Distinct Methods Research Articles

Developments in Emerging Topical Drug Delivery Systems for Ocular Disorders.

According to the current information, using nano gels in the eyes have therapeutic benefits. Industry growth in the pharmaceutical and healthcare sectors has been filled by nanotechnology. Traditional ocular preparations have a short retention duration and restricted drug bioavailability because of the eye's architectural and physiological barriers, a big issue for physicians, patients, and chemists. In contrast, nano gels can encapsulate drugs within threedimensional cross-linked polymeric networks. Because of their distinctive structural designs and preparation methods, they can deliver loaded medications in a controlled and sustained manner, enhancing patient compliance and therapeutic efficacy. Due to their excellent drugloading capacity and biocompatibility, nano-gels outperform other nano-carriers. This study focuses on using nano gels to treat eye diseases and provides a brief overview of their creation and response to stimuli. Our understanding of topical drug administration will be advanced using nano gel developments to treat common ocular diseases such as glaucoma, cataracts, dry eye syndrome, bacterial keratitis, and linked medication-loaded contact lenses and natural active ingredients.

Models and methods for RZ-signals distinction in non-Gaussian noise for information-measurement systems

Abstract Polynomial Decision Rules (DR) for the problems of discrete RZ (Return-to-Zero) signal distinction in asymmetric-excess non-Gaussian noise are proposed. A new approach is proposed, which is based on the use of a modified moment quality criterion of statistical hypothesis testing and the application of higher-order statistics to describe the characteristics of non-Gaussian noise. Simulation of the DR with different parameters of the signal and noise was carried out. It is shown that taking into account the coefficients of skewness and kurtosis of the non-Gaussian noise the efficiency of signal distinction increases with non-linear processing DR compared to known results, which are optimal for the Gaussian noise model. The conducted studies demonstrate a reduction in false decisions in the processing of RZ signals when considering the coefficients of skewness and kurtosis of non-Gaussian noise. Such an increase in efficiency can exceed twofold, depending on the noise parameters. It is shown that the efficiency of the proposed approach is much higher for small SNR (Signal-to-Noise Ratio) values, for example, less than 1.

High Sensitivity and High Throughput Magnetic Flow CMOS Cytometers with 2D Oscillator Array and Inter-Sensor Spectrogram Cross-correlation.

In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors-one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7×7 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.

Archival Research in Sport Management: A Review for Research Methods Courses

The present study aimed to explain archival research and demonstrate its relevance as a distinct research method to include in sport management research methods course instruction. The current essay implicitly shows how archival research can complement other research methods and possibly improve upon their limitations. Furthermore, the study demonstrates that training in archival research can offer skills to students who might pursue employment in the sport industry. The review outlines what archival research is. Next, the various limitations and considerations to aid course instructors and subsequently researcher or practitioner comprehension are provided. Finally, the paper offers a guide for approaching physical archives and outlines expectations for archival research. Techniques necessary for analyzing information gleaned from archival research are presented and explained along with sample course assignments that are available to not only research methods classes but also potentially other coursework.

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Anomaly Detection Algorithms for Low-Dimensional and High-Dimensional Data: A Critical Study

Suspicious events or objects that differ from the norm in data can be discovered using anomaly identification. Identifying anomalies is critical for many applicable domains of life, e.g., preventing credit card theft and spotting intrusions into networks. It is possible to detect anomalies on a global scale as well as at the local level. A global outlier is a data point beyond the norm for the entire dataset, while a local outlier may be inside the norm for the entire dataset but outside the surrounding data points. Data outlier identification methods that are performed locally are inadequate. Therefore, better algorithms are required to investigate the high velocity of data and identify local outliers. Machine learning and data mining techniques need to be investigated to determine the pros and cons of anomaly identification residing inside data. The density based LOF method can be applied as the best choice to identify local outliers. While many variants of LOF exist for low-dimensional data, none are suitable for high-dimensional data. This research study discusses LOF, COF, and CBLOF methods for spotting local outliers in low and high-dimensional data. Regarding the size of the dimension, the performance of density-based algorithms is examined based on accuracy and time complexity. In this scenario, CBLOF achieves outstanding results due to its distinctive method of employing cluster-based local outlier detection.

