Recent publications
In dynamic optimization problems (DOPs), environmental changes can be characterized as various dynamics. Faced with different dynamics, existing dynamic optimization algorithms (DOAs) are difficult to tackle, because they are incapable of learning in each environment to control the search. Besides, diversity loss is a critical issue in solving DOPs. Maintaining a high-diversity over dynamic environments is reasonable as it can address such an issue automatically. In this article, we propose a particle search control network (PSCN) to maintain a high-diversity over time and control two key search actions of each input individual, i.e., locating the local learning target and adjusting the local acceleration coefficient. Specifically, PSCN adequately considers the diversity to generate subpopulations located by hidden node centers, where each center is assessed by significance-based criteria and distance-based criteria. The former enable a small intrasubpopulation distance and a big search scope (subpopulation width) for each subpopulation, while the latter make each center distant from other existing centers. In each subpopulation, the best-found position is selected as the local learning target. In the output layer, PSCN determines the action of adjusting the local acceleration coefficient of each individual. Reinforcement learning is introduced to obtain the desired output of PSCN, enabling the network to control the search by learning in different iterations of each environment. The experimental results especially performance comparisons with eight state-of-the-art DOAs demonstrate that PSCN brings significant improvements in performance of solving DOPs.
Previous studies have shown that pro‐social leaders cooperate, on average, more than pro‐self leaders in social dilemmas. It can, thus, be beneficial for the group to have a pro‐social leader. In this paper we analyze the consequences of a leader informing followers that they are pro‐social (or pro‐self). In doing so, we compare a setting in which the leader's type is truthfully revealed to settings where the leader can ‘hide’ or ‘lie’ about their pro‐sociality. We find that a leader saying they are pro‐social boosts efficiency, even if the signal is not fully credible. Cooperation is highest in a truth setting with a pro‐social leader. We demonstrate that these results are consistent with a belief‐based model of social preference in which the stated type of the leader changes the frame of reference for followers.
Poor-quality products and counterfeit tags within the supply chains pose major challenges. Although several approaches have been suggested, they do not address all the challenges. This paper proposes a blockchain-integrated supply chain to tackle the poor-quality products and counterfeiting tags issues. In this way, the requirements for secure supply chain system are identified, and several algorithms are proposed. The reliable manufacturer selection algorithm improves product quality using participant opinions. Smart traceability algorithm monitors the product path using sensors, preventing the distribution of poor-quality products in the supply chain. Additionally, two counterfeit tag detection algorithms employ a weighted graph to simulate the shortest paths for product distribution to identify cloning, application, and modification attacks on the products tags. Detailed security, scalability, performance, and comparative analyses for the drug supply chain shows the proposed system successfully improves product quality and accurately detects counterfeit tags without a significant drop in performance.
Background
Childhood trauma (CT) increases rates of psychiatric disorders and symptoms, however, the lasting effect of CT into adulthood has little exploration using large-scale samples.
Objectives
This study estimated the prevalence of CT in a large sample of Chinese young adults, examining the risk factors of current psychological symptoms among those with CT experiences.
Methods
117,769 college students were divided into CT and non-CT groups. The propensity score matching method balanced the confounding sociodemographic factors between the two groups, compared to 16 self-reported psychiatric disorders (e.g., depression, anxiety, eating disorder, obsessive-compulsive disorder, autism, social anxiety disorder, post-traumatic stress disorder), and seven current psychiatric symptoms. Hierarchical regression employed the significant risk factors of the seven current psychiatric symptoms.
Results
The prevalence of CT among young adults was 28.76% (95% CI: 28.47–29.04%). Youths with CT experiences reported higher psychiatric disorder rates and current symptom scores (P < 0.001). Sociodemographic factors (females, family disharmony, low socioeconomic status, poor relationship with parents, lower father’s education level) and lifestyle factors (smoking status, alcohol consumption, lack of exercise) were significantly associated with current psychiatric symptoms.
Results
Public health departments and colleges should develop strategies to promote mental health among those who have experienced CT.
