Resource Requirements Research Articles

High-energy blunt pelvic ring injuries and pre-hospital pelvic binder applications – A retrospective assessment based on a prospective registry

IntroductionApplication of a pelvic binder in the pre-hospital settings is a crucial step of high-energy blunt pelvic ring injury (PRI) management protocols. The aim of this retrospective cohort study was to evaluate the percentage of pre-hospital pelvic binder's applications in high-energy blunt type B or C PRI patients managed at a single level I trauma center, and to assess its impact on the medical resource requirements and patients' outcomes. MethodsThe institutional Severely Injured Patients’ Registry was screened for patients meeting the following inclusion criteria: (1) high-energy blunt PRI; (2) admission between 2014.01.01 and 2022.12.31; (3) age ≥16 years; and (4) available computed tomography of the pelvis. Exclusion criteria were: (1) death before admission; (2) low-energy injury; (3) penetrating, blast and electrical injuries; and (4) secondary transfers. Study variables, including pre-hospital application of a pelvic binder and demographic, clinical, management and outcome data were extracted from the registry. Additionally, AO/OTA classifications were determined. ResultsA consecutive series of 262 patients was included into the final analysis. Of these, 58.8 % received a pre-hospital pelvic binder (PPB), increasing from type A (45.1 %) to type B (57.8 %) and type C (73.7 %). Pre-hospital hemodynamic instability was a major factor triggering the use of PPB in high-energy blunt type B PRI patients with PPB in 76.9 % of the cases with hemodynamic instability vs. 51.3 % of the cases without hemodynamic instability (p = 0.009). This difference was not statistically significant for high-energy blunt type C PRI patients (82.8 % vs. 64.3 %, p = 0.200). The presence of a physician on the trauma site contributed to the increased percentage of PRI managed with PPB from 43.5 % to 67.3 % in type B PRI (p = 0.011), and from 50 % to 77.1 % in type C PRI (p = 0.257).This study showed an increased need for packed red blood cells (PRBC) transfusions in subgroups treated with PPB and no statistically significant differences in term of intensive care unit (ICU) and total hospital length of stay and complications between high-energy blunt type B or C PRI patients with or without PPB.Comparison of mortality rates in patients with and without PPB showed a trend towards lower mortality rates with PPB in patients with AIS extremity participating in at least 50 % of the total ISS. ConclusionIn the ideal pre-hospital scenario, every high-energy blunt type B or C PRI patient should be managed with a PPB. Continuous pre-hospital team training and information is crucial to achieve this goal. The preparation of Emergency Department and ICU who admit a patient with PPB should include a sufficient number of PRBC ready for transfusion. Mortality rates did not seem to be affected by PPB, except for a trend towards lower mortality rates in patients in whom their PRI was the major component of the global severity of their injury. This might point out the critical and positive effect of PPB in high-energy blunt PRI patients.

Compact Ultra-Wideband MIMO Antenna for 5G Communication Systems

Ultra-wideband (UWB) technology is highly respected in modern communication systems because it can enable high-speed, high-data-rate communication over short distances. It is still difficult to achieve optimal multiple-input multiple-output (MIMO) performance in real-world multipath settings, even with breakthroughs in traditional antenna designs. UWB-MIMO antenna solutions have been created and simulated in order to address this problem and fulfill present demands. Nevertheless, maintaining high antenna efficiency and the requirement for antenna designs tailored to a particular bandwidth are two of the difficulties UWB systems must overcome. Recently, a variety of optimization strategies have been employed to obtain high gain and UWB performance by fine-tuning antenna characteristics. The major obstacles are resolved by the optimization process, including the time and computational resource requirements. The difficulty is further compounded by the intricacy of accurately modifying antenna settings to maximize gain. A multi-objective advancement for the design and improvement of the reduced UWB-MIMO test radio wire for 5G was offered as a solution to these problems. In order to attain the UWB feature, we first aim to use a single elliptical-square form antenna that operates from 3.4 to 70 GHz with isolation larger than 29.54 dB. Furthermore, we optimize the impedance bandwidth by tuning the parameters of the structural dimensions using the improved proportional topology optimization (IPTO) technique. Additionally, we present the orthogonal correction using the binarized convolutional neural network (BCNN).

