Volume Usage Research Articles

Empirical study of the impact of E-CNY on low-carbon economy from AI trust concerns

The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.

Motivations, barriers, and social media volume usage influence on exercise

Exercise contributes to a healthy lifestyle. Nonetheless, many people are insufficiently active. This study investigated exercise motivation, barriers to exercise, and the impact of social media volume usage among 18-25 year olds from a Midwestern university (USA). Data was collected via survey. Overall, the sample noted the psychological and fitness sub models and the positive health and strength and endurance subscales to be most important. There was a significant difference between college athletes and nonathletes for two sub models and seven subscales. College athletes noted time constraints and nonathletes noted their lack of motivation and time constraints (equally) as top barriers. Overall, the primary barriers to exercise were rated as less impactful (p < .001) by the college athletes (45.3 ± 34.2) compared to nonathletes (69.2 ± 28.3). Social media usage was prevalent in our sample, and it might be differentially impacting users. Highest-volume users rated barriers to be more impactful than lowest-volume users (p = .035). Social media is a current-culture culprit that helps “not enough time” persist as a key barrier to exercise. The impacts of social media on fitness and activity need further investigation.

Optimized deep learning‐based channel estimation for pilot contamination in a massive multiple‐input‐multiple‐output‐non‐orthogonal multiple access system

SummaryOne of the advanced field in 5G cellular networks is the Massive Multiple‐Input‐Multiple‐Output (MIMO), which creates a massive antenna array by offering numerous antennas at the destination. This grows as a hot research topic in the wireless sectors as it enhances the volume and spectrum usage of the channel. The spectral efficiency (SE) is maximized using the abundant antennas employed by MIMO using spatial multiplexing of consumers, which needs precise channel state information (CSI). The SE is affected by both pilot overhead and pilot contamination. To mitigate the contamination and to estimate the suitable channel for communication, an efficient strategy is introduced using the proposed Namib Beetle Aquila optimization (NBAO)_Deep Q network (DQN). Here, the optimal pilot location is identified by employing NBAO, which is an integration of Namib beetle optimization (NBO) and Aquila optimizer (AO). Moreover, DQN is introduced to determine the suitable channel and metrics, such as bit error rate (BER) and normalized mean square error (MSE) is used for evaluation. The normalized MSE channel estimation is utilized to mitigate the effects of pilot contamination. Additionally, designed NBAO + DQN have attained a value of 0.0006 and 0.0005 for BER and normalized MSE.

Insight into the photodegradation of methylisothiazolinone and benzoisothiazolinone in aquatic environments

Methylisothiazolinone (MIT) and Benzisothiazolinone (BIT) are two widely used non-oxidizing biocides of isothiazolinones. Their production and usage volume have sharply increased since the pandemic of COVID-19, inevitably leading to more release into water environment. However, their photochemical behaviors in water environment are still unclear. Therefore, this study investigated photodegradation properties of MIT and BIT in natural water under simulated sunlight. The results demonstrated that direct photolysis was mainly responsible for their photodegradation which occurred through their excited singlet states rather than triplet states. The quantum yields of MIT and BIT photodegradation were 11 - 13.6 × 10−4 and 2.43 - 5.79 × 10−4, respectively. pH had almost no effect on the photodegradation of MIT, while the photodegradation of BIT was significantly promoted under alkaline condition due to abundance of BIT in its deprotonated form (BIT-N−). Cl−, NO3− and dissolved organic matter (DOM) in natural water inhibited the photodegradation of both MIT and BIT, with the light screening effect of DOM being the most significantly inhibitory factor. The addition of other isothiazolinones, which possibly coexisted with MIT and BIT in actual condition, slightly inhibited the photodegradation of MIT and BIT. The estimated half-life under natural sunlight at a 30°N latitude was estimated to be approximately 1.1 days. The photodegradation pathways of MIT and BIT are similar, primarily initiated from the ring-opening at the N-S bond, with Frontier electron densities (FED) calculations suggesting the likelihood of oxidation and ·OH addition reactions at the O, N, and S sites. While the photodegradation products exhibited significantly reduced acute toxicity compared to their parent compounds, they nonetheless posed substantial chronic toxicity. These insights are vital for assessing the ecological impacts of MIT and BIT in aquatic environments.

Systematic Review on the Use of 3D-Printed Models for Planning, Training and Simulation in Vascular Surgery.

