Frequency Ratio Research Articles

Urban flash flood prediction modelling using probabilistic and statistical approaches

The development of a detailed strategy to mitigate the negative consequences of any natural calamities depends on accurately identifying sensitive zones where natural hazards frequently happen. In the present investigations, three widely utilized probabilistic approaches viz., frequency ratio (FR), statistical index (SI), and weighting factor (WF) have been utilized for prediction of flsh flood susceptibility zones in the Coochbehar urban and peri-urban area (CUPUA) (area = 26.22 km2). Ten flash flood conditioning factors have been used in this assessment based on previous literatures and experts' opinions. In the FR model, 29.40 % area is observed in the high and very high flood zones, whereas 36.27 % and 31.16 % area is identified in SI and WF model, respectively. The FR model demonstrates that five conditioning factors, viz., topographic position index (TPI), land use and land cover (LULC), normalized difference vegetation index (NDVI), distance to drainage (DtD) and rainfall were highly impacted in flash flood prediction (FFP) analysis; in SI model, LULC is the major influencing parameter, and in WF model LULC, rainfall, NDVI, and distance to road (DtR) are the effective parameters. The success rate curve of the FR, SI and WF models manifest SI model has highest training (AUC=0.903) and prediction (AUC=0.925) accuracy, and FR and WF also have very good accuracy as their AUC values are 0.899 and 0.877 (in success rate curve) and 0.900 and 0.881 (in prediction rate curve). Therefore, the application of probabilistic approaches in this active flash flood-prone region is excellently performed, and the results of this study will help hydrologists, engineers, and water management administrators to control the areas that are extremely susceptible to flash floods and reduce possible damages.

Landslide Susceptibility Modelling of Central Highland Part of Chaliyar River Basin, Kerala, India with Integrated Algorithms of Frequency Ratio and Shannon Entropy

An integrated landslide susceptibility analysis is carried out for the central highland region of the Chaliyar River Basin in Kerala, India using bivariate statistical methods, namely the Frequency Ratio (FR) and Shannon Entropy (SE). The study addresses the complex nature of landslides, influenced by natural as well as anthropogenic factors, with specific focus on assessing the landslide likelihood of the study area. The methodology involves a systematic approach of collecting the inventory data, identifying various landslide causative factors and developing their corresponding thematic maps, spatial analysis of landslide occurrence and causative factors using GIS software and generation of Landslide Susceptibility Model (LSM) employing FR and SE algorithm, followed by model validation. Various causative factors considered for the study include slope angle, slope aspect, slope curvature, elevation, lithology, drainage density, landuse and landcover (LULC), Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI). The FR and SE algorithm enable the spatial classification of the study area into four landslide susceptibility categories namely Low, Moderate, High, and Very High. Validation of both the LSMs was carried out using Landslide Density Index (LDI) and Area Under the Curve (AUC) methods. LDI demonstrate a positive fit for both the models, which is indicative of reliability of the susceptibility predictions of the study area. A slightly higher AUC value of SE model is an indication of a high accuracy rate of SE model over FR model. This research brings out a robust methodology for predicting and identifying the landslide risks of the study area.The outcomes of this study will help in developing effective strategies to manage the landslide hazards in geologically vulnerable areas. Keywords: Landslide Susceptibility, Landslide Conditioning Factors, Frequency Ratio, Shannon Entropy, Landslide Density Index(LDI), Area Under the Curve (AUC)

A method for rapidly assessing landslide hazard—taking the landslide in Yongxing town, Mingshan area as an example

To overcome the reliance on large samples and high-quality data in existing evaluation methods, while also improving evaluation efficiency and accuracy, this paper proposes a method for rapid landslide hazard assessment. This method utilizes existing research findings and specific analytical techniques for the study area to conduct rapid assessments. Taking the landslide in Yongxing Town, Mingshan Area, Ya’an City, Sichuan Province as an example, the Analytic Hierarchy Process (AHP) is combined with the Information Value (IV) method, Certainty Factor (CF) method, and Frequency Ratio (FR) method from previous studies, The AHP-IV and AHP-FR methods assess the study area as a moderately hazardous zone, while the AHP-CF method assesses it as a slightly hazardous zone. Affected by the strong 2013 Lushan earthquake, the landslide in the study area caused permanent damage. Field investigation results show that the landslide hazard in the study area is moderate, and the AHP-IV and AHP-FR methods are more consistent with the actual field results. The AHP-CF method, due to not considering the water system factor and having certain errors in its discrimination method, leans towards a safer assessment, The results of the three evaluation methods are somewhat consistent.

