Search Results (429)

Antiretroviral therapy (ART) is essential for preventing HIV transmission and improving the health outcomes of people living with HIV (PLHIV). However, many barriers limit PLHIV from starting and adhering to ART, which explains why HIV responses in many settings, including Indonesia, have produced limited gains. This qualitative phenomenological study explored multilevel barriers to ART adherence in urban Yogyakarta (locally known as Jogja) and rural Belu, Indonesia, from the perspectives of PLHIV and healthcare professionals (HCPs). Data were collected through one-on-one in-depth interviews with 92 PLHIV and 20 HCPs. Participants were recruited using the snowball sampling technique. Data were analysed using framework analysis informed by the Access to Healthcare Framework. The findings showed that PLHIV in Belu and Jogja had different experiences in terms of the provision of and ability to access and adhere to ART or HIV treatment. In rural Belu, ART was less available and visible, harder to approach, often unaffordable, less aligned with patients’ needs, and strongly influenced by the widespread use of traditional medicine. PLHIV in Belu also reported a more limited ability to perceive the need for ART, reach services, pay costs, engage in care, and seek ART than those in urban Jogja. Personal, psychological, and social barriers were also reported to hinder PLHIV’s ART adherence in both settings. These findings highlight the need for HIV policies that promote the equitable distribution of ART services and targeted interventions to improve understanding and acceptance of HIV care among PLHIV and the wider community. Full article

One of the most widespread causes of mortality is cancer, and its early diagnosis is a key element of modern medicine. A crucial role is played by imaging techniques such as positron emission tomography (PET), single-photon emission computed tomography (SPECT), and computed tomography (CT), which require the use of appropriate radioactive isotope tracers to enable precise visualization of pathological changes. In recent years, hyperbranched dendrimers have attracted considerable attention due to their unique architecture and functionalization capacity, making them a promising platform for the delivery of radiotracers. The aim of this manuscript is to provide an overview of radiolabeled dendrimer conjugates, their functionalization methods, and the results of preclinical studies confirming their diagnostic potential. It pays particular attention to the stability and radiochemical purity of the systems, and their ability to overcome physiological barriers. The findings presented indicate that dendrimer-based radiotracers constitute a promising class of carriers for diagnostic imaging, although further studies are required to optimize their physicochemical and biological properties. Full article