The use of 3D-printed models in simulation-based training and planning for vascular surgery is gaining interest. This study aims to provide an overview of the current applications of 3D-printing technologies in vascular surgery. We performed a systematic review by searching four databases: PubMed, Web of Science, Scopus, and Cochrane Library (last search: 1 March 2024). We included studies considering the treatment of vascular stenotic/occlusive or aneurysmal diseases. We included papers that reported the outcome of applications of 3D-printed models, excluding case reports or very limited case series (≤5 printed models or tests/simulations). Finally, 22 studies were included and analyzed. Computed tomography angiography (CTA) was the primary diagnostic method used to obtain the images serving as the basis for generating the 3D-printed models. Processing the CTA data involved the use of medical imaging software; 3DSlicer (Brigham and Women's Hospital, Harvard University, Boston, MA), ITK-Snap, and Mimics (Materialise NV, Leuven, Belgium) were the most frequently used. Autodesk Meshmixer (San Francisco, CA, USA) and 3-matic (Materialise NV, Leuven, Belgium) were the most frequently employed mesh-editing software during the post-processing phase. PolyJet™, fused deposition modeling (FDM), and stereolithography (SLA) were the most frequently employed 3D-printing technologies. Planning and training with 3D-printed models seem to enhance physicians' confidence and performance levels by up to 40% and lead to a reduction in the procedure time and contrast volume usage to varying extents.

Comparing the Efficacy of Endovascular Simulation Training in Different Training Formats: A Randomized Controlled Trial

High-fidelity endovascular simulators have demonstrated significant potential in enhancing interventionalists' performance metrics, including procedure time (PT), fluoroscopy time (FT), and contrast volume (CV) usage. However, it is less clear whether trainee performance, when assessed using both multidimensional global and procedure-specific rating scales (PSRS), improves in a manner consistent with these metric parameters. This study aimed to determine whether the structure of the training-conducted in block sessions or weekly intervals-impacts the effectiveness of the training. Twenty students were enrolled in this prospective, randomized, controlled, single-center trial. They were randomly divided into block and weekly training groups, each undergoing 6hrs of supervised endovascular training on a Mentice simulator over a span of 3weeks. At the outset and conclusion of their training, students performed a predefined peripheral endovascular intervention, which was assessed by an experienced interventionalist. Furthermore, participants were prompted to complete a survey at both the beginning and the end of the study. Both groups exhibited significant improvements in PT, FT, CV usage, global rating scale (GRS) scores, and performance-specific rating scale scores. However, no significant difference was observed between the 2 groups. The simulator training notably boosted the trainee's self-confidence. Simulator training not only enhances metric performance parameters but also improves performance as assessed by both GRS and PSRS. Whether the training is conducted in block sessions or weekly intervals did not affect its efficacy. As such, the training format can be tailored to best fit the specific circumstances and setting.

Direct antiglobulin test type, red blood cell distribution width, and estimated glomerular filtration rate for early prediction of in-hospital mortality of patients with COVID-19

ObjectiveThis study aimed to investigate the correlation between COVID-19 and the direct antiglobulin test (DAT) and establish an in-hospital mortality risk predictive model based on the DAT type, which can be used for the early prediction of inpatients with COVID-19. MethodsIn this study, 502 patients admitted to our hospital who underwent DAT testing from January 29 to February 8, 2023, were included (252 DAT-positive and 250 DAT-negative). Among them, 241 cases of COVID-19 were screened(171 DAT-positive and 70 DAT-negative), clinical and laboratory indicators were compared between DAT-positive and DAT-negative groups. Univariate and multivariate logistic regression analysis, the Kaplan-Meier survival curve and receiver operating curves were used to explore the relation between the DAT type and in-hospital mortality of patients with COVID-19. ResultsThe proportion of confirmed COVID-19 cases was higher in the DAT-positive group than in the DAT-negative group (67.9 % vs. 28.0 %, P < 0.05). Patients with COVID-19 in the DAT-positive group had higher age-adjusted Charlson comorbidity index scores, red blood cell distribution width (RDW), lactate dehydrogenase, prothrombin time, D-dimer, creatinine, and high-sensitive cardiac troponin T levels than the negative group (P < 0.05), In contrast, hemoglobin and estimated glomerular filtration rate (eGFR) levels were lower in the DAT-positive group. The DAT-positive group also had a higher red blood cell usage volume and in-hospital mortality rate than the DAT-negative group. The mortality rate of patients with COVID-19 with both IgG and C3d positive was higher than that of the other groups. Multivariate logistic regression analysis showed that RDW and eGFR were associated with mortality in patients with COVID-19. The combined predictive model of DAT type, RDW, and eGFR showed an area under the curve of 0.782, sensitivity of 0.769, and specificity of 0.712 in predicting in-hospital mortality risk in patients with COVID-19. ConclusionThe established predictive model for in-hospital mortality risk of patients with COVID-19 based on DAT type, RDW, and eGFR can provide a basis for timely intervention to reduce the mortality rates of patients with COVID-19. This model is accessible at https://jijijiduola.shinyapps.io/0531// for research purposes.

