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Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion‐activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best‐practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open‐grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data. We show that the accuracy levels of trail‐camera image classification by citizen scientists are affected by habitat type and trail‐camera set up. By comparing the accuracy results from three camera trap citizen science projects, we found that setting trail cameras to capture 3 images per burst, testing the appropriate camera sensitivity, and a shorter field of view resulting from dense vegetation may significantly improve citizen scientist image classification accuracy when compared to classifications by experts.

Synthesis remains a challenge for advancing materials science. A key focus of this challenge is how to enable selective synthesis, particularly as it pertains to metastable materials. This perspective addresses the question: how can “spectator” elements, such as those found in double ion exchange (metathesis) reactions, enable selective materials synthesis? By observing reaction pathways as they happen (in situ) and calculating their energetics using modern computational thermodynamics, we observe transient, crystalline intermediates that suggest that many reactions attain a local thermodynamic equilibrium dictated by local chemical potentials far before achieving a global equilibrium set by the average composition. Using this knowledge, one can thermodynamically “shortcut” unfavorable intermediates by including additional elements beyond those of the desired target, providing access to a greater number of intermediates with advantageous energetics and selective phase nucleation. Ultimately, data-driven modeling that unites first-principles approaches with experimental insights will refine the accuracy of emerging predictive retrosynthetic models for complex materials synthesis. Graphical abstract TOC Diagram:  Schematic representation of the energy bandgaps, photogenerated charge separation, and photocatalytic activities improvement mechanism over g-C 3 N 4 /SmFeO 3 nanosheets Z-scheme based nanocomposites

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO3). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO3 synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl2 and Na2TiO3) that produce BaTiO3 faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

This paper discusses preliminary findings of the study on indigenous knowledge of various cultural, religious, medicinal and culinary uses of the natural resources found in two different indigenous communities in Sarawak, namely the Iban of Banting and the Bidayuh of Padawan. Structured interviews were carried out with each household head in the selected villages to determine indigenous knowledge of these communities on the usage of and reliance on natural resources. Through focus group discussions, indigenous knowledge on the various uses of identified natural resources is still available in these two communities. Their dependence on natural resources as building materials, food and, in the economic aspects still exist. However, their dependence on natural resource is diminishing. The indigenous knowledge gap between the old and younger generation of both Iban dan Bidayuh areas has also been identified. Most of the young people are no longer practicing traditional way of life because they are more comfortable with the modern way of life today. In addition, most Iban people in Banting, and the Bidayuh in Padawan have embraced Christianity and practiced modern way of life.

Objective: To identify factors that mediate or moderate the effects of exercise on postmenopausal sex hormone concentrations. Methods: Postmenopausal women were randomized to 12 months of aerobic exercise for 200 min/week (n = 160)or to a control group (n = 160). Intention-to-treat analyses were performed using general linear models with sex hormone concentrations at 6 and 12 months as the outcome. Mediation by adiposity and insulin was investigated by examining changes in effect estimates after adjustment for changes in these factors over 12 months. Moderation was studied as the interaction between group assignment and eight baseline characteristics. Results: Intervention effects on sex hormone-binding globulin (SHBG) and estradiol changes were attenuated with adjustment for change in overall body fat, while there was less attenuation adjusting for intra-abdominal fat change. Intervention effects on SHBG levels were unaffected by adjustment for insulin change. Significant interactions were identified between treatment and physical fitness (for SHBG and testosterone) and age (for testosterone), implying subgroup differences in intervention effect. Conclusions: Our data suggest that overall fat loss partially mediated exercise-induced changes in estradiol and SHBG concentrations. No previous RCT in postmenopausal women has studied moderators of exercise-induced sex hormone changes; therefore, future studies are needed to corroborate our results.

Originality/value: The paper provides insights on the utilization and management of natural resources by the local Iban community particularly in Nanga Talong, the headwaters of Batang Lupar in Sarawak.

Originality/value: The paper provides insights on the utilization and management of natural resources by the local Iban community particularly in Nanga Talong, the headwaters of Batang Lupar in Sarawak.

Synthesis remains a challenge for advancing materials science. A key focus of this challenge is how to enable selective synthesis, particularly as it pertains to metastable materials. This perspective addresses the question: how can ``spectator'' elements, such as those found in double ion exchange (metathesis) reactions, enable selective materials synthesis? By observing reaction pathways as they happen (\\emph{in situ}) and calculating their energetics using modern computational thermodynamics, we observe transient, crystalline intermediates that suggest that many reactions attain a local thermodynamic equilibrium dictated by local chemical potentials far before achieving a global equilibrium set by the average composition. Using this knowledge, one can thermodynamically ``shortcut'' unfavorable intermediates by including additional elements beyond those of the desired target, providing access to a greater number of intermediates with advantageous energetics and selective phase nucleation. Ultimately, data-driven modeling that unites first-principles approaches with experimental insights will refine the accuracy of emerging predictive retrosynthetic models for complex materials synthesis.

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3,520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO\\(_3\\)). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82,985 possible BaTiO\\(_3\\) synthesis reactions and selected nine for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl\\(_2\\) and Na\\(_2\\)TiO\\(_3\\)) that produce BaTiO\\(_3\\) faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.