Effect of true triaxial principal stress unloading rate on strain energy density of sandstone.

Deep rock are often in a true triaxial stress state. Studying the impacts of varying unloading speeds on their strain energy (SE) density is highly significant for predicting rock stability. Through true triaxial unloading principal stress experiments and true triaxial stress equilibrium unloading experiments on sandstone, this paper proposes a method to compute the SE density in a true triaxial compressive unloading principal stress test. This method aims to analyze the SE variation in rocks under the action of true triaxial unloading principal stresses. Acoustic emission is used to verify the correctness of the SE density calculation method in this paper. This study found that: (1) Unloading in one principal stress direction causes the SE density to rise in the other principal stress directions. This rise in SE, depending on its reversibility, can be categorized into elastic and dissipated SE. (2)When unloading principal stresses, the released elastic SE density in the unloading direction is influence by the stress path and rate. (3) The higher the unloading speed will leads to greater increases in the input SE density, elastic SE density, and dissipative SE density in the other principal stress directions. (4) The dissipated SE generated under true triaxial compression by unloading the principal stress is positively correlated with the damage to the rock; with an increase in unloading rate, there is a corresponding increase in the formation of cracks after unloading. (5) Utilizing the stress balance unloading test, we propose a calculation method for SE density in true triaxial unloading principal stress tests.

Using choice modelling to inform service sustainability for dementia Meeting Centres for people living with dementia in the UK.

OBJECTIVES: This study explores the preferences and willingness-to-pay (WTP) of carers for Meeting Centres (MCs) attributes in assisting individuals with mild to moderate dementia. METHOD: Preferences from 108 carers, gathered through UK-wide MC networks, were collected using a Discrete Choice Experiment survey. The survey incorporated attributes derived from evidence synthesis and lay consultation. A regression model estimated preference weights and marginal WTP for a change in attributes one a time within the MC support 'package.' RESULTS: Carers preferred MCs offering a balanced mix of practical activities and emotional support, along with flexibility without booking requirements and low costs. Social opportunities and the frequency of the meeting were not prioritised. Respondents expressed a WTP of £43 to stay with 'My MC,' the preferred option, compared to transitioning to an alternative in-person MC, all else being equal. Various factors, including attendance modality, the relationship with the supported person, age, and gender, influenced carers' choices. CONCLUSION: These findings offer valuable insights into carers' preferences, priorities, and WTP within MC support for those with mild to moderate dementia. Understanding these factors can guide the implementation and sustainability of MCs, ensuring alignment with carers' needs and preferences and, ultimately, enhancing support for individuals with dementia.

Experimental and Numerical Characterization of Screw Elements Used in Twin-Screw Extrusion.

Hot melt extrusion by a co-rotating twin screw extruder is an important process in the pharmaceutical industry. Especially for quality by design aspects, a comprehensive process understanding is indispensable. The performance of conveying elements was determined as critical process parameter, and therefore an experimental and numerical framework was developed to analyze and compare variations. A test rig capable of measuring volume flow, pressure and torque with high accuracy and precision was designed and built. The 3D simulation was performed using computational fluid dynamics (CFD). A stationary model with impulse transmission and an apparent motion of the screws was applied. The experimental data were fitted to the model of Pawlowski, and parameters for the pressure (A1, A2) and power characteristics (B1, B2) were determined. Good agreement between experimental data and the model was observed. The simulation was significantly faster compared to common methods, and the results were consistent with the literature. Systematic investigations of a native and worn screw were performed with CFD resulting in a transport capacity increase and a pressure build up decrease for all tested screw elements. An experimental and simulation setup was generated to assess the performance of co-rotating twin screw elements. The experiments provided high-quality data, and the simulations exhibited high flexibility with low computational effort.

Examining Audiences' Psychological Reactance to Christian-Sourced Excessive Alcohol Consumption Messages in the United States.

