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BACKGROUND: Ethiopia is recognized as one of the centers of origin for the diversity of many plant species, including medicinal plants. Throughout the country, a large proportion of the population relies on these therapeutic plant species for primary healthcare. While such traditional medicinal knowledge has been documented in some regions, there is a lack of information from the Quara district of northwestern Ethiopia. Therefore, this study aimed to document the indigenous and local knowledge of the use of medicinal plants among three ethnic groups residing in the area. METHODOLOGY: An ethnobotanical study was conducted from August 2022 to October 2023 in ten kebeles of the Quara district. Data were collected through semi-structured interviews, field walks, and focus group discussions with 286 informants using snowball, purposive, and random sampling techniques. Quantitative analyses included Rahman's similarity index (RSI), informant consensus factor (ICF), and direct matrix ranking (DMR). Descriptive statistics were used to analyze basic ethnobotanical data. RESULTS: A total of 128 medicinal plant species from 112 genera and 50 families were documented and used to treat 14 disease categories. There was a 28% overlap in medicinal plant knowledge among the three ethnic groups studied. Fabaceae was the most represented family with 22 species. Trees accounted for the majority of the documented plants (37.5%), and leaves were the most commonly used plant parts (23.1%). Oral administration (56.7%) of plant extracts was the primary mode of remedy preparation and use. The highest ICF value (0.93) was recorded for circulatory and blood-related disorders. The study identified nine plants and 39 therapeutic uses not previously reported in Ethiopia. The RSI showed high overlap with neighboring areas and low similarity with distant areas. Top-ranked multipurpose plants were Ziziphus spina-christi and Terminalia leiocarpa, with agricultural expansion and fuelwood collection identified as major threats. CONCLUSION: The findings demonstrate the rich diversity of medicinal plants and associated traditional knowledge in the Quara district. The high ethnobotanical indices warrant further phytochemical and pharmacological investigations. Integrated conservation efforts are recommended to address the challenges facing these valuable plant resources.

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When a camera lens is directly faced with a strong light source, image flare commonly occurs, significantly reducing the clarity and texture of the photo and interfering with image processing tasks that rely on visual sensors, such as image segmentation and feature extraction. A novel flare removal network, the Sparse-UFormer neural network, has been developed. The network integrates two core components onto the UFormer architecture: the mixed-scale feed-forward network (MSFN) and top-k sparse attention (TKSA), creating the sparse-transformer module. The MSFN module captures rich multi-scale information, enabling the more effective addressing of flare interference in images. The TKSA module, designed with a sparsity strategy, focuses on key features within the image, thereby significantly enhancing the precision and efficiency of flare removal. Furthermore, in the design of the loss function, besides the conventional flare, background, and reconstruction losses, a structural similarity index loss has been incorporated to ensure the preservation of image details and structure while removing the flare. Ensuring the minimal loss of image information is a fundamental premise for effective image restoration. The proposed method has been demonstrated to achieve state-of-the-art performance on the Flare7K++ test dataset and in challenging real-world scenarios, proving its effectiveness in removing flare artefacts from images.

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Employing recent short-term historical rainfall data may enhance the performance of rainwater harvesting systems (RWHs) in response to climate change. However, this assumption lacks extensive research, and the evaluation of RWHs currently relies on long-term historical rainfall time series. This study evaluates the feasibility of this assumption and aims to identify the optimal rainfall time series for evaluating RWH performance under climate change. We evaluated RWHs in residential buildings across 16 Japanese cities utilizing historical rainfall time series of varying lengths and 30-year predicted rainfall time series. The minimum rainfall time series length was obtained based on the similarity index between the evaluation results for historical and future periods. The corresponding optimal series can be determined from the distribution of similarity indices in the minimum length. Finally, we introduce supply pressure indices (SPIs) to summarize the rainfall characteristics of these optimal rainfall time series. Our findings highlight that the minimum rainfall time series length increased from 1 year to 30 years as building non-potable water demand rose and city locations varied. Utilizing rainfall time series incorporating recent rainfall data yielded more dependable evaluation results for RWHs under climate change. These optimal rainfall time series share common characteristics with SPIs ranging from 5.37 to 17.87 mm/d, contingent on the local rainfall patterns. Our study concludes that utilizing recent short-term historical rainfall data is feasible to evaluate and design RWHs under climate change.