COVID-19 restrictions limited interactions of people and resulted in lowered E. coli antimicrobial resistance rates.

Antibiotic resistance is rising globally and is a major One Health problem. How much person-to-person transmission or 'contagion' contributes to the spread of resistant strains compared with antibiotic usage remains unclear. As part of its COVID-19 response, Australia introduced strict people movement restrictions in early 2020. Along with internal lockdown measures, movement of people into Australia from overseas was severely restricted. These circumstances provided a unique opportunity to examine the association of people movements with changes in resistance rates. Monthly resistance data on over 646 000 Escherichia coli urine isolates from 2016 till 2023 were modelled for statistical changes in resistance trends during pre-lockdown, lockdown and post-lockdown periods. Data were available for three clinical contexts (community, hospital and aged-care facilities). Data were also available for antibiotic usage volumes and movements of people into Australia. In 2020, arrivals into Australia decreased by >95%. Antibiotic community use fell by >20%. There were sharp falls in trend rates of resistance for all antibiotics examined after restrictions were instituted. This fall in trend rates of resistance persisted during restrictions. Notably, trend rates of resistance fell in all three clinical contexts. After removal of restrictions, an upsurge in trend rates of resistance was seen for nearly all antibiotics but with no matching upsurge in antibiotic use. Restricting the movement of people appeared to have a dramatic effect on resistance rates in E. coli. The resulting reduced person-to-person interactions seems more closely associated with changes in antibiotic resistance than antibiotic usage patterns.

Long-term functional outcomes and predictors of efficacy in thulium laser enucleation of the prostate (ThuLEP) for benign prostatic hyperplasia (BPH): a retrospective observational study.

Benign prostatic hyperplasia (BPH) is a common urological condition affecting aging men worldwide. Among the treatment options available for BPH, transurethral resection of the prostate (TURP) is the gold-standard invasive intervention. To reduce the TURP-related non-negligible morbidity, loss-of-ejaculation rate, hospitalization, blood loss and catheterization time several laser techniques have been developed, such as the Thulium Laser Enucleation of the Prostate (ThuLEP). To investigate the efficacy outcomes of the ThuLEP as a treatment option for benign prostatic hyperplasia (BPH) we performed a retrospective observational study at Moriggia Pelascini Hospital (Como, Italy) between January 2015 and September 2018. We included 265 patients who underwent ThuLEP at a specific hospital between defined dates. Data on various parameters, including post-void residue volume, peak urinary flow rate (Qmax), International Prostate Symptom Score (IPSS) for urinary symptoms, IPSS Quality of Life (QoL) score, and International Index of Erectile Function (IIEF) score for erectile dysfunction, were collected at baseline and follow-up. The analysis revealed significant improvements in voiding efficiency, urinary flow, urinary symptoms, quality of life, and erectile function following ThuLEP. Furthermore, certain baseline characteristics, such as post-void residue, peak urinary flow rate, age, prostate volume, and aspirin usage, were found to influence treatment outcomes. Despite the study's limitations, these findings contribute to understanding ThuLEP's effectiveness in managing BPH and can aid in making informed clinical decisions for patient care. Prospective studies with longer follow-up periods are recommended to validate and extend these results.

An Analysis of Water Used and Wasted at Operating Room (OR) Scrub Sinks and Call to Action for Reducing Water Waste.

