Search Results (57)

Air quality monitoring (AQM) is key to maintaining healthy air in cities. This is crucial in low- and middle-income countries due to increasing evidence of poor air quality but lack of monitors to consistently collect evaluate air quality data and effect policy changes, mainly because of the costs of monitoring devices. In participating in a challenge for the development of low-cost AQM devices in low-resource regions, an Arduino-based device with sensors for particulate matter size, temperature, and humidity data acquisition was developed for deployment in Port Harcourt, a city in Nigeria’s Niger Delta region, exposed to poor air quality partly due to gas and oil production activities. During the project, challenges to AQM were encountered, including inadequate awareness of air quality issues, lack of necessary AQM device components, unavailability of trained manpower and partnerships, and lack of funding. However, lack of a means of calibrating the device was a major hindrance, as no reference AQM instrument was available, rendering the data acquired largely qualitative, educational, and useless for regulatory purposes. There is an urgent need for AQM in such cities. However, a robust AQM strategy must be designed and used to address these constraints, especially whilst using low-cost devices, for significant progress in acquiring robust air quality data in such low-resource regions to be made. Full article

In orthodontics, the manual tracing of cephalometric radiographs is a common practice, where the Sella Turcica (ST) serves as a reference point. The radiologist often manually traces the outline of the sella using manual tools (e.g., calipers on radiographs). Perhaps the inherent complexity and variability in the shapes of sella and the lack of advanced assessment tools make the classification of sella challenging, as it requires extensive training, skills, time, and manpower to detect subtle changes that often may not be apparent. Moreover, existing semi-supervised learning (SSL) methods face key limitations such as shift invariance, inadequate feature representation, overfitting on small datasets, and a lack of generalization to unseen variations in ST morphology. Medical imaging data are often unlabeled, limiting the training of automated classification systems for ST morphology. To address these limitations, a novel semi-supervised deep subspace embedding (SSLDSE) framework is proposed. This approach integrates real-time stochastic augmentation to significantly expand the training dataset and introduce natural variability in the ST morphology, overcoming the constraints of small and non-representative datasets. Non-linear features are extracted and mapped to a non-linear subspace using Kullback–Leibler divergence, which ensures that the model remains consistent despite image transformations, thus resolving issues related to shift invariance. Additionally, fine-tuning the Inception-ResNet-v2 network on these enriched features reduces retraining costs when new unlabeled data becomes available. t-distributed stochastic neighbor embedding (t-SNE) is employed for effective feature representation through manifold learning, capturing complex patterns that previous methods might miss. Finally, a zero-shot classifier is utilized to accurately categorize the ST, addressing the challenge of classifying new or unseen variations. Further, the proposed SSLDSE framework is evaluated through comparative analysis with the existing methods (Active SSL, GAN SSL, Contrastive SSL, Modified Inception-ResNet-v2) for ST classification using various evaluation metrics. The SSLDSE and the existing methods are trained on our dataset (sourced from PGI Chandigarh, India), and a blind test is conducted on the benchmark dataset (IEEE ISBI 2015). The proposed method improves classification accuracy by 15% compared to state-of-the-art models and reduces retraining costs. Full article

The circular bioeconomy is currently a promising model for repurposing natural sources; these sources include plants due to their abundance of bioactive compounds. This study evaluated the antimicrobial properties of a Chaetomorpha linum extract. Chaetomorpha linum is an invasive macroalga from the Orbetello Lagoon (Tuscany, Italy), which grows in nutrient-rich environments and has been forming extended mats since 2005. The biomass is mechanically harvested and treated as waste, consuming considerable manpower and financial resources. As a potential way to increase the value of such waste, this study found that C. linum extract (CLE) is a source of antimicrobial compounds. The phytochemical characterization of the extract revealed the predominant presence of palmitic acid, a fatty acid with known antimicrobial activity. Based on such findings, four bacterial species of high clinical relevance (Enterococcus faecalis, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli) were tested, revealing a notable antibacterial activity of the extract on Enterococcus faecalis (MIC, 32 μg/mL). Computational analyses identified a potential Enterococcus faecalis molecular target for palmitic acid, offering molecular insights on the interaction. This study presents a comprehensive in vitro and in silico approach for drug and target discovery studies by repurposing C. linum as a source of antimicrobial bioactive compounds. Full article

