Predictable Patterns Research Articles

Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis

The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.

ХАРАКТЕРИСТИКА ПОВРЕЖДЕНИЙ МАЛОБЕРЦОВОЙ КОСТИ ПРИ ПЕРЕЛОМАХ ЛОДЫЖЕК И ДИСТАЛЬНОГО ОТДЕЛА ГОЛЕНИ

The study purpose was the evaluation of fibula injuries in malleolar fractures and distal tibia fractures (segments 44 and 43 according to AO/OTA fracture classification), to reveal the most typical types of injuries and morphological fracture parameters, which could be important for the development of the intramedullary fixator of the fibula. Radiological data of 57 patients with malleolar fractures and 54 patients with distal tibia fractures were evaluated. In ankle injuries the vast majority of fibula fractures (84.2%) were presented by transsyndesmotic injuries (type B according to AO/OTA), majority of fibula fractures (89%) were simple, without signs of comminution, the fracture plane passed at an angle of 33.1°±10.7° to the axis of the fibula, the center of fracture was located at the level of 21.6±8.8 mm from ankle articular surface. In distal tibia fractures fibula injuries were more variable: in 20% of cases there were no fibula fracture at all, 6% involved the proximal part, 27% - shaft and 47% distal part of the fibula. Comminuted fractures were seen in 32% of cases, the center of the fracture was located from 10 mm distal to the tibia articular surface to 317 mm proximal to it (Ме 35 mm, Q1-Q3: 15-68 mm). The revealed data shows that most important for the development of the intramedullary fibular fixator are ankle injuries, which have more predictable pattern.

Differences in left and right lower limb control strategies in coping with visual tracking tasks during bipedal standing.

Differences in motor control between the lower limbs may influence the risk of sports injury and recovery from rehabilitation. In this study, differences in the visual feedback ability of the left and right lower limbs were assessed using visual target tracking tasks. Thirty-four healthy young subjects (aged 20.4 ± 1.2 years) were asked to move their bodies back and forth while tracking a visual target displayed on a monitor in front of them for 30 s. The two target motions were sinusoidal (i.e., predictable patterns) and more complex (random) patterns. To assess the ability of the lower limbs to follow visual target tracking, antero-posterior CoP (right limb, CoPap-r; left limb, CoPap-l) and medio-lateral CoP (right limb, CoPml-r; left limb, CoPml-l) data were measured using a stabilometer. Tracking ability by visual feedback ability was calculated as the difference in displacement between the target signal and the trajectories of the right and left pressure centers as trapezoidal areas, and a smaller sum of area (SoA) over the entire measurement time was defined as a greater tracking ability. Regarding the SoA in the anterior-posterior CoP, the mean SoA in the sinusoidal and random tasks was significantly lower in the CoP-r data than in the CoP-l data, indicating that the right lower limb had a more remarkable ability to follow visual target tracking. Regarding the SoA in the medial-lateral direction (CoP), the mean SoA in the sinusoidal and random tasks did not significantly differ between the two legs. The right lower limb may have a tracking function activated by the target signal when responding to visual stimuli. Identifying the motor strategies of each lower limb in response to visual stimuli will not only help identify potential differences between each lower limb but also suggest the possibility of enhancing the role of each lower limb in balance control.

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems

This paper presents an innovative Convolutional Neural Network (CNN) architecture designed for the prediction of beamforming vectors in Massive Multiple-Input Multiple-Output (MIMO) systems, a cornerstone technology in 5G and emerging wireless communication networks. As mmWave frequencies become central to modern MIMO systems, ensuring precise beam alignment amid susceptibility to blockages is imperative for maintaining robust communication links. Traditional beamforming algorithms, challenged by high complexity and sluggish adaptation to dynamic environmental conditions, are outperformed by the proposed CNN, which effectively captures complex channel patterns for accurate beam prediction. Leveraging a rich dataset from the DeepSense 6G scenarios, the study meticulously preprocesses the data through normalization and scaling before dividing it into training and testing subsets. The CNN, evaluated against real-world signal propagation behaviors, demonstrates a high prediction accuracy in beam indices, particularly in clear line-of-sight scenarios. The paper concludes with an assessment of the model's predictive performance, using Mean Absolute Error as a key metric, and proposes future enhancements to the model's architecture and training process. The implications of this work are significant, heralding a new era of intelligent, self-optimizing wireless networks that can adapt autonomously to environmental changes and user mobility

Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features.

