Periodic Fluctuations Research Articles

Inductance Estimation Based on Wavelet-GMDH for Sensorless Control of PMSM

In permanent magnet synchronous motor (PMSM) sensorless drive systems, the motor inductance is a crucial parameter for rotor position estimation. Variations in the motor current induce changes in the inductance, leading to core magnetic saturation and degradation in the accuracy of rotor position estimation. In systems with constant load torque, the saturated inductance remains constant. This inductance error causes a consistent error in rotor position estimation and some performance degradation, but it does not result in speed estimation errors. However, in systems with periodic load torque, the error in the saturated inductance varies, consequently causing fluctuations in both the estimated position and speed errors. Periodic speed errors complicate speed control and degrade the torque compensation performance. In this paper, we propose a wavelet denoising-group method of data handling (GMDH) based method for accurate inductance estimation in PMSM sensorless control systems with periodic load torque compensation. We present a method to analyze and filter the collected three-phase current signals of the PMSM using wavelet transformation and utilize the filtered results as inputs to GMDH for training. Additionally, a method for magnetic saturation compensation using the inductance parameter estimator is proposed to minimize periodic speed fluctuations and improve control accuracy. To replicate the load conditions and parameter variations equivalent to the actual system, experiments were conducted to measure the speed ripples, inductance variations, and torque component of the current. Finally, software simulation was performed to confirm the inductance estimation results and verify the proposed method by simulating load conditions equivalent to the experimental results.

Archaeal communities change responding to anthropogenic and natural treatments of freeze-thawed soils

The coverage of accumulated snow plays a significant role in inducing changes in both microbial activity and environmental factors within freeze-thaw soil systems. This study aimed to analyze the impact of snow cover on the dynamics of archeal communities in freeze-thaw soil. Furthermore, it seeks to investigate the role of fertilization in freeze-thaw soil. Four treatments were established based on snow cover and fertilization:No snow and no fertilizer (CK–N), snow cover without fertilizer (X–N), fertilizer without snow cover (T-N), and both fertilizer and snow cover (T-X). The research findings indicated that after snow cover treatment, the carbon, nitrogen, and phosphorus content in freeze-thaw soil exhibit periodic fluctuations. Snow covered effectively altered the community composition of bacteria and archaea in the soil, with a greater impact on archaeal communities than on bacterial communities. Snow covered improves the stability of archaeal communities in freeze-thaw soil. Additionally, the arrival of snow also enhanced the correlation between archaea and environmental factors, with the key archaeal phyla involved being Nanoarchaeota and Crenarchaeota. Further research showed that the application of organic fertilizers also had some impact on freeze-thaw soil, but this impact was smaller compared to snow cover. In summary, the arrival of snow could alter the archaeal community and protect nutrient elements in freeze-thaw soil, reducing their loss, and its effect is more pronounced compared to the application of organic fertilizers.

COLD INHIBITION OF BIOACTIVITY OF PROBIOTIC MYCOBACTERIA AND AEROCOCCI IN THE MICROBIOME

The organism of farm animals is constantly under the influence of various factors of the external environment, and one of the negative factors of violation of the technology of keeping and creation of unsatisfactory conditions of existence with deterioration of well-being is cold injury. Mostly it manifests itself as a permanent effect of positive temperatures well below the physiological norm or periodic fluctuations of an uncomfortable temperature. Such a stress factor of low intensity, but permanent over a long period of time, causes negative pathophysiological changes in the macroorganism with the development of immune-depression, metabolic syndrome, perversions of the qualitative composition of the microbiome and, as a result, disruption of native physiological processes of stimulation of cellular mechanisms of nonspecific innate immunity, induced by immune-biological mechanisms of the functioning of neutrophil granulocytes. The negative effect of low-intensity permanent cold stress was studied on a model object - murchakаs, which were kept for two weeks at a temperature of 4-6 °C under conditions of light and movement restriction. Before the start of the experiment, initial microbiological studies of the microbiome of the large intestine were conducted using generally accepted methods. Resident indigenous probiotic prokaryotes - M. vaccae & A. viridans, which possessed typical species characteristics, were isolated. Pathogenic variants of enterobacteria were not isolated, isolated cultures of E. coli were apathogenic and belonged to the usual normal flora. A two-week cold injury resulted in the disappearance of the indigenous probiotic microbiota M. vaccae & A. viridans, which are indicator prokaryotes of the physiological well-being of the macroorganism, from the microbiome. At the same time, the number of potentially pathogenic enteral microorganisms, coccal and rod-shaped, increased. This was correlated with a decrease in the phagocytic activity of native neutrophil granulocytes and their preformed filamentous spatial nucleoprotective mesh structures, which inhibit the de-fragmentary function in relation to genetically non-syngenic objects.

