The swift development of Internet of Things (IoT) technology has led to the expanded application of sensor networks in smart cities. Street lights, as a vital aspect of smart city infrastructure, serve as a reflection of a town's development. While streetlights are crucial for ensuring optimal visibility, road security, and public space utilization at night, they contribute significantly to electricity consumption. Governments across the globe are committed to reducing residential and industrial electricity usage. Regarding electricity usage, the Intelligent Street Lights System based on LED lights is a crucial concept today. This system functions by switching on the LED lights when a vehicle approaches and adjusting them to a dimmed or off state when no vehicle is present. The primary focus of this research paper, implementing an Intelligent Street Lights System based on LED lights, has emerged as a critical approach to achieving energy efficiency and cost reduction. Experimental findings demonstrate the potential for up to 80% energy savings compared to traditional street lamp systems. Additionally, the system enables remote monitoring and intelligent management of urban street light conditions through terminal devices.

SARS-CoV-2 caused the ongoing COVID-19 pandemic, and only a few treatment options are available to mitigate its impact on human health. Hence, there is a need to discover drugs that could be used to treat COVID-19. Several studies have already reported the repurposing of existing drugs to inhibit the receptor-binding domain of SARS-CoV-2. However, the emergence of COVID-19 variants may render current drug candidates ineffective. Here, we report the structure-based drug screening of the DrugBank database against the wild-type, B.1.1.7, B.1.351, and P.1 variants of SARS-CoV-2. Our study revealed that Salmeterol, Abediterol, and Lysophosphatidylglycerol are among the top candidates against all four variants. Furthermore, we showed that Salmeterol forms a stable complex with the receptor binding domain of SARS-CoV-2 variants. Further studies are needed to evaluate the clinical relevance of the drug candidates discovered. Nevertheless, this study provides insight into computational drug design that works against multiple variants of SARS-CoV-2.

Drowsiness driving poses a significant risk to road safety, necessitating effective drowsiness detection models. Most of prior researches has primarily relied on composite facial-based features, mainly focusing on the mouth and/or eye states, to identify drowsiness status. However, these models tend to overlook crucial information from input signals, resulting in suboptimal detection accuracy. Moreover, the absence of suitable algorithms and techniques for extracting other essential facial features, such as the eyebrow and nostril, further impacts the accuracy of drowsiness detection. To address these limitations, this study introduces an innovative algorithm and a technique for extracting drowsiness-related information from the eyebrow and nostril regions. Additionally, we propose a method, leveraging four composite facial-based drowsiness features; eyebrow, nostril, eye, and mouth states as inputs to a Convolutional Neural Network (CNN). A novel multilevel feature fusion method is employed to effectively combine the deep representations of these drowsiness-related features. The final step involves employing a Long-short-term memory (LSTM) recurrent neural network to classify the drowsiness status of drivers. Our proposed model is rigorously evaluated using the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results demonstrate an impressive accuracy in different scenarios, and the average accuracy result reached 0.973, showcasing the effectiveness of our approach in enhancing drowsiness detection accuracy and promoting road safety.

Hydrogels are three-dimensional polymeric networks with promising properties as release systems for fertilizers, pesticides and plant growth-promoting bacteria, as well as improving water availability in crop soils. Most commercial hydrogels for agricultural use are based on synthetic polymers. Due to the increasing attention on the use of biodegradable materials, hydrogels based on natural polymers have gained interest. In this work, the design and development of new hydrogels with superabsorbent characteristics based on gellan gum (GG) and humic acid (HA) were carried out, as an ecological alternative with potential application in the agricultural industry. Hydrogels were obtained by crosslinking the GG chains with spermidine in the presence of HA. The structural and physicochemical properties of the hydrogels were evaluated by FTIR, TGA, SEM and degree of swelling at 25 °C in deionized water and in a soil aqueous extract. Chlorophyll quantification was evaluated in sorghum (Sorghum sp.) seedlings germinated in a growth chamber for plants at 27±1 °C and 12 light hours. The results showed that adding HA to the GG matrix increases the amount of total chlorophylls in sorghum seedlings.

