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Investigation of oscillation and resonance in the renewable integrated DC-microgrid

  • Authors: Habibullah, Mohammad , Mithulananthan, Nadarajah , Shah, Rakibuzzaman , Islam, Md Radiul , Muyeen, S.
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 12, no. 7 (2023), p.
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  • Description: This paper assessed the small-signal stability performance of a multi-converter-based direct current microgrid (DCMG). The oscillation and potential interactions between critical modes are evaluated. First, the complete analytical model of the DCMG is developed with the converter and associated controllers. Three methodologies, impedance scanning, eigenvalue analysis, and time-domain simulation, along with the fast Fourier transform (FFT) analysis, have been used to comprehensively investigate the oscillations and interactions. The simulation results show inherent weak modes, with a wide range of oscillations in the studied DCMG, which may destabilize the system under disturbances. Based on the sensitivity analysis, controller gains and DC-link capacitance are identified as the most critical parameters and substantially influence the weak modes leading to oscillations, interactions, and resonance. Finally, the performance of the various control synthesis methods is compared. This examination would help the researchers, planning, and design engineers to design and stably operate a multi converter-based DC microgrid. © 2023 by the authors.

IoT-based emergency vehicle services in intelligent transportation system

  • Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Khoda, Mahbub , Naha, Ranesh , Khoshkholghi, Mohammad , Aiash, Mahdi
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Sensors Vol. 23, no. 11 (2023), p. 5324
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  • Description: Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.

Key issues of health and safety for workers in residential aged care : an expert study

  • Authors: Seaward, Liz , Morgan, Damian , Thomson, Alana
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Frontiers in Public Health Vol. 10, no. (2023), p.
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  • Description: Introduction: Residential aged care (RAC) represents a fast-growing sector within Australia's health care system and is characterized by high levels of workplace injury. To better understand this injury problem, this study investigated key informant perspectives concerning sector occupational health and safety (OHS) focused on key issues associated with the risk of worker injury. Method: Semi-structured interviews were undertaken with nine key informants representing (OHS) specialists, healthcare employers, regulators, worker association representatives, and academic researchers in OHS or healthcare. Interviews were transcribed verbatim and analyzed using thematic analysis. Results: This study identified six themes on OHS within RAC including (i) the physical and emotional nature of the work, (ii) casualization of employment, (iii) prioritization, (iv) workforce profile, (v) OHS role construction, and (vi) clinical standards. The study highlighted differences in OHS roles between RAC and other safety-critical sectors regarding governance and management of OHS. The key informants identified a propensity within RAC to downplay or disregard worker OHS issues justified through prioritizing resident safety. Further, neither OHS professional nor institutional logics are prominent in RAC leadership and decision-making where the emphasis is placed on mandatory standards to maintain funding purposes. Several recommendations are made to address identified issues. Copyright © 2023 Seaward, Morgan and Thomson.

Keynes from below : a social history of Second World War Keynesian economics

  • Authors: Coventry, Cameron
  • Date: 2023
  • Type: Text , Thesis , PhD
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  • Description: The macroeconomic agenda known as Keynesianism was highly contentious when it was introduced to Australia during the Second World War. Using a ‘history from below’ approach – correctly understood as a society-wide analysis – this thesis reveals the debates and the participants in the social nexus that considered the work of the British economist John Maynard Keynes (1883-1946). It shows that the populous willed a break from the status quo, ranging in favour of socialism to a non-capitalist third way. Support for Keynesianism was isolated to capital and the political right wing, with labour, centrists, the left and far-left strongly opposed. As the war progressed, apathy set in and Keynesianism came to be seen by opponents as either a non-capitalist third way or as the triumph of the possible over the desirable socialist “new order”. From 1936, Keynes had popularised a new economics based on full employment planning that quickly displaced ‘laissez faire capitalism’ in the minds of economists and policymakers. As war broke out, Keynes submitted a war finance plan for public consideration in the United Kingdom. Essentially, the plan addressed the practical aspects of managing an economy experiencing full employment. It contained measures to reduce wage growth to counter rising inflation, welfare for mothers and children to protect their well-being – but also the population growth essential to future economic growth – and the partial repayment of seized wages at the end of the war that would form the basis of post-war “reconstruction”. The Keynes plan generated interest in Australia which rapidly turned to speculation about its applicability. A fierce debate raged, divided on broad political lines, for two years that would shift public opinion and contribute significantly to the rise of the Curtin government (1941-45). However, once enthusiasm for reconstruction waned, it was this government that brought about post-war Keynesianism.
  • Description: Doctor of Philosophy

Knowledge graphs : opportunities and challenges

  • Authors: Peng, Ciyuan , Xia, Feng , Naseriparsa, Mehdi , Osborne, Francesco
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Artificial Intelligence Review Vol. 56, no. 11 (2023), p. 13071-13102
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  • Description: With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs. © 2023, The Author(s).

