A1 scored the lowest mean of vertical marginal gap values (80.284±23.021), while subgroup B2 scored the highest mean (118.597±9.956); following cementation, subgroup A1 scored the lowest mean of vertical marginal gap values (95.45±17.598), and subgroup B2 scored the highest mean of vertical marginal gap values (134.806 ±8.114). Chamfer finishing line design with a planar occlusal reduction scheme could be a preferable occlusal reduction scheme.

Due to the ever increasing global warming, the scientific community is concerned with finding immediate solutions to reduce or utilize carbon dioxide (CO2) and convert it in useful compounds. In this context, the reductive process of CO2 methanation is well investigated and attractive due to its simplicity. However, it requires the development of highly active catalysts. In this mini-review, the focus is on biochar-immobilized nanocatalysts for CO2 methanation. We summarize the recent literature on the topic, reporting strategies for the design of biochar with immobilized nanocatalysts and their performances in CO2 methanation. We review the thermochemical transformation of biomass into biochar and its decoration with CO2 methanation catalysts. We also tackle direct methods of obtaining biochar-nanocatalysts, in one pot, from nanocatalyst precursor-impregnated biomass. We review the effect of the initial biomass nature, as well as the conditions that permit to tune the perfomances of the composite catalysts. Finally, we discuss the CO2 methanation performances and how they could be improved keeping in mind low operation cost and particularly sustainability.

This comprehensive review delves into the intricate relationship between Sodium-GlucoseCo-Transporter 2 (SGLT2) inhibitors and AF, highlighting the multifaceted actions of thesedrugs and their significant cardiovascular benefits. Mechanisms: SGLT2 inhibitors inducesubstantial hemodynamic changes, impacting blood pressure, adipose tissue function, andinflammation. They enhance glycemic control while potentially reducing oxidative stress andinflammation, and they modulate atrial electrophysiology, collectively contributing to the reductionof AF risk. Clinical Evidence: This review synthesizes a wealth of clinical evidence,drawing from observational studies and randomized controlled trials, consistently demonstratinga correlation between SGLT2 inhibitors, particularly dapagliflozin and empagliflozin, anda decreased risk of AF. This protective effect is most pronounced in patients with type 2 diabetesand heart failure, underscoring their potential as therapeutic agents for high-risk populations.Potential Benefits: SGLT2 inhibitors offer a spectrum of potential benefits, includingsubstantial reductions in AF risk, particularly in patients with type 2 diabetes and heart failure.These drugs have consistently demonstrated benefits in reducing heart failure hospitalizations,cardiovascular mortality, and stroke risk, which indirectly contribute to better AF management.Furthermore, SGLT2 inhibitors exhibit renoprotective properties by slowing theprogression of chronic kidney disease and reducing albuminuria, offering substantial benefitsfor AF patients with comorbid kidney disease. Metabolic improvements involving weight loss,blood pressure reduction, and favorable changes in lipid profiles contribute to enhanced cardiovascular health and reduced AF risk. Originally designed for diabetes management, thesemedications offer a holistic approach to AF risk reduction, particularly in patients with type 2diabetes and heart failure.

The Global Hunger Index (GHI) is a vital tool for assessing and understanding the state of hunger and food security across the world. In this review article, we examine the GHI scores of various countries in the years 2000, 2008, 2015, and 2023. We aim to explore the changes in GHI scores over this period, identify trends, and discuss the implications for global food security. The GHI provides a comprehensive snapshot of the world's progress in reducing hunger, and this analysis sheds light on the successes and challenges faced by different countries.

Nickel is widely used in industrial processes and plays a crucial role in many applications. However, most of the nickel resource mainly exists as nickel oxide in laterite nickel ore with complex composition, resulting in difficulty in upgrading the nickel content using physical separation methods. In this study, high-grade ferronickel concentrates were obtained through a carbothermal reduction and magnetic separation using laterite nickel ore and anthracite as raw materials. The effects of different types of additives (CaF2, Na2SO4 and H3BO3), carbon ratio (nc/no in metal oxides) and grinding time on the recoveries and grades of ferronickel concentrates were experimentally investigated, along with the microstructure and chemical composition of the products. CaF2 was proved to be the primary active additive in the aggregation and growth of the ferronickel particles, and the improvement of the grade of the product. Under the optimal conditions of CaF2 addition of 12 wt.%, carbon ratio of 1.4, and grinding time of 240 s, high-grade magnetically separable ferronickel concentrate with nickel grade 8.93 wt.% and iron grade 63.96 wt.% was successfully prepared. This work presents a practical method for the highly efficient recovery and utilization of iron and nickel from low-grade laterite nickel ore, contributing to the development of strategies for the sustainable extraction and utilization of nickel resources.

In the evolving landscape of higher education, St. Michael's College (SMC) recognizes the significant impact of the COVID-19 pandemic on student welfare. This paper emphasizes the critical importance of addressing mental health, time management, and stress in the college environment, underlining their influence on academic success and personal growth. The call to action urges students to prioritize their well-being. Diverse cultural and religious perspectives, including Islam, emphasize the importance of a balanced mind and body. The wide-ranging literature review uncovers empirically supported strategies for mental health, time management, and stress reduction. Personal autonomy emerges as crucial in addressing these challenges. This study underscores the importance of these dimensions, calls for student action, and highlights the diversity of perspectives contributing to holistic well-being.

Microbial-mediated biogeochemical cycling of iron is widespread in anaerobic environments such as sediments and deep Earth aquifers, and is one of the major pathways for metabolising iron in nature. Organic matter containing quinone groups can act as electron shuttles to transfer electrons between microorganisms and Fe(III) oxides, facilitating the biotransformation of iron; this process also influences the transport transformation of pollutants. In this paper, the reduction of Fe(III) oxides by aniline in the heterogeneous iron-reducing bacterium Shewanella oneidensis MR-1 mediated by the electron shuttle AQS was investigated. The results showed that aniline significantly promoted the reduction of Fe(III) by MR-1 in the AQS-mediated reaction, and the best Fe(III) reduction was achieved in the reaction group of "GT+MR-1+0.5mM AQS+3μM aniline", in which the Fe(II) production was significantly higher than that in the reaction groups of only microorganisms and no aniline reaction. The amount of Fe(II) produced was significantly higher than that of the microorganism-only and aniline-free reaction groups, and the addition of aniline also significantly increased the consumption of sodium lactate by MR-1 in the reaction system. The solid mineral Fe(II)-O content increased to 41.32% after the reaction. However, the structure of needle ferrite is relatively stable, and the surface morphology did not change significantly by SEM, and the generation of other secondary minerals was not observed in the XRD results. The results of this experimental study can help to understand the biogeochemical cycle of iron in multicomponent reactions and are of great significance for environmental management.

With the changes in the spatial structure of China's economic development, urban clusters have become the primary carriers of China's regional economy and green growth. The article uses annual data from 2010 to 2021 to study the carbon emission characteristics and carbon reduction pathways of 36 cities in the Yangtze River Delta region. Firstly, cities in the Yangtze River Delta region are divided into six carbon emission types based on decoupling elasticity coefficient and carbon intensity indicators. Then, the STIRPAT model is used to regress the panel data of different carbon emission types for 11 years, analyze the driving factors of carbon emissions, and develop differentiated carbon emission reduction paths for cities with six carbon emission types. According to the results, the city of Type I needs to accelerate low-carbon technology innovation, Type II needs to improve energy efficiency and strengthen low-carbon technology research and development, Type V needs to suppress foreign investment in high energy consumption and high emission projects in the local area, Type VI needs to accelerate the process of new urbanization and optimize industrial structure. Types III and IV have not received effective emission reduction paths, and further research is required on other emission reduction policies and measures.

Environmental tax is the climate policy that offers, in theory, the easiest way for carbon reduction. But in practice, implementation has proven complicated despite public demand for policy action on climate change. This research investigates to reframe environmental taxes in ways more personally engaging to create a moral foundation, and massive participation. As people show rising demand, we aimed to design a tool that responds to public expectations and operates directly at source on emission reducers, viz the trees. Drawing on research from environmental taxes and the evidence of measures taken, we reasoned that an environmental fiscal policy may not intend necessarily to punish the “bads”, but rather might reward the positive attitude and direct it to act. Consequently, we focused on tax reliefs and designed Green Aid, that can address people’s attitude to take active participation into account by incorporating virtuous behaviours into tax relief. Green Aid Tax relief works embedded in the Green Aid Participation Scheme that bears directly on a source of environmental recovery and organizes the global call to public action in a sustained, structured, and collective participation to forestation. Green Aid bridges the action of contributing to carbon reduction with immediate, tangible, and direct benefits. It can be an alternative environmental tax, able to address and operate directly at source on emission reducers and secure effectiveness in carbon reduction and efficiency in terms of public acceptance and viability at a global level.

This work is driven by a practical question, corrections of Artificial Intelligence (AI) errors. Systematic re-training of a large AI system is hardly possible. To solve this problem, special external devices, correctors, are developed. They should provide quick and non-iterative system fix without modification of a legacy AI system. A common universal part of the AI corrector is a classifier that should separate undesired and erroneous behavior from normal operation. Training of such classifiers is a grand challenge at the heart of the one- and few-shot learning methods. Effectiveness of one- and few-short methods is based on either significant dimensionality reductions or the blessing of dimensionality effects. Stochastic separability is a blessing of dimensionality phenomenon that allows one-and few-shot error correction: in high-dimensional datasets under broad assumptions each point can be separated from the rest of the set by simple and robust linear discriminant. The hierarchical structure of data universe is introduced where each data cluster has a granular internal structure, etc. New stochastic separation theorems for the data distributions with fine-grained structure are formulated and proved. Separation theorems in infinite-dimensional limits are proven under assumptions of compact embedding of patterns into data space. New multi-correctors of AI systems are presented and illustrated with examples of predicting errors and learning new classes of objects by a deep convolutional neural network.