A hybrid local search algorithm for the continuous energy-constrained scheduling problem

AbstractWe consider the continuous energy-constrained scheduling problem (CECSP). A set of jobs has to be processed on a continuous, shared resource. A schedule for a job consists of a start time, completion time, and a resource consumption profile. The goal is to find a schedule such that each job does not start before its release time, is completed before its deadline, satisfies its full resource requirement, and respects its lower and upper bounds on resource consumption during processing. The objective is to minimize the total weighted completion time. We present a hybrid local search approach, using simulated annealing and linear programming, and compare it to a mixed-integer linear programming (MILP) formulation. We show that the hybrid local search approach matches the MILP formulation in solution quality for small instances and is able to find a feasible solution for larger instances in reasonable time.

Automatic Planning Method of Construction Schedule under Multi-Dimensional Spatial Resource Constraints

To enhance the application of building information modeling (BIM) in schedule management for engineering projects, improve spatial planning capabilities, and minimize subjective factors in schedule preparation, this paper presents a framework and method for automatic construction schedule planning based on BIM and data-driven approaches under multi-dimensional spatial resource constraints. The method includes data acquisition, construction process reasoning, time parameter calculation, and schedule generation and scheduling. Initially, the construction data from the BIM model and manual input are imported into the reasoning system to establish the logical relationships of the construction process. Construction time parameters and spatial resource requirements are then automatically calculated using MySQL. Subsequently, the spatial layout and resource allocation data exported from the BIM model are compared to assess whether the space meets the project requirements. Finally, a construction schedule that satisfies the space constraints is generated. The method was validated through a case study, demonstrating its effectiveness in automatic schedule planning under human-computer interaction. This study introduces an innovative approach that considers spatial conflicts in the automatic scheduling process, improving planning efficiency and reducing the risk of spatial conflicts on construction sites.

Key-Value Cache Quantization in Large Language Models: A Safety Benchmark

The misuse of large language models (LLMs) is an ongoing concern with the expansion of general public access to LLMs. One reason for expanded access is the development of key-value (KV) cache quantization, a technique that significantly reduces both the large computing resource requirements and memory bottlenecks that are characteristic of LLMs. As more developers and vendors begin to prioritize efficiency in LLM training, protective measures against the misuse of language models become more of an afterthought. To address the expected increase of LLM misuse accompanying KV cache quantization, this paper covers a proof-of-concept benchmark to evaluate the proficiency of LLMs in response safety when tested against a sample of unsafe questions consisting of 13 different question categories. Response safety is a model’s ability to both clearly deny providing a response to a given question and avoid providing any additional information that attempts to provide an accurate answer. By testing the sample against the Meta Llama-2-7B pretrained chat model, we determine response safety fine-tuning considerations that address performance bias among the 13 question categories. We hope this study brings attention to not only the sacrifices of accuracy but also response safety of KV cache quantization in large language models. (Code and data are available at https://github.com/TimochiL/llm_benchmark.) Disclaimer: This paper contains examples of harmful language. Reader discretion is recommended.

Quantum Fourier Transform Using Dynamic Circuits.

In dynamic quantum circuits, classical information from midcircuit measurements is fed forward during circuit execution. This emerging capability of quantum computers confers numerous advantages that can enable more efficient and powerful protocols by drastically reducing the resource requirements for certain core algorithmic primitives. In particular, in the case of the n-qubit quantum Fourier transform followed immediately by measurement, the scaling of resource requirements is reduced from O(n^{2}) two-qubit gates in an all-to-all connectivity in the standard unitary formulation to O(n) midcircuit measurements in its dynamic counterpart without any connectivity constraints. Here, we demonstrate the advantage of dynamic quantum circuits for the quantum Fourier transform on IBM's superconducting quantum hardware with certified process fidelities of >50% on up to 16 qubits and >1% on up to 37 qubits, exceeding previous reports across all quantum computing platforms. These results are enabled by our contribution of an efficient method for certifying the process fidelity, as well as of a dynamical decoupling protocol for error suppression during midcircuit measurements and feed forward within a dynamic quantum circuit that we call "feed-forward-compensated dynamical decoupling." Our results demonstrate the advantages of leveraging dynamic circuits in optimizing the compilation of quantum algorithms.

