Search Strategy Research Articles

Preoperative Smoking-Cessation Interventions to Prevent Postoperative Complications: A Quality Assessment and Overview of Systematic Review Evidence.

Multiple systematic reviews have investigated the effectiveness of preoperative interventions for smoking-cessation, although relatively few have focused on the prevention of surgical complications. This overview of systematic reviews aimed to describe the types of smoking interventions studied to prevent postoperative complications, summarize the results, and evaluate the quality of the reviews and strength of evidence to inform clinicians, health practitioners, policy developers, and government bodies. Comprehensive searches of Cochrane Library, MEDLINE, EMBASE, CINAHL, and Johanna Briggs Institute databases were conducted to identify systematic reviews of preoperative smoking-cessation interventions to prevent surgical complications (inception-May 14, 2024). Search results were independently screened by 2 reviewers for articles meeting the eligibility criteria. Data on key review characteristics and included studies were extracted: aim, search strategy, included studies, risk of bias, population, sample size, intervention, comparator, main findings, and conclusions. Quality appraisal of the reviews was undertaken using the AMSTAR 2 tool and evidence certainty was evaluated using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Sixty-seven full-text articles from 838 citations were screened, resulting in 6 included systematic reviews with 12 primary studies reporting postoperative complications. Four reviews which included all primary studies, provided moderate to high strength of evidence. There was high-certainty evidence suggesting interventions started ≥4 weeks before surgery incorporating multiple behavioral support sessions and pharmacotherapy are needed to prevent postoperative complications, particularly the incidence of surgical site infections. High-certainty evidence also indicates the duration of smoking-cessation is important, with benefits amplified for longer periods. However, medium- to high-certainty evidence suggests interventions initiated <4 weeks before surgery even with multiple behavioral support sessions (with or without pharmacotherapy), and interventions commenced >4 weeks before surgery but with only 1 interventional component, increase quit rates but do not reduce complications. This overview provides the most up-to-date summary and quality assessment of systematic review evidence on the effectiveness of preoperative smoking-cessation interventions to prevent surgical complications. The evidence supports providing smoking-cessation interventions which include multiple behavioral support sessions and pharmacotherapy implemented at least 4 weeks before surgery to reduce postoperative complications. Consequently, anesthesiologists need to work with primary care physicians, consultants, and surgeons to optimize smoking-cessation interventions way in advance of surgery.

Perilymphatic fistula: a historical overview of publication trends

BackgroundPerilymph fistula (PLF) can cause symptoms of dizziness, vertigo, and fluctuating hearing. We hypothesized that publications on PLF have decreased in recent years relative to other inner ear disorders with overlapping symptoms.MethodsWe performed a Scopus search using the terms “perilymphatic fistula” OR “perilymph fistula,” limited to original studies or reviews involving human subjects published in English before 2022. We noted the senior author's institution, country affiliation, and publishing journal. The Kendall rank correlation coefficient test was used to analyze the trend of each variable over the past 30 years. Using the same search strategy, we compared these data to those from vestibular neuritis (VN), Meniere's disease (MD), superior semicircular canal dehiscence (SSCD), and benign paroxysmal positional vertigo (BPPV).ResultsSix hundred and ninety-eight PLF publications were returned. The top publishing country was the United States (n = 334), the top publishing journal was the American Journal of Otology/Otology and Neurotology (n = 68), and the top publishing institution was the University of Pittsburgh (n = 15). In the past 30 years (n = 501), there was no trend in the frequency of annual PLF publications (τ = −0.150, p = 0.265). Still there were positive trends in publications on vestibular neuritis (τ = 0.724, p &amp;lt; 0.001), Meniere's disease (τ = 0.587, p &amp;lt; 0.001), superior canal dehiscence syndrome (τ = 0.840, p &amp;lt; 0.001), and benign paroxysmal positional vertigo (τ = 0.882, p &amp;lt; 0.001) 5,398 PLF cases were identified, of which 4,356 specified the etiology; the majority identified an inciting insult (74.8%, n = 3,257).ConclusionsThe absence of diagnostic criteria for PLF may hinder its appeal to researchers as similar syndromes have had diagnostic consensus documents set forth. Given the greater number of cases with inciting insults, efforts to define a diagnostic criterion should focus on this subtype.

