Journal articles

Rock art is globally recognized as significant, yet the resources allocated to the study and exploration of this important form of cultural heritage are often scarce. In areas where numerous rock art sites exist, much of the rock art is unidentified and therefore remains, unrecorded and unresearched. Manually identifying rock art is time-consuming, tedious, and expensive. Therefore, it is necessary to automate many processes in rock art research, which can be accomplished by Machine Learning. Artificial Intelligence (AI) and Machine Learning (ML) can greatly facilitate rock art research in many ways, such as through Object Recognition and Detection, Motif Extraction, Object Reconstruction, Image Knowledge Graphs, and Representations. This article is a reflective work on the future of ML for rock art research. As a proof-of-concept, it presents a machine learning method based on recent advances in deep learning to train a model to identify images with painted rock art (pictograms). The efficacy of the proposed method is shown using data collected from fieldwork in Australia. Furthermore, our proposed method can be used to train models that are specific to the rock art found in different regions. We provide the code and the trained models in the supplementary section.

The shift toward sustainable agriculture involves replacing inorganic fertilizers with organic alternatives like biosolids. However, concerns arise over emerging contaminants, such as microplastics (MPs), which remain largely unregulated. Despite their common use in Australia, the transfer of MPs from biosolids to agricultural soils remains largely unknown. Herein, we have investigated the abundance, characteristics, and transport of MPs resulting from biosolids application in two Queensland agricultural sites. MP concentrations were significantly higher in biosolid-amended soils (average of 1137 MPs/kg) than in reference sites (average of 36 MPs/kg), correlating with the volume, time since application, and frequency of biosolids application. MPs > 25 μm were predominantly polyethylene, polypropylene, and poly(methyl methacrylate) fragments (up to 85%). Fibers constitute only 15–30% of MPs and mainly in larger sizes (average 1011 μm), whereas fragments (average 188 μm) and beads (average 72 μm) had smaller size ranges. Despite analytical challenges using Raman spectroscopy, detected smaller MPs (1–25 μm) were fragments composed of polyethylene, polypropylene, and poly(vinyl alcohol). This study provides the first report of MPs down to 1 μm in Australian agricultural soils. Our findings suggest a need to assess the long-term impact of MPs in biosolids on soil health and food safety. We call on policymakers to consider the implementation of effective MP source control strategies and the development of guidelines for acceptable biosolids application rates.

Global temperature has risen >1 °C since the preindustrial era, resulting in well-documented adverse climate impacts including extreme weather (floods, droughts, storms, and heat waves), a rise in sea level accompanying melting polar and glacial ice, and disrupted crop growth. These changes are closely correlated with anthropogenic greenhouse gas emissions, predominantly arising from the combustion of nonrenewable fossil fuels. Lignin derived from lignocellulose is the second most abundant biopolymer on Earth, and a rich source of renewable aromatic hydrocarbons to replace those currently obtained from fossil resources. Lignin depolymerization by cleavage of C–O and C–C linkages in the biopolymer can be achieved by direct pyrolysis or catalytic transformations, involving oxidation, hydrolysis, or hydrogenolysis reactions. Hydrogenolysis, in which H2 gas (or in-situ generated reactive H species) is supplied to lignin under relatively mild conditions, has attracted significant attention. This Perspective summarizes recent progress in the development of heterogeneous catalysts for the cleavage of C–O linkages in lignin-derived aromatic ethers by hydrogenolysis: it encompasses strategies using H2, hydrogen transfer, and photocatalysis for aromatic monomers production, and the determination of structure–activity relationships and underlying reaction mechanisms.