Our first objective was to quantify the associations between health-related events (HRE) before insemination, the relative increase in estrus intensity (REI) at insemination, and the probability of cow-level pregnancy per artificial insemination (P/AI) in organic Holstein dairy cows. Quantifying these associations may aid on-farm decision-making, such as setting the voluntary waiting period, choice of type of semen, do-not-breed and culling decisions. A second objective was to develop predictive models to estimate P/AI based on readily available data, and present common goodness-of-fit results also used in the machine learning community. All data were collected from a certified organic dairy farm in the western USA from 2019 to 2021. Health-related and reproduction data were obtained through Dairy Records Management Systems (DRMS; Raleigh, NC, USA). Activity data were collected using pedometers (IceRobotics, Stirling, UK) mounted on the rear legs. The REI, defined as walking steps per hour before insemination divided by the cow’s baseline steps per hour, was available for 17,238 inseminations from 4759 cows. The REI was categorized as ≤200%, >200–400%, >400–600%, or >600%. The HRE were available for 65,684 inseminations from 13,365 cows. The HRE were categorized as mastitis, metabolic disease (i.e., hypocalcemia, ketosis, displaced abomasum, digestive problems), reproductive disease (i.e, metritis, endometritis, pyometra, retained fetal membranes), lameness, 2 different diseases, ≥3 different diseases, or as healthy (none of these diseases prior to insemination). Combinations (COMBO) between REI categories and 0, 1, or ≥2 HRE were also created. Data were split into training and test sets. The training data were used to fit three logistic regression models that included either HRE, or REI, or COMBO. Each of the three models also included the covariates of 3-mo herd-average P/AI prior to insemination, days in milk, and the fixed effects of parity, insemination season, days after the previous insemination or days to 1st insemination. A random effect accounted for repeated inseminations within cow. Parameter estimates, odds ratios, and the estimated marginal means of the estimated P/AI of the fixed effects were obtained from the logistic regression models. The models’ estimates were applied to the test datasets, and discrimination and calibration statistics were calculated to judge goodness-of-fit. Unadjusted mean P/AI were 0.31, 0.28 and 0.28 for the HRE, REI and COMBO training datasets. For the HRE model, estimated P/AI ranged from 0.20 (≥3 different HRE) to 0.30 (healthy). The estimated P/AI associated with four REI categories were not different from 0.27 in the REI model. The estimated P/AI associated with the combinations of HRE and REI in the COMBO model varied from 0.18 after ≥2 HRE and >200–400% REI, to 0.30 when inseminations were in healthy cows with REI >600%. Inseminations in older cows, in the spring, and outside 18–24 d after the previous insemination were also associated with lower estimated P/AI. The area underneath the Receiver Operating Characteristic curve ranged from 0.57 (COMBO) to 0.60 (HRE) for the test data, indicating fair discrimination ability of the models. Calibration plots showed that the prediction models produced unbiased predicted P/AI. In conclusion, the results showed no conclusive evidence of greater estimated P/AI related to greater REI as a measure of estrus activity. More HRE were associated with lower estimated P/AI. Combinations of low REI and more HRE were associated with notably decreased estimated P/AI. The logistic regression models produced unbiased predicted P/AI. We found no evidence that the strength of the relationship between REI and P/AI depended on the HRE category. The applications of the results are as follows. First, these predictive models may help inform insemination decisions in organic dairy cows, although further external validation is recommended, and the discriminatory performance is weak. Second, a variety of goodness-of-fit statistics were calculated to allow comparisons of the current logistic regression analyses with future analyses made by other machine learning techniques. Full article

Guanzhong traditional vernacular dwellings are an important component of traditional architectural heritage. Their architectural decorative motifs are often attached to components such as screen walls, gable end piers, doors and windows, door pillow stones, column bases, tile ends, and roof ridges, making them important visual objects for image based documentation, typological organization, and conservation management of architectural heritage. Since the relevant images are mainly obtained through field photography, field images often contain multiple sources of interference, including weathering, spalling, occlusion, contamination, illumination variation, wall joints, brick joints, shadows, component edges, and damaged textures. These factors lead to blurred motif boundaries, strong background interference, and a high risk of misrecognition. Existing motif recognition methods mainly focus on clear samples and pay insufficient attention to candidate region generation, invalid texture filtering, and the exclusion of low confidence results in complex field scenarios. To address these problems, this study proposes a recognition method for architectural decorative motifs in complex scenarios. Based on field collected images of Guanzhong traditional vernacular dwellings, the method takes architectural decorative motifs and non-motif candidate regions as training objects. SAM is first used to generate potential motif candidate regions. Spatial-prior filtering based on candidate size, aspect ratio, and SAM stability is then introduced to reduce invalid region proposals, while semantic aggregation is used to retain large candidate regions supported by high-confidence local motif evidence. Swin Transformer is further adopted as the classification backbone to extract local texture features and overall structural relationships from candidate regions. A maximum confidence based rejection mechanism is also introduced to enhance the model’s ability to exclude low confidence predictions and regions without motifs. Component-level test-set evaluation and expert-verified complete-field-image evaluation show that the proposed method can reduce the interference of complex backgrounds and invalid textures, and improve the recognition stability of architectural decorative regions in field images. This study provides technical support for image organization, digital documentation, typological indexing, and conservation management of architectural decorative motifs in traditional vernacular dwellings. Full article

