Efficient Technique Research Articles

Weed detection in soybean fields using improved YOLOv7 and evaluating herbicide reduction efficacy.

With the increasing environmental awareness and the demand for sustainable agriculture, herbicide reduction has become an important goal. Accurate and efficient weed detection in soybean fields is the key to test the effectiveness of herbicide application, but current technologies and methods still have some problems in terms of accuracy and efficiency, such as relying on manual detection and poor adaptability to some complex environments. Therefore, in this study, weeding experiments in soybean fields with reduced herbicide application, including four levels, were carried out, and an unmanned aerial vehicle (UAV) was utilized to obtain field images. We proposed a weed detection model-YOLOv7-FWeed-based on improved YOLOv7, adopted F-ReLU as the activation function of the convolution module, and added the MaxPool multihead self-attention (M-MHSA) module to enhance the recognition accuracy of weeds. We continuously monitored changes in soybean leaf area and dry matter weight after herbicide reduction as a reflection of soybean growth at optimal herbicide application levels. The results showed that the herbicide application level of electrostatic spraying + 10% reduction could be used for weeding in soybean fields, and YOLOv7-FWeed was higher than YOLOv7 and YOLOv7-enhanced in all the evaluation indexes. The precision of the model was 0.9496, the recall was 0.9125, the F1 was 0.9307, and the mAP was 0.9662. The results of continuous monitoring of soybean leaf area and dry matter weight showed that herbicide reduction could effectively control weed growth and would not hinder soybean growth. This study can provide a more accurate, efficient, and intelligent solution for weed detection in soybean fields, thus promoting herbicide reduction and providing guidance for exploring efficient herbicide application techniques.

Lung disease detection using EasyNet

One of the major causes of deaths worldwide is the pulmonary diseases. There is an increasing need for an efficient technique that can automatically diagnose these diseases with high accuracy. In this paper, we propose a deep learning architecture to automatically detect pulmonary diseases. The raw pulmonary sound signals are taken from two popular datasets: ICBHI and KAUH datasets. These signals have diverse sampling frequencies of 4 kHz, 10 kHz or 44.1 kHz. The signals from KAUH dataset have a duration of minimum 5 s, while ICBHI signals have a duration of 10 to 90 s. These signals undergo pre-processing, which involves re-sampling them to a common 4 kHz frequency, and segmenting them into frames lasting 3 s. The frames are then normalized and passed to the proposed EasyNet model for training and classification. The EasyNet architecture contains only two convolution layers, which reduces the model complexity. The model’s performance is analyzed for both binary detection as well as multi-class detection. Our method performs well in all the considered evaluation scenarios, and yields an accuracy, sensitivity, and specificity of 1.0 for the KAUH dataset, while for the ICBHI dataset, an accuracy of 0.997, sensitivity of 0.999, and specificity of 0.997 is achieved. For the combined dataset, we have achieved an accuracy of 0.998, with a sensitivity and specificity of 0.999. These values are better than the existing state-of-the-art methods. The proposed architecture is quite simple yet effective in detecting lung diseases.

Microalgal synthesis of the biodiesel employing simultaneous extraction and esterification via heterogeneous catalyst

Within the realm of biorefinery, the third generation of biodiesel production relies on extracting oil from Algae to create renewable fuels. Algae cultivation can occur under precise laboratory conditions, making them a sustainable substitute for Fatty Acid Methyl Ester or biodiesel production. Through analysis of the simultaneous extraction and (trans)esterification process of Algae Chlorella Minutissima utilizing sulfated carbonaceous catalysts, scientists have discovered an efficient technique for producing biodiesel. Various parameters, such as catalyst loading, oil-to-methanol ratio, reaction temperature, and reaction time for FFA reduction and FAME yields, were studied and optimized. Results indicate that utilizing a 10 wt% catalyst loading and a 70:30 volumetric oil to methanol ratio can achieve 67.59 % FFA reduction and a biodiesel yield of 44 mL. This method of producing biodiesel can occur in a single-unit operation, leading to higher yields of renewable fuel at a reduced cost and energy consumption. Finally, the quality of the biodiesel produced met ASTM D6751 standards, confirming its suitability for use.