A bi-population cooperative scatter search algorithm for distributed hybrid flow shop scheduling with machine breakdown

The occurrence of machine breakdowns is a frequent and dynamic phenomenon in the production process. The implementation of effective preventive measures can mitigate such events and result in reduced production costs. This paper investigates the distributed hybrid flow shop scheduling problem with machine breakdown (DHFSSPB) considering short maintenance time. The bi-population cooperative scatter search (BCSS) algorithm is proposed to address the DHFSSPB, wherein the search for the optimal scheduling sequence is transformed into genetic evolution aiming to obtain a gene chain with both minimum lower bound and minimum cost attributes. Firstly, the DHFSSPB problem is modeled through a combination of predictive maintenance strategy and right-shift rescheduling rule. Subsequently, a diversification approach is developed to facilitate attribute inheritance, enhance the efficiency of job allocation, and establish a reference set. The reference set is partitioned into two subpopulations based on lower bound attributes and cost attributes, respectively. The corresponding hybrid search strategies are designed to enhance the efficiency of job sorting and machine selection for subpopulations with distinct attributes. The cooperative evolution between subpopulations occurs through the competitive interaction and fusion of individuals. An enhanced reinforcement learning approach is proposed to expedite the acceleration of individual attribute evolution by leveraging evolutionary knowledge acquired from populations, thereby effectively guiding the evolutionary trajectories of individuals. Additionally, a method for evaluating the population during the learning process is developed based on problem characteristics to enhance learning efficiency. Experimental results demonstrate that BCSS outperforms the comparative algorithm in solving the DHFSSPB.

Bibliometric analysis of the interplay between epilepsy and microglia: trends, hotspots, and emerging research areas.

Epilepsy, a common neurological disorder, has been increasingly associated with neuroinflammation, especially microglia activation. To gain insights into the research trends and patterns in this intersection, we conducted a bibliometric analysis of studies published between 2005 and 2024. Using the Web of Science Core Collection, we identified 1,229 relevant articles and reviews, focusing on the relationship between epilepsy and microglia. We employed the Bibliometrix R package and VOSviewer to analyze the data. Our search strategy combined epilepsy-related terms with microglia and microglial cell keywords. The analysis encompassed publication trends, country and institutional contributions, journal sources, authors, keywords, and thematic evolution. The number of publications has steadily increased, particularly after 2019, indicating growing research interest. The United States, China, and Germany emerged as the most productive countries, with extensive collaboration between China and the United States. Epilepsia and Journal of Neuroinflammation were the most influential journals. Aronica E, Vezzani A, and Engel T were the most prolific authors. Thematic analysis revealed clusters focused on temporal lobe epilepsy, epilepsy-related disorders, and microglia activation. Over the past several years, research has shifted from fundamental brain function studies to in-vivo investigations of specific molecular mechanisms. The CSTB (-) mouse model and NF-κB signaling pathway both merit further in-depth investigation. In conclusion, this bibliometric study reveals a surge in epilepsy-microglia research, led by key countries, journals, and researchers. Temporal lobe epilepsy, epilepsy-related disorders, and microglia activation are focal themes. Future directions include exploring microglia activation mechanisms, utilizing animal models, and interdisciplinary approaches.

The characteristics of high-risk HPV-negative cervical cancer: a systematic review and meta-analysis.

