Types Of Systems Research Articles

Dissipation-Induced Photon Blockade in the Anti-Jaynes–Cummings Model

Due to the fundamental differences between the quantum world and the classical world, some phenomena, such as entanglement and wave–particle duality, only exist in the quantum realm. These peculiar phenomena cannot be demonstrated by classical means: Quantum networks, quantum cryptography, and quantum precision measurements all require quantum sources. Photons are particularly well-suited as quantum sources owing to their minimal interaction with the environment, high flight speed, and ease of interaction with current typical quantum systems. Single-photon sources include pulsed excitation of quantum dots, spontaneous parametric down-conversion, and photon blockade. Herein, we propose that the anti-Jaynes–Cummings model can induce a pronounced photon antibunching effect when subjected to intense cavity dissipation. Similar to the photon blockade caused by strong photon–photon interaction, this antibunching effect is referred to as ’dissipation-induced blockade’. Our findings indicate that the minimum decay rate of a qubit, coupled with a high decay rate for photons, is conducive to achieving strong antibunching within the system. Notably, g(2)(0)<g(2)(τ), a characteristic of photon antibunching, is only valid under the optimal condition Δ=0. Conversely, g(2)(0)<1 is satisfied across all parameters, indicating that g(2)(0)<1 is not a prerequisite for antibunching in the anti-Jaynes–Cummings model. Moreover, under the optimal conditions of the antibunching effect, the average photon number attains its peak value. Consequently, the current anti-Jaynes–Cummings model is promising for developing single-photon sources characterized by excellent purity and average photon number.

Atom-Level Tandem Catalysis in Lithium Metal Batteries.

High-energy-density lithium metal batteries (LMBs) are limited by reaction or diffusion barriers with dissatisfactory electrochemical kinetics. Typical conversion-type lithium sulfur battery systems exemplify the kinetic challenges. Namely, before diffusing or reacting in the electrode surface/interior, the Li(solvent)x + dissociation at the interface to produce isolated Li+, is usually a prerequisite fundamental step either for successive Li+ "reduction" or for Li+ to participate in the sulfur conversions, contributing to the related electrochemical barriers. Thanks to the ideal atomic efficiency (100 at%), single atom catalysts (SACs) have gained attention for use in LMBs toward resolving the issues caused by the five types of barrier-restricted processes, including polysulfide/Li2S conversions, Li(solvent)x + desolvation, and Li0 nucleation/diffusion. In this perspective, the tandem reactions including desolvation and reaction or plating and corresponding catalysis behaviors are introduced and analyzed from interface to electrode interior. Meanwhile, the principal mechanisms of highly efficient SACs in overcoming specific energy barriers to reinforce the catalytic electrochemistry are discussed. Lastly, the future development of high-efficiency atomic-level catalysts in batteries is presented.

Vehicle Detection in Traffic Monitoring

Traffic Monitoring is a challenging task on crowded roads. Traffic Monitoring procedures are manual, expensive, time consuming and involve human operators. Large-scale storage and analysis of video streams were not possible due to limited availability. However, it is now possible to implement object detection and tracking, behavioral analysis of traffic patterns, number plate recognition and surveillance on video streams produced by traffic monitoring. In Big data, video datasets are so large that typical database systems are not able to store and analyses the datasets. Storing and Processing big volumes of data requires Scalability, Fault Tolerance and Availability. Thus, Big Data and Cloud computing are two compatible concepts as cloud enables big data for traffic monitoring. In this paper, we proposed vehicle detection for traffic monitoring. We have applied Support Vector Machine (SVM) machine learning algorithm for detecting vehicles.

Some Findings from the Longitudinal Migration of Starspots

Abstract We present results regarding the longitudinal migrations of cool stellar spots that exhibit remarkable oscillations and explore their possible causes. We conducted analyses using high-quality data from nine target systems of various spectral types, spanning from F to M, which were observed by the Kepler Satellite. The systems in which the behaviour of the spots was examined are as follows: KIC 4357272, KIC 6025466, KIC 6058875, KIC 6962018, KIC 7798259, KIC 9210828, KIC 11706658, KIC 12599700, and KIC 8669092. Basic stellar parameters were calculated from light curve analysis using the PHOEBE V.0.32 software, and light curves were modelled to obtain sinusoidal variations occurring out-of-eclipses phases, induced by rotational modulation. Subsequently, we calculated the minimum times of the obtained sinusoidal variations using the Fourier transform. The distributions of θ min corresponding to these minimum times over time were computed using linear fits to determine the longitudinal migrations of the spotted areas. We then compared the longitudinal migration periods with the stellar parameters found in the literature. In addition, we also found a secondary variation in the spot migrations apart from the linear models. Our results revealed that the longitudinal migration periods vary in relation to the B – V colour index of the stars.