Retraction Note to: An efficient technique to PAPR reduction for LTE uplink using Lonzo’s resampling technique in both SC-LFDMA and SC-DFDMA systems

Retraction Note to: An efficient technique to PAPR reduction for LTE uplink using Lonzo’s resampling technique in both SC-LFDMA and SC-DFDMA systems

Efficient Road Segmentation Techniques with Attention-Enhanced Conditional GANs

Efficient Road Segmentation Techniques with Attention-Enhanced Conditional GANs

Effects of different feeds and stocking densities on growth and survival rates of mud crab (Scylla paramamosain) at the stage from megalopa to crablet-1

Mud crabs (Scylla genus) are luxury foods in high demand internationally. The efficient techniques for mud crab hatcheries are vital for providing breeds for their aquaculture, which is rapidly growing in many countries. This study aims to investigate the effects of different feeds and stocking densities on mud crabs’ growth and survival rates (Scylla paramamosain) in the stage from megalopa to crablet-1 stage. Two separate experiments were conducted indoors in the 60-liter round plastic tanks (containing 50 liters of water at a 28‰ salinity). Experiment 1 investigated four feeds: frozen Artemia biomass, pureed shrimp meat, Lansy pellet feed (48% protein), and NRD pellet feed (55% protein). Megalopae (mean weight of 5.8 mg) were stocked at a density of 10/L. In experiment 2, the megalopae (mean weight of 5.4 mg) were stocked at densities of 20, 30, and 40/L and were fed the Lansy pellet feed, which was the best one selected from experiment 1. High survival rates were obtained at all four feeds (82.2–87.5%) and three stocking densities (88.4–90.1%). The growth performances in Lansy feed and frozen Artemia biomass were better than those in pureed shrimp meat and NRD pellet feed, which was seen through higher indicators of daily weight gain (DWG) and specific growth rate in weight (SGRw) (p < 0.05). Despite the survival rate showing no significant difference among the feeds, their highest value (87.5%) was observed in the Lansy pellet feed. On the other hand, the growth performances showed a significant decrease at stocking densities ≥ 30/L, as complemented by a significant decrease in DWG and SGRw at these stocking densities (p < 0.05). Both survival rate and metamorphosis durations did not show significant differences among the feeds or stocking densities (p > 0.05). The investigated feeds and stocking densities suit the nursing mud crab (S. paramamosain) megalopa. In contrast, the Lansy pellet feeds had a stocking density of 20/L, resulting in the highest nursing efficiency.

Simultaneous removal of anionic and cationic dyes on quaternary mixtures by adsorption onto banana, orange and pomegranate peels

Disposal of untreated wastewater released by industries using dyes, causes serious damage to aquatic life and to the ecosystem in general. Both physical and chemical methods have been used for the removal of these dyes from wastewater; among them, adsorption has been found to be a very efficient technique. In this study, the ability to simultaneously remove cationic (MB) and anionic dyes (RR120, RB5 and RBBR) from a quaternary mixture solution by using zero-cost materials as banana, orange and pomegranate peels, was investigated. The optimal removal rates were achieved at pH 2.0 and a dose of 6.0 g/L. The comparative results of natural adsorbents on dye removal, separately and simultaneously in solution showed that MB adsorption on banana and orange peels was augmented from 89 to 96 and 43 to 87%, respectively, in the presence of anionic dyes (synergistic effect). On the other hand, a competitive effect was determined, mainly between anionic dyes, resulting in a decrease in their respective percentage. Banana peels were selected as the adsorbent material for further study in a quaternary dye mixture containing all dyes. Parameters affecting dye adsorption such as pH solution, initial concentration of dyes, contact time and temperature were examined. Based on the experimental results, two mechanisms for interactions were proposed: (a) the cationic MB molecules are connected to the negatively charged anionic dyes or (b) electrostatic attractions occurred among cationic MB molecules and negatively charged anionic dyes in the solution enhancing the adsorption efficiency. Kinetic experiments showed that equilibrium for anionic dyes reached at around 180 min (3 h), and for MB was reached much faster, at 30 min; 87% of 20 mg/L was found to be removed in just 5 min. Langmuir isotherm model and pseudo-second-order kinetic model were best fitted to the experimental results. According to thermodynamics the spontaneous nature of their adsorption was confirmed.

