Research progress in improving sludge dewaterability: sludge characteristics, chemical conditioning and influencing factors.

Sludge from wastewater treatment processes with high water content and large volume has become an inevitable issue in environmental management. Due to the challenging dewatering properties of sludge, current mechanical dewatering methods are no longer sufficient to meet the escalating water content standards of sludge. This paper summarizes the characteristics of various sludge and raises reasons for the their dewaterability differences. Affected by extracellular polymeric substances, biological sludge is hydrophilic and negatively charged, which limits the dewatering degree. The rheological properties, flocs, ionic composition, and solid phase concentration of the sludge also influence the dewatering to some extent. For these factors, the chemical conditioning measures with simple operation and excellent effect improve its dewaterability, which mainly include flocculation/coagulation, acid/alkali treatment, advanced oxidation, surfactant treatment and combined treatment. There is a growing necessity to explore the development of new chemical conditioning agents, even though traditional agents continue to remain widely used. However, the development of these new agents should prioritize finding a balance between various factors such as efficiency, effectiveness, ease of operation, environmental safety, and cost-effectiveness. Electrochemical dewatering enhances solid-liquid separation, and its coupling with chemical conditioning is also an excellent means to further reduce water content. In addition, the improvement of press filter is an effective way, which is influenced by pressure, processing time, sludge cake thickness and pore structure, filter media etc. In general, it is essential to develop new conditioning agents and enhance mechanical filtration press technology based on a thorough understanding of various sludge properties. Concurrently, an in-depth study of the principles of mechanical pressure filtration will contribute to establishing a theoretical foundation for effective deep sludge dewatering and propel further advancements in this field.

Automatic Kidney Stone Composition Analysis Method Based on Dual-energy CT.

BACKGROUND: The composition of kidney stones is related to the hardness of the stones. Knowing the composition of the stones before surgery can help plan the laser power and operation time of percutaneous nephroscopic surgery. Moreover, patients can be treated with medications if the kidney stone is compounded by uric acid before treatment, which can relieve the patients of the pain of surgery. However, although the literature generally reports the kidney stone composition analysis method base on dual-energy CT images, the accuracy of these methods is not enough; they need manual delineation of the kidney stone location, and these methods cannot analyze mixed composition kidney stones. OBJECTIVE: This study aimed to overcome the problem related to identifying kidney stone composition; we need an accurate method to analyze the composition of kidney stones. METHODS: In this paper, we proposed the automatic kidney stone composition analysis algorithm based on a dual-energy CT image. The algorithm first segmented the kidney stone mask by deep learning model, then analyzed the composition of each stone by machine learning model. RESULTS: The experimental results indicate that the proposed algorithm can segment kidney stones accurately (AUC=0.96) and predict kidney stone composition accurately (mean Acc=0.86, mean Se=0.75, mean Sp=0.9, mean F1=0.75, mean AUC=0.83, MR (Exact match ratio)=0.6). CONCLUSION: The proposed method can predict the composition and location of kidney stones, which can guide its treatment. Experimental results show that the weighting strategy can improve kidney stone segmentation performance. In addition, the multi-label classification model can predict kidney stone composition precisely, including the mixed composition kidney stones.

Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare.

This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the understanding of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently.

Study of the rational dose of propofol in elderly patients under bispectral index monitoring during total intravenous anesthesia: A PRISMA-compliant systematic review.

BACKGROUND: Propofol has been used widely as an anesthetic for elderly patients; however, the drug instructions only indicate that the need for maintenance of general anesthesia in elderly patients is reduced, and not the extent of the reduction. This study has summarized the usage of propofol in total intravenous anesthesia under bispectral index (BIS) monitoring and determined the optimum dosage of propofol for elderly patients. METHODS: The study comprised 156 patients undergoing elective surgery under general anesthesia divided into 2 groups according to their age: the elderly group (O group) and nonelderly group (Y group). BIS monitoring was used in both groups during the operation, and propofol and remifentanil were used to maintain anesthesia. The preoperative special conditions, intraoperative maintenance of propofol, remifentanil, fentanyl, cis-atracurium, vasoactive drug use, and hemodynamic changes were summarized. RESULTS: Propofol maintenance in the O group was 3.372 ±â€Š0.774 mg/(kg h), which was significantly lesser than that in Y group (P < 0.05). The incidence of cardiovascular and cerebrovascular diseases and the use rate of vasoactive drugs in the O group were significantly higher than in the Y group (P < 0.05). CONCLUSION: Propofol maintenance in the O group was significantly lower than that in the nonelderly group; this indicates that the anesthetic drug delivery rate for elderly patients should be reduced.