Consideration of factors of low accrual and methods for setting appropriate accrual periods: Japan Clinical Oncology Group study

BackgroundPoor patient accrual can delay reporting of clinical trials and, consequently, the development of new treatments. For reducing the risk of additional resource requirements, a method for setting planned accrual periods with minimal deviation from the actual accrual periods is desirable. Risk factors for poor patient accrual and the appropriate method of estimating the required accrual period for timely completion of clinical trials were evaluated using the data of trials conducted by the Japan Clinical Oncology Group.MethodsThe study included 199 trials that started patient accrual between January 1, 1990, and June 30, 2021. The explanatory variables included factors that could be evaluated prior to trial commencement. We also evaluated whether the estimation methods for accrual pace could lead to completion within the planned accrual period.ResultsApproximately 23.6% of trials were completed within the planned accrual period. The risk factors for trial extension included planned accrual periods > 3 years (reference group: ≤ 3 years, odds ratio [OR] 0.37, 95% confidence interval [CI]: 0.15–0.92, P = 0.033) and stratified trial design (reference group: nonrandomized phase II trials, nonrandomized phase III trial [OR: 3.28, 95% CI: 0.99–10.9, P = 0.051], randomized phase II trial [OR: 3.91, 95% CI: 0.75–20.30, P = 0.105], and randomized phase III trial [OR: 9.29, 95% CI: 3.39–25.40, P < 0.001]). The method of estimating the accrual pace based on past clinical trials facilitated timely completion of the trial (OR: 3.51; 95% CI: 1.73–7.10, P < 0.001), unlike the estimation method based on survey evaluation of the accrual pace for participating institutions (OR: 1.12, 95% CI: 0.56–2.26, P = 0.751). Furthermore, the discrepancy between planned and actual accrual periods was minimal when using the methods of considering the accrual pace of past clinical trials.ConclusionsConsidering the accrual pace of past clinical trials is useful for estimating the required accrual period if data from past trials are available. When conducting a survey, it is necessary to be cautious of overestimating the cases at each facility.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

Productivity costs of the green energy transition in Africa

AbstractTransitioning to renewable energy generates tangible benefits in terms of the environment and emissions of greenhouse gases. However, the economic productivity costs of switching to clean energy cannot be overlooked, in addition to the large resource requirements, potential disruption in energy systems, risks of job losses, and emergence of new inequalities. Therefore, it is crucial to identify and quantify synergies and trade‐offs. This paper investigates the productivity cost of abandoning the use of fossil fuels in African countries, using econometric techniques applied to panel data, including the fully modified ordinary least square. The paper finds that, while renewable energy supply is beneficial to increase total factor productivity, in general, a shift toward a higher share of renewables in the power mix is not without productivity cost on the economy. The analysis accounts for other sources of productivity, such as human capital and the diffusion of embodied technology imported from abroad into local markets. The data also reveal that the energy transition could have heterogeneous effects depending on the regions' and countries' characteristics and stages of transition.

Adaptive Matching of Discrete Antenna Tuning Units of High Frequency Band to Varying Load Impedance