Influence Analysis of Real Exchange Rate Fluctuations on Trade Balance Data Using Feature Important Evaluation Methods

This study delves into the intricate relationship between fluctuations in the real exchange rate and the trade balance, situated within the framework of a ‘two-country’ trade theory model. Despite a wealth of prior research on the impact of exchange rates on international trade, the precise extent of this influence remains a contentious issue. To bridge this gap, our research adopts a pioneering approach, employing three distinct artificial intelligence-based influence measurement methods: Mean Decrease Impurity (MDI), Permutation Importance Measurement (PIM), and Shapley Additive Explanation (SHAP). These sophisticated techniques provide a nuanced and differentiated perspective, enabling specific and quantitative measurements of the real exchange rate’s impact on the trade balance. The outcomes derived from the application of these innovative methods shed light on the substantial contribution of the real exchange rate to the trade balance. Notably, the real exchange rate (RER) emerges as the second most influential factor within the ‘two-country’ trade model. This empirical evidence, drawn from a panel dataset of 78 nations over the period 1992–2021, addresses crucial gaps in the existing literature, offering a finer-grained understanding of how real exchange rates shape international trade dynamics. Importantly, our study implies that policymakers should recognize the pivotal role of the real exchange rate as a key determinant of trade flow.

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Crimes committed online rank among the most critical global concerns. Daily, they cause country and citizen economies to suffer massive financial losses. With the proliferation of cyber-attacks, cybercrime has also been on the rise. To effectively combat cybercrime, it is essential to identify its perpetrators and understand their methods. Identifying and preventing cyber-attacks are difficult tasks. To combat these concerns, however, new research has produced safety models and forecast tools grounded on artificial intelligence. Numerous methods for predicting criminal behaviour are available in the literature. While they may not be perfect, they may help in cybercrime and cyber-attack tactic prediction. To find out whether an attack happened and, if so, who was responsible, one way to look at this problem is by using real-world data. There is data about the crime, the perpetrator's demographics, the amount of property damaged, and the entry points for the assault. Potentially, by submitting applications to forensics teams, victims of cyber-attacks may get information. This study uses ML methods to analyse cyber-crime consuming two patterns and to forecast how the specified characteristics will furnish to the detection of the cyber-attack methodology and perpetrator. Based on the comparison of eight distinct machine-learning methods, one can say that their accuracy was quite comparable. The Support Vector Machine (SVM) Linear outperformed all other cyber-attack tactics in terms of accuracy. The initial model gave us a decent notion of the assaults that the victims would face. The most successful technique for detecting malevolent actors was logistic regression, according to the success rate. To anticipate who the perpetrator and victim would be, the second model compared their traits. A person’s chances of being a victim of a cyber-attack decrease as their income and level of education rise. The proposed idea is expected to be used by departments dealing with cybercrime. Cyber-attack identification will also be made easier, and the fight against them will be more efficient.

Wellbore instability in naturally fractured formations: Experimental study and 3D numerical investigation

Wellbore instability due to natural fractures is a significant issue impeding drilling operations in shale formations. However, most of the existing literature on wellbore stability analysis focuses on the coupled effects of fluid flow and deformation, and the impact of three-dimensional natural fracture characteristics on wellbore instability is not yet closely examined. This paper carried out a series of experiments to analyze the shale sample's fracture characteristics and shear strength. A fully coupled 3D hydro-mechanical model was then developed using the distinct element method (DEM) to investigate the wellbore instability. Finally, a comprehensive parametric study was performed to analyze the effects of characteristics of fractures (e.g., distribution density, fracture roughness and associated shear strength, dip angle, and seepage time) on wellbore stability under different in-situ stress states. The results show that the shear strength of shale increases with the increase in normal pressure (4 MPa, 6 MPa, and 8 MPa); the shear strength increases by 64.9% when the roughness is 0.325 mm (the largest increase among the three roughness cases). The maximum displacement around the wellbore is 1.3–1.5 times greater under the presence of weak planes than under no weak planes. Fracture roughness poses an important impact on wellbore stability because larger fracture roughness is associated with higher fracture shear strength. As the dip angle of the weak plane increases, the number of yield zones (shear failure) and maximum wellbore displacement increases and then decreases, and the peak occurs at the dip angle of 45°. The seepage time of drilling fluid is positively correlated with the number of yield zones around the wellbore, and the damage rate of drilling fluid to wellbore stability gradually decreases with the seepage time.

