Spin-forbidden Research Articles

Functional Nano-Metallic Coatings for Solar Cells: Their Theoretical Background and Modeling

We have collected theoretical arguments supporting the functional role of nano-metallic coatings of solar cells, which enhance solar cell efficiency via by plasmon-strengthening the absorption of sun-light photons and reducing the binding energy of photoexcitons. The quantum character of the plasmonic effect related to the absorption of photons (called the optical plasmonic effect) is described in terms of the Fermi golden rule for the quantum transitions of semiconductor-band electrons induced by plasmons from a nano-metallic coating. The plasmonic effect related to the lowering of the exciton binding energy (called the electrical plasmonic effect) is of particular significance for metalized perovskite solar cells and is also characterized in quantum mechanics terms. The coupling between plasmons in nanoparticles from a coating with band electrons in a semiconductor substrate significantly modifies material properties (dielectric functions) both of the particles and the semiconductor, beyond the ability of the classical electrodynamics to describe.

A novel spatio-temporal prediction model of epidemic spread integrating cellular automata with agent-based modeling

Since 2019, major infectious disease outbreaks have placed tremendous pressure on global public health systems, triggering extensive research on the predictive modeling of infectious diseases. Cellular Automaton (CA) is primarily used in the spatial prediction of infectious diseases to establish a model to for simulating the interaction between different regions and the infection risk to simulate the transmission process of the disease and predict its development trend. However, CA models are governed by initial fixed rules and local interactions, and often fail to capture the complex dynamics of epidemic transmission, which are influenced by factors such as public behavior and government intervention. In view of these limitations, we propose a factorial simulation model for the spatial spread of epidemics, the CA-ABM, which divides agents into three categories–public, government, and hospital agents–to comprehensively express the macro factors that affect the development of epidemics. Agent-Based Modeling (ABM) influences the transition rules of the CA through agent choices, constraints and supporting behaviors. Focusing on the COVID-19 pandemic in mainland China from February 6 to March 20, 2020, we simulate its spread. The results showed an average improvement of 8.4 % in prediction accuracy, with few errors, RMSE under 200, and R2 values over 0.9 in most provinces, demonstrating strong macro-scale stability. This approach helps regions to understand influencing factors and enables targeted infection risk assessment and prevention. In addition, scenario analysis based on CA-ABM model changes epidemic decision-making from “prediction-response” to “scenario-response” and provides theoretical reference for future epidemic management.

Single-Channel Sleep EEG Classification Method Based on LSTM and Hidden Markov Model

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications.

An Improved Type of Synchronized Space Vector PWM Based on Switching Angles for High‐Power AC Drive Application

Synchronized space vector pulse width modulation (SSVPWM) is widely employed in high‐power AC drive systems. The existing methods are carrier‐based, in which the carrier ratio, position of samples, and switching sequences used to generate every sample for a given SSVPWM are settled, which causes two problems: (i) the control delay changes, and a voltage phase modulation error appears suddenly during transition between different SSVPWMs with different carrier ratios, which causes motor current spikes; (ii) due to the particular switching sequences, some kinds of SSVPWM cannot get into six‐step operation by overmodulation. This paper proposes an improved type of SSVPWM based on switching angles, in which the carrier ratio of a given SSVPWM can change flexibly, and the problems mentioned above can be solved. Theories including switching angle calculation, overmodulation strategy, carrier ratio setting principle, and transition rules between different SSVPWM modes are given in this paper. Meanwhile, the switching angles change linearly with modulation index which makes it easy to implement, and the modulation error in the overmodulation region is effectively limited so that the proposed scheme is suitable in the whole modulation index range. A case study of a 5.5‐kW experimental platform is presented to validate the proposed method. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Bidirectional artificial potential field-based ant colony optimization for robot path planning

