Search Results (35)

Background: Childhood hearing loss is a public health problem of critical importance associated with speech development, academic achievement, and quality of life. Parents’ awareness and knowledge about risk factors contribute to early detection and timely intervention.  Objective: This study aims to examine Jordanian mothers’ knowledge of childhood hearing loss risk factors and investigate the impact of education level and socioeconomic status (SES) on the accuracy and comprehensiveness of this knowledge with the moderating effect of health literacy. Material and Methods: The approach employed an embedded mixed-methods design with a dominant quantitative strand supported by qualitative data, utilizing quantitative surveys (n = 250), analyzed using structural equation modeling (SEM) in SmartPLS, and qualitative interviews (n = 10), analyzed thematically to expand upon the quantitative findings by exploring barriers to awareness and healthcare-seeking behaviors. Results: The accuracy and comprehensiveness of knowledge of hearing loss risk factors were also positively influenced by maternal knowledge of hearing loss risk factors. Maternal knowledge was significantly associated with both education level and socioeconomic status (SES). Furthermore, maternal knowledge and accuracy were significantly moderated by health literacy, such that mothers with higher health literacy exhibited a stronger relationship between knowledge and accuracy. Qualitative findings revealed that individuals encountered barriers to accessing reliable information and comprehending medical advice and faced financial difficulties due to limited options for healthcare services. Conclusions: These results underscore the need for maternal education programs that address specific issues, provide simplified healthcare communication, and enhance access to pediatric audiology services. Future research should explore longitudinal assessments and intervention-based strategies to enhance mothers’ awareness and detect early childhood hearing loss. Full article

In this study, a tubular spot-welded grating sensor composed of a stainless-steel tube fixed to a substrate surface by welding is developed, and the tube is filled with high-performance epoxy resin components after the grating sensor is passed through it. A smart steel strand cable is created by spot welding steel strands using portable spot-welding equipment. This method generates a small current during spot welding, with a voltage of only 3 V to 5 V, and does not damage the internal structure of the steel strand. An equation related to the temperature, tension force, and wavelength fluctuation is presented in this article. A method with a transverse temperature coordinate and a longitudinal wavelength coordinate is used. A formula for the standard temperature calibration of the force values and a procedure for temperature adjustment of the force values are presented. The correlation coefficient between the stress on the steel strand and the wavelength of the tubular spot-welded grating sensor is as high as 0.999 according to static tensile testing, demonstrating good repeatability. The temperature adjustment coefficient for varying temperatures is 0.0264 nm/°C, and the test error is essentially limited to 3.0% F.S. When subjected to a 120 h relaxation test, the steel strand with the tubular spot-welded grating sensor exhibits a relaxation rate of 4.44%. The force value obtained after the relaxation test is 1.2% off from the standard load. A tubular spot-welded grating sensor is welded onto a steel strand within a cable sealing cylinder to create an extruded anchor epoxy-coated steel strand cable. The measured cable force is compared with the standard load. The maximum error is 0.5% F.S. The discrepancy between the measured cable force and the acceleration sensor value is 1.5% in one instance involving an arch bridge employing six smart suspension cables to detect cable forces onsite. The findings provide theoretical and engineering references for smart cables and demonstrate the high accuracy, dependability, and fixation performance of the tubular spot-welded grating sensor smart cable. Full article

The technologies grouped under the term Extended Reality (XR) are constantly evolving. Only fifty years ago, they were delegated to the science fiction strand and were not feasible except in the distant future; today, they are successfully used for personnel training, diagnostic maintenance, education, and more. This article focuses on one such technology: Augmented Reality (AR). In particular, it aims to present an improvement of a software created to monitor the values of Fiber Bragg Grating (FBG) sensors for aeronautical applications. The ability to overlay the status of various network-connected smart elements allows the operator to evaluate actual conditions in a highly intuitive and seamless manner, thus accelerating various activities. It was evaluated in a controlled environment to perform strain and temperature measurements of an Unmanned Aerial Vehicle (UAV), where it demonstrated its usefulness. Full article

Papillomaviruses are double-stranded DNA viruses, and it is essential to clarify their genotypic distribution for their effective prevention and clinical management. In this study, we aimed to evaluate the prevalence of HPV genotypes in the normal oral mucosae of HIV-positive individuals. A systematic literature search was conducted across PubMed, Web of Science, Scopus, and Google Scholar to identify peer-reviewed studies published up to 13 February 2025. The inclusion criteria referred to original research studies reporting on the prevalence and genotype-specific distribution of HPV in the oral mucosae of HIV-positive individuals. Statistical analyses were conducted using the MedicReS E-PICOS AI smart biostatistics software (version 21.3, New York, NY, USA) and the MedCalc statistical software package (MedCalc Software Ltd., Ostend, Belgium). The pooled prevalence estimates were calculated using a random-effects meta-analysis model, and heterogeneity was quantified using the Cochrane Q and I² statistics. The presence of publication bias was assessed via the Begg and Mazumdar rank correlation test. High prevalence and heterogeneity of HPV-58 (6.23%), HPV-16 (4.326%), and HPV-66 (3.733%) were observed, indicating significant variability across populations and methodologies. This supports their association with HPV-related oropharyngeal malignancies and the need for the continuous surveillance of HIV-positive individuals. We also observed the elevated detection of LR-HPV genotypes, particularly HPV-13 (7.16%), HPV-5 (5.64%), and HPV-62 (4.24%). These findings indicate that there is substantial heterogeneity in the prevalence of both HR-HPV and LR-HPV genotypes among HIV-positive individuals, with certain genotypes exhibiting higher detection rates across studies, emphasizing the need for targeted surveillance and preventive strategies in this vulnerable population. The application of advanced data analysis methods is essential in enhancing HPV surveillance and implementing effective control measures in this vulnerable population. Full article

