Reduction Approach Research Articles

Kolam-Inspired Frequency Hopping: An Innovative Approach for Interference Reduction in Wireless Communication

The integration of cultural patterns into engineering applications opens new avenues for innovation and efficiency. This research explores the application of Kolam patterns, a traditional Indian art form characterized by its symmetry and periodicity, in frequency hopping for wireless communication systems. Kolam-inspired frequency hopping sequences are designed and compared with conventional pseudorandom sequences using key performance metrics, including Bit Error Rate (BER) and Signal-to-Interference Ratio (SIR). Simulations conducted under varying conditions of frequencies, number of users, hops, and Signal-to-Noise Ratio (SNR) reveal that Kolam-based sequences demonstrate superior interference management and lower error rates. The results underscore the potential of Kolam patterns to improve spectral efficiency and reliability in modern communication networks. This study paves the way for further exploration of cultural and mathematical patterns in optimizing wireless technologies.

AERA-MIP: emission pathways, remaining budgets, and carbon cycle dynamics compatible with 1.5 and 2 °C global warming stabilization

Abstract. While international climate policies now focus on limiting global warming to well below 2 °C or pursuing a 1.5 °C level of global warming, the climate modelling community has not provided an experimental design in which all Earth system models (ESMs) converge and stabilize at the same prescribed global warming levels. This gap hampers accurate estimations based on comprehensive ESMs of the carbon emission pathways and budgets needed to meet such agreed warming levels and of the associated climate impacts under temperature stabilization. Here, we apply the Adaptive Emission Reduction Approach (AERA) with ESMs to provide such simulations in which all models converge at 1.5 and 2.0 °C warming levels by adjusting their emissions over time. These emission-driven simulations provide a wide range of emission pathways and resulting atmospheric CO2 projections for a given warming level, uncovering uncertainty ranges that were previously missing in the traditional Coupled Model Intercomparison Project (CMIP) scenarios with prescribed greenhouse gas concentration pathways. Meeting the 1.5 °C warming level requires a 40 % (full model range: 7 % to 76 %) reduction in multi-model mean CO2-forcing-equivalent (CO2-fe) emissions from 2025 to 2030, a 98 % (57 % to 127 %) reduction from 2025 to 2050, and a stabilization at 1.0 (−1.7 to 2.9) PgC yr−1 from 2100 onward after the 1.5 °C global warming level is reached. Meeting the 2.0 °C warming level requires a 47 % (8 % to 92 %) reduction in multi-model mean CO2-fe emissions until 2050 and a stabilization at 1.7 (−1.5 to 2.7) PgC yr−1 from 2100 onward. The on-average positive emissions under stabilized global temperatures are the result of a decreasing transient climate response to cumulative CO2-fe emissions over time under stabilized global warming. This evolution is consistent with a slightly negative zero emissions commitment – initially assumed to be zero – and leads to an increase in the post-2025 CO2-fe emission budget by a factor of 2.2 (−0.8 to 6.9) by 2150 for the 1.5 °C warming level and a factor of 1.4 (0.9 to 2.4) for the 2.0 °C warming level compared to its first estimate in 2025. The median CO2-only carbon budget by 2150, relative to 2020, is 800 GtCO2 for the 1.5 °C warming level and 2250 GtCO2 for the 2.0 °C warming level. These median values exceed the median IPCC AR6 estimates by 60 % for the 1.5 °C warming level and 67 % for 2.0 °C. Some of the differences may be explained by the choice of the mitigation scenario for non-CO2 radiative agents. Our simulations highlight shifts in carbon uptake dynamics under stabilized temperature, such as a cessation of the carbon sinks in the North Atlantic and in tropical forests. On the other hand, the Southern Ocean remains a carbon sink centuries after temperatures stabilize. Overall, this new type of warming-level-based emission-driven simulation offers a more coherent assessment across climate models and opens up a wide range of possibilities for studying both the carbon cycle and climate impacts, such as extreme events, under climate stabilization.

A Low Temperature Calciothermic Reduction Approach for the Synthesis of LaB6 Powders

In this work, we report the synthesis of a polycrystalline LaB6 powders with a uniform morphology by a low-temperature metallothermic reduction route using calcium as a reductant. The processing methodology and heat treatment parameters are optimized based on thermodynamic estimates in order to favour the reaction kinetics and for complete formation of LaB6 powders with highest purity. The synthesised powder was subjected to comprehensive characterization studies. X-Ray Diffraction (XRD) revealed the complete formation of LaB6 compound without any traces of reactants. Scanning Electron Microscopy (FE-SEM) along with chemical mapping shows the morphology and elemental distribution of LaB6 powder. X-Ray Photoelectron Spectroscopy (XPS) results show the futuristic peaks with photon energies that are assigned to the core levels of the La and B without significant impurities whereas the Fourier transform infrared (FTIR) spectrum indicates an apparent presence of La-B bonds at 1045.4 cm−1, 585.5 cm−1, and at 463.1 cm−1.

