Range Of Parameters Research Articles

A dynamical computational model of theta generation in hippocampal circuits to study theta-gamma oscillations during neurostimulation.

Neurostimulation of the hippocampal formation has shown promising results for modulating memory but the underlying mechanisms remain unclear. In particular, the effects on hippocampal theta-nested gamma oscillations and theta phase reset, which are both crucial for memory processes, are unknown. Moreover, these effects cannot be investigated using current computational models, which consider theta oscillations with a fixed amplitude and phase velocity. Here, we developed a novel computational model that includes the medial septum, represented as a set of abstract Kuramoto oscillators producing a dynamical theta rhythm with phase reset, and the hippocampal formation, composed of biophysically realistic neurons and able to generate theta-nested gamma oscillations under theta drive. We showed that, for theta inputs just below the threshold to induce self-sustained theta-nested gamma oscillations, a single stimulation pulse could switch the network behavior from non-oscillatory to a state producing sustained oscillations. Next, we demonstrated that, for a weaker theta input, pulse train stimulation at the theta frequency could transiently restore seemingly physiological oscillations. Importantly, the presence of phase reset influenced whether these two effects depended on the phase at which stimulation onset was delivered, which has practical implications for designing neurostimulation protocols that are triggered by the phase of ongoing theta oscillations. This novel model opens new avenues for studying the effects of neurostimulation on the hippocampal formation. Furthermore, our hybrid approach that combines different levels of abstraction could be extended in future work to other neural circuits that produce dynamical brain rhythms.

Maximizing productivity, profitability and water use efficiency in Indian mustard (Brassica juncea) through hydrogel and salicylic acid

Addressing water scarcity in the arid and semi-arid regions of Rajasthan presents a substantial challenge to improve the productivity of Indian mustard [Brassica juncea (L.) Czern.]. The limited availability of water in these areas results in reduced yields of Indian mustard. Therefore, a field experiment was conducted during rabi (winter) seasons of 2018–19, 2019–20 and 2020–21 at the research farm of Sri Karan Narendra Agriculture University, Jobner, Rajasthan, to evaluate the impact of hydrogel and foliar application of salicylic acid on the growth, yield, economic returns and water use efficiency of Indian mustard. The results revealed that the application of hydrogel at a rate of 5.0 kg/ha, in combination with salicylic acid at a concentration of 200 ppm during the flowering and siliqua formation stages through foliar spray, had a significant and highly effective impact. This treatment displayed superior performance across a range of parameters, including growth characteristics, yield attributes, seed yield (1840 kg/ha), stover yield (3847 kg/ha), water use efficiency (8.53 kg/ha-mm), protein content (18.6%), oil content (39.13%), net returns (₹51,710/ha) and benefit-cost ratio (2.03). Furthermore, treatments involved 5.0 kg/ha of hydrogel in conjunction with 100 ppm of salicylic acid, as well as 2.5 kg/ha of hydrogel with 200 ppm of salicylic acid, produced similar results. The combined findings of this experiment endorse the application of hydrogel and salicylic acid as a sustainable and effective strategy for enhancing the productivity, profitability and water use efficiency of Indian mustard in the arid and semi-arid regions of Rajasthan.

Deep learning based ultrasonic reconstruction of rough surface morphology

This paper introduces a methodology to recover the morphology of a complex rough surface from ultrasonic pulse echo measurements with an array of equidistant sensors using the one dimensional convolution neural network (1DCNN). The neural network is trained by the datasets simulated from high-fidelity finite element simulations for surfaces with a range of roughness parameters and is tested on both numerical and real experimental data. To assess the performance of our proposed method, the rough surface reconstruction results from the deep learning approach are compared with those obtained from conventional ultrasonic array imaging methods. Unlike array imaging-based methods that require a large number of sensors (e.g., 128, 64 or 32), the deep learning-based method uses pulse echo signals and can achieve accurate results with much fewer sensors. The developed deep learning approach has the potential to enable low-cost, accurate, and real-time reconstruction of complex surface profiles.