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

This paper presents an investigation into the influence of drag-reducing polymer (DRP) on the flow of water inside and around U-bends. The DRPs tested were partially hydrolysed polyacrylamide (HPAM) and two different molecular weight polyethylene oxides (PEO). The Reynolds number and in-situ concentration ranges of water and DRP were and respectively. The test section was made up of clear polyvinyl chloride (CPVC) pipes that contained two U-bends with radii of curvature (R) of 100 and 200 mm as well as straight sections of 19-mm ID. Higher pressure gradient was recorded in the under-developed flow section after the bend. Drag reduction (DR) increased with in-situ concentration of the DRP and the flowrate up to some threshold values. The concentration and flowrate thresholds were about 20 ppm and 1.07 m/s respectively. DR differed among the different sections considered with the highest values recorded for developed flows upstream of the bend and the least values recorded in the bend. DR at station 1 (immediately after the bend) was generally lower than that at station 2 (further downstream). P-K Plots showed that, the DR envelop for developed flow in straight pipes did not apply to the bend and the under-developed flow section. DR increased with polymer molecular weight and ionic strength in straight sections. However, molecular weight (and polymer shearing) had limited effect on DR in the bends. DR characteristics at the bend suggested that there was a different or additional mechanism. The effect of bend curvature ratio on DR was predominant after the U-bend. The larger the ratio of pipe diameter to bend diameter, the lower the DR in the redeveloping sections after the bend. For R = 100 mm, the highest DR recorded for fully developed flows before bend, in the bend, at stations 1 and 2 were 57, 31, 36 and 33% respectively while that for R = 200 mm were 64, 28, 42 and 49% respectively.

High electricity demand for consumption at current supply level in Indonesia led to the rising cost of electricity bills. This factor is compounded by the fact that many electric generators in Indonesia still use fossil fuels, which contributes to the high basic generation cost. UBL is one of the universities that aim to be a green campus. Our research explores the possibility of installing motion sensor to contribute on the energy efficiency. Although mostly common in developed countries, the use of motion sensor for energy efficiency is still rare, especially in Indonesia. despite rising cost and supply shortages, Indonesian buildings are still of high energy consumption. our experiment shows that simple installation of commercially available motion sensors can contribute to reduce the electricity bill from the increase of energy efficiency. One of the efforts to lower energy demand in consumer side is to use the electricity efficiently, such as turning off lights in a room when it's not in use. This method can be simply done by turning the light switches for office and classrooms, but difficult to do in public spaces such as toilets and corridors. Our experiment shows that simple installation of commercially available motion sensors can contribute to reduce the electricity bill from the increase of energy efficiency. Automatic light switches experimentally installed in sample toilet room prove that electricity consumption from the lamps can contribute to the reduction of total weekly energy that translates into Green House Gas emission reduction.

This paper focuses on parametric model order reduction (PMOR) of guided ultrasonic wave propagation and its interaction with damage in a fiber metal laminate (FML). Structural health monitoring in FML seeks to detect, localize and characterize the damage with high accuracy and minimal use of sensors. This can be achieved by the inverse problem analysis approach which employs the signal measurement data recorded by the embedded sensors in the structure. The inverse analysis requires to solve the forward simulation of the underlying system several thousand times. These simulations are often exorbitantly expensive and triggered the need for improving their computational efficiency. A PMOR approach hinged on the proper orthogonal decomposition method is presented in this paper. An adaptive parameter sampling technique is established with the aid of a surrogate model to efficiently update the reduced-order basis in a greedy fashion. A numerical experiment is conducted to illustrate the parametric training of the reduced-order model. The results show that the reduced-order solution based on the PMOR approach is accurately complying with that of the high fidelity solution.

: Sulfate presence is of great importance for anaerobic digestion as its addition can control the anaerobic microbial community. In this study, the effect of sulfuric acid addition on the perfor-mance of an UASB reactor fed with hydrolyzed starch was investigated. TOC, Fe and SO4-2 re-moval as well as the methane production were monitored under various COD/ SO4-2 ratios, HRTs and OLRs. When HRT was 16 hours and OLR equal to 2 g COD/LUASB-d the methane production was 0.24 L CH4/LUASB-day. After the sulfuric acid addition, TOC removal reached 95%. At HRT, OLR, and COD/ SO4-2 ratio equal to 16 hours, 2 g COD/ LUASB-d and 3.72 respectively, the methane production was 0.267 L CH4/LUASB-d. When the OLR was increased to 5.94 g COD/LUASB-d and COD/SO4-2 ratio was equal to 12.5, the methane production was 3 times higher (0.84 L CH4/LUASB-d) with a methane content in the produced biogas greater than 70%. 16s rRNA gene analysis demonstrated that after the sulfate addition, SRB were enriched and methane was mainly produced by Methanosaeta sp. This study indicates that appropriate sulfate concentrations in the feed in combination with the presence of iron in the UASB reactor lead to enhanced me-thane yields.

To accomplish IMO's emissions reduction targets, the Chinese government has established emission control areas and implemented strict sulfur limitation policies. Faced with the downturn in the shipping industry and the challenge of insufficient supply of compliant fuel, Hong Kong and Shenzhen in China have implemented different low-sulfur fuel subsidy policies. Therefore, it is particularly important to study the air pollution control mechanism considering low sulfur fuel subsidy policy. Firstly, the non-cooperative game models considering low-sulfur fuel subsidies are constructed. Secondly, the mechanisms of various factors affecting port pricing, throughput and profit are analyzed. Then, a case study is conducted by using AIS data of container ships in Shanghai Port and Ningbo Zhoushan Port. The study reveals that whether the container service price of the leader port increases or decreases, the follower port always take corresponding follow-up measures. In both sequential and simultaneous game, the profit difference of the leader port is positively related to the subsidy ratio of the port’s own and negatively related to the coefficient of the competitor's subsidy. In conclusion, the low-sulfur fuel subsidy policy has a significant positive impact on the step-by-step implementation of more stringent pollution reduction policies in port waters.

There are potentially promising mitigation activities for epidemic and pandemic scenarios that are not currently the subject of significant research effort. Large epidemics and pandemics pose risks that are important to mitigate, even if the likelihood of the events is low and uncertain. While some efforts are the subject of extensive funding and consideration, other approaches are neglected. Here, we consider such neglected interventions which could significantly reduce the impact of such an epidemic or large-scale pandemic. These are identified via a narrative literature review of extant literature reviews and overviews of mitigations in epidemic and pandemic situations, followed by consideration of the economic value of information of further study of heretofore neglected interventions and approaches.Based on that analysis, we considered several classes of mitigations, and conducted more exploratory reviews of each. Those discussed include mitigations for (1) reducing transmission, such as personal protective equipment and encouraging improved hygiene, (2) reducing exposure by changing norms and targeted changes for high-risk or critical professions and activities, (3) reducing impact for those infected, and (4) increasing large scale resilience using disaster and infrastructure continuity planning.Some proposed mitigations are found to be of low marginal value. Other mitigations are likely to be valuable, but the concepts or applications are underdeveloped. In those cases, further research, resources, or preparation are valuable for mitigating both routine and extreme disease outbreak events. Still more areas of research are identified as having uncertain value based on specific but resolvable uncertainties. In both of the latter cases, there is no guarantee that mitigations identified as worthy of further consideration will be valuable, but the argument for further research is clear.

Almond is worldwide consumed and renowned as a valuable healthy food. In spite of this, it is also a potent source of allergenic proteins able to trigger several mild to life-threatening immunoreactions. Food processing proved to alter biochemical characteristics of proteins, thus affecting the respective allergenicity. In this paper we investigated the effect of autoclaving, preceded or not by a hydration step, on the biochemical and immunological properties of almond proteins. Any variation in the stability and immunoreactivity of almond proteins extracted from the treated materials, were evaluated by total protein quantification, ELISA assay and protein profiling by electrophoresis-based separation (SDS-PAGE). The autoclaving alone was found to weakly affect almond proteins stability, despite what observed for the combination of hydration and autoclaving, which resulted in a loss of approximately 70% of total protein content compared to untreated sample, and in a final negligible immunoreactivity, as well. The final SDS-PAGE protein pattern recorded for almonds hydrated and autoclaved disclosed significant changes. In addition, the same samples were further submitted to in vitro simulated gastro-duodenal (GI) digestion to evaluate potential changes induced by these processing on allergens digestibility. Digestion products were identified by HPLC-HRMS/MS analysis followed by software-based data mining, and complementary information were provided by analyzing the proteolytic fragments lower that 6 kDa in size. The autoclave based treatment was found not to alter the allergens digestibility, whereas an increased susceptibility to proteolytic action of digestive enzymes was observed in almonds subjected to the combination of prehydration and autoclaving. Finally, the residual immunoreactivity of the GI resistant peptides was investigated in-silico by bioinformatic tools, confirming that by following both approaches, no epitopes survived the almond digestion, thus demonstrating the potential effectiveness of these treatments to reduce almond allergenicity.

This article purposed to examine the impact of integration between Lean accounting (LA) tools & Just in Time (JIT) technique on Cost Reduction (CR) in modern manufacturing environment in Saudi establishments, these two variable moderated by Ethical Standards (ESs). A quantitative methodology was used for realizing the study objectives and answering study questions, the main tool exercised for producing the incipient data was a fully-structured questionnaire with closed ended questions, which was designed and passed around online among a selected sample of (109) managers and accountants in Saudi manufacturing firms. Descriptive and deductive statistics adopted to derive the findings, wherefore the compiled responses analyzed by SPSS & AMOS and the results showed that there is direct and indirect effect of LA tools & JIT technique on CR in Saudi industrial companies, especially when using ESs. These findings could provide decision makers in Saudi manufacturing companies with the acuity of adopting both methods in a combined way to maximize the profits of these firms. The results of this study also suggest that the application of LA tools & JIT technique has a positive effect on the promotion of sustainable manufacture and consequent achievement of high sustainable performance.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

Maritime ports represent an important ecosystem for pollutant emissions and, taking into account the ongoing energy transition, need to adopt new solutions to mitigate current emission levels. These emissions are partially avoidable if ships and vessels docked at the port use electric energy to feed their internal power needs instead of using their internal combustion engines. In Europe, there is an ongoing discussion on including such emissions in the European Emissions Trading System which will represent added costs for maritime operators. Onshore Power Supply systems can contribute to the ongoing energy transition by allowing the use of electric power to feed docked ships. As a first step to contribute to the development of Onshore Power Supply solutions, it is necessary to evaluate the added power needs that these systems would represent for the port. This paper presents a methodology that allows port operators to verify, straightforwardly and transparently, their power needs for Onshore Power Supply applications. The methodology is based on the historical data of docked ships at the port or quay level and provides an energy analysis of each type of vessel to determine the power to be installed at the port so that it is possible to supply energy to different types of ships and vessels simultaneously. Additionally, the implemented methodology provides economic and technical decision support factors by comparing the fuel costs with electric power costs, assessing the potential for this transition to Onshore Power Supply. The methodology is validated using a real case study for the port of Lisbon and obtained results demonstrate the potential for the installation of Onshore Power Supply in medium to large-dimension maritime ports.

