Search Results (87)

Background/Objectives: Traditional dietary monitoring methods such as 24 h recalls rely on self-report, leading to recall bias and underreporting. Similarly, dietary control approaches, including portion control and calorie restriction, depend on user accuracy and consistency. Augmented reality (AR) offers a promising alternative for improving dietary monitoring and control by enhancing engagement, feedback accuracy, and user learning. This systematic review aimed to examine how AR technologies are implemented to support dietary monitoring and control and to evaluate their usability and effectiveness among adults. Methods: A systematic search of PubMed, CINAHL, and Embase identified studies published between 2000 and 2025 that evaluated augmented reality for dietary monitoring and control among adults. Eligible studies included peer-reviewed and gray literature in English. Data extraction focused on study design, AR system type, usability, and effectiveness outcomes. Risk of bias was assessed using the Cochrane RoB 2 tool for randomized controlled trials and ROBINS-I for non-randomized studies. Results: Thirteen studies met inclusion criteria. Since the evidence based was heterogeneous in design, outcomes, and measurement, findings were synthesized qualitatively rather than pooled. Most studies utilized smartphone-based AR systems for portion size estimation, nutrition education, and behavior modification. Usability and satisfaction varied by study: One study found that 80% of participants (N = 15) were satisfied or extremely satisfied with the AR tool. Another reported that 100% of users (N = 26) rated the app easy to use, and a separate study observed a 72.5% agreement rate on ease of use among participants (N = 40). Several studies also examined portion size estimation, with one reporting a 12.2% improvement in estimation accuracy and another showing −6% estimation, though a 12.7% overestimation in energy intake persisted. Additional outcomes related to behavior, dietary knowledge, and physiological or psychological effects were also identified across the review. Common limitations included difficulty aligning markers, overestimation of amorphous foods, and short intervention durations. Despite these promising findings, the existing evidence is limited by small sample sizes, heterogeneity in intervention and device design, short study durations, and variability in usability and accuracy measures. The limitations of this review warrant cautious interpretation of findings. Conclusions: AR technologies show promise for improving dietary monitoring and control by enhancing accuracy, engagement, and behavior change. Future research should focus on longitudinal designs, diverse populations, and integration with multimodal sensors and artificial intelligence. Full article

Background/Objectives: Social capital is a multifaceted concept that comprises structural and cognitive portions, and from the perspective of caregivers, it enables access to assistance and participation, improving well-being in resource-constrained settings. In low- and middle-income countries (LMICs) like Bangladesh, mothers are often the sole carers of children with cerebral palsy (CP), which may affect their social capital and livelihood; however, evidence in this regard is limited. This study assessed the effectiveness of integrated microfinance and community-based rehabilitation (IMCBR) on caregivers’ social capital in rural Bangladesh. Methods: This study was part of a randomized controlled trial (RCT) conducted in Shahjadpur, Sirajganj, with three study arms. Children aged ≤5 years with CP and their primary caregivers were enrolled. Twenty-four clusters (10–14 child–caregiver pairs per cluster) were randomly assigned to Arm-A: IMCBR, Arm-B: community-based rehabilitation (CBR) only, and Arm-C: standard care. Data were collected at the baseline, midline (6 months), and endline (12 months) using a structured questionnaire. Social capital was measured using the Short Adapted Social Capital Assessment Tool (SASCAT), which assesses structural and cognitive dimensions; higher scores indicated greater social capital. The SASCAT was culturally adapted and validated for use in Bangladesh. Descriptive, bivariate, and multivariate analyses were performed. Results: There were 251 dyads enrolled into the trial. At baseline, Arm-A had the lowest social capital scores but showed the greatest improvement by endline (60.0%), followed by Arm-B (54.1%) and Arm-C (6.0%). Structural social capital increased significantly in Arm-A compared with Arm-C (mean difference 2.88; 95% CI: 2.45–3.31; p < 0.001) and in Arm-B compared with Arm-C (mean difference 2.46; 95% CI: 2.04–2.87; p < 0.001). Cognitive social capital increased the most in Arm-B (10.7%), though group differences were not significant (p > 0.05). In Arm-A, improvements in social capital were inversely associated with the child’s Gross Motor Function Classification System (GMFCS) level (β = −0.69; 95% CI: −1.28 to −0.10; p < 0.05). Conclusions: IMCBR significantly improved caregivers’ social capital, particularly its structural components, in rural Bangladesh. Full article