Generation of terahertz radiation through nonlinear Thomson scattering by two moderate-intensity laser fields

A new scheme for the generation of the terahertz (THz) radiation based on the nonlinear Thomson scattering is proposed through two moderate-intensity laser fields. It is found that the THz radiation can be generated in the nonlinear Thomson backscattering spectrum by two moderate-intensity plane wave laser fields with no need of a strong external magnetic field. In this scheme, the amplitude threshold required to generate the THz radiation for two laser fields can be significantly reduced, and the amplitude threshold can even be less than half that of the case of using only a single laser field. It is also shown that in the two moderate-intensity laser fields, the intensity of the THz radiation can also be effectively improved and optimized by increasing the amplitude ratio and reducing the frequency ratio of the two laser fields, and extending the period of the electron motion via the control of the frequency ratio.

Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars

Context. Slowly pulsating B (SPB) stars display multi-periodic variability in the gravito-inertial mode regime with indications of non-linear resonances between modes. Several have undergone asteroseismic modeling in the past few years to infer their internal properties, but only in a linear setting. These stars rotate fast, so that rotation is typically included in the modeling by means of the traditional approximation of rotation (TAR). Aims. We aim to extend the set of tools available for asteroseismology, by describing time-independent (stationary) resonant non-linear coupling among three gravito-inertial modes within the TAR. Such coupling offers the opportunity to use mode amplitude ratios in the asteroseismic modeling process, instead of only relying on frequencies of linear eigenmodes, as has been done so far. Methods. Following observational detections, we derive expressions for the resonant stationary non-linear coupling between three gravito-inertial modes in rotating stars. We assess selection rules and stability domains for stationary solutions. We also predict non-linear frequencies and amplitude ratio observables that can be compared with their observed counterparts. Results. The non-linear frequency shifts of stationary couplings are negligible compared to typical frequency errors derived from observations. The theoretically predicted amplitude ratios of combination frequencies match with some of their observational counterparts in the SPB targets. Other, unexplained observed ratios could be linked to other saturation mechanisms, to interactions between different modes, or to different opacity gradients in the driving zone. Conclusions. For the purpose of asteroseismic modeling, our non-linear mode coupling formalism can explain some of the stationary amplitude ratios of observed resonant mode couplings in single SPB stars monitored during 4 years by the Kepler space telescope.

Active on-site calibration technology of current transformer based on non-rated frequency current injection.

This paper proposes an active on-site calibration method through background current cancellation and non-rated current injection. It can measure the error of the current transformer in service from 1% to 120% rated current percentage without power supply interruption. In order to establish the error relationship between rated frequency and arbitrary frequency, a theoretical analysis of current transformer calibration at the arbitrary frequency has been developed by means of the equivalent circuit. It describes a method to determine the phase angle and ratio errors of the measuring transformers at arbitrary frequencies on the basis of the calibrated error values at rated frequency. To prove the theoretical analysis, an experimental validation was carried out. The experimental results demonstrate that this active onsite calibration is a valid tool for the evaluation of current transformer performances. The calibration results showed that, for both cases (non-rated frequency calibration and mixing frequency calibration), the difference between mean ratio error and rated frequency ratio error was lower than 0.01%, and the difference between mean phase error and rated frequency phase error was lower than 1', which meets the requirement of the 0.2 accuracy class calibration.

