Resource-limited Environments Research Articles

Lightweight Implementation of the Signal Enhancement Model for Early Wood-Boring Pest Monitoring

Wood-boring pests are one of the most destructive forest pests. However, the early detection of wood-boring pests is extremely difficult because their larvae live in tree trunks and have high invisibility. Borehole listening technology is a new and effective method to detect the larvae of insect pests. It identifies infested trees by analyzing wood-boring vibration signals. However, the collected wood-boring vibration signals are often disturbed by various noises existing in the field environment, which reduces the accuracy of pest detection. Therefore, it is necessary to filter out the noise and enhance the wood-boring vibration signals to facilitate the subsequent identification of pests. The current signal enhancement models are all designed based on deep learning models, which have complex scales, a large number of parameters, high demands for storage resources, large computational complexity, and high time costs. They often run on resource-rich computers or servers, and they are difficult to deploy to resource-limited field environments to realize the real-time monitoring of pests; as well, they have low practicability. Therefore, this study designs and implements two model lightweight optimization algorithms, one is a pre-training pruning algorithm based on masks, and the other is a knowledge distillation algorithm based on the separate transfer of vibration signal knowledge and noise signal knowledge. We apply the two lightweight optimization algorithms to the signal enhancement model T-CENV with good performance outcomes and conduct a series of ablation experiments. The experimental results show that the proposed methods effectively reduce the volume of the T-CENV model, which make them useful for the deployment of signal enhancement models on embedded devices, improve the usability of the model, and help to realize the real-time monitoring of wood-boring pest larvae.

Drug resistance profiling, antimicrobial susceptibility and demographic characteristics of children with acute bacterial meningitis in a Southeastern tertiary health facility

Background: Over the years, varying patterns of bacterial susceptibility and multidrug resistance (MDR) rates have been reported in different settings. Detailed evaluation of the drug profile of the bacterial pathogens implicated in children with invasive bacterial infection helps to reduce the heightened risk of adverse events that could follow poorly managed or complicated cases in resource limited environment like ours. This study therefore, aimed to highlight the antibiotic susceptibility and MDR rate, and establish the relationship if any, between demographic characteristics and positive CSF- bacterial isolates of post neonatal children with suspected acute bacterial meningitis (ABM). The findings would guide practitioners on the empirical antimicrobials to consider in the event of clinical suspicion of ABM pending the availability of CSF isolates' antibiogram. Methods: A prospective review of 100 children with clinical suspicion of ABM from January 2016- December 2020. Descriptive statistics, chi square and regression analysis were used to establish MDR rates, Isolates' susceptibility pattern and the relationship between demographic variables and positive isolates respectively. P<0.05 was accepted as significant. Results: Fluroquinolones, cephalosporins, imipenem and aminoglycosides were susceptible anti-microgram in children with ABM. Sixty-four (85.5%) of the isolates showed MDR pattern, and young children (infants and toddlers) were significantly associated with positive CSF bacterial isolates. Conclusions: ABM should be treated with combination of CNS penetrating empirical antibiotics due to rising rate of MDR pathogens. Young children with febrile illnesses should be thoroughly evaluated for possibility of CNS infection.

Medical silos, social identity, and duty of care: A call for health leaders to improve transitions of care.

This article explores the concept of medical silos, particularly within hospital systems, and examines their deeper roots in social identity and the fiduciary duty of care of healthcare providers. While traditional perspectives focus on informational and communication barriers, this analysis highlights how professional identity and moral obligations contribute to the persistence of silos. Social identity theory reveals that strong in-group affiliations, formed during medical training and specialization, fosters collaboration within groups but also create divisions between them. Similarly, the fiduciary duty of care, central to ethical medical practice, may inadvertently reinforce silo boundaries in resource-limited environments. By emphasizing the role of centralized leadership, the article proposes that health system managers and leaders, with the broadest possible duty of care, must take action to dismantle these barriers. Recommendations include re-evaluating policies for patient transitions and fostering integrated care pathways to improve overall system flow, rather than simply balancing the agendas of stakeholders within their silos.

Impact of Nutrient Starvation on Biofilm Formation in Pseudomonas aeruginosa: An Analysis of Growth, Adhesion, and Spatial Distribution.