In order to comprehensive understanding of the characteristics of high-risk HPV-negative cervical cancer and aiming to provide certain evidence in the diagnosis and treatment of this kind of disease, the clinical and pathological characteristics of high-risk HPV-negative (hr-HPV-negative) cervical cancer was evaluated through systematic review and meta-analysis. We employed a systematic search strategy incorporating both MeSH terms and free words across multiple databases including PubMed, Embase, Cochrane Library, Web of Science, CNKI and Wanfang. The strategy was specifically designed to identify articles investigating risk factors or clinical characteristics of hr-HPV-negative cervical cancer. Our database search spanned from their inception until October 2023. The literature selection and data collection processes were guided by predetermined inclusion and exclusion criteria. Each included research was independently evaluated by two reviewers using the Newcastle-Ottawa Scale (NOS) and a meta-analysis was conducted utilizing RevMan5.4. In total, nine studies published between 2013 and 2020 were included. The risk of lymph node metastasis was notably higher in hr-HPV-negative cervical cancer patients compared to hr-HPV-positive counterparts [RR=1.99, 95%CI (1.46-2.72), P<0.0001]. It was found that clinical staging of Ib2 or higher grade was significantly increased in hr-HPV-negative cervical cancer patients, as reported by seven studies [RR=1.42, 95%CI (1.15-1.77), P=0.0001]. Non-squamous carcinoma incidence, reported in seven studies, was significantly higher in hr-HPV-negative patients [RR=2.56, 95%CI (1.63-4.01), P=0.001]. Adenocarcinoma incidence was also significantly higher in hr-HPV-negative cervical cancer patients [RR=2.36, 95%CI (1.44-3.89), P=0.005]. Cervical cancer patients not infected by hr-HPV exhibited increased rates of lymph node metastasis, adenocarcinoma, and non-squamous carcinoma. And they were often diagnosed at more advanced stages. These variables suggest that hr-HPV-negative cervical cancer patients presented with a more severe health status at the time of their initial cancer diagnosis.

A hybrid particle swarm optimisation for flexible casting job shop scheduling problem with batch processing machine

AbstractA flexible casting job shop scheduling problem (FCJSP) with batch processing machines is proposed based on the analysis of the flexible job shop scheduling problem (FJSP) and the study of the expendable casting process. Considering the makespan under the influence of the energy consumption, the authors apply the time execution window to the FCJSP model in conjunction with the characteristics of casting production. A hybrid particle swarm optimisation algorithm (HPSO) is developed to solve the FCJSP. The HPSO employs a block integration decoding rule to address scheduling integration. Particle swarm optimisation is used for global search, employing both discrete and continuous search strategies. Furthermore, the local search employs tabu search with neighbourhood operations based on knowledge‐driven techniques. Simulation experiments demonstrate the feasibility of the proposed optimisation model. In the end, the HPSO algorithm has been successfully applied to the real expendable casting scheduling. The results demonstrate that it is more efficient and robust than previously reported algorithms.

Deep Proteome Analysis of Cerebrospinal Fluid from Pediatric Patients with Central Nervous System Cancer.

The cerebrospinal fluid (CSF) is a key matrix for discovery of biomarkers relevant for prognosis and the development of therapeutic targets in pediatric central nervous system malignancies. However, the wide range of protein concentrations and age-related differences in children makes such discoveries challenging. In addition, pediatric CSF samples are often sparse and first prioritized for clinical purposes. The present work focused on optimizing each step of the proteome analysis workflow to extract the most detailed proteome information possible from the limited CSF resources available for research purposes. The strategy included applying sequential ultracentrifugation to enrich for extracellular vesicles (EV) in addition to analysis of a small volume of raw CSF, which allowed quantification of 1351 proteins (+55% relative to raw CSF) from 400 μL CSF. When including a spectral library, a total of 2103 proteins (+240%) could be quantified. The workflow was optimized for CSF input volume, tryptic digestion method, gradient length, mass spectrometry data acquisition method and database search strategy to quantify as many proteins a possible. The fully optimized workflow included protein aggregation capture (PAC) digestion, paired with data-independent acquisition (DIA, 21 min gradient) and allowed 2989 unique proteins to be quantified from only 400 μL CSF, which is a 340% increase in proteins compared to analysis of a tryptic digest of raw CSF.