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Human milk phospholipids (HMPLs) play an indispensable role in the neurodevelopment and growth of infants. In this study, a total of 37 phospholipid fatty acid (PLFA) species and 139 phospholipid molecular species were detected from human milk and other natural phospholipid sources (including 5 animal-derived species and 2 plant species). Moreover, a similarity evaluation model for HMPLs was established, including phospholipid classes, PLFAs, and phospholipid molecular species, to evaluate their natural substitutes. The closest scores for HMPL substitute in these three dimensions was 0.89, 0.72, and 0.77, which belonged to mare milk, goat milk, and camel milk, respectively. The highest comprehensive similarity score was obtained by camel milk at 0.75, while the lowest score was observed in soybean phospholipid (0.22). Therefore, these results not only monitored the stereochemical structure of HMPLs and their substitutes, but also further provided new insights for the development of infant formulae.

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BACKGROUND: The ethnobotanical analysis of two bordering areas allows for the in-depth understanding of the dynamics of Local Ecological Knowledge, which mirrors the naturalistic, historical, and sociopolitical features of each area. As part of the Interreg Italy-Switzerland B-ICE&Heritage and GEMME projects, this work is an ethnobotanical comparative study of two neighboring Alpine territories: Valmalenco (Italy) and Valposchiavo (Switzerland). METHODS: A total of 471 informants were interviewed on different fields of use (medicinal, food, veterinary, etc.). All data were organized in Excel™ spreadsheets. Informant Consensus Factor was calculated for the pathologies reported. Jaccard's similarity indices were calculated to compare the Valmalenco and Valposchiavo areas. Subsequently, another comparison between Valmalenco/Valposchiavo and Italian/Swiss Alpine neighboring areas was carried out. RESULTS: The number of taxa for Valmalenco was 227 (77 families) and 226 in Valposchiavo (65). Out of the 10 most cited species, 7 were mentioned in both. Arnica montana L. was the most cited in Valmalenco, and Sambucus nigra L. in Valposchiavo. The 5 most cited families were the same. Regarding the medicinal and food fields, the similarity indices were fairly low (0.31 and 0.34 for the species; 0.22 and 0.31 for the uses). Concerning the comparison with Italian and Swiss Alps, similarity values were slightly higher with Italy (Valmalenco food species: 0.38 with Italy and 0.26 with Switzerland, medicinal: 0.26 IT and 0.14 SW; Valposchiavo food species: 0.36 with IT and 0.26 with SW, medicinal: 0.21 IT and 0.14 SW). CONCLUSION: Although Valmalenco and Valposchiavo partly share natural environment, language, history, and culture, they had low similarity indices. They both seemed to have more similarities with Italy than Switzerland, maintaining low values with the surrounding territories too. They showed a common core of Local Ecological Knowledge with several divergent branches possibly due to pivotal historical happenings, as well as more modern external influences.

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BACKGROUND: Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG), TMS-EEG, is a useful neuroscientific tool for the assessment of neurophysiology in the human cerebral cortex. Theoretically, TMS-EEG data is expected to have a better data quality as the number of stimulation pulses increases. However, since TMS-EEG testing is a modality that is examined on human subjects, the burden on the subject and tolerability of the test must also be carefully considered. METHOD: In this study, we aimed to determine the number of stimulation pulses that satisfy the reliability and validity of data quality in single-pulse TMS (spTMS) for the dorsolateral prefrontal cortex (DLPFC). TMS-EEG data for (1) 40-pulse, (2) 80-pulse, (3) 160-pulse, and (4) 240-pulse conditions were extracted from spTMS experimental data for the left DLPFC of 20 healthy subjects, and the similarities between TMS-evoked potentials (TEP) and oscillations across the conditions were evaluated. RESULTS: As a result, (2) 80-pulse and (3) 160-pulse conditions showed highly equivalent to the benchmark condition of (4) 240-pulse condition. However, (1) 40-pulse condition showed only weak to moderate equivalence to the (4) 240-pulse condition. Thus, in the DLPFC TMS-EEG experiment, 80 pulses of stimulations was found to be a reasonable enough number of pulses to extract reliable TEPs, compared to 160 or 240 pulses. CONCLUSIONS: This is the first substantial study to examine the appropriate number of stimulus pulses that are reasonable and feasible for TMS-EEG testing of the DLPFC.

