Minimal Resources Research Articles

Research on seismic performance of a novel fully precast RC frame isolation structure

Precast structure is considered the most promising approach for achieving architectural industrialization and urban functional resilience due to its advantages such as high quality, low cost, minimal on-site work, environmental friendliness, and resource conservation. However, some studies and earthquake damage investigations indicate that the seismic performance of precast structures is relatively poor, and their damage is usually attributed to insufficient bearing capacity and ductility of connections, making post-earthquake repair extremely challenging. While considering isolation technology one of the most innovative achievements in the field of structural engineering, it has been widely used in structures, and numerous isolation buildings have withstood the test of earthquakes. Therefore, isolation technology may be the most promising solution to improve the seismic performance of precast structure systems. This paper proposed a new type of dry-connection fully precast RC frame structure and adopts the isolation technology to guarantee its seismic performance. A half-scaled model of the precast isolation structure was manufactured and tested on the shaking table. The results show that after experiencing rare earthquakes, the superstructure was basically intact and the isolation layer remains undamaged, which could achieve the design objectives of no damage under moderate earthquakes and repairable after severe earthquakes.

A lightweight vortex model for unsteady motion of airfoils

A low-order vortex model has been developed for analysing the unsteady aerodynamics of airfoils. The model employs an infinitely thin vortex sheet in place of the attached boundary layer and a sheet of point vortices for the shed shear layer. The strength and direction of the vortex sheet shed at the airfoil trailing edge are determined by an unsteady Kutta condition. The roll-up of the ambient shear layer is represented by a unique point vortex, which is consistently fed circulation by the last point vortex of the free vortex sheet. The model's dimensionality is reduced by using three tuning parameters to balance representational accuracy and computational efficiency. The performance of the model is evaluated through experiments involving impulsively started and heaving and pitching airfoils. The model accurately captures the dynamics of the development and evolution of the shed vortical structure while requiring minimal computational resources. The validity of the model is confirmed through comparison with experimental force measurements and a baseline unsteady panel method that does not transfer circulation in the free vortex sheet.

Temperature dependence of competitive ability is cold-shifted compared to that of growth rate in marine phytoplankton.

The effect of climate warming on community composition is expected to be contingent on competitive outcomes, yet approaches to projecting ecological outcomes often rely on measures of density-independent performance across temperatures. Recent theory suggests that the temperature response of competitive ability differs in shape from that of population growth rate. Here, we test this hypothesis empirically and find thermal performance curves of competitive ability in aquatic microorganisms to be systematically left-shifted and flatter compared to those of exponential growth rate. The minimum resource requirement for growth, R*-an inverse indicator of competitive ability-changes with temperature following a U-shaped pattern in all four species tested, contrasting from their left-skewed density-independent growth rate thermal performance curves. Our results provide new evidence that exploitative competitive success is highest at temperatures that are sub-optimal for growth, suggesting performance estimates of density-independent variables might underpredict performance in cooler competitive environments.

ENHANCING ENTERPRISE ENERGY MANAGEMENT WITH IOT-BASED MONITORING SYSTEMS

This article offers an in-depth exploration of how IoT technology can revolutionize energy management within enterprises. It provides a comprehensive elucidation of the system's architectural framework, spotlighting its innate capacity for real-time data collection, intricate analysis, and responsive control. The discussion extends to the seamless integration of IoT-enabled sensors, cutting-edge data analytics, and adaptive machine learning algorithms, all orchestrated to orchestrate optimal energy consumption strategies.A pivotal facet of this study underscores the paramount importance of sustainability and cost-effectiveness in the contemporary enterprise landscape. It underscores how the proposed IoT-based system assumes the role of a catalyst in realizing sustainability objectives. It achieves this by vigilant monitoring of energy utilization, waste minimization, and meticulous resource allocation. Moreover, this article presents a panorama of tangible outcomes by examining real-world deployments and substantiated case studies of the IoT-driven energy supply monitoring system. It effectively illustrates the multifaceted benefits, which encompass substantial energy conservation, streamlined operational efficiency, and a markedly reduced carbon footprint.In conclusion, this research accentuates the transformative potential of IoT technology in the realm of enterprise energy supply monitoring. It proffers a comprehensive, forward-looking solution for businesses to not only elevate their sustainability quotient but also optimize their operational prowess, thus contributing substantively to a greener, more efficient future.

