EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10-10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms.

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge-discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery's SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

ASOptimizer: Optimizing antisense oligonucleotides through deep learning for IDO1 gene regulation.

Recent studies have highlighted the effectiveness of using antisense oligonucleotides (ASOs) for cellular RNA regulation, including targets that are considered undruggable; however, manually designing optimal ASO sequences can be labor intensive and time consuming, which potentially limits their broader application. To address this challenge, we introduce a platform, the ASOptimizer, a deep-learning-based framework that efficiently designs ASOs at a low cost. This platform not only selects the most efficient mRNA target sites but also optimizes the chemical modifications for enhanced performance. Indoleamine 2,3-dioxygenase 1 (IDO1) promotes cancer survival by depleting tryptophan and producing kynurenine, leading to immunosuppression through the aryl-hydrocarbon receptor (Ahr) pathway within the tumor microenvironment. We used ASOptimizer to identify ASOs that target IDO1 mRNA as potential cancer therapeutics. Our methodology consists of two stages: sequence engineering and chemical engineering. During the sequence-engineering stage, we optimized and predicted ASO sequences that could target IDO1 mRNA efficiently. In the chemical-engineering stage, we further refined these ASOs to enhance their inhibitory activity while reducing their potential cytotoxicity. In conclusion, our research demonstrates the potential of ASOptimizer for identifying ASOs with improved efficacy and safety.

Cross-cultural equivalence of the Kessler Psychological Distress Scale (K10) across four African countries in a multi-national study of adults.

The Kessler Psychological Distress Scale (K10) has been widely used to screen psychological distress across many countries. However, its performance has not been extensively studied in Africa. The present study sought to evaluate and compare measurement properties of the K10 across four African countries: Ethiopia, Kenya, Uganda, and South Africa. Our hypothesis is that the measure will show equivalence across all. Data are drawn from a neuropsychiatric genetic study among adult participants (N = 9179) from general medical settings in Ethiopia (n = 1928), Kenya (n = 2556), Uganda (n = 2104), and South Africa (n = 2591). A unidimensional model with correlated errors was tested for equivalence across study countries using confirmatory factor analyses and the alignment optimization method. Results displayed 30 % noninvariance (i.e., variation) for both intercepts and factor loadings across all countries. Monte Carlo simulations showed a correlation of 0.998, a good replication of population values, indicating minimal noninvariance, or variation. Items "so nervous," "lack of energy/effortful tasks," and "tired" were consistently equivalent for intercepts and factor loadings, respectively. However, items "depressed" and "so depressed" consistently differed across study countries (R2 = 0) for intercepts and factor loadings for both items. The K10 scale likely functions equivalently across the four countries for most items, except "depressed" and "so depressed." Differences in K10 items were more common in Kenya and Ethiopia, suggesting cultural context may influence the interpretation of some items and the potential need for cultural adaptations in these countries.

Optimizing tensile strength and energy consumption for FDM through Mixed-Integer Nonlinear Multi-objective optimization and design of experiments.

This study presents a methodology for optimizing key parameters of a fused deposition modeling (FDM) printer to minimize energy consumption (EC) while exceeding a specified tensile strength (TS) threshold. Employing Design of Experiments (DoE) with Taguchi and Response Surface analysis, we identify influential parameters affecting TS and EC. A Mixed-Integer Nonlinear Multi-Objective Optimization model is then utilized to balance TS and EC, resulting in optimal parameter values. Validation using fabricated specimens demonstrates less than 5 % error in Tensile Strength and less than 2 % error in Energy Consumption, confirming the efficacy of the proposed methodology.

Managing energy transition alongside environmental protection by making use of AI-led butanol powered SI engine optimization in compliance with SDGs.

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions. The emission and performance parameters were ascertained for eight distinct butanol-gasoline fuel blends. The oxygenated butanol substantially increases engine efficiency and boosts power with lower fuel consumption. The carbon emissions were also observed to be lower in comparison with gasoline. Furthermore, the Artificial Intelligence (AI) approach was used in predicting engine performance running on the butanol blends. The correlation coefficients for the data training, validation, and testing were found to be 0.99986, 0.99942, and 0.99872, respectively. It was confirmed that the ANN predicted results were in accordance with the established statistical criteria. ANN was paired with Response Surface Methodology (RSM) technique to comprehend the influence of the sole design parameters along with their statistical interactions controlling the responses. Similarly, the R2 value of responses in case of RSM were close to unity and mean relative errors (MRE) were confined under specified range. A comparative study between ANN and RSM models unveiled that the ANN model should be preferred. Therefore, a joint utilization of the RSM and ANN can be more effective for reliable statistical interactions and predictions.