The issue of matching the antenna with thetransmitter-receiverplays a primary role in radio communications. The matching problem is relevant for different ranges and is solved in many ways. In the high frequency wave range, matching has a number of features when using an antenna coupler based on discrete elements consisting of capacitance and inductance boxes. The article discusses the concept of fast matching of high-frequency amplifying paths ofdecametric wavestransceivers with non-stationary loads using discrete antenna couplers. An innovative approach has been developed to the tuning process when changing the operating frequency and ensuring self-adaptation of the antenna coupler when changing the impedance, based on methods for quickly searching for the geometric proximity of points. A quick method for configuring the antenna coupler is proposed, eliminating the procedure for recalculating the input impedance based on the results of measuring the input impedance of the antenna coupler. When using it, the speed of setting the antenna coupler at the current operating frequency is limited only by the time of a single state change of the discrete power circuit, which in the case of electromagnetic relays is numerically equal to their switching time. The configuration of the antenna coupler using the method of fast search for the geometric proximity of points is based on a set of frequency cards with settings options. Such cards can be recalculated for the reference plane between the generator and the antenna coupler input for all combinations of discrete elements in accordance with the known matrix model of a discrete power circuit. This eliminates the complicated procedure of recalculating the input impedance of the antenna using complex numbers. The technical result of the proposed innovative method of antenna coupler configuringis simplification, tracking of systematic errors of the impedance meter, elimination of time-consuming calculations with complex numbers, expansion of the method functionality, reduction of the tuning duration and improvement its quality. The practical significance lies in the fact that the implementation of the proposed technical solutions in the high frequency range communications means can significantly (about 100 times) limit the duration of preparation for a radio communication session, eliminate the procedure for recalculating the antenna input impedance and reduce the resource requirements of switching elements.

Efficient Boolean-to-Arithmetic Mask Conversion in Hardware

Masking schemes are key in thwarting side-channel attacks due to their robust theoretical foundation. Transitioning from Boolean to arithmetic (B2A) masking is a necessary step in various cryptography schemes, including hash functions, ARX-based ciphers, and lattice-based cryptography. While there exists a significant body of research focusing on B2A software implementations, studies pertaining to hardware implementations are quite limited, with the majority dedicated solely to creating efficient Boolean masked adders. In this paper, we present first- and second-order secure hardware implementations to perform B2A mask conversion efficiently without using masked adder structures. We first introduce a first-order secure low-latency gadget that executes a B2A2k in a single cycle. Furthermore, we propose a second-order secure B2A2k gadget that has a latency of only 4 clock cycles. Both gadgets are independent of the input word size k. We then show how these new primitives lead to improved B2Aq hardware implementations that perform a B2A mask conversion of integers modulo an arbitrary number. Our results show that our new gadgets outperform comparable solutions by more than a magnitude in terms of resource requirements and are at least 3 times faster in terms of latency and throughput. All gadgets have been formally verified and proven secure in the glitch-robust PINI security model. We additionally confirm the security of our gadgets on an FPGA platform using practical TVLA tests.

Optimizing gene selection for Alzheimer’s disease classification: A Bayesian approach to filter and embedded techniques

Alzheimer’s disease (AD) classification, which is crucial for identifying AD-associated genes, relies heavily on effective feature selection (FS) to tackle the curse of dimensionality. Traditional methods like filter, wrapper, and embedded techniques have their drawbacks, including ignoring feature independence, sensitivity to classifier choices, and high computational costs. Hybrid approaches combining these methods seek to harness their collective strengths but face challenges, particularly in selecting the optimal number of features from each method. This selection is typically manual or requires time-intensive k-fold cross-validation (KFCV), significantly increasing computational demands and complicating the process with the need for extensive parameter optimization across families, thereby escalating the complexity and resource requirements of model development. To overcome these challenges, this work proposes a framework for optimal FS and classification in AD using a combination of filter and embedded techniques, enhanced with hyperparameter tuning. Firstly, gene expression data (GED) from the AD Neuroimaging Initiative (ADNI) is preprocessed. Then, Chi-square filter selection is applied to decrease correlated features. Next, Logistic Regression with ElasticNet penalty (LREN) is employed to further refine the feature set. Finally, Bayesian Optimization (BO) is introduced to automatically determine the optimal number of features (k for Chi-square and max_features for LREN), iteratively evaluating different combinations to find the set that maximizes model accuracy. The selection process is used primarily in the function of BO to tune automatically the (k and max_features while minimizing the number of features and maximizing the accuracy of the Support Vector Machine (SVM). The SVM was used as a classifier to overcome the problem of embedded selection sensitivity to the model of selection. The tuned features are then used to select relevant features from the ADNI dataset and fitted to different models. We evaluated the performance of five classifiers—logistic regression (LR), SVM, Ridge Classifier (RC), stochastic gradient descent classifier (SGD), and Gaussian Naïve Bayes(GNB) across various metrics. Among these, SVM achieved 100% performance in all metrics. This approach significantly reduced the FS time and the number of initial features to 0.6% and 0.02%, respectively. Notably, it identified 6 out of 20 selected features as directly AD-related. Comparative analysis reveals that the proposed method outperforms existing approaches on the ADNI dataset and other datasets. Statistical tests were conducted to assess the significance of the results compared to other methods, confirming a significant improvement and underscoring the effectiveness of the proposed framework in optimal FS and biological relevance confirmation.

