High colloidal stability ZnO nanoparticles independent on solvent polarity and their application in polymer solar cells

Significant aggregation between ZnO nanoparticles (ZnO NPs) dispersed in polar and nonpolar solvents hinders the formation of high quality thin film for the device application and impedes their excellent electron transporting ability. Herein a bifunctional coordination complex, titanium diisopropoxide bis(acetylacetonate) (Ti(acac)2) is employed as efficient stabilizer to improve colloidal stability of ZnO NPs. Acetylacetonate functionalized ZnO exhibited long-term stability and maintained its superior optical and electrical properties for months aging under ambient atmospheric condition. The functionalized ZnO NPs were then incorporated into polymer solar cells with conventional structure as n-type buffer layer. The devices exhibited nearly identical power conversion efficiency regardless of the use of fresh and old (2 months aged) NPs. Our approach provides a simple and efficient route to boost colloidal stability of ZnO NPs in both polar and nonpolar solvents, which could enable structure-independent intense studies for efficient organic and hybrid optoelectronic devices

A large‐scale meta‐analytic atlas of mental health problems prevalence during the COVID‐19 early pandemic

The COVID-19 pandemic and related restrictions can impact mental health. In order to quantify the mental health burden of COVID-19 pandemic, we conducted a systematic review and meta- analysis, searching World Health Organization COVID-19/PsycInfo/PubMed databases (09/29/2020), including observational studies reporting on mental health outcomes in any population affected by COVID-19. Primary outcomes were the prevalence of anxiety, depression, stress, sleep problems, post-traumatic symptoms. Sensitivity analyses were conducted on severe mental health problems, in high-quality studies, and in representative samples. Subgroup analyses were conducted stratified by age, sex, country income level, and COVID-19 infection status. One-hundred-seventy-three studies from February-July 2020 were included (n=502,261, median sample=948, age=34.4 years, females=63%). Ninety-one percent were cross-sectional studies, and 18.5%/57.2% were of high/moderate quality. Highest prevalence emerged for post-traumatic symptoms in COVID-19 infected people (94%), followed by behavioural problems in those with prior mental disorders (77%), fear in healthcare workers (71%), anxiety in caregivers/family members of people with COVID-19 (42%), general health/social contact/passive coping style in the general population (38%), depression in those with prior somatic disorders (37%), and fear in other-than-healthcare workers (29%). Females and people with COVID-19 infection had higher rates of almost all outcomes; college students/young adults of anxiety, depression, sleep problems, suicidal ideation; adults of fear and post-traumatic symptoms. Anxiety, depression, and post- traumatic symptoms were more prevalent in low-/middle-income countries, sleep problems in high-income countries. The COVID-19 pandemic adversely impacts mental health in unique manners across population subgroups. Our results inform tailored preventive strategies and interventions to mitigate current, future, and transgenerational adverse mental health of the COVID-19 pandemic

A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory

An initial public offering (IPO) is a type of public offering in which a company’s shares are sold to institutional and individual investors. While the majority of studies on IPOs have focused on the efficiency of raising capital and price adequacy in IPOs, studies on portfolio allocation strategies for IPO stocks are relatively scarce. This paper develops a machine learning investment strategy for IPO stocks based on rough set theory and a genetic algorithm (GA-rough set theory). To reduce issues of information asymmetry, we use nonfinancial data that are publicly available to individual and institutional investors in the IPO process. Based on the rule sets generated from the training sets, we conduct 120 tests with various conditions involving the target days and the partition of the training and testing sets, and we find excess returns of the constructed portfolios compared to the benchmark portfolios. Investors in IPO stocks can formulate more efficient investment strategies using our system. In this sense, the system developed in this paper contributes to the efficiency of financial markets and helps achieve sustained economic growth

Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities

Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring

Solvent-Free Polycaprolactone Dissolving Microneedles Generated via the Thermal Melting Method for the Sustained Release of Capsaicin

