Recent publications
It is critically important to plan properly for integrating and deploying unmanned aerial vehicles (UAVs) in the bridge inspection process, there is a need for tools to support implementation and decision-making regarding the use of UAVs at specific structures. In this study, a resource estimation tool that can be used to estimate the resources required for UAV-assisted bridge inspections is developed. The tool can aid inspectors in determining the estimated flight time and resources required for using a specific UAV and operator during the inspection of a specific bridge. The tool supports the development of optimal flight paths based on the structural geometry and positioning of structural elements of a bridge, establishes a range of recommended flight speeds for conducting reliable UAV-assisted bridge inspections based on the skill level(s) of the pilot(s) who were involved in conducting inspections. The developed tool also establishes a recommended range of wind speed and the corresponding standoff clearance information for safely conducting UAV-assisted bridge inspections. The tool also provides an estimated number of batteries required to allow the estimated required flight time. In this paper, the development of the tool is described, and the advantages of the tool are illustrated by its application in a case study involving a 10-span steel continuous multi-beam bridge with a reinforced concrete deck. The tool is developed as a spreadsheet and is publicly available through a GitHub page, accessible at https://github.com/ACCESSLab/Resource-Assessment-Tool-for-Effective-UAV-Assisted-Bridge-Inspection .
Cardiovascular disease (CVD) stands as one of the leading causes of morbidity and mortality worldwide, and the continued search for novel therapeutics is vital for addressing this global health challenge. Over the past decade, hydrogen sulfide (H₂S) has garnered significant attention in the field of medical research, as it has been proven to be a cardioprotective gaseous signaling molecule. It joins nitric oxide and carbon monoxide as endogenously produced gasotransmitters. As for its mechanism, H₂S functions through the posttranslational addition of a sulfur group to cysteine residues on target proteins in a process called sulfhydration. As a result, the observed physiological effects of H₂S can include vasodilation, anti-apoptosis, anti-inflammation, antioxidant effects, and regulation of ion channels. Various studies have observed the cardioprotective benefits of H₂S in diseases such as myocardial infarction, ischemia-reperfusion injury, cardiac remodeling, heart failure, arrhythmia, and atherosclerosis. In this review, we discuss the mechanisms and therapeutic potential of H₂S in various CVDs.
Nanocellulose (NC)/graphene oxide (GO) composites are attractive materials with a range of unique features obtained from the integration of NC and GO. These materials have high potential use in various sectors such as biomedicine, wastewater remediation, sensor/biosensor technology, and energy storage/conversion. The simple functionalization and modification of NC or GO afford an opportunity for tailoring these materials for anticipated applications. In wastewater treatment applications, they can be employed as reliable adsorbents for the removal of different pollutants, such as metals, dyes, oils, and pesticides as well as sensors for the detection and monitoring of these pollutants. Besides that, NC/GO composites can be applied as catalysts for catalytic degradation for a wide variety of pollutants. These materials have been also reported to be applicable in biomedical applications such as drug delivery, antibacterial and biosensing. Energy storage applications such as supercapacitors NC/GO-based materials were also utilized. This review summarizes NC/GO hybrid fabrication, characterization, and their application in different fields, i.e. sensing, energy storage, and wastewater remediation. It also covered a broad overview of the status of integrating GO with nanocellulose materials, i.e. bacterial cellulose, cellulose nanofibrils, and cellulose nanocrystals. We concluded with the challenges and outlook for NC/GO-based composites.
Platinum Group of Metals (PGMs) has been at the forefront of emission control in autocatalysts and could be the driving force behind the net-zero agenda, by providing emission-free energy sources. The literature has revealed that the versatility of additive manufacturing (AM) could be used to produce intricate hierarchical structures that increase the active catalytic sites of PGMs in autocatalysts, fuel cells (FCs), and batteries with improved operational efficiency. FCs and batteries with lower PGM loads have proven to perform better than conventional manufactured energy devices with higher PGM loads. The inherent hyperlocal-on-demand nature of AM could be used to disrupt the conventional multiple energy-consuming carbon-intensive supply chain to decarbonize the atmosphere. The synergy between AM and PGMs has contributed greatly to the increase in operational performance of FCs and batteries, compelling several nations to start migrating their energy systems to eco-friendly energy systems.