Binary Ce-doped-ZnO/rGO composite as excellent photocatalyst for bromothymol blue dye degradation

Organic contaminants pose a grave danger to both the surroundings and public health, which emphasizes the urgent need for efficient decomposition techniques. Building a composite and adding a cocatalyst can both enhance the transfer of photogenerated electrons, which are useful strategies to increase photocatalytic efficiency. This study sought to synthesize cerium–doped ZnO–rGO nanoparticles with varying concentrations of Ce for visible–light photocatalytic degradation of bromothymol blue dye in water. The synthesized photocatalysts were characterized using transmission electron microscopy, x–ray diffraction, ultraviolet–visible spectrophotometry, energy dispersive spectroscopy, and scanning electron microscopy. The results disclosed that the photoactivity of the 0.6% Ce–ZnO–rGO was the highest and degraded 92% of bromothymol blue dye under visible light within 180 min (k = 1.072 ×10–2 min–1) compared to the pristine ZnO which only degraded the dye by about 22% (k = 1.15 ×10−3 min−1). The band gap of the pure ZnO was estimated to be 3.09 eV and that of the best performing composite (0.6% Ce) to be 2.81 eV which we suggest are responsible for the improved photodegradation. The pseudo–first–order photocatalytic kinetic has a strong correlation value of 0.99. The Ce–ZnO–rGO composites offer a lot of potential for cleaning the environment.

Integrating ecology, physiology and transcriptomics reveals the response of Zostera marina to rusting of iron transplantation frame

Frame Transplantation System (FTS) is considered an efficient method for seagrass restoration, but the effect of the rusting of iron frame on seagrass restoration remains unclear. We transplanted Zostera marina plants using iron FTS treated with fluorocarbon paint (painted treatment, PT) and traditional unpainted iron FTS (unpainted treatment, UT) under controlled mesocosm conditions for 24 days. Our results showed that the survival rate of Z. marina under the UT was significantly 31.2 % lower than that of the plants under the PT. Soluble sugar content in Z. marina rhizomes under the UT was significantly 2.19 times higher than that of the plants under the PT. Transcriptome analysis revealed differentially expressed genes (DEGs) involved in photosynthesis, metabolism and signal transduction functions. The results provide valuable data that could prove helpful in the development of efficient restoration techniques for Z. marina beds.

Volatile-compound fingerprinting and discrimination of positional isomers in stamp-pad ink tracing using HS-GC-IMS combined with multivariate statistical analysis.

The rising crime rate associated with document forgery has a significant impact on public safety and social stability. In document fraud cases, determining the origin of a particular stamp-pad ink is the most important objective. In this study, a comprehensive analysis of the volatile compounds in quick-drying stamp-pad inks from six commonly used brands were performed for the first time, utilizing a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and multivariate statistical analysis methods. Visual and comparative analysis of the differential volatile components among different stamp-pad ink samples was conducted using fingerprints and volcano plots. A total of 127 volatile compounds were accurately identified, with ketones, esters, alcohols, and aldehydes being the most abundant compounds in the stamp-pad inks. Hierarchical clustering analysis (HCA), including dendrograms and clustering heatmaps, was utilized to explore the correlations between these compounds and the samples. Additionally, the precise identification of positional isomers and functional group isomers of aliphatic compounds was achieved. To achieve accurate discrimination of various stamp-pad ink samples, a multivariate statistical analysis method was utilized to establish a classification model for them. Based on the results obtained from HS-GC-IMS, effective discrimination among different brands of stamp-pad ink samples was achieved through principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The model exhibited excellent performance, with the fit index of dependent variables (R2Y) and the predictive index of the model (Q2) values of 0.99 and 0.984, respectively. These results provided significant theoretical evidence for the application of HS-GC-IMS as an efficient technique in the analysis of volatile compounds, identification of positional isomers and functional group isomers, as well as tracing the origin of stamp-pad ink and analyzing the formation time of documents.

