Economic Costs Research Articles

Innovative materials integrated with PCM for enhancing photovoltaic panel efficiency: An experimental investigation

The electrical power generated by a photovoltaic panel is crucial due to the high demand for electricity. Operating temperatures above the Standard Test Conditions (STC) negatively impact the output power, which is a significant drawback of this technology. Therefore, it is essential to keep the front and back surface temperatures of the photovoltaic panel as low as possible. Phase Change Material (PCM) is a good passive cooling method, but it has low thermal conductivity. This paper introduces a novel material to address this issue. Iron Filling Waste (IFW) is integrated with PCM to cool the photovoltaic panel. This investigation is presented experimentally with three modules. The highest surface temperature of the reference panel, 78 °C, is recorded for the uncooled module, while with PCM- IFW is 70.5 °C. The comparative analysis shows that the PV module cooled by PCM-IFW achieved an efficiency enhancement and power output increase of 39 % and 33 % respectively, compared to the reference panel without cooling. IFW-PCM achieves an average enhancement in efficiency and output power by 23 % and 11 %, respectively, compared with panels that use PCM only. Also, IFW enhances thermal conductivity without any additional economic cost for the cooling systems.

Hydrochar supported strategy for nZVI to remove bisphenol A and Cr(VI): Performance, synergetic mechanism, and life cycle assessment

To comprehensively overcome the shortcomings and extend the application of nanoscale zero-valent iron (nZVI), a promising hydrochar supported strategy was provided. nZVI/Eupatorium adenophorum hydrochar (nZVI/EaHC) composite was attained under relatively mild reaction condition, and life cycle assessment revealed that the greenhouse gases emission, ecotoxicity, and resource consumption of nZVI/EaHC fabrication processes largely reduced in comparison with nZVI and pyrolysis biochar supported nZVI (nZVI/EaBC). Moreover, nZVI/EaHC exhibited an outstanding potential in reduction and immobilization of Cr(VI) as well as degradation of bisphenol A (BPA) via Fenton-like system. The static experiment showed that the removal efficiency achieved 99.21 % within 1 h for Cr(VI) and 97.9 % within 1 min for BPA, which significantly exceeded the performance of nZVI/EaBC, accompanied by a significant detoxification to non-harmful level and mineralization of 87.9 % within 10 min. The dynamic continuous flow column experiment indicated that 90 % Cr(VI) was removed within 40 min and 90 % BPA within 180 min under ultra-low-dosage of catalyst and H2O2. Furthermore, hydrochar supported strategy may reduce the economic cost and avoid secondary pollution due to the outstanding stability under aerobic condition and recyclability in magnetic field. The synergetic mechanisms were further revealed, the abundant oxygen-containing functional groups on EaHC facilitated the generation of electron-rich/poor centers via chemical bond bridge (COFe) and Fe-π interaction, which further enhanced the electron transfer to reduce Cr(VI) or promote the production of multiple reactive species including OH, O2–, and 1O2 to degrade BPA, additionally, persistent free radicals on EaHC also contributed to the degradation of BPA. Therefore, this efficient, applicable, and sustainable strategy may have a greatly practical outlook on nZVI application together with a comprehensive control and utilization of biomass including invasive plants.

Environmental Protection for Bureaucratic Promotion: Water Quality Performance Review of Provincial Governors in China

We show including quantitative environmental targets for bureaucratic promotion incentivizes government officials to enforce environmental regulation but at significant economic costs. Taking advantage of the gradual expansion of China’s water quality performance review (WQPR) over time and space, we find that WQPR significantly reduced ambient concentrations of criteria pollutants, digestive cancer mortality, and GDP growth rate. WQPR’s effects are more pronounced along provincial borders, which are targeted by WQPR, and when provincial governors have more promotion potential. An important mechanism is investment in wastewater treatment facilities. There is evidence that WQPR passed the benefit-cost test, especially in rural areas.

Evaluating potential program cost savings with a single-dose HPV vaccination schedule: a modeling study.