Objectives: This study investigates the impact of nutrient availability on the growth, adhesion, and biofilm formation of Pseudomonas aeruginosa ATCC 27853 under static conditions. Methods: Bacterial behaviour was evaluated in nutrient-rich Luria-Bertani (LB) broth and nutrient-limited M9 media, specifically lacking carbon (M9-C), nitrogen (M9-N), or phosphorus (M9-P). Bacterial adhesion was analysed microscopically during the transition from reversible to irreversible attachment (up to 120 min) and during biofilm production/maturation stages (up to 72 h). Results: Results demonstrated that LB and M9 media supported bacterial growth, whereas nutrient-starved conditions halted growth, with M9-C and M9-N inducing stationary phases and M9-P leading to cell death. Fractal analysis was employed to characterise the spatial distribution and complexity of bacterial adhesion patterns, revealing that nutrient-limited conditions affected both adhesion density and biofilm architecture, particularly in M9-C. In addition, live/dead staining confirmed a higher proportion of dead cells in M9-P over time (at 48 and 72 h). Conclusions: This study highlights how nutrient starvation influences biofilm formation and bacterial dispersion, offering insights into the survival strategies of P. aeruginosa in resource-limited environments. These findings should contribute to a better understanding of biofilm dynamics, with implications for managing biofilm-related infections and industrial biofouling.

An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning

The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and clinicians to make better-informed decisions concerning the management of nodules. However, currently existing nodule classification techniques have a singular function of nodule classification and rely on an extensive amount of high-quality annotation data, which does not meet the requirements of clinical practice. To address this issue, we develop an anthropomorphic diagnosis system of pulmonary nodules (PN) based on deep learning (DL) that is trained by weak annotation data and has comparable performance to full-annotation based diagnosis systems. The proposed system uses DL models to classify PNs (benign vs. malignant) with weak annotations, which eliminates the need for time-consuming and labor-intensive manual annotations of PNs. Moreover, the PN classification networks, augmented with handcrafted shape features acquired through the ball-scale transform technique, demonstrate capability to differentiate PNs with diverse labels, including pure ground-glass opacities, part-solid nodules, and solid nodules. Through 5-fold cross-validation on two datasets, the system achieved the following results: (1) an Area Under Curve (AUC) of 0.938 for PN localization and an AUC of 0.912 for PN differential diagnosis on the LIDC-IDRI dataset of 814 testing cases, (2) an AUC of 0.943 for PN localization and an AUC of 0.815 for PN differential diagnosis on the in-house dataset of 822 testing cases. In summary, our system demonstrates efficient localization and differential diagnosis of PNs in a resource limited environment, and thus could be translated into clinical use in the future.

Conceptual model of the technology for calculating the similarity threshold of two audio sequences

The paper is focused on the pressing problem of speaker verification by means of voice time series comparison. The aim of this paper is to determine the orders of mel-frequency cepstral coefficients that most accurately describe the difference, between an authentic voice and an artificially generated copy for their further use as input to a neural network model in a resource-limited environment. To achieve this goal, the following tasks were accomplished: a conceptual model of the technology for determining the similarity threshold of two audio series was developed; the orders of fine-frequency cepstral coefficients with the most characteristic differences between the recording and the generated voice were determined on the basis of neural network analysis; an experimental study of the dependence of the execution time and computational load on the created feature vector when assessing the degree of similarity of two time series was conducted; and the optimal similarity threshold was determined on the basis of the chosen dataset. The developed model of the technology for determining the similarity threshold was tested on a dataset that is a combination of the DEEP-VOICE dataset and our own dataset. The demonstrated result of applying the developed technology showed an increase of 43% when using the specified MFCCs compared to using all of them. Based on experimental studies, the DTW acceptance threshold was set at 0.37.