In the present study, the changes in the groundwater quality in a Hungarian settlement, Báránd, were examined, nine years after the construction of a sewerage network. The sewerage network in the study area was completed in 2014, with a household connection rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37 dug groundwater wells. Changes in the water quality were assessed using three water quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and geographic information (GIS), data visualization systems, and artificial intelligence (AI). During the evaluation of the quality of the groundwater, eight water chemical parameters were used (pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, COD, Na⁺). Based on interpolated maps and water quality indices, it was established that while an increasing portion of the area exhibits adequate or good water quality compared to the pre-sewerage period, a deterioration has occurred relative to recent years. Even nine years after the sewerage network construction, elevated concentrations of inorganic nitrogen forms and organic matter persist, indicating the continued presence of accumulated pollutants, as confirmed by all three water quality indicators to varying degrees and spatial patterns. The interactive data visualization and cloud-based sharing of the data of the water quality geodatabase were made freely available with the help of Tableau Public. A Feed-Forward Neural Network (FFNN) was developed to predict the groundwater quality, estimating the water quality statuses of three water quality indicators based on water chemistry parameters. The results showed that the applied training algorithms and activation functions proved to be the most effective in the case of different network structures. The most accurate prediction of the WQI and CCME WQI indicators was provided by the Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error (RMSE_WQI = 0.1205, RMSE_{CCME WQI} = 0.1305) and the highest determination coefficient (R²_WQI = 0.9916, R²_{CCME WQI} = 0.9838). For the Cd index, the accuracy of the model was lower (RMSE = 0.1621, R² = 0.9714), suggesting that this indicator is more difficult to predict. With regard to our study, it should be emphasized that data visualization is a particularly practical tool for the post-processing of spatial monitoring data, as it is suitable for displaying information in an intuitive, visual form, for discovering spatial patterns and relationships, and for performing real-time analyses. AI is expected to further increase visualization efficiency in the future, enabling the rapid processing of large amounts of data and spatial databases, as well as the identification of complex patterns. Full article

Illegal logging is a major threat to tropical forests; however, control mechanisms and efforts to combat illegal logging have not effectively curbed fraud in the production chain, highlighting the need for effective methods to verify the geographic origin of timber. This study investigates the application of multi-elemental analysis combined with Principal Component Analysis (PCA) to discriminate the provenance of tropical timber in the Brazilian Legal Amazon. Wood samples of Hymenaea courbaril L. (Jatobá), Handroanthus sp. (Ipê), and Manilkara huberi (Ducke) A. Chevalier. (Maçaranduba) were taken from multiple sites. Elemental concentrations were determined via Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and CA was applied to evaluate geographic differentiation. Significant differences in elemental profiles were found among locations, particularly when using the intermediate disk portions (25% to 75%), and especially the average of all five sampled portions, which proved most effective in geographic discrimination of the trunk. Elements such as Ca, Sr, Cr, Cu, Zn, and B were especially important for spatial discrimination. These findings underscore the forensic potential of multi-elemental wood profiling as a tool to support law enforcement and environmental monitoring by providing scientifically grounded evidence of timber origin. Full article