Lignin and glyoxal are potential sustainable substitutes for preparing lignin-based epoxy resins but exhibit poor flammability; although lignin incorporation enhances the charring capacity of thermosetting materials, intense burning occurs on ignition. A metal-organic framework (P-MOF) comprising cobalt and phosphorus/nitrogen components was therefore synthesized to improve fire safety. Limiting oxygen index (LOI) and vertical burning tests (UL-94) indicate excellent flame retardancy, with a higher LOI value and V-0 rating level achieved after P-MOF addition. Cone calorimeter tests investigating gaseous inhibition of epoxy matrixes reveal that heat release and smoke production dramatically decrease on addition of P-MOF because of fire quenching (phosphorus organic component) and smoke suppression (cobalt component). Synergy between lignin and P-MOF components suppressed combustion of condensed phases, producing more char residues with a higher degree of graphitization. A mechanism for the flame retardancy of the epoxy composite is proposed, which highlights the promise of P-MOF additives for reducing fire hazards due to cobalt-catalyzed CO oxidation during epoxy burning.

We postulate generalized principles for determining catalytic descriptors like the adsorption energy of CO*, across interfacial active sites of gold catalysts having varying coordination numbers and differing proximity to the support. These principles are derived using Density Functional Theory (DFT) calculations, linear scaling relationships, and an electronic structure analysis. Considered supports include two-dimensional (2D) and three-dimensional (3D) carbides and nitrides, doped MgO, and MoS2. We show that the stability of gold atoms, across different coordination numbers, is linearly correlated to the adsorption energy of CO* through site-specific scaling relationships. As per definition, the slopes of these site-specific scaling relationships portray the extent of structure-sensitivity of CO* adsorption. This structure-sensitivity indicates the dependence of adsorption energies of CO* on the coordination number of the Au sites. The site-specific scaling relationships inform that interfacial perturbations are localized at the metal layer proximal to the interface. These perturbations are moreover strongest at low-coordinated gold sites. Interestingly, the interaction energies between adsorbates at higher coverages are insensitive to interfacial perturbations, further demonstrating the localized nature of metal–support interactions. Our interpretations of the slopes of site-specific scaling relationships indicate that the structure-sensitivity of interfacial gold sites is determined by the extent of interfacial charge transfer. The CO* adsorption energy is structure-insensitive on supports that induce a negative charge on interfacial gold atoms. This structure-sensitivity proportionally increases with the progressively increasing positive charge on interfacial gold atoms. Such charge transfer-dependent structure-sensitivity is rationalized using Lewis acid–base interactions. We demonstrate that tuning the adsorption energy of CO* by manipulating interfacial charge transfer can endow a Cu-like reactivity to interfacial Au sites for CO2 electro-reduction. By melding the generalized principles derived in this study, we synthesize a scheme for determining site-specific catalytic descriptors at interfacial active sites of supported gold catalysts.

Objectives This systematic review (PROSPERO CRD42021275421) synthesized evidence on the efficacy of cognitive rehabilitation on cognitive and functional outcomes in adult cancer survivors.

Methods Articles were identified though PubMed/MEDLINE, EMBASE, PsycINFO, and Web of Science from inception through June 30, 2023. Studies included participants ≥18 years old, diagnosed with cancer. Primary outcomes were validated measures of subjective and objective cognition. Articles were dual reviewed for eligibility and data extraction. Risk of bias was assessed with the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields.

Results The search yielded 3,811 articles; 65 full-text articles were reviewed; 53 articles (15 cognitive training, 14 strategy-based, 21 combinations, three inpatient rehabilitation), representing 52 unique studies, were included. Positive effects were observed in at least one objective cognitive measure in 93% of strategy training, 81% of cognitive training, 79% of combination rehabilitation interventions. Positive effects were observed in subjective cognition in 100% of strategy training, 55% of cognitive training, and 92% of combination interventions. Among studies with comparator groups, processing speed improved in 60% of cognitive training studies, while strategy training did not improve processing speed; otherwise, cognitive domain effects were similar between intervention types. Impact on functional outcomes was inconclusive.

Conclusions Cognitive rehabilitation appear beneficial for cancer-related cognitive impairment (CRCI). Differential effects on specific cognitive domains (eg, processing speed) and subjective cognition may exist between intervention types.

Implications for Nursing Practice Nurses should increase patient and provider awareness of the benefits of cognitive rehabilitation for CRCI.