Edge-device collaborative computing for multi-view classification

Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore collaborative inference at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce selective schemes that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.

The ‘soft’ sides of ISO17025

ABSTRACT Where accredited, the vast majority of forensic science laboratories, globally, are accredited to the ISO Standard ISO/IEC17025:2017 (ISO17025). The standard consists of diverse sections including Resource requirements and Process requirements which are afforded much attention. In our view, given significantly less attention are General and Structural requirements which address important issues that are critical, although often neglected, in the quality landscape, such as Impartiality, Confidentiality, Integrity, Culture and Improper influence. We highlight their importance in this article under the heading ‘soft’ clauses and suggest some preventive measures to be taken, as neglecting these aspects could be disastrous in many respects.

Accelerating the convergence of coupled cluster calculations of the homogeneous electron gas using Bayesian ridge regression.

The homogeneous electron gas is a system that has many applications in chemistry and physics. However, its infinite nature makes studies at the many-body level complicated due to long computational run times. Because it is size extensive, coupled cluster theory is capable of studying the homogeneous electron gas, but it still poses a large computational challenge as the time needed for precise calculations increases in a polynomial manner with the number of particles and single-particle states. Consequently, achieving convergence in energy calculations becomes challenging, if not prohibited, due to long computational run times and high computational resource requirements. This paper develops the sequential regression extrapolation (SRE) to predict the coupled cluster energies of the homogeneous electron gas in the complete basis limit using Bayesian ridge regression and many-body perturbation theory correlation energies to the second order to make predictions from calculations at truncated basis sizes. Using the SRE method, we were able to predict the coupled cluster double energies for the electron gas across a variety of values of N and rs, for a total of 70 predictions, with an average error of 5.20 × 10-4 hartree while saving 88.9h of computational time. The SRE method can accurately extrapolate electron gas energies to the complete basis limit, saving both computational time and resources. Additionally, the SRE is a general method that can be applied to a variety of systems, many-body methods, and extrapolations.

RandONets: Shallow networks with random projections for learning linear and nonlinear operators

Deep neural networks have been extensively used for the solution of both the forward and the inverse problem for dynamical systems. However, their implementation necessitates optimizing a high-dimensional space of parameters and hyperparameters. This fact, along with the requirement of substantial computational resources, pose a barrier to achieving high numerical accuracy, but also interpretability. Here, to address the above challenges, we present Random Projection-based Operator Networks (RandONets): shallow networks with random projections and tailor-made numerical analysis methods that learn accurately and fast linear and nonlinear operators. Building on previous works, we prove that RandOnets are universal approximators of linear and nonlinear operators. Due to their simplicity, RandONets provide a one-step transformation of the input space, facilitating interpretability. For the evaluation of their performance, we focus on operators of PDEs. We show, that RandONets outperform by several orders of magnitude, both in terms of numerical approximation accuracy and computational cost, the “vanilla” DeepONets. Hence, we believe that our method will trigger further developments in the field of scientific machine learning, for the development of new ‘’light”schemes that will provide high accuracy while reducing dramatically the computational cost. A MATLAB toolbox for RandONets, including demos, is available on GitHub at https://github.com/GianlucaFabiani/RandONets.