Facilitators, Barriers, and Potential Impacts of Implementation of e-Pharmacy in India and its Potential Impact on Cost, Quality, and Access to Medicines: Scoping Review.

e-Pharmacy can potentially solve problems related to the quality of services and products, cost, and access to medicines in low- and middle-income countries. This review aims to understand the facilitators and barriers to the implementation of e-pharmacy in India. This scoping review aimed (1) to understand the facilitators and barriers to the use of e-pharmacy in India and (2) to estimate the potential for e-pharmacy in India for improving access to medication, improving the quality of services and medicines, and decreasing costs of medications. All published and gray literature from July 1, 2011, to June 30, 2021, relating to e-pharmacy, was searched from MEDLINE, Scopus, ProQuest, and Google using a systematic search strategy. In total, 1464 titles and abstracts were screened, of which 47 full-texts were included in the review. e-Pharmacy can potentially improve access to medications for remote areas, and old and debilitated individuals. e-Pharmacies can enable lean supply chain management, lower cost, and allow easy tracking of dispensed medicines. There is potential for integration of e-pharmacy services into the national program of Bhartiya Jan Aushadhi Pariyojana. However, the country is not adequately regulated to prevent the growth of illicit e-pharmacies. Lack of global accreditation and internet coverage, digital literacy, and transnational access are other challenges. E-pharmacy has the potential to improve universal health coverage in India by improving access to medicines and lowering the overall cost of health care. However, future growth will need specific regulations and accreditation mechanisms. Open Science Forum; https://doi.org/10.17605/OSF.IO/6R9YQ.

Performance Evaluation of a Bioinspired Geomagnetic Sensor and Its Application for Geomagnetic Navigation in Simulated Environment.

For geomagnetic navigation technology, taking inspiration from nature and leveraging the principle of animals' utilization of the geomagnetic field for long-distance navigation, and employing biomimetic technology to develop higher-precision geomagnetic sensors and more advanced navigation strategies, has emerged as a new trend. Based on the two widely acknowledged biological magnetic induction mechanisms, we have designed a bioinspired weak magnetic vector (BWMV) sensor and integrated it with neural networks to achieve geomagnetic matching navigation. In this paper, we assess the performance of the BWMV sensor through finite element model simulation. The result validates its high measurement accuracy and outstanding adaptability to installation errors with the assistance of specially trained neural networks. Furthermore, we have enhanced the bioinspired geomagnetic navigation algorithm and proposed a more advanced search strategy to adapt to navigation under the condition of no prior geomagnetic map. A simulated geomagnetic navigation platform was constructed based on the finite element model to simulate the navigation of the BWMV sensor in geomagnetic environments. The simulated navigation experiment verified that the proposed search strategy applied to the BWMV sensor can achieve high-precision navigation. This study proposes a novel approach for the research of bioinspired geomagnetic navigation technology, which holds great development prospects.

A scoping review to map public-facing websites for non-traumatic wrist disorders with quality evaluation.

Public-facing resources for non-traumatic wrist disorders (NTWD) exist, but care recipients and healthcare professionals alike are uncertain where to access the most useful resources and have raised concerns over the quality of information provided. Previous studies involving stakeholders highlight a need for quality evaluation of these resources. The aim of this study was to identify website resources accessible to UK-based online health seekers and explore their content through quality assessment. A scoping review of public-facing websites was conducted in accordance with Joanna Briggs Institute guidelines and PRISMA-ScR checklist. An a-priori search strategy was performed of publicly accessible websites using lay terms were entered into a simple Google search. The DISCERN tool was used to appraise the quality of health information with additional data charted to pre-determined criteria. The 82 websites meeting inclusion criteria scored an average of 2/5 DISCERN. Nine funding categories existed with private service websites were the most common. 18 different diagnoses were found with twenty different management interventions were recommended. Considerable variation was found in the quality of websites providing information which people with NTWD are likely to access. Quality and trustworthiness of website information on NTWD are not the preserve of any sector or organisation and we identified potential for improvements across the board.

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back-propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSW-NB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.

Association between the use of prostaglandin analogues and ocular surface disease: a systematic review.