Decoding the impact of fiscal decentralization on urban pollution intensity in China: A spatial econometric analysis.

Utilizing city-level data from China, the paper employs a spatial econometric analysis to investigate the impact of fiscal decentralization on urban pollution. Empirical evidence indicates: (1) In the context of the emphasis of ecological civilization construction in China, an increase of fiscal autonomy for local governments is conducive to mitigating urban pollution intensity. Specifically, fiscal decentralization in one city not only promotes a reduction in local pollution intensity but alleviates environmental pollution problems in adjacent cities through spatial spillover effects. (2) Industrial structure upgrading and green technology progress become crucial measures for local governments to realize pollution reduction targets through fiscal expenditure. (3) Heterogeneity analysis reveals that the positive significance of decentralization is most prominent in the eastern China, while local governments with fiscal autonomy in central region tend to transfer pollution to neighboring cities. (4) There is a threshold characteristic for fiscal decentralization to promote a reduction in urban pollution intensity, and its marginal effect becomes more significant accompanied by continuous introduction of sophisticated foreign direct investment. Finally, the paper summarizes the potential significance of fiscal decentralization among Chinese local governments against the background of "Chinese-style decentralization" and proposes corresponding policy recommendations.

Investigating knowledge of DRLs, image quality and radiation dose in PET/CT and CT imaging among medical imaging professionals.

Objective: To investigate the knowledge of diagnostic reference levels (DRLs), image quality, radiation dose and protocol parameters among Jordanian medical imaging professionals (MIPs) involved in PET/CT and CT scan procedures. Materials and methods: A questionnaire was designed and distributed to MIPs in Jordan. The survey comprised four sections: demographic data, MIP knowledge on dose/protocol parameters, image quality, and DRLs. Statistical analyses were performed utilizing Pearson's correlation, t-tests, ANOVA, and linear regression, with a significance level of 95 % and a p-value threshold of <0.05. Results: The study involved 147 participants. Most respondents were male (76.2 %), and most were aged 26-35 years (44.2 %). Approximately 51 % held a bachelor's degree, and the most common range of experience was 3-5 years (28.6 %). Participants showed a moderate level of knowledge regarding dose and protocol parameters, with a mean score of 61.8 %. The mean scores for knowledge of image quality and DRLs were 45.2 % and 44.8 %, respectively. The age group of the MIPs and the total experience were found to have a significant impact on the knowledge of the dose and protocol parameters, as well as the DRLs. Additionally, experience was found to have a significant influence on knowledge of the dose and protocol parameters. The study revealed a positive and significant effect of MIPs' knowledge of dose/protocol parameters and image quality on their knowledge of DRLs. Conclusions: This study indicates that professionals across five specialties who are engaged in PET/CT and CT imaging possess a moderate understanding of dosage and protocol parameters. However, there is a notable gap in knowledge regarding DRLs and image quality. To address this issue, it is recommended that MIPs actively engage in educational programs emphasizing exposure parameters and their impact on image quality. Additionally, access to comprehensive education and training programs will enable MIPs to grasp the complexities of DRLs and their implications, facilitating their implementation in clinical practice.

Grid codes vs. multi-scale, multi-field place codes for space.

Introduction: Recent work on bats flying over long distances has revealed that single hippocampal cells have multiple place fields of different sizes. At the network level, a multi-scale, multi-field place cell code outperforms classical single-scale, single-field place codes, yet the performance boundaries of such a code remain an open question. In particular, it is unknown how general multi-field codes compare to a highly regular grid code, in which cells form distinct modules with different scales. Methods: In this work, we address the coding properties of theoretical spatial coding models with rigorous analyses of comprehensive simulations. Starting from a multi-scale, multi-field network, we performed evolutionary optimization. The resulting multi-field networks sometimes retained the multi-scale property at the single-cell level but most often converged to a single scale, with all place fields in a given cell having the same size. We compared the results against a single-scale single-field code and a one-dimensional grid code, focusing on two main characteristics: the performance of the code itself and the dynamics of the network generating it. Results: Our simulation experiments revealed that, under normal conditions, a regular grid code outperforms all other codes with respect to decoding accuracy, achieving a given precision with fewer neurons and fields. In contrast, multi-field codes are more robust against noise and lesions, such as random drop-out of neurons, given that the significantly higher number of fields provides redundancy. Contrary to our expectations, the network dynamics of all models, from the original multi-scale models before optimization to the multi-field models that resulted from optimization, did not maintain activity bumps at their original locations when a position-specific external input was removed. Discussion: Optimized multi-field codes appear to strike a compromise between a place code and a grid code that reflects a trade-off between accurate positional encoding and robustness. Surprisingly, the recurrent neural network models we implemented and optimized for either multi- or single-scale, multi-field codes did not intrinsically produce a persistent "memory" of attractor states. These models, therefore, were not continuous attractor networks.

FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.

COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classification precision. This article thus proposes a fuzzy-based adaptive convolution neural network (FACNN) model to improve the detection precision by confining the false rates. The feature extraction process between the successive regions is validated using a fuzzy process that classifies labeled and unknown pixels. The membership functions are derived based on high precision features for detection and false rate suppression process. The convolution neural network process is responsible for increasing detection precision through recurrent training based on feature availability. This availability analysis is verified using fuzzy derivatives under local variances. Based on variance-reduced features, the appropriate regions with labeled and unknown features are used for normal or infected classification. Thus, the proposed FACNN improves accuracy, precision, and feature extraction by 14.36%, 8.74%, and 12.35%, respectively. This model reduces the false rate and extraction time by 10.35% and 10.66%, respectively.

Production and testing of mahua oil-based biodiesel synthesized through heterogeneous catalyst using experimental and numerical method.

A two-step treatment  of mahua oil was conducted to synthesize mahua biodiesel using heterogeneous biomass-based catalyst derived from mahua shell. Mahua oil having higher free fatty acid (FFA) content (about 19%) was esterified to reduce the FFA content up to 1%. The esterification process was carried out using 200 mL mahua oil, 5:1 molar ratio (methanol:oil), and 2.25 weight% of H2SO4 at a temperature of 60 °C for 3 h. Post esterification, a set of 16 experiments were created using a Box-Behnken design (BBD)-based response surface methodology (RSM) approach to conduct the transesterification of the esterified oil. Molar ratio, catalyst loading, reaction temperature, and reaction time were the four input variables chosen for the design of experiments. The optimized conditions for maximum biodiesel yield (87.7%) were found to be 14.88 molar ratio, 3.578% catalyst loading, 69.7 °C reaction temperature, and 81.9 min reaction time. The Diesel RK engine simulation tool which was experimentally validated for baseline diesel fuel was used for numerical simulation of mahua biodiesel. The performance, combustion, and emission behavior of mahua biodiesel analyzed using numerical simulation presented the sustainability of mahua biodiesel as an alternate fuel.

Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.

PURPOSE: For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. METHODS: By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. RESULTS: The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. CONCLUSION: We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.

Solving the waste bin location problem with uncertain waste generation rate: A bi-objective robust optimization approach.

An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

An effective and comprehensive optimization strategy for preparing Ginkgo biloba leaf extract enriched in shikimic acid by macroporous resin column chromatography.

INTRODUCTION: Previously reported preparation methods of Ginkgo biloba leaf extract (EGBL) have mainly focused on the enrichment of flavonoid glycosides (FG) and terpene trilactones (TT), which led to the underutilization of G. biloba leaves (GBL). OBJECTIVES: To make full use of GBL, in this study, a comprehensive optimization strategy for preparing EGBL by macroporous resin column chromatography was proposed and applied to enrich FG, TT, and shikimic acid (SA) from GBL. METHODOLOGY: Initially, the static adsorption and desorption were executed to select suitable resin. Then, the influences of solution pH were investigated by the static and dynamic adsorption. Subsequently, eight process parameters were systematically investigated via a definitive screening design (DSD). After verification experiments, scale-up enrichment was carried out, investigating the feasibility of the developed strategy for application on an industrial scale. RESULTS: It was found that XDA1 was the most appropriate adsorbent for the preparation of EGBL at solution pH 2.0. Furthermore, based on the constraints of the desired quality attributes, the optimized ranges of operating parameters were successfully acquired, and the verification experiments demonstrated the accuracy and reliability of using DSD to investigate the chromatography process for the preparation of EGBL. Finally, magnified experiments were successfully performed, obtaining the EGBL containing 26.54% FG, 8.96% TT, and 10.70% SA, which reached the SA level of EGB761, an international standard EGBL. CONCLUSION: The present study not only provided an efficient and convenient approach for the preparation of EGBL enriched in SA but also accelerated efforts to high-value utilization of GBL.

Optimization studies on laccase activity of Proteus mirabilis isolated from treatment sludge of textile industry factories : Optimization of laccase activity of Proteus mirabilis.