Energy-Efficient Virtual Network Embedding: A Deep Reinforcement Learning Approach Based on Graph Convolutional Networks

Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV technology can substantially reduce the operational costs and energy consumption. However, the existing algorithms for energy-efficient VNE have limitations, including manual tuning for heuristic routing policies, inefficient feature extraction in traditional intelligent algorithms, and a lack of consideration of periodic traffic fluctuations. To address these limitations, this paper introduces a novel approach that leverages deep reinforcement learning (DRL) to enhance the efficiency of traditional methods. We employ graph convolutional networks (GCNs) for feature extraction, capturing the nuances of network graph structures, and integrate periodic traffic fluctuations as a key constraint in our model. This allows for the predictive embedding of VNRs that is both energy-efficient and responsive to dynamic network conditions. Our research aims to develop an energy-efficient VNE algorithm that dynamically adapts to network traffic patterns, thereby optimizing resource allocation and reducing energy consumption. Extensive simulation experiments demonstrate that our proposed algorithm achieves an average reduction of 22.4% in energy consumption and 41.0% in active substrate nodes, along with a 23.4% improvement in the acceptance rate compared to other algorithms.

Pore structure expansion and evolution in sandstone with prefabricated crack under freeze-thaw cycles based on CT scanning

Pore structure expansion and evolution in sandstone with prefabricated crack under freeze-thaw cycles based on CT scanning

Industrial technology network security measurement in international trade under discrete hopfield neural network

As global economic integration continues to advance, international trade has become increasingly vital for the economic development and growth of nations. This research aims to assess the trends in industrial technology security within China’s international trade and provide practical guidance for policy-making, corporate strategies, and international cooperation. The significance of the rising trend in security within China’s international trade industry lies in its establishment of a robust foundation for the long-term development of China’s international trade, contributing to its cooperation and competitiveness with other countries. In addressing the limitations of traditional measurement methods and providing a more comprehensive and accurate assessment of industrial technology security, this research presents an approach based on a discrete Hopfield Neural Network (HNN) for evaluating industrial technology security in international trade. This method integrates multiple indicators, including technology gap rates, to construct the Superior Quality Engineering (SQE) comprehensive evaluation model. The research employs a combination model of “entropy-grey relational-Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-discrete HNN” to assess industrial technology security. This research evaluates international trade industry technology security using patent data from 2015 to 2022 as samples. The results indicate an overall upward trend in security in China’s international trade industry. Within this trend, the research observes a stepwise increase in scale components, leading to continuous improvement in security. In terms of quality components, although security develops relatively slowly overall, it exhibits a trend of initial gradual decline followed by rapid growth. Regarding efficiency components, there is overall slow growth with periodic fluctuations. This research outcome provides substantial support for the research of industrial technology in international trade. The proposed method can assist businesses in evaluating their technological security in international trade and offer robust support for international trade decision-making.

Intelligent fault diagnosis and security early warning method of new power system based on system network situation

In the past few decades, China’s power demand has been increasing, and the power fiber plays a key role in ensuring the orderly dispatching of all links of the power system. The study used a wavelet decomposition and reconstruction method, which is a signal processing technique used to decompose complex optical power data into low-frequency and high-frequency signals with different frequency components. Through this decomposition, we can more clearly observe periodic fluctuations, trend changes, and noise components in optical power data. The study also examined different prediction models, including GRU, LSTM, ARMA), etc. The performance of these models in predicting optical power trends is then analyzed, taking into account their accuracy, stability, and computational efficiency. Finally, we carefully evaluated the GRU-ARMA combined prediction model and determined its superior performance in predicting optical power trends. The outcomes show that after adjusting the input data length of the gating cycle cell model and the relevant parameters of the autoregressive sliding mean model, the residual mean value was 0.0141. At the same time, the root mean square error calculated by the combined prediction model of the gating cycle unit-autoregressive moving mean model was 0.000618, which successfully improved the accuracy of predicting the optical power trend of power fiber. This research result provides an important reference for the aging state assessment of power fiber lines, and has an important practical application value for the maintenance of power fiber lines.