Light-assisted thermochemical reduction of CuFe2O4 within a Photo-thermogravimetric analyser for solar fuel production

Abstract Solar thermochemical cycle has emerged as a promising clean energy technology that enables the splitting of water for solar fuel production. However, conventional two-step thermochemical cycles using single-metal oxides require high operating temperature above 1000 °C, especially for the reduction step. Typical solar thermal systems struggle to meet such high temperature requirements, making it vital for reducing the operating temperature. To find a solution enabling lower temperature requirement, we proposed a photo-thermochemical reduction (PTR) strategy, which employs light illumination as assistance, combining both thermally-induced and photo-induced effect for more generation of oxygen vacancies (Vos), with the oxygen carrier copper ferrite (CuFe2O4). Experimental studies were performed in a specially-designed photo-thermogravimetric analyser (photo-TGA) that directly measures the weight change of solid reactants under direct light illumination. The results indicate that the photo-thermochemical reaction achieves a decrease of nearly 40 °C in temperature requirement, giving a higher oxygen release of 21% compared to that driven by pure thermal heating at 800 °C. We also measured an increase of 0.09 in the non-stoichiometry parameter δ in photo-TGA. Additionally, we observed oxygen release increases distinctly with light intensity of incident illumination. From the viewpoint of spectral ranges, ultraviolet and visible light illumination give the primary boost to the generation of photo-induced oxygen vacancies. These results demonstrated the effective assistance of concentrated solar energy to enhance two-step thermochemical cycle for solar fuel production at lower temperature.

Superconducting in-memory computing architecture coupling with memristor synapses for binarized neural networks

Abstract In-memory computing electronic components offer a promising non-von Neumann strategy to develop energy-efficient and high-speed hardware systems for artificial intelligence (AI). However, the implementation of conventional electronic hardware demands a huge computational and power budget, thereby limiting their wider application. In this work, we propose a novel superconducting in-memory computing architecture by coupling the memristor device. Leveraging the phase transition of the superconductor induced by external applied Joule power, we can modulate the state of the bottom superconductor based on memristor resistive states and applied voltages, enabling the execution of in-memory computing operations. We then successfully implement vector-matrix multiplication of input and output signals within the designed array, facilitating its integration into AI systems. Constructing a binarized neural network with superconductor-memristor arrays achieves a high level of accuracy, approximately 97%, in handwritten number classification. Through an evaluation of power consumption in our proposed architecture, we find a remarkable ~48400× advantage in power efficiency compared to typical memristor systems. This marks the inaugural demonstration of a superconducting in-memory computing architecture through memristor coupling, offering a promising hardware platform for various AI systems with superior energy efficiency and computing capacity.

A sub-100 nm thickness flat jet for extreme ultraviolet to soft X-ray absorption spectroscopy.

Experimental characterization of the structural, electronic and dynamic properties of dilute systems in aqueous solvents, such as nanoparticles, molecules and proteins, are nowadays an open challenge. X-ray absorption spectroscopy (XAS) is probably one of the most established approaches to this aim as it is element-specific. However, typical dilute systems of interest are often composed of light elements that require extreme-ultraviolet to soft X-ray photons. In this spectral regime, water and other solvents are rather opaque, thus demanding radical reduction of the solvent volume and removal of the liquid to minimize background absorption. Here, we present an experimental endstation designed to operate a liquid flat jet of sub-micrometre thickness in a vacuum environment compatible with extreme ultraviolet/soft XAS measurements in transmission geometry. The apparatus developed can be easily connected to synchrotron and free-electron-laser user-facility beamlines dedicated to XAS experiments. The conditions for stable generation and control of the liquid flat jet are analyzed and discussed. Preliminary soft XAS measurements on some test solutions are shown.

A novel (2+1)-dimensional Sawada-Kotera type system: multisoliton solution and variable separation solution

A novel (2+1)-dimensional Sawada-Kotera type system: multisoliton solution and variable separation solution

Use of the Community-Derived Open-Source Automated Insulin Delivery Loop System in Type 2 Diabetes.