This study examines how international tourists’ perceptions of environmental, social, and governance (ESG) practices influence their willingness to pay a premium for community-based homestays. Grounded in signaling theory, ESG perception is conceptualized as a credibility signal that reduces perceived uncertainty in community-based accommodation settings. Data were collected from 300 international tourists visiting Mae Kampong Village, Chiang Mai, Thailand, and analyzed using partial least squares structural equation modeling (PLS-SEM). To strengthen predictive assessment, the model was additionally evaluated using PLSpredict, Q²_predict, and the Cross-Validated Predictive Ability Test (CVPAT). The results indicate that ESG perception significantly enhances community sustainability image, trust, and booking intention. Trust partially mediates the relationships between ESG perception and both booking intention and willingness to pay a premium, while booking intention demonstrates the strongest effect on willingness to pay a premium. Community sustainability image does not directly influence booking intention but instead operates indirectly through trust. Environmental concern significantly influences willingness to pay a premium, although its moderating effect is not supported. The findings suggest that tourists in community-based homestay environments rely heavily on trust-based psychological assurance when making accommodation decisions. This study extends ESG tourism research into community-based accommodation contexts and highlights the importance of trust in high-uncertainty tourism environments. The findings also emphasize the importance of transparent ESG communication and trust-building strategies for strengthening sustainable tourism competitiveness. Full article

The deployment of rural electrification actions through off-grid mini-grid solutions is one of the most effective approaches to achieving universal access to electricity in an affordable, reliable, and sustainable way. To assess the sustainability of three mini-grid projects (Sembezea, Mawayela, and Dongane), this study applied a framework that integrates different methods (HOMER, LCA based on SimaPro, and Input–Output) and indicators under the economic, environmental, and social dimensions. Data for the analysis were obtained through site visits in the case study areas, a literature review, and the HOMER and ecoinvent databases. Sembezea and Mawayela were assessed based on their operational experience, whereas the Dongane biogas system is analyzed based on a projected household biodigester experience. The results of this study revealed the considerable benefits of biogas in generating local employment (506 employees) compared to wind/solar PV (98 employees) and hydro/solar PV (91 employees), as it is expected to require a considerable number of employees for feedstock collection for the digester, under the assumed scale and conditions. Additionally, in the long term, biogas would present the lowest cost of electricity at $0.22/kWh compared to wind/solar PV ($0.28/kWh) and hydro/solar PV ($0.60/kWh), thereby improving the ability of the local community to pay for electricity. In contrast, this study concluded that, in terms of environmental impact—particularly CO₂ emissions—biogas has relatively poor environmental performance (4.58 × 10⁻² kg CO₂ eq) compared to wind/solar PV (8.50 × 10⁻⁴ kg CO₂ eq) and hydro/solar PV (3.94 × 10⁻⁴ kg CO₂ eq) in the long term. Nevertheless, biogas presents carbon neutrality as an advantage, in the sense that the CO₂ released during its combustion is assumed to be carbon-neutral. By applying the framework to the aforementioned case studies, the extent to which it is possible to provide an integrated overview of the economic, environmental, and social aspects, as well as the impacts of different HRES options in line with the SDGs, is demonstrated. Full article

In the era of cloud computing and serverless architectures, the security of applications and services has become a critical challenge. Serverless computing, often referred to as function as a service (FaaS), is a cloud computing model that allows developers to build and run applications without the need to manage traditional server infrastructure. Serverless architectures have gained popularity in cloud computing due to their flexibility and ability to scale automatically based on demand. These architectures are based on executing functions without the need to manage the underlying infrastructure. Denial of wallet (DoW) attacks refer to a type of cyberattack that aims to exploit and exhaust the financial resources of an organization by triggering excessive costs or charges within their cloud or serverless computing environment, exploiting characteristics such as the pay-as-you-go model, auto-scaling, limited control, and cost amplification. This research aims to assess existing methods for detecting distributed denial of service (DDoS) attacks and extend their application to detect denial of wallet (DoW) threats, leveraging a dataset tailored to serverless architectures. We investigate various strategies and techniques that employ entropy, machine learning and deep learning algorithms to enable early detection of DDoS and DoW attacks in serverless environments. This research provides insights into the options that are available for detecting DoW attacks in serverless environments, allowing security professionals and developers to make decisions on the most appropriate solutions to protect their applications and cloud services. Full article