This study experimentally investigated the effect of dogmatic and suggestive language in Christian-sourced excessive alcohol consumption messages among college-aged participants who identify as Christians or non-Christians, as well as the role of perceived similarity with the message source, on their self-reported freedom-threat, psychological reactance, and behavioral intentions to consume alcohol. The results from this study support psychological reactance theory and demonstrate the various message strategies to effectively communicate the negative health effects of excessive alcohol consumption to individuals who identify either as Christians or non-Christians.

Use of acoustic signals in Cape fur seal mother-pup reunions: individual signature, signal propagation and pup home range.

The Cape fur seal (Arctocephalus pusillus pusillus) is one of the most colonial mammals, with colonies of up to hundreds of thousands of individuals during the breeding season. During the lactation period, mothers and pups are regularly separated as females undertake multi-day foraging trips at sea. Mothers and pups use a mutual vocal recognition system to reunite after separation. Such communication is highly constrained by both high background noise and risk of individual confusion owing to the density of seals. This study aimed to experimentally assess the acoustic features relevant for mother-pup vocal identification and the propagation properties of their calls. Playback experiments revealed that mother and pup individual vocal signatures rely on both temporal and frequency parameters: amplitude and frequency modulations, timbre and fundamental frequency (f0). This is more parameters than in any colonial species studied so far. The combinational use of acoustic features reinforces the concept that both environmental and social constraints may have acted as selective pressures on the individual vocal recognition systems. Theoretical propagation distances of mother and pup vocalisations were estimated to be below the range of distances at which mother-pup reunions can occur. This suggests that Cape fur seals may have strong abilities to extract vocal signals from the background noise, as previously demonstrated in the highly colonial king penguin. Investigating the transmission of information throughout the propagation of the signal as well as the ability of the receiving individual to decipher vocal signatures is crucial to understanding vocal recognition systems in the wild.

A jsPsych touchscreen extension for behavioral research on touch-enabled interfaces.

Online experiments are increasingly gaining traction in the behavioral sciences. Despite this, behavioral researchers have largely continued to use keyboards as the primary input devices for such online studies, overlooking the ubiquity of touchscreens in everyday use. This paper presents an open-source touchscreen extension for jsPsych, a JavaScript framework designed for conducting online experiments. We additionally evaluated the touchscreen extension assessing whether typical behavioral findings from two distinct perceptual decision-making tasks - the random-dot kinematogram and the Stroop task - can similarly be observed when administered via touchscreen devices compared to keyboard devices. Our findings indicate similar performance metrics for each paradigm between the touchscreen and keyboard versions of the experiments. Specifically, we observe similar psychometric curves in the random-dot kinematogram across the touchscreen and keyboard versions. Similarly, in the Stroop task, we detect significant task, congruency, and sequential congruency effects in both experiment versions. We conclude that our open-source touchscreen extension serves as a promising tool for data collection in online behavioral experiments on forced-choice tasks.

Short-term cultured tumor fragments to study immunotherapy combinations based on CD137 (4-1BB) agonism.

Biomarkers for cancer immunotherapy are an unmet medical need. The group of Daniela Thommen at the NKI recently reported on novel methodologies based on short-term cultures of patient-derived tumor fragments whose cytokine concentrations in the supernatants and activation markers on infiltrating T cells were associated with clinical response to PD-1 blockade. We set up a similar culture technology with tumor-derived fragments using mouse tumors transplanted into syngeneic immunocompetent mice to test an agonist anti-CD137 mAb and its combinations with anti-PD-1 and/or anti-TGF-ß. Increases in IFNγ concentrations in the tissue culture supernatants were detected upon in-culture activation with the anti-CD137 and anti-PD-1 mAb combinations or concanavalin A as a positive control. No other cytokine from a wide array was informative of stimulation with these mAbs. Interestingly, increases in Ki67 and other activation markers were substantiated in lymphocytes from cell suspensions gathered at the end of 72 h cultures. In mice bearing bilateral tumors in which one was excised prior to in vivo anti-CD137 + anti-PD-1 treatment to perform the fragment culture evaluation, no association was found between IFNγ production from the fragments and the in vivo therapeutic outcome in the non-resected contralateral tumors. The experimental system permitted freezing and thawing of the fragments with similar functional outcomes. Using a series of patient-derived tumor fragments from excised solid malignancies, we showed IFNγ production in a fraction of the studied cases, that was conserved in frozen/thawed fragments. The small tumor fragment culture technique seems suitable to preclinically explore immunotherapy combinations.