There is limited evidence on the magnitude of the potential program cost savings associated with the World Health Organization-endorsed single-dose schedule for the human papillomavirus (HPV) vaccine. The objective of this analysis was to model the delivery and vaccine procurement cost implications of the new schedule. The analysis leveraged primary data during a study evaluating the HPV vaccine delivery costs and operational context in 5 countries (Ethiopia, Guyana, Rwanda, Sri Lanka, and Uganda) implementing a two-dose schedule. To estimate the cost for the single-dose schedule, we adjusted the two-dose schedule cost estimates to account for differences in the frequency of activities, whether activities differed by HPV vaccine dose or session, and differences in relative quantity or storage volume of HPV vaccines delivered. We estimated the cost per dose and cost per adolescent receiving the full (single-dose or two-dose) vaccination schedule in 2019 US dollars from a health system perspective. Modeled results found that cost per dose would increase under a single-dose schedule, whereas cost per adolescent receiving the full schedule would decrease. The financial cost for vaccine procurement and delivery per adolescent receiving the full schedule ranged from $9.64 (Sri Lanka) to $23.43 (Guyana) under a two-dose schedule and decreased to $4.84 and $12.34, respectively, under a single-dose schedule, reflecting savings up to 50%. For economic costs, the range for a single-dose schedule was $7.86 (Rwanda) to $28.53 (Guyana). A single-dose HPV vaccination schedule could provide cost savings to immunization programs and enhance program affordability and sustainability.

Analysis of the therapeutic effort and economic cost of the care provided to a group of hospitalized end-of-life patients

Analysis of the therapeutic effort and economic cost of the care provided to a group of hospitalized end-of-life patients

Silver‐Nanoparticles‐Based Composites for Antimicrobial Applications: An Update

AbstractAntimicrobial resistance (AMR), which develops into superbugs, poses a significant challenge to global health leading to 4.95 million deaths in 2019, necessitating the exploration of alternative strategies. AMR is responsible for a significant economic cost estimated by the World Bank, where AMR could result in additional healthcare costs of US$ 1 trillion by 2050. Thus, overcoming these drawbacks is of great importance. Fortunately, the advent of 21st century nanotechnology provides an ample opportunity to develop diverse nanomaterials along with specific functionalization to treat bacteria, fungi, or viruses more effectively with the combination of innovative technologies. Among these, silver nanoparticles (AgNPs) are considered a great boon in the area of AMR, which has a long history of practice. However, in recent years, Ag‐based composites have been designed by scientists to enhance their antimicrobial effects at minimal concentrations, thereby depending on synergism. Thus, in this review, we provide an update on the recent advances in Ag‐based composites with metals, polymers, and carbon for various antimicrobial applications. In addition, we focused on the mechanisms that assist in tackling microbes and the toxicity of AgNPs. Thus, in this review, we highlight the synergistic effects of AgNP‐based composites in combating several microorganisms.

An innovative process for clean ammonium-free vanadium precipitation and one-step preparation of vanadium dioxide based on hydrothermal enhancement of organic alcohols

As a transition metal oxide, VO2 has excellent optical and electrical properties and is widely used in many fields. The production of VO2 by the reduction of NH4VO3 and V2O5 will inevitably produce ammonia–nitrogen wastewater and NH3 emission during the production of NH4VO3 and V2O5, which will cause serious environmental pollution and will increase economic costs. The large-scale and low-cost synthesis of VO2 still faces great challenges. In this paper, VO2(B) was successfully prepared by a one-step hydrothermal method using NaVO3 as the vanadium source and CH3CH2OH as the reducing agent, and the optimal conditions for vanadium precipitation are as follows: vanadium concentration = 30 g/L, CH3CH2OH dosage = 20 % (percentage of solution volume), initial pH = 1, reaction temperature = 220 °C, reaction time = 12 h. The vanadium precipitation efficiency under the optimal conditions could reach 98.93 %, and the purity of VO2(B) could reach 98.59 %. The precipitation products were characterized by XRD, TG, FTIR, XPS, and SEM-EDS. The mechanism of VO2(B) preparation by ethanol reduction was proposed. This process is characterized by green, clean, and high efficiency. At the same time, the vanadium precipitation effect and vanadium precipitation products of various alcohols were studied to provide technical and theoretical support for the preparation of VO2(B).