The continuous emergence of new illegal compounds, particularly psychoactive chemicals, poses significant challenges for current drug detection methods. Developing new protocols and kits for each new drug requires substantial time, effort, and dedicated manpower. Whole-cell bacterial bioreporters have been proven capable of detecting diverse hazardous compounds in both laboratory and field settings, identifying not only single compounds but also chemical families. We present the development of a microbial bioreporter for the detection of cocaine, the nervous system stimulant that is the second-most widely used illegal drug in the US. Escherichia coli was transformed with a plasmid containing a bacterial luxCDABEG bioluminescence gene cassette, activated by a cocaine-responsive signaling cascade. The engineered bioreporter is demonstrated to be a sensitive and specific first-generation detection system for cocaine, with detection thresholds of 17 ± 8 μg/L and 130 ± 50 μg/L in a buffer solution and in urine, respectively. Further improvement of the sensor’s performance was achieved by altering the nucleotide sequence of the PBen gene promoter, the construct’s sensing element, using accelerated site-directed evolution. The applicability of ready-to-use paper strips with immobilized bioreporter cells was demonstrated for cocaine detection in aqueous solutions. Full article

E’bian Yi Autonomous County is a mineral-rich area located in a complex geological structure zone. The region experiences frequent geological disasters due to concentrated rainfall, steep terrain, and uneven vegetation cover. In particular, during the rainy season, large amounts of rainwater rapidly accumulate, increasing soil moisture and slope pressure, making landslides and debris flows more likely. Additionally, human activities such as mining, road construction, and building can alter the original geological structure, exacerbating the risk of geological disasters. According to publicly available data from the Leshan government, various types of geological disasters occurred in 2019, 2020, 2022, and 2023, resulting in economic losses and casualties. Although some studies have focused on geological disaster issues in E’bian, these studies are often limited to specific areas or types of disasters and lack comprehensive spatial and temporal analysis. Furthermore, due to constraints in technology, funding, and manpower, geophysical exploration, field geological exploration, and environmental ecological investigations have been challenging to carry out comprehensively, leading to insufficient and unsystematic data collection. To provide data support and monitoring for regional territorial spatial planning and geological disaster prevention and control, this paper proposes a new method to study the correlation between soil moisture changes and geological disasters. Six high-resolution Landsat remote sensing images were used as the main data sources to process the image band data, and terrain factors were extracted and classified using a digital elevation model (DEM). Meanwhile, a Normalized Difference Vegetation Index–Land Surface Temperature (NDVI-LST) feature space was constructed. The Temperature Vegetation Drought Index (TVDI) was calculated to analyze the variation trend and influencing factors of soil moisture in the study area. The research results showed that the variation in soil moisture in the study area was relatively stable, and the overall soil moisture content was high (0.18 < TVDI < 0.33). However, due to the large variation in topographic relief, it could provide power and be a source basis for geological disasters such as landslide and collapse, so the inversion value of TVDI was small. The minimum and maximum values of the correlation coefficient (R²) were 0.60 and 0.72, respectively, indicating that the surface water content was relatively large, which was in good agreement with the calculated results of vegetation coverage and conducive to the restoration of ecological stability. In general, based on the characteristics of remote sensing technology and the division of soil moisture critical values, the promoting and hindering effects of soil moisture on geological hazards can be accurately described, and the research results can provide effective guidance for the prevention and control of geological hazards in this region. Full article