Modeling of 3D Brick‐and‐Mortar Structures Using Cohesive Zone Finite Elements

Nacre‐inspired brick‐and‐Mortar composite structures exhibit exceptional combinations of properties as well as a highly tuneable mechanical response, due to their large range of design parameters. Understanding the effect of these parameters on the response is essential to optimally design these structures and can be guided by modeling. Traditional models only consider 2D geometries and limited attempts at modeling 3D geometric designs exist. Herein, 3D brick‐and‐mortar structures using a finite element in conjunction with an experimentally calibrated cohesive zone model to represent the layers are proposed. The model is successfully validated against experimental results for a nonplanar brick assembly using so‐called osteomorphic bricks. The capabilities of the model are further demonstrated through a parametric study, where the effect of brick shape, number of bricks, and soft layer material properties on the structure mechanical properties (elastic modulus, yield strength and toughness) are investigated. Numerical results show that toughness is significantly increased by transitioning from a “two‐peak” failure mechanism to a “peak‐plateau‐peak,” which is controlled by the brick shape. It is also shown that 3D structures may exhibit significant out‐of‐plane deformation involving the cooperative motion of many bricks, which may contribute to their improved toughness compared to 2D structures.

Ti-B-O System for Catalyzing Boron Nitride Nanotube Growth.

Chemical vapor deposition (CVD) stands out as the most promising method for cost-effective production of high-quality boron nitride nanotubes (BNNTs). Catalysts play a crucial role in BNNT synthesis. This work delves into the impact of oxygen (O) on Ti-based catalysts during the CVD growth of BNNTs. In contrast to the B/TiB2 nanoparticles (NPs) and B/TiN NPs systems, the oxygen-containing precursor B/TiO2 NPs remarkably catalyzes the growth of high-quality and high-purity BNNTs across a wider range of synthesis parameters. Subsequent analyses reveal that TiBO3 acts as an active catalyst, facilitating BNNT growth in Ti-based catalyst systems. Moreover, the nanocomposite film synthesized from BNNTs and PVDF-HFP exhibits excellent mechanical properties and heat dissipation capabilities. Utilizing the nanocomposite film as a thermal interface material effectively enhances the heat dissipation for a 5 W light-emitting diode (LED) chip. Consequently, our research confirms the effectiveness of the Ti-B-O system in catalyzing BNNT growth.

Improving the performance of gasoline fuels by adding methanol and methyl tertiary-butyl ether along with metal oxides titanium oxide and magnesium oxide

These variables include the type of nanoparticle additives integrated into the fuel blend and the operational engine speed. The focus is on two specific oxygenated additives, methanol and isobutanol, in conjunction with Methyl Tertiary-Butyl Ether (MTBE) and two metal oxide nanoparticles, namely magnesium oxide and titanium oxide. The experimental framework entails testing these new fuel blends in a controlled environment, utilizing a four-stroke engine linked to a dynamometer and a speed analyzer. The tests are conducted at rotational speeds of 750 and 1250 rpm. The comprehensive data collected encompass a range of parameters, including engine power and torque, as well as the emissions of carbon dioxide (CO2), nitrogen oxides (NOx), unburned hydrocarbons (HC), and carbon monoxide (CO). The study meticulously analyzes data to build a robust model, significantly enhancing engine performance while substantially lowering emissions. It finds that adding specific additives to gasoline notably reduces carbon monoxide and hydrocarbons. Testing gasoline with methanol, MTBE, and titanium oxide at 750 RPM showed a notable shift in emissions: nitrogen oxide increased by 40.5%, while hydrocarbons, CO, and CO2 saw reductions of 17.5%, and from 2.4% to 7.8%–1.197% and 7.661%, respectively, underlining the need for careful fuel composition management for environmental benefits.