Background: No published data are available on the use of the community-derived open-source Loop hybrid closed-loop controller ("Loop") by individuals with type 2 diabetes (T2D). Methods: Through social media postings, we invited individuals with T2D currently using the Loop system to join an observational study. Thirteen responded of whom seven were eligible for the study, were using the Loop algorithm, and provided data. Results: Mean (±standard deviation) age was 61 ± 13 years, and mean body mass index was 31 ± 5 kg/m2. All but one participant were using noninsulin glucose-lowering medications. Self-reported mean hemoglobin A1c decreased from 7.3% ± 1.1% before starting Loop to 6.0% ± 0.5% on Loop. Time in range 70-180 mg/dL increased from 84% to 93%. The amount of time in hypoglycemia was extremely low before and with Loop (time <54 mg/dL was 0.04% ± 0.06% vs. 0.09% ± 0.07%, respectively). No severe hypoglycemia or diabetic ketoacidosis events were reported while using Loop. Conclusion: These data, though limited, suggest that the Loop system is likely to be effective when used by individuals with T2D and should be evaluated in large-scale studies. Clinical Trial Registration numbers: NCT05951569.

Light recovery after maize harvesting promotes soybean flowering in a maize-soybean relay strip intercropping system.

Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to yield. Soybeans adapt to light conditions in intercropping by adjusting the onset of reproduction and the inflorescence architecture to optimize reproductive success. Maize-soybean strip intercropping (MS), maize-soybean relay strip intercropping (IS), and sole soybean (SS) systems are typical soybean planting systems with significant differences in light environments during growth periods. To elucidate the effect of changes in the light environment on soybean flowering processes and provide a theoretical basis for selecting suitable varieties in various planting systems to improve yields, field experiments combining planting systems (IS, MS, and SS) and soybean varieties (GQ8, GX7, ND25, and NN996) were conducted in 2021 and 2022. Results showed that growth recovery in the IS resulted in a balance in the expression of TERMINAL FLOWER 1 (TFL1) and FLOWERING LOCUS T (FT) in the meristematic tissues of soybeans, which promoted the formation of new branches or flowers. IS prolonged the flowering time (2-7 days) and increased the number of forming flowers compared with SS (93.0 and 169%) and MS (67.3 and 103.3%) at the later soybean flowering stage. The higher carbon and nitrogen content in the middle and bottom canopies of soybean contributed to decreased flower abscission by 26.7 and 30.2%, respectively, compared with SS. Canopy light environment recovery promoted branch and flower formation and transformation of flowers into pods with lower flower-pod abscission, which contributed to elevating soybean yields in late-maturing and multibranching varieties (ND25) in IS.

Effects of drip and flood irrigation on soil heavy metal migration and associated risks in China

BackgroundDifferent irrigation methods have variable effects on the physiochemical properties of soils. The predicted widespread replacement of flood irrigation with drip irrigation will alter the micro-environment of the associated soils in terms of migration, transformation, morphology, and toxicology of heavy metals and pesticides. The dynamics of heavy metals in soil under drip irrigation have only been investigated with regard to the use of sewage and reclaimed water; studies comparing these patterns across different irrigation methods are scarce. PurposeHere, we aimed to investigate the effects of drip and flood irrigation on heavy metal distribution in soil and the related risks in three typical agricultural systems in China. MethodsWe used fixed-point sampling, digestion analytical methods, descriptive analyses, one-way analysis of variance (ANOVA), Nemerow pollution index analyses, and correlation analyses to investigate the different effects of drip and flood irrigation on heavy metals (Cd, Cr, Cu, Pb, and Zn) in soils from Hebei, Xinjiang, and Ningxia Provinces, China. Results and DiscussionAnalyses of soil samples collected from drip-irrigated fields revealed significant differences in the concentrations of soil heavy metals at depths of 0–60 cm. Drip irrigation had a greater effect on heavy metal content in the soil surface than flood irrigation. In addition, heavy metals likely accumulated at the edges of the wetting fronts following prolonged irrigation. In comparison to drip irrigation, flood irrigation elevated pollution levels in the soil. The amount of fertiliser affected the leaching and migration of heavy metals in the soils by altering the physiochemical characteristics of the drip irrigation solution. ConclusionDrip irrigation alters the dynamics of heavy metals in soil by influencing their migration and accumulation patterns. This effect can be attributed to the methods of adding water and fertilisers to the soil under drip irrigation. Altering these variables can impact the level of heavy metals that bioaccumulate in crops. This study provides a scientific basis for reducing heavy metal pollution under drip irrigation.

Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex systems and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems.

Exciplex Formation by Complexation of an Electron-Accepting Guest in an Electron-Donating Pillar[5]arene Host Liquid.