In hydraulic systems, energy dissipation can be significant. The pressure drops that can occur in the hydraulic circuit, influenced by the adopted drive architecture, result in an absorbed power often significantly greater than that required by the mechanical system. In this paper, a comparative study of energy efficiency among five drive common architectures in industrial hydraulic axes is carried out. The analysis is applied to a hydraulic blanking press with variable speed and force, a fairly frequent industrial system, e.g. in the production of semi-finished brass products. Standard, regenerative, high-low, variable displacement pump and variable speed drive for a fixed displacement pump configurations have been analysed and compared. An adequate and optimized sizing of the various components of the system has been carried out in each case and subsequently the energy consumption has been estimated for a load cycle common to all the considered cases. The results show that the choice of power generation architecture of the hydraulic system has a very significant impact on energy efficiency and consequently operating costs and carbon footprint. The performed quantification of the potential energy efficiency of the considered drive architectures can be very useful in guiding energy-conscious choices.

Since Hydrogen Sulfide (H2S) was recognized as a gas transmitter, its detection and quantifica-tion have become a hot research topic among chemists and biologists. In this area. fluorescent probes have shown great advantages: fast and strong responds, low detection limit and easy ma-nipulation. Here we developed a new fluorescent probe that detected H2S selectively among various bioactive and inorganic salts. This probe was based on the core structure of fluorescein and reacted with H2S through azide-reduction. Great linearity was achieved correlating fluores-cence intensity and H2S concentrations in solution. The detection of H2S in cancer cells was also achieved.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Concept sustainable of banking is based on principle realization growth and profit for the bank and shareholders, but only with achievement social goals, as and care for conservation life environment. Providing new ones banking service and creation new ( green ) banking jobs, except for _ profitable, they achieve and spreads social and ecological goals, chances are with creation competitive advantages, both in countries Western Balkans observed in the work, so and in all countries of the world In this one work we will focus on analysis macroeconomic parameters in countries Western Balkans works tests influence selected indicators on performance banking system, with special in retrospect on social and component protection life environment. The results indicate that rate unemployment the most significant indicator potentially risky events for bankers, as and banks they are not recognized in national conservation strategies _ life environment which are in WB countries currently on strength. Accordingly, the former is necessary to carry out education foreign affairs, and in parallel to promote green loans like they live and in the long term more favorable of traditional loans, with update the existing one regulatory frame in the domain protection life environment.

The reckless use of non-replenishable fuels by the growing population for energy and the resultant incessant emission of hazardous gases and waste products into the atmosphere insisted scientists to fabricate materials capable of managing these global threats at once. In recent studies, photo-catalysis employed focus on utilizing renewable solar energy to initiate chemical processes with the aid of semiconductors and highly selective catalysts. A wide range of nanoparticles have showcased promising photocatalytic properties. Ultrasmall metal nanoclusters (MNCs) with sizes below 2 nm, stabilized by ligands shows discrete energy levels, exhibits unique optoelectronic properties which are vital to photocatalysis. In this review, we intend to compile information on varying photocatalytic efficiency of metal NCs with respect to changes in their properties. Review discusses about the photocatalytic activity of atomically precise ligand-protected MNCs and its hybrids in the domain of in energy conversion processes like photodegradation of dyes, Oxygen Evolution Reaction (ORR), Hydrogen Evolution Reaction (HER) and CO2 Reduction Reaction (CO2RR), and photosensitizer in solar cell.

The paper proposes a new principle of finding and removing elements of mathematical model, redundant in terms of parametric identification of the model. It allows reducing computational and time complexity of the applications built on the model. Especially this is important for AI based systems, systems based on IoT solutions, distributed systems etc. Besides, the complexity reduction allows increasing an accuracy of mathematical models implemented. Despite the model order reduction methods are well known, they are extremely depended however on the problem area. Thus, proposed reduction principles can be used in different areas, what is demonstrated in this paper. The proposed method for the reduction of mathematical models of dynamic systems allows also the assessment of the requirements for the parameters of the simulator elements to ensure the specified accuracy of dynamic similarity. Efficiency of the principle is shown on the ordinary differential equations and on the neural network model. The given examples demonstrate efficient normalizing properties of the reduction principle for the mathematical models in the form of neural networks.

With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

Twin pregnancies are highly undesirable in dairy cattle; they compromise the health and wellbeing of a cow, and its incidence dramatically impairs the farm economy. Recently, a genomic prediction for twin pregnancies has been developed. The objective of this study was to assess cow, environmental and management risk factors affecting the incidence of twin pregnancies in high-producing dairy cows in their first lactation, with special emphasis placed on the genomic prediction values for twin pregnancy. Our study population of primiparous cows proved valuable in identifying factors other than genomic predictive values influencing the twin pregnancy rate. The odds ratio for twin pregnancy was 0.85 (p <0.0001) for each unit of a prediction value increase, 3.5 (p = 0.023) for cows becoming pregnant during the negative photoperiod, and 0.33 (p = 0.016) for cows producing ≥42 kg of milk at AI, compared to the remaining cows producing <42kg of milk. As a general conclusion, the practical implication of our findings is that genomic prediction values can identify the risk of twin pregnancy at herd level. Given the cumulative effect of genomic selection, selecting for animals with reduced genetic risk of twin pregnancies can contribute to reduce its incidence in dairy herds.

One of the challenges of deploying Wireless Sensor Networks (WSN) at large scale is to operate for prolonged periods of time relying on the limited amounts of energy stored in its nodes. Generally, data communication is the main factor of draining energy. Consequently, reducing the amount of transmitted data by the nodes is of great interest. At the same time, reducing data to be transmitted should not decrease the quality of the deduced information. Solving this problematic is what we call in this article "Ensuring the Quality of Information Service (QoIS)". This document provides a state-of-the-art for the QoIS regarding WSNs. At first, we give a quick overview of the Internet of Things (IoT) paradigm and the WSN technologies in order to understand how data is gathered and used in these networks. Also, we introduce a novel concept: QoIS, in contrast with classic QoS. Then, it is expected a summary of criteria that asses the QoIS in WSNs plus some techniques to improve it. In this article, we will focus on data management techniques. These techniques can improve raw data, information and/or knowledge quality in energy-constrained WSNs. Finally, we give some conclusions and a glimpse about our future work.

In this work, Density Functional Theory (DFT) calculations were employed to study the photocatalytic CO2 to CO reduction by a series of Pt(II) square planar complexes with general formula [Pt(5-R-dpb)Cl] (dpb = 1,3-di(2-pyridyl)benzene anion, R = H, N,N-dimethylaniline,T thiophene, diazaborinine). The CO2 to CO conversion process is thought to proceed via two main steps, namely the photocatalytic/reduction step and the main catalytic step. The simulated absorption spectra exhibit strong bands in the 200 – 400 nm UV-Vis region. The calculated excited state reduction potentials are in the range 0.32 — -0.36 V revealing that the reductive quenching of the T1 state of the complexes could be modulated with suitable changes of the N^C^N pincer ligands. The CO2 fixation and activation by the ‘real’ three coordinated Pt(II) catalyst/intermediate is predicted to be favorable with Pt-CO2 bond dissociation energies, D0 in the range -36.9—-10.3 kcal/mol. The nature of the Pt-CO2 bond is complex with covalent, hyperconjugative and H-bonding interactions prevailing the repulsive electrostatic interactions. The main catalytic cycle is estimated to be a favorable exergonic process.

This paper presents three types of sliding mode controllers for a magnetic levitation system. First, a proportional-integral sliding mode controller (PI-SMC) is designed using a new switching surface and a proportional plus power rate reaching law. The PI-SMC is more robust than a feedback linearization controller in the presence of mismatched uncertainties and outperforms the SMC schemes reported recently in the literature in terms of the convergence rate and settling time. Next, to reduce the chattering phenomenon in the PI-SMC, a state feedback-based discrete-time SMC algorithm is developed. However, the disturbance rejection ability is compromised to some extent. Furthermore, to improve the robustness without compromising the chattering reduction benefits of the discrete-time SMC, mismatched uncertainties like sensor noise and track input disturbance are incorporated in a robust discrete-time SMC design using multirate output feedback (MROF). With this technique, it is possible to realize the effect of a full-state feedback controller without incurring the complexity of a dynamic controller or an additional discrete-time observer. Also, the MROF-based discrete-time SMC strategy can stabilize the magnetic levitation system with excellent dynamic and steady-state performance with superior robustness in the presence of mismatched uncertainties. The stability of the closed-loop system under the proposed controllers is proved by using the Lyapunov stability theory. The simulation results and analytical comparisons demonstrate the effectiveness and robustness of the proposed control schemes.