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Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data association. In the first step, objects of interest are detected in each frame of a video. The second step establishes the correspondence between these detected objects across different frames to track their trajectories. This paper proposes an efficient and unified data association method that utilizes a deep feature association network (deepFAN) to learn the associations. Additionally, the Structural Similarity Index Metric (SSIM) is employed to address uncertainties in the data association, complementing the deep feature association network. These combined association computations effectively link the current detections with the previous tracks, enhancing the overall tracking performance. To evaluate the efficiency of the proposed MOT framework, we conducted a comprehensive analysis of the popular MOT datasets, such as the MOT challenge and UA-DETRAC. The results showed that our technique performed substantially better than the current state-of-the-art methods in terms of standard MOT metrics.

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The distribution and composition of the vegetation are greatly affected by the edaphology and topography of an area. The current study explores the vegetation structure of the herbaceous layer at various habitats of district Kohat for the first time. A survey was conducted during the spring seasons of 2021, 2022 and 2023 selecting 40 sites on the basis of edaphology, topography, altitude, aspect and status. Data was collected via quadrat approach to establish plant communities by species Importance Value (IV), point out dominant species by Total IV (TIV) and dominant families via Total Family IV (TFIV). The quantitative biological spectrum was also calculated. Communities' phytosociological characteristics were analyzed via various diversity indices (Shannon's Index (H), Simpson's Index (D), Species Richness (SR), Evenness (E) and Maturity index (Mi)) while similarity between the communities was calculated by using Sorensen's Index. The findings revealed a total of 253 species belonging to 57 families having the dominant species Cynodon dactylon (L.) Pers. (TIV, 484.3) followed by Saussuria heteromalla (D. Don) Hand. (TIV, 360.4), Anagallis arvensis L. (TIV, 353.2) and Aristida adscensionis L. (TIV, 349.65). Among 40 plant communities, Poaceae (TFIV, 2706.6), Asteraceae (TFIV, 2018.8), Fabaceae (TFIV, 1071.5) and Brassicaceae (TFIV, 825.9) were the dominant families. Therophytes (TIV, 7882) class was the dominant life form class followed by hemicryptophytes (TIV, 2517) while microphylls (TIV, 4669) class was the dominant leaf size class followed by nanophylls (TIV, 5469). Environmental factors i.e. topography and edaphic characteristics, showed significant effects on the diversity of the communities. The study concludes in a diverse pattern of distribution with a rich flora in the area warranting its documentation which will preserve the valuable species opening vistas for future research.

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Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction. It also takes advantage of the information from the membership function of the last iteration to compute neighborhood impact. This strategic refinement enhances the algorithm's adaptability to complex image structures, effectively addressing challenges such as intensity irregularities and contributing to heightened segmentation accuracy. We compare the segmentation accuracy of esFCM against four variants of FCM, Gaussian Mixture Model (GMM) and FSL and ANTs algorithms using four various dataset, employing three measurement criteria. Comparative assessments underscore esFCM's superior performance, particularly in scenarios involving added noise and bias fields.The obtained results emphasize the significant potential of the proposed method in the segmentation of MRI images.

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Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold's cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold's cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.

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Long-term occupation of coal gangue dumping sites (CGDS) may destroy ecological environment of nearby area. However, how the CGDS affects the distribution pattern of soil seed banks and vegetation in the nearby area is not clear. In this study, we investigated soil seed bank and vegetation at different distances from the second CGDS of Yangchangwan in Ningdong mining area, Lingwu, Ningxia. The results showed that soil seed bank was mainly distributed in 0-10 cm layer and decreased with increasing soil depth. Species richness of soil seed bank and vegetation first increased and then tended to be stable with increasing distance to the CGDS. The influence range of CGDS on soil seed banks was 300-500 m and was 100-300 m on aboveground vegetation. The CGDS did not affect the vertical distribution pattern of soil seed bank, but significantly affected the horizontal distribution pattern of soil seed banks and aboveground vegetation. The key area of vegetation restoration around the CGDS was between 100 m and 300 m.