A multi-dimensional comparative study on the performance of algae removal using various flotation

Flotation is considered the most cost-effective and efficient algae-water separation technology. However, there are various flotation techniques for algae removal, such as coagulation-flotation (CF), foam flotation (FF), and positively charged bubble flotation (PF). It remains unclear which method is most suitable for removing algae from water bodies and under what specific conditions each technique is most effective. This study systematically compares CF, FF, and PF in terms of algal cell removal efficiency, concentration ratio, flotation kinetics, impact on algal cells, removal efficiency of algal organic matter (AOM) and microcystins (MC-LR), as well as economic cost analysis. CF is better suited for algae removal in water bodies, including drinking water sources, using fixed installations on shore due to its high removal efficiency, high concentration ratio, low chemical dosage, and minimal residuals. FF is more appropriate for non-drinking water sources as it can remove algae and further control algal growth; however, its residual CTAB may pose a threat to drinking water safety. PF is most suitable for in situ algae removal within water bodies, primarily because it does not require stirring or coagulation. Instead, modified bubbles can be directly introduced into the algal distribution layer, where they adhere to algal cells, facilitating algae-water separation. All three flotation methods are economically feasible for algae removal. For FF, the costs of chemicals and electricity are nearly equal, while for CF and PF, the primary cost is electricity. This study provides data to support the selection of appropriate flotation technologies for emergency removal of algal blooms in water bodies.

A study on the scaled intelligent supply mode of biomass briquette fuel in China

In the backdrop of China's ‘Carbon peak, Carbon neutrality’ goal, to reduce the pollution caused by carbon emissions to the atmosphere, this study investigates the supply of biomass briquette fuel. The supply of biomass briquette fuel is generally divided into two stages: the first stage involves the collection, storage, and transportation of biomass straw to the biomass briquette production base; the second encompasses the processing of the biomass straw into briquette fuel at the production base, followed by its transportation to the biomass boiler heating center for utilization. In this paper, a mathematical model is developed based on carbon emission and economic cost. The model is then calculated with an optimized intelligent algorithm, and the results are subjected to sensitivity analysis. The results demonstrate that: (1) Compared to the pre-optimization phase, the optimized model increases profits by 26.31 % and reduces carbon emissions by 12.64 %. (2) The adaptive genetic algorithm shows significantly better convergence speed and accuracy compared to the traditional genetic algorithm, making it more suitable for intelligent calculations of biomass briquette fuel supply. (3) As the weight coefficient of carbon emissions increases, economic costs continue to decrease with increasing sensitivity, while carbon emissions continue to decrease with decreasing sensitivity. These results provide references for further optimizing the weight coefficients of economic costs and carbon emissions in the supply of briquette fuel. This study contributes to reducing carbon emissions and provide references for the rational and intelligent supply of biomass briquette fuel.

Damocles's Switchboard: Information Externalities and the Autocratic Logic of Internet Control

Abstract This paper advances a theory for the autocratic logic of internet control. Politically motivated internet control generates a positive externality for domestic data-intensive firms and a negative externality for domestic knowledge-intensive research entities. Exploiting a major internet control shock in 2014, I find that Chinese data-intensive firms gained 26 percent in revenue over other Chinese firms as the result of internet control. The same shock incurred a 10 percent decline in research quality from Chinese researchers, conditional on the knowledge intensity of their discipline. It also reduced the research quality from Chinese researchers relative to their US counterparts by 22 percent in all disciplines. Due to the positive data externality, internet control enacted to prevent domestic threats challenges the state's competing need for data sovereignty against foreign threats. Meanwhile, the state shields certain foreign knowledge-intensive actors from the negative knowledge externality to avoid the immediate economic costs they might otherwise impose. Qualitative evidence supports both implications, highlighting the centrality of short-term interests and foreign actors in autocratic decision making.