Hydrogen Production through Distinctive C-C Cleavage during Acetic Acid Reforming at Low Temperature.

Acetic acid reforming is a green method for sustainable hydrogen production owing to its renewable source from biomass conversion. However, conventional acetic acid reforming would produce various byproducts, including CO, CH4 and so on. Here, we develop a distinctive method for selective hydrogen production from C-C directional cleavage during acetic acid reforming. Completely different from conventional acetic acid reforming process, acetic acid would react with water over organoruthenium catalyst during its C-C cleavage at low temperature, then produce methanol and formic acid (CH3COOH+H2O→CH3OH+HCOOH). Lastly, methanol and formic acid could further decompose into hydrogen and carbon dioxide over organoruthenium selectively. As a result, there is little CO and CH4 produced in the first step of C-C bond cleavage during acetic acid reforming at 100 °C. Hydrogen production rate is up to 26.8 molH2/(h-1*mol-1 Ru) at 150 °C through a tandem catalysis. A mechanism for C-C cleavage of acetic acid is proposed based on intermediate product analysis and density functional theory (DFT) calculation. Firstly, the C-C single bond was transformed into C=C double bond by dropping one H atom to organoruthenium. Then the coming H2O molecule reacted with the C=C bond by an addition reaction, forming methanol and formic acid. This research not only proposes distinctive reaction pathway for hydrogen production from acetic acid reforming, but also provides some inspiration for selective C-C bond cleavage during ethanol reforming.

Crack Propagation in Heterogeneous Gravity Dams Due to Overflow Using Polygonal Grain-Based Distinct Element Method

Crack Propagation in Heterogeneous Gravity Dams Due to Overflow Using Polygonal Grain-Based Distinct Element Method

Growth, survival and reproductive traits of chemical induced and tetraploid-derived triploid Hong Kong oysters (Crassostrea hongkongensis)

Chemical induction (inhibition of polar body II release) and crossing between tetraploid and diploid oysters (mating triploids) are 2 distinct and typical methods for obtaining triploids, and systematic comparative analysis on the phenotypic traits of these two triploids is rare. We tracked and compared the growth, survival, reproductive characteristics and ploidy composition of diploids, induced triploids, and mating triploids, and conducted interaction analysis between genotypes and sites. The results showed that the shell height of larvae showed a significant decrease trend of 3 NTD > 3 NCB > 2 N, while the D larval rate and survival rate of 3 NCB were the lowest, which may be related to the long-term effects of CB toxicity. During offshore farming, from the 180th to 700th day, the shell heights of the three groups followed a significantly decreasing order at both sites, namely 3 NTD > 3 NCB > 2 N (except on the 270th day at Beihai site), and the triploid advantage of shell height in 3 NTD was generally higher than that in 3 NCB, with the same trend in the whole weight. Genotypes and sites always had significant effects on shell height, but their interactions were not significant. The incremental survival rates of the 2 triploid groups were higher than that of the diploid at both sites on the 270th and 700th day, while that of the 3 NTD was significantly higher than the 3 NCB only on the 270th day. In addition, both triploids had some individuals fertile and could produce mature gametes, but the proportion was much lower than that of diploids. Triploid gonadal development is characterized by feminization and a high proportion of hermaphroditism. The fertility ratio and gamete production of 3 NTD were higher than that of 3 NCB, but the relatively high fertility of 3 NTD had no significant effect on its growth and survival. Ploidy composition tracking found that the ploidy composition of 2 N and 3 NTD groups was stable, but the ploidy composition of 3 NCB varied over time. These results indicate that 3 NTD has better performance and higher triploid advantages than 3 NCB, making it more suitable for the commercial production and promotion of triploid C. hongkongensis.

