Mixed Set Research Articles

Experiences of people with disability living in permanent supportive housing in the context of the National Disability Insurance Scheme

AbstractPeople with disabilities are more likely to be homeless than the general population and experience additional barriers to accessing housing. Despite this, there is limited research exploring the intersection of homelessness and disability, or the impact of responses such as permanent supportive housing (PSH) from the perspective of people with disabilities. This paper reports on research that explored the experiences of tenants with disabilities in a single site mixed tenancy PSH setting and the extent to which the housing and supports they received addressed psychosocial, welfare and health needs. The research also explored experiences of accessing Australia's National Disability Insurance Scheme (NDIS) as well as other community supports designed to promote inclusion. We found benefits included a sense of stability and ‘home’ whilst challenges related to the limits of congregate housing for this tenant cohort and workforce capacity to manage significant complexity and competing needs. The findings highlight the need for trauma‐informed practices and design, and commitment to workforce development and capacity consistent with tenant needs. We also identified significant challenges and barriers at the interface with the NDIS and other funded services with implications for more assertive in reach education, support and planning to promote community participation.

Prevention and Treatment of Neonatal Infections in Facility and Community Settings of Low- and Middle-Income Countries: A Descriptive Review

Introduction: We present a robust and up-to-date synthesis of evidence on the effectiveness of interventions to prevent and treat newborn infections in low- and middle-income countries (LMICs). Newborn infection prevention interventions included strategies to reduce antimicrobial resistance (AMR), prevention of healthcare-associated infections (HAIs), clean birth kits (CBKs), chlorhexidine cleansing, topical emollients, and probiotic and synbiotic supplementation. Interventions to treat suspected neonatal infections included prophylactic systemic antifungal agents and community-based antibiotic delivery for possible serious bacterial infections (PSBIs). Methods: A descriptive review combining different methodological approaches was conducted. To provide the most suitable recommendations for real-world implementation, our analyses considered the impact of these interventions within three distinct health settings: facility, mixed, and community. Results: In facility settings, the strongest evidence supported the implementation of multimodal stewardship interventions for AMR reduction and device-associated infection prevention bundles for HAI prevention. Emollients in preterm newborns reduced the risk of invasive infection compared to routine skin care. Probiotics in preterm newborns reduced neonatal mortality, invasive infection, and necrotizing enterocolitis (NEC) risks compared to standard care or placebo. There was insufficient evidence for synbiotics and prophylactic systemic antifungals in LMICs. In mixed settings, CBKs reduced neonatal mortality risk compared to standard care. In community settings, chlorhexidine umbilical cord cleansing reduced omphalitis risk compared to dry cord care. For the treatment of PSBIs, purely domiciliary-based antibiotic delivery reduced the risk of all-cause neonatal mortality when compared to the standard hospital referral. Conclusion: Strategies for preventing HAIs and reducing AMR in healthcare facilities should be multimodal, and strategy selection should consider the feasibility of integration within existing newborn care programs. Probiotics are effective for facility-based use in preterm newborns; however, the establishment of high-quality, cost-effective mass production of standardized formulations is needed. Chlorhexidine cord cleansing is effective in community settings to prevent omphalitis in contexts where unhygienic cord applications are prevalent. Community-based antibiotic delivery of simplified regimens for PSBIs is a safe alternative when hospital-based care in LMICs is not possible or is declined by parents. More randomized trial evidence is needed to establish the effectiveness of CBKs, emollients, synbiotics, and prophylactic systemic antifungals in LMICs.