Low-carbon optimal dispatch of park integrated energy system considering coordinated ammonia production from multiple hydrogen energy

The utilization of ammonia energy has garnered considerable global attention as a viable approach to alleviate pressure on energy supply within park integrated energy systems (PIES) and enhance energy efficiency. However, the introduction of ammonia production requires the system to increase large amounts of hydrogen, diversifying the sources of hydrogen and challenging the coordinated operation of PIES. In this paper, the inertia characteristics of the gas and thermal energy delivery process are utilized to increase the flexibility of the PIES operation. A technological framework for coordinated ammonia production from multi-hydrogen energy in PIES under consideration of having inertia characteristics and a low-carbon operation strategy for ammonia-doped combustion in thermal generation is constructed. Limiting carbon emissions through a stepped carbon trading mechanism in this strategy. In addition, while addressing the ammonia-doped requirement for thermal generation combustion, and by flexibly setting the ammonia doping ratio of thermal generation combustion, the operation of PIES under different ammonia-doped ratios has been analyzed in detail. The results demonstrate that the proposed model enhances the utilization of renewable energy, leading to reduction in total economic costs by 4.52 % and total carbon emissions by 24.81 %. This makes the operation of PIES more economical, low-carbon, and flexible.

Deep reinforcement learning-based multi-objective optimization for electricity–gas–heat integrated energy systems

With the increasing global attention on energy efficiency and carbon emissions, the optimization of integrated energy systems (IES) has become the key to improve energy efficiency and reduce pollution emissions. However, most of the existing optimization methods cannot effectively deal with the complexity of high dimensional continuous action space. Therefore, this paper focuses on a novel multi-objective optimization strategy for the electricity–gas–heat integrated energy systems (EGH-IES). Firstly, considering the absorption capacity of wind power and the emission of pollutant gases, a multi-objective optimization model is constructed based on the mechanism model and operation constraints of each device in EGH-IES, in which the integrated operation cost and the environmental factors are taken as optimization objectives. Then, the multi-objective optimization problem is designed as the optimal strategy of interaction learning between agent and environment in reinforcement learning, and the output power of the devices constitutes the action of reinforcement learning. Additionally, the Ornstein–Uhlenbeck process is introduced to enhance the training efficiency and exploration performance of the agent, and the deep deterministic policy gradients (DDPG) algorithm is employed to optimize the action, thus the output power of the appliances could be obtained. Finally, the simulation results show that compared with deep Q network (DQN) method and proximal policy optimization (PPO) method, the reward function value of the proposed method increases by 2.43% and 6.09%, respectively, which represents a reduction in economic cost and pollutant emissions. These verify the effectiveness and superiority of the proposed multi-objective optimization scheme in cost reduction and benefit improvement for the EGH-IES.

Effect of working fluid charging amount on system performance in an ocean thermal energy conversion system

In ocean thermal energy conversion (OTEC) systems, the initial working fluid charging amount is crucial as it relates to the system’s performance and economic cost. But currently, there is no clear standard for it, and as the working fluid circulating pump is the most direct and only means to regulate the working fluid flow, it is necessary to investigate the coupled effects of it and the working fluid charging amount on the system performance. To this issue, a corresponding experimental facility was established. The coupled effects were investigated by energy and exergy analysis under varying temperature difference. The results showed that varying working fluid charging amount changed the physical state of the working fluid, and the effect on the system varied under dissimilar temperature difference conditions. Compared to the standard charge (8 kg), 75 % of the standard charging amount diminished the system thermal efficiency by 17 % and exergy efficiency by 16 % at small temperature difference (20 °C), whereas a charging amount of more than 100 % resulted in improved thermal efficiency by 14 % and exergy efficiency by 13 %. At large temperature difference (28 °C), 75 %, 125 % and 150 % of the standard working fluid charging amount all improved system performance (thermal efficiency by maximum 16 %, exergy efficiency by maximum 21 %). Besides, an increase in circulating pump frequency improved system performance regardless of the temperature difference and working fluid charging amount. This research innovatively investigates the above problems in the application scenario of OTEC, and provides theoretical and data support for the development of OTEC technology.