EFFECT OF DIFFERENT PROCESSING METHODS ON THE PROXIMATE COMPOSITION, MINERAL, AND ORGANOLEPTIC PROPERTIES OF DOMESTICATED GRASSCUTTER (Thryonomys swinderianus)

Wildlife plays a crucial role in supplying energy, protein, and various components essential for human diets in tropical regions. In urban areas, there is still substantial consumption of bush meat, such as grasscutter, which serves as a significant source of nutrition. The study examined the proximate composition, mineral content, and sensory properties of processed grasscutter (Thryonomys swinderianus) meat using three distinct preparation methods. The grasscutter samples were acquired from the Department of Forestry and Wildlife Management at the Federal University of Agriculture, Abeokuta, Nigeria. The meat underwent smoking, boiling, and oven drying after slaughtering and washing. A portion of each processed grasscutter meat was collected, placed in sterile containers, and transported to the laboratory for proximate and mineral analysis. A questionnaire was employed for organoleptic evaluation and twelve taste panelists comprising randomly selected students, non-academic staff members, and lecturers from the department participated in the assessment. The proximate analysis followed the standard procedure outlined in the AOAC method (2005). Results indicated that the crude protein content was 20.06% for smoked and 16.40% for oven-dried grasscutter meat. The oven-dried meat exhibited lower fat content. Sensory evaluation suggested that oven-dried meat was perceived as the most favorable, with the highest mean values for aroma (8.42±0.26), taste (7.67±0.36), flavor (8.42±0.99), texture (7.67±0.36), acceptability (7.67±0.36), and the lowest for odor (2.08±0.31) compared to boiled and smoked samples. Moreover, oven-dried grasscutter meat demonstrated elevated levels of magnesium (7.45%) and potassium (27.43%). It is therefore recommended that the oven drying method of meat processing should be...

The Observation of Actors’ Vocal Emotion Exercises with Deep Learning and Spectral Analysis

This paper presents two distinct methods that demonstrate the increased intensity of a specific emotion when the induced emotion is trained daily for 30 days. For this study, four actors participated in a 30-day exercise trial and were recorded each day using high-level audio equipment. The first method supporting our hypothesis is a deep learning approach. A convolutional neural network pre-trained on Mel-frequency cepstral coefficients analyzed the actors' recordings and delivered the intensity of the detected emotion. The CNN tested 3,561 segments of 0.2-second length, and the results showed a higher level of intensity on the final day of training for each participant. The second method is spectral analysis. The spectrograms generated on the first and final days of the experiment showed that the spectral composition on the final day had a wider range of frequencies than on the first day, further supporting our hypothesis.

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students.