Unveiling the Essential Parameters Driving Mineral Reactions during CO2 Storage in Carbonate Aquifers through Proxy Models

Numerical simulation is a commonly employed technique for studying carbon dioxide (CO2) storage processes in porous media, particularly saline aquifers. It enables the representation of diverse trapping mechanisms and the assessment of CO2 retention capacity within the subsurface. The intricate physicochemical phenomena involved necessitate the incorporation of multiphase flow, accurate depiction of fluid and rock properties, and their interactions. Among these factors, geochemical reaction rates and mechanisms are pivotal for successful CO2 trapping in carbonate reactive rocks. However, research on kinetic parameters and the influence of lithology on CO2 storage remains limited. This limitation is partly due to the challenges faced in laboratory experiments, where the time scale of the reactions and the lack of in situ conditions hinder accurate measurement of mineral reaction rates. This study employs proxy models constructed using response surfaces calibrated with simulation results to address uncertainties associated with geochemical reactions. Monte Carlo simulation is utilized to explore a broader range of parameters and identify influential factors affecting CO2 mineralization. The findings indicate that an open database containing kinetic parameters can support uncertainty assessment. Additionally, the proxy models effectively represent objective functions related to CO2 injectivity and mineralization, with calcite dissolution playing a predominant role. pH, calcite concentration, and CO2 injection rate significantly impact dolomite precipitation, while quartz content remains unaffected.

Laser marking on polyoxymethylene (POM) polymer substrate for a lean manufacturing application

AbstractLaser marking is a critical production process step in most automated production systems. However, some materials such as polyoxymethylene (POM) can be quite challenging to laser to 80 kHz.mark, hence the need to develop new laser marking methods. By employing a wide range of marking parameters (marking speed 15–2000 mm/s, laser power from 10 to 100% of 30 W maximum, and frequencies mainly from 1 to 400 kHz) and characterization techniques, and with the use of marker inks as masking agents successful laser marking POM is demonstrated irrespective of its presentation (white or black coloured) using a new method akin to Laser Polymer Tattooing (LPT), and without prior modification of polymer composition (by addition of laser marking additives). Results indicate laser marking of acceptable quality can be obtained with black POM at marking speeds up to 2000 mm/s. For white POM, markings of acceptable quality were obtained at markings speeds up to 100 mm/s, with laser power ≥ 80% (of 30 W maximum), and with frequencies in the range of 20 kHz with the help of a masking agent. The effects of the laser marking parameters on marking quality and the changes induced in the material are presented and discussed. The wide range of marking parameters employed enabled the determination of feasible marking parameters for the development of a lean manufacturing-based automatic and self-evaluating and improving laser marking system with minimal human intervention.

Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability.

In order to ensure that drug administration is safe during pregnancy, it is crucial to have the possibility to predict the placental permeability of drugs in humans. The experimental method which is most widely used for the said purpose is in vitro human placental perfusion, though the approach is highly expensive and time consuming. Quantitative structure-activity relationship (QSAR) modeling represents a powerful tool for the assessment of the drug placental transfer, and can be successfully employed to be an alternative in in vitro experiments. The conformation-independent QSAR models covered in the present study were developed through the use of the SMILES notation descriptors and local molecular graph invariants. What is more, the Monte Carlo optimization method, was used in the test sets and the training sets as the model developer with three independent molecular splits. A range of different statistical parameters was used to validate the developed QSAR model, including the standard error of estimation, mean absolute error, root-mean-square error (RMSE), correlation coefficient, cross-validated correlation coefficient, Fisher ratio, MAE-based metrics and the correlation ideality index. Once the mentioned statistical methods were employed, an excellent predictive potential and robustness of the developed QSAR model was demonstrated. In addition, the molecular fragments, which are derived from the SMILES notation descriptors accounting for the decrease or increase in the investigated activity, were revealed. The presented QSAR modeling can be an invaluable tool for the high-throughput screening of the placental permeability of drugs.