Beyond Housing First: rethinking neoliberal policies impacting homelessness.

This article critically examines the Housing First model within the broader context of neoliberal policies impacting homelessness, particularly at the intersection of mental illness, poverty, and addiction. While Housing First is celebrated for its effectiveness in providing immediate housing to chronically homeless individuals, this model's alignment with neoliberal principles prioritizes cost effectiveness and visible outcomes over comprehensive care. As a harm reduction approach, Housing First often overlooks the underlying mental health and addiction issues that maintain homelessness, resulting in a cycle of dependency rather than long-term recovery. In this article, we argue that the reduction in funding for transitional housing and mental health services, driven by neoliberal policies, has exacerbated the challenges faced by marginalized populations. A call is made for a shift toward more holistic and integrated approaches that balance immediate housing solutions with robust mental health care and social support systems, aiming for sustained recovery, independence, and social reintegration for individuals experiencing homelessness.

A comparative study on the industrial flotation performance of fine coal using conventional and turbulent microbubble mechanical flotation cells

ABSTRACT Fine coal frequently exhibits complications such as diminished recovery and compromised selectivity in the flotation process. This study presents a novel reactor/separator dual-stage flotation device, the turbulent microbubble mechanical flotation cell (TMFC), on the basis of the principles of forced mineralization and static separation. The TMFC implemented in industrial settings was carried out, facilitating a comparative analysis with a conventional mechanical flotation machine (XJM) to elucidate the flotation behavior across particles of varying sizes. In terms of recovery efficiency, the TMFC significantly increased the concentrated yield by 3.29% while simultaneously increasing the ash in the tailings by 10.59%. This enhancement is attributed to the more vigorous turbulent environment within the TMFC, which helps in transforming bubbles into microbubbles, synergistically boosting collision and adhesion. In terms of selectivity, the flotation selectivity index for the −75 μm significantly improved, especially for the −45 μm, which surpassed that of XJM by 10.79%. The intense turbulent conditions in the TMFC generated a scrubbing effect that efficiently reduced the coating, significantly enhancing the quality of the concentrate. This research evaluated the industrial performance of the TMFC in fine particle flotation, proposing a novel technical approach for significant desulfurization and ash reduction in fine coal.

Do Cognitive Enhancers Have the Potential to Change the Structure of Harm Reduction in the Future?

: Human progress across various dimensions has led to changes in approaches aimed at achieving greater success. Throughout human history, people have utilized plants and medicines from their surroundings to enhance productivity. However, this use has evolved over time, and it is clear that the use of these resources will continue to increase in the future. These tools will not only improve cognitive functions but also help achieve maximum productivity. In this article, we explore the history of cognitive enhancers, assess the current situation, and discuss their future. It seems likely that, in the future, hallucinogens such as Magic Mushrooms will play a key role in enhancing productivity. As a result, the human species will need a harm reduction approach to address this phenomenon for various reasons.

Design of a Low-Power Dynamic Latched Comparator for Biomedical Applications

Abstract The latched comparator is a fundamental component of all ADC topologies. Thermal noise, kickback noise, and offset voltage impact the energy efficiency of the comparator significantly. In cardiac IMD ADCs, latched comparator kickback noise may impact resolution, accuracy, and settling time. This paper describes the design and development of an a low-power dynamic latched comparator optimised for biomedical applications and running at 1V. The suggested comparator is achieved on a 45 nm CMOS technology node, having the main goal of reducing kickback noise. The main concept of the recommended comparator has been utilizing the novel resetting approach and kickback noise reduction approach to preserve the charge and kickback noise, respectively. This approach aids in minimizing delay, power consumption, and kickback noise using shared charging logic. The charging-sharing technique involves using a single-pass transistor positioned across two output nodes. The pass transistor distributes the charge evenly across two output nodes throughout the reset phase. Due to the shared charging of the load capacitances, the voltage at the output nodes would not drop under the threshold value. As a result, the input signal could be evaluated more quickly during the regenerative stage. The comparator's delay is calculated using a rigorous statistical analysis considering the circuit's random factors. The offset of the proposed comparator is verified using thorough Monte Carlo simulations. The comparator reduces power while preserving noise. Also, the measured simulation results of recommended comparator outperform state-of-the-art comparators regarding kickback noise, delay, power consumption, and power delay product (PDP). The input voltage inversely affects the comparator's delay. Further, the simulation shows that the comparator consumes 9.36μW at 1 V and 1GHz sampling frequency. The simulation results confirm that the suggested comparator effectively reduces power consumption by atleast 66.98%. The proposed comparator has less PDP value by 44.44%. Also, suggested comparator effectively reduces offset voltage by 7.6% but with a corresponding increase in the area by 5%. Additionally, the comparator successfully reduces kickback noise by 44.6%. The obtained results are not silicon proven and bound to be in the pre-silicon stage. The low-power behaviour of the suggested technique is validated using analytical derivations, PVT corner analysis, and post-layout simulation.