With rapid global urbanization, glass curtain wall buildings have been widely adopted due to aesthetics and natural lighting. However, during summer time, intense solar radiation leads to significant indoor heat gain, which adversely affect thermal comfort and energy efficiency. The conventional air conditioning systems are typically equipped with a cooling capacity sufficient to maintain an indoor air temperature at the design values specified in the Design standard for energy efficiency of public buildings, which fails to account for the effects of radiation temperature, potentially resulting in reduced thermal comfort and energy inefficiency. By integrating the Thermal Comfort Tool to calculate the PMV index, this study evaluates the affection of solar heat gain on indoor occupants’ thermal comfort and proposes an optimized air temperature control strategy to realize thermal comfort. Based on the dynamic air temperature strategy, an energy consumption model is developed to evaluate the affection of solar radiation on energy consumption for glass curtain wall buildings based on the PMV index. The synergistic effects of shading measures are then evaluated. This study conducts simulation analysis using an office building with a glass curtain wall located in Beijing as a case study. When accounting for radiant heat gain, a significant portion of the time (53.89%) fall outside the thermal comfort range, even when the air conditioning is set to the designated temperature. To maintain thermal comfort, the air conditioning temperature must be lowered by 1.4–3.5 °C, resulting in a 28.08% increase in energy consumption. To address this issue, this study finds that installing interior shading can reduce radiant heat gain. Under the same thermal comfort conditions, the required air temperature reduction is only 0.8–2.1 °C, leading to a 24.26% reduction in energy consumption compared to the case without interior shading. Full article

There is a significant portion of the South African population with unknown HIV status, which slows down epidemic control despite the progress made in HIV testing. Machine learning (ML) has been effective in identifying individuals at higher risk of HIV infection, for whom testing is strongly recommended. However, there are insufficient predictive models to inform targeted HIV testing interventions in South Africa. By harnessing the power of supervised ML (SML) algorithms, this study aimed to identify the most consistent predictors of HIV testing in repeated adult population-based surveys in South Africa. The study employed four SML algorithms, namely, decision trees, random forest, support vector machines (SVM), and logistic regression, across the five cross-sectional cycles of the South African National HIV Prevalence, Incidence, and Behavior and Communication Survey (SABSSM) datasets. The Human Science Research Council (HSRC) conducted the SABSSM surveys and made the datasets available for this study. Each dataset was split into 80% training and 20% testing sets with a 5-fold cross-validation technique. The random forest outperformed the other models across all five datasets with the highest accuracy (80.98%), precision (81.51%), F₁-score (80.30%), area under the curve (AUC) (88.31%), and cross-validation average (79.10%) in the 2002 data. Random forest achieved the highest classification performance across all the dates, especially in the 2017 survey. SVM had a high recall (89.12% in 2005, 86.28% in 2008) but lower precision, leading to a suboptimal F₁-score in the initial analysis. We applied a soft margin to the SVM to improve its classification robustness and generalization, but the accuracy and precision were still low in most surveys, increasing the chances of misclassifying individuals who tested for HIV. Logistic regression performed well in terms of accuracy = 72.75, precision = 73.64, and AUC = 81.41 in 2002, and the F₁-score = 73.83 in 2017, but its performance was somewhat lower than that of the random forest. Decision trees demonstrated moderate accuracy (73.80% in 2002) but were prone to overfitting. The topmost consistent predictors of HIV testing are knowledge of HIV testing sites, being a female, being a younger adult, having high socioeconomic status, and being well-informed about HIV through digital platforms. Random forest’s ability to analyze complex datasets makes it a valuable tool for informing data-driven policy initiatives, such as raising awareness, engaging the media, improving employment outcomes, enhancing accessibility, and targeting high-risk individuals. By addressing the identified gaps in the existing healthcare framework, South Africa can enhance the efficacy of HIV testing and progress towards achieving the UNAIDS 2030 goal of eradicating AIDS. Full article