The Development of Simulated Dust-Devil-Like Vortices

Abstract In this study, the cause of rotation in simulated dust-devil-like vortices is investigated. The analysis uses a numerical simulation of an initially resting, dry, atmosphere, in which uniform surface heating leads to the development of a growing convective boundary layer (CBL). As soon as convective mixing sets in, regions of weak vertical vorticity develop at the lowest model level. Using forward trajectories, this vorticity is shown to originate from horizontal baroclinic production and simultaneous reorientation into the vertical within the descending branches of the convective cells. The requirement for vertical vorticity production in the downdraft cells is shown to be a nonaxisymmetric horizontal footprint of the downdraft regions. The resulting vertical vorticity is not initially associated with rotation. However, as the CBL matures, like-signed vortex patches merge, the vertical vorticity magnitude increases due to stretching, and deformation in the vortex patch decreases, leading to the development of vortices. The ultimate origin of the vortices is thus initially horizontal vorticity that has been produced baroclinically and that has subsequently been reoriented into the vertical in sinking air. Significance Statement Dust devils are concentrated vortices consisting of rapidly rising buoyant air, which may pose a risk to small aircraft and light structures on the ground. Although these vortices are a common occurrence in convective boundary layers, the origin of the vorticity within these vortices has not yet been fully established. The present study uses a numerical simulation of an evolving convective boundary layer and analyzes air parcel trajectories to identify the origin of vertical vorticity at the surface during dust-devil formation. The work contributes an answer to the long-standing question of what causes dust devils to spin.

Denoising diffusion probabilistic model enhanced tool condition monitoring method under imbalanced conditions

Abstract In recent years, tool condition monitoring (TCM) based on deep learning has been widely considered and achieved remarkable success. However, these methods typically require relatively large training samples to produce significant results, which are both imbalanced and rather troublesome to obtain in the practical application of TCM. To address this issue, a novel TCM method combined with multiscale permutation entropy (MPE), denoising diffusion probabilistic model (DDPM) and a residual network (ResNet) is proposed under conditions of sample imbalance. First, the one-dimensional sensing signal data is converted to a grayscale recurrence plot (RP) by minimizing the MPE of the signals in each channel. Second, combine and splice these grayscale RPs from different channels in each sample into color RPs. After that, the generated RP images using DDPM are added to the imbalanced dataset to augment the data to achieve a balanced state of the dataset. Finally, the balanced mixed data set of real and fake samples is input into a ResNet for recognition and monitoring tool conditions. TCM experiments are conducted to verify the performance of the proposed method with imbalanced dataset, and the results of experimental investigation demonstrate that the accuracy of the proposed method improved by 2%–18.8% compared to that of the other four sample augmentation methods using ResNet18 when the imbalance rate is 1:200.

Artificial intelligence can be trained to predict c-KIT-11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides.

Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene (c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. A relevant performance drop could be observed when the stain-scanner combination of training and test data set differed. Multi-institutional data sets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant (ie, intra-institutional). In summary, DLM-based morphological examination can predict c-KIT-11-ITD with high accuracy in canine MCTs in HE slides. However, staining protocol and scanner type influence accuracy. Larger data sets of scans from different laboratories and scanners may lead to more robust DLMs to identify c-KIT mutations in HE slides.

Empirical, computational studies and non-covalent interactions analysis of a novel salt with cadmium transition metal precursor

In this research paper, (C3H5N2)6[CdCl4][CdCl6] was successfully synthesized using a slow evaporation process. The structure was confirmed through single-crystal X-ray crystallography, FT-IR, and thermal analysis. The material was found to crystallize in the tetragonal system (space group I41/a) and the following parameters a = b = 12.0872 (8) Å; c = 24.6985 (16) Å, the crystal packing shows parallel layers of cations and stacks of discrete anions positioned at y = 1/4 and 3/4. The junction between the monoprotonated imidazolium cations and the anions, along with the crystal structure stability, relies on Cl···H−N, and Cl···H−C hydrogen bonds. Computational investigations, conducted using the B3LYP method with 6–311++G(d,p) and LANL2DZ mixed basis set, demonstrated close alignment between the computed and the experimental data, providing insights into the material's geometrical and vibrational properties. The non-covalent interactions were studied through Atoms-In-Molecule (AIM) and Reduced Density Gradient (RDG) analysis and quantitatively using the Hirshfeld surfaces associated with 2D fingerprint plots. Furthermore, thermal stability was assessed through Thermogravimetric and Differential Scanning Calorimetry (TG–DSC) analysis.

Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image.

Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR. A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively. For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87. Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.