Modelling individual plants’ growth: competition of Viola arvensis and wheat

IntroductionCompetition by weeds is a severe threat to agricultural crops. While these days the broadcast of herbicides over the entire field is common praxis, new technologies promise to reduce chemical output by reducing the area sprayed. The maximum precision would be a single plant treatment. This precision will allow a single plant management, which requires single plant management decisions, which is far beyond the possibilities of current praxis. A plant specific management decision can only be made on the basis of a model simulation.Materials and methodsA simulation model was developed to evaluate the effect of spatially explicit weed management covering interaction between single plants. The governing equations consist of coupled nonlinear differential equations for growth and competition of crop and weed plants in a spatial setting i.e. a coordinate is assigned to each plant. The mutual interaction is determined by the parameters strength and range of competition. Furthermore, an experiment was carried out parallel to the development of the model involving wheat and Viola arvensis (Murr.), in which coordinates and growth curves for a large number of plants (~600) were recorded allowing for a reasonable parameterization of the model.Results and discussionThe model is able to evaluate spatially explicit management measures such as weed strip control based on the height growth of single plants. The model is capable of evaluating a variety of control measures such as the frequency and spatial allocation of treatments. In particular, the effect of the width of a treatment zone around the rows of the crop was simulated.ConclusionIn future, the developed model could be extended to a decision support system for single plant weed management. Making decisions plant-by-plant, allows to orchestrate the weed management in a way that takes into account competing goals in plant protection: yield and biodiversity.

A theoretical framework for BL Her stars. II. New period-luminosity relations in Gaia passbands

In the era of the Hubble tension, it is crucial to obtain a precise calibration of the period-luminosity ($PL$) relations of classical pulsators. Type II Cepheids (T2Cs; often exhibiting negligible or weak metallicity dependence on $PL$ relations) used in combination with RR Lyraes and the tip of the red giant branch may prove useful as an alternative to classical Cepheids for the determination of extragalactic distances. We present new theoretical $PL$ and period-Wesenheit ($PW$) relations for a fine grid of convective BL Her (the shortest period T2Cs) models computed using mesa-rsp in the $Gaia$ passbands and we compare our results with the empirical relations from $Gaia$ DR3. Our goal is to study the effect of metallicity and convection parameters on the theoretical $PL$ and $PW$ relations. We used the state-of-the-art 1D non-linear radial stellar pulsation tool mesa-rsp to compute models of BL Her stars over a wide range of input parameters:\ metallicity ($-2.0\; dex Fe/H dex $), stellar mass ($0.5M_ odot -0.8M_ odot $), stellar luminosity ($50L_ odot -300L_ odot $), and effective temperature (across the full extent of the instability strip; in steps of 50K). We used the Fourier decomposition technique to analyse the light curves obtained from mesa-rsp and $Gaia$ DR3 and then compared the theoretical and empirical $PL$ and $PW$ relations in the $Gaia$ passbands. The BL Her stars in the All Sky region exhibit statistically different $PL$ slopes compared to the theoretical $PL$ slopes computed using the four sets of convection parameters. We find the empirical $PL$ and $PW$ slopes from BL Her stars in the Magellanic Clouds to be statistically consistent with theoretical relations computed using the different convection parameter sets in the $Gaia$ passbands. There is a negligible effect coming from the metallicity on the $PL$ relations in the individual $Gaia$ passbands. However, there is a small but significant negative coefficient of metallicity in the $PWZ$ relations for the BL Her models using the four sets of convection parameters. This could be attributed to the increased sensitivity of bolometric corrections to metallicities at wavelengths shorter than the $V$ band. Our BL Her models also suggest a dependence of the mass-luminosity relation on metallicity. We found the observed Fourier parameter space to be covered well by our models. Higher mass models ($> 0.6\ odot $) may be needed to reliably model the observed light curves of BL Her stars in the All-Sky region. We also found the theoretical light curve structures (especially the Fourier amplitude parameters) to be affected by the choice of convection parameters.