Cardiac autonomic function in REM-related obstructive sleep apnoea: insights from nocturnal heart rate variability profiles

PurposeIn light of the reported association between REM-related obstructive sleep apnoea (OSA) and heightened cardiovascular risk, this study aims to compare cardiac autonomic function in patients with REM-OSA and OSA independent of sleep stage. We hypothesized that REM-OSA patients would exhibit higher sympathetic cardiac modulation based on heart rate variability (HRV) profiles.MethodsHRV was compared between the OSA group (AHI ≥ 5 events/h, n = 252) and the REM-OSA group (AHI ≥ 5 events/h, AHIREM:AHINREM ≥ 2, n = 137). Time- and frequency-domain measures of HRV were analysed during N2 and REM sleep.ResultsClinical characteristics between the two test groups differed significantly, 45% of REM-OSA patients were female, with mild OSA (median, interquartile range (IQR)) AHI of 10 (7) events/h. Only 26% of the OSA cohort were female with moderate OSA (AHI = 17 (20) events/h, p < 0.001). Compared with the OSA group, the low frequency to high frequency ratio (LF:HF) and LF power were lower and HF power was higher in the REM-OSA group during N2 (LF:HF, p = 0.012; LF; p = 0.013; HF, p = 0.007) and in REM sleep (LF:HF, p = 0.002; LF, p = 0.004; HF, p < 0.001). Patient sex and OSA severity had a significant combined effect on average N to N interval, LF power, and LF:HF ratio during N2 and REM sleep (all p < 0.001).ConclusionContrary to our hypothesis, REM-OSA patients demonstrated consistently higher cardiac vagal modulation, reflecting better cardiac autonomic adaptation. These results were attributed to differences in OSA severity and sex in these two groups, both independently affecting HRV. This study emphasises the need for future research into the underlying pathophysiology of REM-OSA and the potential implications of sex and OSA severity on cardiovascular risk.

Landslide susceptibility mapping using frequency ratio method along the Bhaderwah-Bani Road, Jammu and Kashmir, India

This study outlines a method for landslide susceptibility mapping utilizing remote sensing and geographic information systems (GIS). Since the terrain from Bhaderwah to Bani in J&amp;K is prone to landslide threats, the study has been conducted there. The purpose of the current study is to determine and identify the significant terrain elements causing landslides. Nine contributing factors on landslides were taken into account throughout the analysis. Thematic data layers are created in the GIS domain based on local events. Topographic maps, satellite images, on-site observations and publicly available published maps are used to gather the landscape data. To determine the standardized scores of criteria expressing their factor of importance for a given decision problem in terms of thematic parameters, categories, and their normalized weights the Frequency ratio (FR) method is used to digitize these maps along with tabular data and to create a GIS database. A particular landslide susceptibility zonation (LSZ) map was created on a GIS platform by statistically combining weightages from several thematic maps. Using FR, the LSZ map was divided into five separate susceptibility zones. According to the study's finding, 8% of the area is in a very high susceptibility zone, with the remaining 12%, 15%, 23%, and 42% falling into high, moderate, low, and very low susceptibility zones, respectively. The outcomes of the current study might aid policy and decision makers in moving forward with regional development initiatives including land use planning.

Faktor-faktor yang Berhubungan dengan Kualitas Tidur Pasien dengan Lupus Eritematosus Sistemik

Introduction. Sleep disturbances are often found among Systemic Lupus Erythematosus (SLE) patients. Poor sleep may cause low quality of life. Study about sleep quality among SLE patients and related factors in Indonesia is scarce. This study aimed to know factors related to poor sleep quality among SLE patients. Methods. This cross-sectional study was conducted among SLE patients at Cipto Mangunkusumo Hospital Jakarta Indonesia. Sleep quality was assessed by Pittsburgh Sleep Quality Index (PSQI), with global PSQI score &gt;5 indicates poor sleep quality. Factors evaluated were symptoms of depression and anxiety by Hospital Anxiety Depression Scale (HADS), pain by Visual Analogue Scale (VAS), Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K), autonomic dysfunction by Low Frequency/High Frequency (LF/HF) ratio from Heart Rate Variability (HRV), and high sensitivity C-Reactive Protein (hs-CRP) level. Bivariate analysis was done by Chi Square or Fisher’s test. Multivariate analysis was done by logistic regression, p value &lt;0.05 was considered statistically significant. Results. Total of 166 SLE patients were included. Mean age was 32.7 years old with the majority in age group of 21-30 years old and female. Poor sleep quality in SLE patients was found in 82.5% of subjects. Symptoms of depression were significantly associated with poor sleep quality in SLE patients, especially sleep efficiency. Anxiety was associated with subjective month- and week-long sleep quality, sleep disturbances, and sleep medication usage. Musculoskeletal component of the SLEDAI-2K was associated with month- and week-long sleep quality, sleep latency, and daytime dysfunction. Hematologic involvement was associated with daytime dysfunction, whereas mucocutaneous involvement was associated with sleep disturbances. Conclusions. Symptoms of depression were significantly associated with poor sleep quality in SLE patients, especially sleep efficiency. Anxiety was associated with subjective month- and week-long sleep quality, sleep disturbances, and sleep medication usage.