Sixty-eight years after the Brown v. Board of Education decision led to a national reduction of teachers of color, even as numbers of students of color grew, a collaborative effort to racially re-diversify the education profession is forming in the Dayton, Ohio region. This article describes this ongoing movement to increase the number of racially and ethnically marginalized educators, led by seven school districts, five institutions of higher education (IHEs), a county educational services center, and two nonprofit organizations. Initial work was supported by leveraging grant funds. The project seeks to (1) identify seventh- through eleventh-grade students interested in becoming educators, (2) direct students into model pathways and critical supports from high school through college graduation and licensure, (3) offer support for minoritized future and new teachers that fosters confidence and promotes belonging, and (4) facilitate the emergence of collectively-designed professional development for continuous renewal of culturally responsive and inclusive cultures in all education spaces. Strategies include identifying prospective teachers within high schools, establishing peer cohorts, facilitating critical mentoring, mapping grow-your-own pipelines and pathways, providing support at crucial stages in college, developing positive cultures in programs and districts, facilitating ongoing professional development that centers marginalized perspectives, and providing logistical support for emerging networks and organic affinity groups. Lack of support, student debt, isolation, discrimination, and unwelcoming and unresponsive school environments are common barriers that require ongoing dismantling.
Purpose of Review
Diabetic neuropathy is a common complication of diabetes mellitus (DM) and can affect up to 50% of DM patients during their lifetime. Patients typically present with numbness, tingling, pain, and loss of sensation in the extremities. Since there is no treatment targeting the underlying mechanism of neuropathy, strategies focus on preventative care and pain management.
Recent Findings
Up to 69% of patients with diabetic neuropathy receive pharmacological treatment for neuropathic pain. The United States Food and Drug Administration (FDA) confirmed four drugs for painful diabetic neuropathy (PDN): pregabalin, duloxetine, tapentadol, and the 8% capsaicin patch. Nonpharmacological treatments such as spinal cord stimulation (SCS) and transcutaneous electrical nerve stimulation (TENS) both show promise in reducing pain in DM patients.
Summary
Despite the high burden associated with PDN, effective management remains challenging. This update covers the background and management of diabetic neuropathy, including its epidemiology, pathogenesis, preventative care, and current therapeutic strategies.
Understanding a protein’s function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning as a powerful tool, achieving significant success in protein function prediction. Their strength lies in their ability to automatically learn informative features from protein sequences, which can then be used to predict the protein’s function. This study builds upon these advancements by proposing a novel model: CNN-CBAM+BiGRU. It incorporates a Convolutional Block Attention Module (CBAM) alongside BiGRUs. CBAM acts as a spotlight, guiding the CNN to focus on the most informative parts of the protein data, leading to more accurate feature extraction. BiGRUs, a type of Recurrent Neural Network (RNN), excel at capturing long-range dependencies within the protein sequence, which are essential for accurate function prediction. The proposed model integrates the strengths of both CNN-CBAM and BiGRU. This study’s findings, validated through experimentation, showcase the effectiveness of this combined approach. For the human dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +1.0 % for cellular components, +1.1 % for molecular functions, and +0.5 % for biological processes. For the yeast dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +2.4 % for the cellular component, +1.2 % for molecular functions, and +0.6 % for biological processes.
