Original Time Research Articles

Comparative Mitogenome of Phylogenetic Relationships and Divergence Time Analysis within Potamanthidae (Insecta: Ephemeroptera).

Potamanthidae belongs to the superfamily Ephemeroidea but has no complete mt genome released in the NCBI (except for two unchecked and one partial mt genome). Since the sister clade to Potamanthidae has always been controversial, we sequenced seven mt genomes of Potamanthidae (two species from Rhoenanthus and five species from Potamanthus) in order to rebuild the phylogenetic relationships of Potamanthidae in this study. The divergence time of Potamanthidae was also investigated by utilizing five fossil calibration points because of the indeterminate origin time. In addition, because Rhoenanthus coreanus and Potamanthus luteus are always in low-temperature environments, we aimed to explore whether these two species were under positive selection at the mt genome level. Amongst the 13 PCGs, CGA was used as the start codon in COX1, whereas other genes conformed to initiating with an ATN start codon. From this analysis, UUA (L), AUU (I), and UUU (F) had the highest usage. Furthermore, the DHU arm was absent in the secondary structure of S1 in all species. By combining the 13 PCGs and 2 rRNAs, we reconstructed the phylogenetic relationship of Potamanthidae within Ephemeroptera. The monophyly of Potamanthidae and the monophyly of Rhoenanthus and Potamanthus were supported in the results. The phylogenetic relationship of Potamanthidae + (Ephemeridae + Polymitarcyidae) was also recovered with a high prior probability. The divergence times of Potamanthidae were traced to be 90.44 Mya (95% HPD, 62.80-121.74 Mya), and the divergence times of Rhoenanthus and Potamanthus originated at approximately 64.77 Mya (95% HPD, 43.82-88.68 Mya), thus belonging to the late Pliocene Epoch or early Miocene Epoch. In addition, the data indicated that R. coreanus was under negative selection and that ATP8 and ND2 in Potamanthidae had a high evolutionary rate.

Gendered labor: Appearance management and the unequal extraction of effort and time among ambassadors

This article examines the gendered appearance management ambassadors do to properly represent states. Drawing on feminist sociologist theorizations of labor, and attentive to intersections with sexuality, class, and Western standards, the study shows the unequal extraction of time and effort from men and women as they manage their appearance in diplomacy. Theoretically, I argue that the gendered management of dress, hair, and adornments are better conceived of as labor than as habitus or capital. Empirically, the study focuses on appearance management among ambassadors posted to Washington D.C. 2018–2020. Relying on a wealth of data—an original time survey, ambassador interviews, and observations—the study makes three empirical claims about the diplomatic embodiment of states: (a) diplomatic appearance standards are largely gender binary, with male uniformity and female variation; (b) more time and effort is demanded from female than male ambassadors; and (c) diplomatic appearance standards demand gender labor that is inextricably linked with the management of sexuality, upper class conventions, and Western physical appearance norms for representing states. In short, individuals presenting as men or women (although apparently not nonbinary) may now represent states as ambassadors, but more labor is extracted from women in doing so.

From simple to complex: a sequential method for enhancing time series forecasting with deep learning

Abstract Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network.

Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing.

Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator's cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system.

A novel multi-step-ahead approach for cloud server aging prediction based on hybrid deep learning model

Predicting software aging in cloud servers is essential for preventing cloud system breakdown and unexpected failures. However, the aging time series data in cloud servers often exhibits non-linearity and stochastic fluctuations, posing challenges for accurate prediction. To address this issue, this study proposes a novel multi-step ahead approach for cloud server aging prediction. Firstly, the original aging time series data is denoised using discrete wavelet transformation (DWT) and then reconstructed into multi-dimensional data by phase state reconstruction (PSR) to capture potential aging patterns. Subsequently, a deep long short-term memory network (DLSTM) is developed to predict the aging trend. Additionally, a stacked auto-encoder LSTM (SAE-LSTM) is designed to optimize the initial weight matrix of DLSTM to enhance prediction abilities. Finally, multi-step prediction experiments are conducted on our time series datasets from OpenStack cloud servers and a public dataset from Alibaba Cluster Trace Program. The comparative analysis of the proposed method with five baseline methods confirms its superiority in terms of prediction accuracy. For response time and free memory test sets, the range of root mean square error (RMSE) values are produced by the proposed method distributed in [0.1688–0.1931] and [139.05–146.05] respectively, with respect to the multi-step ahead prediction horizon from 1 up to 10. These results demonstrate that the proposed method can tackle the limitations of one-step ahead prediction, effectively extracting relevant aging features, and achieving low prediction accuracy. The study provides valuable insights into revealing future aging trends in cloud servers and offers a reference for implementing preventive maintenance and prognostics in cloud servers.