Atmospheric Pressure Portable Catalytic Thermal Plasma System for Fast Synthesis of Aqueous NO3 and NO2 Fertilizer from Air and Water

AbstractMeaningful deployment of plasma water-based nitrogen fixation in agricultural application is hindered primarily due to its poor synthesis rate in compact systems. The study reports a directly deployable thermal plasma based portable catalytic compact system, offering typical synthesis rate as high as 1035 mg/min for nitrate and 635 mg/min for nitrite directly from naturally abundant atmospheric air and water. Developed technology is clean, sustainable, easily decentralizable, and completely free from fossil fuels and harmful intermediates like ammonia. The system avoids safety hazards and costs related to the requirements of continuous energy resources, pressurized environment for synthesis, regulated storage, refrigeration need, transportation of raw materials and distribution of fertilizer, as may be required by other competing technologies. Described system, consisting of air plasma torch, reaction chamber, water injection manifold and catalytic bed creates a unique nascent reactive plasma environment at ambient pressure that auto activates the catalyst in the field of thermal plasma for highly efficient fixation of nitrogen. Presented results indicate that use of combination catalysts with mechanically enhanced surface area allows drastic enhancement in the nitrogen fixation. Possible reaction chemistries, results of trials with different catalysts, time evolution of concentration, auto-conversion from nitrite to nitrate in aqueous media, time stability of concentration of the synthesized nitrate and observed remarkable effectiveness in the actual field trials are presented. Achieved synthesis rates are compared with those reported in literature in the area of thermal and non-thermal plasma.

Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.

The Universal Module for Integration of an Intelligent Assistant into iOS Applications

Investigated current implementations of the integration of intelligent assistants into mobile applications. Identified key disadvantages of existing implementations and formed the criteria for a universal intelligent assistant. Developed a proprietary software module for integrating an intelligent assistant into iOS application, which provides autonomy, minimal resource requirements, and simplifies the development process. Created a photo editor application to test the operation of the software module. The test results were presented and further development prospects were described.

An Infrastructure’s emissions assessment tool: AMICO - Account Method of Infrastructures embodied CarbOn

Abstract In 2013, the UK Infrastructure Carbon Review report highlighted how the infrastructure sector contributed to the national total emissions and, therefore, had a key role in contributing to the national reduction, adopting targeted strategies to reduce them. The main objective of AMICO’s research project: “Account Method of Infrastructures embodied CarbOn”, is the development of a tool for the analysis and parametric evaluation of carbon emissions (Embodied Carbon, EC) and energy resource requirements (Embodied Energy, EE) related to the design and construction of infrastructures (railway tunnels, bridges, highways …) AMICO tool is designed to be used for tendering, contract start-up and construction of infrastructure by Webuild Group (Italian Joint Stock Company) as part of a research project funded by MIMIT - Ministry of Business and Made in Italy, with the scientific support of the Politecnico di Torino (Department of Architecture and Design). AMICO aims to assess potential impacts in terms of greenhouse gas emissions (GHG) and potential primary energy resource needs over the infrastructure life cycle and at different design process stages, allowing designers and constructors to evaluate decarbonisation in their choices. AMICO is currently organised into two calculation models: 1. An Excel spreadsheet file, divided into worksheets containing algorithms for accounting of EC and EE, organised into two usage models: Basic, which will be used for tenders and the early design stage, and Advanced, which will be used in the detailed design or construction stage. 2. A code for the BIM environment that accounts for impacts (EE and EC) related to infrastructure systems (as defined by UNICLASS) combined and concurrently with design in the BIM environment (this section is not the focus of this paper). An innovation aspect is sharing a database of environmental data about materials and processes (transportation, construction, demolition) directly related to the Bill of Quantity without using Life Cycle Assessment software. The database will be integrated into the Datalake to speed up and optimise infrastructure design and construction work. You can set the database to the location of the work. Through data digitisation, AMICO is designed to be as automated, easy to fill in, and intuitive as possible.