The accumulation of ¹³⁷Cs and ⁹⁰Sr radionuclides by dominants and codominants of pine forest communities in the Chernihiv region (Ukraine) was studied. The studied communities belong to the the Peucedano-Pinetum association. Our studies have confirmed the species specificity of the accumulation of 137Cs and 90Sr radionuclides by plants of biotopes of forest ecosystems. According to the indications of the specific activity of ¹³⁷Cs, the plants form the following row (as they decrease): Pleurozium schreberi – Ptilium crista castrensis – Pinus sylvestris – Betula pendula – Frangula alnus. The decrease in the specific activity of ⁹⁰Sr is observed in the following order: Frangula alnus – Betula pendula – Ptilium crista castrensis – Pleurozium schreberi – Pinus sylvestris.

The obtained data on the accumulation of ¹³⁷Cs and ⁹⁰Sr radionuclides indicate that the accumulation of ⁹⁰Sr by plants is less intense than that of ¹³⁷Cs. In the soils of natural ecosystems, the distribution of radiostrontium is similar to the distribution of radioactive cesium. Nevertheless, ⁹⁰Sr migrates down the soil profile more intensively and its main part is in the root-bearing soil layer. Therefore, the high bioavailability of ⁹⁰Sr can subsequently cause significant equal accumulations of it by representatives of the vegetation cover of the forest biocenosis.

In the last decades, bioengineering research has played a prominent role in the field of dental implant rehabilitation. Dental implants represent, nowadays, the main treatment option for edentulism. Periodontitis, a broadly diffused biofilm-induced chronic disease involving tooth-supporting tissues, represents the main cause of tooth loss in adult patients and may affect, at the same time, the long term prognosis of implant rehabilitations. The aim of the present systematic review and meta-analysis was to assess the influence of history of periodontitis on dental implant loss.

The protocol of the present PRISMA-driven meta-analysis was registered on PROSPERO. Literature searches were conducted up to January 2023. All prospective clinical studies comparing implant loss (or implant survival) in history of periodontitis patients (HPP) and periodontally healthy patients (PHP) were included. The quality of the included studies was assessed using the Newcastle-Ottawa Scale or the RoB2. A meta-analysis was performed on implant loss at the available follow-up time points. Sub-analyses were performed for the reported type and severity of periodontitis.

From 10549 initial records, 16 articles were finally included. A high-quality level for the main part of the included studies was found. Meta-analysis showed that HPP had a higher risk for implant loss compared to PHP with a >3 years follow-up period. Sub-analyses revealed that history of aggressive (vs chronic) and severe (vs moderate) periodontitis showed a significantly higher risk of implant loss.

Moderate evidence exists showing that history of periodontitis increases the risk of implant loss, particularly for longer follow-up and for history of aggressive and severe periodontitis. Further well-designed and well-conducted prospective studies are needed in order to consolidate the available scientific evidence on the risk of dental implant loss in HPP.

This study evaluated copper sulfide penetration dynamics during electrophoresis in the root canal system of mandibular anterior teeth. Twenty intact mandibular anterior teeth were included, extracted for prosthetic purposes from patients aged 40 to 65 years. The research comprised two phases: macroscopic and histological analysis. In the first group (n=8), electrophoresis involved a copper-calcium hydroxide-based compound (Cupral™) administered in two sessions, with a 7-day interval between them. The second group (n=8) received a combination-paste of Cupral and Ca(OH)₂-highly dispersed paste. A third group (n=4) served as a comparison group. Electrophoresis was conducted by immersing teeth with opened endodontic cavities in a plastic container filled with 0.9 % saline. Canal entrances were explored using a hand K-file, followed by canal drying and application of the corresponding paste to create an endodontic reservoir. Electrophoretic current was then applied at an intensity of 5 mA x min per canal. Teeth were fixed in 10% neutral buffered formalin for histological examination. Macroscopically, the first group exhibited aesthetically unacceptable turquoise blue staining of the crown and root. In contrast, the second group showed no staining of this intensity. Histological examination of the first group revealed copper sulfide penetration into predentin and dentinal tubules, while the intertubular dentinal matrix remained unstained. The second group exhibited slight impregnation of copper sulfide in some areas of predentin, with uneven distribution within the dentinal tubules. The third group displayed a normal dentin structure in the root canal walls. This research provides valuable insights for improving endodontic outcomes and expanding treatment options available to clinicians.