The attenuation of revenge-related responses after a major service failure is not simply caused by the passage of time—as is assumed in prior work. Instead, we propose that the effect of time is enhanced by the completion of multiple surveys that allow customers to constructively make sense of their service failures. We document this sensemaking-based attenuation effect by conducting four longitudinal experiments; each of them includes a series of three to four surveys completed over four to eight weeks. Doing so, we make three key contributions. First, all studies show that customers having the opportunities to complete a series of sensemaking-inducing surveys report fewer revenge-related responses than participants completing a single survey (i.e., a control group) for the same period. Second, we document the process at play by manipulating the contents of surveys (i.e., “cognitions and emotions” vs. “only cognitions” vs. “only emotions”) and by showing the mediation roles played by sensemaking and benevolent trusting beliefs. Third, we identify quality of pre-failure relationship as a boundary condition whereby the attenuation is stronger when relationship quality is weaker. Finally, we explain how sensemaking can be prompted by marketers to appease their customers.

The use of checklists in surgery is a best practice.1 There is a plethora of evidence that suggests using the WHO Surgical Safety Checklist (SSC) reduces complications such as pneumonia,2 intraoperative blood loss,2 3 sepsis,2 unplanned intubation,2 urinary tract infections,2 wound infections,2–4 30-day readmissions and 30-day mortality.2–4 The SSC has three components, which need to be carried out for each phase of a surgical procedure, including sign-in, timeout and sign-out.5 The SSC serves as an aide memoir that includes vital information to prompt team discussions and actions that may otherwise be overlooked or forgotten, thereby promoting clear, consistent and timely communications among team members that prevents errors and enhances patient safety.6 Importantly, the SSC is more than merely a routine activity. When used as intended, it can promote effective teamwork and communication and enable surgical team members to voice their concerns regardless of their professional role.7

Workload can be regarded as the “cost” (in information processing terms) of performing a given flight task. Predictive models of workload are valuable at the early stages of system design to identify potentially excessive demands on the human operator before major system development commences. This paper describes the early development of a task-based predictive workload method. It utilizes a scenario-based timeline analysis supplemented by characterizations of the cognitive nature of the pilot’s tasks using Wickens’ multiple resource theory, followed by the identification of likely task conflicts and estimates of the influence of the task environment. Workload predictions were made for a manual approach, landing and roll-out scenario in a modern airliner. Predictions were compared against corresponding Bedford scale workload estimates. Results show that the predictive workload technique produced comparable estimates of cognitive workload measured using the Bedford scale.

Nutritional intake during pregnancy can affect gestational length, fetal development, and impact postnatal growth and health in offspring. Perturbations in maternal nutrition with either an excess or deficiency in nutrients during pregnancy may have harmful effects on the offspring’s development and increase the risk of developing chronic diseases later in life. In pregnancy, nutrients transfer from the mother to the fetus via the placenta. Essential fatty acids, linoleic acid (LA) and alpha linoleic acid (ALA), can only be obtained in the diet. In Western countries, the ratio of LA and ALA in the diet has increased dramatically in recent decades. Some animal and human studies have found a correlation between maternal intake of LA and birth weight; however, the association varies. In contrast, some human studies have demonstrated inconclusive findings regarding the correlation between cord blood levels of LA and birth outcomes. In addition, high dietary LA intake in animal studies in pregnancy increased the production of inflammatory markers such as prostaglandins, leukotrienes, cytokines, and tumour necrosis factor-alpha. This review aims to highlight the effect of high dietary LA intake during pregnancy on birth outcomes, obesity, maternal inflammatory markers, and the transfer of fatty acids across the placenta.