To model the volume of water used and wasted during wet scrubs at operating room (OR) scrub sinks and identify factors for reducing water waste. Wasteful consumption of water by US healthcare systems has not been well characterized. This is a two-component observational study. The first was an observational study of handwashing practices and water usage at scrub sinks in the OR at a single medical center. The second component was a series of two anonymous surveys of surgeons and OR staff to assess hand scrub practices and perspectives. Data from both components were used to estimate the volume of water used and wasted annually at OR scrub sinks. The median total volume of water wasted at OR scrub sinks for 34,554 cases over one year is 337,595.6 L (interquartile range 139,010.0;911,210.5). This represents approximately 34.2% of the total volume of water usage associated with wet scrubs (i.e.,water used during scrubbing and wasted after the conclusion of the scrub). Other pertinent findings are that attending surgeons and OR staff perform water scrubs in 25.9% of cases; there are significant differences in scrub type preferences among OR users; the median volume of water wasted in a single wet scrub at timer-controlled sinks is 10 L; and significantly more water is wasted at timer-controlled sinks than knee-operated sinks. OR wet scrubs are a source of enormous water waste. We identified scrub sink characteristics and OR user beliefs and behaviors as modifiable factors for water waste reduction. We encourage all institutions and OR users to carefully examine their facility characteristics and practices to implement plans that will conserve water without compromising patient safety.

Data and Energy Impacts of Intelligent Transportation—A Review

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles.

Mechanistic Understanding of Sieving Lithium Ions Using a Biobased Sorbent Technology for Sustainable Lithium Reclamation and Cleansing Brines.

Low-cost environmentally benign materials that can be produced in a large scale to extract lithium from brine resources could drive the lithium market toward a clean technology with high lithium recovery and production. Herein, we have investigated the utilization of a novel, environmentally benign, and low-cost biobased sorbent for the extraction of lithium from lithium-rich solutions. This biobased molecular sieving sorbent, iron(III)-tannate (Fe(III)-TA), belongs to a novel class of coordination polymer frameworks derived from a natural polyphenol-tannic acid (TA)-coordinated with iron(III) metal cations. Its lithium adsorption and kinetic isotherm studies conducted using lithium-rich aqueous solutions confirm the sorbent's dual function for lithium sieving via physisorption, chemisorption, and mass transfer diffusion processes. The adsorption equilibrium and kinetic isotherm models combined with the external and internal mass transfer diffusion models reveal a mechanistic pathway for lithium-ion adsorption. Aiding by forming a fluid film for external mass transfer diffusion of lithium ions, analytes adsorb onto the sorbent surface via physisorption and chemisorption followed by the internal mass transfer diffusion, occupying lithium ions in the sorbent's pores. The lithium adsorption efficiency studies conducted for brines with different concentrations of interference alkali and alkaline cations evidence that the sorbent's affinity for lithium ions strongly depends on the analyte concentration. The results evidence that the sorbent has the ability to lower the brine's salinity and significantly reduces the ratios of Mg/Li and Ca/Li by 4-fold and 10-fold, respectively, yielding lithium-rich solutions. Thus, implementing this innovative biobased sorbent technology as an add-in step into traditional lithium extraction and refining processes, one can design a cost-effective pathway to yield lithium-rich leachate by reducing the Mg/Li and Ca/Li ratio. Nonetheless, the present work demonstrates that Fe(III)-tannate is an effective multifunctional sorbent for sieving lithium from lithium-rich aqueous solutions as well as for desalinating brine resources to recover usable water. Thus, this biobased sorbent offers the possibility of effective application of lithium reclamation and remediation of brine, mitigating the environmental impact of brine discharge and large volume of freshwater usage for lithium extraction and refining.

Optimizing additive manufacturing: Minimizing support structures through constraint-based design

Support structures are critical in Additive Manufacturing (AM) but pose challenges due to increased time and material costs for post-processing. This study introduces a novel constraint-based strategy to refine structural parameters while reducing reliance on support structures. By integrating this technique with topology optimization, component angulations are calibrated to align with AM constraints, enabling optimal designs without the need for support. Implementation of this constraint was done using MATLAB, transferring models to Cura software for support identification during printing. To validate the results, stress distribution was scrutinized in the ABAQUS environment, revealing a significant reduction in stress levels due to the proposed methodology, consistent with existing studies on stress reduction phenomena. Moreover, the study demonstrates a substantial decrease in material volume usage, leading to cost savings and promoting sustainable manufacturing practices. This reduction in material usage also enhances structural integrity, minimizing the risk of failure and improving overall performance.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Abstract 93: Upgrading to Endovascular Therapy-Performing Hospital Increases Acute Ischemic Stroke Admissions