(1) Background: Dissolving microneedles (DMNs), a transdermal drug delivery system, have been developed to treat various diseases in a minimally invasive, painless manner. However, the currently available DMNs are based on burst release systems due to their hydrophilic backbone polymer. Although hydrophobic biodegradable polymers have been employed on DMNs for sustained release, dissolution in an organic solvent is required for fabrication of such DMNs. (2) Method: To overcome the aforementioned limitation, novel separable polycaprolactone (PCL) DMNs (SPCL-DMNs) were developed to implant a PCL-encapsulated drug into the skin. PCL is highly hydrophobic, degrades over a long time, and has a low melting point. Under thermal melting, PCL encapsulated capsaicin and could be fabricated into a DMN without the risk of toxicity from an organic solvent. (3) Results: Optimized SPCL-DMNs, containing PCL (height 498.3 ± 5.8 µm) encapsulating 86.66 ± 1.13 µg capsaicin with a 10% (w/v) polyvinyl alcohol and 20% (w/v) polyvinylpyrrolidone mixture as a base polymer, were generated. Assessment of the drug release profile revealed that this system could sustainably release capsaicin for 15 days from PCL being implanted in porcine skin. (4) Conclusion: The implantable SPCL-DMN developed here has the potential for future development of toxicity-free, sustained release DMNs

TAROGE-M: radio antenna array on antarctic high mountain for detecting near-horizontal ultra-high energy air showers

Abstract The TAROGE-M radio observatory is a self-triggered antenna array on top of the ???2700 m high Mt. Melbourne in Antarctica, designed to detect impulsive geomagnetic emission from extensive air showers induced by ultra-high energy (UHE) particles beyond 1017 eV, including cosmic rays, Earth-skimming tau neutrinos, and particularly, the ???ANITA anomalous events??? (AAE) from near and below the horizon. The six AAE discovered by the ANITA experiment have signal features similar to tau neutrinos but that hypothesis is in tension either with the interaction length predicted by Standard Model or with the flux limits set by other experiments. Their origin remains uncertain, requiring more experimental inputs for clarification. The detection concept of TAROGE-M takes advantage of a high altitude with synoptic view toward the horizon as an efficient signal collector, and the radio quietness as well as strong and near vertical geomagnetic field in Antarctica, enhancing the relative radio signal strength. This approach has a low energy threshold, high duty cycle, and is easy to extend for quickly enlarging statistics. Here we report experimental results from the first TAROGE-M station deployed in January 2020, corresponding to approximately one month of livetime. The station consists of six receiving antennas operating at 180-450 MHz, and can reconstruct source directions of impulsive events with an angular resolution of ???0.3??, calibrated in situ with a drone-borne pulser system. To demonstrate TAROGE-M's ability to detect UHE air showers, a search for cosmic ray signals in 25.3-days of data together with the detection simulation were conducted, resulting in seven identified candidates. The detected events have a mean reconstructed energy of 0.95-0.31+0.46 EeV and zenith angles ranging from 25?? to 82??, with both distributions agreeing with the simulations, indicating an energy threshold at about 0.3 EeV. The estimated cosmic ray flux at that energy is 1.2-0.9+0.7 ?? 10-16 eV-1 km-2 yr-1 sr-1, also consistent with results of other experiments. The TAROGE-M sensitivity to AAEs is approximated by the tau neutrino exposure with simulations, which suggests comparable sensitivity as ANITA's at around 1 EeV energy with a few station-years of operation. These first results verified the station design and performance in a polar and high-altitude environment, and are promising for further discovery of tau neutrinos and AAEs after an extension in the near future

Synthesis of Pt-CeVO4 nanocomposites and their enhanced photocatalytic hydrogen evolution activity under sunlight

The environmental pollution problem caused by fossil fuels, a nonrecyclable resource, becomes more serious every year. Therefore, the development of technology to produce alternative energy in a carbon–neutral way is urgent. In this regard, solar-powered H2 production from water using particulate photocatalysts is considered the most economical and robust approach to producing carbon–neutral H2 fuels. Using Pt-CeVO4 nanocomposites with controllable amounts of Pt nanoparticles (NPs) on CeVO4 as a photocatalyst, a superior H2 production rate of 220.68 mmol g-1h−1 was achieved, which was five times higher than that of Pt NPs. In the Pt-CeVO4 catalyst, CeVO4 affected the electron density of Pt through upward band bending, which dramatically improved the H2 generation ability. Our research is a competent study that satisfies the dual purpose of 1) achieving maximum reaction efficiency using a small amount of noble metal while providing important insights that 2) proper contact of metal and semiconductor materials can exponentially enhance photocatalytic performance. © 2023 The Korean Society of Industrial and Engineering ChemistryFALS