Knowledge-based intelligent text simplification for biological relation extraction

  • Authors: Gill, Jaskaran , Chetty, Madhu , Lim, Suryani , Hallinan, Jennifer
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Informatics Vol. 10, no. 4 (2023), p.
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  • Description: Relation extraction from biological publications plays a pivotal role in accelerating scientific discovery and advancing medical research. While vast amounts of this knowledge is stored within the published literature, extracting it manually from this continually growing volume of documents is becoming increasingly arduous. Recently, attention has been focused towards automatically extracting such knowledge using pre-trained Large Language Models (LLM) and deep-learning algorithms for automated relation extraction. However, the complex syntactic structure of biological sentences, with nested entities and domain-specific terminology, and insufficient annotated training corpora, poses major challenges in accurately capturing entity relationships from the unstructured data. To address these issues, in this paper, we propose a Knowledge-based Intelligent Text Simplification (KITS) approach focused on the accurate extraction of biological relations. KITS is able to precisely and accurately capture the relational context among various binary relations within the sentence, alongside preventing any potential changes in meaning for those sentences being simplified by KITS. The experiments show that the proposed technique, using well-known performance metrics, resulted in a 21% increase in precision, with only 25% of sentences simplified in the Learning Language in Logic (LLL) dataset. Combining the proposed method with BioBERT, the popular pre-trained LLM was able to outperform other state-of-the-art methods. © 2023 by the authors.

Large positive ecological changes of small urban greening actions

  • Authors: Mata, Luis , Hahs, Amy , Palma, Estibaliz , Backstrom, Anna , Johnston, Nikolas , King, Tyler , Olson, Ashley , Renowden, Christina , Smith, Tessa , Vogel, Blythe , Ward, Samantha
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Ecological Solutions and Evidence Vol. 4, no. 3 (2023), p.
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  • Description: The detrimental effects of environmental change on human and non-human diversity are acutely manifested in urban environments. While urban greenspaces are known to mitigate these effects and support functionally diverse ecological communities, evidence of the ecological outcomes of urban greening remains scarce. We use a longitudinal observational design to provide empirical evidence of positive ecological changes brought about by greening actions. We collected a plant–insect interactions data set 1 year before, and for 3 years after, a greenspace received a small greening action within a densely urbanised municipality. We then assessed how (i) insect species richness; (ii) the probabilities of occurrence, survival and colonisation of the insect community; and (iii) the plant–insect network structure varied across the 4 years of the study. As we understand, this is the first study to apply statistical and network analytical frameworks to quantitatively track how positive ecological changes accrue over time at a site after the implementation of a specific urban greening action. We show how a small greening action quickly led to large positive changes in the richness, demographic dynamics and network structure of a depauperate insect community. An increase in the diversity and complexity of the plant community led to, after only 3 years, a large increase in insect species richness, a greater probability of occurrence of insects within the greenspace and a higher number and diversity of interactions between insects and plant species. We demonstrate how large positive ecological changes may be derived from investing in small greening actions and how these contribute to bring indigenous species back to greenspaces where they have become rare or been extirpated by urbanisation. Our findings provide crucial evidence that supports best practice in greenspace design and contributes to re-invigorate policies aimed at mitigating the negative impacts of urbanisation on people and other species. © 2023 The Authors. Ecological Solutions and Evidence published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Leached copper correlation with dissolved organic carbon in sloped vineyard soil