Energy Prices and Their Impact on US Stock Indices: A Wavelet- based Quantile-on-Quantile Regression Approach

This study delves into the effects of crude oil and gas prices on the United States’ (US) conventional, Islamic, and environmental, social, and governance (ESG) stock indices from January 2013 to December 2022. Decomposing original time series data to minimise inherent fluctuations and using the Quantile-on-Quantile (QQ) regression approach presents a nuanced view of how these energy prices impact different stock indices. The findings reveal that crude oil prices have a variable impact on the indices: high prices negatively influence the indices, low prices have a positive effect, and moderate prices yield a moderate positive impact. After data decomposition, this positive influence diminishes in higher quantiles, indicating an emerging neutral effect in stabilised conditions. In contrast, gas prices show a limited impact, with high prices slightly benefiting conventional and ESG indices but less so for the Islamic index. This suggests a more pronounced influence of oil prices on the indices, likely due to the dependence of many listed companies on oil. The study emphasises the importance of considering oil-related risks in investment strategies and highlights the asymmetric impact of crude oil prices on the US stock indices. These findings have significant implications for investors and policymakers. They underscore the need for careful consideration of oil price dynamics in investment decisions and the importance of staying vigilant against shifts in oil prices that could lead to market instability.

An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. Using a panel data structure, credit risk scores can be integrated with an APC framework using either logistic regression or machine learning. Such APC scores for default, payoff, and other key rates fit naturally into forward-looking cash flow estimates. Given an economic scenario, every applicant at the time of origination can be assigned profit and profit volatility estimates so that underwriting can truly be account-level. This process optimizes the most fallible part of underwriting, which is setting cutoff scores and assigning loan pricing and terms. This article provides a summary of applications of APC models across portfolio management roles, with a description of how to create the models to be directly integrated. As a consequence, cash flow calculations are available for each account, and cutoff scores can be set directly from portfolio financial targets.

Inferring the Focal Depths of Small Earthquakes in Southern California Using Physics-Based Waveform Features

ABSTRACT Determining the depths of small crustal earthquakes is challenging in many regions of the world, because most seismic networks are too sparse to resolve trade-offs between depth and origin time with conventional arrival-time methods. Precise and accurate depth estimation is important, because it can help seismologists discriminate between earthquakes and explosions, which is relevant to monitoring nuclear test ban treaties and producing earthquake catalogs that are uncontaminated by mining blasts. Here, we examine the depth sensitivity of several physics-based waveform features for ∼8000 earthquakes in southern California that have well-resolved depths from arrival-time inversion. We focus on small earthquakes (2<ML<4) recorded at local distances (<150 km), for which depth estimation is especially challenging. We find that differential magnitudes (Mw/ML–Mc) are positively correlated with focal depth, implying that coda wave excitation decreases with focal depth. We analyze a simple proxy for relative frequency content, Φ≡log10(M0)+3log10(fc), and find that source spectra are preferentially enriched in high frequencies, or “blue-shifted,” as focal depth increases. We also find that two spectral amplitude ratios Rg 0.5–2 Hz/Sg 0.5–8 Hz and Pg/Sg at 3–8 Hz decrease as focal depth increases. Using multilinear regression with these features as predictor variables, we develop models that can explain 11%–59% of the variance in depths within 10 subregions and 25% of the depth variance across southern California as a whole. We suggest that incorporating these features into a machine learning workflow could help resolve focal depths in regions that are poorly instrumented and lack large databases of well-located events. Some of the waveform features we evaluate in this study have previously been used as source discriminants, and our results imply that their effectiveness in discrimination is partially because explosions generally occur at shallower depths than earthquakes.

