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This study introduces a groundbreaking application of the Bellman-Ford algorithm for optimizing evacuation routes in multi-floor academic buildings, extending its traditional use in single-source shortest-path problems to address complex multiple- source multiple-exit (MSME) problems. A comprehensive computational model was developed, reflecting real-world evacuation scenarios and incorporating key constraints and assumptions. The model was rigorously benchmarked against a Dijkstra's algorithm-based model, revealing a 3.5% improvement in the number of evacuees evacuated after the initial 9 seconds. Detailed simulation results and extended data analysis further substantiate these findings. While the current model assumes perfect evacuee compliance and overlooks human behavior, future research could address these limitations to enhance the model's realism. This study significantly advances the field of emergency evacuation planning, offering valuable insights for emergency response practitioners, facility managers, and policymakers.

Various efforts have been made worldwide to reduce energy use for heating, ventilation, and air-conditioning (HVAC) systems and lower carbon dioxide (CO2) emissions. Research and development are essential to ensuring the efficient use of renewable energy systems. This study proposes a multiple sources and multiple uses heat pump (MMHP) system that can efficiently respond to heating, cooling, and domestic hot water (DHW) loads using multiple natural heat sources. The MMHP system uses ground and air heat as its primary heat sources and solar heat for heat storage operations and ground temperature recovery. For the efficient use of each heat source, it also determines the heat source required for operation by comparing the heat source temperatures in the same time zone. A model for predicting the heat source temperatures, electricity use, and coefficient of performance (COP) was constructed through simulation. To analyze the efficiency of the proposed system by comparing the existing air source heat pump with ground source heat pump systems, a performance analysis was conducted by setting regional and system configurations as case conditions. The results demonstrate that the electricity use of the MMHP system was 13–19% and 1–3% lower than those of air source heat pump (ASHP) and ground source (GSHP) systems, respectively. In addition, the MMHP system was the most favorable in regions with a low heating load.

Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.

The ability to meaningfully and critically integrate multiple texts is vital for twenty-first-century literacy. The aim of this systematic literature review is to synthesize empirical studies in order to examine the current state of knowledge on how intertextual integration can be promoted in educational settings. We examined the disciplines in which integration instruction has been studied, the types of texts and tasks employed, the foci of integration instruction, the instructional practices used, integration measures, and instructional outcomes. The studies we found involved students from 5th grade to university, encompassed varied disciplines, and employed a wide range of task and text types. We identified a variety of instructional practices, such as collaborative discussions with multiple texts, explicit instruction of integration, modeling of integration, uses of graphic organizers, and summarization and annotation of single texts. Our review indicates that integration can be successfully taught, with medium to large effect sizes. Some research gaps include insufficient research with young students; inadequate consideration of new text types; limited attention to students' understandings of the value of integration, integration criteria, and text structures; and lack of research regarding how to promote students' motivation to engage in intertextual integration.

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications. [Display omitted] •A modified RAP-MUSIC method to locate multiple brain sources in EEG/MEG is proposed.•The new method, TRAP-MUSIC, solves a hidden deficiency in the original algorithm.•TRAP-MUSIC applies a recursive dimension reduction of the signal-space estimate.•The improvements in performance come without any computational cost.

Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additionally, we compute novel domain-specific time-series features that allow the system to better model the pattern of energy consumption of the household. The experimental analysis and evaluation were performed on one of the most extensive datasets for household electrical energy consumption, Pecan Street, containing almost four years of data. Multiple test cases show that the proposed model provides accurate load forecasting results, achieving a root-mean-square error score of 0.44 kWh and mean absolute error score of 0.23 kWh, for short-term load forecasting for 300 households. The analysis showed that, for hourly forecasting, our model had 8% error (22 kWh), which is 4 percentage points better than the benchmark model. The daily analysis showed that our model had 2% error (131 kWh), which is significantly less compared to the benchmark model, with 6% error (360 kWh).

For power system engineers, automated load frequency control (LFC) for multi-area power networks has proven a difficult problem. With the addition of numerous power generation sources, the complexity of these duties becomes even more difficult. The dynamic nature of linked power networks with varied generating sources, such as gas, thermal, and hydropower plants, is compared in this research. For the study to be more accurate, frequency and tie-line power measurements are used. For precise tuning of proportional-integral-derivative (PID) controller gains, the Bald Eagle Sparrow search optimization (BESSO) technique was used. The BESSO algorithm was created by combining the characteristics of sparrows and bald eagles. The performance of BESSO is determined by comparing its findings to those acquired using traditional approaches. In terms of Integral Time Absolute Error (ITAE), which is the most important criterion used to reduce system error, the findings presented in this study indicate the effectiveness of the BESSO-PID controller. Finally, sensitivity analysis and stability analysis proved the robustness of the developed controller. The settling times associated with the tie-line power flow, frequency variation in area-1, and frequency variation in area-2, respectively, were 10.4767 s, 8.5572 s, and 11.4364 s, which were all less than the traditional approaches. As a result, the suggested method outperformed the other strategies.

We conducted a systematic review of research involving K-12 students that examined associations among individual differences factors (e.g., working memory) and intertextual integration. We identified 25 studies published in 23 peer-reviewed journal articles and two dissertations/theses. These examined a wide range of individual difference factors, which we organized into four categories: (a) language and literacy, (b) cognition and metacognition, (c) knowledge and beliefs, and (d) motivation, emotion, and personality. We found large variation in the participants, tasks, and document types, and little systematic replication across studies. Nonetheless, results generally showed that variation in literacy, cognition, metacognition, knowledge, beliefs, and motivation are positively and moderately associated with intertextual integration. We discuss the limitations of this work and offer four recommendations for future research.

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.

Decision-making on the basis of multiple information sources is common. However, to what extent such decisions differ from those with a single source remains unclear. We combined cognitive modelling and neural-mass modelling to characterise the neurocognitive process underlying perceptual decision-making with single or double information sources. Ninety-four human participants performed binary decisions to discriminate the coherent motion direction averaged across two independent apertures. Regardless of the angular distance of the apertures, separating motion information into two apertures resulted in a reduction in accuracy. Our cognitive and neural-mass modelling results are consistent with the hypotheses that the addition of the second information source led to a lower signal-to-noise ratio of evidence accumulation with two congruent information sources, and a change in the decision strategy of speed–accuracy trade-off with two incongruent sources. Thus, our findings support a robust behavioural change in relation to multiple information sources, which have congruency-dependent impacts on selective decision-making subcomponents.