Multilevel depression status detection based on fine-grained prompt learning

As a common psychological disorder, depression is generally detected based on scales and interviews, which are often affected by subjective or environmental factors. In order to assist in the diagnosis, automatic depression detection (ADD) is developed to provide an objective, efficient, and convenient technique based on the analysis of different psychophysiological data. Among them, text is one of the most frequently used modalities in the research field of ADD. Despite its success, several key issues still need to be addressed. Specifically, binary detection performed in the classical ADD methods cannot satisfy the demand for early detection tasks. Therefore, a more fine-grained analysis of depression status is highly needed. Moreover, due to factors such as small dataset size and improper organization of interview texts by selecting response texts only or choosing topics subjectively, insufficient learnable representations for depression detection are inevitable. To tackle these issues, we propose a multilevel depression status detection approach based on fine-grained prompt learning, called MDSD-FGPL. In our method, we design multiple sets of prompts from coarse-grained to fine-grained and therefore train the language model with features extracted from different depressive levels. We also reorganize the interview text at the question-response level and utilize the attention mechanism to capture and fuse the key features. Specifically, two model branches are constructed based on pre-trained language models for extracting comprehensive semantic features and depression-specific semantic tendencies, respectively. Our method was evaluated on the DAIC-WOZ dataset, showing satisfying performance in fine-grained depression detection. Additionally, our method achieves a state-of-the-art F1-score of 0.8276 in the coarse-grained binary classification.

Energy-Efficient Strategies for Mitigating Airborne Pathogens in Buildings—Building Stage-Based Sustainable Strategies

The coronavirus disease (COVID-19) pandemic has had widespread global effects. The advent of novel variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, along with the spread of diverse airborne viruses across different geographical locations, has caused reflective apprehension on a global scale. This resurgence emphasises the critical importance of carefully constructed structures installed with efficient ventilation systems, including both natural and mechanical ventilation techniques, as well as mixed-mode ventilation approaches in buildings. Building engineering and architectural designs must go beyond traditional considerations of economics and structural durability in order to protect public health and well-being. To attain a high quality of life, it is necessary to prioritise sustainability, energy efficiency, and the provision of safe, high-quality indoor environments. Empirical scientific investigations underscore the pivotal role played by conducive indoor environments in averting the transmission of viral diseases such as COVID-19 and mitigating challenges associated with sick building syndrome, primarily stemming from suboptimal indoor air quality. This work provides a summary and a SWOT (strength, weakness, opportunities, and threat) analysis of strategies designed for engineers, architects, and other experts in the field to implement. These strategies are intended for integration into new constructions and the retrofitting of extant structures. Their overarching objective is the minimisation of viral transmission within indoor spaces, accomplished in an energy-efficient manner consonant with sustainable development objectives. The significance of these strategies lies in their ability to impact changes to national and international building codes and regulations, strengthening infrastructures against probable airborne viral threats. Encompassing both object-centric and subject-centric approaches, these strategies collectively furnish a holistic framework for mitigating the dissemination of pathogens, exemplified by the SARS-CoV-2 virus and similar airborne viruses, across diverse typologies of buildings.

Chemical characterization and sensory properties of apple brandies aged with different toasted oak chips and ultra-high-pressure treatments

Aging is an important processing step of producing high quality apple brandy. In this study, apple brandies aged by traditional method and using three different toasted oak chips combined with or without ultra-high-pressure (UHP) treatment were prepared to compare their differences in chemical characterization and sensory properties. The results indicated that the brandies aged with toasted oak chip increased the levels of total acidity, volatile acidity and phenolic compounds. It also had the desirable color and taste. The brandy aged with toasted oak chip combined with UHP reached the highest levels of total acidity (1.06 g/L), total phenolic content (284.92 mg/L) and aromatic esters (49.37 %). Therefore, the aging with high toasted oak chip combined with UHP treatment could cut the traditional aging time to meet the same quality as traditional aging method. The results are very useful to develop a fast and efficient aging technique for brandy production.

Powdered Cellulose Microblasting for Dry Cleaning Printed Works on Paper.