Yeasts are the usual contaminants in fruit juices and other beverages, responsible for the decrease in the quality and shelf-life of such products. Preservatives are principally added to these beverages to enhance their shelf-life. With the increasing consumer concern towards chemical food additives, plant-derived antimicrobials have attracted the attention of researchers as efficient and safer anti-yeast agents. However, the methods currently used for determining their anti-yeast activity are time- and material-consuming. In this study, the anti-yeast effect of plant phenolic compounds in apple and orange juice food models using microtiter plates has been evaluated in order to validate the modified broth microdilution method for screening the antimicrobial activity of juice preservative agents. Among the twelve compounds tested, four showed a significant in vitro growth-inhibitory effect against all tested yeasts (Saccharomyces cerevisiae, Zygosaccharomyces bailii, and Zygosaccharomyces rouxii) in both orange and apple juices. The best results were obtained for pterostilbene in both juices with minimum inhibitory concentrations (MICs) ranging from 32 to 128 μg/mL. Other compounds, namely oxyresveratrol, piceatannol, and ferulic acid, exhibited moderate inhibitory effects with MICs of 256–512 μg/mL. Furthermore, the results indicated that differences in the chemical structures of the compounds tested significantly affected the level of yeast inhibition, whereas stilbenes with methoxy and hydroxy groups produced the strongest effect. Furthermore, the innovative assay developed in this study can be used for screening the anti-yeast activity of juice preservative agents because it saves preparatory and analysis time, laboratory supplies, and manpower in comparison to the methods commonly used. Full article

Co-curricular participation is a required component of the pharmacy program. Assessment of co-curricular activities has proven challenging due to lack of manpower to address the workload of reviewing multiple critical reflections. This project documented the professionalization impact of co-curricular involvement and secondarily explored the utility of our assessment tool, the Co-curricular Impact Scale (CIS), developed to streamline the assessment process. First- through third-professional-year students (P1, P2, P3) participated in five co-curricular domains: (i) professional development/education; (ii) patient care service; (iii) legislative advocacy; (iv) leadership/service to the pharmacy profession; and (v) healthcare-related community service. For the CIS, 16 questions were developed and mapped to 11 educational outcomes and included assessing the impact of immersing in an authentic learning experience, collaborating with healthcare professionals, and preparing for the pharmacist role. A group of 296 students rated the impact of participation as low, moderate, or significant for five events annually. Based on 717 entries, the two attributes deemed most impactful were: “Activity immersed me in an authentic learning experience” (95% ≥ Moderate Impact) and “Activity improved my self-confidence” (93% ≥ Moderate Impact). P1 students found slightly less impact in co-curricular participation (83.5%) than P2 (88.4%) and P3 (86.8%) counterparts. The CIS proved to be an efficient method to collate impact of co-curricular involvement upon student professionalization. Full article

(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor requirements and improves yield by efficiently performing all the cultivation activities related to field vegetables, particularly onions and garlic, is essential. In this study, we propose methods to identify onion and garlic plants with the best growth status and accurately predict their live bulb weight by regularly photographing their growth status using a multispectral camera mounted on a drone. (2) Methods: This study was conducted in four stages. First, two pilot blocks with a total of 16 experimental units, four horizontals, and four verticals were installed for both onions and garlic. Overall, a total of 32 experimental units were prepared for both onion and garlic. Second, multispectral image data were collected using a multispectral camera repeating a total of seven times for each area in 32 experimental units prepared for both onions and garlic. Simultaneously, growth data and live bulb weight at the corresponding points were recorded manually. Third, correlation analysis was conducted to determine the relationship between various vegetation indexes extracted from multispectral images and the manually measured growth data and live bulb weights. Fourth, based on the vegetation indexes extracted from multispectral images and previously collected growth data, a method to predict the live bulb weight of onions and garlic in real time during the cultivation period, using functional regression models and machine learning methods, was examined. (3) Results: The experimental results revealed that the Functional Concurrence Regression (FCR) model exhibited the most robust prediction performance both when using growth factors and when using vegetation indexes. Following closely, with a slight distinction, Gaussian Process Functional Data Analysis (GPFDA), Random Forest Regression (RFR), and AdaBoost demonstrated the next-best predictive power. However, a Support Vector Machine (SVM) and Deep Neural Network (DNN) displayed comparatively poorer predictive power. Notably, when employing growth factors as explanatory variables, all prediction models exhibited a slightly improved performance compared to that when using vegetation indexes. (4) Discussion: This study explores predicting onion and garlic bulb weights in real-time using multispectral imaging and machine learning, filling a gap in research where previous studies primarily focused on utilizing artificial intelligence and machine learning for productivity enhancement, disease management, and crop monitoring. (5) Conclusions: In this study, we developed an automated method to predict the growth trajectory of onion and garlic bulb weights throughout the growing season by utilizing multispectral images, growth factors, and live bulb weight data, revealing that the FCR model demonstrated the most robust predictive performance among six artificial intelligence models tested. Full article