Abstract PS07-09: Prenatal BRCA1 epimutations is a major cause of triple-negative breast cancer

Abstract Background: Low level normal cell BRCA1 epimutations have been associated with an increased risk of triple-negative breast cancer (TNBC). However, the fraction of TNBCs that may have BRCA1 epimutations as their underlying cause is unknown. Neither are the time of occurrence and the potential inheritance pattern of BRCA1 epimutations established. Methods: To address these questions, we analyzed BRCA1 methylation status in breast cancer tissue and matched white blood cells (WBC) from 411 patients with primary breast cancer, including 66 TNBCs. Samples were analyzed by a highly sensitive next-generation sequencing (NGS) assay on an Illumina MiSeq sequencer, allowing allele-resolved methylation assessment. Further, to assess the time of origin and the characteristics of normal cell BRCA1 methylation, we analyzed umbilical cord samples from 1260 newborn girls and 200 newborn boys.To assess potential Mendelian heritage, we analyzed BRCA1 methylation status in WBCs from 575 mothers and 531 fathers of newborn girls with (n = 102) and without (n = 473) WBC BRCA1 methylation. Results: We found concordant tumor and mosaic WBC BRCA1 epimutations in 10 out of 66 patients with TNBC and in four out of six patients with estrogen receptor (ER)-low expression (< 10%) of tumors (combined 14 out of 72; 19.4%, CI: 11.1-30.5). These exceeded the number of tumors harboring germline (n = 5) or somatic (n = 4) BRCA1 mutations. Notably, BRCA1 methylation and BRCA1 mutations were mutually exclusive. Contrasting the findings in TNBC and ER-low exprssion tumors, we found WBC and tumor BRCA1 methylation concordance in only three out of 221 patients with ER >10+% tumors and zero out of 116 patients with HER2 positive tumors. Intraindividually, BRCA1 epimutations affected the same allele in normal and tumor cells. Assessing BRCA1 methylation in umbilical cord WBCs from newborn girls, we found mosaic, predominantly monoallelic BRCA1 epimutations, with qualitative features similar to those in adults, in 113/1260 (9.0%) of individuals. We found no correlation between WBC BRCA1 methylation in newborns and methylation status in their mothers, fathers, or any parent. Notably, WBC BRCA1 methylation occurred at a significantly lower frequency in newborn boys ( 9/200; 4.5%) as compared to newborn girls (p = 0.038). Similarly, WBC BRCA1 methylation was found less common among fathers (16/531; 3.0%), as compared to mothers (46/575; 8.0%; p = 0.0003). Conclusions: Our findings suggest prenatal BRCA1 epimutations might be the underlying cause of around 20% of TNBC and low-ER expressing breast cancers. Such constitutional mosaic BRCA1 methylation likely arise through gender-related mechanisms in utero, independent of Mendelian inheritance. Citation Format: Per Lonning, Hans Petter Eikesdal, Elisabet Ognedal, Bjornar Gilje, Steinar Lundgren, Egil Blix, Helge Espelid, Jürgen Geisler, Stephanie Geisler, Emiel Janssen, Synnøve Yndestad, Laura Minsaas, Beryl Leirvaag, Reidun Lillestol, Stian Knappskog, Oleksii Nikolaienko. Prenatal BRCA1 epimutations is a major cause of triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS07-09.

Novel f-CaO soft sensor for cement clinker based on integrated model of dual-parallel structure.

Aiming at the problem that the cement production process is inherently affected by uncertainty, time delay, and strong coupling among variables, this paper proposed a novel soft sensor of free calcium oxide in a cement clinker. The model utilizes a dual-parallel integrated structure with an optimized integration of one-dimensional convolutional neural networks, long and short-term memory networks, graphical neural networks, and extreme gradient boosting. The proposed model can mitigate the risks associated with overfitting while incorporating the strengths of each individual model and excels in extracting both local and global features as well as temporal and spatial characteristics from the original time series data, ensuring its stability. The experimental results demonstrate that this dual-parallel integrated model exhibits superior robustness, predictive accuracy, and generalization capabilities when compared to single models or enhancements made to other deep learning algorithms.