Point-of-care ultrasound in resource-limited environments: a review

Ultrasound is a standard assessment modality in hospital and prehospital settings. It is becoming more accessible in remote, austere and resource-limited environments. Aim: This article aims to review the literature on the use of point-of-care ultrasound (POCUS) imaging in low- to medium-income countries (LMICs) and by practitioners working in remote and austere out-of-hospital environments. Methods: Fifteen articles published between 2019 and 2023 found via PubMed were identified and analysed. Results: The 15 papers present the use of ultrasound imaging by practitioners in LMICs: six were systemic reviews; five were narrative reviews; two were randomised clinical trials; and two were case studies. Conclusions: Few articles focusing on POCUS use in LMICs were published over the five-year period. However, the number of publications increased over the years. Of the 15 studies in this review, six (40%) were systematic reviews, five (33%) were narrative reviews, two (13%) were random clinical trials and two (13%) were case studies. All studies were conducted in district hospitals or other resource-limited environments. The studies have revealed a significant increase in POCUS use over the past few years, with the majority of the devices now being smartphone-based. This is a promising development in POCUS use in austere environments.

A lightweight visual mamba network for image recognition under resource-limited environments

The Vision Transformers (ViTs) models show great potential in recognition due to their excellent self-attention mechanisms. However, they often need more computational complexity, making achieving high performance in resource-constrained environments challenging. This paper proposes a lightweight visual model (called LightViM) to tackle these challenges, specifically devised for recognition challenges under resource-constrained environments. Considering the imperative of reducing the model’s computational complexity and resource consumption , we propose lightweight local–global feature fusion modules based on Mamba (LGF-Mamba) aimed at integrating spatially detailed local information with globally contextualized features while maintaining lower linear time complexity. In LGF-Mamba, Mamba sub-blocks are first designed to extract global information; then, for the rapid extraction of local features, LocalE module is designed and integrated into LGF-Mamba, exploring more comprehensive feature representation. This mechanism efficiently directs the network to integrate features from different spatial scales, thereby improving the recognition performance in resource-constrained environments. Experimental results indicate that, in comparison to other leading-edge lightweight methods, the proposed procedure simultaneously achieves superior recognition performance and minimal resource consumption.

Managing Patients With COVID-19 in Armenia Using a Remote Monitoring System: Descriptive Study.

The COVID-19 pandemic has imposed immense stress on global health care systems, especially in low- and middle-income countries (LMICs). Armenia, a middle-income country in the Caucasus region, contended with the pandemic and a concurrent war, resulting in significant demand on its already strained health care infrastructure. The COVID@home program was a multi-institution, international collaboration to address critical hospital bed shortages by implementing a home-based oxygen therapy and remote monitoring program. The objective of this study was to describe the program protocol and clinical outcomes of implementing an early discharge program in Armenia through a collaboration of partner institutions, which can inform the future implementation of COVID-19 remote home monitoring programs, particularly in LMICs or low-resource settings. Seven hospitals in Yerevan participated in the COVID@home program. A web app based on OpenMRS was developed to facilitate data capture and care coordination. Patients meeting eligibility criteria were enrolled during hospitalization and monitored daily while on oxygen at home. Program evaluation relied on data extraction from (1) eligibility and enrollment forms, (2) daily monitoring forms, and (3) discharge forms. Over 11 months, 439 patients were screened, and 221 patients were managed and discharged. Around 94% (n=208) of participants safely discontinued oxygen therapy at home, with a median home monitoring duration of 26 (IQR 15-45 days; mean 32.33, SD 25.29) days. Women (median 28.5, mean 35.25 days) had similar length of stay to men (median 26, mean 32.21 days; P=.75). Despite challenges in data collection and entry, the program demonstrated feasibility and safety, with a mortality rate below 1% and low re-admission rate. Opportunities for operational and data quality improvements were identified. This study contributes practical evidence on the implementation and outcomes of a remote monitoring program in Armenia, offering insights into managing patients with COVID-19 in resource-constrained settings. The COVID@home program's success provides a model for remote patient care, potentially alleviating strain on health care resources in LMICs. Policymakers can draw from these findings to inform the development of adaptable health care solutions during public health crises, emphasizing the need for innovative approaches in resource-limited environments.

Model development for bespoke large language models for digital triage assistance in mental health care

Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) — a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content.A significant problem for the United Kingdom’s National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data (NHS Digital, 2024), in each month of 2023, there were between 370,000 and 470,000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient’s EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients.The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions.We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and its architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data are appropriately controlled and governed. Code available at: https://github.com/NtaylorOX/BespokeLLM_Triage.