Associations of hyperthyroidism with epilepsy: a Mendelian randomization study

Prior studies have revealed an increased susceptibility to epilepsy in hyperthyroid individuals, but the genetic basis of the hyperthyroidism–epilepsy relationship is not fully comprehended, prompting this study to explore this potential association. We conducted a two-sample Mendelian randomization (TSMR) study to explore the relationship between hyperthyroidism and epilepsy by utilizing aggregated statistics from Genome-Wide Association Studies (GWAS). Data for hyperthyroidism were derived from a GWAS encompassing 462,933 participants, while epilepsy data were sourced from the International League Against Epilepsy (ILAE) consortium. Five distinct methods were employed for TSMR analysis, which included the inverse variance weighting method, MR Egger method, weighted median method, simple model, and weighted model. In our sensitivity analysis, we employed the MR Egger and MR PRESSO methods to assess pleiotropy, and inverse variance weighting and MR Egger in Cochran’s Q statistics to assess heterogeneity. In the IEU database, utilizing the MR-Egger method, we obtained an odds ratio (OR) of 2.631 (95% CI 0.608, 9.796) with a p-value of 0.122. Meanwhile, employing the Weighted Median method yielded an OR of 1.813 (95% CI 0.786, 4.181) with a p-value of 0.163. The IVW method exhibited an OR of 1.986 (95% CI 1.127, 3.502) with a p-value of 0.018. In the assessment of heterogeneity, the MR-Egger method produced a Q statistic of 65.205, accompanied by a p-value of 0.087, while the IVW method recorded a Q statistic of 66.668 with a p-value of 0.083. The multifactorial analysis results showed an intercept term with a standard error (SE) value of 0.009 and a p-value of 0.291. In the FinnGen database, employing the MR-Egger method for all epilepsy data, we observed an OR of 0.952 (95% CI 0.831, 1.093) with a p-value of 0.539. Simultaneously, the Weighted Median method produced an OR of 0.986 (95% CI 0.953, 1.021) with a p-value of 0.423. The IVW method indicated an OR of 0.992 (95% CI 0.965, 1.019) with a p-value of 0.541. The MR-Egger method’s assessment of heterogeneity resulted in a Q statistic of 2.671, associated with a p-value of 0.445, while the IVW method generated a Q statistic of 3.011 with a p-value of 0.556. The multifactorial analysis results displayed an intercept term with a SE-value of 0.019 and a p-value of 0.601. Sensitivity analysis found no evidence of horizontal pleiotropy or heterogeneity. Hyperthyroidism was found to be causally related to all epilepsy but had no effect on other types of epilepsy.

Efficient implicit constraint handling approaches for constrained optimization problems

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput. Methods Appl. Mech. Eng. 363:112917, 2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.

Uncertainty in Distinct Element Modeling of Freestanding Structures Considering Stiffness Parameters

ABSTRACT Numerical studies on freestanding structures typically leverage the Distinct Element Method where the response is dictated by joints; however, the response carries significant uncertainty due to joint stiffness parameters. This study aims to quantify the influence of joint stiffness parameters on the global response of freestanding structures subjected to seismic excitation in a probabilistic formulation. By using a range of joint stiffness values with rectangular rigid blocks of different aspect ratios, the impact of joint stiffness parameters is evaluated against a large suite of historical ground motions. Results indicate that increasing contact stiffness results in increased stability against overturning.

A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model

In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.

The mitochondrial genome of Huaaristarchorum (Heude, 1889) (Gastropoda, Cerithioidea, Semisulcospiridae) and its phylogenetic implications.

Research on complete mitochondrial genomes can help in understanding the molecular evolution and phylogenetic relationships of various species. In this study, the complete mitogenome of Huaaristarchorum was characterized to supplement the limited mitogenomic information on the genus Hua. Three distinct assembly methods, GetOrganelle, NovoPlasty and SPAdes, were used to ensure reliable assembly. The 15,691 bp mitogenome contains 37 genes and an AT-rich region. Notably, the cytochrome c oxidase subunit I (COX1) gene, commonly used for species identification, appears to be slow-evolving and less variable, which may suggest the inclusion of rapidly evolving genes (NADH dehydrogenase subunit 6 [ND6] or NADH dehydrogenase subunit 2 [ND2]) as markers in diagnostic, detection, and population genetic studies of Cerithioidea. Moreover, we identified the unreliability of annotations (e.g., the absence of annotations for NADH dehydrogenase subunit 4L [ND4L] in NC_037771) and potential misidentifications (NC_023364) in public databases, which indicate that data from public databases should be manually curated in future research. Phylogenetic analyses of Cerithioidea based on different datasets generated identical trees using maximum likelihood and Bayesian inference methods. The results confirm that Semisulcospiridae is closely related to Pleuroceridae. The sequences of Semisulcospiridae clustered into three clades, of which H.aristarchorum is one; H.aristarchorum is sister to the other two clades. The findings of this study will contribute to a better understanding of the characteristics of the H.aristarchorum mitogenome and the phylogenetic relationships of Semisulcospiridae. The inclusion of further mitochondrial genome sequences will improve knowledge of the phylogeny and origin of Cerithioidea.