The Costs and Cost-Effectiveness of a Two-Dose Oral Cholera Vaccination Campaign: A Case Study in a Refugee Camp Setting in Thailand

Oral cholera vaccination (OCV) campaigns are increasingly used to prevent cholera outbreaks; however, little is known about their cost-effectiveness in refugee camps. We conducted a cost-effectiveness analysis of a pre-emptive OCV campaign in the Maela refugee camp in Thailand, where outbreaks occurred with an annual incidence rate (IR) of up to 10.7 cases per 1000. Data were collected via health sector records and interviews and household interviews. In the base-case scenario comparing the OCV campaign with no campaign, we estimated the campaign effect on the cholera IR and case fatality rate (CFR: 0.09%) from a static cohort model and calculated incremental cost-effectiveness ratios for the outcomes of death, disability-adjusted life-years (DALYs), and cases averted. In sensitivity analyses, we varied the CFR and IR. The household economic cost of illness was USD 21, and the health sector economic cost of illness was USD 51 per case. The OCV campaign economic cost was USD 289,561, 42% attributable to vaccine costs and 58% to service delivery costs. In our base case, the incremental cost was USD 1.9 million per death averted, USD 1745 per case averted, and USD 69,892 per DALY averted. Sensitivity analyses that increased the CFR to 0.35% or the IR to 10.4 cases per 1000 resulted in a cost per DALY of USD 15,666. The low multi-year average CFR and incidence of the cholera outbreaks in the Maela camp were key factors associated with the high cost per DALY averted. However, the sensitivity analyses indicated higher cost-effectiveness in a setting with a higher CFR or cholera incidence, indicating when to consider campaign use to reduce the outbreak risk.

Cost-utility analysis of once-weekly insulin icodec and once-daily insulin glargine in patients with type 2 diabetes receiving basal-bolus insulin therapy in China.

The purpose of this study is to explore the rational pricing range for the once-weekly administration of insulin icodec in the treatment of type 2 diabetes patients in China who have already received basal insulin therapy. The data foundation of this study originates from the ONWARDS 4 clinical trial and research materials on Chinese type 2 diabetes patients. By comprehensively applying cost-utility analysis methods and binary search techniques, the appropriate price positioning of insulin icodec was determined from the perspective of China's healthcare system. In the long-term treatment simulation, we found that insulin icodec and insulin glargine performed similarly in terms of quality-adjusted life years (QALYs), with 10.15 and 10.07 years, respectively. Although the annual cost of insulin icodec was initially assumed to be equivalent to that of insulin glargine, in-depth analysis revealed that insulin icodec may have higher cost-effectiveness potential. Further price sensitivity analysis indicated that the reasonable cost range of insulin icodec lies between $851.95 and $1358.25. After fine-tuning through univariate sensitivity analysis, this cost range was revised to $784.90 to $1145.96, a conclusion that was robustly validated in subsequent probabilistic sensitivity analysis and scenario simulations. The conclusion drawn from this study is that, with insulin glargine as the cost reference, the economic cost of insulin icodec for Chinese type 2 diabetes patients is expected to range from $784.90 to $1145.96, providing a reference basis for clinical decision-making and healthcare policy formulation.

Cost-utility and clinical outcome analysis of surgery or oral corticosteroids treatment in patients with chronic rhinosinusitis with nasal polyps

Background: Surgery and oral corticosteroids are recommended therapies for chronic rhinosinusitis with nasal polyps (CRSwNP) patients who are nonresponsive to intranasal corticosteroid treatment. Objective: This study aimed to compare the effectiveness of these 2 treatments in terms of improving sinus-related symptoms, enhancing quality of life, and economic costs and duration costs. Methods: This prospective study enrolled CRSwNP patients. All participants were instructed to complete the 22-item Sino-Nasal Outcome Test (SNOT-22), visual analog scale, and 36-item short-form (SF-36) questionnaire at baseline and 3 months after treatment. The nasal polyp score (NPS) was assessed via endoscopic examination. Additionally, patients were requested to maintain records of economic direct costs, other indirect costs, and duration costs throughout the 3-month period and to report them during each follow-up visit. Results: A total of 40 patients who underwent surgery or received oral corticosteroids were enrolled in this study. After 3 months of treatment, significant improvements were observed between baseline and 3 months after treatment in the NPS, total SNOT-22 score, and nasal congestion, runny nose, facial pain, olfactory function, and overall symptoms measured. Only headache and sleep order were improved in the surgery group. Both physical functioning and general health, as measured by the SF-36, improved after both treatments, and the role-physical, bodily pain, and social functioning domains of the SF-36 improved only in the surgery group. The changes in the NPS, nasal congestion, runny nose, olfactory function, and sleep disorders in the surgery group were greater than those in the oral corticosteroid group (P values = 0.0003, 0.0092, 0.0258, 0.0284, and 0.0164, respectively). Changes in the total SNOT-22 score and SF-36 subscores were not different between the 2 treatment groups. The direct economic costs and duration costs of surgical treatment were 4.5 times and 17.0 times higher, respectively, than those of oral corticosteroid treatment. Conclusions: Both surgical and oral corticosteroid treatments effectively improved clinical symptoms and quality of life in patients with CRSwNP. Patients who underwent surgery exhibited greater improvements in sinus-related symptoms. Nevertheless, oral corticosteroid treatment presented notable advantages in terms of economic cost and duration cost of disease-related care.

How Do People Make Sense of Intimate Partner Violence (IPV) in the Context of Perpetrator PTSD Diagnostic Status and Gender Identity?

Background: Intimate partner violence (IPV) has profound effects on victim physical and mental health, considerable social and economic costs and is a significant public health concern. Research suggests that the way the public make sense of IPV may vary in different contexts, as certain contextual factors may be used to explain, excuse or legitimise IPV. PTSD diagnostic status and perpetrator gender may be such contextual factors, but little research exists in this area. Objectives: This study explored how the public make sense of IPV in the context of perpetrator diagnostic status (PTSD or no PTSD) and gender identity (female, male or nonbinary) by exploring IPV ratings and IPV discourses. Methods: Two hundred and sixty-five (265) community participants were recruited via snowball sampling and presented one of six versions of the same story containing an account of IPV. Stories were identical across conditions bar perpetrator diagnostic status and gender identity. Participants rated the degree to which they thought the behaviour in the story constituted IPV. Approximately half the participants also completed a story completion task, and stories were analysed for themes. Results: IPV ratings were weighted towards abuse and did not vary with diagnostic status or gender identity. Five themes were identified in participant stories: (i) characterise the behaviour; (ii) trajectory of IPV; (iii) show compassion towards victim of IPV; (iv) hold victim of IPV responsible; and (v) context influences decision-making. Limitations, implications and directions for future research are discussed.

Novel cost-effective method for forecasting COVID-19 and hospital occupancy using deep learning