Characteristics of the mandible structures have been relevant in anthropological and forensic studies for sex prediction. This study aims to evaluate the coronoid process, condyle, and sigmoid notch patterns in sex prediction through supervised machine learning algorithms. Cephalometric radiographs from 410 dental records of patients were screened to investigate the morphology of the coronoid process, condyle, and sigmoid notch and the Co-Gn distance. The following machine learning algorithms were used to build the predictive models: Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Logistic Regression, Multilayer Perceptron Classifier, Random Forest Classifier, and Support Vector Machine (SVM). A 5-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and ROC curves were constructed. All tested variables demonstrated statistical significance (p < 0.10) and were included in the construction of the predictive model. The Co-Gn variable stood out as the most important among the evaluated independent variables, showing greater relevance in three of the four algorithms used in assessing feature importance. In the analysis of the models' performance, the AUC ranged from 0.82 [95% CI = 0.72-0.93] to 0.66 [95% CI = 0.53-0.76] for the test data, and from 0.83 [95% CI = 0.80-0.87] to 0.71 [95% CI = 0.61-0.75] for cross-validation. The precision of the models ranged from 0.83 [95% CI = 0.75-0.91] to 0.68 [95% CI = 0.58-0.78] in the test phase, and from 0.78 [95% CI = 0.74-0.82] to 0.69 [95% CI = 0.65-0.75] in cross-validation. The SVM, KNN, and Gradient Boosting Classifier algorithms stood out with the highest AUC and precision values in both cross-validation and testing. The use of condyle, coronoid process, and sigmoid notch characteristics, in combination with supervised machine learning predictive models, shows potential for contributing to sex prediction based on morphometric bone characteristics, particularly regarding the distance between the condyle and gnathion. However, given the study's limitations, these findings should be interpreted with caution.

Technological Uncertainty and Catch-Up Patterns: Insights of Four Chinese Manufacturing Sectors

Existing literature highlights the relevance of technology uncertainty and processes in latecomers’ catch-up but largely ignores the potential interplay between the two as well as the catch-up cycle. Thus, the purpose of this study is to answer the research question of how process patterns throughout the various stages of the catch-up cycle hinge on technological uncertainty. Based on observations of the manufacturing industries of general equipment, pharmaceuticals, home appliances, and electronic device manufacturing in China, our findings reveal that the patterns differ in the early and late stages of the catch-up cycle, due to different technological uncertainties. We demonstrate the features of patterns in conjunction with the catch-up cycle: predictive or explanatory patterns as fundamental ones in the early stage; and convergent and divergent hybrid modes comprised of the aforementioned fundamental patterns in the late stage. Our results provide fresh insights into the catch-up process literature and offer valuable practical implications for latecomers.

Monitoring drought using explainable statistical machine learning models

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models. The model utilized factors such as wind speed, temperature, and humidity to better understand the importance of certain features in drought prediction. We hypothesized that explainable machine learning models can effectively forecast future drought severity. Results of experiments with a public domain data set showed that explainable statistical machine learning models predict drought severity with average precision and recall values of 0.55 and 0.56, respectively. We identified a subset of meteorological indicators as major contributors to predicting the occurrence of drought. The experiments used K- nearest neighbors (KNN) and random forest algorithms to predict drought severity. The random forest algorithm achieved the highest accuracy at 200 trees and 30 depth, while KNN achieved the highest accuracy when K was set to one. Feature importance analysis showed that surface pressure, temperature range at two meters, and maximum temperature at two meters were the most important features. In conclusion, statistical machine learning models provide insights into factors that impact drought and can enable researchers to identify steps to prevent natural disasters.

Gradient-Based Predictive Pulse Pattern Control with Active Neutral Point Potential Balancing for Medium-Voltage Induction Motor Drives

Gradient-Based Predictive Pulse Pattern Control with Active Neutral Point Potential Balancing for Medium-Voltage Induction Motor Drives

Deciphering the microbial signature of death: advances in post-mortem microbial analysis

Cadaver decomposition is a natural phenomenon intimately affected by numerous organisms such as insects, fungi, animals, and bacteria where they use the decaying body as their nutrition source. These organisms can be utilized in forensic science to estimate the post-mortem interval (PMI). The post-mortem interval refers to the time that has passed since the death of a person until the body was found. Forensic entomology is one of the popular approaches where successive colonization of insects on cadaver is studied to estimate PMI. However, sometime this method does not provide consistent results due to lack of insect activities during cold environment conditions or when crime scene is indoor. Therefore, a new approach is needed to aid forensic scientists to estimate PMI. Recently, researchers have noted that microbial communities have shown a predictable and clockwise successional pattern on decomposing cadavers and suggested this could be utilized to estimate PMI when this approach is etched with other established methods. The purpose of this review is to summarize some of the studies that have been conducted on the utility of microbial communities in estimating PMI and discuss the role of microbial communities in cadaver decomposition.