While groundwater-dependent ecosystems (GDEs) occupy only a small portion of the Earth’s surface, they hold significant ecological value by providing essential ecosystem services such as habitat for flora and fauna, carbon sequestration, and erosion control. However, GDE functionality is increasingly threatened by human activities, rainfall variability, and climate change. To address these challenges, various methods have been developed to assess, monitor, and understand GDEs, aiding sustainable decision-making and conservation policy implementation. Among these, remote sensing and advanced machine learning (ML) techniques have emerged as key tools for improving the evaluation of dryland GDEs. This study provides a comprehensive overview of the progress made in applying advanced ML algorithms to assess and monitor GDEs. It begins with a systematic literature review following the PRISMA framework, followed by an analysis of temporal and geographic trends in ML applications for GDE research. Additionally, it explores different advanced ML algorithms and their applications across various GDE types. The paper also discusses challenges in mapping GDEs and proposes mitigation strategies. Despite the promise of ML in GDE studies, the field remains in its early stages, with most research concentrated in China, the USA, and Germany. While advanced ML techniques enable high-quality dryland GDE classification at local to global scales, model performance is highly dependent on data availability and quality. Overall, the findings underscore the growing importance and potential of geospatial approaches in generating spatially explicit information on dryland GDEs. Future research should focus on enhancing models through hybrid and transformative techniques, as well as fostering interdisciplinary collaboration between ecologists and computer scientists to improve model development and result interpretability. The insights presented in this study will help guide future research efforts and contribute to the improved management and conservation of GDEs. Full article

Gold nanoparticles (AuNPs) are a promising tool for drug delivery due to their unique chemical properties that make them biocompatible and easy to functionalize. However, when AuNPs are introduced into biological systems, they are coated by the so-called protein corona (PC), which affects their biodistribution and limits their therapeutic efficacy. The functionalization of AuNPs with endogenous carbohydrates can be a possible strategy to reduce immune recognition, thus enhancing their biocompatibility and circulation time. Suitable candidates for this approach are the ABO blood sugar antigens, di- and tri-saccharides that represent the terminal portion of some glycolipids and glycoproteins present on the surface of human red blood cells and other tissues. In this work, we illustrate the synthesis of trisaccharide antigen A derivative, whose last step is worthy of investigation. During the final hydrogenolysis reaction, intended to remove protecting groups, an unexpected side reaction occurred, the isolated product bearing an N,N-dimethyl moiety on the anomeric propyl linker. This side reaction might be ascribed to the in situ formation of formaldehyde and successive imine formation and reduction. The obtained compound can be used as a monomeric control compound in biochemical and structural biology studies involving ABO blood sugar antigens. Full article

The advancement of computational tools for cartometric analysis has opened new avenues for the identification and understanding of stemmatic relationships between historical maps through the analysis of their planimetric distortions. The 19th-century Western cartographic depiction of Jerusalem serves as an ideal case study in this context. The challenges of conducting comprehensive onsite surveys—due to limited time and local knowledge—combined with the fascination surrounding the area’s representation, resulted in a proliferation of maps marked by frequent errors, distortions, and extensive copying. How can planimetric similarities and differences between maps be measured, and what insights can be derived from these comparisons? This paper introduces a methodology aimed at detecting and segmenting regions of local planimetric similarity across maps, corresponding to the portions that were either copied between them or derived from a common source. To detect these areas, the ground control points from the georeferencing process are employed to deform a common lattice grid for each map. These grids, triangulated to maintain shape rigidity, can be partitioned under conditions of geometric similarity, allowing for the segmentation and clustering of locally similar regions that represent shared areas between the maps. By integrating this segmentation with a filter on the intensity of distortion, the areas of the grid that are almost non-deformed, and thus not relevant for the study, can be excluded. To showcase the support this methodology offers for close reading, it is applied to the maps in the dataset depicting the Russian Compound. The methodology serves as a tool to assist in constructing the genealogy of the area’s representation and uncovering new historical insights. A larger dataset of 50 maps from the 19th century is then used to identify all the local predecessors of a given map, showcasing another application of the methodology, particularly when working with extensive collections of maps. These findings highlight the potential of computational cartometry to uncover hidden layers of cartographic knowledge and to advance the digital genealogy of map collections. Full article