Development of instructional design principles for using ICT in resource-limited learning environments: a case of Bangladesh

The integration of ICT in education in developing countries has become a significant focus in various ODA projects and educational research. With the increasing demand for teachers to incorporate ICT in their classrooms, the precise methods for effectively integrating ICT resources have not been thoroughly explored. Therefore, this research aimed to develop the most optimal instructional design principles that may systematically guide teachers in planning their lessons using ICT, specifically in the context of Bangladesh. By employing the design and development research method, this research developed an initial set of instructional design principles based on literature review and field research, then elaborated on the principles through three sets of internal validation tests and a usability test by conducting in-depth interviews to confirm the applicability of the principles in real classroom settings in Bangladesh. The final version of instructional design principles is composed of five components, ten principles, and 22 specific guidelines. Two distinct features of the principles are discussed for further research. In discussion, this research underscores the necessity of providing practical instructional design principles to guide teachers in effectively utilizing ICT, particularly within a resource-limited learning environment like Bangladesh.

Characterizing the soil microbial community associated with the fungal pathogen Coccidioides immitis.

Coccidioidomycosis is a fungal disease affecting humans and other mammals, caused by environmental pathogens of the genus Coccidioides . Understanding the ecological factors that shape the distribution of Coccidioides in soils is important for minimizing the risk of human exposure, though this remains challenging due to the pathogen's highly variable spatial distribution. Here, we examined associations between the soil microbial community and Coccidioides immitis presence within the Carrizo Plain National Monument-a minimally disturbed grassland ecosystem, and the site of a longitudinal study examining the effects of rodents and their burrows on C. immitis presence in soils. Using internal transcribed spacer 2 (ITS2) and 16S sequencing to characterize the soil fungal and bacterial communities, we found over 30 fungal species, including several other members of the Onygenales order, that co-occurred with Coccidioides more frequently than expected by chance. Coccidioides -positive samples were significantly higher in microbial diversity than negative samples, an association partly driven by higher Coccidioides presence within rodent burrows compared to surface soils. Soil source ( i.e., rodent burrow versus surface soil) explained the largest amount of variation in bacterial and fungal community diversity and composition, with soils collected from rodent burrows having higher microbial diversity than those collected from adjacent surface soils. While prior evidence is mixed regarding associations between Coccidioides and microbial diversity, our study suggests that favorable microhabitats such as rodent burrows can lead to a positive association between soil diversity and Coccidioides presence, particularly in otherwise resource-limited natural environments.

Detecting emerald ash borer boring vibrations using an encoder-decoder and improved DenseNet model.

Forest ecosystems are under constant threat from wood-boring pests such as the Emerald ash borer (EAB), which remain elusive owing to their hidden life cycles within tree trunks. Early detection is vital to mitigate economic and ecological damage. The main current monitoring method is manual detection which is ineffective at early stages of infestation. This study introduces VibroEABNet, a deep learning-based joint recognition network designed to enhance the detection of EAB boring vibration signals, with a novel approach integrating denoising and recognition modules. The proposed VibroEABNet model demonstrated exceptional performance, achieving an average accuracy of 98.98% across multiple signal-to-noise ratios (SNRs) in test datasets and a remarkable 97.5% accuracy in real forest datasets, surpassing traditional models and other deep learning networks evaluated in this study. These findings were supported by rigorous noise resistance analysis and real dataset evaluation, indicating the model's robustness and reliability in practical applications. Furthermore, the model's efficiency was highlighted by its inference time of 26 ms and a compact model size of 8.43 MB, underscoring its suitability for deployment in resource-limited environments. The development of VibroEABNet marks a significant advancement in pest detection methodologies, offering a scalable, accurate and efficient solution for early monitoring of wood-boring pests. The integration of a denoising module within the network structure addresses the challenge of environmental noise, one of the primary limitations in acoustic monitoring of pests. Currently, this research is limited to a specific pest. Future work will focus on the applicability of this network to other wood-boring pests. © 2024 Society of Chemical Industry.

Making gene editing accessible in resource limited environments: recommendations to guide a first-time user.