Basic Experimental Study on the Application of Natural Frequency to Transfer Learning

Integrating artificial intelligence (AI) into structural health monitoring systems significantly improves the structural integrity and safety of buildings. This integration necessitates extensive data on various structural damage scenarios; however, acquiring comprehensive damage-state data for real-world buildings is difficult, resulting in data scarcity and imbalances between intact- and damage-state datasets. Transfer learning offers a compelling alternative that enables the utilization of damage-state data acquired from scaled experimental models for real-world building applications. This study explores the use of natural frequency ratios before and after damage to facilitate transfer learning. This study established an equation that describes the relationship between the natural frequency ratio before and after damage, accounting for scaling impacts. The natural frequency ratios for both the prototype and scaled models were experimentally determined and compared, focusing on bolted joints in the steel structure. The results of the experiment were consistent with those of the finite-element analysis. The experimentally obtained natural frequency ratios of the prototype and scaled models under identical damage conditions exhibited high congruence. The experimental and FEA results demonstrated analogous patterns of decreasing natural frequency ratio with increasing damage severity. These results indicate that natural frequency ratio data from various damage conditions in scaled models could mitigate data scarcity issues and train AI models for real-world building applications.

Integration of geospatial-based algorithms for groundwater potential characterization in Keiskamma Catchment of South Africa

Groundwater supports over 2.4 billion people across the globe and is critical to food security. The spatial dynamics of groundwater vary from place to place. The irregularity of groundwater resource exploitation is recognized in drought-prone areas, putting pressure on the resource. Hence, accurate groundwater potential characterization is critical for sustainable development and management of groundwater, particularly in drought-prone environments. Therefore, this study aimed at utilizing remote sensing satellite data and geospatial-based (analytical hierarchy process (AHP) and frequency ratio (FR)) algorithms to characterize groundwater potential zones (GWPZs) in the Keiskamma Catchment of South Africa. Seven (7) selected factors, including geology, soil type, slope, rainfall, drainage density, lineament density, and land use land cover, were assigned weights based on the AHP and FR algorithms. The validation results showed that the FR model performed better than the AHP, with the area under curve (AUC) accuracies of 62% and 50%, respectively. Based on the findings of this study, we infer that FR is more reliable than AHP when characterizing GWPZ. Lastly, GWPZ maps produced will be beneficial for improving efficient planning, management strategies, and decision-making.

Chatter and Surface Waviness Analysis in Oerlikon Face Hobbing of Spiral Bevel Gears

A vectorized analytical model for the cutting dynamics in the spiral bevel gear face hobbing process has been developed, which is based on machine tool kinematics and vibration vectorization. The structural modal parameters of the cutter head spindle system are obtained through experimental modal analysis with hammer impact testing. The analytical model is utilized to simulate the generation of simulated vibration acceleration signals during spiral bevel gear hobbing. A wavelet threshold denoising method is applied to process the simulated vibration signals of the spiral bevel gear face hobbing with added white noise. Signal processing methods, including short-time Fourier transform are employed for time-domain analysis, frequency-domain analysis, and time–frequency-domain analysis of measured signals and simulated signals, thereby extracting the corresponding statistical features. In addition to the results of the experimental modal analysis, the causes of chatter in spiral bevel gear hobbing are discussed in detail, revealing that the main factor is cutter head vibration in the Y direction of the Hunan ZDCY CNC EQUIPMENT YKA2260 machine tool used in this research. The error in the time-domain characteristic parameters between simulated signals and measured vibration acceleration signals is within 15%, with a difference of 3.5% in spectral peak values. The predicted tooth surface morphology from simulation matches the actual morphology on the workpiece, comprehensively validating the reliability of the cutting dynamics model for the spiral bevel gear face hobbing process. Another conclusion drawn from numerical simulation experiments is that the amount of tooth surface waviness of the spiral bevel gears is the ratio of tool chatter frequency to cutting fundamental frequency.