  • Authors: Filipovi, Lana , Defterdarović, Jasmina , Chen, Rui , Krevh, Vedran , Gerke, Horst , Baumgartl, Thomas , Kovač, Zoran , Ondrašek, Gabrijel , Ružičić, Stanko , He, Hailong , Dusek, Jaromir , Filipović, Vilim
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Water (Switzerland) Vol. 15, no. 4 (2023), p.
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  • Description: The solubility and mobility of copper (Cu) in soil is strongly influenced by the presence of dissolved organic carbon (DOC); however, the interactions between Cu and DOC are complex and not yet fully understood. In this study, Cu and DOC concentrations were measured monthly for two years in leachates from self-constructed lysimeters installed at inter- and intra-row vineyard hilltop, backslope, and footslope areas at the SUPREHILL Critical Zone Observatory, Croatia. The aim was to quantify Cu and DOC leaching from the hilltop towards the backslope and the footslope. The assumed strong relationship between Cu and DOC in the leachates was statistically analyzed and explained using chemical equilibrium software. Leachates were analyzed for pH, EC, DOC, Cu, and major ion concentrations. The highest Cu concentrations found in leachates from the intra-row footslope suggested Cu downhill transport. Although not strong, a significant positive correlation between Cu and DOC in footslope leachates confirmed the relevance of Cu complexation by DOC. Speciation confirmed that more than 99.9% of total Cu in leachates was found as a Cu-DOC complex. Data implied the role of soil water flow pathways in explaining Cu downhill transport. Critical timing for applying Cu fungicides at sloped vineyards was highlighted. © 2023 by the authors.

Lean and its impact on sustainability performance in service companies: results from a pilot study

  • Authors: Lizarelli, Fabiane , Chakraborty, Ayon , Antony, Jiju , Jayaraman, Raja , Carneiro, Matheus , Furterer, Sandy
  • Date: 2023
  • Type: Text , Journal article
  • Relation: TQM Journal Vol. 35, no. 3 (2023), p. 698-718
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  • Description: Purpose: The purpose of this empirical research is to understand the application of Lean practices (technical and social) and tools in the service sector, whose implementation is less studied, despite its economic relevance. The study aims to extend previous studies that focused on the relationship between Lean and operational and financial performance, and analyzing the impact on sustainability, encompassing economic, social and environmental perspectives. Design/methodology/approach: A pilot survey was conducted with Lean experts in European service companies. The authors have utilized various professional contacts on LinkedIn and a satisfactory response rate was obtained for analysis. Findings: The results of the study showed that there are several motivating factors for the implementation of Lean, the highlights being improving customer satisfaction, efficiency, delivery and cost reduction. The most frequently used Lean tools are related to the identification of improvement opportunities and causes of problems. The pilot survey also made it possible to identify the greater use of technical practices than social practices. The sustainability performance analysis showed that the better performance of service companies is in the economic dimension. Originality/value: The authors have identified no empirical studies linking Lean and sustainable performance in the service sector. This study bridges this cognitive gap through a pilot study and therefore makes an original contribution to the current literature. © 2022, Emerald Publishing Limited.

Lessons to learn, discourses to change, relationships to build : how decolonising race theory can articulate the interface between school leadership and Aboriginal students’ schooling experiences

  • Authors: Burgess, Cathie , Fricker, Aleryk , Weuffen, Sara
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Australian Educational Researcher Vol. 50, no. 1 (2023), p. 111-129
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  • Description: When conversations about Aboriginal student educational success emerge, they are usually focussed on the high levels of underachievement and disengagement. School leadership is seen as critical to contributing to student outcomes. For Aboriginal students, creating inclusive learning environments that support culture and identity, and building trusting relationships with families and community members are also critical goals. As part of the Aboriginal Voices project, this paper uses Decolonising Race Theory (Moodie, 2018) to analyse interviews with four Principals in urban, regional, and rural locations to understand their perceptions and experiences of leading Aboriginal education in schools. From the interviews, three key themes emerged: leading culture, identity and school–community relationships, leading curriculum, pedagogy and teacher development, and leading student participation and achievement. Decolonising Race Theory (Moodie, 2018) is applied as an analytical tool to view these themes through a critical Indigenous lens to understand the Principals’ discourses around Aboriginal student experiences at school and their role in improving outcomes. This revealed contradictory positionings within and between Principal comments, from blaming students and their families for their underachievement, to implementing cultural programmes to build confidence to become self-determining adults. This data provides new ways of thinking through discourses about Aboriginal students and their families, communities, schools, teachers and Principals, and challenges some of the ‘regimes of truth’ that position these groups in particular ways. © 2022, The Author(s).