Patients with glaucoma often experience chronic ocular surface diseases, potentially underestimated in frequency and severity. To provide updated estimates of ocular surface diseases linked to prostaglandin analogue antiglaucoma eye medication, a systematic review was conducted. Twenty-seven publications were selected from databases like PubMed, Scopus, and Web of Science, following a search strategy targeting glaucoma and prostaglandins while excluding certain medications '(Glaucoma AND prostaglandins OR 'prostaglandin analogues')('eye drops' OR 'artificial tears' OR 'ocular surface' OR 'dry eye' OR 'dry eye syndrome' OR 'ocular surface disease' OR 'tear film') NOT ('beta blockers' OR 'alpha adrenergic agonists' OR 'carbonic anhydrase inhibitors' OR 'rho-quinase')'. The review revealed a correlation between prostaglandin analogue use and ocular surface damage, assessing parameters such as tear break-up time, Schirmer test value, ocular surface staining, hyperaemia score, and meibomian gland characteristics. Some studies explored switching patients to alternative glaucoma medications, noting varied effects on ocular surface parameters. Comparisons suggested better tolerance and outcomes with preservative-free options over prostaglandins. Additionally, the impact of treatment duration and diquafosol on ocular health, including meibomian gland loss, was examined across different formulations. Although a link between prostaglandin analogues (with or without preservatives) and ocular surface damage was established, inconsistencies in methodologies and assessment across studies were noted. This comprehensive review, spanning a decade of glaucoma research, underscores the need for re-evaluation of treatment strategies in ophthalmology. It stresses the significance of informed decision-making for enhanced glaucoma care, taking into account the observed effects of various medications on eye health.

Areas of uncertainty on the diagnosis, treatment, and follow-up of hypophosphatemia in adults: an Italian Delphi consensus.

The study aimed to present the results of a Delphi consensus involving Italian experts focusing on the management of hypophosphatemia in adults. A multidisciplinary advisory board of nine physicians, experts in hypophosphatemia management, was established. Next, a literature search was performed to identify international guidelines, consensus, and clinical pathways, which were later presented to the advisory board. Collaboratively, the advisory board and authoring team selected key statements for the consensus process and focused on areas of uncertainty related to the management of hypophosphatemia. The advisory board also indicated the experts to be invited to participate in the consensus process. The Delphi method was employed to reach a consensus. The literature search yielded one guideline, five consensus documents, and one clinical pathway. While our search strategy aimed to identify documents on the management of all types of hypophosphatemia, most of the guidelines and consensus documents retrieved focused on X-linked hypophosphatemia. The consensus process focused on 11 key issues, achieving strong convergence (over 70% consensus) in the first Delphi round for 8 out of the 11 statements. Three statements proceeded to the second round, with strong agreement reached for two. Notably, consensus was not reached for the statement concerning the measurement of fibroblast growth factor 23 for diagnostic purposes. The study revealed that the community of clinical experts is well-informed and in agreement regarding hypophosphatemia management. It emphasized the importance of developing clear national guidance documents to support clinicians and multidisciplinary teams in patient management. These documents are crucial not only for healthcare professionals but also for those responsible for defining pathways and services, facilitating a more accurate management of hypophosphatemic patients.

Consulting with an embedded librarian: student perceptions on the value of required research meetings.

This qualitative research project was undertaken to discover how students perceive the embedded librarian in their nursing class. The researchers determined how a required group research meeting was valued by students and whether that value warranted the necessary time and energy by an embedded librarian. Researchers conducted focus groups with twenty-three students from two different sections of the same nursing research methods undergraduate course. Students' responses to a series of five questions were recorded within Zoom and supplemented by handwritten notes. Data was coded by hand and patterns that emerged from the five focus groups were analyzed. Participants reported overall satisfaction with the embedded librarian and students felt they benefitted from the required research meeting with the librarian, which was part of the searching assignment rubric and closely tied to the assignment itself. Based on the data, a required research meeting with an embedded librarian, who is familiar with the course assignments, reinforces classroom instruction, point-of-need assistance with search strategies, and the opportunity to strengthen the relationship with the librarian for future research needs.

What is the State of Organisational Compassion-Based Interventions Targeting to Improve Health Professionals' Well-Being? Results of a Systematic Review.