A Lightweight Image Encryption Scheme Using DNA Coding and Chaos

Protecting transmitted multimedia data such as images is a significant concern. This work proposes an encryption algorithm for greyscale images using a Pseudo-Random Number Generator (PRNG), DNA coding, and pixel sum. The proposed approach is implemented on a Genesys 2 FPGA using minimal hardware resources and can operate at a maximum frequency of 110.8 MHz. In addition, several performance evaluation tests are conducted for multiple images, including statistical analysis of the encrypted image, keyspace analysis, and differential attack analysis. The system is compared to recent works with respect to encryption quality and used hardware resources. The proposed scheme outperformed recent chaos-based image encryption schemes.

Event Transformer+. A multi-purpose solution for efficient event data processing.

Event cameras record sparse illumination changes with high temporal resolution and high dynamic range. Thanks to their sparse recording and low consumption, they are increasingly used in applications such as AR/VR and autonomous driving. Current top-performing methods often ignore specific event-data properties, leading to the development of generic but computationally expensive algorithms, while event-aware methods do not perform as well. We propose Event Transformer+, that improves our seminal work EvT with a refined patch-based event representation and a more robust backbone to achieve more accurate results, while still benefiting from event-data sparsity to increase its efficiency. Additionally, we show how our system can work with different data modalities and propose specific output heads, for event-stream classification (i.e., action recognition) and per-pixel predictions (dense depth estimation). Evaluation results show better performance to the state-of-the-art while requiring minimal computation resources, both on GPU and CPU.

Parametrization of coefficients for sub-grid modeling of pitch-angle diffusion in global magnetospheric hybrid-Vlasov simulations

Sub-grid models are key tools to accurately describe the physical processes at play in a system when high-resolution simulations are not feasible. We previously developed a sub-grid model for pitch-angle diffusion in hybrid-Vlasov simulations of Earth's magnetosphere. However, a more precise description of the pitch-angle diffusion coefficient is required to apply this model to global simulations. In this study, we use an existing method to parametrize pitch-angle diffusion coefficients from monotonic distribution functions and adapt it to bi-Maxwellian distributions. We determine these coefficients for various values of the ion temperature anisotropy and plasma β∥. We use these newly parametrized coefficients in our sub-grid model and show that it accurately models reduction of temperature anisotropy in both local simulations and global simulations of the Earth's magnetosphere, while using minimal computational resources.

Multi‐layer task scheduling and resource allocation schemes considering idle resource and task priority in IoT networks

AbstractWith more and more interconnected smart devices (ISDs) accessing the Internet of Things (IoT), massive and diverse tasks need to be transformed and computed. Mobile edge computing enables the offloading of tasks to nearby servers to enhance processing efficiency, which makes ISDs idle, causing resource waste and failing to satisfy the high real‐time requirements of tasks. Besides, when tasks with different priorities are processed in the order they are generated, it will be difficult for IoT to guarantee a timely response to high‐priority tasks. To address the aforementioned issues, we establish an edge‐terminal‐local architecture by software‐defined networking to centrally manage idle ISD resource (2ISDR). Then the proposed two‐step scheduling mechanism with preemptive priority queue ensures the real‐time responses to high‐priority tasks, and the minimum resource allocation coefficients make offloading effective. Finally, we also propose a modified NSGA‐III algorithm named MNSGA‐III, which is designed to make decisions about offloading and solve resource allocation for tasks, and we correct infeasible solutions by a two‐step correction function to ensure the feasibility of MNSGA‐III. Experimental results show that the method can ensure a timely response to high‐priority tasks and optimize processing time, energy consumption, and economic cost through the utilization of 2ISDR.