Recently many researches of EOS (Electric Arc Furnace Oxidizing Slag) on application to construction industry have been carried out with increasing its production and limited reclamation site. EOS can be used as a fine aggregate for construction material, however, its engineering properties vary with the manufacturing process and producing district, causing a quality differences in material performance. In the work, EOS is obtained from steel manufacturing plants in South Korea and the engineering properties are evaluated for EOS and the cement mortar with EOS, respectively. From the tests, EOS is mainly made up of CaO, SiO₂, and FeO with 18.2% of larnite which has a crystal structure of β-C₂S with similar cement mineral. EOS mortar shows an increasing compressive strength with more EOS content, which is affected by a considerable amount of larnite (β-C2S) in EOS. The EOS based mortar with ERS (Electric Arc Furnace Reduction Slag) shows unsatisfactory results over the criteria for rate of change, which implies that more consideration must be taken for the usage of the mixed ERS and EOS for cement mortar due to swelling effect of ERS on dimensional stability.

SDG target 12.3 calls for halving food waste at retail, food service, and household by 2030. The food waste index for this purpose includes parts attached to food that are usually removed intentionally ("unavoidable food waste") and allows counting the conversion to animal feed and utilisation as biomaterial as means of reduction. In Japan, the "Food Waste Recycling Law" which has been in effect since 2001, promotes the recycling of food waste generated from businesses, designating feed conversion as top priority. On the other hand, based on the more recent "Food Loss Reduction Promotion Act" of 2019, the government is currently promoting the reduction of avoidable food waste, as to meet the SDG target. Based on statistical sources and interviews with Ministry officers, this paper explains the history and achievements of Japan's 23-year policy based on the Food Recycling Law, as well as the tension between the two legislations.

Recent wastewater testing suggests the poliovirus is reemerging, raising questions about the efficacy of the current inactivated polio vaccine and mass vaccination to eradicate poliomyelitis. The present study reassessed reported outcomes from the placebo-control study of the 1954 Salk inactivated poliomyelitis vaccine field trial, which studied a population of 1,829,916 U.S. school children from the first three grades. The placebo-control study of the trial reported that the polio vaccine was 80% to 90% effective in reducing cases of laboratory confirmed paralytic poliomyelitis, 60% to 70% effective against type 1 of the virus, and greater than 90% effective against types 2 and 3 of the poliovirus. However, these outcomes are reported as relative risk reductions, and the absolute risk reductions of the 1954 polio vaccine field trial were never reported to the public. The present paper’s analysis reveals to the public for the first time that the inactivated poliomyelitis vaccine's absolute risk reduction of laboratory confirmed paralytic poliomyelitis in the placebo-control study is 0.01% to 0.02%, an extraordinarily low clinical effect that impugns the Salk polio vaccine’s reported effectiveness. Furthermore, absolute risk reductions for the three poliovirus types are 0.01%, 0.00%, and 0.01%, respectively. Outcome reporting bias in the 1954 inactivated poliomyelitis vaccine field trial casts doubt on eradication of the poliovirus through mass vaccination.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. In many instances, utility companies pay for both energy and peak demand. PV systems can effectively reduce energy consumption during their operational periods, while, their contribution during peak hours, particularly in the early morning and late evening, is often limited. To address this issue, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV-battery systems remains a challenge. This study proposes a novel statistical model to optimize PV-battery system sizing for enhanced peak demand reduction. The model aims to flatten 95% of peak demand days up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV-battery system successfully flattens 95% of daily peak demand up to 2000 kW, yielding a financial benefit of $812,648 over 20 years.

We review a modern differential geometric description of the fluid isotropic motion and featuring it the diffeomorphism group structure, modelling the related dynamics, as well as its compatibility with the quasi-stationary thermodynamical constraints. There is analyzed the adiabatic liquid dynamics, within which, following the general approach, there is explained in detail, the nature of the related Poissonian structure on the fluid motion phase space, as a semidirect Banach groups product, and a natural reduction of the canonical symplectic structure on its cotangent space to the classical Lie-Poisson bracket on the adjoint space to the corresponding semidirect Lie algebras product. We also present a modification of the Hamiltonian analysis in case of the isotermal liquid dynamics. We study the differential-geometric structure of the adiabatic magneto-hydrodynamic superfluid phase space and its related motion within the Hamiltonian analysis and invariant theory. In particular, we construct an infinite hierarchies of different kinds of integral magneto-hydrodynamic invariants, generalizing those before constructed in the literature, and analyze their differential-geometric origins. A charged liquid dynamics on the phase space invariant with respect to an abelian gauge group transformation is also investigated, some generalization of the canonical Lie-Poisson type bracket is obtained.

Sulfur dioxide (SO2) reduction remains an area of global necessity further enhanced by the current international focus on pandemic diseases mitigation, elimination of air pollution, and promotion of renewable green energy. The dynamics of chemical bond-strength breaking and reformation in the transition state (TS) is a fundamental process in the reduction of SO2 by CO. Density Functional Theory (DFT) has been used to determine optimal TS reaction pathway via a novel triple-hybrid catalyst utilizing doubly-charged negative atomic V, Mn, and Au. The triple-hybrid catalyst is furthermore tailored to the subsequent minimization of each individual step of the 3-Step SO2 reduction by CO chemical reaction. Each optimized step 1, 2, and 3 is minimized with doubly-charged V, Mn, and Au, respectively, with TS barrier reductions ranging from 1.18 eV to 0.002 eV. Super-benzene, armchair (6, 6) single wall carbon nanotube, and fullerene TS reaction pathways have also been calculated to compare the nanoscale catalytic effectiveness with that of the atomic scale transition metals.

The introduction of ChatGPT in academia has raised significant concerns, particularly regarding issues like plagiarism and the ethical use of generative text for academic purposes. Existing literature has predominantly focused on ChatGPT adoption, leaving a notable gap in addressing the strategies users employ to mitigate these emerging challenges. To bridge this gap, this research utilizes the Uncertainty Reduction Theory (URT) to formulate user strategies for reducing uncertainty. These strategies include both interactive and passive approaches. Concurrently, the study identifies key sources of uncertainty, which include concerns related to transparency, information accuracy, and privacy. Additionally, it introduces the concepts of seeking clarification and consulting peer feedback as mediating roles to facilitate reduced uncertainty. We tested these hypotheses with a sample of Indonesian users (N = 566) using structural equation modeling via Smart-PLS 4.0 software. The results confirm that interactive Uncertainty Reduction Strategies (URS) are more effective in reducing uncertainty when using ChatGPT compared to passive URS. Transparency concerns, information accuracy, and privacy concerns are identified as factors that increase the level of uncertainty. In contrast, consulting peer feedback is the most effective strategy for reducing uncertainty compared to seeking clarification at the individual-system level. Insights from the mediating effects confirm that consulting peer feedback significantly mediates uncertainty sources to reduce uncertainty. The study discusses various strategies for the ethical use of ChatGPT by users in the educational context and contributes significantly to theoretical development.

Exploring effective oxygen reduction reaction (ORR) electrocatalysts is crucial for progress in solid-state alkaline zinc-air batteries (ZAB). In this paper, silver-manganese dioxide-carbon nanotubes (SMC) ternary composites, which function as Electrocatalyst for air Electrodes, was produced via the pyrolysis of silver permanganate under microwave irradiation in one step. A scanning electron microscope (SEM), X-ray diffraction (XRD), and an energy dispersion spectrometer (EDS) have consistently determined that SMC consists of silver and alpha-manganese dioxide, which anchored on the surface of CNTs. Through polarization and chronoamperometery curves, the electrocatalytic activity of SMC for ORR was examined. The results reveal that SMC has higher catalytic activity than the chemically produced electrocatalyst for ORR in alkaline condition. Finally, a solid-state zinc-air cell with an electrocatalyst was constructed and tested. Constant current discharge curve of the zinc-air cell with SMC has a long discharge voltage plateau and a 60.03 mAh capacity at 30 mA · cm−2 . The mechanism of improvement in electrocatalytic activity was also discussed. In a summary, the strategy described in this study and the electrocatalyst produced can be regarded as an effective method for making a robust oxygen reduction catalyst toward solid-state zinc-air batteries.

The combination of e-commerce and the green economy allows enterprises to distribute their green products directly to consumers through Internet channels. This paper investigates the impact of service spillovers on emission reduction and channel decisions in a two-echelon supply chain,in which the supplier is a Stackelberg leader and the retailer is a follower.By comparing the equilibrium outcomes of the single-channel and dual-channel supply chain in a setting with and without retail services, we observe that the supplier prefers to encroach on the market when services that retail locations provide largely spillover to and benefit the direct sales channel. Contrary to popular belief, a higher degree of service spillovers is beneficial for the retailer to get more returns under the dual-channel structure, whereas supplier encroachment will lead to a decline in the service level if the spillover degree is relatively low. In addition, the emission reduction level of products under supplier encroachment is always higher than that employed in the single-channel structure if consumers have both low-carbon preference and high degree of service sensitivity. Furthermore, we extend the equilibrium decisions under a carbon cap-and-trade (CCT) mechanism; the results fully verify the above conclusions. Interestingly, the implementation of a carbon price not only motivates carbon emission abatement and supplier encroachment, but also helps achieve a win-win outcome for supply chain members when service spillovers happen.

Reactive oxygen species and reactive nitrogen species in semen are both essential for fertilization process; however, they become harmful at excessive levels, causing oxidative stress and nitrosative stress, respectively. Recently, oxidation–reduction potential (ORP) has been used as a marker for measuring oxidative stress, and this study investigated the possibility of comprehensively evaluating nitrosative stress. The correlation between standardized ORP (sORP) and 8-hydroxy-2′-deoxyguanosine (8-OHdG) and NOx per unit sperm was tested. Based on the previously reported cutoff value, samples were classified into oxidative stress-positive and negative groups. Thereafter, a receiver operating characteristic curve was drawn on 8-OHdG and NOx, and each optimal cutoff value was determined. Both standardized 8-OHdG and NOx significantly correlated with sORP. Although both correlated significantly with sORP in the oxidative stress group, no correlation was found in the nonoxidative stress group. The optimal standardized 8-OHdG level to determine oxidative stress status was 0.52 ng/106 spermatozoa, and the standardized NOx was 0.17 µM/106 spermatozoa/mL. The ORP is a simpler and quicker assay that can comprehensively assess oxidative and nitrosative stresses. Optimal cutoff values for both stresses were established in this study.