The designer nuclease, CRISPR-Cas9 system has advanced the field of genome engineering owing to its programmability and ease of use. The application of these molecular scissors for genome engineering earned the developing researchers the Nobel prize in Chemistry in the year 2020. At present, the potential of this technology to improve global challenges continues to grow exponentially. CRISPR-Cas9 shows promise in the recent advances made in the Global North such as the FDA-approved gene therapy for the treatment of sickle cell anaemia and β-thalassemia and the gene editing of porcine kidney for xenotransplantation into humans affected by end-stage kidney failure. Limited resources, low government investment with an allocation of 1% of gross domestic production to research and development including a shortage of skilled professionals and lack of knowledge may preclude the use of this revolutionary technology in the Global South where the countries involved have reduced science and technology budgets. Focusing on the practical application of genome engineering, successful genetic manipulation is not easily accomplishable and is influenced by the chromatin landscape of the target locus, guide RNA selection, the experimental design including the profiling of the gene edited cells, which impacts the overall outcome achieved. Our assessment primarily delves into economical approaches of performing efficient genome engineering to support the first-time user restricted by limited resources with the aim of democratizing the use of the technology across low- and middle-income countries. Here we provide a comprehensive overview on existing experimental techniques, the significance for target locus analysis and current pitfalls such as the underrepresentation of global genetic diversity. Several perspectives of genome engineering approaches are outlined, which can be adopted in a resource limited setting to enable a higher success rate of genome editing-based innovations in low- and middle-income countries.

Amino acid sensitive latent fingermark detection formulations based upon improvised carrier solvents for resource-limited environments: A proof-of-concept study

HFE-7100 and petroleum ether (bp 40–60 °C) are routine carrier solvents used in amino acid sensitive fingermark reagents such as 1,2-indanedione/zinc chloride (IND-Zn) and ninhydrin. However, limited resource jurisdictions face major challenges in sourcing laboratory-grade reagents due to budgetary and geographical restrictions. Common hydrocarbon solvents, available from hardware stores and similar outlets, may offer a more readily available and inexpensive alternative for such jurisdictions. This study assessed the range of improvised carrier solvents for their suitability in the IND-Zn formulation and found that eight different hydrocarbon solvents were able to develop fingermarks when substituted into the formulation. The formulation based on shellite solvent outperformed all other improvised formulations tested, providing high-quality fingermarks and similar sensitivity to a petroleum ether (bp 40–60 °C) based formulation across six porous substrates using six donors. A similar approach was applied to ninhydrin, where it was discovered that formulations using kerosene, white spirits and shellite as carrier solvents performed comparably to a formulation based on petroleum ether (bp 40–60 °C). By chemically characterising each solvent using gas chromatography-mass spectrometry, this study also provided a greater understanding of how the chemical composition of a carrier solvent impacts the efficacy of the resulting IND-Zn formulation, emphasising that hydrocarbon solvents should contain short-chain alkanes and minimal aromatic content to give a more volatile formulation. Additionally, the identity of the solvent was found to cause slight differences in the colour and luminescence intensity of ink diffusion on porous substrates. These results indicate that improvised hydrocarbon solvents are suitable alternatives to common carrier solvents with their operational use offering major benefits for limited resource jurisdictions by reducing costs and supply chain risks. A thorough risk assessment must be conducted by each jurisdiction that intends to use this method as health and safety considerations are concerns with these improvised solvents.

Effects of periodic bottlenecks on the dynamics of adaptive evolution in microbial populations.

Population bottlenecks can impact the rate of adaptation in evolving populations. On the one hand, each bottleneck reduces the genetic variation that fuels adaptation. On the other hand, each founder that survives a bottleneck can undergo more generations and leave more descendants in a resource-limited environment, which allows surviving beneficial mutations to spread more quickly. A theoretical model predicted that the rate of fitness gains should be maximized using ~8-fold dilutions. Here we investigate the impact of repeated bottlenecks on the dynamics of adaptation using numerical simulations and experimental populations of Escherichia coli. Our simulations confirm the model's prediction when populations evolve in a regime where beneficial mutations are rare and waiting times between successful mutations are long. However, more extreme dilutions maximize fitness gains in simulations when beneficial mutations are common and clonal interference prevents most of them from fixing. To examine these predictions, we propagated 48 E. coli populations with 2-, 8-, 100-, and 1000-fold dilutions for 150 days. Adaptation began earlier and fitness gains were greater with 100- and 1000-fold dilutions than with 8-fold dilutions, consistent with the simulations when beneficial mutations are common. However, the selection pressures in the 2-fold treatment were qualitatively different from the other treatments, violating a critical assumption of the model and simulations. Thus, varying the dilution factor during periodic bottlenecks can have multiple effects on the dynamics of adaptation caused by differential losses of diversity, different numbers of generations, and altered selection.