Partially “invisible” pulsation of asymmetric soliton molecules

Pulsation is a universal phenomenon in all disciplines of nonlinear science, which generally exhibit various nonlinear soliton dynamic modes in nonlinear optics. With the emergence of real-time spectral measurement technology, a new phenomenon of optical soliton pulsation, “invisible” soliton pulsation, has gradually attracted researchers’ attention. In this paper, by solving the coupled Ginzburg–Landau equation, we have discovered the partially “invisible” pulsation phenomenon of asymmetric soliton molecules (SMs) composed of two unequal-intensity pulses in a normal-dispersion Mamyshev oscillator for the first time. It is indicated that the fluctuation periods of each internal pulse in asymmetric SMs in terms of peak power, energy, and relative phase difference are all four roundtrips. However, the oscillation period of asymmetric SMs energy is two roundtrips, only half of the above period. Further research shows that the oscillation of the relative phase difference between two internal pulses in SMs is related to the variation of their intensity difference, which can also affect the position change of the spectral modulation peak. This work will enrich the research on “invisible” soliton pulsation and is of great significance for promoting the development of nonlinear dissipative systems.

Mechanism Analysis of the Effect of Wake Vortex on Cylinder Vibration in Flow Induced Motion

Abstract Unsteady Reynolds-averaged Navier–Stokes (RANS) method combined with Spalart–Allmaras turbulence model and dynamic mesh technology was used to investigate the impact of wake vortex on the vibration response of a cylinder. By analyzing the phase difference between the wake vortex force and the displacement under different mass parameters in flow-induced vibration (FIV), the study reveals that the influence of wake vortex on the cylinder varies significantly in different vibration branches. The wake vortex of the initial branch enhances the cylinder's vibration, whereas the wake vortices of the upper, lower, and desynchronized branches suppress the vibration. At the critical point between the initial branch and the upper branch of vortex-induced vibration (VIV), there is a 90 degree phase jump, and the instantaneous phase difference fluctuation between the wake vortex force and displacement of the VIV branch remains relatively constant. In the galloping branch, there are wake vortices in different directions that affect the cylinder's vibration every quarter of the vibration period, and the phase difference undergoes periodic large fluctuations (either in-phase or out-of-phase), with the result that the wake vortex force periodically promotes or restrains the cylinder's vibration, which can serve as a novel criterion for identifying the occurrence of galloping. Furthermore, when varying the mass parameters at a constant reduced velocity, the impact of the wake vortex in the initial branch is relatively insignificant. However, as the mass ratio increases in other vibration branches, the suppressive effect increases, and the wake vortex force can prevent VIV induced galloping phenomenon by affecting the vibration intensity.

Turning The Tide: Live-Bed Scale Experiments Of Bar-Dominated Estuaries And Effects Of Dredging On Intertidal Habitat

Many large sand-dominated estuaries and tidal basins have complex channel and bar patterns, wherein a continuous deep channel suitable for shipping is rarely naturally present, which requires dredging. The intertidal area has important, often protected habitats for macro-benthic species, wader birds and other species. The question is to what degree channel and shoal dimensions and the amount of intertidal area are affected by dredging and disposal for access to ports. While numerical modelling is conducted for these systems with some success, complementary scale experiments with live beds have challenged the fields of coastal engineering and coastal morphodynamics for over a century. Compared to scale models for rivers, the scale issues in tidal experiments are more complicated and very few attempts have been pursued. For example, Reynolds (1889) conducted the first scale experiments of an estuary by a periodic sea level fluctuation, but found that even fine sand was hard to mobilize, while the bed surface was dominated by ripples rather than bars. Similarly, Tambroni et al. (2005) carefully scaled sediment mobility in a converging channel and obtained large-scale morphological patterns with gentle bars nearly overwhelmed by ripples. Stefanon et al. (2010) present a careful attempt at erosive tidal channel network development in a scale model with low-density sediment, which showed unexplained, over-large scour holes at channel junctions. The key issues for tidal experiments are to obtain sufficient sediment mobility (expressed as Shields or Rouse number) and to avoid over-large ripples and scours, and to this end classic river scale modelling is inadequate. Here we assess whether our recent innovation solves these scale issues. The objective of this abstract is to elucidate the scaling of experiments that enable study of morphodynamic estuaries in many aspects including effects of sediment management by dredging.