ASSESSMENT OF PHYSICOCHEMICAL PARAMETERS OF WATER IN AN INDUSTRIAL SCALE IN POND RACEWAY SYSTEM

The In-pond raceway system is a highly sophisticated innovative aquaculture technology. Many research has been done on the physicochemical characteristics of water. The present study was conducted to measure the physicochemical parameters of water in an In-Pond Raceway System (IPRS). The study employed standard and instrumental methods to investigate a range of physicochemical parameters, including water temperature, pH, DO, TDS, conductivity, ammonia, nitrite, and phosphate. One-way Analysis of Variance was conducted to calculate the variation of physicochemical parameters among the study sites. Statistical analysis showed there was no significant variation of physicochemical parameters (p>0.05) among the experimental sites except DO and ammonia (p<0.05). The experimental study showed that, all the key physicochemical parameters water in IPRS production zone were within the optimum ranges when compared with standard water quality parameters which is the prerequisite to introduce maximum survivability of fish production in IPRS system.

The SAMI galaxy survey: predicting kinematic morphology with logistic regression

ABSTRACT We use the SAMI (Sydney-AAO Multi-object Integral field spectrograph) galaxy survey to study the the kinematic morphology–density relation: the observation that the fraction of slow rotator galaxies increases towards dense environments. We build a logistic regression model to quantitatively study the dependence of kinematic morphology (whether a galaxy is a fast or slow rotator) on a wide range of parameters, without resorting to binning the data. Our model uses a combination of stellar mass, star formation rate (SFR), r-band half-light radius, and a binary variable based on whether the galaxy’s observed ellipticity (ϵ) is less than 0.4. We show that, at fixed mass, size, SFR, and ϵ, a galaxy’s local environmental surface density (log10(Σ5/Mpc−2)) gives no further information about whether a galaxy is a slow rotator, that is, the observed kinematic–morphology density relation can be entirely explained by the well-known correlations between environment and other quantities. We show how our model can be applied to different galaxy surveys to predict the fraction of slow rotators which would be observed and discuss its implications for the formation pathways of slow rotators.

Structured model conserving biomass for the size-spectrum evolution in aquatic ecosystems.

Mathematical modelling of the evolution of the size-spectrum dynamics in aquatic ecosystems was discovered to be a powerful tool to have a deeper insight into impacts of human- and environmental driven changes on the marine ecosystem. In this article we propose to investigate such dynamics by formulating and investigating a suitable model. The underlying process for these dynamics is given by predation events, causing both growth and death of individuals, while keeping the total biomass within the ecosystem constant. The main governing equation investigated is deterministic and non-local of quadratic type, coming from binary interactions. Predation is assumed to strongly depend on the ratio between a predator and its prey, which is distributed around a preferred feeding preference value. Existence of solutions is shown in dependence of the choice of the feeding preference function as well as the choice of the search exponent, a constant influencing the average volume in water an individual has to search until it finds prey. The equation admits a trivial steady state representing a died out ecosystem, as well as-depending on the parameter-regime-steady states with gaps in the size spectrum, giving evidence to the well known cascade effect. The question of stability of these equilibria is considered, showing convergence to the trivial steady state in a certain range of parameters. These analytical observations are underlined by numerical simulations, with additionally exhibiting convergence to the non-trivial equilibrium for specific ranges of parameters.

Geometrically thick equilibrium tori around a dyonic black hole with quasi-topological electromagnetism

We study geometrically thick and non-self gravitating equilibrium tori orbiting a static spherically symmetric dyonic black hole with quasi-topological electromagnetism. Our results show that the electric and magnetic charges together with the coupling parameter in the quasi-topological electromagnetism lead to a much richer class of equilibrium tori. There are a range of parameters which allow for the existence of double tori. The properties of double equilibrium tori become far richer. There exist transitions between single torus and double tori solutions as we change the specific angular momentum of the fluid. These properties of equilibrium tori could help to understand the dyonic black hole and its thick accretion disk.