Laccase is an exothermic enzyme with copper in its structure and has an important role in biodegradation by providing oxidation of phenolic compounds and aromatic amines and decomposing lignin. The aim of this study is to reach maximum laccase enzyme activity with minimum cost and energy through optimization studies of Proteusmirabilis isolated from treatment sludge of a textile factory. In order to increase the laccase enzyme activities of the isolates, medium and culture conditions were optimized with the study of carbon (Glucose, Fructose, Sodium Acetate, Carboxymethylcellulose, Xylose) and nitrogen sources (Potassium nitrate, Yeast Extract, Peptone From Soybean, Bacteriological Peptone), incubation time, pH, temperature and Copper(II) sulfate concentration then according to the results obtained. Response Surface Method (RSM) was performed on six different variables with three level. According to the data obtained from the RSM, the maximum laccase enzyme activity is reached at pH 7.77, temperature 30.03oC, 0.5 g/L CuSO4, 0.5 g/L fructose and 0.082 g/L yeast extract conditions. After all, the laccase activity increased 2.7 times. As a result, laccase activity of P. mirabilis can be increased by optimization studies. The information obtained as a result of the literature studies is that the laccase enzymes produced in laboratory and industrial scale are costly and their amounts are low. This study is important in terms of obtaining more laccase activity from P.mirabilis with less cost and energy.

Optimization of the fermentation media and growth conditions of Bacillus velezensis BHZ-29 using a Plackett-Burman design experiment combined with response surface methodology.

Introduction: Bacillus velezensis occurs extensively in the soil environment. It produces a range of antimicrobial compounds that play an important role in the field of biological control. However, during the actual application process it is often affected by factors such as the medium formulation and fermentation conditions, and therefore biocontrol measures often do not achieve their expected outcomes. Methods: In this study, the B. velezensis BHZ-29 strain was used as the research object. The carbon and nitrogen sources, and inorganic salts that affect the number of viable bacteria and antibacterial potency of B. velezensis BHZ-29, were screened by a single factor test. A Plackett-Burman design experiment was conducted to determine the significant factors affecting the number of viable bacteria and antibacterial potency, and a Box-Behnken design experiment was used to obtain the optimal growth of B. velezensis BHZ-29. The medium formula that produced the highest number of viable bacteria and most antibacterial substances was determined. The initial pH, temperature, amount of inoculant, liquid volume, shaking speed, and culture time were determined by a single factor test. The factors that had a significant influence on the number of viable bacteria of B. velezensis BHZ-29 were selected by an orthogonal test. A Box-Behnken design experiment was conducted to obtain the optimal fermentation conditions, and highest number of viable bacteria and antibacterial titer. Results: Molasses, peptone, and magnesium sulfate had significant effects on the viable count and antibacterial titer of B. velezensis BHZ-29. The viable count of B. velezensis BHZ-29 increased from 7.83 × 109 to 2.17 × 1010 CFU/mL, and the antibacterial titer increased from 111.67 to 153.13 mm/mL when the optimal media were used. The optimal fermentation conditions for B. velezensis BHZ-29 were as follows: temperature 25.57°C, pH 7.23, culture time 95.90 h, rotation speed 160 rpm, amount of inoculant 2%, and liquid volume 100 ml. After the optimization of fermentation conditions, the number of viable bacteria increased to 3.39 × 1010 CFU/mL, and the bacteriostatic titer increased to 158.85 mm/ml.The plant height and leaf number of cotton plants treated with BHZ-29 fermentation broth were higher than those of cotton inoculated with Verticillium dahliae. The number of bacteria was 1.15 × 107 CFU/g, and the number of fungi was 1.60 × 105 spores/g. The disease index of the cotton seedlings treated with the optimized fermentation broth was 2.2, and a control effect of 93.8% was achieved. B. velezensis BHZ-29 could reduce the disease index of cotton Verticillium wilt and had a controlling effect on the disease. The best effect was achieved in the treatment group with an inoculation concentration of 2 × 108 CFU/ml, the disease index was 14.50, and a control effect of 84.18% was achieved. Discussion: The fermentation process parameters of the number of viable bacteria and antibacterial titer by strain B. velezensis BHZ-29 were optimized to lay a foundation for the practical production and application of strain B. velezensis BHZ-29 in agriculture.

Continual improvement of CRISPR-induced multiplex mutagenesis in Arabidopsis.