Symmetry concepts in parametrized dynamical systems may reduce the number of external parameters by a suitable normalization prescription. If, under the action of a symmetry group G, parameter space A becomes a (locally) trivial principal bundle, A ~ A/G x G, then the normalized dynamics only depends on the quotient A/G. In this way, the dynamics of fractional variables in homogeneous epidemic SI(R)S models, with standard incidence, absence of R-susceptibility and compartment independent birth and death rates, turns out to be isomorphic to (a marginally extended version of) Hethcote's classic endemic model, first presented in 1973. The paper studies a 10-parameter master model with constant and I -linear vaccination rates, vertical transmission and a vaccination rate for susceptible newborns. As recently shown by the author, all demographic parameters are redundant. After adjusting time scale, the remaining 5-parameter model admits a 3-dimensional abelian scaling symmetry. By normalization we end up with Hethcote's extended 2-parameter model. Thus, in view of symmetry concepts, reproving theorems on endemic bifurcation and stability in such models becomes needless.

The effects of global warming combined with the progressive expansion of urbanization have considerably increased exposure to urban flooding and runoff widespread erosion risk, also causing shallow landslides and mud flows, respectively in urbanized areas of lowland and hill/foothill environments. Increasing urban flooding resilience has become a priority at virtually all levels of governance. The analysis of a different hazard scenarios, in which various hydro-meteorological conditions and management alternatives are examined, should act as the basis for the effective design and evaluation of interventions to improve urban flooding resilience. Turin Metropolitan Area (TMA), located in north-western Italy, represents a unique case due to its complex orography, with a mountainous sector in the west side and a flat or hilly part in the east side. During the warm season, these environmental conditions make the urban area prone to severe atmospheric convection causing frequent hailstorms and rainstorms of high intensity that may impact on urban infrastructures (i.e., drainage system and road network), thus requiring an adequate management as a key factor to reducing risk and losses. The urban areas of TMA are monitored by polarimetric Doppler weather radars and by a dense rain gauges network. Analyzing several case studies of urban flooding, this research work assesses the feasibility of a meteorological radar early warning system based on the identification of rainfall thresholds that characterize urban flooding, occurring in the lowlands, and the runoff erosion phenomena affecting the urbanized hills and foothills.

The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

A review of influence of drag-reducing agents on curved pipe flows is presented in this work. In addition, this review outlined proposed mechanism, friction factor and fluid flux models for drag-reducing agents in curved pipe flows. Our finding reveals that drag reduction by additives in curved pipes is quite significant but generally lower than the corresponding drag reduction in straight pipes. It decreases with increase in curvature ratio and more pronounced in the transition and turbulent flow regimes. Drag reduction strongly depends on the polymers and surfactants’ concentrations as well as the bubble fraction of micro-bubbles. It is also reported that drag reduction in curved pipes depends on temperature and existence of dissolved salts in the fluids. Maximum drag reduction asymptote differed between straight and curved pipes and between polymer and surfactant. No definite conclusion could be drawn as regards drag reduction for two-phase flow in curved pipes due to the limited studies in this area. Many questions such as the mechanism of drag reduction in curved pipes and how drag-reducing agents interact with secondary flows still remained unanswered. Hence, some research gaps have been identified with recommendations for areas of future researches.

Two ferrocene derivatives, namely, 1,2-(tetramethylene)-ferrocene and 1,2,1’,2’-bis(tetramethylene)-ferrocene, were synthesized in a four-step reaction sequence starting from ferrocene. Friedel-Craft acylation of ferrocene with succinic anhydride gave mono- or bis(3-carboxypropinoyl)ferrocene depending on the stoichiometry of succinic anhydride. The reduction of the keto groups to methylene, followed by ring-closing using trifluoroacetic anhydride gave 1,2-(α-ketotetramethylene)-ferrocene or 1,2,1’,2’-bis(α-ketotetramethylene)ferrocene. The diastereomeric mixture of the latter diketones was separated by column chromatography, characterized by single-crystal X-ray analysis, and assigned their stereochemistry. Reduction of the keto groups to methylene under Clemmensen conditions gave homoannular mono- or bis(tetramethylene) ferrocene derivatives. The molecular structure of 1,2-(tetramethylene)-ferrocene revealed that the ipso carbon atoms of the cyclopentadienyl group are 0.023(3) Å farther away from Fe(II) compared to the remaining three carbon atoms. Both complexes exhibit lower half-wave oxidation potentials than ferrocene, possibly due to the electron-releasing effects of the tetramethylene bridges.

The past is an important lesson for facing the future with greater awareness. In this context, school plays a key role to spread knowledge on natural phenomena and to promote behavior change. Together with researchers, teachers can be strong allies to build more resilient future citizens. The Istituto Nazionale di Geofisica e Vulcanologia (INGV) school training activities provide tools to get ready for the next earthquake and/or tsunamis. About 5000 students from both Middle schools (ISCED 2) and High schools (ISCED 3) were involved in active learning activities based on a flipped-up approach during special online scientific events in the Pandemic. Online lab activities were conducted respectively during the European Researchers’ Night (“Earthquakes: history teaches us the future: researchers for a day with experimentation in didactics for ESL”) and during the World Water Day 2021 and the World Earth Day 2021 (“Tsunamis: history teaches us the future researchers for a day with experimentation in didactics for ESL”). These two Episodes of Situated Learning (ELS) experiences triggered students’ interest, favoring remote learning, developing life skills, focusing on historical seismic studies of both past earthquakes and tsunamis.

Steels studs are an inevitable part of drywall construction as they are lightweight and offer the required structural stability. However, the studs act as sound bridges between the plasterboards reducing the overall sound insulation of the wall. Overcoming this often calls for wider cavity walls and complex stud decoupling fixtures that increase the installation cost while reducing the floor area. As an alternative approach, this research reveals the potential of perforated studs to improve the acoustic insulation of drywall partitions. The acoustic and structural performance is characterized using a validated finite element model that acted as a prediction tool in reducing the number of physical tests required. The results established that an acoustic numerical model featuring fluid-structure-interaction can predict the weighted sound reduction index of a stud wall assembly at an accuracy of ±1 dB. The model was used to analyze six perforated stud designs and found to outperform the sound insulation of non-perforated drywall partitions by reducing the sound bridging. Overall, the best performing perforated stud design was found to offer improvements in acoustic insulation of up to 4 dB, while being structurally compliant.

In fuel-cell technological development, one of the most important objectives is to minimize the amount of Pt, the most employed material as oxygen reduction and methanol oxidation electro-catalyst. In this paper we report the synthesis and characterization of Te nanotubes (TeNTs) decorated with Pt nanoparticles, readily prepared from stirred aqueous solutions of PtCl2 containing a suspension of TeNTs and ethanol acting as a reducing agent, avoiding the use of any hydrophobic surfactants as capping stabilizing substance. The as obtained TeNTs decorated with Pt nanoparticles (TeNTs/PtNPs) have been fully characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), selected area diffraction patterns (SAD), X-ray photoelectron spectroscopy (XPS) and cyclic voltammetry (CV). We demonstrate that the new material can be successfully employed in fuel cell either as anodic (for methanol oxidation reaction) and cathodic (for oxygen reduction reaction) electrode with high efficiency in terms of related mass activities and on-set improvement. Remarkably, the cell operates in aqueous electrolyte buffered at pH 7.0, thus avoiding acidic or alkaline conditions that may lead e. g. to Pt dissolution (at low pH) and paving the way for the development of biocompatible devices and on chip fuel cells.

Red mud is a hazardous waste of alumina industry that contains high amounts of iron, aluminum, titanium and REEs. One of the promising methods for the extraction of iron from red mud is car-bothermic reduction with the addition of sodium salts. This research focuses on the process of hy-drochloric high-pressure acid leaching using 10–20% HCl of two samples of non-magnetic tailings obtained by 60-minute carbothermic roasting of red mud at 1300 °C and the mixture of 84.6 wt. % of red mud and 15.4 wt. % Na2SO4 at 1150 °C, respectively, with subsequent magnetic separation of metallic iron. An influence of temperature, leaching duration, solid-to-liquid-ratio and acid con-centration on dissolution behavior of Al, Ti, Mg, Ca, Si, Fe, Na, La, Ce, Pr, Nd, Sc, Zr were studied. Based on the investigation of the obtained residues, mechanism of passing of valuable elements into the solution was proposed. It has shown that 90% Al, 91% Sc and above 80% of other REEs can be dissolved under optimal conditions; Ti can be extracted into the solution or the residue depending on the leaching temperature and acid concentration. Based on the research results, novel flowsheets for red mud treatment were developed.

Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for augmenting farmers the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents the result of a predictive digital twin case study that employs six regression algorithms namely Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system.

The increasing prevalence of periprosthetic femoral fractures, specifically in the vicinity of the hip, has emerged as a significant issue in recent times. Consequently, there is a need for a thorough examination to enhance the effectiveness of management and treatment approaches. The findings emphasized a significant disparity in the occurrence and characteristics of these fractures, as multiple studies have highlighted the efficacy of various treatment strategies, such as open reduction and internal fixation, as well as the utilization of cortical strut allografts. Furthermore, the research has identified potential risk factors that have an impact on the characteristics of fractures, providing valuable insights that could be crucial in the development of preventive strategies. This research provides a thorough examination of periprosthetic femoral fractures, highlighting the importance of a cohesive treatment algorithm to improve the handling of such fractures. Moreover, it promotes the need for a collaborative endeavor in conducting research in this field, cultivating a more profound comprehension that has the potential to drive progress in therapeutic approaches, ultimately enhancing patient results over an extended period of time. It is crucial that forthcoming research endeavors persist in expanding upon these discoveries, striving towards a unified methodology in tackling this substantial clinical obstacle.