We present a novel system, a liquid-state pillar[5]arene decorated with tri(ethylene oxide) chains, that brings electron-donor and electron-acceptor molecules into proximity for efficient exciplex formation. The electron-accepting guests exhibit a blue-purple emission from a localized excited state upon excitation in common solvents. However, directly dissolving the guests in the electron-donating pillar[5]arene liquid (a bulk system) results in visible green emission from the formed exciplexes. In the bulk system, the guest molecules are always surrounded by excess pillar[5]arene molecules, resulting in the formation of mainly inclusion-type exciplexes. In the bulk system, energy migration occurs between the pillar[5]arene molecules. Excitation of the pillar[5]arenes results in a more intense green exciplex emission than that observed upon direct excitation of the guests. In summary, the pillar[5]arene liquid is a novel system for achieving efficient exciplex formation and energy migration that is different from typical solvent and solid systems.

НАУКОВА КОНЦЕПЦІЯ CТВОРЕННЯ ВИСОКОВОЛЬТНИХ МОБІЛЬНИХ ЕЛЕКТРОТЕХНІЧНИХ СИСТЕМ РЕЗОНАНСНОГО ТИПУ ЗІ ШВИДКОДІЮЧИМИ КЕРУВАННЯМ І ПАРАМЕТРИЧНОЮ СТАБІЛІЗАЦІЄЮ РЕЖИМІВ НАВАНТАЖЕННЯ

A new scientific concept has been developed for the creation of high-voltage electro-technical systems (ETS) of a reso-nant type based on the implementation in them of series high-quality inductive-capacitive circuits (ICC) and high-frequency (HF) resonant currents. This makes it possible to repeatedly increase the alternating voltage on the reactive elements of the ICC and on the load connected in parallel to one of the elements, and to ensure a significant decrease in the impulse energy of the ETS without changing their average power, as well as to increase the speed of control and parametric stabilization of the load modes of the ETS even with a rapid decrease in the electrical resistance of this load to almost zero, as in the case of electrical breakdown of its insulation. The generation of resonant currents with a fre-quency of up to 40 kHz in the ICC, which has a coil with an inductance of ~ 25 mH and a Q factor up to 270 (made of multi-core twisted copper wires of the " litzendraht " type) and a ceramic capacitor with a capacity of ~ 3.5 nF and a Q factor &gt; 1000, ensures the realization of the QICC up to 260 and the corresponding increase in the ratio of the ETS out-put voltage to the input voltage without the use of step-up transformers. When connecting such an ETS to a source of alternating voltage of 220 V, a voltage of up to 40 kV can be generated at the output of the ETS. A significant increase in the frequency of currents leads to a significant decrease in the mass-dimensional characteristics of ETS and the pos-sibility of creating their samples mobile (weighing up to 5 kg), and if necessary, autonomous, in particular with power supply from batteries. The new scientific concept is aimed at creating a resonance-type ETS for safe and reliable moni-toring and diagnostics of the technical condition of high-voltage insulation of power cables, powerful electric machines and other energy objects of the critical infrastructure of Ukraine. Such ETS can also be effectively used for charging to high voltages capacitive energy storage (CES) in technological installations for the production of electro-spark micro- and nano-powders with unique properties, as well as for high-voltage electro-hydro-pulse processing of various mate-rials and environments. It has been established that the power of these ETS increases with a decrease in the ratio of the capacitances of the capacitors ICC and CES. References 40, figures 4.

Mengukur Citra Carrefour BG Junction Surabaya

A retail business is a business selling products and services that have been added to meet the needs of individuals, families or other end users. Retail business in Indonesia is divided into 2 groups, namely traditional retail and modern retail. Modern retail is basically a development of traditional retail. Retail business does not always have to be done in a shop, but can also be done via telephone or internet, also called retail or non-shop retail. The characteristics of a traditional retail business are simple, the place is not too large, the goods sold are not too many type, the management or management system is still simple, does not offer shopping convenience and there is still a process of haggling over prices with traders, and the products sold are not displayed openly, so customers do not know whether the retailer has the goods they are looking for or not. Meanwhile, modern retail businesses On the contrary, it offers a spacious place, there are many types of goods sold, the management system is well managed, offers shopping convenience, the selling price is fixed (fixed price), so there is no bargaining process and there is a self-service or self-service system, as well as displays products on open shelves, so customers can see, choose, and even try products first before deciding to buy. Of all the retail companies, there is one large company that also dominates the retail business currently, namely Carrefour. Carrefour is an international supermarket group, headquartered in France. Carrefour is the second largest retail group after Wal-Mart. The first Carrefour store opened on June 3, 1957, in Annecy near an intersection