Periodic approaching flows and pollutant dispersion of a single-sided generic building with natural ventilation

In real urban environments, wind velocities often exhibit significant time-dependent variations. However, the impact of these fluctuations on ventilation performance is not frequently addressed in the literature. To accurately predict the building ventilation performance, this paper intends to elucidate the influence of the unsteady approaching flow. Four distinct inflows, characterized by periodic sinusoidal fluctuations, are evaluated using large-eddy simulation (LES). A single-sided building model with natural ventilation is constructed and validated through a wind tunnel experiment. Four periodic inflows consist of combinations of two frequencies (f = 0.1 and 0.3 Hz) and two amplitudes (A = 0.06 and 0.09). This paper mainly compares the results of airflow patterns and ventilation performance with those of stable inflows. The results indicate that periodic inflows cause greater fluctuations in the velocity fields on the leeward side. At higher frequencies and lower amplitudes, the vertical distribution of indoor bottom vortices is relatively dispersed. It shows that periodic flows lead to noticeable changes in the ventilation rate, with a deviation between instantaneous and average Q* ranging from 13.8% to 16.5%. Compared to the stable inflow, the impacts of periodic inflows on ventilation performance vary over time; 6% and 9% variations in incoming velocities result in deviations of up to 17.5% in ventilation rates. The frequencies and amplitudes of the periodic inflows do not exhibit a linear relation with ventilation rate. Therefore, it is prudent to be cautious about adopting averaged incoming wind velocities in predicting ventilation performance. This study helps in the more accurate prediction of single-sided natural ventilation.

Dynamics and control of a plant-herbivore model incorporating Allee's effect

This research focuses on the interaction between the grape borer and grapevine using a discrete-time plant-herbivore model with Allee's effect. We specifically investigate a model that incorporates a strong predator functional response to better understand the system's qualitative behavior at positive equilibrium points. In the present study, we explore the topological classifications at fixed points, stability analysis, Neimark-Sacker, Transcritical bifurcation and State feedback control in the two-dimensional discrete-time plant-herbivore model. It is proved that for all involved parameters ς1,ϱ1,γ1 and ϒ1, discrete-time plant-herbivore model has boundary and interior fixed points: c1=(0,0), c2=(ς1−1ϱ1,0) and c3=(ϒ1(1−γ1)2γ1−1,γ1(2ς1+ϱ1ϒ1−2)−ϱ1ϒ1+1−ς12γ1−1) respectively. Then by linear stability theory, local dynamics with different topological classifications are investigated at fixed points: c1=(0,0), c2=(ς1−1ϱ1,0) and c3=(ϒ1(1−γ1)2γ1−1,γ1(2ς1+ϱ1ϒ1−2)−ϱ1ϒ1+1−ς12γ1−1). Our investigation uncovers that the boundary equilibrium c2=(ς1−1ϱ1,0) experiences a transcritical bifurcation, whereas the unique positive steady-state c3=(ϒ1(1−γ1)2γ1−1,γ1(2ς1+ϱ1ϒ1−2)−ϱ1ϒ1+1−ς12γ1−1) of the discrete-time plant-herbivore model undergoes a Neimark-Sacker bifurcation. To address the periodic fluctuations in grapevine population density and other unpredictable behaviors observed in the model, we propose implementing state feedback chaos control. To support our theoretical findings, we provide comprehensive numerical simulations, phase portraits, dynamics diagrams, and a graph of the maximum Lyapunov exponent. These visual representations enhance the clarity of our research outcomes and further validate the effectiveness of the chaos control approach.