A cow’s lifetime productivity is influenced not only by breed and age at first calving, but also by feeding conditions and appropriate supplementation. The objective of the study was to determine the effect of two different lick supplementation strategies between weaning and first conception on the calving percentage and weight over the first three calving seasons. In the study, 24 Bonsmara heifers were divided into two groups of 12 animals each after weaning. The two heifer groups received the same mineral lick during summer. During winter months (April – September), the one group received a protein rich winter lick (400g/kg protein) while the second group received a production lick (winter lick supplemented with yellow maize in a ratio of 2:1). The heifers were bred naturally at 24 months during a three-month summer breeding season (December – February). Calving percentage and reconception rate of the group which received production lick in the winter was 92%, 50% and 58% for the first, second and third calving seasons. This was significantly higher than the calving percentage of 67%, 42% and 33% for the group which received only winter lick. Cow weight at calving and calf 205 day corrected weaning weight were higher over the three consecutive calving seasons for the heifers receiving production lick during winter. These results indicate that it may be worthwhile to provide good supplementation before the first breeding season for the incentive of a better calving percentage and higher calf weaning weights up to the third calving season.
The success of weed control is critical for our food security. Non-chemical weed control is a promising technique in sustainable agriculture to ensure the food security. In this review, multiple directed energy weed control methods are reviewed with a specific focus on laser and optical radiation weed control. The mechanisms of the weed control in terms of adverse ablation, radiation thermal effects, and molecular-level damages are systematically reviewed. In particular, the underlying mathematical models determining the dose and response relationship of the weed control are also analyzed for a rigorous study of the physical law of the control process. Challenges of applying the techniques into practice are also illustrated to guide practical weed control applications.
Chitinases, enzymes that degrade chitin, have long been studied for their role in various biological processes. They play crucial roles in the moulting process of invertebrates, the digestion of chitinous food, and defense against chitin-bearing pathogens. Additionally, chitinases are involved in physiological functions in crustaceans, such as chitinous food digestion, moulting, and stress response. Moreover, chitinases are universally distributed in organisms from viruses to mammals and have diverse functions including tissue degradation and remodeling, nutrition uptake, pathogen invasion, and immune response regulation. The discovery of these diverse functions expands our understanding of the biological significance and potential applications of chitinases. However, recent research has shown that chitinases possess several other functions beyond just chitin degradation. Their potential as biopesticides, therapeutic agents, and tools for bioremediation underscores their significance in addressing global challenges. More importantly, we noted that they may be applied as bioweapons if ethical regulations regarding production, engineering and application are overlooked.
The yield of green energy from solid-state anaerobic co-digestion (SSAD) has recently been enhanced by incorporating innovative pretreatment methods and nanoparticles. However, the environmental consequences of employing new processes have not been fully examined. In this study, the environmental impacts of three high-methane-yielding scenarios including SSAD of corn stover blended with dairy manure (DM) denoted as (SYM1), calcium hydroxide-pretreated corn stover (CpCS) blended with DM (SYM2), and the CpCS blended with DM and nanoparticles (SYM3) were assessed and compared the baselines of solid-state and semi-solid-state anaerobic digestion using a life cycle assessment (LCA) approach. The approach investigated the best management practices that would result in high methane yield and low environmental impact. Results of the life cycle assessment indicate the inclusion of calcium hydroxide and nanoparticle has minimal negative environmental impact. There was an environmental gain in GWP when corn stover was co-digestion with DM (SYM1) relative to DM mono-digestions (baselines) and the carbon footprint of SYM1 was less by more than 85% compared to SYM2 and SYM3. However, the large volume of untreated corn stover harnessed for SYM1 scenario resulted in over 75% fossil fuel depletion compared to the other scenarios. The surplus methane from the SYM3 (at least twofold of other scenarios and baselines) in conjunction with being the least with the environmental implication makes the scenario the most attractive option for on-farm practice capable of harnessing the growing organic waste volume. These outcomes can guide trade-off between pretreatment and nanoparticle application to reduce solid-state anaerobic digestion’s negative environmental impact.
Backgrounds
Urban and peri-urban fragments are vital for biodiversity conservation, requiring genetic assessment of tree species in fragmented forests. The aim of this study was to analyze the genetic variability and diversity for adult individuals of J. micrantha along an urban-rural gradient in the Araucaria Forest. Fifteen individuals were sampled, with five from each remaining forest type. Initially, 10 ISRR primers were tested. Five mother trees were chosen from each site (urban, peri-urban, and rural) with a minimum distance of 100 m. The experimental design was a RCBD with 15 progenies, three provenances, three blocks, and 20 plants per plot, totaling 900 seedlings.
