As a contribution to the graphene-based nanoferrite composites, this article is intended to present Mn, Co, and Co-Mn nanoferrites for the preparation and investigation of such samples. Nanoparticles of Co ferrite, Mn ferrite, and Co-Mn ferrite were chemically synthesized by the coprecipitation method. The composites of ferrite/graphene were made by incorporating weight ratios of 25% graphene to 75% ferrite. Various structural and characterizing investigations of ferrite samples and ferrite/graphene composites were performed, including XRD, EDX, SEM, VSM hysteresis loops, AC conductivity, and dielectric behavior. The investigations ensured the formation of the intended nanoferrite powders, each having a single-phase crystal structure with no undesired phases or elements. All samples exhibit a soft magnetic behavior. They show a semiconducting behavior of AC electrical conductivity as well. This was proved by the temperature dependence of the AC’s electrical conductivity. Whereas the dielectric function and loss tangent show an expected, well-explained behavior, the ferrite/graphene composite samples have lower saturation magnetization values, lower AC conductivity, and dielectric constant values than the pure ferrites but still have the same behavior trends as those of the pure ferrites. The values obtained may represent steps on developing new materials for expected applications, such as manufacturing supercapacitors and/or improved battery electrodes.

Microplastics (MP) were recognized as an emergent pollution problem due to their ubiquitous nature and bioaccumulative potential. Those present in salt for consumption could represent a human exposure route through dietary uptake. The current study, conducted in Bangladesh, reports microplastics contamination in coarse salt prepared for human consumption. Sea salt samples were collected from eight representative salt pans located in the country's largest salt farming area, in the Maheshkhali Channel, along the Bay of Bengal. Microplastics were detected in all samples, with mean concentrations ranging from 78 ± 9.33 to 137 ± 21.70 particles kg⁻¹, mostly white and ranging in size from 500–1000 µm. The prevalent types were: fragments (48%) > films (22%) > fibers (15%) > granules and lines (both 9%). Fourier transform mid-IR and near-IR spectra (FT-MIR-NIR) analysis registered terephthalate (48%), polypropylene (20%), polyethylene (17%), and polystyrene (15%) in all samples. These results contribute to the MP's pollution knowledge in sea salts to understand and reduce this significant human exposure route and environmental pollution source in the future.

The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event extraction has become a research hotspot in academia. Many academic conferences publish it as an evaluation task and provide a series of high-quality annotation data. This article aims at the characteristics of discrete attributes of tumor-related medical events and proposes a medical event. The standard extraction method realizes the combined extraction of the primary tumor site and primary tumor size characteristics, as well as the extraction of tumor metastasis sites. In addition, given the problems of the small number and types of annotation texts for tumor-related medical events, a key-based approach is proposed. A pseudo-data-generation algorithm that randomly replaces information in the whole domain improves the transfer learning ability of the standard extraction method for different types of tumor-related medical event extractions. The proposed method won third place in the clinical medical event extraction and evaluation task of the CCKS2020 electronic medical record. A large number of experiments on the CCKS2020 dataset verify the effectiveness of the proposed method.

Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite the use of different artificial methods for cultivation, hydroponic approaches using the IoT prove to give the best results. The presented study consists of potential artificial approaches used for cultivation and the selection of hydroponics as the best approach out of these based on different parameters. This paper also provides a comparative analysis of six present hydroponic approaches. The research work on different factors of saffron, such as the parameters responsible for growth, reasons for the decline in growth, and different agronomical variables, has been shown graphically. A smart hydroponic system for saffron cultivation has been proposed using the NFT (nutrient film technique) and renewable sources of energy.

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.