To identify and assess the state of knowledge regarding compassion-based interventions and outcomes, targeted to the organisational level, that aim to improve health professionals' well-being. Systematic review. Using the PICO model, the clinical question and search strategy were structured. The searches were performed on 20 September 2022 and 26 December 2023 in the Scopus, CINAHL, EMBASE, PsycINFO and ProQuest Dissertations & Theses Global databases. Content analysis was applied to analyse data, and the PRISMA and SWiM guidelines were followed for reporting. Thirty-eight studies, mostly from the United Kingdom and the United States, met the inclusion criteria and were quality assessed and analysed. Compassion-based interventions that target the organisational level are quite new, thus representing a burgeoning initiative. In this review, many included quantitative studies revealed significant methodological challenges in effectively measuring organisational compassion (interpersonal relationships, organisational culture and retention/turnover). However, the review findings overall indicate that interpersonal connections between colleagues that foster a sense of community, through shared experiences, mindfulness and (self-)compassion practices and social activities, may be a protective factor for well-being. Further, the review emphasises the crucial role of management support in catalysing organisational changes to improve health professionals' well-being. Evidence strongly suggests that fostering human interconnectedness among health professionals is associated with enhanced well-being. Further rigorous studies are needed to validate these findings, clarify the organisational cultural aspects of compassion and develop an effective outcome measurement tool for organisational compassion. Organisational compassion-based interventions may help foster a culture of compassion within organisations, enhance health professionals' capacity for compassion and benefit both their well-being and the quality of care provided to patients and relatives. This review is part of a larger project about compassion and includes two patient representatives (mothers of children with cancer) in the research team.

The effects of the traffic signs information volume on the visual characteristics and workload of novice and experienced drivers.

The effects of traffic sign information volume (TSIV) on the visual characteristics and workload of novice and experienced drivers were investigated in this study. TSIV plays a crucial role in road traffic safety, and understanding its impact on drivers is essential for designing effective traffic sign systems. This research aimed to compare the visual characteristics and workload of novice and experienced drivers under varying TSIV doses through simulated driving tests. The objective was to provide insights for optimizing the design of road TSIV. Six TSIV levels were considered: S0, S1, S2, S3, S4, and S5, representing different workload levels. Participants, including both novice and experienced drivers, were involved in simulated driving scenarios with varying TSIV levels. Eye movement data was collected using an eye tracker device. The study was conducted in China, and appropriate driving simulators and equipment were utilized. The findings revealed several valuable results. Experienced drivers exhibited a higher proportion of saccade behavior in the 30-90 ms time period and did not show rapid saccade behavior during the 0-30 ms period, indicating superior visual search strategies. Both novice and experienced drivers demonstrated improved visual cognitive abilities at the S3 level of TSIV, which corresponds to normal and safe driving conditions. Furthermore, a majority of both groups had saccade amplitudes in the range of 0°-4°, with experienced drivers showing a slightly higher proportion. About 82% of experienced drivers had saccade behavior within the range of 0°-2°, compared to 75% of novice drivers. The study concludes that the S3 level of TSIV, corresponding to 30 bits/km, is optimal for both novice and experienced drivers. This level promotes better visual performance and reduces visual workload, indicating that drivers' information acquisition capacity and visual search strategies are maximized while keeping the workload associated with driving at a minimum. These findings have significant implications for enhancing driving safety.

Mental Wellbeing Among Workers Approaching Retirement: a Scoping Review

Increasing numbers of older workers have to postpone retirement to be eligible for pension income. Therefore, knowledge of how to ensure their mental wellbeing is becoming increasingly salient, especially in Nordic countries, where mental health problems are the most important reason for workers leaving the labor market prematurely. Yet, no systematic overview of what influences older workers’ mental wellbeing currently exists. This scoping review aims to build such knowledge and identify gaps in the literature. Employing a multi-faceted search strategy, we identified and reviewed 23 articles. The mental wellbeing of older workers is influenced by (a) working onditions, (b) workers’ socio-economic and psychological resources, and (c) uncertainties in the statutory framework. Research gaps include a lack of knowledge on (i) how the interaction between factors at the individual, workplace, and welfare state levels impacts older workers’ mental wellbeing, and (ii) the determinants of older workers’ mental wellbeing in Nordic countries.

Usability evaluation and reporting for mobile health apps targeting patients with skin diseases: a systematic review.