The emergence of the COVID-19 pandemic in 2019 and its rapid global spread put healthcare systems around the world to the test. This crisis created an unprecedented level of stress in hospitals, exacerbating the already complex task of healthcare management. As a result, it led to a tragic increase in mortality rates and highlighted the urgent need for advanced predictive tools to support decision-making. To address these critical challenges, this research aims to develop and implement a predictive system capable of predicting pandemic evolution with accuracy (in terms of Mean Absolute error (MAE), Root Mean Square Error (RMSE), R2, and Mean Absolute Percentage Error (MAPE)) and low computational and economic cost. It uses a set of interconnected Long Short Term-memory (LSTM) with double bidirectional LSTM (BiLSTM) layers together with a novel preprocessing based on future time windows. This model accurately predicts COVID-19 cases and hospital occupancy over long periods of time using only 40% of the set to train. This results in a long-term prediction where each day we can query the cases for the next three days with very little data. The data utilized in this analysis were obtained from the “Hospital Insular” in Gran Canaria, Spain. These data describe the spread of the coronavirus disease (COVID-19) from its initial emergence in 2020 until March 29, 2022. The results show an improvement in MAE (< 161), RMSE (< 405), and MAPE (> 0.20) compared to other studies with similar conditions. This would be a powerful tool for the healthcare system, providing valuable information to decision-makers, allowing them to anticipate and strategize for possible scenarios, ultimately improving public health outcomes and optimizing the allocation of healthcare and economic resources.

Enhancing Diagnostic Accuracy of Fresh Vertebral Compression Fractures with Deep Learning Models.

Retrospective study. The study aimed to develop and authenticated a deep learning model based on X-ray images to accurately diagnose fresh thoracolumbar vertebral compression fractures. In clinical practice, diagnosing fresh vertebral compression fractures often requires MRI. However, due to the scarcity of MRI resources and the high time and economic costs involved, some patients may not receive timely diagnosis and treatment. Using a deep learning model combined with X-rays for diagnostic assistance could potentially serve as an alternative to MRI. In this study, the main collection included X-ray images suspected of thoracolumbar vertebral compression fractures from the municipal shared database between December 2012 and February 2024. Deep learning models were constructed using frameworks of EfficientNet, MobileNet, and MnasNet respectively. We conducted a preliminary evaluation of the deep learning model using the validation set. The diagnostic performance of the models was evaluated using metrics such as AUC value, accuracy, sensitivity, specificity, F1 score, precision, and ROC curve. Finally, the deep learning models were compared with evaluations from two spine surgeons of different experience levels on the control set. This study included a total of 3025 lateral X-ray images from 2224 patients. The dataset was divided into a training set of 2388 cases, a validation set of 482 cases, and a control set of 155 cases. In the validation set, the three groups of DL models had accuracies of 83.0%, 82.4%, and 82.2%, respectively. The AUC values were 0.861, 0.852, and 0.865, respectively. In the control set, the accuracies of the three groups of DL models were 78.1%, 78.1%, and 80.7%, respectively, all higher than spinal surgeons and significantly higher than junior spine surgeon. This study developed deep learning models for detecting fresh vertebral compression fractures, demonstrating high accuracy.

Multi-objective optimization of non-fossil energy structure in China towards the carbon peaking and carbon neutrality goals

Numerous nations and regions are earnestly dedicated to propelling energy transition. China accounts for 31.78 % of the total global carbon emissions, and China's energy structure holds paramount significance in the broader context of global initiatives. China has officially proposed to achieve the target of carbon peak by 2030 and carbon neutrality by 2060, imposing substantial pressure on the transformation of the energy structure. In this study, we propose a multi-objective optimization model for the non-fossil energy structure, encompassing three primary optimization objectives: maximizing power generation, minimizing economic costs, and reducing CO₂ emissions. The optimization involves determining the proportion of seven key non-fossil energy sources—hydropower, onshore wind, offshore wind, photovoltaic (PV), bioenergy, geothermal, and nuclear power—while adhering to constraints related to power demand, nuclear safety, and other factors. The analysis explores the installed capacity, power generation, power generation costs, and CO₂ emission reductions of non-fossil energy under distinct scenarios in 2025, 2030, and 2060. The findings reveal that by 2030, the optimal share of power generation should be sequenced as onshore wind, hydropower, nuclear power, offshore wind, PV, bioenergy, and geothermal. Subsequently, by 2060, the top three developed power should be onshore wind, nuclear power, and hydropower.