Analyzing the Day of the Week Effect: A Study on Stock Market Returns

The study investigates the Day of the Week (DOW) effect on stock market returns in Malaysia, Indonesia, and the Dow Jones Islamic Market Index (DJIM). Utilizing GARCH models to examine stock return volatility, the study identifies significant calendar anomalies in stock returns, particularly lower returns on Mondays and higher returns on Fridays. These findings challenge the Efficient Market Hypothesis (EMH), suggesting predictable patterns in markets, particularly in emerging economies. This research provides insights into investor behavior and trading strategies, offering practical implications for market practitioners and academics.

Factors Influencing Stock Price Prediction: Demographics, Mental State, Company Origin, and Past Performance

This study investigates the interplay between socio-demographic factors, mental state, and stock price prediction behavior among a sample of 150 individuals in Ulaanbaatar, Mongolia. Participants were tasked with completing incomplete graphs, varying in context in three survey groups, allowing for an exploration of how age, gender, and past value perceptions, and contextual things influence prediction patterns. The research aims to understand the cognitive biases underpinning investment decisions, particularly in a context of limited financial literacy. By analyzing completed graphs, the study identifies distinct decision-making tendencies across age and gender groups. Additionally, it examines whether the type of given context of the graph impacts prediction behavior in a certain way. While emotional bias was found to be less influential, the study reveals significant differences in predictions based on contextual differences, also the study revealed certain age and gender group tendencies bias proneness for certain biases. These findings contribute to a deeper understanding of investor psychology and provide insights for financial advisors seeking to tailor strategies based on client demographics.

Prediction of skin cancer invasiveness: A comparative study among the regions of Brazil

ContextSkin cancer is the most incident neoplasia in Brazil, and their invasiveness can be impacted by various factors, including geographical aspects. Identifying these factors is important for improving diagnosis and treatment. ObjectiveThe research focused on analyzing the impact of region on the invasiveness of skin cancer in Brazil, through the identification of regional predictive patterns. MethodsAn analysis and processing of data from the Hospital Cancer Registries (RHC) of Brazil's National Cancer Institute (INCA) were conducted, followed by the application of machine learning algorithms. The SHapley Additive exPlanations (SHAP) approach was employed to provide explanations for the developed artificial intelligence models. ResultsIt was revealed that geography plays a significant role in predicting the invasiveness of skin cancer, reinforcing the need to consider regional specificities in future studies. ConclusionsThe study identified that regional characteristics of Brazil impacts the prediction of the invasiveness of skin cancer. Despite limitations, such as the issue of data imbalance, the findings are important for developing more effective policies in the fight against skin cancer in the Brazil.

Robust Financial Market Share Prediction using Intuitionistic Possibility Fermatean Neutrosophic Soft Set

An addition of soft set theory, Neutrosophic soft set theory offers a versatile framework for handling indeterminacy and uncertainty in data. Using this theory for the prediction of market share includes representing market data in a neutrosophic soft-set format, where elements pose truth, indeterminacy, and false degrees. The predictive model is constructed to estimate future market shares with consideration for ambiguity and uncertainty by analyzing previous market factors and trends affecting market dynamics within these frameworks. The stock market prediction pattern is interpreted as a significant action and it is more beneficial. Therefore, stock prices will result in substantial profits from sound taking choices. Thus, stock market forecasting is a main task for investors to spend their money to create maximum profit due to the noisy and stagnant data. Stock market prediction uses learning tools and mathematical strategies. Therefore, this manuscript offers the design of Financial Market Share Prediction using the Intuitionistic Possibility Fermatean Neutrosophic Soft Set (FMSP-IPFNS) technique. In the FMSP-IPFNS technique, a three-stage approach is followed. Firstly, the data normalization process is executed using a min-max scalar approach. Secondly, the prediction process can be carried out using the IPFNS approach. Thirdly, the parameter adjustment of the IPFNS approach takes place using the grasshopper optimization algorithm (GOA). To validate the performance of the FMSP-IPFNS system, a sequence of experimentations were tested. The obtained values demonstrate the promising results of the FMSP-IPFNS system compared to other models