Results
The average percentage of polymorphic loci was 93.33%. The urban population showed a greater loss of genetic diversity (H=0.1806). 79% of the genetic diversity was found within populations. The observed gene flow value (Nm) was 1.8790, indicating that there were no random losses of alleles within populations. The fragments did not exhibit significant differences, but there were significant differences among the progenies. The stem diameter (SD) and the height-diameter relationship (H/SD) emerged as the key traits for selecting new individuals due to their higher heritability (< 0.50), accuracy (< 0.70), and relative coefficient of variation (< 7%).
Conclusion
The urban fragment is the most affected, but gene flow between fragments prevents the random loss of alleles. The analysis suggests that these fragments form a unique population, despite geographic barriers. Thus, the three fragments can be considered when choosing superior individuals for future progeny tests in genetic improvement programs for the species.
Keywords:
Caroba; Progeny test; Genetic parameters; Genetic conservation.
In this study, a novel time-driven mathematical model for trust is developed considering human-multi-robot performance for a Human-robot Collaboration (HRC) framework. For this purpose, a model is developed to quantify human performance considering the effects of physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload and workloads due to the robots’ mistakes, and task complexity. The performance of multi-robot in the HRC setting is modeled based upon the rate of task assignment and completion as well as the mistake probabilities of the individual robots. The human trust in HRC setting with single and multiple robots are modeled over different operation regions, namely unpredictable region, predictable region, dependable region, and faithful region. The relative performance difference between the human operator and the robot is used to analyze the effect on the human operator’s trust in robots’ operation. The developed model is simulated for a manufacturing workspace scenario considering different task complexities and involving multiple robots to complete shared tasks. The simulation results indicate that for a constant multi-robot performance in operation, the human operator’s trust in robots’ operation improves whenever the comparative performance of the robots improves with respect to the human operator performance. The impact of robot hypothetical learning capabilities on human trust in the same HRC setting is also analyzed. The results confirm that a hypothetical learning capability allows robots to reduce human workloads, which improves human performance. The simulation result analysis confirms that the human operator’s trust in the multi-robot operation increases faster with the improvement of the multi-robot performance when the robots have a hypothetical learning capability. An empirical study was conducted involving a human operator and two collaborator robots with two different performance levels in a software-based HRC setting. The experimental results closely followed the pattern of the developed mathematical models when capturing human trust and performance in terms of human-multi-robot collaboration.
The rising widespread oil‐impacted wastewater warrants an urgent call for innovative approaches to the handling of oily wastewater. A variety of techniques has been investigated to treat oil‐impacted water, and they are found to be inefficient. Electrospun nanofibers emerge as the viable technique to treat oily wastewater precisely owing to their high specific surface areas and interconnected nanoscale pore structures. In this review, a brief background on the study is provided followed by the environmental pollution by the oily wastewater. Subsequent to that, the polyvinylidene fluoride (PVDF) modification methods are also presented followed by the physicochemical properties of both the electrospun PVDF blends and the PVDF‐based composites. Furthermore, the performances of the PVDF‐based composites in oil/water separation are described. It is concluded with the future prospects for using PVDF‐based composites for oil/water separation.
Peroxidative damage to human spermatozoa has been shown to be the primary cause of male infertility. The possible role of nitric oxide (NO) in affecting sperm motility, capacitation, and acrosome reaction has been reported, too. The overproduction of NO by the enzyme inducible nitric oxide synthase (iNOS) could be responsible as it has been implicated in the pathogenesis of many diseases. There have been many studies on regulating iNOS function in various tissues, especially by protein–protein interaction; however, no study has looked for iNOS-interacting proteins in the human testis. Here, we have reported the identification of two proteins that interact with iNOS. We initially undertook a popular yeast two-hybrid assay to screen a human testis cDNA library in yeast using an iNOS-peptide fragment (amino acids 181–335) as bait. We verified our data using the mammalian chemiluminescent co-IP method; first, employing the same peptide and, then, a full-length protein co-expressed in HEK293 cells in addition to the candidate protein. In both cases, these two protein partners of iNOS were revealed: (a) sperm acrosome-associated 7 protein and (b) retinoblastoma tumor-suppressor binding protein.