Assessing vaccine hesitancy among healthcare providers in Brazil: the influence of vaccine status and professional experience

ObjectiveAssess the occurrence of vaccine hesitancy among pediatricians and their patients and identify potential predictors to mitigate hesitancy among them. MethodsThe study is a cross-sectional survey using an online questionnaire sent to pediatricians affiliated with the Brazilian Society of Pediatrics. The data was analyzed using statistical methods such as exploratory factor analysis, principal component analysis, correspondence analysis, and generalized linear mixed models. ResultsA total of 982 respondents, with a majority being females (77.4%), participated in the research. Among them, the proportion of pediatricians with complete vaccine status was 41.14%, while 90.6% had undergone medical residency. Furthermore, 9.3% worked in public healthcare settings, 30.4% in private settings, and 60.3% in mixed healthcare settings. The analysis revealed a significant association between vaccine status and pediatricians’ misconceptions about COVID-19 vaccines, with those having complete vaccine status showing lower misconceptions (mean difference of -0.15, p = 0.010). Moreover, pediatricians with medical residency experience exhibited fewer misconceptions about COVID-19 vaccines (mean difference of -0.33, p = 0.002). Additionally, correspondence analysis unveiled the presence of two distinct profiles among pediatricians, showcasing variations in vaccine education, professional experience, and vaccine confidence perceptions. ConclusionThe study highlights the influence of vaccine status and medical residency experience on pediatricians’ attitudes and misconceptions about vaccines, emphasizing the need for targeted educational interventions to promote vaccine confidence and combat hesitancy within the healthcare provider community.

Machine learning for nano-level defect detection in aligned random carbon nanotubes-reinforced electrically conductive nanocomposite

Machine learning allows fast nano-scale defect detection in polymer-impregnated aligned carbon nanotube (CNT) nanocomposites. Digital twins were populated by TEM-validated geometry; considered defects were flat cracks and close-to-spherical voids. Finite-element analysis of piezoresistive response was conducted by embedment of CNT network into matrix. Identification of a defect by change in CNT network piezoresistivity was challenged by: (1) randomness of CNTs’ shapes and placement, ML training happened on random realisations; (2) high strength of CNTs leading to the preservation of conductive paths along CNTs and changes only in conductivities of tunnelling contacts. “Artificial approximation“ was introduced to economise computer time multi-fold: ML was trained on cases with artificially degraded tunnelling conductivities within the defect. Three ML models: XGBoost, fully connected, and convolution neural networks were employed. All models managed the task for near-spherical voids, but performed poorly for flat cracks, due to the limited number of tunnelling contacts in crack volume. When trained on the mixed set of voids and cracks, both neural networks demonstrated the ability to learn the difference and detected even cracks, while XGBoost was not up to the challenge. By metrics, the convolutional neural network demonstrated the highest accuracy of predictions.

Unraveling Strong Supramolecular Assembly of a Novel Pyrene Based Hg(II)‐complex: Insights from Hirshfeld Surface, FMO and Molecular Electrostatic Potential (MEP) Analyses

AbstractA rare pyrene‐based coordination compound of Hg(II) [Hg(L)2] (1) (where, HL=2‐((Pyren‐1‐ylmethylene)amino)benzenethiol) was synthesized and characterized by single crystal X‐ray diffraction (SC‐XRD) analysis. Crystallographic analysis revealed that complex 1 has seesaw geometry with HL coordinated as a bidentate ligand to a metal ion. The role of weak forces like π⋅⋅⋅π and CH⋅⋅⋅π interactions in influencing the self‐assembly process appears to be of importance. The electronic structure of the complex was predicted using DFT calculations with mixed basis set. DFT calculated structural parameters are in good agreement with the experimentally obtained parameters from XRD. Molecular reactivity and stability of the complex has been assessed through frontier molecular orbital analysis. Evaluation of molecular electrostatic potential (MEP) displays the electrophilic and nucleophilic reactivity sites. The Hirshfeld surface analysis clearly indicates C−H⋅⋅⋅π interactions and π⋅⋅⋅π stacking interactions are responsible to extend the supramolecular network of the complex. Analyses of the Finger print plots suggest that H⋅⋅⋅H and H⋅⋅⋅C contacts are major interactions for stabilizing the molecular crystal.