Study on rotating stall characteristics of centrifugal pumps based on gamma transition model

The phenomenon of rotating stall in centrifugal pumps is closely associated with the evolution of the blade boundary layer. Aiming to accurately predict the characteristics of the boundary layer, this study investigates the phenomenon of rotating stall in centrifugal pump impellers using the gamma (γ) transition model. The accuracy of the numerical simulation was confirmed by comparing its conclusions with the results of the testing. In calculations considering transition characteristics, the distribution of low-pressure areas inside the impeller is relatively discontinuous, while the pressure distribution is more uniform. However, in calculations without considering transition, the low-pressure regions in neighboring flow channels exhibit a tendency to be interconnected, resulting in a more variable pressure distribution, and the pressure contour at the outlet is closer to parallel. The dynamic characteristics of the centrifugal pump impeller rotating stall were obtained through the dynamic mode decomposition method, including the frequency, structure, and dynamic evolution process of the stall vortex. Through modal reconstruction, it was discovered that the impeller's rotation causes the stall vortex to undergo periodic fluctuations. The stall vortex is not stationary but moves synchronously with the rotation of the blades. At different time points, the stall vortex exhibits periodic changes. At the blade suction entrance, the stall vortex initially appears. Subsequently, multiple vortex structures resulted in channel blockage. After a period of development, the excess vortex structures merge to generate a typical “8” shaped vortex structure and move toward the exit. Finally, the exit stall vortex disappears, and a new vortex structure is generated at the inlet of the blade suction surface.

Permafrost temperature dynamics and its climate relations in various Tibetan alpine grasslands

Permafrost temperature is a vital indicator of climate and permafrost changes, benefiting ecosystem development and informing local climate strategies. Alpine grasslands impact moisture and heat exchange between the surface and atmosphere, thereby affecting the thermal state of underlying permafrost. This study analyzed permafrost temperatures (2004–2019) from various alpine grasslands (including alpine meadow, alpine steppe, alpine desert grassland, and barren land) in the Beiluhe region of the Tibetan Plateau and revealed their connections to climate change and controlling factors, using time–frequency analysis. The findings revealed that in the time–frequency domain, permafrost temperatures exhibited multiple time scales characteristics, driven by climate fluctuations. Changes in the active layer closely followed monthly climate variations, while permafrost dynamics responded to annual climate changes. Significant oscillations with periods of 10–11, 8–9, and 14 years were observed in the surface, permafrost table, and deep permafrost layers, respectively. Among the different types of alpine grasslands, alpine meadows proved to be the most sensitive to climate change, with the intensity of periodic fluctuations initially decreasing and then increasing with depth in alpine meadows, while it consistently decreased with depth in the other three alpine grasslands. The impact of air temperature, precipitation, and wind speed on permafrost dynamics exhibited depth-dependent variations in the time–frequency domain, contrasting with the time domain where permafrost temperature changes were predominantly associated with air temperature across all depths.

Microbial landscape of periodontal pockets against local antibacterial therapy of chronic generalized periodontitis: a multicenter clinical trial