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3–5% better accuracy results. Full article

Anticancer therapy urgently needs the development of novel strategies. An innovative molecular target is represented by circular RNAs (circRNAs), single-strand RNA molecules with the 5′ and 3′ ends joined, characterized by a high stability. Although circRNA properties and biological functions have only been partially elucidated, their relationship and involvement in the onset and progression of cancer have emerged. Specific targeting of circRNAs may be obtained with antisense oligonucleotides and silencing RNAs. Nanotechnology is at the forefront of research for perfecting their delivery. Continuous efforts have been made to develop novel nanoparticles (NPs) and improve their performance, materials, and properties regarding biocompatibility and targeting capabilities. Applications in various fields, from imaging to gene therapy, have been explored. This review sums up the smart strategies developed to directly target circRNAs with the fruitful application of NPs in this context. Full article

The use of DNA structures in creating multimodal logic gates bears high potential for building molecular devices and computation systems. However, due to the complex designs or complicated working principles, the implementation of DNA logic gates within molecular devices and circuits is still quite limited. Here, we designed simple four-way DNA logic gates that can serve as multimodal platforms for simple to complex operations. Using the proximity quenching of the fluorophore–quencher pair in combination with the toehold-mediated strand displacement (TMSD) strategy, we have successfully demonstrated that the fluorescence output, which is a result of gate opening, solely relies on the oligonucleotide(s) input. We further demonstrated that this strategy can be used to create multimodal (tunable displacement initiation sites on the four-way platform) logic gates including YES, AND, OR, and the combinations thereof. The four-way DNA logic gates developed here bear high promise for building biological computers and next-generation smart molecular circuits with biosensing capabilities. Full article

According to current regulations, welding is strictly prohibited for prestressed and tension cables. In response, this article proposes the use of a portable spot-welding machine to spot weld steel strands. This method generates a small current during spot welding, with a voltage of only 3 V to 5 V, and does not damage the internal structure of the steel strand. To effectively monitor cable tension in cable-supported structures, a novel approach utilizing a chip-based, encapsulated spot-welded strain sensor was investigated. The strain sensing capability, temperature sensitivity, stress relaxation, and static load responses were investigated on the proposed smart steel strand cables with spot-welded strain sensors. The theoretical analyses and finite element simulations revealed that the strain transfer efficiency of the spot-welded strain sensor exceeded 96%. The experimental results demonstrated that the load-strain relationship of the smart steel strand cable had a fitting degree greater than 0.999, and the tension errors obtained under different loads were within 1.26%. The tension full capacity errors measured at different temperatures were generally within 1.0%. The relaxation rate of the smart steel strand cable after 120 h was 3.78% and reduced the sensor accuracy error by 3.97%. Thus, the proposed strain sensor equipped with a smart steel strand cable is suitable for use in long-term tension monitoring. Full article

Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active sensing methods are commonly used in this field. However, there appears to be a problem of “energy saturation” in the utilization of piezoceramic active sensing methods. In this study, a smart anchor washer with semi-cylinders was developed to alleviate the saturation problem. An intelligent monitoring system is formed by combining the upper and lower annular cylinders with two piezoelectric patches. The piezoelectric patch on the upper annular cylinder is used as an actuator to emit signals through the contact interface of the smart anchor washer, which are then received by the piezoelectric patch on the lower annular cylinder. Based on wavelet packet decomposition, we investigate the correlation between the energy of the received signal and the applied tension force. Finally, a prestressing force index is developed for monitoring prestressing force using Shannon entropy. It is found that the index decreases with the increase in tension. The proposed design and index are also sensitive to early monitoring of prestressing force and can be used to monitor the entire prestressing process of steel strands. Full article

