Carbazole-based molecules play a significant role in dye-sensitized solar cells (DSSCs) due to their advantageous properties. Carbazole derivatives are known for their thermal stability, high hole-transport capability, electron-rich (p-type) characteristics, elevated photoconductivity, excellent chemical stability, and commercial availability. This review focuses on DSSCs, including their structures, working principles, device characterization, and the photovoltaic performance of carbazole-based derivatives. Specifically, it covers compounds such as 2,7-carbazole and indolo[3,2-b]carbazole, which are combined with various acceptors like benzothiadiazole, thiazolothiazole, diketopyrrolopyrrole, and quinoxaline, as reported over the past decade. The review will also outline the relationship between molecular structure and power-conversion efficiencies. Its goal is to summarize recent research and advancements in carbazole-based dyes featuring a D-π-A architecture for DSSCs. Additionally, this review addresses the evolution of carbazole-based hole-transport materials (HTMs), which present a promising alternative to the costly spiro-OMeTAD. We explore the development of novel HTMs that leverage the unique properties of carbazole derivatives to enhance charge transport, stability, and overall device performance. By examining recent innovations and emerging trends in carbazole-based HTMs, we provide insights into their potential to reduce costs and improve the efficiency of DSSCs.

This study reports the gas-phase homolytic P–H BDEs of a set of 30 phosphine-type oxides (i.e., R¹R²P(=O)H) obtained using the W1w thermochemical protocol. We note that the P–H BDEs (at 298 K) of the species in this dataset differ by as much as 157.2 kJ mol^–1, with (H₂B)₂P(=O)H having the lowest BDE (249.3 kJ mol^–1) and F₂P(=O)H having the highest (406.5 kJ mol^–1). Furthermore, using the full set of 30 all-electron, non-relativistic, vibrationless bottom-of-the-well W1w P–H BDEs as reference values, we have identified several well-performing DFT methods that could be applied to the computation of the P–H BDEs of phosphine-type oxides. The best-performing DFTs (in conjunction with the A'VTZ basis set) were shown to be MN12-SX (MAD = 1.7 kJ mol^–1) and MN12-L (MAD = 2.7 kJ mol^–1).

Suicide is a major global public health concern, and the application of artificial intelligence (AI) methods, such as natural language processing (NLP), machine learning (ML), and deep learning (DL), has shown promise in advancing suicide prediction and prevention efforts. Recent advancements in AI – particularly NLP and DL have opened up new avenues of research in suicide prediction and prevention. While several papers have reviewed specific detection techniques like NLP or DL, there has been no recent study that acts as a one-stop-shop, providing a comprehensive overview of all AI-based studies in this field. In this work, we conduct a systematic literature review to identify relevant studies published between 2019 and 2023, resulting in the inclusion of 156 studies. We provide a comprehensive overview of the current state of research conducted on AI-driven suicide prevention and prediction, focusing on different data types and AI techniques employed. We discuss the benefits and challenges of these approaches and propose future research directions to improve the practical application of AI in suicide research. AI is highly capable of improving the accuracy and efficiency of risk assessment, enabling personalized interventions, and enhancing our understanding of risk and protective factors. Multidisciplinary approaches combining diverse data sources and AI methods can help identify individuals at risk by analyzing social media content, patient histories, and data from mobile devices, enabling timely intervention. However, challenges related to data privacy, algorithmic bias, model interpretability, and real-world implementation must be addressed to realize the full potential of these technologies. Future research should focus on integrating prediction and prevention strategies, harnessing multimodal data, and expanding the scope to include diverse populations. Collaboration across disciplines and stakeholders is essential to ensure that AI-driven suicide prevention and prediction efforts are ethical, culturally sensitive, and person-centered.

Classification of animal species using animal sounds is a critical issue for bioacoustics work. Especially the determination of anurans (frogs or toads) species can be used as an indicator of climate change. However, counting and classifying anurans in their natural habitat is challenging. Therefore, computer-assisted intelligent systems must be used to determine anuran types correctly. This work collected a new anuran sound dataset and proposed a hand-modeled sound classification system. The collected dataset contains 1536 anuran sounds belonging to 26 anuran species. Furthermore, an improved one-dimensional local binary pattern (1D-LBP) and Tunable Q Wavelet Transform (TQWT) based feature extraction method has been proposed to generate features at both frequency and space domains. Our proposed hand-modeled anuran sound classification architecture comprises of feature extractor (TQWT + improved 1D-LBP), iterative neighborhood component analysis (INCA) selector and k nearest neighbor (kNN) classifier. Our proposed 1D-LBP and TQWT-based anuran sound classification model has obtained a classification accuracy of 99.35% in classifying 26 anuran species. Moreover, we discussed explainable results. In the future, we plan to validate this work by increasing more species in each group.

Background: Epilepsy is one of the most commonly seen neurologic disorders worldwide and has generally caused seizures. Electroencephalography (EEG) is widely used in seizure diagnosis. To detect epilepsy automatically, various machine learning (ML) models have been introduced in the literature, but the used EEG signal datasets for epilepsy detection are relatively small. Our main objective is to present a large EEG signal dataset and investigate the detection ability of a new hypercube pattern-based framework using the EEG signals.

Material and method: This study collected a large EEG signal dataset (10,356 EEG signals) from 121 participants. We proposed a new information fusion-based feature engineering framework to get high classification performance from this dataset. The dataset consists of 35 channels, and our proposed feature engineering model extracts features from each channel. A new hypercube-based feature extractor has been proposed to generate two feature vectors in the feature extraction phase. Various statistical parameters of the signals have been used to create a feature vector. Multilevel discrete wavelet transform (MDWT) has been applied to develop a multileveled feature extraction function, and seven feature vectors have been extracted. In this work, we have extracted 245 (=35 × 7) feature vectors, and the most valuable features from these vectors have been selected using the neighborhood component analysis (NCA) selector. Finally, these selected features were fed to the k nearest neighbors (kNN) classifier with the leave one subject out (LOSO) cross-validation (CV) strategy. These results have been voted/fused to obtain the highest classification performance. Results: In this work, we have attained 87.78% classification accuracy using voting these vectors and 79.07% with LOSO CV with the EEG signals.

Conclusions: The proposed fusion-based feature engineering model achieved satisfactory classification performance using the largest EEG signal datasets for epilepsy detection.