Spinning waveforms in cubic effective field theories of gravity

We derive analytic all-order-in-spin expressions for the leading-order time-domain waveforms generated in the scattering of two Kerr black holes with arbitrary masses and spin vectors in the presence of all independent cubic deformations of Einstein-Hilbert gravity. These are the two parity-even interactions I1 and G3, and the parity-odd ones Ĩ1 and G~3. Our results are obtained using three independent methods: a particularly efficient direct integration and tensor reduction approach; integration by parts combined with the method of differential equations; and finally a residue computation. For the case of the G3 and G~3 deformations we can express the spinning waveform in terms of the scalar waveform with appropriately shifted impact parameters, which are reminiscent of Newman-Janis shifts. For I1 and Ĩ1 similar shifts occur, but are accompanied by additional contributions that cannot be captured by simply shifting the scalar I1 and Ĩ1 waveforms. We also show the absence of leading-order corrections to gravitational memory. Our analytic results are notably compact, and we compare the effectiveness of the three methods used to obtain them. We also briefly comment on the magnitude of the corrections to observables due to cubic deformations.

An edge server placement based on graph clustering in mobile edge computing

With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. In this paper, we investigate the issue of edge server placement and user allocation to reduce transmission delay between base stations and servers, and balance the workload of individual servers. To this end, we propose a graph clustering-based edge server placement model by fully considering the constraints such as the distance, coverage area and number of channels of base stations. The model mainly consists of a two-layer graph convolutional network (GCN) component and a differentiable version of K-means clustering component, which transforms the server placement problem into an end-to-end learning optimization problem on a graph. It trains the GCN network to achieve the best clustering results with the expectation of average delay and load balancing as the loss function to obtain the edge server placement and user assignment scheme. We conducted experiments based on the Shanghai Telecom dataset, and the results show the effectiveness of our approach in both latency reduction and load balancing.

A machine learning approach for solution space reduction in aircraft disruption recovery

A machine learning approach for solution space reduction in aircraft disruption recovery

Beams on elastic foundation: A variable reduction approach for nonlinear contact problems

Beams on elastic foundations are applied to a vast number of engineering problems. Several elastic foundation models are available, from the simplest Winkler element with one parameter to complex models with more parameters and nonlinear characteristics. Analytical and numerical approaches have been developed in the literature for the solution of this problem, often specialized for a particular application. In this paper, a novel numerical approach that can be applied to any combination of beam and foundation models is presented. The method is based on independent meshes for the beam and for the foundation. The independent discretization of the foundation opens the possibility to model any kind of foundation behaviour, including nonlinearities, discontinuities and space-dependent properties. The two meshes are then connected by a variable reduction approach, formulated by standard finite element procedures. Such an approach allows to refine the discretization of the foundation without affecting the dimension of the solving system, i.e. with a limited effect on the computational time. Additionally, a relevant advantage of the presented method is that, contrary to most approaches described in the literature, gaps between the beam and the foundation can be straightforwardly included by an energy-based formulation. Examples of applications to linear, nonlinear, and foundation with gaps are reported in the paper. This innovative approach not only simplifies the modelling process but also offers significant computational advantages, making it a versatile and efficient tool for a wide range of engineering applications involving beam-foundation interactions.