The carbapenem-resistant Acinetobacter baumannii isolate BAL062 is a clinical reference isolate used in several recent experimental studies. It is from a ventilator-associated pneumonia (VAP) patient in an intensive care unit at the Hospital for Tropical Diseases (HTD), Ho Chi Minh City, Vietnam in 2009. Here, BAL062 was found to belong to the B sub-lineage of global clone 2 (GC2) isolates in the previously reported outbreak (2008 and 2012) of carbapenem-resistant VAP A. baumannii at the HTD. While related sub-lineage B outbreak isolates were extensively antibiotic-resistant and carry GC2-associated genomic resistance islands, AbGRI1, AbGRI2, and AbGRI3, BAL062 has lost AbGRI3 and three aminoglycoside resistance genes, armA, aacA4, and aphA1, leading to amikacin, tobramycin and kanamycin susceptibility. The location of Tn2008VAR found in the chromosome of this sub-lineage was also corrected. Like many of the outbreak isolates, BAL062 carries the KL58 gene cluster at the capsular polysaccharide (CPS) synthesis locus and an annotation key is provided. As information about K type is important for the development of novel CPS-targeting therapies, the BAL062 K58-type CPS structure was established using NMR spectroscopy. It is most closely related to K2 and K93, sharing similar configurations and linkages between K units, and contains the rare higher monosaccharide, 5,7-diacetamido-3,5,7,9-tetradeoxy-d-glycero-l-manno-non-2-ulosonic acid (5,7-di-N-acetyl-8-epipseudaminic acid; 8ePse5Ac7Ac), the 8-epimer of Pse5Ac7Ac (5,7-di-N-acetylpseudaminic acid). Inspection of publicly available A. baumannii genomes revealed a wide distribution of the KL58 locus in geographically diverse isolates belonging to several sequence types that were recovered over two decades from clinical, animal, and environmental sources.

Freemium services such as music streaming, video sharing, and mobile games are often supported using advertisements, whereby consumer attention to these advertisements can be considered a proxy form of payment for using the service. The current research develops and empirically tests a conceptual model underpinned by cognitive appraisal theory, examining the influence of control features within freemium services, such as offering consumers choice over when to view advertisements and timing prompts (countdowns indicating length of advertisement). This research considers the influence of these control features on perceived advertising intrusiveness and psychological reactance, as well as the impacts on the advertising outcomes of brand recognition and attitudes toward the advertisement, toward the freemium platform, and toward the brand advertised. Three experimental studies (n = 178, n = 123, n = 224) showed control features significantly decrease perceived advertising intrusiveness. Results revealed that when control features are not provided, there is a significant indirect negative effect on advertising outcomes (recognition and attitude) via the mediators of advertising intrusiveness and psychological reactance. This research is one of the first studies to demonstrate both theoretical and empirical understanding of how control provided to consumers via choice and timing prompts can mitigate the negative effects of advertising within freemium services.

Biocrusts play a wide range of ecological roles, especially in modifying surface soil properties and as a natural agent to combat soil degradation in fragile semi-arid and arid ecosystems globally. We examined the importance of biocrust on soil properties and quality in a semi-arid alluvial fan in northeastern Iran. The soil quality index (SQI) was used as the method for the quantitative evaluation of soil with or without biocrusts, using standard linear and nonlinear scoring methods. For this purpose, 17 biological, chemical, and physical soil properties were integrated into two indicator selection methods (total and minimum data set) and SQI has been calculated by the weighted additive method. The spatial patterns of SQI showed that overall the soil quality was (47%) increased in biocrust compared to non-biocrust soil. The increase of microbial biomass nitrogen, basal soil respiration, and microbial biomass carbon, and the decrease of available sodium and percentage sand were the most important indicators controlling the soil quality changes by biocrusts. In addition, biocrusts improved the soil structure by reducing bulk density and increasing the mean weight diameter of aggregates and available water capacity. Cyanobacteria communities of biocrusts were the initiators of soil quality improvement, while moss and cyanolichen communities played a major role from the moderate stages of biocrust occurrence. It is concluded that globally, in dry lands, biocrusts should be considered as signatures of enhanced soil quality.