Network analysis combined with experimental assessment to explore the therapeutic mechanisms of New Shenqi Pills formula targeting mitochondria on senile diabetes mellitus.

Background: The escalation of global population aging has accentuated the prominence of senile diabetes mellitus (SDM) as a consequential public health concern. Oxidative stress and chronic inflammatory cascades prevalent in individuals with senile diabetes significantly amplify disease progression and complication rates. Traditional Chinese Medicine (TCM) emerges as a pivotal player in enhancing blood sugar homeostasis and retarding complication onset in the clinical management of senile diabetes. Nonetheless, an evident research gap persists regarding the integration of TCM's renal tonification pharmacological mechanisms with experimental validation within the realm of senile diabetes therapeutics. Aims: The objective of this study was to investigate the mechanisms of action of New Shenqi Pills (SQP) in the treatment of SDM and make an experimental assessment. Methods: Network analysis is used to evaluate target pathways related to SQP and SDM. Mitochondrial-related genes were obtained from the MitoCarta3.0 database and intersected with the common target genes of the disease and drugs, then constructing a protein-protein interaction (PPI) network making use of the GeneMANIA database. Representative compounds in the SQP were quantitatively measured using high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) to ensure quality control and quantitative analysis of the compounds. A type 2 diabetes mice (C57BL/6) model was used to investigate the pharmacodynamics of SQP. The glucose lowering efficacy of SQP was assessed through various metrics including body weight and fasting blood glucose (FBG). To elucidate the modulatory effects of SQP on pancreatic beta cell function, we measured oral glucose tolerance test (OGTT), insulin histochemical staining and tunel apoptosis detection, then assessed the insulin-mediated phosphoinositide 3-kinase (PI3K)/protein kinase A (Akt)/glycogen synthase kinase-3ß (GSK-3ß) pathway in diabetic mice via Western blotting. Additionally, we observe the structural changes of the nucleus, cytoplasmic granules and mitochondria of pancreatic islet ß cells. Results: In this investigation, we identified a total of 1876 genes associated with senile diabetes, 278 targets of SQP, and 166 overlapping target genes, primarily enriched in pathways pertinent to oxidative stress response, peptide response, and oxygen level modulation. Moreover, an intersection analysis involving 1,136 human mitochondrial genes and comorbidity targets yielded 15 mitochondria-related therapeutic targets. Quality control assessments and quantitative analyses of SQP revealed the predominant presence of five compounds with elevated concentrations: Catalpol, Cinnamon Aldehyde, Rehmanthin D, Trigonelline, and Paeonol Phenol. Vivo experiments demonstrated notable findings. Relative to the control group, mice in the model group exhibited significant increases in body weight and fasting blood glucose levels, alongside decreased insulin secretion and heightened islet cell apoptosis. Moreover, ß-cells nuclear condensation and mitochondrial cristae disappearance were observed, accompanied by reduced expression levels of p-GSK-3ß protein in islet cells (p < 0.05 or p < 0.01). Conversely, treatment groups administered SQP and Rg displayed augmented expressions of the aforementioned protein markers (p < 0.05 or p < 0.01), alongside preserved mitochondrial cristae structure in islet ß cells. Conclusion: Our findings suggest that SQP can ameliorate diabetes by reducing islet cell apoptosis and resist oxidative stress. These insulin-mediated PI3K/AKT/GSK-3ß pathway plays an important regulatory role in this process.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil.

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Similar experimental study on diffusion and distribution of diesel exhaust in confined space of a coal mine.