Population aging and multimorbidity challenge health system sustainability, but the role of assistance-related variables rather than individual pathophysiological factors in determining patient outcomes is unclear. To identify assistance-related determinants of sustainable hospital healthcare, all patients hospitalised in an Internal Medicine Unit (n = 1073) were enrolled in a prospective year-long observational study and split 2:1 into a training (n = 726) and a validation subset (n = 347). Demographics, comorbidities, provenance setting, estimates of complexity (cumulative illness rating scale, CIRS: total, comorbidity, CIRS-CI, and severity, CIRS-SI subscores) and intensity of care (nine equivalents of manpower score, NEMS) were analysed at individual and Unit levels along with variations in healthcare personnel as determinants of in-hospital mortality, length of stay and nosocomial infections. Advanced age, higher CIRS-SI, end-stage cancer, and the absence of immune-mediated diseases were correlated with higher mortality. Admission from nursing homes or intensive care units, dependency on activity of daily living, community- or hospital-acquired infections, oxygen support and the number of exits from the Unit along with patient/physician ratios were associated with prolonged hospitalisations. Upper gastrointestinal tract disorders, advanced age and higher CIRS-SI were associated with nosocomial infections. In addition to demographic variables and multimorbidity, physician number and assistance context affect hospitalisation outcomes and healthcare sustainability. Full article

(1) Background: Type 2 Diabetes (T2D) is associated with reduced muscle mass, strength, and function, leading to frailty. This study aims to analyze the movement patterns (MPs) of older individuals with T2D across varying levels of physical capacity (PC). (2) Methods: A cross-sectional study was conducted among individuals aged 60 or older with T2D. Participants (n = 103) were equipped with a blinded continuous glucose monitoring (CGM) system and an activity monitoring device for one week. PC tests were performed at the beginning and end of the week, and participants were categorized into three groups: low PC (LPC), medium PC (MPC), and normal PC (NPC). Group differences in MPs and physical activity were analyzed using non-parametric Kruskal–Wallis tests for both categorical and continuous variables. Dunn post-hoc statistical tests were subsequently carried out for pairwise comparisons. For data analysis, we utilized pandas, a Python-based data analysis tool, and conducted the statistical analyses using the scipy.stats package in Python. The significance level was set at p < 0.05. (3) Results: Participants in the LPC group showed lower medio-lateral acceleration and higher vertical and antero-posterior acceleration compared to the NPC group. LPC participants also had higher root mean square values (1.017 m/s²). Moreover, the LPC group spent less time performing in moderate to vigorous physical activity (MVPA) and had fewer daily steps than the MPC and NPC groups. (4) Conclusions: The LPC group exhibited distinct movement patterns and lower activity levels compared to the NPC group. This study is the first to characterize the MPs of older individuals with T2D in their free-living environment. Several accelerometer-derived features were identified that could differentiate between PC groups. This novel approach offers a manpower-free alternative to identify physical deterioration and detect low PC in individuals with T2D based on real free-living physical behavior. Full article