Long-term analysis of soil water regime and nitrate dynamics at agricultural experimental site : field-scale monitoring and numerical modeling using HYDRUS-1D

  • Authors: Krevh, Vedran , Filipović, Lana , Petošić, Dragutin , Mustać, Ivica , Bogunović, Igor , Butorac, Jaminka , Kisić, Ivica , Defterdarović, Jasmina , Nakić, Zoran , Kovač, Zoran , Pereira, Paulo , He, Hailong , Chen, Rui , Toor, Gurpal , Versini, Antoine , Baumgartl, Thomas , Filipović, Vilim
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Agricultural Water Management Vol. 275, no. (2023), p.
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  • Description: Intensive agricultural practices increase agrochemical pollution, particularly nitrogen (N) based fertilizers, which present an environmental risk. This study aims to evaluate long-term (2009–2020) data on soil water regime and nitrate dynamics at an agricultural experimental site on fine-textured soils and to better understand the implications of N management in relation to groundwater pollution. The field site is located in the Biđ field (eastern Croatia), in the proximity of the Sava river. Zero-tension lysimeters were installed at six selected locations. Lysimeters were used to monitor the water regime, i.e., outflows in which nitrate concentration was measured, while additional soil-water samples were collected via 4 and 15-meter-deep monitoring wells. Soil hydraulic parameters were estimated by combining the laboratory measurements, and estimation in RETC software. Water regime and nitrate leaching in lysimeters were simulated using HYDRUS-1D for each year to allow crop rotation and to evaluate their effects individually. The HYDRUS-1D model successfully reproduced lysimeter outflows and nitrate dynamics, which was confirmed with high R2 values (water: 93% above 0.7, and nitrate: 73% above 0.7) indicating the good performance of the model simulating nitrification chain reactions. Principal component analysis (PCA) was performed to identify the relationships among all soil properties and environmental characteristics. The results showed the complex interaction of soil hydraulic properties, precipitation patterns, plant uptake, and N application. All locations have a decreasing trend of nitrate leaching over the investigation period. Most of the lysimeter outflows and elevated nitrate concentrations were connected to the wet period of the year when the soil was saturated, and evapotranspiration was low. The results of this study show that it is important to optimize N fertilizer applications for each particular environmental condition to reduce nitrate loss. The study indicates the importance of long-term field studies, key for agro-hydrological modeling and the improvement of agricultural practices. © 2022 The Authors

Long-term occupational exposures on disability-free survival and mortality in older adults

  • Authors: Alif, Sheikh , Benke, Geza , Kromhout, Hans , Vermeulen, Roel , Tran, Cammie , Ronaldson, Kathlyn , Walker-Bone, Karen , Woods, Robyn , Beilin, Lawerence , Tonkin, Andrew , Owen, Alice , McNeil, John
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Occupational Medicine Vol. 73, no. 8 (2023), p. 492-499
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  • Description: Background The impact of long-term occupational exposures on health in older adults is increasingly relevant as populations age. To date, no studies have reported their impact on survival free of disability in older adults. Aims We aimed to investigate the association between long-term occupational exposure and disability-free survival (DFS), all-cause mortality and cause-specific mortality in initially healthy older adults. Methods We analysed data from 12 215 healthy participants in the ASPirin in Reducing Events in the Elderly (ASPREE) study whose mean age was 75 years. Their work history was collated with the ‘ALOHA-plus JEM’ (Job Exposure Matrix) to assign occupational exposures. The primary endpoint, DFS, was a composite measure of death, dementia or persistent physical disability. The secondary endpoint, mortality, was classified according to the underlying cause. Cox proportional hazard models were used to calculate hazard ratios and 95% confidence intervals, adjusted for confounders. Results A total of 1835 individuals reached the DFS endpoint during the median 4.7 years follow-up period. Both ever-high and cumulative exposure to all dusts and all pesticides during a person’s working years were associated with reduced DFS. Compared to no exposure, men with high exposure to dusts and pesticides had a reduced DFS. Neither of these exposures were significantly associated with all-cause mortality. Men with high occupational exposure to solvents and women exposed to dusts experienced higher all-cause and cancer-related mortality. Conclusions Long-term occupational exposure to all dusts and pesticides was associated with a reduced DFS and increased mortality in community-dwelling healthy older adults. © The Author(s) 2023. Published by Oxford University Press on behalf of the Society of Occupational Medicine.