As a non-renewable resource, phosphorus enjoys the dual characteristics of polluting and resource. Specifically, polluting refers to excessive phosphorus discharged into the waterbody will lead to eutrophication of the waterbody, thus affecting the water quality, while resource refers to the application of appropriate amount of phosphorus can promote plant growth and improve crop yield. Therefore, how to effectively remove phosphorus from bodies of water and recycle it as a soil conditioner or fertilizer for agricultural land applications has been a hot issue in global research. Here, this paper begins with a description of the current phosphorus crisis facing mankind and summarizes the wide range of uses of phosphorus as a resource. Then, two types of document (article and review article) published up to July 14, 2023 were collected and analyzed from the Web of Science core databases and a bibliometric survey was conducted using VOSviewer and Bibliometrix software. Here, bibliometric analysis of publication evolution, top 10 languages, top 15 research areas, institutions, countries/regions, journals, keyword co-occurrence network, thematic map, highly cited papers, author networks, production over the last six years of most relevant authors were taken into account and analyzed in detail. In summary, this paper seeks to provide an insightful description and analysis of current research progress in the field from a bibliometric point of view. Finally, future trends and perspectives of phosphorus recovery with biochar are explored in depth, providing a potential pathway for phosphorus recycling and solving the phosphorus crisis.

Different analytes have been proposed as biomarkers of periodontitis, such as macrophage inflammatory protein (MIP-1α) and matrix metalloproteinase-8 (MMP-8). The aim of this proof-of-concept study is to test a novel highly sensitive point-of-care test (POCT), based on an optical biosensor exploiting the principles of surface plasmon resonance (SPR), for the analysis of salivary MIP-1α and MMP-8.
Antibody self-assembled monolayers (SAMs) for plasmonic detection of MIP-1α and MMP-8 were realized on suitably modified plastic optical fibers. Biomarker levels were quantified by analysing the shift of the resonance wavelength of a white light source determined by the specific antigen-antibody binding upon the SAM and detected by a spectrometer connected to the biosensor.
Dose-response curves were realized by serial dilutions of human recombinant MIP-1α and MMP-8 in both PBS (phosphate buffered saline) and real matrix (saliva), showing a very low limit of detection (LOD) and a high selectivity. It was also possible to discriminate, by measuring the biomarker salivary levels, between a periodontitis patient and a periodontally healthy subject.
The present SPR-based POCT demonstrated to be employable to detect and quantify MIP-1α and MMP-8 levels in both buffer solution and saliva with low LOD and high selectivity, opening interesting perspectives for future clinical applications in terms of on-site detection and real-time monitoring of periodontal health conditions, early diagnosis, as well as timely and targeted therapy of periodontitis.

Wastewater treatment is of great importance for reducing pollutants and improving the water environment. The complexity of natural conditions, multiple processing technologies, and variation in parameters result in an inaccurate description of the wastewater treatment system, which is not conducive to efficient management. The traditional environmental model has certain limitations, mainly including the lack of effective algorithms for processing defective data, excessive calculation time, and poor scalability of scenarios. As an emerging and powerful technology, artificial intelligence (AI) has shown its role in modeling complex phenomena to improve the efficiency of system management, which provides a path to problem solving. The current study presents a bibliometric analysis of the application of AI technology in wastewater treatment and reveals publication characteristics and country distribution. Furthermore, an overview about the characteristics of various AI algorithms in the applications of wastewater treatment are introduced. Specific applications of AI technology in wastewater treatment are also highlighted, including the prediction of water quality indicators, simulation of microbial action, system monitoring and diagnosis, optimization of system energy, regulation of wastewater reuse, and innovative applications in the future. Finally, the challenges of AI application are summarized, mainly including algorithm defects caused by the lack of wastewater treatment knowledge of the developers, imperfect engineering database in large-scale water treatment operation system, and insufficient investment in system resources and hardware facilities. This review provides an insightful discussion for researchers to achieve accurate wastewater treatment management in the future.