Modeling landslide susceptibility using alternating decision tree and support vector

Globally, but especially in the Chinese Loess Plateau, landslides are considered to be one of the most severe and significant geological hazards. The purpose of this study is to design two ensemble machine learning methods, which are denoted as ADTree-Dagging and SVM-Dagging, for modeling landslide susceptibility in Lanzhou City (China). For this aim, the slope units extracted by the curvature watersheds method are used to construct landslide susceptibility modeling, and ten landslide conditioning factors are included in the landslide susceptibility evaluation (altitude, slope angle, slope aspect, cutting depth, surface roughness, relief amplitude, gully density, rainfall, distance to roads, and lithology). The conditioning factors selection and spatial correlation analysis were implemented by using the correlation attribute evaluation method and the frequency ratio model. The comprehensive performance of the models was tested using the receiver operating characteristic (ROC), area under the ROC curve (AUC), the root mean square error (RMSE), and several other performance metrics. For the training dataset, the results show that the SVM-Dagging model acquire the largest AUC value (0.953), lowest RMSE (0.3125), highest positive predictive value (96.0%), highest negative predictive value (91.2%), highest sensitivity (91.6%), highest specificity (95.8%), highest accuracy (93.6%), and highest Kappa (0.873). Similar results are observed in the validation dataset. Results demonstrated that the Dagging technique has improved significantly the prediction ability of SVM and ADTree models. The Dagging method can combine different models by leveraging the strengths of each model to create methods with higher flexibility than traditional machine learning methods. Therefore, in this study, the proposed new models can be applied for land-use planning and management of landslide susceptibility in the study area and in other areas containing similar geological conditions.

Modeling of groundwater productivity in the Alfred Nzo District, South Africa, using relative frequency ratio and Shannon entropy models

Study regionAlfred Nzo District, Eastern Cape Province, South Africa. Study focusThe study aimed to identify groundwater potential zones in the Alfred Nzo District using advanced geographic information systems and remote sensing applications by integrating relative frequency ratio (RF) with predictor rate and Shannon entropy (SE) models. Seven influential factors that are thought to control groundwater availability in the Alfred Nzo District were considered to identify groundwater potential zones. These factors are drainage density, lineament density, rainfall, slope, land use and land cover, lithology, and soil class. The factors were weighted in the RF and SE approaches. About 70 % of boreholes were used as training datasets, and 30 % were used for validation. Groundwater potential zones were delineated by integrating all these factors and their corresponding weights in ArcGIS software. The accuracy of each model was evaluated using receiver operating characteristic curve and area under curve techniques. New hydrological insights for the regionFive groundwater potential zones were found in the research area, which includes very poor, poor, moderate, good, and very good, by RF and SE models. The results of the models revealed that the SE model, with a success rate of 79.80 %, performed better than the RF, with a success rate of 75.10 %. The results of this research could be helpful in adequately managing groundwater resources in the research area.

The use of transvaginal ultrasound alters physiologic uterine peristalsis in gynecologic participants

To study whether transvaginal ultrasound (TVUS) affected the uterine peristalsis (UP) patterns in nonpregnant participants. Institutional review board-approved, prospective observational cohort study. The noninvasive UP imaging (UPI) system uses electrode patches placed on the patient's skin just above the pubic bone and on the low back to quantify the 3-dimensional electrical activation pattern during UP by calculating peristalsis frequency, duration, magnitude, and activation ratio. A 20-minute UPI scan was completed without TVUS followed by a 10-minute UPI scan acquired simultaneously during TVUS examination as a comparison. University medical center. Twenty-eight participants with regular menstrual cycles not taking hormonal medication and with a normal uterus were included in analysis. Subjects were imaged longitudinally during the four phases of the menstrual cycle (menses, proliferative, periovulatory, and secretory) with a UPI scan followed by concurrent TVUS and UPI scan. Serum hormone levels (estradiol and progesterone) and TVUS evaluating follicular development were obtained during each visit to confirm menstrual cycle phase. Duration, frequency, magnitude, and activation ratio of the UP waves. With the use of simultaneous TVUS, UP waves had a change in at least one of the outcomes measured in all visits. The frequency, magnitude, and duration were significantly higher with TVUS use in all phases of the menstrual cycle. The activation ratio was higher with TVUS during all phases except the periovulatory phase. This study demonstrated that TVUS may inherently affect UP waves. Therefore, noninvasive technology may more accurately measure physiologic peristalsis waves.