Birdwatching preferences reveal synergies and tradeoffs among recreation, carbon, and fisheries ecosystem services in Pacific Northwest estuaries, USA

Coastal ecosystems provide multiple ecosystem services that are valued in diverse ways. The Nisqually River Delta (the Delta), an estuary in Puget Sound, Washington, U.S.A., is co-managed by the Nisqually Indian Tribe and the Billy Frank Jr. Nisqually National Wildlife Refuge. In an ecosystem services assessment, we used different service-appropriate methods including citizen science, statistical and geospatial models, and scenario analysis to evaluate three ecosystem services – recreational birdwatching, soil carbon accumulation and fishery production – indicated as priorities for the Refuge, Nisqually Indian Tribe, and surrounding communities. We developed a generalized additive mixed model set based on eBird mobile application birdwatching observations to understand the biological and landscape features that influence birdwatching and to project birdwatching visitation based on scenarios of Delta habitat change. We evaluated ecosystem service synergies and tradeoffs associated with habitat change for three coastal habitat types using scenario outputs from the birdwatching model and published results on Delta soil carbon accumulation and fishery production. The highest-ranked birdwatching models explained 88 % of the deviance and showed that visitation was greatest in winter months when distance to major cities was approximately 20 km. Recreational birdwatching increased with increasing area of forested wetland, emergent wetland, aquatic vegetation bed, open access, and total estuary. With increasing forested and emergent wetland area, recreational birdwatching, out-migrating juvenile Chinook salmon weight and soil carbon accumulation all increased. With increasing aquatic vegetation bed (resulting from sea level rise), recreational birdwatching increased, but salmon weight and soil carbon accumulation decreased. We identified practical ways in which ecosystem services may be incorporated into adaptive management frameworks that support climate adaptation decision making. This study illustrated how use of ecosystem services can help managers make decisions that have greater benefit for wildlife and people, communicate the societal value of decisions and increase local support and participation.

Application of horticulture therapy as a health intervention for elderly people in nursing homes: a scoping review†

Abstract Objective To review the scope of interventional studies on horticultural therapy (HT) applied to elderly people in nursing institutions to support the efficient implementation of HT among this target group. Methods In accordance with the scoping review framework proposed by the Joanna Briggs Institute (JBI), the PubMed, JSTOR, Web of Science, CNKI, and Wanfang databases were searched. Data from the retrieved literature were summarized and analyzed. Results In all, 18 studies were included in this review. The target population groups of interventions included self-supporting elderly people, cognitively impaired elderly people, elderly people with negative emotions, and elderly people with frailty living in nursing institutions. HT interventions, including planting, craft activities, derivative activities, and outdoor viewing activities, are implemented indoors, outdoors, or in mixed settings. The most common duration of the intervention was 8 weeks, the most common frequency was once per week, and the most common session duration was 60 min. Conclusions The measurements used in HT interventions included assessments of physical, psycho-mental, and social health; quality of life; and activity effects. Future studies should include partially dependent groups and completely dependent groups of elderly individuals, interventions that last at least 6 months, scientifically designed activity intensity and safety-guarantee plans, and outcome-evaluation indicators such as compliance and intervention benefits.

Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy.

The diagnosis of faults in wind turbine gearboxes based on signal processing represents a significant area of research within the field of wind power generation. This paper presents an intelligent fault diagnosis method based on ensemble-refined composite multiscale fluctuation-based reverse dispersion entropy (ERCMFRDE) for a wind turbine gearbox vibration signal that is nonstationary and nonlinear and for noise problems. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and stationary wavelet transform (SWT) are adopted for signal decomposition, noise reduction, and restructuring of gearbox signals. Secondly, we extend the single coarse-graining processing method of refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE) to the multiorder moment coarse-grained processing method, extracting mixed fault feature sets for denoised signals. Finally, the diagnostic results are obtained based on the least squares support vector machine (LSSVM). The dataset collected during the gearbox fault simulation on the experimental platform is employed as the research object, and the experiments are conducted using the method proposed in this paper. The experimental results demonstrate that the proposed method is an effective and reliable approach for accurately diagnosing gearbox faults, exhibiting high diagnostic accuracy and a robust performance.