Background
Despite increasing recognition of long COVID, the psychosocial impacts of the lived experience on individuals remain underexplored. This systematic review sought to fill this gap by identifying key themes that describe the psychosocial dimensions of long COVID.
Objective
The aim of this study is to identify key themes illustrating the psychosocial aspects of individuals' lived experience of long COVID.
Search Strategy
Searches were conducted in multiple databases and grey literature sources for qualitative studies published between November 2019 and June 2024.
Inclusion Criteria
Eligible studies involved adult participants self‐reporting long COVID. The studies needed to provide qualitative data that could be synthesised thematically.
Data Extraction and Synthesis
Data extraction and thematic synthesis were conducted by at least two independent reviewers at each stage. Quality appraisal was performed using the Critical Appraisal Skills Programme tool.
Results
The review included 34 studies. Thematic synthesis yielded five themes: ‘Debilitation’, ‘Uncertainty’, ‘Sources of Support’, ‘Meaning Making: Adjusting to a New Normal’ and ‘Experiences with Healthcare Services’. Individuals with long COVID reported experiencing physical, economic, and social challenges. Uncertainty and scepticism from others caused anxiety. Support from healthcare services, friends and online groups played an important role. Acceptance and gratitude were found to be meaningful in adjusting to the new normal. Experiences with healthcare services varied.
Discussion and Conclusions
This review provides valuable insights into the psychosocial impact of long COVID, highlighting the profound changes and challenges that individuals face. Healthcare services should adopt a holistic approach to integrate psychosocial support within their management strategies, to improve overall patient outcomes.
Background
Community pharmacies in England offer convenient and safe disposal of unwanted medicines, including antimicrobials, and better uptake of this service could limit environmental antimicrobial resistance. However, there is limited information on the extent and nature of antibiotic returns to community pharmacies. The impact of an antibiotic amnesty campaign promoting antibiotic disposal through community pharmacies was evaluated with the intention of collecting detailed information on the antibiotics returned.
Methods
An antibiotic amnesty campaign was delivered by community pharmacies in the Midlands (England) with an audit of returned antibiotics conducted in 19 community pharmacies in Leicestershire. Detailed information on antibiotics returned for disposal was gathered during the month-long amnesty campaign and again 3 months later in the same pharmacies.
Results
Antibiotics accounted for 3.12%–3.35% of all returned medicines. The amnesty campaign led to a significant increase in defined daily doses of returned antibiotics compared to the post-amnesty period (P = 0.0165), but there was no difference in the overall number of returned medicines. Penicillins were the most commonly returned antibiotics in both periods (29.3% and 42.5% of packs, respectively), while solid oral dose formulations predominated. A total of 36.6% of antibiotics returned during the amnesty period were expired, increasing to 53.4% in the post-amnesty period. Amnesty conversations had a significant impact on the number of antibiotic returns but campaign posters did not.
Conclusions
Antibiotic conversations can increase the amount of antibiotics returned to community pharmacies for safe disposal, and passive campaign materials had limited impact. More research is needed to identify the most effective interventions to increase returns.
This longitudinal study examines the effects of a pre-study abroad (SA) ped-agogic intervention and subsequent SA experience on second language (L2) Mandarin fluency. It explores two temporal aspects of oral fluency-planning time and speech rate-along with one performance measure, duration of response. Additionally, L2 contact data were included as a supplementary variable in the analysis. The experimental group was assessed at three points: before instruction (T1), after 2 weeks of instruction (T2), and post-SA (T3). A non-instructed control group that participated in the SA period provided baseline data. Both groups demonstrated improved fluency after the SA period, with the experimental group showing superior performance in planning time, speech rate, and duration of response. The greatest reduction in between-group differences occurred at T2 and persisted over time. These findings highlight that combining targeted instruction with exposure is highly effective, with L2 contact strongly correlating with overall fluency gains.
Background
Obesity has a significant impact on healthcare resources with limited accessible support available through the NHS. This service evaluation determines 24-month efficacy of referral to an open-group behavioral program by BMI category and socioeconomic status.