CRISPR/Cas9 is currently the most powerful tool to generate mutations in plant genomes and more efficient tools are needed as the scale of experiments increases. In the model plant Arabidopsis, the choice of the promoter driving Cas9 expression is critical to generate germline mutations. Several optimal promoters have been reported. However, it is unclear which promoter is ideal as they have not been thoroughly tested side by side. Furthermore, most plant vectors still use one of the two Cas9 nuclear localization sequence (NLS) configurations initially reported. We genotyped more than 6000 Arabidopsis T2 plants to test seven promoters and six types of NLSs across 14 targets to systematically improve the generation of single and multiplex inheritable mutations. We found that the RPS5A promoter and bipartite NLS were individually the most efficient components. When combined, 99% of T2 plants contained at least one knockout (KO) mutation and 84% contained 4- to 7-plex KOs, the highest multiplexing KO rate in Arabidopsis to date. These optimizations will be useful to generate higher-order KOs in the germline of Arabidopsis and will likely be applicable to other CRISPR systems as well.

Life regression based patch slimming for vision transformers.

Vision transformers have achieved remarkable success in computer vision tasks by using multi-head self-attention modules to capture long-range dependencies within images. However, the high inference computation cost poses a new challenge. Several methods have been proposed to address this problem, mainly by slimming patches. In the inference stage, these methods classify patches into two classes, one to keep and the other to discard in multiple layers. This approach results in additional computation at every layer where patches are discarded, which hinders inference acceleration. In this study, we tackle the patch slimming problem from a different perspective by proposing a life regression module that determines the lifespan of each image patch in one go. During inference, the patch is discarded once the current layer index exceeds its life. Our proposed method avoids additional computation and parameters in multiple layers to enhance inference speed while maintaining competitive performance. Additionally, our approach1 requires fewer training epochs than other patch slimming methods.

Optimization of Hypericum Perforatum Microencapsulation Process by Spray Drying Method.

Hypericum perforatum (HP) contains valuable and beneficial bioactive compounds that have been used to treat or prevent several illnesses. Encapsulation technology offers protection of the active compounds and facilitates to expose of the biologically active compounds in a controlled mechanism. Microcapsulation of the hydroalcoholic gum arabic and maltodextrin have hot been used as wall materials in the encapsulation of HP extract. Therefore, the optimum microencapsulation parameters of Hypericum perforatum (HP) hydroalcoholic extract were determined using response surface methodology (RSM) for the evaluation of HP extract. Three levels of three independent variables were screened using the one-way ANOVA. Five responses were monitored, including total phenolic content (TPC), 2,2-Diphenyl-1-picrylhydrazyl (DPPH), carr index (CI), hausner ratio (HR), and solubility. Optimum drying conditions for Hypericum perforatum microcapsules (HPMs) were determined: 180 °C for inlet air temperature, 1.04/1 for ratio of maltodextrin to gum arabic (w/w), and 1.98/1 for coating to core material ratio (w/w). TPC, antioxidant activity, CI, HR, and solubility values were specified as 316.531 (mg/g GAE), 81.912%, 6.074, 1.066, and 35.017%, respectively, under the optimized conditions. The major compounds of Hypericum perforatum (hypericin and pseudohypericin) extract were determined as 4.19 µg/g microcapsule and 15.09 µg/g microcapsule, respectively. Scanning electron microscope (SEM) analysis revealed that the mean particle diameter of the HPMs was 20.36 µm. Based on these results, microencapsulation of HPMs by spray drying is a viable technique which protects the bioactive compounds of HP leaves, facilitating its application in the pharmaceutical, cosmetic, and food industries.

Unleashing the potential of Cajanus cajun biochar polymer composite for Cu (II) removal: mechanism, modification, and application.

This study introduces a cost-effective approach to fabricating a porous and ionically surface-modified biochar-based alginate polymer networks composite (SBPC) through air drying. The study critically analyzes the role and concentrations of various components in the success of SBPC. Characterization techniques were employed to evaluate the microstructure and adsorption mechanism, confirming the ability of the adsorbent's carboxyl and hydroxyl groups to eliminate various heavy metal ions in water simultaneously. The SBPC demonstrated high copper binding capacities (937.4 mg/g and 823.2 mg/g) through response surface methodology (RSM) and column studies. It was also influential in single and natural systems, exhibiting competitive behavior and efficient removal of Cu2+. The Langmuir isotherm and pseudo-second-order kinetics strongly correlate with experimental data, with R2 values of 0.98 and 0.99, respectively. SBPC showed remarkable stability, up to 10 desorption cycles, and achieved 98% Cu2+ adsorption efficiency and 91.0% desorption. Finally, the cost analysis showed a cost of 125.68 INR/kg or 1.51 USD/kg, which is very low compared to the literature. These results highlight the potential of SPBC and show that it provides an efficient and cost-effective solution for removing Cu2+ from a real system.