Background. A microbial nature of periodontitis requires an accurate analysis of the pathogenic potential of the periodontal pocket microflora thereby enabling correct diagnosis to be made and the most effective pharmacological agent to be selected for treating chronic periodontitis. Metronidazole demonstrates excellent results for anaerobic microorganisms in the periodontal pocket, whereas minocycline hydrochloride, moxifloxacin and amoxicillin/clavulanate are efficient for aerobic microorganisms. However, specialists find considerably relevant to analyze the correlations between specific bacterial associations in the periodontal pocket against local antibacterial therapy, as well as to track the contents of periodontal pockets long after antibacterial therapy. Therefore, a microbiological analysis of periodontal pocket contents is considered necessary in order to select and monitor the effectiveness of local antibacterial therapy.Objective. To evaluate the effectiveness of local antibacterial therapy for chronic generalized periodontitis by means of analysis of changes in the microbiological composition of periodontal pockets.Methods. The clinical trial involved 144 patients with chronic generalized periodontitis. The study included examination of patients and analysis of clinical data bases of the Department for General Practice Dentistry, Kuban State Medical University and the Dentistry Department, Stavropol State Medical University of the Ministry of Health of the Russian Federation. The trial included patients aged 30 to 60 with a diagnosis of chronic generalized periodontitis of different severity. All periodontitis patients were divided into 2 groups depending on the therapy: main and control groups. Each group had three subgroups depending on the severity of the pathologic process. The first subgroup of the control group (chronic generalized periodontitis of mild degree) consisted of 16 people (11.1%), the second subgroup (chronic generalized periodontitis of moderate degree) — 31 people (21.5%), the third subgroup (chronic generalized periodontitis of severe degree) — 7 people (4.9%). The first, second and third subgroups of the main group consisted of 28 (19.4%), 48 (33.3%) and 14 persons (9.7%) respectively. The severity of periodontitis was determined by the depth of the periodontal pocket, degree of bone resorption (according to x-ray examination), and pathologic tooth mobility. A local anti-inflammatory therapy for the patients of the control group included oral baths with propolis (15–20 drops of 10% propolis tincture diluted in 100 ml of warm water) 3 times a day for 7 days starting from the first visit. At the final stage of treatment after preliminary scaling and rootplaning, patients of the control group received antiseptic gel with 10% metronidazole and 2% chlorhexidine bigluconate to be injected into periodontal pockets with over 4 mm depth. Additionally, applications of adhesive dental films Diplene Denta L (with lincomycin) and Diplene Denta M (with metronidazole) were prescribed to be done in the home. In addition to the above-mentioned comprehensive therapy, patients of the main group received a single insufflation of minocycline hydrochloride microspheres into each periodontal pocket. The tools for the patients of the main group included plastic cannulas with minocycline hydrochloride microspheres in the form of powder in the tip of the cartridge, and a special syringe-insufflator. The technique of antibiotic insufflation into the periodontal pocket was preliminary trained on the model of jaws with the studied pathology. Statistical analysis of the results was performed using variance analysis in the Excel Microsoft Office 2016 (Microsoft, USA).Results. According to the systematic observation of the patients from the control group, periodic fluctuations in microbial population in the majority of pathologic pockets obtained a specific regularity: a decrease in the number of microorganisms on day 21 was followed by its increase in the subsequent period of observation after 30 and especially 60 days. According to the ongoing follow-up of the patients of the main group, fluctuations of microbial population in the majority of pathologic pockets obtained a certain regularity: a significant reduction in the number of microorganisms on day 21 was followed by its slight rise in the subsequent period of observation after 30 and a slight increase again in 60 days. Fluctuations in the number of microorganisms of periodontal pockets in the main group reveal their specific character: their number remained invariably lower than that of the control group throughout the observation period, while the total number of detected periodontopathogens decreased.Conclusion. The established correlations between the main indicators of microbiological status of periodontal pockets and the severity of chronic generalized periodontitis within different periods after the local therapy confirm the positive dynamics of treatment in the main group, where patients were insufflated with minocycline hydrochloride microspheres, proved by a change in the typology of microbial status of periodontal pockets and a number of periodontopathogenic microorganisms.

Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period

Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period

МЕТОДИ ПРОГНОЗУВАННЯ ЧАСОВИХ РЯДІВ З ВИРАЖЕНОЮ СЕЗОННІСТЮ НА ОСНОВІ ТРАНСФОРМЕРІВ

Forecasting time series data with seasonal patterns remains a significant challenge in various domains. The complex and non-stationary nature of such data requires advanced modeling techniques capable of capturing intricate temporal dependencies and periodic fluctuations. Traditional statistical methods like ARIMA models often struggle to adequately represent the underlying dynamics, motivating the exploration of more flexible and powerful approaches. In recent years, deep learning architectures, particularly transformer-based models, have demonstrated remarkable success in handling sequential data and capturing long-range dependencies. Their self-attention mechanisms and parallel processing capabilities make them well-suited for time series forecasting tasks involving seasonality. This study provides a comprehensive evaluation of three prominent transformer-based models: Temporal Fusion Transformer (TFT), PatchTST, and DLinear. Each model brings unique architectural innovations and training strategies to tackle the intricacies of seasonal time series forecasting effectively. The experimental results, evaluated on a real-world product sales dataset, reveal the potential of these models to outperform traditional methods and achieve superior forecasting accuracy. However, it is crucial to consider the trade-off between model complexity, computational resources, and the specific requirements of the forecasting task at hand. As the field of time series forecasting continues to evolve, transformer-based models offer a promising direction for handling complex seasonal patterns, paving the way for more accurate and reliable predictions across various industries. Nevertheless, further research is necessary to address the challenges of model interpretability, efficient parameter optimization, and the incorporation of domain-specific knowledge into these architectures.