This work investigates a new nanostructured gas-diffusion-layer (nano-GDL) to improve performance of air-cathode Single-Chamber-Microbial-Fuel-Cells (a-SCMFCs). The new nano-GDLs improves the direct oxygen-reduction-reaction by exploiting the best of nanofibers from electrospinning in terms of high surface ratio to volume and high porosity, and laser-based processing to promote adhesion. Nano-GDLs by electrospinning were fabricated directly collecting two nanofibers mats on the same carbon-based electrode, acting as the substrate. Each layer was designed with a specific function: water resistant, oxygen permeable polyvinylidene-difluoride (PVDF) nanofibers served as a barrier to prevent water-based electrolyte leakage, while an inner layer of cellulose nanofibers was added to promote oxygen diffusion towards the catalytic sites. The maximum current density obtained for a-SCMFCs with the new nano-GDLs is (132.2 ± 10.8) mA m^-2, and it doubles the current density obtained with standard PTFE-based GDL (58.5 ± 2.4 mA m^-2), used as reference material. The energy recovery (EF) factor, i.e. the ratio of the power output to the inner volume of the device, was then used to evaluate the overall performance of a-SCMFCs. a-SCMFCs with nano-GDL provided an EF value of 60.83 mJ m^-3: one order of magnitude higher than the value of 3.92 mJ m^-3 obtained with standard GDL

Ever since the tremendous success of the rapid vaccine development against COVID‑19, its availa-bility, distribution, and deployment have been a significant concern; however, evidence suggests that vaccine hesitancy has become a greater problem. Therefore, trust in vaccines is crucial and critically dependent on regimes' ability to communicate the benefits of immunization. This study investigated ideas on ways to reduce barriers to COVID-19 vaccination uptake. It explores methods to overcome COVID-19 vaccine barriers through qualitative research: interviews and group discussions involving healthcare providers, administration personnel, teachers, and individuals with chronic conditions across urban (Mombasa) and rural (Kilifi) Kenya. Audio-recorded discussions were transcribed and thematically analyzed across locations. Five themes emerged in our results regarding how to reduce barriers to the COVID vaccine in the context of Kenya, including awareness campaigns, engaging diverse stakeholders, using various communication techniques, capacity building to increase vac-cination centers and trained staff, and lastly, revising relevant government health policies and guidelines. These results indicate the importance of adopting multiple approaches, as no single strategy could boost vaccine acceptance. Moreover, this study provides recommendations for con-ceiving actionable interventions to potentially boost vaccine demand and maintain routine immun-ization in Kenya.

Rather high speed of modern water bikes that they develop using only human muscle power increases their popularity. For example, the maximum speed of prototypes reaches the value 3 m/s. Similar vehicles can be used not only for recreation and fitness, but also for transportation. To increase their speed and tonnage, we recommend improving the pontoon shape and using electrical power. The total drag and maximal speeds of the vehicles with the human muscle and electrical power are estimated. Expected success in improving the pontoon shape opens wide prospects for the use of these special shaped hulls in shipbuilding.

The high level of noise in helicopter cabins considerably compromises the comfort and safety of the pilot and passengers. To verify the feasibility and effectiveness of microperforated panel composite sound absorption structures for noise suppression in helicopter cabins, simulation and experimental studies were conducted on a model of a light helicopter cabin. First, three microperforated composite sound absorption structures for the helicopter cabin wall panel were designed. Then, a finite element model of the main gear/body acoustic vibration coupling was established to obtain the target frequencies of the microperforated composite sound absorption structures; the acoustic effect was verified by simulation. Finally, a model helicopter cabin equipped with the three microperforated composite sound absorption structures was built, and a cabin noise test was performed. The test results showed that the combined microperforated panel acoustic structure and microperforated panel–porous material composite structure realized an overall cabin sound pressure level attenuation of 8–10 dB, on average, in the wide frequency range of 500–2000 Hz, with an amplitude of more than 20 dB. The microperforated panel–acoustic supermaterial composite structure achieved low-frequency sound absorption in the frequency range of 300–450 Hz. The sound absorption effect reached 50%, and it also exhibited good noise reduction effects in the middle- and high-frequency bands.

We refute a recently proposed thought experiment namely the Dead-Alive Physicist (DAP) whose authors claim it to be the falsification of the von Neumann interpretation in Quantum Mechanics. We discover major flaws in their model and our justification serves the purpose of disregarding further claims and assertions made in the paper at various stages. Thus, in a yet another situation, it is proven that the formalism advanced by von Neumann is non-trivial and non-falsifiable.

The so-called Born-Huang ansatz is a fundamental tool in the context of ab-initio molecular dynamics, viz., it allows to effectively separate fast and slow degrees of freedom and thus treating electrons and nuclei at different mathematical footings. Here we consider the use of a Born-Huang-like expansion of the three-dimensional time-dependent Schr\"odinger equation to separate transport and confinement degrees of freedom in electron transport problems that involve geometrical constrictions. The resulting scheme consists of an eigenstate problem for the confinement degrees of freedom (in the transverse direction) whose solution constitutes the input for the propagation of a set of coupled one-dimensional equations of motion for the transport degree of freedom (in the longitudinal direction). This technique achieves quantitative accuracy using an order less computational resources than the full dimensional simulation for a prototypical two-dimensional constriction.

Disaster Risk Reduction (DRR) is a high priority on the agenda of main stakeholders involved in sustainable development and Earth Observation (EO) can provide useful, timely and economical information in this context. This short communication outlines the European Space Agency’s (ESA) specific initiative to promote the use and uptake of satellite data in the global development community: ‘Earth Observation for Sustainable Development (EO4SD)’. One activity area under EO4SD is devoted to Disaster Risk Reduction: EO4SD DRR. Within this project, a team of European companies and institutions are tasked to develop EO services for supporting the implementation of DRR in International Financial Institutions’ (IFI) projects. Integration of satellite-borne data and ancillary data to generate insight and actionable information is thereby considered a key factor for improved decision making. To understand and fully account for the essential user requirements (IFI & Client States), engagement with technical leaders is crucial. Fit-for-purpose use of data and comprehensive capacity building eventually ensure scalability and long-term transferability. Future perspectives of EO4SD and DRR regarding mainstreaming are also highlighted.

This study investigated the possible usage of beeswax oleogels instead of milk fat as a fat source in ice cream production and konjac gum as a stabilizer instead of salep. For this aim, 12 different ice cream samples were prepared using various fat and oil sources (milk fat and oleogel), stabilizers (salep and konjac gum), and emulsifiers (monoglyceride (MG), palsgaard (PG), and none emulsifier emulsifier-free (NE)). It was determined that the overrun ratio in ice cream samples containing milk fat was higher than in oleogel and the viscosity of the ice cream mix containing palsgaard and oleogel was greater than that of the other treatments (milk fat, MG, and NE). While the first dripping time of the samples with PG and konjac gum was longer compared to the samples without emulsifier (NE) and Monoglyceride (MG), the complete melting times of the samples were close to each other. Whereas the dissolution rate of the samples with salep was higher than that of konjac gum, the fat destabilization ratios of the samples with oleogel and konjac gum were lower than those of milk fat and salep. The fat destabilization ratio of samples containing PG as an emulsifier was statistically higher (p <0.01) than that of samples containing MG and NE. It was found that the unsaturated fatty acid (C18:1, C18:2, and C18:3) content of the samples containing oleogel was significantly higher (p <0.01) than that of the samples containing milk fat. However, butyric and caproic acids, which are aliphatic fatty acids, were found to be deficient in the samples to which oleogel was added instead of milk fat. As to the microscopic appearance, while water and oil particles were not homogeneously dispersed in the ice cream samples with oleogel, they were fully homogeneously dispersed in the milk fat-added ice cream samples. In addition, it was determined that panelists preferred the C samples (milk fat as fat source, salep and PG as emulsifier).Among the samples with added oleogel as oil source, they liked the J sample more (oleogel as fat source,conjak gum and none emulgator). Therefore, the C and J samples can be recommended for consumption

There are two reasons to have an efficient algorithm for identifying all maximal Lyndon substrings of a string: firstly, Bannai et al. introduced in 2015 a linear algorithm to compute all runs of a string that relies on knowing all maximal Lyndon substrings of the input string, and secondly, Franek et al. showed in 2017 a linear equivalence of sorting suffixes and sorting maximal Lyndon substrings of a string, inspired by a novel suffix sorting algorithm of Baier. In 2016, Franek et al. presented a brief overview of algorithms for computing the Lyndon array that encodes the knowledge of maximal Lyndon substrings of the input string. Among the presented were two well-known algorithms for computing the Lyndon array: a quadratic in-place algorithm based on iterated Duval's algorithm for Lyndon factorization, and a linear algorithmic scheme based on linear suffix sorting, computing inverse suffix array, and applying to it the Next Smaller Value algorithm. Duval's algorithm works for strings over any ordered alphabet, while for linear suffix sorting, a constant or an integer alphabet is required. The authors at that time were not aware of Baier's algorithm. In 2017, our research group proposed a novel algorithm for the Lyndon array. Though the proposed algorithm is linear in the average case and has O(n log(n)) worst-case complexity, it is interesting as it emulates the fast Fourier algorithm's recursive approach and introduces tau-reduction that might be of independent interest. In 2018, we presented a linear algorithm to compute the Lyndon array of a string inspired by Phase I of Baier's algorithm for suffix sorting. This paper presents theoretical analysis of these two algorithms and provides empirical comparisons of both their C++ implementations with respect to iterated Duval's algorithm.

The increasing number of caesarean section (CS) is a major challenge in modern obstetrics. This rise is not supported by better neonatal and maternal outcomes. The prevention of unnecessary primary CS is a global priority. This study focuses on effectiveness and safety of yoga practice during pregnancy and it’s connection to perinatal outcome. We conducted a single-blinded, randomized controlled clinical trial, with healthy singleton primiparas, randomized at first trimester of pregnancy. A total of 214 participants were randomized, 106 in yoga and 108 in controls. The study group performed yoga exercise while the controls received standard antenatal care. The yoga exercises were performed once a week, lasted 90 minutes, under the supervision of a certified yoga teacher. All pregnant women from study group completed 12 yoga exercises. The results showed that the yoga group had a lower rate of urgent intrapartum CS (p-0.004) and more frequent spontaneous vaginal delivery (p-0.009), while the control group had a higher rate of induced labour (p-0.032). Conclusion: Yoga practice in pregnancy is a promising intervention that has a potential to decrease intrapartum urgent CS rates and assures natural and spontaneous vaginal birth; it’s highly recommendable to healthy pregnant women.