Subduction plate interface shear stress associated with rapid subduction at deep slow earthquake depths: example from the Sanbagawa belt, southwestern Japan

Abstract. Maximum shear stress along an active deformation zone marking the subduction plate interface is important for understanding earthquake phenomena and is an important input parameter in subduction zone thermomechanical modeling. However, such maximum shear stress is difficult to measure directly at depths more than a few kilometers and is generally estimated by simulation using a range of input parameters with large associated uncertainties. In addition, estimated values generally represent maximum shear stress conditions over short observation timescales, which may not be directly applicable to long-timescale subduction zone modeling. Rocks originally located deep in subduction zones can record information about deformation processes, including maximum shear stress conditions, occurring in regions that cannot be directly accessed. The estimated maximum shear stress is likely to be representative of maximum shear stress experienced over geological timescales and be suitable to use in subduction zone modeling over timescales of millions to tens of millions of years. In this study, we estimated maximum shear stress along a subduction plate interface by using samples from the Sanbagawa metamorphic belt of southwestern (SW) Japan, in which slivers of mantle-wedge-derived serpentinite are widely distributed and in direct contact with metasedimentary rocks derived from the subducted oceanic plate. These areas can be related to the zone of active deformation along the subduction plate interface. To obtain estimates of maximum shear stress at the subduction interface, we focused on the microstructure of quartz-rich metamorphic rocks – quartz is the main component of the rocks we collected and its deformation stress is assumed to be roughly representative of the stress experienced by the surrounding rock and plate interface deformation zone. Maximum shear stress was calculated by applying deformation temperatures estimated by the crystallographic orientation of quartz (the quartz c-axis fabric opening-angle thermometer) and the apparent grain size of dynamically recrystallized quartz in a thin section to an appropriate piezometer. Combined with information on sample deformation depth, estimated from the P–T (pressure–temperature) path and deformation temperatures, it is suggested that there was nearly constant maximum shear stress of 15–41 MPa in the depth range of about 15–30 km, assuming plane stress conditions even when uncertainties related to the measurement direction of thin section and piezometer differences are included. The Sanbagawa belt formed in a warm subduction zone. Deep slow earthquakes are commonly observed in modern-day warm subduction zones such as SW Japan, which has a similar thermal structure to the Sanbagawa belt. In addition, deep slow earthquakes are commonly observed to be concentrated in a domain under the shallow part of the mantle wedge. Samples showed the depth conditions near the mantle wedge, suggesting that these samples were formed in a region with features similar to the deep slow earthquake domain. Estimated maximum shear stress may not only be useful for long-timescale subduction zone modeling but also represent the initial conditions from which slow earthquakes in the same domain nucleated.

Self-locking Kirigami surfaces via controlled stretching

Kirigami provides a powerful strategy to transform two-dimensional elements into complex three-dimensional functional structures with lengths ranging from nanoscale to microscale and macroscale. The stability and programmability of forming three-dimensional structures through mechanical actuation, whether external or self-balancing, are crucial. Here, we offer a system that performs the 2D to 3D transformation through sequential in-plane tension and release. As a result, the 3D state is obtained by out-plane popping and rotation and shows a self-locking behavior. The range of geometric parameters for kirigami elements with different stability properties is determined theoretically. The in-plane tension conditions are also calculated to break the transition point of the forming process. The horizontal and vertical modular array analysis demonstrates the scalability and programmability from the self-locking elements to the Kirigami surfaces. We expect that the kirigami pattern and design approach will serve for innovative systems, including tunable antennas, flexible electronics, and medical devices.

Enhancing Heart Disease Prediction using Advanced Feature Engineering and Ensemble Learning Techniques

This study introduces a holistic model for predicting heart disease ,integrating advanced algorithms with a focus on feature engineering. The dataset encompasses a diverse range of patient parameters, including demographics, lifestyle factors, and medical history. Feature engineering involves a meticulous process of selecting, transforming, and augmenting relevant features to enhance the model's ability to discern patterns and relationships within the data. This stage is essential for increasing the predicted accuracy of the model and gleaning insightful information from intricate datasets. The logistic regression algorithm is employed to establish a baseline predictive model, providing insights into the individual contribution of each feature. Subsequently, a neural network is implemented to capture intricate non-linear dependencies and interactions within the data, further refining the predictive capabilities. Results indicate that the incorporation of feature engineering significantly improves the model's performance compared to traditional approaches. Early experiments demonstrate promising accuracy rates in heart disease prediction, showcasing the potential for early detection and proactive healthcare interventions. This method not only enhances predictive modeling for heart disease but also emphasizes the significance of feature engineering in maximizing the full capabilities of machine learning algorithms for medical applications.