Outpatient-department-based intensified tuberculosis case-finding activity in the tertiary care hospitals in West Bengal, India.: An observational study on the yields and outputs

BackgroundImproved tuberculosis (TB) case detection using intensified case finding (ICF) is one of the cornerstones of the Stop TB Strategy. A Non-Government Organization (NGO)-led project took place in two tertiary care hospitals of West Bengal for outpatient department (OPD)-based ICF activities. Methodology: A secondary analysis was conducted with the routinely collected project data to assess the yields of the model. Risk ratios have been calculated to identify associations of attrition before TB testing and diagnosis. ResultsA total of 2324 presumptive TB patients (PTBP) were identified from a screening of 105,391 OPD attendees. The screening yield of PTBP was 2.2 % (2.1%–2.3 %). Total 143 TB patients were detected with testing of 770 (out of 2324 identified) presumptive cases. The pretesting attrition rate was 66.9 % (64.9%–68.8 %). The screening yield of TB was 0.14 % (0.11%–0.16 %).Being younger than fifteen years of age, of the Islam religion, a homemaker or unemployed was independently associated with “attrition before TB tests”.Being Hindu, a homemaker or a student was independently associated with “diagnosis with TB” among the tested. ConclusionsThe OPD-based screening yield of TB is low. However, minimal resource requirements in this model advocate for its utility. Similar model can be replicated in other hospitals at low resource settings with appropriate setting-specific modifications.

Simultaneous Detection of CO and NO2 Gases using Interaction Analysis of SnS2 Sensor Array Response

Developing a multi-analyte gas sensing system that simultaneously detects trace levels of CO and NO2 at low temperatures is necessary for the Internet of Things (IoT) based air quality monitoring applications. Nevertheless, gas sensors operating at low temperatures are nonspecific and rarely detect target gases at lower ppb levels in the air. Herein, an array of two SnS2 sensors with different bias voltages has been developed and characterized upon exposure to individual and binary mixtures of CO and NO2 gases at different concentrations. The developed gas sensors array achieved the lower detection limit of 45 ppb for NO2 and 150 ppb for CO. Further, co-adsorption-induced interaction analysis was carried out to predict the target gas concentration in the binary mixture using the mixed gas response. The mean absolute percentage error of 7.86% is observed in predicting the target gas concentrations in the binary mixture, which indicates the high prediction accuracy of proposed method. As a minimal resource intensive approach, the proposed method can be used in air quality monitoring applications that require low-power and low-cost sensors.

Multi-client distributed blind quantum computation with the Qline architecture

Universal blind quantum computing allows users with minimal quantum resources to delegate a quantum computation to a remote quantum server, while keeping intrinsically hidden input, algorithm, and outcome. State-of-art experimental demonstrations of such a protocol have only involved one client. However, an increasing number of multi-party algorithms, e.g. federated machine learning, require the collaboration of multiple clients to carry out a given joint computation. In this work, we propose and experimentally demonstrate a lightweight multi-client blind quantum computation protocol based on a recently proposed linear quantum network configuration (Qline). Our protocol originality resides in three main strengths: scalability, since we eliminate the need for each client to have its own trusted source or measurement device, low-loss, by optimizing the orchestration of classical communication between each client and server through fast classical electronic control, and compatibility with distributed architectures while remaining intact even against correlated attacks of server nodes and malicious clients.

Towards a sustainable environment and carbon neutrality: Optimal sizing of standalone, green, reliable, and affordable water-power cogeneration systems

Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.

Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier

AbstractEpilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

A New Socio-Hydrology System Based on System Dynamics and a SWAT-MODFLOW Coupling Model for Solving Water Resource Management in Nanchang City, China

To address the issue of seasonal water resource shortages in Nanchang City, a multi-system coupling socio-hydrology simulation method was proposed. This approach involves dynamically integrating a centralized socio-economic model with a distributed surface water groundwater numerical model to explore the intricate relationships between the socio-economic system, the surface water–groundwater integrated system, and the outcomes related to seasonal water resource shortages. Taking Nanchang City as an example, this study conducted research on the water resource supply and demand balance, as well as the groundwater emergency supply, using the multi-system coupling model. Three scenarios were established: status quo, developing, and water-saving. The results show that with the increasing total water demand of social and economic development, the severity of the water resource shortage will be most pronounced in 2030. The minimum water resources supply and demand ratios for the status quo, developing, and water-saving scenarios are projected to be 0.68, 0.52, and 0.77, respectively. To meet residents’ water needs during drought conditions, emergency groundwater supply efforts are investigated. According to the simulation results, groundwater emergency supply would increase the total population by 24.0 thousand, 49.4 thousand, and 11.2 thousand people, respectively, in the status quo, developing, and water-saving scenarios. In the water-saving scenario, the Youkou and Xiebu water sources can serve as suitable emergency water sources. In the status quo scenario, the Youkou water source is the most viable emergency water source. However, in the developing scenario, relying solely on any single water source for emergency supply could have an irreversible impact on the aquifer. Therefore, considering the simultaneous use of multiple water sources is recommended, as it can fulfill water demands while ensuring the sustainable utilization of groundwater resources.

First-principles study of gallenene-based nanogap architecture for DNA nucleobase identification

In this study, we explore the potential of using a nanogap architecture based on gallenene, a single layer of Ga crystal, for DNA nucleobase identification via its quantum capacitance profiling and optical absorbance. This emerging technology offers several advantages, including minimum resources and time as well as cost-effectiveness. The results find that nucleobases exhibit a weak binding with gallenene resulting in a shorter resident time within the nanogap. Moreover, the degree of interaction between gallenene and nucleobases leads to modulation in the quantum capacitance of the system. On the other hand, we predict an anisotropic response in the optical absorption, indicating the potential for polarization-dependent nucleobase optical identification using the nanogap architecture. These results highlight the promising prospects of employing the gallenene-based nanogap architecture for sensing applications.

Minimum Resource Threshold Policy Under Partial Interference

– When developing policies for prevention of infectious diseases, policymakers often set specific, outcome-oriented targets to achieve. For example, when developing a vaccine allocation policy, policymakers may want to distribute them so that at least a certain fraction of individuals in a census block are disease-free and spillover effects due to interference within blocks are accounted for. The paper proposes methods to estimate a block-level treatment policy that achieves a pre-defined, outcome-oriented target while accounting for spillover effects due to interference. Our policy, the minimum resource threshold policy (MRTP), suggests the minimum fraction of treated units required within a block to meet or exceed the target level of the outcome. We estimate the MRTP from empirical risk minimization using a novel, nonparametric, doubly robust loss function. We then characterize statistical properties of the estimated MRTP in terms of the excess risk bound. We apply our methodology to design a water, sanitation, and hygiene allocation policy for Senegal with the goal of increasing the proportion of households with no children experiencing diarrhea to a level exceeding a specified threshold. Our policy outperforms competing policies and offers new approaches to design allocation policies, especially in international development for communicable diseases.

Using responsive feedback from routine monitoring data to guide course corrections for a family planning intervention in Nigeria.