Lost at starting line : predicting maladaptation of university freshmen based on educational big data

  • Authors: Guo, Teng , Bai, Xiaomei , Zhen, Shihao , Abid, Shagufta , Xia, Feng
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of the Association for Information Science and Technology Vol. 74, no. 1 (2023), p. 17-32
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  • Description: The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long-term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of “at-risk” students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real-world education datasets demonstrate that the MASTER framework outperforms other state-of-art methods. © 2022 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.

Machine learning-based agoraphilic navigation algorithm for use in dynamic environments with a moving goal

  • Authors: Hewawasam, Hasitha , Kahandawa, Gayan , Ibrahim, Yousef
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Machines Vol. 11, no. 5 (2023), p. 513
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  • Description: This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.

Malicious node detection using machine learning and distributed data storage using blockchain in WSNs

  • Authors: Nouman, Muhammad , Qasim, Umar , Nasir, Hina , Almasoud, Abdullah , Imran, Muhammad , Javaid, Nadeem
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 11, no. (2023), p. 6106-6121
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  • Description: In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage. © 2013 IEEE.

Malignant and non-malignant oral lesions classification and diagnosis with deep neural networks

  • Authors: Liyanage, V.iduni , Tao, Mengqiu , Park, Joon , Wang, Kate , Azimi, Somayyeh
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Dentistry Vol. 137, no. (2023), p.
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  • Description: Objectives: Given the increasing incidence of oral cancer, it is essential to provide high-risk communities, especially in remote regions, with an affordable, user-friendly tool for visual lesion diagnosis. This proof-of-concept study explored the utility and feasibility of a smartphone application that can photograph and diagnose oral lesions. Methods: The images of oral lesions with confirmed diagnoses were sourced from oral and maxillofacial textbooks. In total, 342 images were extracted, encompassing lesions from various regions of the oral cavity such as the gingiva, palate, and labial mucosa. The lesions were segregated into three categories: Class 1 represented non-neoplastic lesions, Class 2 included benign neoplasms, and Class 3 contained premalignant/malignant lesions. The images were analysed using MobileNetV3 and EfficientNetV2 models, with the process producing an accuracy curve, confusion matrix, and receiver operating characteristic (ROC) curve. Results: The EfficientNetV2 model showed a steep increase in validation accuracy early in the iterations, plateauing at a score of 0.71. According to the confusion matrix, this model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions was 64% and 80% respectively. Conversely, the MobileNetV3 model exhibited a more gradual increase, reaching a plateau at a validation accuracy of 0.70. The MobileNetV3 model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions, according to the confusion matrix, was 64% and 82% respectively. Conclusions: Our proof-of-concept study effectively demonstrated the potential accuracy of AI software in distinguishing malignant lesions. This could play a vital role in remote screenings for populations with limited access to dental practitioners. However, the discrepancies between the classification of images and the results of "non-malignant lesions" calls for further refinement of the models and the classification system used. Clinical significance: The findings of this study indicate that AI software has the potential to aid in the identification or screening of malignant oral lesions. Further improvements are required to enhance accuracy in classifying non-malignant lesions. © 2023 The Author(s)

Management using continence products : report of the 7th international consultation on incontinence

  • Authors: Murphy, Cathy , Fader, Mandy , Bliss, Donna , Buckley, Brian , Cockerell, Rowan , Cottenden, Alan , Kottner, Jan , Ostaszkiewicz, Joan
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Continence Vol. 8, no. (2023), p.
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  • Description: Aim: To summarise the available evidence on the use of continence products to manage urinary or faecal incontinence published since the 6th International Consultation on Incontinence (2017) and provide key recommendations for the use of products in each group. Methods: A series of systematic reviews (grouped according to pre-determined topics) and evidence updates were undertaken and reported descriptively by members of an international committee to update the 6th Consultation. Results: The available evidence is presented for 13 categories of continence management products. Some categories (female mechanical urinary incontinence devices, products for preventing/treating incontinence-associated dermatitis and urinary catheters) had at least one new randomised controlled trial. Other categories had small-scale or qualitative studies, reviews or no new associated evidence. A summary of key research priorities is provided. Discussion: This paper provides a summary of the evidence available for a range of continence management products. Some product categories have a larger body of new and existing evidence than others, but there continues to be a lack of research to guide decision-making on the wide range of continence management products. Clinicians and other decision-makers remain largely dependent on expert opinion and individual user circumstances and preferences. We summarise specific areas where more. © 2023 The Authors

Managing depression with complementary and alternative medicine therapies: a scientometric analysis and visualization of research activities