This article examines how the redistributive design of crisis-time fiscal policy shaped Belgian federal public debt trajectories from 1912 to 2024. Drawing on a reconstructed debt-to-GDP series and historical–institutional analysis, it identifies a secular transformation in the distributive logic of fiscal adjustment. From 1912 to the late 1970s, broadly speaking, debt surges were addressed through explicitly progressive instruments grounded in the ability-to-pay principle, and on the view that capital should be taxed at least as heavily as labour. From the 1980s onward, this paradigm gave way to a competitiveness-oriented model that eroded tax progressivity, detached capital from the global tax base, and shifted the fiscal burden onto consumption and labour—disproportionately affecting middle-income earners. The evidence presented in this article points to three plausible determinants of this transformation: the role of mass warfare in legitimising progressive taxation; the ideological shift from Keynesian interventionism to supply-side orthodoxy; and the twin constraints of internal federalisation and external Europeanisation. Furthermore, the timing and modalities of these adjustments appear to have been significantly shaped by linguistic party fragmentation and the recurrent use of emergency executive powers—a pattern that was increasingly mirrored in the European Union’s own governance. Ultimately, since 2020, crisis management has relied almost exclusively on debt-financed expenditure. While the EU has temporarily acted as a redistributive counterweight to domestic fiscal paralysis, these ad hoc supranational interventions have left Belgium’s underlying debt trajectory unchanged. Full article

In urban areas, green spaces have become the main refuge for biodiversity, providing essential habitat and resources for urban-adapted species. However, scientific evidence on the fitness consequences of urban green space management for urban populations remains scarce, limiting our ability to design successful conservation and management strategies. Here, we assess the fitness consequences of different levels of management practices in green spaces (i.e., high for areas with continuous intervention such as regular mowing and irrigation, and low for areas with minimal, sporadic maintenance) based on a 19-year long-term monitoring of the Eurasian Tree Sparrow (Passer montanus), a species with high behavioural plasticity in response to human-altered habitats. We formulated a unistate capture–mark–recapture model to estimate age-dependent survival while accounting for uncertainty in recapture probability. Furthermore, by means of GLMMs, we tested if the level of management influences reproductive parameters (i.e., breeding failure, number of eggs, nestlings, fledglings, brood number from the same year, breeding success). We found that high urban green space management caused a decline in adult survival, but we found no effect on juvenile survival. We also found lower breeding failure, a greater number of eggs, and larger brood numbers in the low management areas, but no differences were found in the number of nestlings and fledglings. Consequently, we found no differences in overall breeding success. Our results highlight the reduction in survival in a near-threatened passerine species due to routine green urban space management, in addition to differences in reproductive parameters depending on the degree of green urban space management. Overall, we confirm that the same species show several reproductive strategies with different breeding effort to reach similar breeding success, whatever the human context is. However, birds pay the cost in adult survival, and probably in shortening life span. Therefore, the management of urban green spaces has a negative impact on biodiversity in cities. It is necessary to review the management practices of these urban areas and promote practices that are friendly to biodiversity. Full article

Photoacoustic imaging (PAI) is an emerging hybrid biomedical imaging modality that combines the high molecular contrast of optical excitation with the deep tissue penetration of ultrasound detection. This review presents recent advances in PAI-based techniques for the detection and characterization of gynecological diseases in women, with particular focus on endometriosis and uterine-related disorders. We summarize the application of PAI across preclinical and translational studies, highlighting progress in photoacoustic microscopy, spectroscopic photoacoustic imaging, and endoscopic and probe-based implementations for non-invasive, high-resolution tissue evaluation. The role of functional and contrast-enhanced PAI approaches is discussed, emphasizing their ability to enhance diagnostic sensitivity, enable longitudinal monitoring, and provide detailed information on vascular, biochemical, and structural tissue characteristics. Furthermore, the expanding applications of PAI in assessing uterine, cervical, and ovarian pathologies, including tumor detection and tissue remodeling, are reviewed. Finally, current challenges, limitations, and future directions toward clinical translation are addressed. Collectively, this review underscores the potential of photoacoustic imaging as a powerful, non-invasive platform for early diagnosis, disease monitoring, and improved management of women’s health conditions. Full article