Regime switching in coupled nonlinear systems: Sources, prediction, and control—Minireview and perspective on the Focus Issue

Regime switching, the process where complex systems undergo transitions between qualitatively different dynamical states due to changes in their conditions, is a widespread phenomenon, from climate and ocean circulation, to ecosystems, power grids, and the brain. Capturing the mechanisms that give rise to isolated or sequential switching dynamics, as well as developing generic and robust methods for forecasting, detecting, and controlling them is essential for maintaining optimal performance and preventing dysfunctions or even collapses in complex systems. This Focus Issue provides new insights into regime switching, covering the recent advances in theoretical analysis harnessing the reduction approaches, as well as data-driven detection methods and non-feedback control strategies. Some of the key challenges addressed include the development of reduction techniques for coupled stochastic and adaptive systems, the influence of multiple timescale dynamics on chaotic structures and cyclic patterns in forced systems, and the role of chaotic saddles and heteroclinic cycles in pattern switching in coupled oscillators. The contributions further highlight deep learning applications for predicting power grid failures, the use of blinking networks to enhance synchronization, creating adaptive strategies to control epidemic spreading, and non-feedback control strategies to suppress epileptic seizures. These developments are intended to catalyze further dialog between the different branches of complexity.

Thermogenic adipose tissues: promising therapeutic targets for metabolic diseases: Thermogenic fats and obesity.

The ongoing increase in the prevalence of obesity and its comorbidities such as cardiovascular disease, type 2 diabetes (T2D) and dyslipidemia warrants discovery of novel therapeutic options for these metabolic diseases. Obesity is characterized by white adipose tissue expansion due to chronic positive energy balance as a result of excessive energy intake and/or reduced energy expenditure. Despite various efforts to prevent/reduce obesity including lifestyle and behavioral interventions, surgical weight reduction approaches and pharmacological methods, there has been limited success in significantly reducing obesity prevalence. Recent research has shown that thermogenic adipocyte (brown and beige) activation or formation, respectively, could potentially act as a therapeutic strategy to ameliorate obesity and its related disorders. This can be achieved through the ability of these thermogenic cells to enhance energy expenditure and regulate circulating levels of glucose and lipids. Thus, unraveling the molecular mechanisms behind the formation and activation of brown and beige adipocytes holds the potential for probable therapeutic paths to combat obesity. In this review, we provide a comprehensive update on the development and regulation of different adipose tissue types. We also emphasize recent interventions in harnessing therapeutic potential of thermogenic adipocytes by bioactive compounds and new pharmacological anti-obesity agents.

Improving baseline reduction for emotion recognition based on electroencephalogram signals

The baseline reduction method has been widely used to define electroencephalogram (EEG) signal patterns. However, because the baseline signal in this approach contains artifacts, the baseline reduction approach cannot perform optimally. As a result, decreasing artifacts in the baseline signal is critical. The mean, Gaussian, and Savitzky-Golay filters will be compared in this study to minimize artifacts in the baseline signal. Three secondary datasets are utilized to evaluate these approaches' capacity to remove artifacts. These three strategies are also tested with the convolution neural network classification algorithms. When applied to the dataset for emotion analysis using physiological signals (DEAP) and a dataset for multimodal research of affect, personality traits, and mood on individuals and groups (AMIGOS) datasets, the mean filter can increase baseline reduction performance based on twenty-four test scenarios. On the data readiness for machine learning research (DREAMER) dataset, however, the Gaussian filter is preferable. The relative difference approach was employed in this study's baseline reduction process to generate EEG signal patterns that are easy to recognize throughout the classification phase, which impacts increasing accuracy.

Eliminating noise from a speech signal based on a pair of filters

Today, the increase in the number of devices working based on speech interfaces increases the importance of speech quality. However, even a minimal noise level can seriously affect the accuracy of speech signal recognition and processing. Therefore, denoising speech signals is an important task in signal processing, and it also serves to improve speech quality in telecommunications, speech recognition systems, and other speech-related applications. Applying existing noise reduction filters separately may not always be effective. Therefore, in this research, a noise reduction approach based on the sequential application of filters is proposed. Based on literature analysis, filters such as low-pass, band-pass, Kalman, Butterworth, and elliptic filters were selected, and pairs were formed based on them. Pairs of filters were applied to speech signals with different levels of noise, and the resulting filtered speech signals were evaluated based on the PESQ evaluation criterion. The purpose of this study is to determine the optimal pair of filters that can minimize the impact of noise on the quality of speech signals using the PESQ evaluation criterion. The results of the experiments showed that it is optimal to use a pair of band-pass and Butterworth filters at a low level of noise, a pair of low-pass and elliptic filters at a medium level, and a pair of band-pass and elliptic filters at a high level of noise. The results obtained are important and practical in the development of other hybrid methods of noise reduction.