Optimizing energy storage plant discrete system dynamics analysis with graph convolutional networks

Addressing the challenges of suboptimal model performance and excessive parameters and operations in the optimization of energy storage power plants utilizing Graph Convolutional Network (GCN), this paper introduces a novel approach – the packet-switched graph convolutional network. Initially, a GCN extreme learning machine is established. Drawing inspiration from this solid foundation, we have innovatively crafted a group exchange graph convolution module. This module leverages group graph convolution techniques to amalgamate unique node feature information, tailored to diverse topology graph matrices based on various groupings. This innovative approach ensures that information flows freely and effectively among distinct groupings. Furthermore, we have designed a cutting-edge timing depth separation convolution module, comprising two innovative components. The first component introduces timing depth separation convolution, revolutionizing the original timing convolution module. The second component, the packet-switching graph convolutional network, revolutionizes the time sequence depth separation convolution process. It achieves this by employing 1 × 1 convolutional layers between different feature fusion packets, enabling seamless information exchange between distinct packets. Experimental results demonstrate the efficacy of the proposed model, with root mean square error (RMSE) metrics and root mean square error (MAE) metrics for single-step prediction reaching 46.08 and 26.22 at 60 min, respectively. In multi-step testing, the proposed model exhibits a 14.71 % reduction in RMSE error at the 15-min scale and a 9.29 % reduction at the 60-min scale compared to the benchmark model. This performance improvement enhances the operational efficiency and reliability of the energy storage plant, particularly under dynamic changes in the time series.

Cost Overrun and Delays in Construction Management by Using Primavera

Abstract: Project management is the practice of organizing, planning, carrying out, overseeing, and wrapping up a team's work in order to accomplish particular objectives and satisfy particular success criteria within a given time frame. Each project is unique, and managing it is necessary to meet the project's need for increased productivity. Notwithstanding the centuries-long evolution of the construction industry and the decades-long quest for novel solutions by researchers, a variety of obstacles still need to be overcome in order to improve the speed, safety, affordability, and accuracy of the construction process. Utilizing a program called Prima Vera p6, which is a tool for project planning and progress tracking, the study focuses on an ongoing residential building construction project (G+5) . This project is a G+5 storey residential building with a rectangular shape RCC framed construction with a total size of 1391 Sq. Mtr. And the floor height is 3m. We used Primavera to calculate project quantities, schedule, and estimate the project. We created a Gantt chart and a network diagram for this project and completed it in a timely and cost-effective manner. It gathers all relevant data from the relevant agencies and closely monitors the work processes. Through our work on it, we have learned a great deal about the challenges that businesses encounter on a daily basis. Though every project is unique, as I've already shown, we may learn from our mistakes and prevent them in future endeavors. Using Primavera P6, a project management tool, a comparison of the original and actual time and cost is conducted in this study. The project monitoring process itself might not be completed effectively even with well-established planning and policies.

Maternal behavior of dairy cows and suckling behavior of dairy calves in different cow-calf contact conditions

Cow-calf contact (CCC) has been suggested as an alternative to the common practice of early separation of dairy cow and calf. Increasing CCC poses challenges by reducing the machine milk yield and increasing separation stress. Part-time CCC may prove more feasible than full-time contact for farmers but may affect the maternal behavior of the cows. Gradually reducing the daily contact could gradually wean the calf off milk, preparing it for separation. This study aims to investigate 1) how cows' maternal behaviors toward their own calf are affected by part-time contact, and 2) whether calves' suckling behavior is reduced by reducing daily cow contact. Fifty-six dairy cows and their calves were assigned to either full-time (23 h/d) or part-time (10 h/d) cow-calf contact for 7 weeks (main rearing period). This was followed by 2 weeks of either reduced contact (50% in wk 8, then 25% of original contact time in wk 9) or unchanged contact. First, the maternal behavior of full and part-time cows was observed for 24 h in the third week of the main rearing period. Part-time cows spent less time nursing and grooming their own calf than full-time cows did. However, the amount of nursing in the inverse parallel position and the probability of a cow nursing a calf other than her own did not differ between the 2 treatments. Second, calf suckling behavior was observed for 24 h in both weeks of the reduced contact period. Averaged across both observations, total suckling time (i.e., time spent suckling any cow) was lower in part-time calves that experienced reduced contact compared with part-time calves with unchanged contact. In wk 9, time spent suckling only the dam was lower for full-time calves with reduced contact compared with part-time calves with unchanged contact. In conclusion, although we found a lower nursing and grooming time in part-time cows compared with full-time, we found no difference between treatments with regards to nursing in the inverse parallel position and nursing a calf other than the cow's own. The effect of gradually reducing cow-calf contact on calves' suckling time requires more research.