Illness is more prevalent than injury in trail runners participating in a mountainous ultra trail race

ABSTRACT Objectives Trail running is a popular off-road sport involving running in natural environments over various terrains, often in remote locations. This study aims to investigate the epidemiology and risk factors of injuries and illnesses, i.e. medical encounters, on race day among trail runners in a high-altitude ultra trail race. Methods This descriptive cross-sectional study on an ultra trail race (38 km, 65 km and 100 km) in South Africa, included participants 18 years or older. Of the 331 race participants, 285(86.1%) consented to participate in the study. Data collection included demographic details, injuries (body region, specific body area, tissue type, pathology) and illnesses (organ system, symptom cluster, etiology). Risk factor analysis includes sex, age, weight, height, race distance, illness and injury history, training and running experience. Frequency (n, %), prevalence (%) and odds ratios (OR; 95%CI) are reported. Results Eighty-nine (31.2%) individuals reported 131 medical encounters [49 injuries (37.4%); 82 illnesses (62.6%)]. Injuries were sustained by 14.7% of athletes, and 22.5% reported illnesses. For injuries, the lower limb was mainly involved (n = 41; 83.7%). Most injuries affected the foot (n = 18; 36.7%), ankle (n = 10; 20.4%) and knee (n = 7; 14.3%). Tissue types mainly involved skin (n = 21; 42.8%), ligament (n = 7; 14.3%) and muscle (n = 7; 14.3%). Multiple (n = 45; 54.9%) and gastrointestinal (n = 17; 20.7%) organ systems were mainly involved in illnesses. Only 100 km runners reported dehydration (n = 28; 31.5%), and one in every six of these runners (n = 5; 17.9%) did not finish. Runners reporting fatigue (n = 21; 23.6%) had a high (n = 8; 38.1%) did not finish rate. Two in every five participants (n = 36; 40.4%) with a medical encounter, did not finish. No medical encounter-associated risk factors were identified. Conclusions Illnesses were more common than injuries during the mountainous ultra trail race. Sustaining a medical encounter increased the chance of not completing the race. Further research on the epidemiology of race day medical encounters in trail running is required.

Exploring different approaches for landslide susceptibility zonation mapping in Manipur: a comparative study of AHP, FR, machine learning, and deep learning models

ABSTRACT The movement of rock, soil, and other debris down a slope or incline is a geological phenomenon known as a landslide. To analyze the landslide susceptibility (LS) in Manipur, the study develops and compares six heterogeneous models, specifically the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Deep Learning (DL) models were considered. The study found that the DL is the most intriguing model, with a total accuracy of 97.2%, followed by the RF, KNN, SVM, AHP, and FR, with respective accuracy levels of 94.5%, 93.1%, 92.6%, 85.5%, and 76.9%.