Usability is an important method for evaluating mobile health apps from a user perspective. Yet, many publicly available apps lack adequate attention to their design, development, and evaluation. To assess usability evaluation and reporting for mobile health apps targeting patients with skin diseases. The protocol was registered with PROSPERO (CRD42022347184). A search strategy combined terms for usability evaluation, user experience, skin disease and mobile health apps (search date from 2012 to 2023). Six databases (Embase, Medline, CINAHL, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, Scopus) were searched, identifying 18052 results. Nine studies (comprising 9 apps) were included in the final analysis. Skin conditions/issues targeted included skin cancer (n=3), sun protection (n=3), and one study identified for each of the following: chronic pruritus/acne/psoriasis/rosacea/laser treatments/actinic damage/monitoring benign moles/alopecia/inflammatory rash/cutaneous leishmaniasis/spina bifida. All studies assessed app usability and feasibility, with the majority concluding that the apps were deemed useful and easy to use. Qualitative methods, such as usability questionnaires and semi-structured interviews, were predominantly employed. Common emerging themes included ease of use and navigation, comprehensibility, security/privacy concerns, data sharing issues, customizability, costs, and the ability to track progress or self-monitor. While smartphone applications for skin disorders show promising usability across diverse diseases, the limited literature compared to the rapid app development highlights the need for meticulous user-centered design and rigorous evaluation. The study emphasises the importance of evaluating and reporting usability findings to optimize the long-term adoption of mobile health apps, particularly those targeting skin diseases.

Pavement Distress Detection Based on the Staged Deep Learning Classifier Approach

Pavement crack detection is a critical task that is essential to ensure the safety of roads and highways. Accurate detection and classification of pavement cracks can help transportation agencies to identify potential maintenance needs and plan appropriate interventions. In this project, we propose a staged deep learning classifier approach for pavement crack detection to provide accurate and comprehensive information about the pavement condition. In this proposed pavement crack detection model, there are four main phases: pre-processing, feature extraction, feature selection, and crack detection. Firstly, raw images are pre-processed through median filtering and histogram equalization for noise removal and contrast enhancement. Next, features such as the improved local binary pattern, gray-level co-occurrence matrix, and gray-level run length matrix are extracted from the pre-processed images. The optimal features are selected using a hybrid optimization model, the tuna customized honey badger optimization algorithm, which combines the honey badger algorithm and tuna swarm optimization. This new hybrid optimization model will enhance the search strategy for solving optimization problems. The loss function of an augmented convolutional neural network (A-CNN) is modified to compute the root mean square error instead of the entropy-based loss function. The final detected outcomes (presence or absence of cracks) are acquired from the A-CNN.

An evaluation of the efficiency and effectiveness of diagnostic tests for foot and mouth disease: are novel diagnostic tests for FMD more feasible than conventional tests in Southeast Asia?

Foot and mouth disease (FMD) remains endemic in many areas of continental Southeast Asia (SEA). It is responsible for substantial economic losses in the smallholder sector and threatens livelihoods. In recent years, novel diagnostic tests have been developed which reportedly detect FMD virus more effectively and efficiently. This critically appraised topic (CAT) aimed to evaluate the feasibility of these diagnostic tests for FMD in SEA compared to conventional tests. Relevant studies that evaluate diagnostic tests are identified and critically assessed, and recommendations are made on suitable potential diagnostic tests for use in the smallholder sector in SEA. A systematic search of electronic databases (CABI: CAB Abstracts, Web of Science Core Collections) was carried out to identify relevant studies that compared novel and conventional diagnostic tests. The search strategy initially identified 12 papers, of which six fulfilled all the inclusion criteria and were selected for this review. Most of the selected studies had limitations in design and comparability, making it difficult to validly compare the effectiveness and efficiency of the relevant diagnostic tests. These limitations include variation in sample characteristics, methodology, measurable outcomes and the different aspects of the diagnostic tests that each study focused on. Most studies concluded that novel diagnostic tests were more effective and efficient than conventional tests: had greater analytical sensitivity and specificity, were more robust, had a wider range of processable sample types and serotypes, could detect various diseases, had faster testing speeds and provided greater value for money. However, strong recommendations on which specific diagnostic test to rely on could not be made, since there was conflicting evidence and multiple confounding factors. Overall, the evidence found did not entirely apply to the target scenario, being SEA smallholder farms. Recommendations for the target scenario were also made based on the study findings.