Ant colony optimization (ACO) is a common approach for addressing mobile robot path planning problems. However, it still encounters some challenges including slow convergence speed, susceptibility to local optima, and a tendency to falling into traps. We propose a bidirectional artificial potential field-based ant colony optimization (BAPFACO) algorithm to solve these issues. First, the bidirectional artificial potential field is introduced to initialize the grid environment model and restrict direction selection to jump out of the trap. Second, an adaptive heuristic function is presented to strengthen directionality of the algorithm and reduce the turning times. Third, a pseudo-random state transition rule based on potential difference between starting and ending nodes is developed to accelerate convergence speed. Finally, an improved pheromone update strategy incorporating pheromone diffusion mechanism and elite ants update strategy is proposed to help getting out of local optima. To demonstrate the advantages of BAPFACO, the validation of the performance in six different complexity environments and comparative experiments with other conventional search algorithms and ACO variants are conducted. The results of experiment show that compared to various ACO variants, BAPFACO have advantages in terms of reducing the turning times, shortening path length, improving convergence speed and avoiding ant loss. In complex environments, compared to IHMACO, the average path length enhancement percentage (PLE) of BAPFACO is 20.98%, the average iterations enhancement percentage (IE) of BAPFACO is 20.00% and the average turning times enhancement percentage (TE) of BAPFACO is 49.43%. These results firmly demonstrate the efficiency and practicality of the BAPFACO algorithm for mobile robot in path planning.

Cointegration Analysis of Crop Yields and Extreme Weather Factors Using Actuaries Climate Index with Application of Bonus–Malus System

This article analyzes the long-term temporal co-movement of the extreme weather variables in the Actuaries Climate Index (ACI) and crop yields, modeling their relationship using an error correction model (ECM). The analysis suggests that significant weather variables can serve as trigger parameters in the pricing framework of weather index crop insurance. To address the challenge of weather index crop insurance while preserving the advantages of a bonus–malus system (BMS), we propose a transition rule that distinguishes between damages caused by severe weather and those resulting from the policyholder’s decisions. Subsequently, we also explore the challenges of implementing such a new hybrid BMS for crop insurance where extreme weather outcomes are integrated into the classical BMS.

Higher-order network information propagation model based on social impact theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

Predicting CO2 emissions through partitioned land use change simulations considering urban hierarchy

ABSTRACT An understanding of the relationship between CO2 emissions and land use can provide a deeper understanding of how urbanization affects climate change, provide insights into sustainable urban development, and reveal CO2 emission dynamics in the context of urbanization. However, the accuracy of these predictions is limited due to the poor understanding of the influence of differences in urban hierarchy on land use transition rules. This study proposes an integrated CO2 emission prediction method that utilizes a newly developed future land use simulation (partitioned-FLUS) model and a Long Short-Term Memory (LSTM) model. Notably, the partitioned-FLUS model considers regional heterogeneities based on urban hierarchies. Based on data from the Yangtze River Delta, zonal partitioning was found to improve Kappa coefficients by at least 13% compared to conventional FLUS models. The results indicated that a significant urbanization belt was likely to form both along and south of the Yangtze River over the next 30 years; resulting in areas of concentrated CO2 emissions. The relationship between land use and CO2 emissions varied greatly between cities of different urban hierarchies. This study suggests that construction land and farmland in Level 1 and 2 cities should be reallocated to Level 3 cities, thereby reducing carbon emissions.

On complexity of colloid cellular automata

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from them. We analyze the space-time complexity of Boolean circuits derived from the electrical responses of colloids-specifically ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and their combinations in response to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata-arrays of binary state machines that update their states synchronously according to the same rule-creating the colloid cellular automata. We then analyze the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging

Background and Objective:Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages. Methods:In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals. Results:We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively. Conclusions:The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.

Emergence of Self-Replicating Hierarchical Structures in a Binary Cellular Automaton.

We have discovered a novel transition rule for binary cellular automata (CAs) that yields self-replicating structures across two spatial and temporal scales from sparse random initial conditions. Lower-level, shape-shifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicate. When the initial distribution of live cells is sufficiently sparse, these clusters coalesce into larger formations that also self-replicate. These formations may further form the boundaries of an expanding complex on an even larger scale. This rule, dubbed "Outlier," is rotationally symmetric and applies to 2-D Moore neighborhoods. It was evolved through genetic programming during an extensive search for rules that foster open-ended evolution in CAs. While self-replicating structures, both crafted and emergent, have been created in CAs with state sets intentionally designed for this purpose, the Outlier may be the first known rule to facilitate nontrivial emergent self-replication across two spatial scales in binary CAs.

Research on double‐USVs fuzzy‐priority NSB behavior fusion formation control method for oil spill recovery

AbstractReplacing manned ships with unmanned surface vehicle (USV) for oil spill containment can reduce the consumption of manpower and resources. This article studies the formation control method of dual USVs during the process of capturing oil spill, based on the engineering background of towing oil boom by dual USVs oil spill recovery system. To calculate the drag force of the oil boom acting on the USV, the shape of the oil boom is simplified into a catenary, the oil boom is modeled, and the hydrodynamic numerical simulation is carried out. To address the issue of “winding” phenomenon and “towing separation,” the formation behavior is designed when double USVs are towing oil fences to capture oil spill. In response to the problem of low task execution efficiency caused by fixed behavior priority in traditional null‐space‐based (NSB) behavior fusion methods, a fuzzy‐priority NSB (FNSB) behavior fusion formation method is proposed by combining fuzzy control with NSB behavior fusion method. In the FNSB behavior fusion formation control method, a smooth transition rule is introduced to make the behavior priority change, USVs can still maintain good formation performance, ensuring the smooth execution of oil spill recovery tasks. Simulation shows that FNSB behavior fusion formation method based on flexible transition rules can improve the rounding efficiency by 26.6% in the environment without obstacles and 37.2% in the environment with multiple obstacles compared with the NSB method. The effectiveness and practicality of this method have been verified through simulation experiments and field experiments on the “Dolphin” series of small USVs.

Excited-State Symmetry Breaking and Localization in a Noncentrosymmetric Electron Donor-Acceptor-Donor Molecule.

Electronic excitation in quadrupolar conjugated molecules rapidly localizes on a single electron donor-acceptor (DA) branch when in polar environments. The loss of center of inversion upon this excited-state symmetry breaking (ES-SB) can be monitored by exploiting the relaxation of the exclusion rules for IR and Raman vibrational transitions. Here, we compare ES-SB in a right-angled (1) and a centrosymmetric (2) DAD dyes using time-resolved IR spectroscopy. We show that the localization of the excitation can also be identified with the bent molecule 1. We find that contrary to dye 2, subpopulations with localized and delocalized excitation coexist for 1 in weak to medium polar solvents. This difference originates from the torsional disorder present in the excited state of 1 but not of 2. Additionally, irreversible localization in a bent molecule is shown to require higher solvent polarity than in a centrosymmetric one.

A multiclass logistic regression, Markov chain, and cellular automata integration for modelling urban land use change

Cellular Automata (CA) models employ simple transition rules and neighbourhood interactions to determine cells' evolution. Integrating CA with other algorithms is often essential in order to obtain necessary statistical inferences. The integration of Binary Logistic Regression (BLR) and Markov chain (MC) with CA forms common CA integrations. For convenience, urban land use change is often modelled as a binary process (e,g. ‘undeveloped land’ and ‘developed land’), whereas in the real world urban land use change phenomenon could go beyond two processes (e. g. ‘undeveloped land’, ‘sparsely developed land’, ‘semi-highly developed land’, ‘highly developed land’, ‘very highly developed land’, etc.). The BLR-MC-CA hybrid model yields a binary outcome. In order to extend the BLR-MC-CA hybrid model to multivariate problems, we introduce a novel hybrid model, by simply replacing the BLR with the new Multiclass Logistic Regression (MLR) to obtain the new MLR-MC-CA hybrid model. Satellite remote sensing imageries acquired in 1984, 2000, and 2020 are used as input data. Four scenarios of urban land use change are modelled: ‘nonurban region’, ‘highest urban region’, ‘high urban region’, and ‘medium urban region’. These four scenarios are modelled with twelve urban land use drivers: distance to water body, distance to residential areas, distance to industrial and commercial areas, distance to major roads, distance to railway, distance to Lagos Island, distance to international seaport, distance to University of Lagos, income potential, population potential, distance to international airport, and distance to Lagos State University. Urban transitions 1984–2000 and 2000–2020 are used to simulate the urban states in 2000 and 2020 respectively; as well as to obtain the urban state in 2060. The validation of the experiment is largely based on McNemar's test and Receiver Operating Characteristics (ROC) curves. The results of the experiment indicate significant fit between the reference and the predicted data.

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

A Review of Improved Methods for Ant Colony Optimization in Path Planning

_ Ant colony optimization is an intelligent evolutionary algorithm that has been widely used in the field of path planning, and the research on its improved methods is proliferating. In this paper, the improvement methods of single and hybrid ant colony optimization in path planning are reviewed in terms of initial pheromone concentration, heuristic function, state transition rule, pheromone update rule, and parameter optimization, respectively. The future research directions of ant colony optimization have been prospected in terms of theoretical aspects, parameter setting and optimization, integration with other algorithms, and multiobjectives. Keywords ant colony optimization; path planning; single ant colony optimization; hybrid ant colony optimization; improvement methods

Sustainability in the Public Healthcare Sector: Insights From an Analytical Hierarchy Process Analysis.

Background This study aimed to identify sustainability priorities within Oman's healthcare sector using the analytical hierarchy process (AHP) methodology. Mainly, it focused on assessing the relative importance of economic, environmental, and social factors and their sub-elements in sustaining Oman's healthcare system. Methodology A semi-quantitative, cross-sectional design was employed to collect data from 23 Omani healthcare experts with at least 10 years of experience in five different public hospitals in Oman. The AHP methodology was used to analyze pairwise comparisons of sustainability factors and derive their priorities. The consistency ratio was calculated to ensure the reliability of the analysis, and the transitivity rule was applied to address inconsistencies in pairwise comparisons. Results The findings revealed a strong emphasis on environmental and social sustainability, with economic considerations ranking comparatively lower. Circular practices emerged as a key priority within the environmental domain, while patient satisfaction stood out within the social domain. The analysis showed the integration between healthcare, environmental health, and societal well-being, emphasizing the need for holistic approaches to sustainability. Conclusions This study contributes to the understanding of healthcare sustainability by providing empirical evidence of stakeholder preferences within Oman's healthcare system. By quantitatively assessing the relative importance of economic, environmental, and social factors, it informs decision-making and resource allocation toward sustainable healthcare development. The findings also support the argument for integrated approaches to healthcare sustainability that balance economic efficiency with environmental protection and social inclusion.

Study of the Selection Rules of Molecular Polaritonic Transitions by Two-Photon Absorption Spectroscopy

Study of the Selection Rules of Molecular Polaritonic Transitions by Two-Photon Absorption Spectroscopy

Latent Profiles and Transitions of Bedtime Procrastination Among Chinese College Students: The Predictive Roles of Anxiety, Depression, Problematic Smartphone Use and Self-Control.

Bedtime procrastination (BP) has become an important factor affecting individual well-being. This study aimed to assess the stability and changes in BP and examine risk and protective factors. The study recruited 1423 respondents. Latent profile analysis was used to identify subgroups of BP and latent transition analysis to determine transition probabilities for each subgroup. Logistic regression examined associations between identified classes and related factors. Three subgroups of BP were identified. In terms of stability and changes, the moderate bedtime procrastination group showed the highest stability (66%), followed by the severe bedtime procrastination group (62.4%), and the mild bedtime procrastination group had a 52% probability of switching to moderate bedtime procrastination. In terms of influencing factors, more problematic phone use (PSU) (OR: 1.08; 95% CI = 1.05-1.12), more depression (OR: 1.17; 95% CI = 1.06-1.29) and anxiety (OR: 1.16; 95% CI = 1.05-1.28) are all factors that aggravate the transition from mild to moderate sleep procrastination. Similarly, PSU (OR: 1.15; 95% CI = 1.12-1.19), anxiety (OR: 1.10; 95% CI = 1.06-1.14), and depression (OR: 1.10; 95% CI = 1.06-1.14) increased the risk of severe bedtime procrastination. Self-control emerged as a protective factor against BP. This study identified three subgroups of BP at two time points and the rule of transition for each subgroup. Our findings indicate that BP were relatively stable, with some changes over time. The results also highlight the important function that PSU, depression, anxiety, and self-control can play in preventing and intervening in BP.

Adapting German energy transition rules for Iraq through industry, flexibility, and demand management

The study delves into the potential application of Germany energy transition methodologies to Iraq, emphasizing the industrial sector role, the imperativeness of adaptability, and the crucial role of demand response management. Germany achievements in evolving towards a greener and more efficient energy framework offer vital lessons for nations, such as Iraq, aiming for similar objectives. However, the contrasting socio-economic and energy landscapes of both countries call for distinct approaches. The study delves into these variances, exploring how Germany energy transition strategies can be tailored to Iraq unique situation. The discussion underscores the integration of the industrial sector in the transition and the requisite for a malleable strategy. Additionally, the article sheds light on the significance of adept demand response management, a cornerstone of Germany energy shift. Included is a case study that illustrates the customization of German methodologies for Iraq, exploring possible advantages and challenges. The outcomes act as a guide for Iraqi journey towards sustainable energy, drawing inspiration from Germany successful trajectory. The insights and suggestions presented might also resonate with other countries eyeing or initiating their energy metamorphosis, advancing the collective vision of a sustainable tomorrow.