Optimization of column-in-column system based on non-conventional TMD concept

Previous studies showed that the non-conventional tuned mass damper (TMD) with large mass ratio outperforms the traditional one in respect to the structural vibration control efficiency and robustness. The authors recently proposed a novel control system based on the non-conventional TMD concept, namely the column-in-column (CIC) system, which was composed of a primary column, a secondary column and a series of interconnected springs/dashpots. In this novel system, the secondary column connected to the primary one with springs/dashpots could act as a large TMD to attenuate adverse vibration of the primary structure, and its control effectiveness was preliminarily verified through numerical investigations. However, the control robustness was found not sufficient based on the existing optimal formulae in the literature for CIC optimization. This paper presents a new design method for optimizing the CIC system for better harnessing its control effectiveness and robustness in reducing structural response. The CIC system consists of two columns with distributed mass and uniform flexural rigidity, connected by springs along the column height. The effective modal mass and modal stiffness of a specific vibration mode of the system can be straightforwardly calculated by using the Euler-Bernoulli beam theory. Considering the fundamental vibration mode of the system as the target response mode for vibration control, the multiple degree-of-freedom (DOF) CIC is simplified into a two DOF with three springs (2DOF3S) structural model, the design parameters, i.e., the optimal tuning frequency ratio and damping ratio of this system are derived to minimize the displacement response of the primary structure. To demonstrate the proposed design method, the effectiveness and robustness of the CIC system in controlling seismic induced structural vibrations are assessed in both the frequency and time domains. Results show that the derived optimum formulae are applicable for optimizing the TMD system with consideration of the distributed mass and deformation of the mass damper, i.e., a column in this study. The optimized CIC system has good control robustness even if the CIC is mistuned with its design parameters deviate from the optimal values by ±50 %. The conventional TMD system with lumped mass as the mass damper is a special case of the proposed CIC system. The optimized CIC system has favorable control efficiency in mitigating the seismic responses of structures with reduction ratios averagely varying within the range of 15 % ∼ 55 % with a probability of 92.67 %. It is concluded that the proposed method is reliable and can be used for the optimum design and control of the CIC system.

Observation of Boyer-Wolf Gaussian modes

Stable laser resonators support three fundamental families of transverse modes: the Hermite, Laguerre, and Ince Gaussian modes. These modes are crucial for understanding complex resonators, beam propagation, and structured light. We experimentally observe a new family of fundamental laser modes in stable resonators: Boyer-Wolf Gaussian modes. By studying the isomorphism between laser cavities and quadratic Hamiltonians, we design a laser resonator equivalent to a quantum two-dimensional anisotropic harmonic oscillator with a 2:1 frequency ratio. The generated Boyer-Wolf Gaussian modes exhibit a parabolic structure and show remarkable agreement with our theoretical predictions. These modes are also eigenmodes of a 2:1 anisotropic gradient refractive index medium, suggesting their presence in any physical system with a 2:1 anisotropic quadratic potential. We identify a transition connecting Boyer-Wolf Gaussian modes to Weber nondiffractive parabolic beams. These new modes are foundational for structured light, and open exciting possibilities for applications in laser micromachining, particle micromanipulation, and optical communications.

Linkages between gully erosion susceptibility and hydrological connectivity in Tropical sub-humid river basin: Application of Machine learning algorithms and Connectivity Index

Hydrological connectivity from upslope to downslope of valley floor and main channel, triggered the gully initiation and associated land degradation continue occurring off-site erosion as considered most effective drivers on potential sediment detachment. Present study attempted to identify the linkage between gullies erosion susceptibility (GES) and hydrological connectivity pathway in sub-tropical humid river basin Kangsabati (KRB) using four machine-learning algorithms (MLALs) such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN) for GES mapping, and connectivity index (IC) for hydrological connectivity mapping. Thirty-five controlling factors were selected using Boruta’s approach to produce GES mapping, while frequency ratio (FR) was applied to determine the significant role in each individual class of controlling factors on degree of gully susceptibility. To achieve the efficiency of using MLALs, AUC of ROC including sensitivity, specificity, accuracy, F, and Kappa index were employed to compare in each model. In testing datasets, AUC values reveals that RF (0.99) and XGB (0.99) were well performed and predicted to GES followed by ANN (0.97) and SVM (0.87). FR depicts the most contributing factors of barren land and laterite followed by rainfall erosivity, degraded forest, single crop, and elevation to GES. IC result showed that values range (−11.52 to 0.49) address the three connectivity categories i.e. not connected (NC), gully connected but not reach (CNR), and gully well connected (WC). Correlation analysis clarified that double crop (R = 0.82), topographical wetted index (R = 0.77) and slope (R = 0.0.44) are prolonged WC for large downslope in north-western sub-basins of upper catchment and western, eastern sub-basins of lower catchment, while dense forest (R = -0.71) and vegetation cover (R = -0.82) forms NC gullies and CNR to channel due to interrupted upslope in central sub-basins of upper and lower catchment. Finally, research findings could provide to take strategy for sustainable land uses policy.