To address escalating climate challenges, China has implemented a multi-tiered low-carbon policy framework aimed at achieving carbon peaking and carbon neutrality, profoundly reshaping firms’ strategic and financial behaviors. Using a panel of Chinese listed firms from 2007 to 2022, this study examines how low-carbon policies affect corporate earnings management choices and the underlying mechanisms. The results show that low-carbon policies significantly restrain accrual-based earnings management while simultaneously promoting real earnings management, indicating a clear substitution effect; these findings remain robust across multiple robustness checks. Mechanism analyses reveal that rising financing costs and enhanced digital transformation induced by low-carbon policies curb accrual-based earnings management, whereas increased financial risk and weakened debt-paying ability stimulate real earnings management. Further heterogeneity analyses suggest that the inhibitory effect on accrual-based earnings management is stronger among firms subject to greater analyst coverage and media scrutiny, while the shift toward real earnings management is more pronounced among firms with weaker profitability and those located in regions with lower innovation capacity. Overall, this study deepens the understanding of the microeconomic consequences of low-carbon policies and provides policy-relevant insights for refining green regulatory frameworks and promoting sustainable corporate development. Full article

This exploratory study examines how journalists in/coordinating investigations use a chatbot designed to reduce power asymmetries during remote work. Twelve freelancers across Africa, Europe, and India tested Obruche, a chatbot advisor covering risk mitigation, pay equality, tension de-escalation, and intellectual property protection. Drawing on the Unified Theory of Acceptance and Use of Technology, semi-structured interviews were coded for Performance Expectancy, Effort Expectancy, Facilitating Conditions, and Social Influence. Results show journalists gravitate towards chatbots that are cognisant of their location-specific challenges and able to provide information that facilitates access to media outlets or peers for future collaborations. Next-best-action responses that expanded user queries or offered role-play scenarios also left journalists feeling supported, less lonely, and not judged. However, the chatbot’s female persona, scepticism of artificial intelligence, and chatbot novelty may reduce user acceptance. Obruche’s potential areas of intervention are linked to eight types of organisational power. The chatbot mainly assisted journalists to confront or rebalance Control of Knowledge and Information, and Control of Scarce Resources, aiding users’ Ability to Cope with Uncertainty. This research contributes to recent qualitative studies on journalists’ well-being by demonstrating how chatbots can mitigate power imbalances between dispersed teams of journalists. The benefits and concerns presented may inform future designs of similar team-mediation chatbots. Full article

The optimal placement of samplers and sensors in water distribution systems (WDSs) and wastewater collection systems (WCSs) is fundamental to effective monitoring, early contamination detection, and system protection. The goal of optimal sensor/sampling placement (OSP) is to maximize the ability to detect, monitor, and track critical variables, such as contaminants or temperature, while maintaining cost-effectiveness and operational efficiency. In practice, OSP problems are inherently multi-objective and typically involve trade-offs between cost minimization, spatial and temporal coverage, detection accuracy, and robustness under uncertainty. This paper presents a comprehensive review of recent single- and multi-objective optimization strategies for source detection and monitoring, drawing on approaches developed in various research fields. The reviewed literature is systematically organized according to problem formulation, objective functions, optimization techniques, and decision-making strategies, paying particular attention to their applicability in real-world WDSs and WCSs. Beyond summarizing existing methods, this review critically examines key methodological assumptions and limitations that hinder practical implementation. These include sparse sensor deployment, budget constraints, and modeling and sensor uncertainty. Finally, the paper identifies open challenges and outlines potential directions for future research aimed at improving the robustness, scalability, and practical relevance of OSP strategies. Full article