Land subsidence (LS) is among the most prominent forms of subsurface erosion and geomorphological hazards. This study used two deep learning (DL) models consisting of the hybrid CNN-RNN and ensemble DL (EDL) merged with two dense models. The main variables controlling LS (consisting of environmental, hydrological, hydrogeological, digital elevation model, and soil characteristics), were used as the input for the predictive DL models. Likewise, to establish the degree of performance of each parameter, different control points have been established. We then trained and tested our DL models using the receiver-operating characteristic-area under curve (ROC-AUC) and precision-recall plots. The measures based on the game theory consisting of permutation feature importance measure (PFIM) and SHapley Additive exPlanations (SHAP) were employed to assess the features relative importance and interpretability of the predictive model output. Our findings show that the ensemble CNN-RNN model performed well with the ROC-AUC curve (0.95) of class 1 (land subsidence) for training data for detecting and mapping land subsidence compared to EDL with the ROC curve (0.93) of class 1 (land subsidence) for training datasets. The CNN-RNN also performed well with the precision-recall curve (0.954) of class 1 for testing data for detecting and mapping land subsidence compared to the EDL model with the precision-recall curve (0.95) of class 1. The results of this research revealed that much of the study area is susceptible to land subsidence. The results of the model sensitivity analysis suggested that the groundwater drop rate is the most sensitive for the model. Based on the SHAP values, the groundwater drop rate was identified as the most contributed feature to the model output. The importance of this study is at a broader level, especially in arid and semiarid environments with similar geomorphological, and climatic conditions.

Land subsidence (LS) and pipe collapse (PC) as the major types of geomorphologic hazards lead to noticeable changes in landscape alterations, land damage, loss of soil and water, surface erosion, and sediment buildup in affected areas. To overcome this, the susceptibility to LS and CP was investigated using three deep learning convolutional neural network (DL-CNN) architectures, including Res-Net, AlexNet, and VGG-Network. We used various predictor variables, and then, trained and tested our DL-CNN models using ReLu, Cross-Entropy, and Adam as activation, loss, and optimization functions, respectively. Our findings showed that DL-CNN models achieved an overall accuracy of 0.9836, 0.9721, and 0.9642 for the Res-Net, AlexNet, and VGG-Network, respectively, for CP sensitivity detection. In addition, the Res-Net, AlexNet, and VGG-Network with an overall accuracy of 0.9698, 0.9654, and 0.9519, respectively, showed satisfying performances for LS detection. We also applied univariate summary statistics, including L(r), the pair correlation function (g(r)), and the O-ring function (O(r)), to investigate the spatial pattern and distribution of CP and LS. The L(r) function graph showed that the spatial patterns of CP and LS were clustered across all the investigated distance scales. The value of this function fell outside the Monte Carlo range, indicating that the accumulation of CP and LS at the mentioned distance scale was statistically significant. The results of the O(r) function for the distribution pattern of CP in the study area indicated that this phenomenon was mostly distributed next to each other, implying the facilitating effect of CP on the creation and expansion of each other across all the investigated distance scales. Similarly, the univariate function g(r) also showed the dispersed distribution of subsidence LS at all distances next to each other. In summary, the results of this research revealed that much of the study area was susceptible to CP and LS. The proposed methodology and findings of this study would be useful for land managers, stakeholders, and researchers.

Objectives: Beliefs and knowledge about pain can influence how an individual approaches pain management. At present, there have been no investigations into the beliefs and knowledge that females hold about their pelvic pain; therefore, pain intervention approaches may not be targeted at current patient understandings. Exploring beliefs and knowledge may help inform and subsequently improve pain intervention outcomes for pelvic pain by allowing more tailored content to be delivered. This study aimed to identify beliefs and knowledge that females hold about their pelvic pain.

Methods: Semi-structured interviews (n=12) were conducted. Based on the Common Sense Model, interview questions investigated participants’ beliefs and knowledge related to the identity (diagnostic labels), cause, consequence, control, and timeline of their pelvic pain. Transcribed interviews were analysed using an Interpretive Description Framework.

Results: Participants reported holding biopsychosocial understandings of pain; however, many described their own pain experience in a way that was more aligned with the biomedical model of pain, suggesting incongruence in pain understandings. Participants valued their ability to take charge of their pain management, highlighting bodily autonomy and being proactive in pain management approaches. This approach to management did not provide total control over participants’ pelvic pain. Pelvic pain was perceived as disruptive of life, both at present and into the future, with predictability of pain varying over time.

Discussion: This study provides insight into the perspectives of females with pelvic pain. Recommendations for clinicians are provided with a focus on delivering targeted education and equipping patients with helpful representations of their pain.