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PURPOSE: This study aims to evaluate the viability of utilizing the Structural Similarity Index (SSI*) as an innovative imaging metric for quality assurance (QA) of the multi-leaf collimator (MLC). Additionally, we compared the results obtained through SSI* with those derived from a conventional Gamma index test for three types of Varian machines (Trilogy, Truebeam, and Edge) over a 12-week period of MLC QA in our clinic. METHOD: To assess sensitivity to MLC positioning errors, we designed a 1 cm slit on the reference MLC, subsequently shifted by 0.5-5 mm on the target MLC. For evaluating sensitivity to output error, we irradiated five 25 cm × 25 cm open fields on the portal image with varying Monitor Units (MUs) of 96-100. We compared SSI* and Gamma index tests using three linear accelerator (LINAC) machines: Varian Trilogy, Truebeam, and Edge, with MLC leaf widths of 1, 0.5, and 0.25 mm. Weekly QA included VMAT and static field modes, with Picket fence test images acquired. Mechanical uncertainties related to the LINAC head, electronic portal imaging device (EPID), and MLC during gantry rotation and leaf motion were monitored. RESULTS: The Gamma index test started detecting the MLC shift at a threshold of 4 mm, whereas the SSI* metric showed sensitivity to shifts as small as 2 mm. Moreover, the Gamma index test identified dose changes at 95MUs, indicating a 5% dose difference based on the distance to agreement (DTA)/dose difference (DD) criteria of 1 mm/3%. In contrast, the SSI* metric alerted to dose differences starting from 97MUs, corresponding to a 3% dose difference. The Gamma index test passed all measurements conducted on each machine. However, the SSI* metric rejected all measurements from the Edge and Trilogy machines and two from the Truebeam. CONCLUSIONS: Our findings demonstrate that the SSI* exhibits greater sensitivity than the Gamma index test in detecting MLC positioning errors and dose changes between static and VMAT modes. The SSI* metric outperformed the Gamma index test regarding sensitivity across these parameters.

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Purpose: To explore 'magnesium myristate' for its dual functionality as a lubricant and binder in the formulation of tablets. Methods: Using (DoE), tablet formulations using magnesium myristate and conventional excipients (magnesium stearate and PVP K30) were developed by wet granulation technique. The prepared granules and formulated tablets were evaluated for pre- and post-compression parameters, respectively. Results: Magnesium myristate exhibited excellent flow properties. The optimized formulations containing magnesium myristate exhibited increased hardness and in vitro drug release in comparison to conventional excipients. f2 similarity index for in vitro drug release showed no significant variations with optimized formulations and with the marketed formulations. Conclusion: Magnesium myristate shows a promising replacement for conventional excipients as both a lubricant and binder in tablet formulation.

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Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.

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Purpose: Over the past decade, the diagnostic information of the patients are digitally recorded and transferred. During the transmission of patients data, the security and authenticity of the information has to be ensured. Medical image watermarking technology has recently advanced because it can be used to conceal patient information while ensuring the authenticity. We propose a multiple watermarking method for securing clinical medical images. Approach: In this watermarking method, the quality feature property and private label property information are embedded as watermarks in the original image. Initially, medical images are divided into the region of interest (ROI) and non-interest region (NIR). Second, a two-level discrete wavelet transform (DWT) is applied to the ROI and the coefficients LL1 (LL2, LH2, HL2, HH2), LH1, HL1, and HH1 are generated. Watermarks are embedded using the DWT low-frequency sub-band (LL2) by quantizing the low-frequency coefficients. Next, the NIR is separated into non-overlapping 8×8 blocks, and a discrete cosine transform (DCT) is applied for each block. The DCT coefficients of each block are sorted using the zigzag transform. For embedding, eight intermediate frequency coefficients are used. Finally, the feature information is embedded in the ROI, and the tag information is embedded in the NIR. Results: The performance of the DWT-DCT watermarking method is calculated using the metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure, and mean square error. The proposed method obtained the better PSNR value of 45.76 dB. Conclusions: The proposed model works well for clinical medical images when compared with the existing techniques.

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BACKGROUND: To quantitatively evaluate the effectiveness of the Noise2Noise (N2N) model, a deep learning (DL)-based noise reduction algorithm, on enhanced depth imaging-optical coherence tomography (EDI-OCT) images with different noise levels. METHODS: The study included 30 subfoveal EDI-OCT images averaged with 100 frames from 30 healthy participants. Artificial Gaussian noise at 25.00, 50.00, and 75.00 standard deviations were added to the averaged (original) images, and the images were grouped as 25N, 50N, and 75N. Afterward, noise-added images were denoised with the N2N model and grouped as 25dN, 50dN, and 75dN, according to previous noise levels. The choroidal vascularity index (CVI) and deep choroidal contrast-to-noise ratio (CNR) were calculated for all images, and noise-added and denoised images were compared with the original images. The structural similarity of the noise-added and denoised images to the original images was assessed by the Multi-Scale Structural Similarity Index (MS-SSI). RESULTS: The CVI and CNR parameters of the original images (68.08 ± 2.47 %, and 9.71 ± 2.80) did not differ from the only 25dN images (67.97 ± 2.34 % and 8.50 ± 2.43) (p:1.000, and p:0.062, respectively). Noise reduction improved the MS-SSI at each noise level (p < 0.001). However, the highest MS-SSI was achieved in 25dN images. CONCLUSIONS: The DL-based N2N denoising model can be used effectively for images with low noise levels, but at increasing noise levels, this model may be insufficient to provide both the original structural features of the choroid and structural similarity to the original image.