Introduction: Given the impact of endovascular therapy (EVT) on clinical outcomes and the use of pre-hospital bypass protocols to route acute ischemic stroke (AIS) patients to EVT-performing hospitals (EPHs), there is substantial interest in upgrading stroke center certification to become EPHs. Here, we assess trends in patient volumes of AIS admissions at three hospitals after upgrade, in comparison to one long-standing EPH and six non-EPHs within the same urban area. Methods: From our prospectively collected, multi-institutional registry, we identified consecutive AIS admissions at ten hospitals in the Houston metropolitan area from November 2017 through March 2022. One hospital had been an EPH throughout the study period (Group A: EPH 0), three transitioned to EPHs in November 2017 (Group B: EPH 1, EPH 2, EPH 3), and six were non-EPHs (Group C). Primary outcomes were trends in volume of total AIS admissions over time determined by linear regression. Secondary outcomes included trends in volumes of tPA usage, LVO admissions, and mechanical thrombectomy (MT) determined by linear regression. Results: Among 13,099 patients with AIS presenting to ten hospitals, median age was 67 [IQR 57-78], 45% were female, median NIHSS was 4 [IQR 1-11], 17.5% were given tPA, 22% had LVO, and 9.5% underwent mechanical thrombectomy. Centers that became EPH in 2017 experienced an overall increase in AIS admissions (p=0.0007) at rates of 8 per year for EPH 1 (p=0.005), 5 per year for EPH 2 (p&lt;0.001) and 4 per year for EPH 3 (p=0.11). AIS admissions did not change significantly at the original EPH (p=0.13) nor at three of the non-EPHs (p=0.24, 0.39, 0.82), and volumes decreased at the other three non-EPHs (p=0.009, 0.02, 0.09). Changes in LVO volumes did not reach significance for any group. Volumes of tPA use and MT increased for group B (p&lt;0.005) but did not change significantly for group A (p&gt;0.18), and tPA use decreased for group C (p=0.03). Conclusions: In this observational cohort study, upgrading to EPH status resulted in increasing AIS admissions over time, as well as increases in tPA use and MT. Volumes at the previously established EPH did not change significantly over the study period, while AIS admissions decreased slightly at the non-EPHs in the surrounding area.

Photothermal-driven flow with water droplets for effective removal of indoor fine particulate matters

Airborne particulate matter (PM) causes hazardous problems in human health, and water droplets have been utilized for PM removal in the air. However, since conventional wet scrubbing techniques mostly rely on mechanically generated water droplets, they have intrinsically limited in collection efficiencies of a single droplet. In addition, PM capturing strategies for removal of fine PM need to be established to enhance PM removal performance based on flow dynamics. Here, we propose a steam flow of water droplets generated by a photothermal membrane for air purification. The generated flow captures PM efficiently by the aid of the coupled effects of water droplets and hydrodynamic flow. The flow achieves high and sustainable deposition constants of 0.458 and 0.472 h−1 for PM1.0 and PM2.5 within 1 h at 4 solar intensity, which is 2.20 and 2.24 times higher than those of without solar irradiation, respectively. The deposition constants of PM using steam flow were higher than that of previous wet scrubbing methods despite a low volume of water usage. Our photothermal-driven flow with water droplets could be utilized as effective indoor air purification system that is low cost, energy-efficient, and environmentally-friendly.

Reporting Application of Groundwater Use Volume by Industrial Taxpayers

The Bandung District's Regional Revenue Agency (BAPENDA), primarily tasked with managing local taxes, currently utilizes computer technology for most of its operations, albeit not to its full potential. Currently, manual techniques that utilize paper are still utilized to report the amount of groundwater consumption by taxpayers. As a result, this can affect the accurate computation of groundwater taxes and lead to delays in distributing information regarding groundwater taxes. This study aims to develop an application that enables taxpayers to report the amount of groundwater they use efficiently. This application will also facilitate the access of groundwater usage reports by Tax Division I, hence enhancing convenience. The Final Project utilizes a descriptive research methodology, whereas the system development process employs OOAD (Object-Oriented Analysis and Design). This application system is designed using the PHP programming language and utilizes the MySQL XAMPP database. The chosen framework is CodeIgniter due to its extensive range of capabilities that facilitate the creation of robust and high-performing web applications. To summarize, this research has effectively developed an application that allows taxpayers in the Bapenda of Bandung District to submit their groundwater usage volume. This application simplifies the reporting procedure for taxpayers and assists Tax Division I in accessing groundwater usage statistics.

Efficacy and safety of intercostal nerve anastomosis in immediate subpectoral prosthetic breast reconstruction after nipple-areola-sparing mastectomy: a randomized, controlled, open-label clinical study.

This aims to investigate the efficacy and safety of intercostal nerve anastomosis among breast cancer patients who undergo immediate subpectoral prosthetic breast reconstruction after nipple-areola-sparing mastectomy. From 2022 to 2023, female patients between the ages of 20 and 60 diagnosed with stage I-IIIA breast cancer, who required and were willing to undergo immediate subpectoral prosthetic breast reconstruction after nipple-areola-sparing mastectomy, were screened and assigned to take the operation with (treatment group) or without (control group) intercostal nerve anastomosis (the nerves with appropriate length and thickness were selected from the 2nd-4th intercostal nerves, which were then dissociated and anastomosed to the posterior areola tissue). A radial incision at the surface projection of the tumor location was used. The patients' breast local sensation was assessed using Semmes-Weinstein monofilaments before the operation as well as at 10 days, 3 months, and 6 months postoperatively. Furthermore, the patients' quality of life was evaluated 6 months postoperatively using the EORTC QLQ-C30 questionnaire. Adverse events, operation duration, drainage volume, and the duration of drainage tube carrying time were also monitored and recorded. Compared to the pre-operative period, a significant decrease in local sensation was observed 10 days after surgery in both groups. However, the control group showed a significant reduction in sensation at 3 and 6 months postoperatively, while the treatment group showed noticeable recovery. A statistically significant difference (P < 0.001) in local sensation between the pre-operative and post-operative periods was observed at the final follow-up in the two groups. By the time of 3 and 6 months postoperatively, a significant difference was seen in the local sensation between the two groups. Intercostal nerve anastomosis was found to significantly improve the patients' quality of life, including emotional (P = 0.01), physical (P = 0.04), and social functioning (P = 0.02) and pain (P = 0.04). There were no significant differences in general characteristics (such as age, BMI, and subtypes). Although intercostal nerve anastomosis increased the duration of operation by around 20min (P < 0.001), it did not affect the volume or duration of postoperative drainage tube usage between the two groups. This study indicated that intercostal nerve anastomosis improved the local sensation and quality of life of patients who underwent immediate subpectoral prosthetic breast reconstruction after nipple-areola-sparing mastectomy. https://www.chictr.org.cn/showproj.html?proj=42487, identifier ChiCTR1900026340.

Рупия против мировых резервных валют в российско-индийской торговле

This article analyses possible connection between sharp depreciation of the Russian Rouble in the second half of 2023 and the growing usage of Indian Rupees for settlement of Russo-Indian trade. Some experts explained that depreciation by the ‘Rupee trap’, which was created by quick growth of Russian exports to India while the opposite flow was not significantly changed. They suggested that superior export revenues of Russian companies from exports to India were accumulated in Rupee accounts in Indian banks. Then somehow, these Rupees have penetrated the foreign exchange market in Russia and have depreciated the Rouble exchange rate. Author has analysed available statistics from both countries and came to conclusion that described problem is factitious. It is true that the usage of Rupee for invoicing in the external trade of India has increased in the last couple of years. However, the real volume of such usage is much smaller than the volume of Indian external trade. Indian Rupee is not a freely convertible currency, so exchange of Rupee assets for other currencies is a subject of a range of restrictions and quotas. Foreign exporters to India are aware of this and in practice agree to accept Rupees only in limited amounts. A number of top-ranking people from Russia and India, who were well informed about mutual trade, confirmed such practice. Therefore, there is no reason to consider that Russian exporters might accumulate too big Rupee assets. In addition, statistics of currency invoicing of Russian external trade in 2022–2023 suggest that volumes of inward and outward flows of non-reserve currencies (like Renminbi, Rupee, Dirham etc.) are about equal. This means that demand/offer of such currencies are balanced. However, for the same period, the demand for Roubles outside Russia was bigger than their offer from Russia. So all together, this does not confirm assertions that increase of Rupee (or other non-reserve currency) assets was the main cause of the sharp Rouble depreciation in 2023.