Suicide is a major global public health concern, and the application of artificial intelligence (AI) methods, such as natural language processing (NLP), machine learning (ML), and deep learning (DL), has shown promise in advancing suicide prediction and prevention efforts. Recent advancements in AI – particularly NLP and DL have opened up new avenues of research in suicide prediction and prevention. While several papers have reviewed specific detection techniques like NLP or DL, there has been no recent study that acts as a one-stop-shop, providing a comprehensive overview of all AI-based studies in this field. In this work, we conduct a systematic literature review to identify relevant studies published between 2019 and 2023, resulting in the inclusion of 156 studies. We provide a comprehensive overview of the current state of research conducted on AI-driven suicide prevention and prediction, focusing on different data types and AI techniques employed. We discuss the benefits and challenges of these approaches and propose future research directions to improve the practical application of AI in suicide research. AI is highly capable of improving the accuracy and efficiency of risk assessment, enabling personalized interventions, and enhancing our understanding of risk and protective factors. Multidisciplinary approaches combining diverse data sources and AI methods can help identify individuals at risk by analyzing social media content, patient histories, and data from mobile devices, enabling timely intervention. However, challenges related to data privacy, algorithmic bias, model interpretability, and real-world implementation must be addressed to realize the full potential of these technologies. Future research should focus on integrating prediction and prevention strategies, harnessing multimodal data, and expanding the scope to include diverse populations. Collaboration across disciplines and stakeholders is essential to ensure that AI-driven suicide prevention and prediction efforts are ethical, culturally sensitive, and person-centered.

Psychiatry shares most ethical issues with other branches of medicine, but also faces special challenges. The Code of Ethics of the World Psychiatric Association offers guidance, but many mental health care professionals are unaware of it and the principles it supports. Furthermore, following codes of ethics is not always sufficient to address ethical dilemmas arising from possible clashes among their principles, and from continuing changes in knowledge, culture, attitudes, and socio-economic context. In this paper, we identify topics that pose difficult ethical challenges in contemporary psychiatry; that may have a significant impact on clinical practice, education and research activities; and that may require revision of the profession's codes of ethics. These include: the relationships between human rights and mental health care, research and training; human rights and mental health legislation; digital psychiatry; early intervention in psychiatry; end-of-life decisions by people with mental health conditions; conflicts of interests in clinical practice, training and research; and the role of people with lived experience and family/informal supporters in shaping the agenda of mental health care, policy, research and training. For each topic, we highlight the ethical concerns, suggest strategies to address them, call attention to the risks that these strategies entail, and highlight the gaps to be narrowed by further research. We conclude that, in order to effectively address current ethical challenges in psychiatry, we need to rethink policies, services, training, attitudes, research methods and codes of ethics, with the concurrent input of a range of stakeholders, open minded discussions, new models of care, and an adequate organizational capacity to roll-out the implementation across routine clinical care contexts, training and research.

Soil is the basis of bamboo growth and quality formation of bamboo shoots and has an important contribution to the sustainable development of agriculture. To this end, We studied the soil properties and microbial communities of Dendrocalamus brandisii by collecting twenty-one soil samples from its seven typical geographic provenances in Yunnan Province, China. Bacterial 16S rRNA gene amplicons were used to detect soil bacteria and predict bacterial functions using Tax4Fun. The results indicated that the soil bacterial diversity indices (ACE, Chao1, Simpson, and Shannon) were significantly different among different geographical provenances. The dominant bacterial groups at the phylum level in all seven regions were Proteobacteria (19.78~29.06%), Actinobacteria (13.53~30.01%), Chloroflexi (8.03~31.47%), and Acidobacteria (7.12~19.17%), with markedly different constitution proportions. Total phosphorus, available potassium, and pH were the main environmental factors affecting soil bacterial communities. There were significant differences in the secondary metabolic pathways and phenotypes of soil bacterial functions, exhibiting a diversity of functions. The geographical variables of the soil bacterial community in D. brandisii varied with spatial scales. Environmental factors such as available potassium (AK), pH, and total nitrogen (TN) have an impact on soil bacterial communities.