Enhancing the transferability of adversarial attacks with diversified input strategies

Abstract Adversarial examples are often used to test and evaluate the security and robustness of image classification models. Though adversarial attacks under white-box setting can achieve a high attack success rate, due to overfitting, the success rate of black-box attacks is relatively low. To this end, this paper proposes diversified input strategies to improve the transferability of adversarial examples. In this method, various transformation methods are applied to randomly transform the original image multiple times, thereby generating a batch of transformed images. Then, in the process of back-propagation, the loss function gradient of the transformed images is calculated, and a weighted average of the obtained gradient values is performed to generate adversarial perturbation, which is iteratively added to the original image to generate adversarial examples. Meanwhile, by increasing the variety of data augmentation transformation types and the number of input images, the proposed method effectively alleviates overfitting and improves the transferability of adversarial examples. Extensive experiments on the ImageNet dataset indicate that the proposed method can perform black-box attacks better than benchmark methods, with an average of 97.2% success rate attacking multiple models simultaneously.

Short-term Electricity Load Forecasting Based on Improved Seagull Algorithm Optimized Gated Recurrent Unit Neural Network

INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads. Power load changes have characteristics such as non stationarity, randomness, seasonality, and high volatility. Therefore, how to construct accurate short-term power load forecasting models has become the key to the normal operation and maintenance of power.OBJECTIVES: Accurate short-term power load forecasting helps to arrange power consumption planning, optimize power usage and largely reduce power system losses and operating costs.METHODS: A hybrid decomposition-optimization-integration load forecasting method is proposed to address the problems of low accuracy of current short-term power load forecasting methods.RESULTS: The original power load time series is decomposed using the complete ensemble empirical modal decomposition method, while the correlation of power load influencing factors is analysed using Pearson correlation coefficients. The seagull optimisation algorithm is overcome to fall into local optimality by using the random adaptive non-linear adjustment strategy of manipulated variables and the differential variational Levy flight strategy, which improves the search efficiency of the algorithm. Then, the The gated cyclic unit hidden layer parameters are optimised by the improved seagull optimisation algorithm to construct a short-term electricity load forecasting model.The effectiveness of the proposed method is verified by simulation experimental analysis. The results show that the proposed method has improved the accuracy of the forecasting model.CONCLUSION: The CEEMD method is used to decompose the original load time series, which improves the accuracy of the measurement model. The GRU prediction model based on improved SOA optimization not only has better prediction accuracy than other prediction models, but also consumes the least amount of time compared to other prediction models.

Lineage Analysis and Birthdating of Drosophila Central Complex Lineages.

From insects to humans, the nervous system generates complex behaviors mediated by distinct neural circuits that are composed of diverse cell types. During development, the spatiotemporal gene expression of the neural progenitors expands the diversity of neuronal and glial subtypes. Various neural stem cell-intrinsic and -extrinsic gene programs have been identified that are thought to play a major role in generating diverse neuronal and glial cell types. Drosophila has served as an excellent model system for discovering the fundamental principles of nervous system development and function. The sophisticated genetic tools allow us to link the origin and birth timing (the time when a particular neuron is born during development) of neuron types to unique neural stem cells (NSCs) and to a developmental time. In Drosophila, a special class of NSCs called Type II NSCs has adopted a more advanced division mode to generate lineages for the higher-order brain center, the central complex, which is an evolutionarily conserved brain region found in all insects. Type II NSCs, similar to the human outer radial glia, generate intermediate neural progenitors (INPs), which divide many times to produce about eight to 10 progeny. Both Type II NSCs and INPs express distinct transcription factors and RNA-binding proteins that have been proposed to regulate the specification of cell types populating the adult central complex. Here, we describe the recently invented lineage filtering system, called cell class-lineage intersection (CLIn), which enables the tracking and birthdating of the Type II NSC lineages. Using CLIn, one can easily generate clones of different Type II NSCs and identify not only the origins of neurons of interest but also their birth time.