In the confined space of the underground coal mine, which is dominated by transportation lanes, explosion-proof diesel-powered trackless rubber-wheeled vehicles are becoming the main transportation equipment, and the exhaust gas produced by them is hazardous to the health of workers and pollutes the underground environment. In this experiment, a similar test platform is built to study the effects of wind speed, vehicle speed, and different wind directions on the diffusion characteristics of exhaust gas. In this paper, CO and SO2 are mainly studied. The results show that the diffusion of CO and SO2 gas is similar and the maximum SO2 concentration only accounts for 11.4% of the CO concentration. Exhaust gas is better diluted by increasing the wind speed and vehicle speed, respectively. Downwind is affected by the reverse wind flow and diffuses to the driver's position, which is easy to cause occupational diseases. When the wind is a headwind, the exhaust gases spread upwards and make a circumvention movement, gathering at the top. When the wind speed and vehicle speed are both 0.6 m/s, the CO concentration corresponds to the change trend of the Lorentz function when the wind is downwind and the CO concentration corresponds to the change trend of the BiDoseResp function when the wind is headwind. The study of exhaust gas diffusion characteristics is of great significance for the subsequent purification of the air in the restricted mine space and the protection of the workers' occupational health.

A robust triphenylamine-based monolithic polymer network for selective sieving of CO2 and PM from flue gas.

The increasingly urgent issue of climate change is driving the development of carbon dioxide (CO2) capture and separation technologies in flue gas after combustion. The monolithic adsorbent stands out in practical adsorption applications for its simplified powder compaction process while maintaining the inherent balance between energy consumption for regeneration and selectivity for adsorption. However, optimizing the adsorption capacity and selectivity of CO2 separation materials remains a significant challenge. Herein, we synthesized monolithic polymer networks (N-CMPs) with triphenylamine adsorption sites, acid-base environment tolerance, and precise narrow microchannel pore systems for the selective sieving of CO2 and particulate matter (PM) in flue gas. The inherent continuous covalent bonding of N-CMPs, along with their highly delocalized π-π conjugated porous framework, ensures the stability of the monolithic polymer network's adsorption and separation capabilities under wet and acid-base conditions. Specifically, under the conditions of 1 bar at 273 K, the CO2 adsorption capacity of N-CMP-1 is 3.35 mmol/g. Attributed to the highly polar environment generated by triphenylamine and the inherent high micropore/mesopore ratio, N-CMPs exhibit an excellent ideal adsorbed solution theory (IAST) selectivity for CO2/N2 under simulated flue gas conditions (CO2/N2 = 15:85). Dynamic breakthrough experiments further visualize the high separation efficiency of N-CMPs in practical adsorption applications. Moreover, under acid-base conditions, N-CMPs achieve a capture efficiency exceeding 99.76 % for PM0.3, enabling the selective separation of CO2 and PM in flue gas. In fact, the combined capture of hazardous PM and CO2 from the exhaust gases produced by the combustion of fossil fuels will play a pivotal role in mitigating climate change and environmental issues until low-carbon and alternative energy technologies are widely adopted.

Efficacy of acupuncture in animal models of various ovarian dysfunctions: a systematic review and meta-analysis.

Objective: This study aims to assess the comprehensive and integrated modulatory effects of acupuncture and electroacupuncture on various ovarian dysfunctions. Methods: We systematically searched for articles on animal experiments related to polycystic ovary syndrome (PCOS), premature ovarian failure (POF), premature ovarian insufficiency (POI), and perimenopausal syndrome (PMS) across multiple databases, including PubMed, Web of Science, Cochrane Library, Embase, and four Chinese language databases. The search covered the period from inception to November 2023. We conducted a comparative analysis between the acupuncture group and the model group (untreated) based on eligible literature. Our primary outcomes encompassed serum sex hormones (Luteinizing hormone, Follicle-stimulating hormone, Testosterone, Estradiol, Progesterone, and Anti-Müllerian hormone) and ovarian weight. Dichotomous data were synthesized to establish the relative risk (RR) of notable post-treatment improvement, while continuous data were pooled to determine the standardized mean difference (SMD) in post-treatment scores between the groups. Statistical analyses, including sensitivity analysis, Egger's test, and the trim-and-fill method, were executed using Stata 15.0 software. Results: The meta-analysis encompassed 29 articles involving a total of 623 rats. In comparison to rat models of PCOS, the experimental group exhibited a reduction in serum levels of LH, T and LH/FSH ratio. However, no statistically significant differences were observed in AMH, FSH, E2 levels, and ovarian weight between the two groups. In the ovarian hypoplasia model rats, both acupuncture and electroacupuncture interventions were associated with an increase in E2 levels. However, the levels of LH and FSH did not exhibit a significant difference between the two groups. Conclusions: Acupuncture or electroacupuncture facilitates the restoration of ovarian function primarily through the modulation of serum sex hormones, exerting regulatory effects across various types of ovarian dysfunction disorders. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022316279.

