Reports that promote persulfate-based advanced oxidation process (AOP) as a viable alternative to hydrogen peroxide-based processes have been rapidly accumulating in recent water treatment ...literature. Various strategies to activate peroxide bonds in persulfate precursors have been proposed and the capacity to degrade a wide range of organic pollutants has been demonstrated. Compared to traditional AOPs in which hydroxyl radical serves as the main oxidant, persulfate-based AOPs have been claimed to involve different in situ generated oxidants such as sulfate radical and singlet oxygen as well as nonradical oxidation pathways. However, there exist controversial observations and interpretations around some of these claims, challenging robust scientific progress of this technology toward practical use. This Critical Review comparatively examines the activation mechanisms of peroxymonosulfate and peroxydisulfate and the formation pathways of oxidizing species. Properties of the main oxidizing species are scrutinized and the role of singlet oxygen is debated. In addition, the impacts of water parameters and constituents such as pH, background organic matter, halide, phosphate, and carbonate on persulfate-driven chemistry are discussed. The opportunity for niche applications is also presented, emphasizing the need for parallel efforts to remove currently prevalent knowledge roadblocks.
Full text
Available for:
IJS, KILJ, NUK, PNG, UL, UM
In this letter, a cooperative non-orthogonal multiple access system with imperfect successive interference cancellation is investigated in an underlay cognitive radio network. Considering that the ...channel coefficients between the primary source and the secondary receiving nodes follow Rayleigh distribution, we derive an exact closed form of the exact outage probability for each secondary destination. Also, asymptotic expressions for the outage probability are derived: 1) when the interference constraint goes to infinity and 2) when the transmit powers at the secondary source and relay go to infinity. The simulation results verify our analytical results.
The number of studies employing artificial intelligence (AI), specifically machine and deep learning, is growing fast. The majority of studies suffer from limitations in planning, conduct and ...reporting, resulting in low robustness, reproducibility and applicability. We here present a consented checklist on planning, conducting and reporting of AI studies for authors, reviewers and readers in dental research.
Lending from existing reviews, standards and other guidance documents, an initial draft of the checklist and an explanatory document were derived and discussed among the members of IADR’s e-oral network and the ITU/WHO focus group “Artificial Intelligence for Health (AI4H)”. The checklist was consented by 27 group members via an e-Delphi process.
Thirty-one items on planning, conducting and reporting of AI studies were agreed on. These involve items on the studies’ wider goal, focus, design and specific aims, data sampling and reporting, sample estimation, reference test construction, model parameters, training and evaluation, uncertainty and explainability, performance metrics and data partitions.
Authors, reviewers and readers should consider this checklist when planning, conducting, reporting and evaluating studies on AI in dentistry.
Current studies on AI in dentistry show considerable weaknesses, hampering their replication and application. This checklist may help to overcome this issue and advance AI research as well as facilitate a debate on standards in this fields.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Printing technology can be used for manufacturing stretchable electrodes, which represent essential parts of wearable devices requiring relatively high degrees of stretchability and conductivity. In ...this work, a strategy for fabricating printable and highly stretchable conductors are proposed by transferring printed Ag ink onto stretchable substrates comprising Ecoflex elastomer and tough hydrogel layers using a water‐soluble tape. The elastic modulus of the produced hybrid film is close to that of the hydrogel layer, since the thickness of Ecoflex elastomer film coated on hydrogel is very thin (30 µm). Moreover, the fabricated conductor on hybrid film is stretched up to 1780% strain. The described transfer method is simpler than other techniques utilizing elastomer stamps or sacrificial layers and enables application of printable electronics to the substrates with low elastic moduli (such as hydrogels). The integration of printed electronics with skin‐like low‐modulus substrates can be applied to make wearable devices more comfortable for human skin.
Printable and highly stretchable conductors are realized by transferring printed Ag ink onto stretchable substrates comprising Ecoflex and tough hydrogel layers. The elastic modulus of the produced hybrid film is close to that of the hydrogel layer, since the thickness of Ecoflex coated on hydrogel is very small. The fabricated conductor on hybrid film is stretched up to 1780% strain.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Objectives
The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)—odontogenic keratocysts, dentigerous cysts, and periapical ...cysts—using dental panoramic radiography and cone beam computed tomographic (CBCT) images based on a deep convolutional neural network (CNN).
Methods
The GoogLeNet Inception‐v3 architecture was used to enhance the overall performance of the detection and diagnosis of OCLs based on transfer learning. Diagnostic indices (area under the ROC curve AUC, sensitivity, specificity, and confusion matrix with and without normalization) were calculated and compared between pretrained models using panoramic and CBCT images.