The cotton mealybug Phenacoccus solenopsis (Hemiptera: Pseudococcidae) is a polyphagous invasive species native to America and considered one of the major cotton pests in Asia. It is currently threatening horticultural and ornamental protected crops in Mediterranean countries. Due to ecological and environmental concerns, the conventional chemical control of P. solenopsis in new areas of introduction is being replaced by exploring the potential of indigenous natural enemies as a sustainable biological control tool. After P. solenopsis introduction in Sicily (Italy), field surveys were conducted on native natural enemies attacking the mealybug to select promising biocontrol agents for field applications. For the first time, Aenasius arizonensis (Hymenoptera: Encyrtidae) was reported in Europe, and the native Anagyrus matritensis (Hymenoptera: Encyrtidae) was recorded in association with P. solenopsis. The two parasitoid species were identified by morphological features and molecularly using a portion of the mitochondrial cytochrome oxidase subunit I (mtCOI) gene. Because of missing information, additional morphological features were provided for the morphological identification of A. matritensis. In addition, the generalist predators Cryptolaemus montrouzieri, Hippodamia variegata and Parexochomus nigripennis (Coleoptera: Coccinellidae) were also recorded attacking the invasive mealybug. Full article

This study investigated the combined effects of calcium carbide waste (CCW) and lateritic soil (LS) on sustainable concrete’s fresh and mechanical properties as a construction material for infrastructure development. The study will explore the possibility of using easily accessible materials, such as lateritic soils and calcium carbide waste. Therefore, laterite soil was used to replace some portions of fine aggregate at 0% to 40% (interval of 10%) by weight, while CCW substituted the cement content at 0%, 5%, 10%, 15%, and 20% by weight. A response surface methodology/central composite design (RSM/CCD) tool was applied to design and develop statistical models for predicting and optimizing the properties of the sustainable concrete. The LS and CCW were input variables, and compressive strength and splitting tensile properties are response variables. The results indicated that the combined effects of CCW and LS improve workability by 18.2% compared to the control mixture. Regarding the mechanical properties, the synergic effects of CCW as a cementitious material and LS as a fine aggregate have improved the concrete’s compressive and splitting tensile strengths. The contribution of LS is more pronounced than that of CCW. The established models have successfully predicted the mechanical behavior and fresh properties of sustainable concrete utilizing LS and CCW as the independent variables with high accuracy. The optimized responses can be achieved with 15% CCW and 10% lateritic soil as a substitute for fine aggregate weight. These optimization outcomes produced the most robust possible results, with a desirability of 81.3%. Full article

In contemporary times, a significant portion of the population experiences symptoms of temporomandibular joint (TMJ) dysfunction. The objective of this study was to evaluate the effects of a single-session TMJ soft tissue therapy on the TMJ and cervical spine mobility as well as on body balance and the foot load distribution. This study was a parallel-group, randomized, controlled trial with a 1:1 allocation ratio. Fifty women aged 20–30 years diagnosed with myofascial pain in the TMJ area were included in the study and divided into two groups. The experimental group received TMJ soft tissue therapy. The following research tools were used: a Hogetex electronic caliper, a CROM Deluxe, and a FreeMed Base pedobarographic platform. In the experimental group, an increase in mobility within all assessed jaw and cervical spine movements was observed. This change was statistically significant (p < 0.05) for lateral movement to the left, abduction, and protrusion of the jaw (an increase of 10.32%, 7.07%, and 20.92%, respectively) and for extension, lateral bending to the right and left, and rotation to the right and left, of the cervical spine (an increase of 7.05%, 7.89%, 10.44%, 4.65%, and 6.55%, respectively). In the control group, no significant differences were observed. No significant changes were observed in the load distribution and body balance assessment. A single session of TMJ soft tissue therapy increases jaw and cervical spine mobility but does not impact body balance or foot load distribution in static conditions in women diagnosed with myofascial pain in the TMJ area. Full article