Release kinetics and biological properties of active films based on cellulose nanocrystal-chitosan in combination with γ-irradiation to mitigate microbial load in rice

Nanoemulsions (NEs) of two antimicrobial formulations (AF-1 and AF-2), a mixture of essential oils and citrus extracts, were prepared by microfluidization. For optimization, a central composite design was used to ascertain the effect of independent variables (pressure, number of cycles, and emulsifier concentration) on the physical properties of NEs [i.e., droplet size (DS), polydispersity index (PDI), zeta potential (ζ), and encapsulation efficiency (EE)] and compare with coarse emulsion. The optimized AF-1 and AF-2 values had DS of 116 and 40 nm, PDI of 0.17 and 0.2, ζ-potential of 49 and 32.3 mV, and EE of 77 and 79%, respectively. The microbicidal properties of the NEs against Escherichia coli O157:H7, Salmonella Typhimurium, Aspergillus niger, Penicillium chrysogenum, and Mucor circinelloides were significantly (P ≤ 0.05) higher than the coarse emulsion. The NEs of both AFs were incorporated into a chitosan-based (CH) film. In situ tests with rice using bioactive CH-based nanocomposite films (BCHNF) reinforced with cellulose nanocrystals (CNCs) were tested against selected bacteria and fungi with and without γ-irradiation (750Gy) and stored for eight weeks. The BCHNF alone reduced 50–71 % of bacterial and fungal growth compared to the controls, while the combined action of γ-irradiation and films reduced 73–93 % of the tested microbial growth compared to untreated controls. Incorporation of CNC into BCHNF demonstrated a predictable pattern of slower release of active components (32–39 %) over the eight week period compared to the bioactive films without CNC. The sensory qualities of rice did not change significantly after BCHNF treatments.

The important factors nurses consider when choosing shift patterns: A cross-sectional study.

To gain a deeper understanding of what is important to nurses when thinking about shift patterns and the organisation of working time. A cross-sectional survey of nursing staff working across the UK and Ireland collected quantitative and qualitative responses. We recruited from two National Health Service Trusts and through an open call via trade union membership, online/print nursing profession magazines and social media. Worked versus preferred shift length/pattern, satisfaction and choice over shift patterns and nurses' views on aspects related to work and life (when working short, long, rotating shifts) were analysed with comparisons of proportions of agreement and crosstabulation. Qualitative responses on important factors related to shift preferences were analysed with inductive thematic analysis. Eight hundred and seventy-three survey responses were collected. When nurses worked long shifts and rotating shifts, lower proportions reported being satisfied with their shifts and working their preferred shift length and pattern. Limited advantages were realised when comparing different shift types; however, respondents more frequently associated 'low travel costs' and 'better ability to do paid overtime' with long shifts and 'healthy diet/exercise' with short shifts; aspects related to rotating shifts often had the lowest proportions of agreement. In the qualitative analysis, three themes were developed: 'When I want to work', 'Impacts to my life outside work' and 'Improving my work environment'. Reasons for nurses' shift preferences were frequently related to nurses' priorities outside of work, highlighting the importance of organising schedules that support a good work-life balance. General scheduling practices like adhering to existing shift work guidelines, using consistent and predictable shift patterns and facilitating flexibility over working time were identified by nurses as enablers for their preferences and priorities. These practices warrant meaningful consideration when establishing safe and efficient nurse rosters. This survey was developed and tested with a diverse group of stakeholders, including nursing staff, patients, union leads and ward managers. The Strengthening the Reporting of Observational Studies (STROBE) checklist for cross-sectional studies was used to guide reporting.

Fanatic Consumer Behavior, Devotion, and Rational Addiction: Unveiling the Distinctive Consumption Patterns of Fans

Fan behavior, characterized by exceptional consumption patterns, stands distinct from typical consumer behavior and has become a focal point in fan theory studies. Scholars have applied various concepts to explain this phenomenon, including fanatic consumer behavior, consumer devotion, and rational addiction. The concept of fanatic consumer behavior emphasizes fans' deep immersion and compulsive engagement in interests like collecting and discussing related merchandise, which aids in forming their unique identity and self-concept. Consumer devotion portrays a religious-like zeal in fans, evident in behaviors such as collecting memorabilia, participating in community activities, and displaying a deep commitment to their favored entities. Rational addiction, as per the economic theory, suggests fans exhibit predictable and habitual consumption patterns, contrasting the random behaviors of ordinary consumers. These theories together offer a comprehensive understanding of fan consumption behaviors, enriching the broader field of consumer behavior research by highlighting the unique and passionate engagement of fans in their areas of interest.