Dataset on carbonation and chloride-induced steel corrosion in cementitious mortars.

Machine learning (ML) has seen success in civil and structural engineering, but its application to forecasting corrosion of steel reinforcement in concrete structures is limited due to small datasets from isolated studies. Moreover, the existing corrosion dataset of reinforced concrete typically lacks sufficient and comprehensive material and environmental information that enables reliable corrosion prediction of reinforced concrete under complex corrosion scenarios. This work aims to bridge the gap by compiling and building a comprehensive corrosion dataset focusing on carbon steel in cementitious mortars. This dataset involves 46 distinct mortar mixtures with embedded steel bars. The samples first underwent accelerated corrosion testing (either by carbonation or chloride contamination), followed by investigating their corrosion behaviours under varying relative humidity (RH) conditions. Corrosion data were obtained during this period, in which all corrosion measurements were conducted in laboratory settings and the results are tabulated in spreadsheet format (.xlsx). The dataset encompasses mixture parameters, material properties, environmental parameters, and electrochemical parameters. This extensive dataset provides valuable corrosion data for training ML models to predict steel corrosion across various corrosion-related variables.

Novel approach for characterizing clinical load application of superelastic orthodontic wires.

OBJECTIVE: Current standardized in vitro bending experiments for orthodontic archwires cannot capture friction conditions and load sequencing during multi-bracket treatment. This means that clinically relevant forces exerted by superelastic wires cannot be predicted. To address these limitations, this study explored a novel test protocol that estimates clinical load range. METHODS: The correction of a labially displaced maxillary incisor was simulated using an in vitro model with three lingual brackets. Deflection force levels derived from four different protocols were designed to explore the impact of friction and wire load history. These force levels were compared in nickel-titanium (NiTi) archwires with three commonly used diameters. The unloading path varied between protocols, with single or multiple sequences and different load orders and initial conditions. RESULTS: Deflection forces from the new protocol, employing multiple continuous load/unload cycles (CCincr), consistently exceeded those from the conventional protocol using a single continuous unloading path (CUdecr). Mean differences in plateau force ranged from 0.54 N (Ø 0.014" wire) to 1.19 N (Ø 0.016" wire). The CCinr protocol also provided average force range estimates of 0.47 N (Ø 0.012" wire), 0.89 N (Ø 0.014" wire), and 1.15 N (Ø 0.016" wire). SIGNIFICANCE: Clinical orientation towards CUdecr carries a high risk of excessive therapeutic forces because clinical loading situations caused by friction and load history are underestimated. Physiological tooth mobility using NiTi wires contributes decisively to the therapeutic load situation. Therefore, only short unloading sequences starting from the maximum deflection in the load history, as in CCincr, are clinically meaningful.

Influence of Extrusion Screw Speed and CNT Concentration on the Mechanical and EMI Properties of PC/ABS Based Nanocomposites.