Building on the job demands–resources (JD-R) and allostatic load (AL) models, in the present study we examined the role of smart working (SW) in the longitudinal association between workload/job autonomy (JA) and a possible biomarker of work-related stress (WRS) in the hair—namely, the cortisol–dehydroepiandrosterone sulfate (DHEA(S)) ratio—during the COVID-19 pandemic. Overall, 124 workers completed a self-report questionnaire (i.e., psychological data) at Time 1 (T1) and provided a strand of hair (i.e., biological data) three months later (Time 2, T2). Results from moderated multiple regression analysis showed that SW at T1 was negatively associated with the hair cortisol/DHEA(S) ratio at T2. Additionally, the interaction between workload and SW was significant, with workload at T1 being positively associated with the hair cortisol/DHEA(S) ratio at T2 among smart workers. Overall, this study indicates that SW is a double-edged sword, with both positive and negative consequences on employee wellbeing. Furthermore, our findings suggest that the hair cortisol/DHEA(S) ratio is a promising biomarker of WRS. Practical implications that organizations and practitioners can adopt to prevent WRS and promote organizational wellbeing are discussed. Full article

Herein, we report on a smart biosensing platform that exploits gold nanoparticles (AuNPs) functionalized through ssDNA self-assembled monolayers (SAM) and the DNA-directed immobilization (DDI) of DNA-protein conjugates; a novel, high-sensitivity optical characterization technique based on a miniaturized gel electrophoresis chip integrated with online thermal lens spectrometry (MGEC-TLS), for the high-sensitivity detection of antigen binding events. Specifically, we characterized the physicochemical properties of 20 nm AuNPs covered with mixed SAMs of thiolated single-stranded DNA and bio-repellent molecules, referred to as top-terminated oligo-ethylene glycol (TOEG6), demonstrating high colloidal stability, optimal binder surface density, and proper hybridization capacity. Further, to explore the design in the frame of cancer-associated antigen detection, complementary ssDNA fragments conjugated with a nanobody, called C8, were loaded on the particles and employed to detect the presence of the HER2-ECD antigen in liquid. At variance with conventional surface plasmon resonance detection, MGEC-TLS characterization confirmed the capability of the assay to titrate the HER2-ECD antigen down to concentrations of 440 ng/mL. The high versatility of the directed protein-DNA conjugates immobilization through DNA hybridization on plasmonic scaffolds and coupled with the high sensitivity of the MGEC-TLS detection qualifies the proposed assay as a potential, easily operated biosensing strategy for the fast and label-free detection of disease-relevant antigens. Full article

The smart strand technique has been recently developed as a cost-effective prestress load monitoring solution for post-tensioned engineering systems. Nonetheless, during its lifetime under various operational and environmental conditions, the sensing element of the smart strand has the potential to fail, threatening its functionality and resulting in inaccurate prestress load estimation. This study analyzes the effect of potential failures in the smart strand on impedance characteristics and develops a 1D convolutional neural network (1D CNN) for automated fault diagnosis. Instead of using a realistic experimental structure for which transducer faults can be hard to control accurately, we adopt a well-established finite element model to conduct all experiments. The results show that the impedance characteristics of a damaged smart strand are relatively different from other piezoelectric active sensing devices. While the slope of the susceptance response is widely accepted as a promising fault indicator, this study shows that the resistance response is more favorable for the smart strand. The developed network can accurately diagnose the potential faults in a damaged smart strand with the highest testing accuracy of 94.1%. Since the network can autonomously learn damage-sensitive features without pre-processing, it shows great potential for embedding in impedance-based damage identification systems for real-time structural health monitoring. Full article

Despite the progress made in the diagnoses and therapy of gastrointestinal cancers, these diseases are still plagued by a high mortality. Thus, novel therapeutic approaches are urgently required. In this regard, small interfering RNA (siRNA), double-stranded RNA molecules able to specifically target the mRNA of pathological genes, have the potential to be of therapeutic value. To be effective in the human body, siRNAs need to be protected against degradation. Additionally, they need to target the tumor, leaving the normal tissue untouched in an effort to preserve organ function. To accomplish these tasks, siRNAs have been formulated with smart delivery systems such has polymers and lipids. While siRNA protection is not particularly difficult to achieve, their targeting of tumor cells remains problematic. Here, after introducing the general features of gastrointestinal cancers, we describe siRNA characteristics together with representative delivery systems developed for gastrointestinal cancers. Afterward, we present a selection of research papers employing siRNAs against upper- and lower- gastrointestinal cancers. For the liver, we also consider papers using siRNAs to combat liver cirrhosis, a relevant risk factor for liver cancer development. Finally, we present a brief description of clinical trials employing siRNAs for gastrointestinal cancers. Full article

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This “smart” SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor’s response. This can be explained by considering the aptamers’ conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule. Full article

The anticipated electrification of the transport sector may lead to significant increase in the future peak electricity demand, resulting in potential violations of network constraints. As a result, a considerable amount of network reinforcement may be required in order to ensure that the expected additional demand from electric vehicles that are to be connected will be safely accommodated. In this paper we present the Backwards Induction Framework (BIF), which we use for identifying the optimal investment decisions, for calculating the option value of smart charging of EV and the cost of stranded assets; these concepts are crystallized through illustrative case studies. Sensitivity analyses depict how the option value of smart charging and the optimal solution are affected by key factors such as the social cost associated with not accommodating the full EV capacity, the flexibility of smart charging, and the scenario probabilities. Moreover, the BIF is compared with the Stochastic Optimization Framework and key insights are drawn. Full article