Human behaviors driving disease emergence.

Interactions between humans, animals, and the environment facilitate zoonotic spillover-the transmission of pathogens from animals to humans. Narratives that cast modern humans as exogenous and disruptive forces that encroach upon "natural" disease systems limit our understanding of human drivers of disease. This review leverages theory from evolutionary anthropology that situates humans as functional components of disease ecologies, to argue that human adaptive strategies to resource acquisition shape predictable patterns of high-risk human-animal interactions, (2) humans construct ecological processes that facilitate spillover, and (3) contemporary patterns of epidemiological risk are emergent properties of interactions between human foraging ecology and niche construction. In turn, disease ecology serves as an important vehicle to link what some cast as opposing bodies of theory in human ecology. Disease control measures should consider human drivers of disease as rational, adaptive, and dynamic and capitalize on our capacity to influence ecological processes to mitigate risk.

Impact of accrual and real earnings management on the stock liquidity: the month-of-the-year and Ramadan effects

Purpose The abnormality of the month-of-the-year and Ramadan effects has extensively existed in the stock and other markets. The commercial strategy pattern and the computation of such predictable patterns in the market allow investors to make money. By using anomalies such as the month-of-the-year and the Ramadan effects on earnings management (EM), it is possible to achieve such a goal. This study aims to investigate the month-of-the-year effect and the Ramadan effect on the relationship between accrual earnings management and real earnings management (AEM and REM, respectively) and liquidity in the Iranian capital market. Design/methodology/approach This empirical analysis comprises a panel data set of 80 listed firms (400 observations) on the Tehran Stock Exchange from 2016 to 2020. Findings The findings exhibit that when AEM and REM increase, information asymmetry also increases. The simultaneous increase of these variables leads to a decrease in stock liquidity. Furthermore, the results indicate that the month-of-the-year and Ramadan effects intensify the negative relationship between AEM and REM with stock liquidity. Therefore, EM is affected by the investor’s behavior in specific months. Practical implications Anomalies caused by the Ramadan effect and the month-of-the-year effect on reducing liquidity in the Iranian stock market were confirmed. Investors can use these anomalies to identify predictable patterns, exchange securities according to those patterns and earn abnormal returns. Originality/value To the best of the authors’ knowledge, this is the first study that empirically examined the simultaneous effect of Gregorian and Islamic calendar anomalies on the relationship between EM and liquidity, and while helping managers and other readers, it can be the basis for future research.

Metabolic characteristics of granulosa cell tumor: role of PPARγ signaling†.

Granulosa cell tumors are relatively rare, posing challenges for comprehension and therapeutic development due to limited cases and preclinical models. Metabolic reprogramming, a hallmark of cancer, manifests in granulosa cell tumors with notable lipid accumulation and increased expression of peroxisome proliferator-activated receptor gamma (PPARγ), a key lipid metabolism regulator. The roles of these features, however, remain unclear. In our previous work, we established a granulosa cell tumor model in mice by introducing a constitutively active Pik3ca mutant in oocytes, enabling the study of predictable tumor patterns from postnatal day 50. In this study, we characterized metabolic alterations during tumorigenesis (postnatal day 8 to day 50) and tumor growth (day 50 to day 65) in this model and explored the impact of PPARγ antagonism on human granulosa cell tumor proliferation. The tumor exhibited significant lipid accumulation, with PPARγ and the proliferation marker Ki67 co-localizing at postnatal day 65. Transcriptome analysis demonstrates that pathways for lipid metabolism and mitochondrial oxidation are promoted during tumorigenesis and tumor growth, respectively. Overlappingly upregulated genes during tumorigenesis and tumor growth are associated with lipid metabolism pathways. Correspondingly, mouse granulosa cell tumor shows overexpression of peroxisome proliferator-activated receptor gamma and DGAT2 proteins at postnatal day 65. Furthermore, GW9662 reduces the proliferation of KGN human granulosa cell tumor cells and decreases the phosphorylation of AKT and SMAD3. Our findings identify metabolic abnormalities in ooPIK3CA* granulosa cell tumor model and suggest peroxisome proliferator-activated receptor gamma as a potential driver for primary granulosa cell tumor growth.