Fibroepithelial lesions (FL) of the breast, in particular Phyllodes tumors (PT) and fibroadenomas, pose a significant diagnostic challenge. There are no generally accepted criteria that distinguish benign, borderline, malignant PT, and FA. Combined genome-wide DNA methylation and copy number variant (CNV) profiling is an emerging strategy to classify tumors. We compiled a series of patient-derived archival biopsy specimens reflecting the FL spectrum and histological mimickers including clinical follow-up data. DNA methylation and CNVs were determined by well-established microarrays. Comparison of the patterns with a pan-cancer dataset assembled from public resources including "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) suggests that FLs form a methylation class distinct from both control breast tissue as well as common breast cancers. Complex CNVs were enriched in clinically aggressive FLs. Subsequent fluorescence in situ hybridization (FISH) analysis detected respective aberrations in the neoplastic mesenchymal component of FLs only, confirming that the epithelial component is non-neoplastic. Of note, our approach could lead to the elimination of the diagnostically problematic category of borderline PT and allow for optimized prognostic patient stratification. Furthermore, the identified recurrent genomic aberrations such as 1q gains (including MDM4), CDKN2a/b deletions and EGFR amplifications may inform therapeutic decision-making.

Background: This article evaluates the perception of drug use control and strategies in Valencia City (Spain) in a general and clinical population, in two independent studies. Material and Methods: 1071 people participated. In the Study 1 (n= 924) the entire sample came from general population (GP), and in the Study 2 (n=147), 68 were drug users being treated in an Addictive Behaviors Unit (ABU), and 79 people of the GP. The drug use control perception and strategies in both subgroups were compared. The participants filled in the Drug Use Strategies Scale and a Drug Use Survey. Results: A high level of perception of drug control in GP was obtained (72,7% in the Study 1 and 67,5% in the Study 2), and 32.5% in ABU subgroup. People in the PG and drug users in treatment differ in some control strategies. A predictive profile of the perception of control was obtained for the Study 2. Conclusion: The high degree of perception of controlling drug use in the GP, and partially in drug users being treated, and the specific control strategies reported suggests that moderate use and drug control strategies are a great value alternative to bear in mind compared to abstinence.

The processing of woody biomass waste piles for use as fuel instead of burning them was investigated. At each landing slash pile location, a 132 kW grapple excavator was used to transfer the waste piles into a 522 kW horizontal grinder. Economies of scale could be expected when grinding a larger pile, although the efficiency of the loading operation might be diminished. Here, three piles were ground and the operations were time-studied: Small (20 m long × 15 m wide × 4 m high), Medium (30 × 24 × 4 m), and Large (35 × 30 × 4 m) piles. Grinding the Medium pile was found to be the most productive at 30.65 bone dry tons per productive machine hour without delay (BDT/PMH₀), thereby suggesting that there might be an optimum size of slash pile for a grinding operation. We also examined modeling of the excavator and grinder operations, and we observed that the constructed simulation model well-replicated the actual operations. Based on the modeling, we estimated that the productivity of grinding at a landing area of 710 m² of slash pile location was 31.24 BDT/PMH₀, which was the most productive rate.

The complex progression, specialized care, and comorbidities of chronic diseases place a financial and health burden on society. In some instances, the high hospital readmission rate is also a result of chronic diseases. The readmission rates can be decreased if the quality of life for patients is increased and healthcare costs are reduced through effective management and preventive measures. As a result, healthcare professionals, service providers, and policy-makers are looking for innovative approaches to reduce healthcare costs while enhancing the quality of care. There is an emerging trend in deploying technological advancements like artificial intelligence (AI), the Internet of Things (IoT), sensors, wearables, social media, mobile apps, and genomics to decrease hospital readmissions. In some instances, predictive analytics, early warning systems, personal-ized care management, remote monitoring and Telehealth, decision support systems, patient education and engagement, and other areas of artificial intelligence and machine learning have outperformed traditional approaches in lowering hospital readmissions. Although AI effectively reduces readmission, there are potential risks if it is not combined with efficient interventions. Therefore, real-time monitoring and intervention are required for AI systems to increase patient safety and decrease hospital readmissions. To achieve autonomy, prevent harm, achieve fairness, and achieve explicability, the ethical principles relating to AI systems should be taken into ac-count. This study examines the significant impact on health and the economy of hospital read-missions for chronic diseases and the role of AI in reducing readmissions.

The dilatometer curves of continuous cooling transformation of 12Mn steel were measured with Formastor-FⅡthermal mechanical simulator．The steel's undercooled austenite phase continuous cooling transformation curves ( CCT curves) were established by means of the dilatometer method and the metallographic-hardness measurement method． The effect of cooling rate on microstructure and hardness of the steel was studied． CCT curve of test steel was simulated by JMatPro. The results show that the Ac1 and Ac3 of the experimental steel are 692 ℃ and 855 ℃ ; the microstructure obtained is made of ferrite，pearlite and bainite．The ferrite transformation and pearlite transformation occur at a slower cooling rate，in which the ferrite is dominant. When the cooling rate is greater than 4.25 ℃ / s bainite transformation happens．As the cooling rate increases，microstructure or grains become finer．The hardness of the tested steel with increasing cooling rate shows a trend of first fast increase and soon decrease．The simulation results are consistent with the measured CCT law. The high temperature mechanical properties of 12mn steel round billet were tested by gleeble-1500d thermal / mechanical simulator. The tensile strength, reduction of area and stress-strain curves of the billet were obtained in the range of 600-1300 ℃.

This article offers a theoretical framework to contextualise the concept of "Safety Culture" in the field of public safety and Disaster Risk Reduction (DRR), with the aim of supporting its understanding and measurement in our communities and finally attaining more disaster resilient societies. The work discusses the role of cultural dimensions in risk management and DRR and highlights the relevance of building knowledge and practices based on the consideration of culture and cultural variability in all phases of the risk management process. After an overview of the Safety Culture concept, including its origins, developments and applications, the text explores the transferability of this concept to the context of public safety. Then, based on the existing Safety Culture models, metrics and indicators from other sectors, the paper proposes an original definition of Safety Culture for the DRR context, shaped on a series of elements and dimensions specifically outlined for this context. Finally, the expected benefits of the application of Safety Culture concepts and techniques for further advances in DRR is discussed.

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

Various species of plants, bacteria, fungi, insects, invertebrates, and vertebrate animals produce unique small molecules, peptides, or proteins (biomolecules) for their defensive purposes. These biomolecules, also known as toxins, are poisonous (toxic) to humans and could be misused due to relatively easy accessibility and affordability. Toxins are not contagious and replicative; their effect or symptoms depend on their concentration (dose), which can take hours to days after exposure. Furthermore, for nefarious actors, preparing low-effort homemade explosives (HMEs) using household items or easily accessible chemical precursors is not difficult. To mitigate the biosecurity risks, biological scientists, law enforcement investigators, and public health response professionals should be aware of the new threats and challenges posed by low-effort toxins and HMEs. The chemical and biological investigation, evidence, and countermeasures training program was aimed at preparing biological scientists, law enforcement investigators, and public health representatives to mitigate the biosecurity risks from low-effort toxins and HMEs. The tailor-made eight-day training program containing lectures, round table discussions, immersion scenarios, practical exercises, practical assessment, and tabletop exercises helped participants to understand and close their skill gaps. The training also covered various elements associated with chemical, biological, radiological, nuclear, and explosive (CBRNE) threats from nefarious individuals and groups. Some highlights of the training are reported in this report.

Digital twins are the foundation of autonomous off-road vehicles. Dynamic reduction methods are one of several ways to develop digital twins for off-road vehicles. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results of digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built control, electrical, and control systems.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. So, this paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Non-Linear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) Methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

This work aims to study and analyze a hybrid power system for electric vehicles consisting of a dual low and high-rate lithium battery block and a fuel cell. In this configuration, the high-rate lithium battery powers the electric vehicle in high power demand processes like acceleration mode or on an uphill road; the low-rate battery operates at a low output power range, servicing the auxiliary systems and low power loads and the fuel cell supplies energy at intermediate power demand conditions, normal driving mode, constant velocity, or flat and downhill terrain. The dual power system improves global efficiency since every power unit operates optimally depending on driving conditions. Power sharing optimizes the lithium battery performance and fuel cell capacity, minimizing the size and weight of each energy system and enlarging the driving range. A comparative study between different lithium battery configurations and fuel cell shows an efficiency improvement of 31.4% for the hybrid dual battery block and fuel cell operating in low, high, and intermediate output power ranges, respectively. The study bases on a simulation process recreating current driving conditions for electric cars in urban, peripheral, and intercity routes. An alternative solution consisting of a hybrid system, fuel cell and a high rate lithium battery produces a 29 % power gain.

Research for a theory of quantum gravity has recently led to the use of presheaf topos. Quantum uncertainty is linked to the truth values of intuitionistic logic. This paper proposes transposing this model into a classic probability context, that of conditional mathematical expectations. A simulation of Brownian motion is offered for illustrative purposes.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

Ultrasonic sensors are inexpensive and can take measurements with high accuracy, even with simple hardware configurations, so they are used in various fields. When multiple ultrasonic sensors exist in the measurement space, crosstalk occurs due to other nodes, which causes measurement errors. Crosstalk includes not only receiving homogeneous signals from other nodes, but also overlap by other signals, and interference by heterogeneous signals. This paper proposes a method that uses frequency sweep keying modulation to be robust against overlap and a faster region-based convolutional neural networks (R-CNN)-based demodulator to reduce the interference caused by heterogeneous signals. The demodulator works by training faster R-CNN with the spectrograms of various received signals and classifying the received signals using a faster R-CNN. Experiments implementing an ultrasonic crosstalk environment showed that compared to on-off keying (OOK), phase-shift keying (PSK), and frequency-shift keying (FSK), the proposed method can implement CDMA even with shorter codes, and is robust against overlap. Compared to correlation-based frequency sweep keying, the time-of-flight error was reduced by approximately 75%. While existing demodulators did not consider heterogeneous signals, the proposed method ignored approximately 99% of the OOK and PSK signals, and approximately 79% of the FSK signals. The proposed method performs better than the existing methods and is expected to be used in various applications.