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Artificial Intelligence-Based Intrusion Detection System for Cloud Computing

Abstract: It is possible to communicate with others and do business across this global network, which is comprised of hundreds of millions of computers running a variety of hardware and software configurations. This makes it simpler for hackers to abuse resources and conduct Internet attacks since computers are linked to one another. There are significant roadblocks to the development of a security-oriented approach that may be flexible and adaptive in light of the expanding number of Internet assaults. Threats from the internet could be identified using an intrusion detection system (IDS). The utilization of an intrusion detection system, or IDS, is necessary to preserve network security. Because the cloud platform is continuously expanding and becoming more prevalent in our everyday lives, it is imperative that we develop an effective IDS for it as well. On the other hand, typical intrusion detection systems may encounter difficulties when used in the cloud. A cloud segment may get overburdened by the pre-determined IDS architecture because of the added detection overhead. In the context of a networked system with an adaptable architecture. Using a neural network-based IDS, we show how to make full utilize available resources while not putting undue strain on any single cloud server. The proposed IDS uses a neural network machine learning to identify new threats even more effectively

On the boundary stabilization of the KdV–KdV system with time-dependent delay

The boundary stabilization problem of the Boussinesq KdV–KdV type system is investigated in this paper. An appropriate boundary feedback law consisting of a linear combination of a damping mechanism and a delay term is designed. Then, considering time-varying delay feedback together with a smallness restriction on the length of the spatial domain and the initial data, we show that the problem under consideration is well-posed. The proof combines Kato’s approach and the fixed-point argument. Last but not least, we prove that the energy of the linearized KdV–KdV system decays exponentially by employing the Lyapunov method.

Comparison of scoring systems for predicting short- and long-term type 2 diabetes remission after bariatric surgery.

Our study aimed to compare the short- and particularly long-term type 2 diabetes mellitus (T2DM) remission prediction abilities of ABCD, individualised metabolic surgery (IMS), DiaRem2, Ad-DiaRem and DiaBetter scoring systems in Turkish adult type 2 diabetic morbidly obese patients who underwent bariatric surgery. Our study was planned as a retrospective cohort study. A total of 137 patients with T2DM, including 78 sleeve gastrectomy (SG) and 59 Roux-en-Y gastric bypass (RYGB) patients, were included in the 1st-year evaluation after bariatric surgery, and a total of 115 patients with T2DM, including 64 SG and 51 RYGB patients, were included in the evaluation at the end of the 5th year. In the 1st year after bariatric surgery, area under the ROC curve (AUC) values for diabetes remission scores were 0.863 for Ad-DiaRem, 0.896 for DiaBetter, 0.840 for DiaRem2, 0.727 for ABCD and 0.836 for IMS. At 5 years after bariatric surgery, the AUC values for diabetes remission were 0.834 for Ad-DiaRem, 0.888 for DiaBetter, 0.794 for DiaRem2, 0.730 for ABCD and 0.878 for IMS. According to our study, the DiaBetter score provided a better AUC value than the other scores both in the short and long term but showed similar predictive performance to Ad-DiaRem in the short term and IMS in the long term. We believe that DiaBetter and Ad-DiaRem scores might be more appropriate for short-term assessment and DiaBetter and IMS scores for long-term remission assessment.

New computational method for calculating of water droplet–clay contact angles: Based on molecular dynamics

The measurement of the water contact angle (θ) on the surface of clay minerals poses a significant challenge due to their microscopic particle sizes ranging from micrometers to nanometers and their extensive specific surface areas. We introduce an advanced computational framework based on molecular dynamics simulations. This framework leverages the coordination number (CN) differences of the water molecules and effectively eliminates the gaseous water molecules in the droplet equilibrium system. Additionally, our framework uses an alpha-shaped probe ball to accurately determine the three-dimensional (3D) coordinates of the droplet interface and utilizes a feed-forward neural network (FFNN) to optimize the objective function of the 3D-interface; this ensures highly accurate characterization of the interface contour and an accurate computation of θ. Our approach exhibits outstanding performance in typical clay mineral surface-water wetting systems; it significantly overcomes the limitations of the transformed cylindrical coordinate system algorithm and accurately captures the wettability variations under complex environmental conditions. This new θ computational framework provides crucial assistance for the in-depth exploration of the wettability characteristics of clay minerals in complex environments.