Machine learning (ML) algorithms may be effective at improving the HCV care cascade. One ML algorithm, developed using U.S. ambulatory electronic medical records (EMR), demonstrated the ability to identify people infected with HCV earlier than conventional testing strategies among those with indications for screening. We evaluated the potential cost-effectiveness of ML-enabled screening for the early identification of undiagnosed HCV among people in care in the U.S. An HCV natural history Markov model was developed to evaluate the cost-effectiveness of the ML algorithm-enabled screening compared to conventional testing over the training data period. Based on the training data, the ML algorithm identified patients on average 6.5 months earlier than conventional testing strategies. We compared the status quo to intervention scenarios using the ML algorithm at different recall levels (proportion of HCV patients identified, 5–100%). We identified the optimal algorithm recall level, which maximized health (measured in quality-adjusted life years, QALYs) while staying under a willingness-to-pay threshold of USD$100,000/QALY gained. ML-enabled screening was cost-effective (ICER < $100 k/QALY gained) in identifying undiagnosed HCV patients for recall levels up to 30%. The optimal recall level was 30% (Precision 0.27%), which resulted in a mean ICER of $94,022/QALY gained. ML-enabled screening for the early identification of undiagnosed HCV patients could be cost-effective in the U.S. Prospective evaluation of real-world effectiveness is warranted. Full article

Background/Objectives: Identification of tools to minimize opioid-related harms is critical in the U.S. The purpose of this study was to better understand community responder and healthcare provider perceptions and preferences regarding the design and function of a potential new opioid sensor device (OSD). Methods: Adults aged ≥ 18 years employed as community responders or healthcare providers in Alabama were recruited via email to participate in an anonymous online cross-sectional survey informed by the Unified Theory of Acceptance and Use of Technology (UTAUT). Primary outcomes were assessed via multiple-choice and 7-point Likert-type scales (1 = strongly disagree, 7 = strongly agree) and included the following topics: (1) past OSD utilization (4 items); (2) perceived importance of OSD design elements (15 items); (3) OSD function and cost preferences (3 items); and (4) UTAUT measures including perceived usefulness of OSDs (3 items), ease of use (4 items), social factors (4 items), resources (4 items), concerns (3 items), and intentions (3 items). Differences in UTAUT measures across professions were assessed via Mann–Whitney U tests, and predictors of OSD utilization intention were analyzed via multiple linear regression. Results: Respondents (N = 145) included pharmacists (40.0%), nurses (23.4%), physicians (14.5%), behavioral health (4.8%), social work (4.8%), and law enforcement (0.7%). Availability in hospital emergency departments was rated as the most important device element (mean [SD] score: 6.66 [0.80]), followed by sensitivity and specificity of the test (6.42 [0.98]), rapid detection time (6.42 [0.88]), ability to detect opioids in a broad range of substance (6.42 [0.93]), and availability in law enforcement offices (6.33 [1.08]). A 2–5 min detection time was rated as reasonable by 32.6% of respondents, with 53.0% preferring to pay <USD 15 per test. There were no statistically significant differences in UTAUT scale scores across professions. Perceived usefulness (β = 0.493; p < 0.001), social acceptance (β = 0.281; p = 0.023), and resource availability (β = 0.708; p = 0.002) were positive predictors and perceived ease of use was a negative predictor (β = −0.472; p = 0.007) of intention to use an OSD. Conclusions: Newly developed OSDs should consider prioritizing accessibility in hospital emergency departments and law enforcement offices, ability to detect a broad range of opioids, detection time between 2 and 5 min, and cost less than USD 15 per test. Future research may explore perspectives from a more diverse sample across multiple states and different professional roles. Full article