Results
The pretrained model using CBCT images showed good diagnostic performance (AUC = 0.914, sensitivity = 96.1%, specificity = 77.1%), which was significantly greater than that achieved by other models using panoramic images (AUC = 0.847, sensitivity = 88.2%, specificity = 77.0%) (p = .014).
Conclusions
This study demonstrated that panoramic and CBCT image datasets, comprising three types of odontogenic OCLs, are effectively detected and diagnosed based on the deep CNN architecture. In particular, we found that the deep CNN architecture trained with CBCT images achieved higher diagnostic performance than that trained with panoramic images.
Full text
Available for:
CMK, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The transferred microbiota from mother to baby constitutes the initial infant gastrointestinal microbiota and has an important influence on the development and health of infants in human. However, ...the reproductive tract microbiota of avian species and its inheritance have rarely been studied. We aimed to characterize the microbial community in the chicken reproductive tract and determine the origin of the chicken embryo gut microbiota. Microbiota in four different portions of chicken oviduct were determined using 16S rRNA metagenomic approach with the IonTorrent platform. Additionally, we analyzed the mother hen's magnum and cloaca, descendent egg, and embryo gut microbiota. The microbial composition and relative abundance of bacterial genera were stable throughout the entire chicken reproductive tract, without significant differences between the different parts of the oviduct. The chicken reproductive tract showed a relatively high abundance of Lactobacillus species. The number of bacterial species in the chicken reproductive tract significantly increased following sexual maturation. Core genera analysis detected 21 of common genera in the maternal magnum and cloaca, descendent egg shell, egg white, and embryo gut. Some elements of the maternal oviduct microbiota appear to be transferred to the embryo through the egg white and constitute most of the embryo gut bacterial population.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In this letter, we investigate cooperative device-to-device (D2D) communication in an uplink cellular network, where D2D users act as relays for cellular users. We derive the outage probability of a ...cellular user and the average achievable rate from a D2D transmitter to a D2D receiver in analytic form. We obtain optimal spectrum and power allocation to maximize the total average achievable rate under the outage probability constraint. The validity of the analysis is verified by computer simulations.
High peak-to-average power ratio of the transmit signal is a major drawback of multicarrier transmission such as OFDM or DMT. This article describes some of the important PAPR reduction techniques ...for multicarrier transmission including amplitude clipping and filtering, coding, partial transmit sequence, selected mapping, interleaving, tone reservation, tone injection, and active constellation extension. Also, we make some remarks on the criteria for PAPR reduction technique selection and briefly address the problem of PAPR reduction in OFDMA and MIMO-OFDM.
Macrophages play an important role in the innate and adaptive immune responses of organ systems, including the lungs, to particles and pathogens. Cumulative results show that macrophages contribute ...to the development and progression of acute or chronic inflammatory responses through the secretion of inflammatory cytokines/chemokines and the activation of transcription factors in the pathogenesis of inflammatory lung diseases, such as acute lung injury (ALI), acute respiratory distress syndrome (ARDS), ARDS related to COVID-19 (coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)), allergic asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF). This review summarizes the functions of macrophages and their associated underlying mechanisms in the development of ALI, ARDS, COVID-19-related ARDS, allergic asthma, COPD, and IPF and briefly introduces the acute and chronic experimental animal models. Thus, this review suggests an effective therapeutic approach that focuses on the regulation of macrophage function in the context of inflammatory lung diseases.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and ...pathology. The aim of the current study was to evaluate the efficacy of deep CNN algorithms for detection and diagnosis of dental caries on periapical radiographs.
A total of 3000 periapical radiographic images were divided into a training and validation dataset (n = 2400 80%) and a test dataset (n = 600 20%). A pre-trained GoogLeNet Inception v3 CNN network was used for preprocessing and transfer learning. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for detection and diagnostic performance of the deep CNN algorithm.
The diagnostic accuracies of premolar, molar, and both premolar and molar models were 89.0% (80.4–93.3), 88.0% (79.2–93.1), and 82.0% (75.5–87.1), respectively. The deep CNN algorithm achieved an AUC of 0.917 (95% CI 0.860–0.975) on premolar, an AUC of 0.890 (95% CI 0.819–0.961) on molar, and an AUC of 0.845 (95% CI 0.790–0.901) on both premolar and molar models. The premolar model provided the best AUC, which was significantly greater than those for other models (P < 0.001).
This study highlighted the potential utility of deep CNN architecture for the detection and diagnosis of dental caries. A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs.
Deep CNN algorithms are expected to be among the most effective and efficient methods for diagnosing dental caries.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
PDF