Occupational exposure and risk assessment for agricultural workers of thiamethoxam in vineyards

© 2022 The AuthorsDermal & inhalation exposure was examined and according to these results, risk assessment of agricultural workers to thiamethoxam was performed during pesticide mixing/loading and hand-held sprayer application (11 replicates, each of about 1000 L of spray suspension) in vineyards. For the whole body dosimetry (WBD), clothing (Outer and inner), gauze, and nitrile gloves were analyzed to determine dermal exposure using whole-body dosimetry exposure protocol. The inhalation exposure was measured using a glass fiber filter with an IOM sampler. Analytical method validation of exposure matrices was evaluated including the field recovery and breakthrough test. The dermal exposure amount during mixing/loading was 0.163 mg (0.0004% of the total mixed/loaded active ingredient [a.i.]), whereas there was no inhalation exposure. The gloves (0.154 mg, 94.5%) were the most exposed body parts followed by the chest and stomach (0.009 mg, 5.5%). During application, the dermal and inhalation exposure amounts were 32.3 mg (0.07% of the total applied a.i.) and 10.8 µg (2.4 × 10−6% of the total applied a.i), respectively. The shin (35.1%) had the highest exposure to pesticides, followed by the chest & stomach (15.6%) and pelvis (12.6%). In case of mixing/loading, the amounts of actual dermal exposure (ADE) and actual inhalation exposure (AIE) were 0.0 and 0.0 μg/day, while those of ADE and AIE were 4707.6 and 15.8 μg/day for application. In risk assessment of the two different scenarios, the risk index was much lower than 1 (mixing/loading:0.000, application:0.014), indicating that vineyard workers are at low risk of thiamethoxam exposure. To determine the validity of the risk assessment using WBD method, the urinary metabolite was analyzed. Comparison of biomonitoring data and WBD exposure data show a reliable correlation (r = 0.885, p = 0.0003), suggesting that these are suitable methods to estimate exposure.N

DataSheet_1_Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.docx

Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.</p

Film-trigger applicator (FTA) for improved skin penetration of microneedle using punching force of carboxymethyl cellulose film acting as a microneedle applicator

Abstract Background Dissolving microneedle (DMN) is a transdermal drug delivery system that creates pore in the skin and directly deliver drug through the pore channel. DMN is considered as one of the promising system alternatives to injection because it is minimally invasive and free from needle-related issues. However, traditional DMN patch system has limitations of incomplete insertion and need of complex external devices. Here, we designed film-trigger applicator (FTA) system that successfully delivered DMN inside the skin layers using fracture energy of carboxymethyl cellulose (CMC) film via micropillars. We highlighted advantages of FTA system in DMN delivery compared with DMN patch, including that the film itself can act as DMN applicator. Methods FTA system consists of DMNs fabricated on the CMC film, DMN array holder having holes aligned to DMN array, and micropillars prepared using general purpose polystyrene. We analyzed punching force on the film by micropillars until the film puncture point at different CMC film concentrations and micropillar diameters. We also compared drug delivery efficiency using rhodamine B fluorescence diffusion and skin penetration using optical coherence tomography (OCT) of FTA with those of conventional DMN patch. In vivo experiments were conducted to evaluate DMN delivery efficiency using C57BL/6 mice and insulin as a model drug. Results FTA system showed enhanced delivery efficiency compared with that of the existing DMN patch system. We concluded CMC film as a successful DMN applicator as it showed enhanced DMN penetration in OCT and rhodamine B diffusion studies. Further, we applied FTA on shaved mouse dorsal skin and observed successful skin penetration. The FTA group showed higher level of plasma insulin in vivo than that of the DMN patch group. Conclusions FTA system consisting of simple polymer film and micropillars showed enhanced DMN delivery than that of the existing DMN patch system. Because FTA works with simple finger force without sticky patch and external devices, FTA is a novel and promising platform to overcome the limitations of conventional microneedle patch delivery system; we suggest FTA as a next generation applicator for microneedle application in the future