A dense micro-electromechanical system (MEMS)-based seismic network in populated areas: rapid estimation of exposure maps in Trentino (NE Italy)

Abstract. The micro-electromechanical system (MEMS)-based seismic network of Trentino (NE Italy) consists of 73 low-cost accelerometers installed close to inhabited areas. These sensors have a suitable sensitivity to detect moderate-to-strong earthquakes but are able to record even weaker seismicity. The densely distributed peak ground acceleration values recorded by MEMS and other types of stations are integrated within the existing seismic monitoring procedure in order to automatically obtain a complete set of strong motion parameters a few minutes after the origin time. The exposure of the resident population and critical buildings is estimated by quantifying the different levels of shaking, which is expressed according to the Mercalli–Cancani–Sieberg intensity scale. These types of results, summarized in synthetic portable document format (PDF), can be useful for civil protection purposes to evaluate the state of emergency after a strong earthquake in a timely manner and to choose how and where to activate first aid measures and targeted structural monitoring.

Cholinergic interneurons in the dorsal striatum play an important role in the acquisition of duration memory.

The present study aimed to examine the effect of cholinergic interneuron lesions in the dorsal striatum on duration-memory formation. Cholinergic interneurons in the dorsal striatum may be involved in the formation of duration memory since they are among the main inputs to the dorsal striatal muscarinic acetylcholine-1 receptors, which play a role in the consolidation of duration memory. Rats were sufficiently trained using a peak-interval 20 s procedure and then infused with anti-choline acetyltransferase-saporin into the dorsal striatum to cause selective ablation of cholinergic interneurons. To make the rats acquire new duration-memories, we trained them with a peak interval 40 s after lesion. Before lesion, the peak times (an index of duration memory) for sham-lesioned and lesioned groups were similar at approximately 20 s. In the peak interval 40 s session, the peak times for the sham-lesioned and lesioned groups were approximately 30 and 20 s, respectively. After additional peak interval 40 s sessions, the peak times of both groups were shifted to approximately 40 s. Those results suggest that the cholinergic interneuron lesion delayed new duration-memory acquisition. Subsequent experiments showed that cholinergic interneuron lesions did not retard the shift of peak time to the original target time (20 s). Following experiment without changing the target time after lesion showed that cholinergic interneuron lesions did not change their peak times. Our findings suggest that cholinergic interneurons in the dorsal striatum are involved in new duration-memory acquisition but not in the utilization of already acquired duration memory and interval timing.

An ensemble-based stage-prediction machine learning approach for classifying fetal disease

Fetal diseases often lead to the death of many babies during pregnancies. Machine learning and deep learning are promising technologies providing efficient and effective detection and treatment of various fetal diseases. We contribute to the medical field by addressing the critical challenge of fetal disease classification, a concern affecting females and infants. This study utilizes 22 features associated with fetal heart rate extracted from 2126 patient records within the Cardiotocography(CTG) datasets. Our classification system offers a cost-effective, efficient, and accurate solution. It classifies fetal diseases into three categories: Normal, Suspect, and Pathological, based on preprocessed data that underwent MinMax Scaling and employed dimensionality reduction techniques, including Principal Component Analysis(PCA) and Autoencoders. By incorporating dimensionality reduction techniques, the computation time has been reduced from 9 to 26 s to just 4 and 15 s, which is less than half of the original computation time. We assessed the performance of 11 standard machine learning algorithms and various performance metrics to identify the best classification model. We have applied the K-fold Cross-Validation technique to validate our model to improve machine learning models and identify the most effective algorithm. When the results are compared, it is observed that Extreme Gradient Boosting (XGBoost) gained the highest accuracy of 0.99% also highest precision 0.93% and outperformed all the other machine learning algorithms.

Archaeological and molecular evidence for ancient chickens in Central Asia

The origins and dispersal of the chicken across the ancient world remains one of the most enigmatic questions regarding Eurasian domesticated animals. The lack of agreement concerning timing and centers of origin is due to issues with morphological identifications, a lack of direct dating, and poor preservation of thin, brittle bird bones. Here we show that chickens were widely raised across southern Central Asia from the fourth century BC through medieval periods, likely dispersing along the ancient Silk Road. We present archaeological and molecular evidence for the raising of chickens for egg production, based on material from 12 different archaeological sites spanning a millennium and a half. These eggshells were recovered in high abundance at all of these sites, suggesting that chickens may have been an important part of the overall diet and that chickens may have lost seasonal egg-laying