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Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems.

Key Clinical Message Meningiomas are slow‐growing tumors that develop from the arachnoid cap cells' meningothelial cells. Males are more likely to develop intra‐parenchymal meningiomas, which also manifest earlier than ordinary meningiomas and are uncommon. Meningiomas are slow‐growing neoplasms which arise from the meningothelial cells of the arachnoid cap cells. Unlike other meningiomas, intra‐parenchymal meningiomas do not originate from dura. Intra‐parenchymal meningiomas are more common in males and develop earlier than regular meningiomas. Because of the rare occurrence the intra‐parenchymal meningiomas, they are commonly misdiagnosed. Figure A and B: CT scan showing a well marginated hyperdense extra‐axial lesion in the posterior aspect of left Sylvian fissure.

“Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.

Renewable energy sources like solar and wind face an insurmountable obstacle in the form of environmental change-induced discontinuity and instability. Since hydropower is quick to respond and doesn't cost much to alter, it was a common choice for electric energy system correction. A cascade hydropower (CHP) station compensates the hydro power-solar-wind energy system that we present in this study, which considers several long-term goals. Among the model's objectives is the optimization of the power system's annual total power generation while simultaneously minimizing power output variations. As a prerequisite for optimizing hydropower, this model first determines the total Photovoltaic (PV) and wind power, and then feeds those numbers into the power grid. In order to obtain a set of solutions for the model that has been proposed, we suggest an enhanced non-dominated sorting whale optimization algorithm (NSWOA). According to the findings, decision-makers have access to a plethora of options for optimal selection through the revised NSWOA, and hydropower's superior modifying capabilities more than compensate for the PV and wind power's deficiencies.

In the recent era, the rapidly increasing trend of e-commerce business creates opportunities for logistics service providers to grow globally. With this growth, the concern regarding the implementation of sustainability in logistic networks has received attention in recent years. Thus, in this work, we have focused on the vehicle routing problem (VRP) to deliver the products in a lesser time horizon with driver safety concern considerations in business (B2B) e-commerce platforms. We proposed a sustainable logistics network that captures the complexities of suppliers, retailers, and logistics service providers. A mixed-integer nonlinear programming (MINLP) approach is applied to formulate a model to minimize total time associated with order processing, handling, packaging, shipping, and vehicle maintenance. Branch-and-bound algorithms in the LINGO optimization tool and genetic algorithm (GA) are used to solve the formulated mathematical model. The computational experiments are performed in eight different case scenarios (small-sized problem to large-sized problem) to validate the model.

Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational, organizational, or regulatory factors. These changes, referred to as incremental concept drift, gradually alter the behavior or structure of processes, making their detection and localization a challenging task. Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift, particularly from a control-flow perspective. The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs, with a specific emphasis on the structural evolution of control-flow semantics in processes. We propose DriftXMiner, a control-flow-aware hybrid framework that combines statistical, machine learning, and process model analysis techniques. The approach comprises three key components: (1) Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals; (2) a Temporal Clustering and Drift-Aware Forest Ensemble (DAFE) to capture distributional and classification-level changes in process behavior; and (3) Petri net-based process model reconstruction, which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores. Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time. The framework achieves a detection accuracy of 92.5%, a localization precision of 90.3%, and an F1-score of 0.91, outperforming competitive baselines such as CUSUM + Histograms and ADWIN + Alpha Miner. Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures. DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic, process-aware systems. By integrating statistical signal accumulation, temporal behavior profiling, and structural process mining, the framework enables fine-grained drift explanation and supports adaptive process intelligence in evolving environments. Its modular architecture supports extension to streaming data and real-time monitoring contexts.

Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable to all mechanical devices that are susceptible to failure or operational degradation. The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation. The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature. It is observed that, in the settings without concept drift in the data, methods like SVM and Random Forest performed better compared to deep neural networks. However, this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift. In this perspective, DNN generated the stable drift detection method; it reported an accuracy of and an AUC of while detecting only a single drift point, indicating the stability to perform better in detecting and adapting to new data in the drifting environments under industrial measurement settings.

The absence, destruction, or loss of β-cells of pancreas results in type 1 diabetes (insulin-dependent diabetes mellitus [IDDM]). Presently, diagnosis and periodic monitoring of diabetes is achieved by evaluating blood glucose levels as it is relatively invasive and dreaded by children. In the light of this, present study was planned to compare salivary glucose values with blood glucose values and the biochemical characteristics of saliva in IDDM children were evaluated and obtained results were compared with the salivary parameters of normal children. Thirty IDDM children and 30 healthy children were selected for the study. Fasting blood sample and unstimulated salivary sample were collected from all the subjects and were subjected for analysis. A weak positive correlation was noticed between fasting blood glucose and salivary glucose values in IDDM children. But a mean average of salivary glucose was high in IDDM children when compared with healthy children. The biochemical parameters like acid phosphatase, total protein count, and α-amylase were increased, whereas salivary urea did not show significant variation between the groups. With presently used diagnostic armamentarium, estimation of salivary glucose cannot replace the standard method of estimation of glucose in diabetic mellitus children. The established relationship was very weak with many variations.

This paper presents the design methodology of a permanent magnet motor with line-start capability, developed specifically for single-stage centrifugal pump applications. This motor configuration integrates the direct-on-line (DOL) start-up reliability of squirrel cage induction motors with the high efficiency synchronous permanent magnet machines. The design aims to enable seamless transition from asynchronous startup to synchronous operation without the use of external controllers. Electromagnetic parameters are evaluated through analytical modelling, and the design is further validated using Finite Element Analysis (FEA). The rotor is optimized through techniques such as magnetic skewing and core shaping to minimize torque ripple and enhance transient response, meeting out IE4 efficiency.

This study aims to evaluate the clinical, radiographic, and histological success rate of antioxidant mix as a new pulpotomy agent for primary teeth. Commercially available antioxidants, namely Antioxidants plus trace elements (OXIn-Xt(tm), India) were used. This prospective study was carried out on 36 primary molar teeth in 32 children, with age that ranged from 6 to 9 years. Regular conventional pulpotomy procedure followed by placement of antioxidant mix over the radicular orifice was done. Recall was scheduled for 3, 6, and 9 months, respectively, after treatment. Thirty-six pulpotomized primary molars were available for follow-up evaluations. Scanning electron microscopy analysis of samples showing convex shaped hard tissue barrier formation may be proof of the role of antioxidant material in localization and direction and morphology of the hard tissue barrier. One tooth which presented with pain was assessed as unsuccessful. Quite promising clinical, radiographic, and histological results of antioxidants in the present study shows their potential to be an ideal pulpotomy agent.