This study evaluates the practical feasibility of using powdered cellulose microblasting for dry cleaning paper-based printed artworks in a real setting of conservation treatment. The control parameters used for this purpose are the potential morphological changes in the surface, the level of cleanliness achieved, and the amount of residue remaining in the artwork after the treatment. In this study, cleaning of a lithography was conducted entirely with powdered cellulose microblasting. The outcomes were evaluated before and after treatment using optical microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and spectrophotometry. The results indicate that powdered cellulose microblasting is a feasible and efficient technique for conducting the dry cleaning of printed works on paper without causing morphological changes to their surface. Additionally, it offers significant benefits by enabling precise treatment control, reducing cleaning time, and using materials stable in the long term and compatible with the substrate. Moreover, it mitigates the long-term negative effects caused by synthetic polymer residues from the cleaning materials commonly used in the dry cleaning of paper.

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.

Efficient routing method for IoT networks using bee colony and hierarchical chain clustering algorithm

Hierarchical chain communication is an efficient technique for saving sensor energy while maintaining load balance and scalability. However, the long delay caused by the formation of long chains is a significant research gap in this paradigm. In this article, a two-level hierarchical chain routing scheme based on the bee colony algorithm called PEG_ABC is proposed to reduce transmission delay and take advantage of the benefits of chain communication. Initially, the remaining sensor energy, Euclidean distance between sensors, and Euclidean distance between sensors and base station are simultaneously adjusted to the fitness function of the bee colony algorithm to select the most suitable chain leader at the first level. Also, in our proposed method at the second level, the collected data in the leader nodes of each level is transferred to the BS through end-to-end path formation between the leaders based on the bee colony algorithm. At this level, the data transmission delay and remaining energy in the leader nodes adjust the fitness function of the bee colony algorithm. Simulation results show the efficiency of the proposed PEG_ABC scheme compared to literature research in terms of energy consumption, network lifetime, number of packets sent to BS, and transmission delay. Also, this article presents a relationship based on extensive simulation results to determine the optimal number of clusters in hierarchical chain communication.

Energy Efficient Routing Technique Using Enthalpy Ant Net Routing for Zone-Based MANETS

Routes discovery that can provide reliable data transmission in Mobile Ad-hoc Networks is challenging due to its wireless channel characteristics and dynamic transmission environment. Ad-hoc networks frequently experience link failure because nodes are mobile and their positions are not fixed. The cellular ad hoc community's dynamic nature makes it possible to analyze multi-route routing protocols specifically. One of the most trustworthy and environment-friendly routing methods is the Zone Routing Protocol used in Mobile Ad Hoc Networks. Offering timely and trustworthy communication services, however, depends critically on maintaining the Quality of Service, electricity performance, and outstanding resource management. In this research, we suggested an energy-efficient routing method for zone-based MANETs called enthalpy and net routing. First, a completely fuzzy quarter clustering based on an Energy guide is used to perform the clustering. With the help of fuzzy memberships, the AFCM set of rules permits the input of statistics for each elegance. In the second, the best route is selected using Enthalpy Ant Net Routing (EANR) while taking the following factors into account: community disconnection, channel error, buffer overflow, and contention at the link layer. The results of the experiment show that the collection of rules performs better than many existing algorithms in terms of community longevity, electricity consumption, and other metrics.

Analysis of Financing Strategies of ECOVACS Robotics Based on PEST

The artificial intelligence business has a wide potential as a young industry that continues to influence human social and economic growth. The development of efficient finance techniques has emerged as a crucial challenge for artificial intelligence businesses as they look to expand their markets and implement their plans. In order to sort out and analyze the strategic influencing factors on the development of artificial intelligence in China, particularly the status quo and issues of enterprise financing, this paper uses ECOVACS Robotics as a research sample. Financing is the primary source of funds necessary for the survival and development of enterprises. The demand for financing capital from businesses will rise as the worldwide market for service robotics continues to grow rapidly. According to the report, ECOVACS should adopt a targeted approach by bolstering technological advancement, boosting visibility, boosting legal support, boosting funding sources, and directing the targeted strategy in various ways.