Voters use traditional paper ballots, a method limited by the factors of time and space, to ensure their voting rights are exercised; this method requires a lot of manpower and resources. Duplicate voting problems may also occur, meaning the transparency and reliability of the voting results cannot be guaranteed. With the rapid developments in science and technology, E-voting system technology is being adopted more frequently in election activities. However, E-voting systems still cannot address the verifiability of the election process; the results of a given election and the credibility of the host organization will be questioned if the election’s verifiability cannot be ensured. Elections may also pose a series of problems related to privacy, security, and so on. To address these issues, this paper presents a public, and verifiable E-voting system with hidden statistics; this system is based on commitment, zk-SNARKs, and machine learning. The system can deal with a large number of candidates, complex voting methods, and result functions in counting both hidden and public votes and can satisfy the requirements of verifiability, privacy, security, and intelligence. Our security analysis shows that our scheme achieves privacy, hidden vote counting and verifiability. Our performance evaluation demonstrates that our system has reasonable applications in real scenarios. Full article

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system’s normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset’s optimization to demonstrate the expanded dataset’s dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks. Full article

Previous crisis communication research has been based on qualitative methods such as interviews or questionnaires, which require considerable manpower, material resources, and time to focus on specific topics. The current situation needs to be reflected timelier. With the rise of social communities, community users’ comments have gradually become an important reference for other community members. Twitter is one of the most popular social media in the world. During the COVID-19 pandemic, people were restricted by rules and government policies, such as wearing masks, maintaining social distancing, and avoiding crowding. This led people to spend time on devices. By using devices, most people are involved in social media activities. This study aims to discover the awareness Indonesians display in the text they upload to Twitter. Using the Twitter crawling technique, we collected data. We also analyzed the text with text mining techniques and latent semantic analysis (LSA) with semiotic methods. The crisis communication was classified, and the definition of crisis terminology was improved in social media. Full article

Tremendous amount of financial resources and manpower have been invested to understand the function of numerous genes that are deregulated during the carcinogenesis process, which can be targeted for anticancer therapeutic interventions. Death-associated protein kinase 1 (DAPK-1) is one of the genes that have shown potential as biomarkers for cancer treatment. It is a member of the kinase family, which also includes Death-associated protein kinase 2 (DAPK-2), Death-associated protein kinase 3 (DAPK-3), Death-associated protein kinase-related apoptosis-inducing kinase 1 (DRAK-1) and Death-associated protein kinase-related apoptosis-inducing kinase 2 (DRAK-2). DAPK-1 is a tumour-suppressor gene that is hypermethylated in most human cancers. Additionally, DAPK-1 regulates a number of cellular processes, including apoptosis, autophagy and the cell cycle. The molecular basis by which DAPK-1 induces these cell homeostasis-related processes for cancer prevention is less understood; hence, they need to be investigated. The purpose of this review is to discuss the current understanding of the mechanisms of DAPK-1 in cell homeostasis-related processes, especially apoptosis, autophagy and the cell cycle. It also explores how the expression of DAPK-1 affects carcinogenesis. Since deregulation of DAPK-1 is implicated in the pathogenesis of cancer, altering DAPK-1 expression or activity may be a promising therapeutic strategy against cancer. Full article

Despite decades of research on the impact of interprofessional collaboration (IPC), we still lack definitive proof that team-based care can lead to a tangible effect on healthcare outcomes. Without return on investment (ROI) evidence, healthcare leaders cannot justifiably throw their weight behind IPC, and the institutional push for healthcare manpower reforms crucial for facilitating IPC will remain variable and fragmentary. The lack of proof for the ROI of IPC is likely due to a lack of a unifying conceptual framework and the over-reliance on the single-method study design. To address the gaps, this paper describes a protocol which uses as a framework the Quadruple Aim which examines the ROI of IPC using four dimensions: patient outcomes, patient experience, provider well-being, and cost of care. A multimethod approach is proposed whereby patient outcomes are measured using quantitative methods, and patient experience and provider well-being are assessed using qualitative methods. Healthcare costs will be calculated using the time-driven activity-based costing methodology. The study is set in a Singapore-based national and regional center that takes care of patients with neurological issues. Full article