The construction of optimized biological fuel cells requires a cathode which combines the longevity of a microbial catalyst with the power density of an enzymatic catalyst. Laccase secreting fungi were grown directly on the cathode of a biological fuel cell to facilitate the exchange of inactive enzymes with active enzymes with the goal of extending the lifetime of laccase cathodes. Additionally, a functionally graded coating was developed to increase enzyme loading at the cathode. Directly incorporating the laccase producing fungus at the cathode extends the operational lifetime of laccase cathodes while eliminating the need for frequent replenishment of the electrolyte. Additionally, the hybrid microbial-enzymatic cathode addresses the issue of enzyme inactivation by using the natural ability of fungi to exchange inactive laccases at the cathode with active laccases. Finally, enzyme adsorption was increased through the use of a functionally graded coating containing an optimized ratio of titanium dioxide nanoparticles and single walled carbon nanotubes. The hybrid microbial-enzymatic fuel cell combines the higher power density of enzymatic fuel cells with the longevity of microbial fuel cells and demonstrates the feasibility of a self-regenerating fuel cell in which inactive laccases are continuously exchanged with active laccases.

In recent years, distributed generation technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of Distributed Generation (DG) and meet the challenges after power grid access, Active Distribution Network (ADN) is considered as the future development direction of traditional distribution network because of its ability of active management. Nowadays, multi-scenario analysis is widely used in the research of optimal allocation of distributed power supply in active distribution network. Aiming at the problems that may arise when using multi-scenario analysis to plan DG with uncertainties in large-scale scenarios, a scenario reduction method based on improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the method is verified. At present, there are few studies on the optimal allocation of DG in ADN under fault state. In this paper, comprehensive safety indicators are introduced. Considering the timing characteristics of DG and the influence of active management mode, a bi-level programming model is established, which aims at minimizing the investment of annual life cycle and the removal of active power. The bi-level model is a complex mixed integer non-linear programming model. A hybrid algorithm combining cuckoo search algorithm and primal dual interior point method is used to solve the model. Finally, through the simulation of the IEEE-33 node system, the superiority of the scenario reduction method and the comprehensive security index used in this paper to optimize the configuration of DG in ADN is verified.

Thin Slab Casting and Directing Rolling (TSCDR) has become a major process for flat- rolled production. However, the elimination of slab reheating and limited number of thermomechanical deformation passes leave fewer opportunities for austenite grain refinement resulting in some large grains persist in the final microstructure. In order to achieve excellent Ductile to Brittle Transaction (DBTT) and Drop Weight Tear Test (DWTT) properties in thicker gauge high strength low alloy products, it is necessary to control austenite grain coarsening prior to the onset of thermomechanical processing. This contribution proposes a suite of methods to refine the austenite grain from both theoretical and practical perspective including: increasing cooling rate during casting, liquid core reduction, increasing austenite nucleation sites during the delta ferrite to austenite phase transformation, controlling holding furnace temperature and time to avoid austenite coarsening, and producing new alloy with two phase pinning to arrest grain coarsening. These methodologies can not only refine austenite grain size in the slab center, but also improve the slab homogeneity.

Is immediate action at a distance, like gravitational attraction, imaginable using the contrary concept of close up, tactile, events? Tactile events, defined with the term ‘tap-tapping’ as a blind man does, described in a two-way spiritual interaction theory, are implemented in physics to understand gravitation from this respect. The quantum mechanical wave function reduction during measurements receives a new approach. Formulated is a new proof for Einstein’s Equivalence Principle, extending it beyond locality, and a sketch of how tactile interaction could explain dark energy and an accelerated expansion of the universe. Dark energy and dark matter are examples starting from which to discuss properties of matter and space and gravitation as immediate tactile action rather than mediated action such as electromagnetism.

A Demand-side management technique are deployed along with battery energy-storage systems (BESSs) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand

In the context of global changes, Nature-Based Solutions (NBSs) increasingly draw attention as a possible way to reduce disaster risk associated with extreme hydro-meteorological events while providing human well-being and biodiversity benefits at the same time. The PHUSICOS platform is dedicated to gather and analyse relevant NBSs used to reduce disaster risk associated with extreme hydro-meteorological events in mountainous and hilly lands. To design the platform, an in-depth review of 11 existing platforms has been performed. The platform currently references 152 literature NBS cases and is continuously enriched with demonstrator sites through the contribution of NBS community. The platform also proposes a qualitative assessment of the NBSs collected according to 15 criteria related with five ambits: disaster risk reduction, technical and economical feasibility, environment, society and local economy. This paper presents the structure of the platform and a first analysis of its content.

Decades of research suggest that Mindfulness-Based Stress Reduction (MBSR) training supports a greater capacity to live with chronic medical conditions and contributes to lowering stress levels. This paper introduces a model for a Mindfulness-Based Recovery from Stroke (MBRfS) for promoting stroke recovery, informed by the lived experience of the author (a stroke survivor and certified MBSR instructor), the research literature regarding MBSR training, and the specific challenges of stroke recovery. Four themes emerged from the autoethnographic analysis that informed the proposed model: Readiness to accept the stroke event and the acquired brain injury; Navigating uncertainties of stroke recovery with awareness and self-responsibility for outcomes; Trusting the inherent wisdom of the body as a stroke recovery “teacher”; and Increased capacity to integrate complex emotions with self-compassion, and a sense of wholeness. A four component MBRfS model is offered, which consists of an integration amongst a modified MBSR framework, emergent attitudinal themes, and insights from the autoethnographic vignettes. The MBRfS model offers a path for providing participants with a supportive experience within stroke recovery. Recommendations and suggestions for future studies are offered to support the development of MBRfS for stroke survivors and their caregivers, as well as contributing to health care providers.

Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, often found both in open pits and in waste and tailing disposal facilities. The development of slope monitoring and modeling programs usually entails the exploitation of in situ and remote sensing data together with the application of numerical modeling, and it plays an important role in the definition of prevention and mitigation measures aimed at minimizing the impact of slope failures in mining areas. Here we present a new methodology combining satellite radar interferometry and 2D finite element modeling for slope stability analysis at a regional scale, applied within slope unit polygons. We studied a former mining area in southeast Spain, and the method proved useful in detecting and characterizing a considerably large number of unstable slopes. Out of 1,959 slope units used for the spatial analysis of the radar interferometry data, 43 were unstable, with varying values of safety factor and landslide size. Out of the 43 active slope units, 21 exhibited line of sight velocities greater than the maximum error obtained through the validation analysis (2.5 cm/year). Eventually, this work discusses the possibility of using the results of the proposed approach to devise a proxy for landslide hazard. The proposed methodology can help to provide non-expert final users with intelligible, clear and easily comparable information to analyze slope instabilities in different settings, not limited to mining areas.

The urgency for a shift towards cleaner and more efficient energy alternatives for power generation is underscored by the escalating demand for energy and the detrimental environmental impacts of conventional fossil fuels. In the case of automotive power, electric vehicles are being adopted in many applications, however, challenges remain such as extended charging periods and limited ranges between charges. Therefore, this research study conducts a techno-economic analysis of renewable energy technologies (including solar modules, wind turbines and piezoelectric materials) to augment the efficiency and sustainability of electric buses. The study examines the scope for these technologies to be adopted on double-decker electric buses in London in the United Kingdom. The solar modules are placed on the rooftop and sides of the bus generating 15.9 kWh/day and the wind turbine in the front bumper of the bus generates 8.3 kWh/day. However, the piezoelectric material generated only 22.6 Wh/day, rendering it an impractical inclusion in further analysis. Therefore, both the solar modules and wind turbines combined generate 24.2 kWh/day, which can increase the driving range by 16.3 km per day and this results in savings of 19.36 minutes for charging at the stations. Investing in such projects would have a positive return as the internal rate of return (IRR) and net present value (NPV) are 2.8% and £11,175 respectively. The annual revenue would be £6,712, and the greenhouse gas (GHG) reduction would be two metric tons annually. Electricity exported to the grid, the electricity export rate, and the initial investment are identified as key factors influencing power outage in a sensitivity analysis. In conclusion, this numerical modelling study paves the way for experimental validation towards implementation of renewable energy technologies on electric bus fleets.

Variational Auto-Encoders (VAEs) are deep generative models used for unsupervised learning, however their standard version is not topology-aware in practice since the data topology may not be taken into consideration. In this paper, we propose two different approaches with the aim to preserve the topological structure between the input space and the latent representation of a VAE. Firstly, we introduce InvMap-VAE as a way to turn any dimensionality reduction technique, given an embedding it produces, into a generative model within a VAE framework providing an inverse mapping into original space. Secondly, we propose the Witness Simplicial VAE as an extension of the Simplicial Auto-Encoder to the variational setup using a witness complex for computing the simplicial regularization, and we motivate this method theoretically using tools from algebraic topology. The Witness Simplicial VAE is independent of any dimensionality reduction technique and together with its extension, Isolandmarks Witness Simplicial VAE, preserves the persistent Betti numbers of a data set better than a standard VAE.

Earth Observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: Atmosphere, Water, Agriculture, Land, Disaster Risk Reduction (DRR), Cultural Heritage, Energy, Marine Safety and Security (MSS) and Big Earth Data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and Land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high resolution and near real time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems and services.

Compressive sensing (CS) is a technique that allows a signal vector to be reconstructed with a number of linear measurements far fewer than its size. In this study, optical hybrid cryptography with simultaneous compressive sensing based on orthogonal matching pursuit (OMP) is proposed as an alternative solution to the two important problems in communications, namely, effective / efficient / signal processing and secure encryption/decrypton. The whole system is able to both compress and encrypt data with attack immunity.