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OBJECTIVE: Interindividual similarity refers to how similarly individuals respond when receiving the same stimulus or intervention. In this study, we aimed to examine interindividual similarity in adults with ADHD. METHOD: We used the cosine similarity index of ex-Gaussian reaction time (RT) vectors of mu, sigma, and tau parameters during a Stroop task. RESULTS: Our results demonstrate that the ADHD group exhibits a reduced interindividual similarity index in their ex-Gaussian RT vectors for congruent stimuli compared to the healthy control group. Importantly, we did not find significant differences in the interindividual similarity index to incongruent stimuli between both groups, thus suggesting that this reduced index was selective for congruent stimuli. CONCLUSION: Our findings highlight that ADHD adults exhibit more significant interindividual differences in cognitive functioning when processing congruent stimuli than healthy controls. These results provide new insights into the selective mechanisms underlying ADHD and may contribute to developing new targeted interventions for this disorder.

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Music-oriented auditory attention detection (AAD) aims at determining which instrument in polyphonic music a listener is paying attention to by analyzing the listener's electroencephalogram (EEG). However, the existing linear models cannot effectively mimic the nonlinearity of the human brain, resulting in limited performance. Thus, a nonlinear music-oriented AAD model is proposed in this paper. Firstly, an auditory feature and a musical feature are fused to represent musical sources precisely and comprehensively. Secondly, the EEG is enhanced if music stimuli are presented in stereo. Thirdly, a neural network architecture is constructed to capture nonlinear and dynamic interactions between the EEG and auditory stimuli. Finally, the musical source most similar to the EEG in the common embedding space is identified as the attended one. Experimental results demonstrate that the proposed model outperforms all baseline models. On 1-s decision windows, it reaches accuracies of 92.6% and 81.7% under mono duo and trio stimuli, respectively. Additionally, it can be easily extended to speech-oriented AAD. This work can open up new possibilities for studies on both brain neural activity decoding and music information retrieval.

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&lt;b&gt;Background and Objective:&lt;/b&gt; South Sulawesi, one of the Indonesian provinces, is a producer of oranges with various varieties grown extensively for export and domestic use. Information about the diversity of oranges is crucial for plant breeding and germplasm conservation. This study aims to analyze the diversity of oranges from several plantation centers in South Sulawesi based on morphological and anatomical characteristics. &lt;b&gt;Materials and Methods:&lt;/b&gt; Orange leaf samples were collected from five plantation locations in South Sulawesi, namely Pangkep, Sidrap, Bantaeng, North Luwu and Selayar Regencies. The morphological characteristics were identified using descriptors from the International Plant Genetic Resources Institute and Tjitrosoepomo. The anatomical characteristics were identified by preparing stomata slides observed under a microscope at a magnification of 200-400x. Similarity analysis between orange varieties was conducted using the NTSYS software and presented in the form of a dendrogram. &lt;b&gt;Results:&lt;/b&gt; The results of the diversity analysis of 13 orange varieties showed morphological variability in tree form and leaf shape, while anatomical characteristics showed variability in stomata size and stomata index. The similarity analysis showed that morphological characteristics formed clusters consisting of seeded selayar (SB), kaffir lime (NN), JC-selayar (JS), selayar-selayar (SS), batu (B), japansche citroen (JC) and dekopon (D) varieties, which had a 75% similarity with siam (SI) and sweet santang (SM) varieties. Meanwhile, the anatomical cluster analysis showed that the JC and SM orange varieties had a 79% similarity with the D variety. &lt;b&gt;Conclusion:&lt;/b&gt; The dendrogram diagram can serve as a basis for determining desired plant traits in plant breeding activities.

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Background: The increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles. Methodology: A prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection. Results: The study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker. Conclusion: Grammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers.