Search Results (21,088)

The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse configurations feasible within an acceptable cost range. This study proposes a hierarchical inverse-analysis framework integrating a genetic algorithm (GA) and linear programming (LP). The upper-level GA explores a broad space of exogenous conditions, including selected fuel-price assumptions, technology-cost conditions, equipment capacities, end-use electrification rates, CO₂-capture installation rates, and CO₂-storage limits, while the lower-level LP rigorously optimizes operations for each candidate. The framework applies explainable AI (SHAP) to identify dominant cost-determining factors and their interactions, and employs k-means clustering to compress the high-dimensional feasible solution space into illustrative archetypes. As an illustrative demonstration, the framework is applied to a hypothetical 2050 net-zero case for the Kanto region. The framework, under the assumed conditions, generates diverse feasible solutions, identifies influential cost-related conditions and their interactions, and organizes the generated solution set into five illustrative archetypes. The proposed framework extends energy system modeling beyond single-optimum solutions toward interpretable decision-support analytics for long-term net-zero planning under deep uncertainty. Full article

In the highly volatile cryptocurrency market, trading decision support based on price prediction remains a challenging task. Although machine learning and deep learning techniques have been widely applied to cryptocurrency price prediction, many existing approaches rely on correlation-based black-box models, which limits interpretability and robustness. In this study, we employed a NOTEARS-Linear-based Prediction Model (NLBPM) that directly incorporated causal structures inferred through a causal discovery method as structural constraints within the prediction model. Unlike conventional approaches that focus primarily on minimizing prediction error, the NLBPM emphasized return maximization as its objective function, thereby prioritizing practical economic value. Using Bitcoin as a case study, we constructed a model to predict the direction of price movement four hours ahead and evaluated its performance using a rolling-window scheme with a one-month sliding window. Analysis of the inferred causal structures showed that the returns improved when trades were executed only during rolling-window trials in which specific directed edges to the target variable were detected. Based on this finding, we proposed a causal filter strategy that restricts trading to periods in which specific directed edges to the target variable are detected. In the data period analyzed in this study, the selected edge was the one from the opening price (Open) to the target variable. Backtesting experiments incorporating a transaction fee of 0.1% demonstrated that, while the benchmark LSTM model achieved a negative monthly average return of −3.20% and the NLBPM without filtering yielded −0.72%, the NLBPM with the Open filter attained a higher monthly average return of 10.35%. This study supports the usefulness of using inferred causal structure for cryptocurrency trading decision support. Full article

Urban centers today face unprecedented challenges in energy management, emissions, waste disposal, and public services, as nearly 70% of the global population is projected to live in cities by 2050. The complexity and rapid evolution of urban systems underscore the pressing need for stability, innovation, and adaptability, particularly regarding sustainability and digital transformation. AI-powered simulation models have emerged as transformative tools, capable of simplifying, predicting, and managing highly intricate urban systems while offering policymakers valuable insights for strategic planning. However, the integration of AI in smart and sustainable urban development presents critical legal, ethical, and regulatory concerns. This study examines these questions by evaluating existing and emerging frameworks addressing algorithmic transparency, data protection, stakeholder engagement, and sustainable development in prominent urban models including Amsterdam, Copenhagen, Singapore, Tokyo, Bangalore, and Nairobi. Comparative analysis is conducted through a doctrinal desk review, focusing on statutory provisions, international policies (EU, UN-Habitat), ISO Smart City standards, and local governance charters. Key issues addressed include the risk that AI models, if unregulated, become opaque “black boxes” that obscure both decision-making logic and accountability. Without robust standards, there is no guarantee of interoperability, revision, or representation of public interest. Equitable management, access, and inclusive participation are vital to responsible AI frameworks in urban planning. This article advances a comprehensive legal and policy framework for ensuring accountability, transparency, and stability in AI-driven city governance, bridging gaps between technological innovation, urban studies, and regulatory oversight. The proposed governance structure empowers cities to adopt multi-level, authority-driven mechanisms that safeguard the common good while leveraging AI’s potential in sustainable urban transformation. Full article

While plug-in hybrid electric vehicles (PHEVs) can significantly reduce fuel consumption and emissions, their real-world benefits strongly depend on heterogeneous driver usage patterns. Understanding these usage patterns is therefore essential for optimizing energy management and electrification policies. This study presents a data-driven framework for identifying and characterizing PHEV driving behavior using two primary indicators: the Utility Factor (UF), which quantifies the proportion of electric-mode driving, and the annual vehicle kilometers traveled (VKT), which measures driving intensity. A Gaussian Mixture Model (GMM) is employed in a transformed feature space characterized by the logit of UF and the logarithm of VKT to capture nonlinear relationships and diverse usage patterns. The Bayesian Information Criterion (BIC) is used to find the optimal number of behavioral clusters. To assess the robustness of the clustering, we perform a bootstrap stability analysis and compare it with k-means and a density-based clustering method (DBSCAN). Based on an analysis of real-world PHEVs, three distinct usage patterns are identified. The dominant cluster (96.6%) exhibits moderate electric usage (UF = 0.38) with an annual mileage of 18,617 km and fuel consumption of 3.89 L/100 km, whilst two smaller clusters represent near-full electric operation (2.3%, UF about 1.0, negligible fuel) and low-mileage users (1.1%, approximately 2444 km/year). The clustering demonstrates high assignment confidence (posterior entropy $<{10}^{-4}$) and moderate stability, with a mean Adjusted Rand Index (ARI) of 0.448. The findings indicate that the proposed probabilistic clustering framework offers a comprehensible and statistically robust method for identifying diverse PHEV usage patterns. These insights can support adaptive energy management strategies, effective planning of charging infrastructure, and evidence-based policies to maximize the real-world electrification benefits of PHEVs. Full article

Background and Clinical Significance: Lambert–Eaton myasthenic syndrome (LEMS) is mediated by antibodies against P/Q-type voltage-gated calcium channels (VGCCs) and is classified as paraneoplastic (T-LEMS) or non-paraneoplastic (NT-LEMS). Cerebellar degeneration is recognized in T-LEMS, but pathological confirmation in NT-LEMS has not been reported. Case Presentation: A 79-year-old man developed progressive ataxic gait and dysarthria at age 76 and was diagnosed with LEMS based on repetitive nerve stimulation findings and anti-P/Q-type VGCC antibodies. No malignancy was identified during more than 40 months of surveillance, and comprehensive autopsy revealed no occult tumor. After hospitalization for erythroderma and pneumonia, he died of respiratory failure. Postmortem examination revealed severe Purkinje cell loss with Bergmann gliosis in the anterior lobe and tuber vermis, accompanied by torpedoes and empty baskets, without significant inflammation. These findings indicate that NT-LEMS can reach the same VGCC-associated Purkinje cell endpoint previously documented only in paraneoplastic LEMS, despite different upstream triggers. Conclusions: This first autopsy-confirmed case of NT-LEMS with cerebellar degeneration supports a shared, non-inflammatory VGCC-mediated pathway of Purkinje cell injury across LEMS subtypes. Full article

Background/Objectives: This study aimed to identify dietary patterns among community-dwelling older adults in northern Thailand and examine their associations with nutrient intake, serum biomarkers, and anthropometric indicators. Methods: A cross-sectional study was conducted among 117 older adults in Lampang and Chiang Mai provinces in Northern Thailand. Dietary intake was assessed using a semiquantitative food frequency questionnaire. Dietary patterns were derived using principal component analysis, and participants were classified into mutually exclusive dietary pattern groups based on their highest factor scores, which was defined as the dominant pattern. Differences in nutrient intake were evaluated using the Kruskal–Wallis test. Serum biomarkers and anthropometric indicators were assessed using an analysis of covariance adjusted for age, sex, residential area, total energy intake, and body mass index. Results: Four dietary patterns were identified: diverse traditional, processed staple-based, tropical fruit, and a Western diet high in fats and sweets. Dietary fiber intake differed significantly among the four patterns, with the highest level in the diverse traditional and lowest in the processed staple patterns. No significant differences were observed in total energy or major nutrient intake. Total cholesterol levels differed significantly, with lower levels in the traditional diet pattern than in the tropical fruit and Western diet patterns. Conclusions: Dietary patterns among older adults in northern Thailand may be associated with dietary fiber intake and serum total cholesterol levels. A traditional diet rich in vegetables and fish may be linked to more favorable nutrient intake and lipid profiles. Full article

This study established a sensor structure and readout circuit for identifying the point of force application within a single cable piezoelectric sensor. The sensor is highly flexible and enables wide-area sensing because it can be fabricated in long, continuous form. To determine the point of force application, the central electrode of the cable sensor was designed to have high resistance, and charge amplifiers were connected to both ends of the cable. The generated charge was divided and measured by the two amplifiers in a proportion corresponding to the point of force application. The position was predicted using a normalized ratio calculated from the charge quantities measured by the two amplifiers. The principle was first verified by accurately identifying the loaded section in a five-segment cable connected through discrete resistors. For cable sensors with a high-resistance central electrode, we predicted the point of force application with a root mean square error of 25 mm by interpolating the relationship between the point of force application and the normalized charge ratio with a smoothing-spline calibration model. Full article

This paper introduces quantum risk measurement models focusing on the evolution of risk observation over time triggered by the arrival of information. These models address the contextuality of risk-related decision-making with greater flexibility and greater coherence than conventional approaches. A typical example of such information is the commencement of external support for a risk-sharing pool. This paper investigates the amplification and attenuation of perceived risk within risk-sharing pools, driven by the arrival of exogenous information using open system quantum theory models. Rather than adopting a closed system framework—where the quantum model employs a Hermitian Hamiltonian resulting in unitary time evolution—an open system approach is implemented utilizing a non-Hermitian Hamiltonian and non-unitary time evolution to describe the dynamics of risk observation. The perceived risk is measured by a quantum risk measure operator. While unitary time evolution preserves the sum of the eigenvalues of this operator, keeping the magnitude of the expectation value under strict constraints, our proposed open system framework breaks these limitations. By employing a non-Hermitian Hamiltonian and non-unitary transformations, the model captures the risk dynamics of amplification or attenuation of the size more realistically, allowing exogenous information to alter the scale of observed risk fundamentally, flexibly, and significantly. Agent-based models provide less structural insight, and the jump diffusion models do not treat mind state interactions. Numerical simulations demonstrate that this model successfully accounts for both risk amplification and attenuation—phenomena that occur naturally in the real world but cannot be explained by unitary transformations. The time evolution described by the Schrödinger equation shows step-by-step effects of perceived risk size changes and risk-based interactions. Practical applications include scenarios where new informational shocks alter the perceived severity of risk, such as the formalization of risk pooling rules or the establishment of new regulatory frameworks. Full article

Although the use of three-dimensional-printed (3D-printed) denture base resins is becoming increasingly widespread in digital dentistry, information regarding the reinforcing effects of cellulose nanofibers and their most favorable concentration remains limited. We evaluated the effects of adding cellulose nanofibers (CNFs, 0–2.0 wt%) on the mechanical and physical properties of three-dimensional-printed denture base resins. The addition of cellulose nanofibers improved flexural strength (FS) and Vickers hardness (HV), with more balanced performance observed at low concentrations; no significant differences were observed in flexural modulus. Color stability did not show a linear concentration-dependent trend, and the 1.0 wt% group exhibited the most favorable overall performance. Water sorption slightly increased at higher CNF concentrations. SEM observations revealed relatively uniform CNF dispersion at lower concentrations, whereas agglomeration and void formation occurred at higher concentrations. Appropriate CNF incorporation may be a promising strategy for reinforcing 3D-printed denture base materials. Full article

Pandemic response requires not only epidemiological control but also the allocation of limited social resources across competing uses. This paper develops an integrated economic–epidemiological framework to examine how resources should be allocated among goods production, preventive intervention, and therapeutic intervention during an infectious disease outbreak. Building on the susceptible–infected–recovered (SIR) model, the analysis treats the infection rate and the recovery rate as policy-sensitive variables shaped by preventive and therapeutic resource allocation. The objective is intentionally parsimonious and focuses on output preservation and resource allocation under epidemic constraints; infections affect the economy indirectly by reducing effective labor input and output. Two epidemiological environments are considered: one with permanent immunity after recovery and another with possible reinfection. The results reveal a robust timing pattern across both environments. Preventive allocation tends to peak before the surge in infections, whereas therapeutic allocation tends to move more closely with the infection trajectory. The analysis also makes explicit the opportunity cost of intervention: allocating more resources to prevention or treatment reduces the resources available for goods production. Phase-diagram representations clarify the mechanism behind this timing distinction, and sensitivity analyses over alternative curvature parameters confirm that the qualitative ordering of the peaks is robust. These findings suggest that the effectiveness of pandemic response depends not only on the total amount of intervention resources, but also on their timing and functional allocation. By linking epidemic dynamics, resource scarcity, and policy timing within a unified optimization framework, the paper contributes to economic–epidemiological modeling and offers implications for pandemic preparedness, health-system resilience, and the design of response strategies for future infectious disease emergencies. Full article

Peritoneal metastasis (PM) is the most frequent and lethal pattern of dissemination in gastrointestinal malignancies. Despite advances in systemic chemotherapy, outcomes remain poor because the unique biology of PM, characterized by poor vascularization and the peritoneal–plasma barrier (PPB), limits drug penetration and contributes to treatment resistance. To address these challenges, several locoregional treatment strategies have been developed, including cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy (CRS + HIPEC) and pressurized intraperitoneal aerosol chemotherapy (PIPAC). However, their widespread adoption is constrained by invasiveness, strict patient selection, and inconsistent survival benefits. Normothermic intraperitoneal and systemic treatment (NIPS) has emerged as a practical and less invasive alternative, particularly in East Asia. Through an implanted intraperitoneal port, NIPS enables repeated drug administration, providing sustained regional exposure while imposing minimal procedural burden. Importantly, it can be readily integrated with systemic chemotherapy, making it suitable for long-term multimodal treatment. Among available agents, paclitaxel (PTX) is particularly well suited for intraperitoneal administration because of its prolonged retention within the peritoneal cavity and limited systemic absorption. These pharmacokinetic properties allow high local drug concentrations with relatively low systemic toxicity. Consequently, PTX-based NIPS represents a biologically rational and clinically feasible treatment strategy for PM. This review summarizes the pharmacological rationale, clinical evidence, and emerging innovations in drug formulation and delivery that may further enhance the efficacy of PTX-based intraperitoneal chemotherapy for this challenging disease. Full article

Background: The Physical Internet (PI) promotes resource sharing among independent firms. This can improve logistics efficiency, but shared route capacity and limited compensation may also create unequal outcomes among firms. Methods: This study develops a framework for coordinated logistics planning under shared route capacity constraints. The framework includes two coordination rules. Model 3.3 is an efficiency-oriented participation-guaranteeing rule with individual rationality constraints. Model 3.4 is a fairness-oriented rule that minimizes the maximum firm-level disadvantage under a limited compensation budget. Numerical experiments are conducted using a stylized Japanese domestic consumer goods distribution network. Results: Coordinated planning reduces total logistics cost compared with decentralized sequential allocation. Model 3.3 achieves the lowest system cost but gives benefits unevenly. Model 3.4 gives more balanced firm-level outcomes and improves the worst-off firm in the tested scenarios. The results also show that substantial fairness improvements can be obtained through small route-allocation changes. Conclusions: The study shows how two coordination rules can be used in PI-oriented logistics coordination. Model 3.3 is useful when firms need a no-loss guarantee, especially in an early stage. Model 3.4 is useful in a mature or repeated coordination stage, where the platform needs to avoid excessive disadvantage. Full article

Economic well-being is essential for assessing sustainability of human settlement in urbanizing regions; however, the geographic factors linking settlement characteristics to residents’ well-being remain underexplored, particularly in counties in China undergoing urban–rural transformation. In this study, six representative Chinese counties (Yanshou, Wafangdian, Bazhou, Yugan, Yongsheng, and Raoping) with varying urbanization levels are investigated to establish a multidimensional evaluation framework and reveal the geographic factors underlying economic well-being. Through original household surveys conducted across these six geographically and economically diverse counties, we collected primary data from 1659 households; these data provide unique insights into residents’ lived experiences. By integrating these original survey data with objective indicators from statistical yearbooks and geographic features from multisource spatial data, key drivers were identified using Pearson correlation and random forest models. The results show the following trends: (1) significant county-level variation in subjective well-being, with Wafangdian ranking the highest and Bazhou ranking the lowest, while well-being aligned more closely with economic development levels; (2) income and happiness were the dominant determinants of subjective well-being, with work-related factors also contributing substantially, whereas nighttime light intensity, building density, and construction land area drove fusion well-being; and (3) multifactor modeling demonstrated strong explanatory power for fusion well-being (training set R² = 0.8313; validation set R² = 0.7531), indicating generalizability. The primary data collection across varied settlement settings provides strong empirical grounding. The findings reveal the spatial differentiation of economic well-being in urbanizing settlements, offering empirical support for targeted settlement planning and urban governance policies to improve sustainability and residents’ well-being in developing countries. Full article

Background: Cracked teeth may present with variable and atypical symptoms, sometimes mimicking non-odontogenic orofacial pain conditions, making diagnosis challenging, particularly when cracks appear limited to enamel. Case presentation: A 36-year-old woman presented with intermittent pain in a mandibular molar radiating to the ipsilateral temporal region. Clinical examination identified a crack line on the lingual surface of the mandibular first molar. Pulp sensibility testing (cold test and electric pulp test), occlusal loading tests, and cone-beam computed tomography (CBCT) were performed. CBCT was used primarily to exclude vertical root fracture and periapical pathology, and no radiographic abnormalities were identified. Differential diagnosis was conducted using structured diagnostic frameworks, including the International Classification of Orofacial Pain and the International Classification of Headache Disorders (3rd edition). Diagnostic local anaesthesia eliminated both biting pain and referred pain, supporting an odontogenic source. Collectively, the findings suggested that the enamel crack was the most likely source of odontogenic pain, although a definitive causal relationship could not be established. Because pulp sensibility remained normal, conservative management was selected. Crack sealing with a methyl methacrylate-based adhesive resin resulted in complete symptom resolution that was maintained throughout a 3-year follow-up period without the need for root canal treatment. Conclusions: Although the diagnosis remained probabilistic, the structured diagnostic approach, together with the favourable clinical response after crack sealing, supported the enamel crack as the most likely source of odontogenic pain. Full article

Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application. Full article