Magnon bands in pyrochlore slabs with Heisenberg exchange and anisotropies

The pyrochlore lattice is a versatile venue to probe the properties of magnetically ordered states induced or perturbed by anisotropic terms like the Dzyaloshinskii–Moriya interactions or single-ion anisotropy. Several such ordered states have been investigated recently as precursors of topological magnons and the associated surface states. In parallel, there has been recent progress in growing thin films of magnetic materials with this lattice structure along high symmetry directions of the lattice. In both cases, an account of the magnetic excitations of relevant Hamiltonians for finite slabs is a necessary step in the analysis of the physics of these systems. While the analysis of bulk magnons for these systems is quite common, a direct evaluation of the magnon spectra in the slab geometry, though required, is less frequently encountered. We study here magnon bands in the slab geometry for a class of spin models on the pyrochlore lattice with Heisenberg exchange, Dzyaloshinskii–Moriya interaction and spin-ice anisotropy. For a range of model parameters, for both ferromagnetic and antiferromagnetic exchange, we compute the classical ground states for different slab orientations and determine the spin wave excitations above them. We analyze the ferromagnetic splay phase, the all-in-all-out (AIAO) phase and a coplanar phase and evaluate magnon dispersions for slabs oriented perpendicular to the [111], [100] and [110] directions. For all the phases considered, depending on the slab orientation, magnon band structures can be non-reciprocal and we highlight the differences in the three orientations from this point-of-view. Finally, we present details of the surface localized magnons for all the three slab orientations in the phases we study. For the ferromagnetic splay phase and the AIAO phase we analyze surface states associated with point degeneracies or nodal lines in the bulk spectrum by computing the magnonic Berry curvature and Weyl charges or Chern numbers associated with it.

Enhanced Irradiance Levels using Synergistically Engineered Monochromatic Wavelength Ultraviolet-C Arrays Configuration

Contamination in healthcare environment poses risk of microbial transmission to healthy people. Recently, conventional ultraviolet (UV) technologies, particularly low-pressure mercury lamps have caught researcher’s attention for disinfection. Nonetheless, drawbacks such as hazardous components, limited lifespan, warm-up time, maintenance and fragility have driven researchers to delve into light emitting diode (LED) technologies as an appealing alternative. However, due to the diminishing intensity exhibited by LEDs over extended distances, prior studies primarily focused on short-distance investigations. Consequently, the phenomenon remained insufficient owing to the limited exploration in this field. Furthermore, frequently investigated LEDs in ambient settings, potentially created a gap in completely understanding the capability for irradiance attainability. Hence, this study aimed to overcome this limitation by evaluating the effectiveness of UV-C LEDs with synergistic arrangements within an enclosed chamber, offering optimum settings for examining irradiance performance. The analyzed recorded data aimed to identify trends in emission performance over different distances (5 to 60 cm), providing insights into how the arrangement of LEDs could influence irradiance output. The study unveiled that the synergistic effects of 6, 8, and 10-LED, when strategically engineered, significantly contributed to enhanced intensity. Notably, 10-SEMW LEDs exhibited a remarkable ability to maintain significant irradiance levels across extended distances. This was evident in the range between 0.037 and 0.016 mW.cm⁻² observed at 5 and 60 cm, respectively. In conclusion, the findings stress the importance of strategically arranging LEDs in a synergistic configuration to achieve optimal irradiance levels across a diverse range of spatial parameters for effective long-range disinfection.