This paper aims to promote the use of simple interrupted time series (ITS) analyses of routine data as a responsive feedback tool to improve public health programs. Although advanced ITS techniques exist, their use is often not feasible due to limitations in funding or research capacity. We propose an Excel-based analysis that requires minimal resources or statistical expertise, and illustrate it by measuring the effect of a radio campaign to promote a family planning call center in Nigeria on the demand for family planning information. We used a single group interrupted time series design (ITS) as a responsive feedback mechanism to determine whether the radio campaign influenced use of the Honey&Banana call center. ITS is ideal when there is no control group. ITS uses the pre-intervention trend to predict what would have happened if the intervention were absent. After conducting ITS analyses, the results show that the number of calls requesting family planning information increased throughout the campaign period, with a gain of about 500 additional calls per month, and then decreased after the campaign ended. However, the number of calls gained from the campaign was substantially lower than anticipated. While end-of-project impact evaluations are necessary, there should be regular feedback system to provide program implementers with information about the status of the project, such as failures, successes, and areas of improvements. This would allow implementers to make necessary adjustments as needed throughout the intervention period. The finding that the radio campaign was not living up to expectations helped Honey&Banana program implementers to end the campaign prematurely and re-allocate resources to a more promising activity. Our research shows that basic Excel-based ITS analysis of routine data can be a useful tool for receiving regular feedback to guide programming improvements for organizations that have limited resources and/or research capacity.

A robust architecture of ring oscillator PUF: Enhancing cryptographic security with configurability

Physically Unclonable Functions (PUFs) are becoming more widely recognized as tamper-resistant, high-entropy hardware security primitives. Leveraging the intrinsic manufacturing properties of integrated circuits, PUFs provide a lightweight, cost-effective technique for device identification and cryptographic key generation. Despite the existence of various PUF designs and architectures, the ring oscillator (RO) PUF stands out as one of the most notable. This significance is due to its simplicity of implementation and outstanding performance metrics. However, traditional RO-PUFs are often classified as weak PUFs because they support a limited size of input-output combinations. This paper introduces a configurable inversion unit designed to construct a lightweight, robust architecture configurable (RAC) RO-PUF. The proposed unit comprises an XOR gate, an XNOR gate, and a multiplexer. The RACRO-PUF significantly increases the size of generated output bits while efficiently utilizing minimal hardware resources. The performance of the RACRO-PUF stands out in evaluations, recording a uniqueness of 49.78 %, uniformity of 49.42 %, reliability of 97.72 %, and impressive randomness of 98.34 %.

Optimal Energy-efficient Resource Allocation and Fault Tolerance scheme for task offloading in IoT-FoG Computing Networks

Due to technology development in the recent past, it is observed that an exponential growth in the usage of high speed intelligent devices which includes smart objects, smart home, smart vehicles etc. Therefore, for the efficient transmission between different smart objects/devices situated across different regions, an effective communication is required and the corresponding technology is called as ‘Internet of Things (IoT)’. This leads to an issues in various aspects like, increase in complexity, low data rate & latency and security may reduce the performance of the existing technologies. Therefore, there is a huge requirement of advanced technology which may support ultra-low latency transmission for the task received from the devices. Fog computing (FC) is one of an emerging technology that can improve network performance and provide resource-constrained applications for the IoT devices. In this paper, the Optimal Energy-efficient Resource Allocation (OEeRA) algorithm is proposed based on Minimal Cost Resource Allocation (MCRA) and Fault Identification and Rectification (FIR) algorithms for effective task offloading of IoT-FoG computing networks. The MCRA algorithm is proposed to assign at least one FN and Resource Block (RB) for each device, and also it ensures that each FN is connected with one or more RBs and devices. The leftover RBs are collected and stored in the buffer to replace the faulty RBs, as proposed in the FIR algorithm, which achieves better processing and response time with higher fault detection accuracy. The energy efficiency (EE) of the proposed OEeRA algorithm is computed through MCRA and FIR algorithms by varying FN, RB, and IoT devices. The performance analysis shows that the proposed algorithm achieved the maximum EE of 6.12 × 109 bit/J, 5.69 × 1010 bit/J, and 3.019 × 1010 (bit/J) for varying RBs, IoTs, and FNs, respectively.

Methodological analysis of a community-based training initiative using the EPIS framework: an ongoing initiative to empower 10 million bystanders in CPR and bleeding control

BackgroundOut-of-hospital cardiac arrest (OHCA) and life-threatening bleeding from trauma are leading causes of preventable mortality globally. Early intervention from bystanders can play a pivotal role in increasing the survival rate...