Enhancing MILD Combustion of Natural Gas with Additives: A Taguchi Method Approach for CO2 Emission Reduction

Enhancing MILD Combustion of Natural Gas with Additives: A Taguchi Method Approach for CO2 Emission Reduction

A New Approach to Reduce the Position Data of Moving Objects Using Neural Network

ABSTRACTIn this study, an ANN‐based Global Navigation Satellite System (GNSS) location data reduction approach was proposed. As GNSS location data becomes more common, efficient data reduction techniques are needed to reduce transmission, storage, and processing costs. This involves selecting key points from the original trajectory to maintain integrity, eliminating redundancy, and lowering transmission and storage expenses. In this study, we proposed a new method for reducing GNSS location data in both online and offline settings, utilizing an ANN trained with a mathematically generated dataset. ANN has not been used in data reduction in the literature. The approach involves training the ANN with a window size of 3 and a threshold value of 10°, followed by using the trained model for data reduction. Experimental results show that the ANN achieves a reduction rate of around 59.18% compared to the original trajectory. Notably, the ANN yields a significantly lower RMSE compared to a mathematical method, particularly in areas requiring precision. Despite the slightly slower computation times, the ANN remains suitable for real‐time applications, demonstrating its efficacy for GNSS location data reduction. Our study highlights its online capability, reasonable reduction rates, and low RMSE values, distinguishing it from existing literature. This method shows potential for scenarios where balancing reduction rates with data quality is crucial.

“Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region”

Landslide susceptibility map (LSM) plays an important role in providing the knowledge of slopes prone to future landslides. However, the applicability of LSM is often hindered due to high cost of data collection especially in mountainous region such as Himalayas. Therefore, this study proposes transfer learning approach (TL) to improve the performance of LSM by transferring the information from the data rich region (source) to data scare region (target). Two machine learning based model are trained in source area which is then transferred for prediction in target area and a source trained model combined with the knowledge of target is used for the prediction in the target area. The applicability of the proposed approach is tested considering Mandi district as source area and Kullu and Rudraprayag districts as target areas. Independent variables which included 11 landslide conducive factors from the source and target area, the independence was critically analysed using multi-collinearity test, while the geomorphic similarity was assessed using KL divergence method. Efficient machine learning method such as random forest (RF) and multi-layer perceptron (MLP) was used to train the models in both areas, statistical measure such as AUC-ROC, precision, recall, F-score, and accuracy were used to evaluate the performance of the LSMs. The results demonstrated the proposed approach for target area 1, the AUC value increased from 0.908 (Target trained on itself), to 0.942 (Target-Transfer Learned (TL)) and 0.959 (Target Combined) for RF and 0.896, 0.907 and 0.946 for MLP. Additionally, an increase in the precision was observed in RF while all other statistical measures increased to 0.023 for precision, 0.022 for recall, 0.023 for F-score and 0.066 for accuracy in the case of MLP. While on the other hand, in the case of target area 2, the AUC value for RF, ranged from 0.95 (Target area trained on itself), 0.80 (target TL) and 0.98 (target combined) and for MLP the AUC ranged from 0.82 for the source trained model while 0.84 for LSM obtained when both target and source data was used. Thus, the results revealed that proposed TL approach can be effective in improving the performance of LSM in data scare region in Himalayan region, thereby providing a promising approach in overcoming the issue of data limitation.

Combined Open Reduction and Transantral Endoscopic Approach for Internal Fixation of Orbital Floor Fractures: A Tertiary Care Center Experience with Decision-Making Algorithm

Combined Open Reduction and Transantral Endoscopic Approach for Internal Fixation of Orbital Floor Fractures: A Tertiary Care Center Experience with Decision-Making Algorithm

A Reduced Order Modeling Approach to Capture Transport within a Three-Electrode System Towards Fast Charge Applications

In support of GM's traction battery efforts, we derive and implement a reduced order modeling method to describe the electrochemical performance of a battery cell through the combination of a modified Newman Pseudo-2-Dimensional model and a three-electrode experimental apparatus. To assess the capability of the method, we compare the modeling results with experimental data for a lithiated graphite electrode and a lithiated nickel rich electrode system. The model is applied to simulate the electrochemical and transport processes within the battery cell to predict the negative electrode potential and positive electrode potential with respect to a lithium iron phosphate reference electrode, as well as the terminal voltage. The manufacture and set up of the three-electrode system, model development and reduction approaches, as well as a discussion of applicability to fast charge considerations to avoid lithium plating at the anode during a charging event will be presented.