From passion to profit: the growth of Mambokadzi Bath and Body

Learning outcomes By the end of the lesson, students should be able to identify and think critically about common business problems and come up with possible solutions, explain how a business owner’s attributes can influence the business decisions they make, use available information to assess the viability and sustainability of a business model/strategy, determine the strengths and weaknesses of a particular marketing strategy and suggest ways to improve it and use the information provided to evaluate the risks and benefits of business growth strategies and determine their appropriateness for specified circumstances. Case overview/synopsis Mambokadzi is a small business with considerable potential in Zimbabwe’s beauty and personal care industry. The case study spans several years, highlighting Sharon Chingwaro’s journey as she navigates the difficulties of starting and growing a business with limited resources. The case study delves into various aspects of running a small business, such as production, marketing, distribution, human resources and intellectual property. It also discusses the value of resilience and endurance, as well as the need for resourcefulness when starting and growing a business under difficult circumstances. This case study is intended to teach entrepreneurship and marketing strategies to both under- and postgraduate students, with a particular emphasis on the difficulties of starting and growing a business in a limited-resource environment. It has applications in a variety of fields, including entrepreneurship, strategic management and marketing. Complexity academic level This case study is suitable for undergraduate and postgraduate students. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship.

LVD-YOLO: An efficient lightweight vehicle detection model for intelligent transportation systems

Vehicle detection is a fundamental component of intelligent transportation systems. However, current algorithms often encounter issues such as high computational complexity, long execution times, and significant resource demands, making them unsuitable for resource-limited environments. To overcome these challenges, we propose LVD-YOLO, a Lightweight Vehicle Detection Model based on YOLO. This model incorporates the EfficientNetv2 network structure as its backbone, which reduces parameters and enhances feature extraction capabilities. By utilizing a bidirectional feature pyramid structure along with a dual attention mechanism, we enable efficient information exchange across feature layers, thereby improving multiscale feature fusion. Additionally, we refine the model's loss function with SIoU loss to boost regression and prediction performance. Experimental results on the PASCAL VOC and MS COCO datasets show that LVD-YOLO outperforms YOLOv5s, achieving a 0.5% increase in accuracy while reducing FLOPs by 64.6% and parameters by 48.6%. These improvements highlight its effectiveness for use in resource-constrained environments.

Highly sensitive detection of carbendazim in agricultural products using colorimetric and photothermal lateral flow immunoassay based on plasmonic gold nanostars

The wide use and high toxicity of carbendazim (CBD) in agriculture pose unprecedented demands for convenient, sensitive, and cost-effective on-site monitoring. Herein, we propose a novel colorimetric and photothermal dual-mode lateral flow immunoassay (LFIA) based on plasmonic gold nanostars (AuNSs) for CBD detection in agricultural products. The AuNSs were synthesized via a rapid seed-mediated growth method (with growth time of ∼5 s). A stable immunoprobe was formed by adsorbing CBD antibodies onto AuNSs. This immunoprobe exhibited high conversion efficiency and sensitivity in photothermal detection with a low limit of detection (LOD) of 0.28 ng mL−1. The LOD of the colorimetric mode was higher (0.48 ng mL−1). The results of CBD detection in various agricultural products aligned well with ultra-performance liquid chromatography tandem mass spectrometry. Overall, our LFIA shows excellent sensitivity, specificity, reproducibility, and rapidness in CBD detection, and thus is a highly potential on-site platform in resource-limited environments.

External Support and Demographic Factors as Differentials of Achievement Goal Orientation among Adolescent Students in Resource-Limited Environments

External Support and Demographic Factors as Differentials of Achievement Goal Orientation among Adolescent Students in Resource-Limited Environments