  • Authors: Zhao, Fei-Yi , Xu, Peijie , Zheng, Zhen , Conduit, Russell , Xu, Yan , Yue, Li-Ping , Wang, Hui-Ru , Wang, Yan-Mei , Li, Yuan-Xin , Li, Chun-Yan , Zhang, Wen-Jing , Fu, Qiang-Qiang , Kennedy, Gerard
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Frontiers in Psychiatry Vol. 14, no. (2023), p.
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  • Description: Background: Complementary and Alternative Medicine (CAM) interventions may prove to be an attractive option for the treatment of depression. The aim of this scientometric analysis is to determine the global scientific output of research regarding managing depression with CAM and identify the hotspots and frontiers within this theme. Methods: Publications regarding the utilization of CAM for treating depression were collected from the Web of Science Core Collection from 1993 to 2022, and analyzed and visualized by Bibliometrix R-package, VOSviewer, and CiteSpace. Results: A total of 1,710 publications were acquired. The number of annual publications showed an overall rapid upward trend, with the figure peaking at 179 in 2021. The USA was the leading research center. Totally 2,323 distinct institutions involving 7,638 scholars contributed to the research theme. However, most of the cooperation was limited to within the same country, institution or research team. The Journal of Alternative and Complementary Medicine was the most productive periodical. The CAM therapies of most interest to researchers were acupuncture and body–mind techniques, such as yoga, meditation and mindfulness. Systematic review and meta-analysis are commonly used methods. “Inflammation,” “rating scale” and “psychological stress” were identified as the most studied trend topics recently. Conclusion: Managing depression with evidence-based CAM treatment is gaining attention globally. Body–mind techniques and acupuncture are growing research hotspots or emerging trending topics. Future studies are predicted to potentially investigate the possible mechanisms of action underlying CAM treatments in reducing depression in terms of modulation of psychological stress and inflammation levels. Cross-countries/institutes/team research collaborations should be encouraged and further enhanced. Copyright © 2023 Zhao, Xu, Zheng, Conduit, Xu, Yue, Wang, Wang, Li, Li, Zhang, Fu and Kennedy.

Matching the model to the available data to predict wheat, barley, or canola yield : a review of recently published models and data

  • Authors: Clark, Robert , Dahlhaus, Peter , Robinson, Nathan , Larkins, Jo-ann , Morse-McNabb, Elizabeth
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: Agricultural Systems Vol. 211, no. (2023), p.
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  • Description: CONTEXT: Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. OBJECTIVE: This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. METHODS: A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. RESULTS AND CONCLUSIONS: The literature search produced 11, 908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. SIGNIFICANCE: The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. © 2023 The Authors

Maternal attachment state of mind and perinatal emotional wellbeing : findings from a pregnancy cohort study

  • Authors: Galbally, Megan , Watson, Stuart , Lewis, Andrew , Power, Josephine , Buus, Niels , van Ijzendoorn, Marinus
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Affective Disorders Vol. 333, no. (2023), p. 297-304
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  • Description: Objectives: Maternal attachment state of mind is an important potential predictor of risk and resilience to perinatal emotional wellbeing and early parenting. To explore maternal attachment in relation to perinatal depression and emotional wellbeing. Methods: This study drew on data collected within an ongoing cohort from 170 women recruited in early pregnancy, including 67 who met criteria for Major Depression. Maternal attachment state of mind was assessed with the Adult Attachment Interview (AAI) in pregnancy. Additional measures included the Structured Clinical Interview for the DSM (SCID), at 12 months the Strange Situation Procedure (SSP), Child Trauma Questionnaire (CTQ), Parenting Stress Index, and antenatal maternal hair cortisol concentrations (HCC). Limitations: Sample size to be able to undertake all analyses using the 4 way classifications, cortisol measurement is limited to hair only and there is no prospectively collected measure of childhood trauma in mothers. Conclusions: This study found that maternal attachment, specifically the Non-Autonomous states of mind, adjusted for clinical depression, was associated with higher cortisol in pregnancy and higher depressive symptoms across pregnancy and the postpartum. Furthermore, separately those with depression and Non-Autonomous states of mind also had higher postpartum parenting stress. There was no significant intergenerational concordance between AAI and SSP attachment classifications. Our findings support future research exploring the role of maternal attachment state of mind in understanding perinatal depression and emotional wellbeing. © 2023 The Author(s)