Effect of different hydrophilic heads on strong interfacial activities of non-edible oil-derived ultra-long chain (UC24:1) Bio-based zwitterionic surfactants for tertiary oil recovery

One of the cross-cutting issues in green petrochemistry is discovering an efficient and sustainable technique for chemically enhanced oil recovery (EOR). Developing eco-friendly and renewable surfactants with strong interfacial activity as greener chemical materials for EOR has been a challenging topic. In this work, a new series of eco-friendly and high-performance ultra-long chain fatty acid-derived bio-based zwitterionic surfactants with different hydrophilic heads (the 2-(dimethyl(3-(tetracos-15-enamido)propyl)ammonio)acetate (TAPAA), 3-(dimethyl(3-(tetracos-15-enamido)propyl)ammonio)− 2-hydroxypropane-1-sulfonate (TAPAHPS), and 4-(dimethyl(3-(tetracos-15-enamido)propyl)ammonio)butane-1-sulfonate (TAPABS)), was developed from the non-edible fatty acid (Z)− 15-tetracosenoic acid (UC24:1) by a simple and effective method. ESI-MS, 1HNMR, and 13C NMR were used for their structural analyses. In alkali- and polymer-free systems, 0.5 g/L concentration of each surfactant could reduce the interfacial tension between Daqing crude oil and simulated formation groundwater to ultra-low value (1.971 × 10−3, 4.44 × 10−3 and 6.538 × 10−4 mN/m for TAPAA, TAPAHPS and TAPABS, respectively). These surfactants also demonstrated strong electrolyte tolerance, up to 2000 mg/L and 200 g/L of Ca2+ and NaCl, respectively. They were also structurally thermal stable up to 75 °C and biodegradable. This work consolidates our hypothesis that ultra-long chain (UC24:1) bio-based zwitterionic surfactants are of greater use than short-chain surfactants or traditional alkali-surfactant-polymer (ASP) systems for EOR applications, as they show strong interfacial activity at lower dosages in alkali- and polymer-free systems.

Simultaneous multi-region detection of GABA+ and Glx using 3D spatially resolved SLOW-editing and EPSI-readout at 7T

GABA+ and Glx (glutamate and glutamine) are widely studied metabolites, yet the commonly used magnetic resonance spectroscopy (MRS) techniques have significant limitations, including sensitivity to B0 and B1+-inhomogeneities, limited bandwidth of MEGA-pulses, high SAR which is accentuated at 7T. To address these limitations, we propose SLOW-EPSI method, employing a large 3D MRSI coverage and achieving a high resolution down to 0.26 ml.Simulation results demonstrate the robustness of SLOW-editing for both GABA+ and Glx against B0 and B1+-inhomogeneities within the range of [-0.3, +0.3] ppm and [40 %, 250 %], respectively.Two protocols, both utilizing a 70 mm thick FOV slab, were employed to target distinct brain regions in vivo, differentiated by their orientation: transverse and tilted. Protocol 1 (n = 11) encompassed 5 locations (cortical gray matter, white matter, frontal lobe, parietal lobe, and cingulate gyrus). Protocol 2 (n = 5) involved 9 locations (cortical gray matter, white matter, frontal lobe, occipital lobe, cingulate gyrus, caudate nucleus, hippocampus, putamen, and inferior thalamus).Quantitative analysis of GABA+ and Glx was conducted in a stepwise manner. First, B1+/B1–-inhomogeneities were corrected using water reference data. Next, GABA+ and Glx values were calculated employing spectral fitting. Finally, the GABA+ level for each selected region was compared to the global Glx within the same subject, generating the GABA+/Glx_global ratio.Our findings from two protocols indicate that the GABA+/Glx_global level in cortical gray matter was approximately 16 % higher than in white matter. Elevated GABA+/Glx_global levels acquired with protocol 2 were observed in specific regions such as the caudate nucleus (0.118±0.067), putamen (0.108±0.023), thalamus (0.092±0.036), and occipital cortex (0.091±0.010), when compared to the cortical gray matter (0.079±0.012).Overall, our results highlight the effectiveness of SLOW-EPSI as a robust and efficient technique for accurate measurements of GABA+ and Glx at 7T. In contrast to previous SVS and 2D-MRSI based editing sequences with which only one or a limited number of brain regions can be measured simultaneously, the method presented here measures GABA+ and Glx from any brain area and any arbitrarily shaped volume that can be flexibly selected after the examination. Quantification of GABA+ and Glx across multiple brain regions through spectral fitting is achievable with a 9-minute acquisition. Additionally, acquisition times of 18–27 min (GABA+) and 9–18 min (Glx) are required to generate 3D maps, which are constructed using Gaussian fitting and peak integration.