Mixed Fair Division: A Survey

Fair division considers the allocation of scarce resources among agents in such a way that every agent gets a fair share. It is a fundamental problem in society and has received significant attention and rapid developments from the game theory and artificial intelligence communities in recent years. The majority of the fair division literature can be divided along at least two orthogonal directions: goods versus chores, and divisible versus indivisible resources. In this survey, besides describing the state of the art, we outline a number of interesting open questions and future directions in three mixed fair division settings: (i) indivisible goods and chores, (ii) divisible and indivisible goods (mixed goods), and (iii) indivisible goods with subsidy which can be viewed like a divisible good.

An engineered prodrug selectively suppresses β-lactam resistant bacteria in a mixed microbial setting.

The rise of β-lactam resistance necessitates new strategies to combat bacterial infections. We purposefully engineered the β-lactam prodrug AcephPT to exploit β-lactamase activity to selectively suppress resistant bacteria producing extended-spectrum-β-lactamases (ESBLs). Selective targeting of resistant bacteria requires avoiding interaction with penicillin-binding proteins, the conventional targets of β-lactam antibiotics, while maintaining recognition by ESBLs to activate AcephPT only in resistant cells. Computational approaches provide a rationale for structural modifications to the prodrug to achieve this biased activity. We show AcephPT selectively suppresses gram-negative ESBL-producing bacteria in clonal populations and in mixed microbial cultures, with effective selectivity for both lab strains and clinical isolates expressing ESBLs. Time-course NMR experiments confirm hydrolytic activation of AcephPT exclusively by ESBL-producing bacteria. In mixed microbial cultures, AcephPT suppresses proliferation of ESBL-producing strains while sustaining growth of β-lactamase-non-producing bacteria, highlighting its potential to combat β-lactam resistance while promoting antimicrobial stewardship.

The efficacy of Relative Strength Index (RSI) in KLSE market trade

The Relative Strength Index (RSI) is a widely used technical indicator in stock trading because of its simple application. However, its application combined with fundamental analysis in Kuala Lumpur Stock Exchange (KLSE) is yet to be explored extensively. Hence, this study aimed to investigate the effectiveness of RSI as a predictive tool in the KLSE market. First, 39 stocks that met the set of criteria guided by fundamental soundness were identified. Trading signals are then initiated based on the RSI indicators, which are superimposed on the charts of the identified stocks. The return on investment (ROI) was then estimated and benchmarked against KLCI performance over a period of five years starting from 21st July 21, 2018. It was found that the average ROI was 3.46% against KLCI’s -24.37% over the same period of 5 years, measured between 21st July 21, 2018, 21st July 21, 2023. The average ROI improves as the number of trade iterations increases to five. The peak average ROI is approximately 22%. Technology is the top performer, recording an ROI of more than 40%. In conclusion, the combination of RSI and fundamental analysis yielded a mixed set of results. Although it outperforms KLCI, the annualised ROI is only slightly above 4%. Nevertheless, the results of this study provide valuable insights into the application of the RSI as a trading signal generator in the KLSE market.

Patagonian dust, Agulhas Current, and Antarctic ice-rafted debris contributions to the South Atlantic Ocean over the past 150,000 years