Polycaprolactone (PCL) is one of the durable polymers with potential in a plethora of healthcare applications. Its biological properties, degradability, chemical properties, and mechanical properties can further be modified to manufacture desired products for modern biomedical applications. Electrospinning of PCL offers the opportunity to design treatment materials that resemble human tissues and facilitate regeneration at the target site. The resultant materials can also be modified by loading other active functional materials to broaden their applications. Herein, the recent advances in the preparation and modification of PCL‐based materials for healthcare applications are elucidated. The challenges and future trends for its application in modern biomedical applications are also outlined.
Innovative techniques such as the “omics” can be a powerful tool for the understanding of intracellular pathways involved in homeostasis maintenance and identification of new potential therapeutic targets against endocrine-metabolic disorders. Over the last decades, proteomics has been extensively applied in the study of a wide variety of human diseases, including those involving the endocrine system. Among the most endocrine-related disorders investigated by proteomics in humans are diabetes mellitus and thyroid, pituitary, and reproductive system disorders. In diabetes, proteins implicated in insulin signaling, glucose metabolism, and β-cell activity have been investigated. In thyroid diseases, protein expression alterations were described in thyroid malignancies and autoimmune thyroid illnesses. Additionally, proteomics has been used to investigate the variations in protein expression in adrenal cancers and conditions, including Cushing’s syndrome and Addison’s disease. Pituitary tumors and disorders including acromegaly and hypopituitarism have been studied using proteomics to examine changes in protein expression. Reproductive problems such as polycystic ovarian syndrome and endometriosis are two examples of conditions where alterations in protein expression have been studied using proteomics. Proteomics has, in general, shed light on the molecular underpinnings of many endocrine-related illnesses and revealed promising biomarkers for both their detection and treatment. The capacity of proteomics to thoroughly and objectively examine complex protein mixtures is one of its main benefits. Mass spectrometry (MS) is a widely used method that identifies and measures proteins based on their mass-to-charge ratio and their fragmentation pattern. MS can perform the separation of proteins according to their physicochemical characteristics, such as hydrophobicity, charge, and size, in combination with liquid chromatography. Other proteomics techniques include protein arrays, which enable the simultaneous identification of several proteins in a single assay, and two-dimensional gel electrophoresis (2D-DIGE), which divides proteins depending on their isoelectric point and molecular weight. This chapter aims to summarize the most relevant proteomics data from targeted tissues, as well as the daily rhythmic variation of relevant biomarkers in both physiological and pathophysiological conditions within the involved endocrine system, especially because the actual modern lifestyle constantly imposes a chronic unentrained condition, which virtually affects all the circadian clock systems within human’s body, being also correlated with innumerous endocrine-metabolic diseases.
Introduction
Since digital transformation has become a priority in the higher education landscape, it is unlikely that higher education institutions will return to traditional face-to-face teaching and learning. Many higher education institutions have adopted a hybrid approach to teaching and learning in a post-Covid-19 setting. This unplanned forced change has raised concerns about the quality of online teaching and learning, as well as issues related to the student experience thereof. Therefore, it is necessary to consider possible factors that may influence students’ perceived enjoyment of the online teaching and learning experience. To date, very few studies have considered the antecedents of perceived enjoyment of online teaching and learning. The purpose of this paper was to determine the influence of selected factors of online teaching and learning on the perceived enjoyment of students.
Methods
Quantitative data was collected, and the final sample consisted of 501 students enrolled at higher education institutions.
Results
The findings showed that cognitive benefits, perceived usefulness and perceived ease of use are statistically significantly correlated with students’ perceived enjoyment of the online learning experience.
Discussion
The current study contributes to existing knowledge regarding the intention of continued use of online teaching and learning. The findings of this study are also practically relevant for enhancing students’ online learning experiences in a post-Covid-19 setting.
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