Forecasting Bitcoin Trends Based on the ARIMA Model

This research paper aims to conduct a time series forecasting of the bitcoin mean weighted price using the data from Kaggle. The data has a one-minute resolution and includes the following variables: timestamp, open, high, low, close, volume (BTC), volume (currency), and weighted price. Data analysis was achieved using R, a statistical computing and graphics programming language. The main findings of this research paper were that the bitcoin mean weighted price had a strong upward trend and exhibited high volatility over time. The time series also had weak seasonal and significant random components, indicating periodic fluctuations and noise in the data. Four years of data were used to estimate the mean change for the following month. The results indicate that while the expected value may rise somewhat, it will do so with significant variability and unpredictability. The main implications of this research paper were that there was a potential for profit or loss depending on the timing and strategy of buying or selling bitcoins.

Investigation and analysis of ship to platform collision incidents on the UK continental shelf: highlighting trends between the enforcement of offshore regulations and the occurrence of vessel to platform collision incidents

An offshore installation is exposed to ship collision risk from in-field and passing vessels. Both categories of collision have occurred on the United Kingdom Continental Shelf (UKCS), and both have the potential to result in catastrophic damage to the installation, although to date only significant consequences have been observed in UK waters. World-wide, catastrophic collisions with installations have occurred resulting in severe damage to vessels and installations, leading to loss of life and environmental damage. This led to the authors investigate the occurrence and reporting of ship to platform collisions on the UKCS 1971–2017. It was found that there were periodic fluctuations in the occurrence of collision accidents. This research analyses the accident trends in order to determine the whether the fluctuations were a regular occurrence and, if so, what was the cause. 511 ship to platform collision incidents between 1971 and 2017 were subject to analysis, in terms of damage classification, the vessel type, month of occurrence and a comparison of the release of regulations with the fluctuation of incidents. Subsequently, this analysis has identified a key trend between the reporting of offshore collision incidents and the release and enforcement of offshore Safety Case regulations.

Temporal dynamics and forecasting of aerosol optical depth in megacities Lahore and Karachi: Insights from the Indo-Gangetic Basin and southern Pakistan, and implications for Sustainable development

This scientific study investigates the temporal dynamics and forecasts aerosol optical depth (AOD) in Lahore and Karachi, Pakistan's two most populated cities. Lahore, in the center of the Indo-Gangetic Basin, and Karachi, on the southern coast, have issues from growing urbanization and significant aerosol emissions. Understanding AOD trends is critical for successful environmental management, public health planning, and climate change mitigation. Using the Box-Jenkins ARIMA models, this work systematically analyses monthly AOD fluctuations from July 2002 to May 2023 to confirm their predicting accuracy. The autocorrelation function (ACF) and partial autocorrelation function (PACF) have been employed to estimate the suitable model and identify significant lags to maintain prediction accuracy. The study concludes with a judgment that Model A is ideal for the ecology of Karachi and Model B for the ecology of Lahore. The forecast till January 2030 is available to look at several trends on AOD, namely, periods of seasonal solid fluctuations for both sites. This diversity emphasizes the need for specific actions, such as stricter environmental regulations, air quality monitoring, and providing greener transport solutions, which will result in blue skies. Utilizing the in-depth observations and forecast of the changing distribution of AOD values, the study helps develop approaches for reducing aerosol pollution. This is also one of the key contributions. We investigated critical environmental problems in the cities of Karachi and Lahore and provided means for such issues' resolution, enabling sustainable development and clean air.