Disentangling inputs of aeolian dust, ice-rafted debris (IRD), and eroded continental detritus delivered by ocean currents to marine sediments provide important insights into Earth System processes and climate. This study uses Sr-Nd-Pb isotope ratios of the continent-derived (lithogenic) fraction in deep-sea core TN057-6 from the subantarctic Southern Ocean southwest of Africa over the past 150,000 y to identify source regions and quantify their relative contributions and fluxes utilizing a mixing model set in a Bayesian framework. The data are compared with proxies from parallel core Ocean Drilling Program Site 1090 and newly presented data from potential South America aeolian dust source areas (PSAs), allowing for an integrated investigation into atmospheric, oceanic, and cryospheric dynamics. PSA inputs varied on glacial/interglacial timescales, with southern South American sources dominating up to 88% of the lithogenic fraction (mainly Patagonia, which provided up to 68%) during cold periods, while southern African sources were more important during interglacials. During the warmer Marine Isotope Stage (MIS) 3 of the last glacial period, lithogenic fluxes were twice that of colder MIS2 and MIS4 at times, and showed unique isotope ratios best explained by Antarctic-derived IRD, likely from the Weddell Sea. The IRD intrusions contributed up to 41% at times and followed Antarctic millennial warming events that raised temperatures, causing instability of icesheet margins. High IRD was synchronous with increased bioavailable iron, nutrient utilization, high biological productivity, and decreased atmospheric CO2. Overall, TN057-6 sediments record systematic Southern Hemisphere climate shifts and cryospheric changes that impacted biogeochemical cycling on both glacial/interglacial and subglacial timescales.

Exploration of supramolecular interactions, Hirshfeld surface, FMO, molecular electrostatic potential (MEP) analyses of pyrazole based Zn(II) complex

The supramolecular interactions of title compound [Zn(HL2)](ClO4)2(MeOH)2] (1) where, HL2=((Z)-N’-(5-methyl-1H-pyrazole-3-carbonyl)pyrazine-2-carbohydrazonamide) are explored in detail. With a detailed scrutiny of visualizing of title compound 1, the crystal packing is mainly directed by strong hydrogen bonding interactions observed N–H⋯N and N–H⋯O but, in addition, weak interactions such as C–H⋯π, π⋯π are also sighted to stabilize the crystal self-assembly. Electronic structure of the complex is determined using DFT calculations at B3LYP level with mixed basis set. Theoretically calculated structural parameters are in good match with the experimentally obtained parameters from XRD. TDDFT calculation is performed to simulate the UV–Vis absorption spectra of the complex. Molecular reactivity and stability of the complex has been assessed through frontier molecular orbital analysis and also through evaluation of molecular electrostatic potential (MEP). Hirshfeld surface analysis, which uses molecular surface contours and 2D fingerprint plots to visually analyze intermolecular interactions in crystal structures, has been used to examine molecular morphologies. The Hirshfeld surface and fingerprint plots are accompanied by crystal structure analysis allowed for the discovery of the important intermolecular interactions.

Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop-specific approaches.

Crop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop-specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets. Our results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61-98.76%), Mixed (90.76-92.31%), and Field (68.56-73.81%). Although CSMs were found to reduce inter-crop disease misclassifications, they also led to a slight increase in intra-crop misclassifications. Our findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field-specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry.

Women's Perceived HIV Stigma and Testing in Stann Creek District, Belize: A Population-Representing, Mixed-Methods Study.

Stigma affects adversely the HIV prevention continuum and care cascade. Our population-based, mixed-methods study aimed to assess women's perceived HIV stigma and discriminatory attitudes, and their relation with HIV testing in a high-prevalence area in Belize. This population-representing household survey in the mixed urban-rural setting of Stann Creek District, Belize, collected data from 236 women age 15 to 49 years. We analyzed HIV testing rates, HIV prevention and transmission knowledge, perceived stigma manifestations, and participant attitudes. Concurrently, a nested qualitative component of study cognitive interviews with a purposive sample of 23 women explored HIV stigma in their community. A vast majority of women (96%) perceived HIV stigma manifestations in their communities as pervasive and a deterrent to people from testing. Discriminatory attitudes (16% believe children with HIV should not attend school) and HIV misconceptions (53% fear acquiring HIV through saliva) tended to be more common in nonurban areas and among women with less formal education. Stigma persisted even with high HIV testing rates among women. Qualitative findings triangulated survey results and, taken together, suggest that prejudices held against people with HIV led to avoidance of HIV preventive measures such as testing and status disclosure, fueled by a strong distrust of the medical care system regarding confidentiality of HIV test results. Misconceptions about HIV and stigmatizing attitudes remain pervasive among women in Stann Creek, Belize. Health literacy, stigma interventions, and expansion of routine confidential testing to include men are needed to address the HIV and stigma syndemic in Belize.