Abstract
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), learns by training and is restricted by the amount and quality of training data. Training involves a tradeoff between prediction bias and variance controlled by model complexity. Increased model complexity decreases prediction bias, increases variance, and increases overfitting possibilities. Overfitting is a significantly smaller training prediction error relative to the trained model prediction error for an independent validation set. Uncertain data generate risks for AI–ML because they increase overfitting and limit generalization ability. Specious confidence in predictions from overfit models with limited generalization ability, leading to misguided water resource management, is the uncertainty-related negative consequence. Improved data is the way to improve AI–ML models. With uncertain water resource data sets, like stream discharge, there is no quick way to generate improved data. Data assimilation (DA) provides mitigation for uncertainty risks, describes data- and model-related uncertainty, and propagates uncertainty to results using observation error models. A DA-derived mitigation example is provided using a common-sense baseline, derived from an observation error model, for the confirmation of generalization ability and a threshold identifying overfitting. AI–ML models can also be incorporated into DA to provide additional observations for assimilation or as a forward model for prediction and inverse-style calibration or training. The mitigation of uncertain data risks using DA involves a modified bias–variance tradeoff that focuses on increasing solution variability at the expense of increased model bias. Increased variability portrays data and model uncertainty. Uncertainty propagation produces an ensemble of models and a range of predictions.

Abstract
Population ageing is the most important social demographic and healthcare challenge worldwide. There is an increased demand for long-term assistance, particularly institution-based services, for older adults. This study aimed to synthesize qualitative evidence on the transition experience of new residents to nursing homes. A systematic search and meta-synthesis were performed in PubMed, Embase, CINAHL, China National Knowledge Infrastructure, and China Wanfang Database from their inception to February 2024. The CASP evaluation for qualitative studies was used for quality appraisal, while meta-aggregation guided data analysis. Results showed twenty studies concerned the following topics: the pressure and challenges, the transition process, and the needs and expectations of new residents. The analysis identified 77 well-defined findings, eight categories, and three synthesized insights. The transition of new residents to nursing homes is dynamic and can be influenced by key factors, including personal philosophy, maintaining identity, and accepting one’s situation. Families, institutions, and other stakeholders must work together to provide personalized care, services, and satisfactory social support for new residents. Tailored intervention research is required in the future to promote nursing home adjustment.

Abstract
This study focuses on parameter optimization and detection signal comparison of planar coil
particle detection sensors, and conducts in-depth analysis of different core materials. Through finite
element simulation analysis and experimental verification, the influence of coil turns, diameter,
thickness and other parameters on magnetic field distribution and particle detection signals was
systematically explored, and the sensor chip structure design was optimized. The study found that a
planar coil with 60 turns and an inner diameter of 1.2 mm performed best. At the same time, the
enhancement effect of different magnetic core materials (including iron core, silicon steel sheet and
permalloy) on the magnetic field and detection signal was investigated. It was found that the
permalloy magnetic core can significantly improve the sensor performance. Copper particles have
effective detection capabilities. This research provides theoretical and experimental basis for
optimizing the detection sensor of metal particle contaminants in oil, and has important academic
value and application prospects.

Abstract
Improving energy management is essential for sustainability since building energy use is rising.
This study is a trailblazer in using multi-criteria decision-making (MCDM) methodologies for the
real-time operational optimization of building energy systems. Data collection and pre-processing,
feature extraction, feature selection, classification, trust authentication, encryption, and decryption
are among the techniques used in this approach. Pre-processing procedures for the raw data include
feature encoding, dimension reduction, and normalization approaches. The Hybrid Grey Level Cooccurrence
Matrix Fast Fourier Transform (HGLCM-FFT) method is used for feature extraction.
Filter-based methods are used for feature selection, including IG, CS, symmetric uncertainty, and
gain ratio. The Hierarchical Gradient Boosted Isolation Forest (HGB-IF) technique is used for the
classification. Distributed Adaptive Trust-Based Authentication (DAT-BA), a security architecture
in distributed cloud environments, uses trust authentication. The Particle Swarm Optimized
Symmetrical Blowfish (PSOSB) method is used for encryption and decryption. The suggested work
is implemented using OS Python – 3.9.6; the performance of the proposed model is Attack
Detection Rate, False alarm rate, True positive rate, Network usage, CPU usage, Encryption time,
encryption time and Throughput.