Accurate forecasting of the available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for hydropower plants in energy production planning for revenue maximization, as well as for environmental impact prevention and flood control upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos, namely, the Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with popular single-algorithm approaches such as ANN, and NARX models. The results demonstrated the superiority of the proposed PSO-ANN approach over the other two models, in addition to being comparable to those proposed by previous studies. Full article

Malaria is a serious health problem worldwide affecting mainly children and socially vulnerable people. The biological particularities of P. vivax, such as the ability to generate dormant liver stages, the rapid maturation of gametocytes, and the emergence of drug resistance, have contributed to difficulties in disease control. In this context, developing an effective vaccine has been considered a fundamental tool for the efficient control and/or elimination of vivax malaria. Although recombinant proteins have been the main strategy used in designing vaccine prototypes, synthetic immunogenic peptides have emerged as a viable alternative for this purpose. Considering, therefore, that in the Brazilian endemic population, little is known about the profile of the humoral immune response directed to synthetic peptides that represent different P. vivax proteins, the present work aimed to map the epitope-specific antibodies’ profiles to synthetic peptides representing the linear portions of the ookinete and sporozoite cell passage protein (CelTOS), thrombospondin-related adhesive protein (TRAP), and cysteine-rich protective antigen (CyRPA) proteins in the acute (AC) and convalescent phases (Conv30 and Conv180 after infection) of vivax malaria. The results showed that the studied subjects responded to all proteins for at least six months following infection. For IgM, a few individuals (3–21%) were positive during the acute phase of the disease; the highest frequencies were observed for IgG (28–57%). Regarding the subclasses, IgG2 and IgG3 stood out as the most prevalent for all peptides. During the follow-up, the stability of IgG was observed for all peptides. Only one significant positive correlation was observed between IgM and exposure time. We conclude that for all the peptides, the immunodominant epitopes are recognized in the exposed population, with similar frequency and magnitude. However, if the antibodies detected in this study are potential protectors, this needs to be investigated. Full article

Mineral exploration works conducted in the Alto Guaporé Gold Province (AGGP), situated in the southwest region of the Amazon Craton in Brazil, faces the challenges of many gold provinces around the world, i.e., declines in the discoveries of new economic deposits and increases in exploration costs. Ground geophysical methods, combined with structural analyses and geological mapping, are valuable tools that have potential to improve accuracy in selecting exploration targets and in determining drilling locations. AGGP deposits are primarily associated with regional N20°–W50° inverse faulting and sheared geologic contacts between Meso-Neoproterozoic siliciclastic metasedimentary rocks and Mesoproterozoic basement (granite and volcano–sedimentary sequences). Mining currently occurring in the central portion of the province drives exploration works towards the many existing targets at the area. Among them, the ABP target is one of the most promising for being located few kilometers north of the Pau-a-Pique mine. At the ABP target, gold is associated with hydrothermal alteration located in the sheared contacts and in the hinge zone of folded metasedimentary sequence. Hydrothermal phases include Fe-oxides, sulfide (py), muscovite and quartz veins. In this study, we use magnetic and geoelectric (induced polarization) surveys coupled with structural and geological mapping to identify potential footprints within the ABP target. The results from induced polarization (IP) profiles successfully mapped the shape and orientation of the main structures down to approximately 350 m at the ABP target, indicating potential locations for hydrothermal alteration hosting gold. Additionally, 3D magnetic data inversions illustrated the distribution of magnetic susceptibilities and magnetization vectors associated with shear zone structures and isolated magnetic bodies. Magnetic data highlighted fault zones along the contacts between metamorphic rocks and granites, while IP data identified areas with high chargeability, correlating with sulfidation zones mineralized with gold. These findings suggest a metallogenic model where gold deposits are transported through deep structures connected to regional faults, implying significant tectonic and structural control over gold deposition. The results underscore the potential of multiparameter geophysics in identifying and characterizing deposits in both deep and strike, thereby advancing our understanding of mineral occurrences in the region and enhancing the search for new mineralized zones. Full article