This study investigates the effect of extrusion screw speed and carbon nanotube (CNT) concentration on the thermal, mechanical, and electromagnetic interference shielding effectiveness (EMI SE) properties of Polycarbonate (PC)/acrylonitrile-butadiene-styrene (ABS) and its polymer nanocomposites (PNCs) by means of design of experiments (DoE) approach. A masterbatch method was employed to obtain the best dispersion of the CNTs throughout the polymer matrix. This study evaluates the thermo-mechanical characterisation of the polymers and PNCs at varying screw speeds to assess filler matrix bonding. The results highlight that CNT concentration has a significant effect on all mechanical properties, while screw speed only affects the Charpy impact strength and flexural properties of the samples. Compounding at 200 rpm has the best flexural and tensile strength, which is attributed to the best filler matrix bonding (highest storage modulus) of the PNCs. The best EMI SE results were obtained at 10 wt.% CNTs. This research contributes valuable insights into the effect of CNT concentration and extrusion screw speed on the mechanical, thermal and EMI SE properties of PC/ABS and its PNCs.

Laboratory and Numerical Investigation of Pre-Tensioned Reinforced Concrete Railway Sleepers Combined with Plastic Fiber Reinforcement.

This research investigates the application of plastic fiber reinforcement in pre-tensioned reinforced concrete railway sleepers, conducting an in-depth examination in both experimental and computational aspects. Utilizing 3-point bending tests and the GOM ARAMIS system for Digital Image Correlation, this study meticulously evaluates the structural responses and crack development in conventional and plastic fiber-reinforced sleepers under varying bending moments. Complementing these tests, the investigation employs ABAQUS' advanced finite element modeling to enhance the analysis, ensuring precise calibration and validation of the numerical models. This dual approach comprehensively explains the mechanical behavior differences and stresses within the examined structures. The incorporation of plastic fibers not only demonstrates a significant improvement in mechanical strength and crack resistance but paves the way for advancements in railway sleeper technology. By shedding light on the enhanced durability and performance of reinforced concrete structures, this study makes a significant contribution to civil engineering materials science, highlighting the potential for innovative material applications in the construction industry.

Thermo-Mechanical Behavior and Strain Rate Sensitivity of 3D-Printed Polylactic Acid (PLA) below Glass Transition Temperature (Tg).

This study investigated the thermomechanical behavior of 4D-printed polylactic acid (PLA), focusing on its response to varying temperatures and strain rates in a wide range below the glass transition temperature (Tg). The material was characterized using tension, compression, and dynamic mechanical thermal analysis (DMTA), confirming PLA's strong dependency on strain rate and temperature. The glass transition temperature of 4D-printed PLA was determined to be 65 °C using a thermal analysis (DMTA). The elastic modulus changed from 1045.7 MPa in the glassy phase to 1.2 MPa in the rubber phase, showing the great shape memory potential of 4D-printed PLA. The filament tension tests revealed that the material's yield stress strongly depended on the strain rate at room temperature, with values ranging from 56 MPa to 43 MPA as the strain rate decreased. Using a commercial FDM Ultimaker printer, cylindrical compression samples were 3D-printed and then characterized under thermo-mechanical conditions. Thermo-mechanical compression tests were conducted at strain rates ranging from 0.0001 s-1 to 0.1 s-1 and at temperatures below the glass transition temperature (Tg) at 25, 37, and 50 °C. The conducted experimental tests showed that the material had distinct yield stress, strain softening, and strain hardening at very large deformations. Clear strain rate dependence was observed, particularly at quasi-static rates, with the temperature and strain rate significantly influencing PLA's mechanical properties, including yield stress. Yield stress values varied from 110 MPa at room temperature with a strain rate of 0.1 s-1 to 42 MPa at 50 °C with a strain rate of 0.0001 s-1. This study also included thermo-mechanical adiabatic tests, which revealed that higher strain rates of 0.01 s-1 and 0.1 s-1 led to self-heating due to non-dissipated generated heat. This internal heating caused additional softening at higher strain rates and lower stress values. Thermal imaging revealed temperature increases of 15 °C and 18 °C for strain rates of 0.01 s-1 and 0.1 s-1, respectively.

A hybrid method of system dynamics and design of experiments for investigating the economic and environmental indicators of electricity industry.

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.