Methods
This retrospective, longitudinal study examined weight outcomes of adults living in England referred by healthcare professionals to Slimming World during 2016 who recorded at least 1 weight change. Primary outcome was % weight change at 3, 6, 12, and 24 months. Socioeconomic status was measured using the Index of Multiple Deprivation (IMD). Data from a post-referral questionnaire investigated self-reported changes in dietary and activity behaviors.
Results
Twenty-seven thousand five hundred sixty (15.6% male) records were analyzed. Mean (SD) age and BMI on joining were 48.6 (14.80) years and 37.1 (6.31) kg/m²; 91.7% had a BMI > 30 kg/m². Mean (SD) % weight change was −5.6 (3.79), −7.1 (5.71), −7.5 (6.88), and −7.3 (6.88) at 3, 6, 12, and 24-months, respectively. At 24- months, differences in weight loss between BMI category were significant, ranging from 0.29% (35-<40 vs 40+) to 1.33% (25-<30 vs 40+). For IMD quintile only comparisons against Q1 and Q2 were significant, ranging between 0.36% (Q2 vs Q3) to 0.94% (Q1 vs Q5). Five thousand eight hundred sixty-two (21.2%) completed the post-referral questionnaire. There were no BMI category effects on dietary behaviors but changes in physical activity behaviors were lower within the higher categories albeit effect sizes were small (all ges < 0.001). IMD quintile influenced changes for sugary drinks, watching TV and avoiding moderate activity although effect sizes were small (all ges < 0.01).
Conclusion
Following 12-week referral to a commercial weight management organization, a mean weight loss of over 7% was reported at 24-months. Adults with higher BMIs and a greater level of deprivation can benefit from the practical support offered as part of the referral, supporting weight loss and weight loss maintenance albeit with some inequality.
This research aims to enhance the surface quality, mechanical properties, and biocompatibility of PEEK (polyether–ether–ketone) biomimetic dental implants through laser polishing. The objective is to improve osseointegration and implant durability by reducing surface roughness, increasing hydrophilicity, and enhancing mechanical strength. The methodology involved fabricating PEEK implants via FDM and applying laser polishing. The significant findings showed a 66.7% reduction in surface roughness, Ra reduced from 2.4 µm to 0.8 µm, and a 25.3% improvement in hydrophilicity, water contact angle decreased from 87° to 65°. Mechanical tests revealed a 6.3% increase in tensile strength (96 MPa to 102 MPa) and a 50% improvement in fatigue resistance (100,000 to 150,000 cycles). The strength analysis result showed a 10% increase in stiffness storage modulus from 1400 MPa to 1500 MPa. Error analysis showed a standard deviation of ±3% across all tests. In conclusion, laser polishing significantly improves the surface, mechanical, and biological performance of PEEK implants, making it a promising approach for advancing biomimetic dental implant technology.
Introduction
As parental mental illness is a global public health concern, rigorous qualitative research is central to understanding families' experiences, needs and outcomes to inform optimal service provision in adult mental health and children's social services.
Methods
The current review identified, appraised and synthesized international qualitative research exploring Families and Parent Mental Illness (FaPMI) research to determine the focus, findings and outcomes and to summarize the recommendations made about the direction of future research. Findings are classified according to outcomes for children, parents, and families.
Results
While some children experienced positive outcomes from a parent's illness, most faced impacts on their social-emotional wellbeing, school performance, increased caregiving responsibilities, strained parent relationships, and lack of understanding about parental mental illness. Some family members endured abuse and struggled to adapt to an ill parent's unpredictable needs, with reluctance to discuss the situation. Parents found parenting challenging yet viewed having children as a protective factor. Future research should gather diverse perspectives, explore within-family factors and social environments, develop and test interventions, and address methodological issues like sampling.
Discussion
This review highlights the centrality of qualitative data in comprehensively understanding and evaluating outcomes of parental mental illness on families and provides clear recommendations regarding future research.
Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve, while Extreme Gradient Boosting (XGBoost) outperforms other traditional classifiers in terms of F-1 score. Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost, delivering superior results across all metrics, particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique. The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets. These factors include the importance of selecting appropriate datasets for training and testing, choosing the right classifiers, employing effective techniques for processing and handling imbalanced datasets, and identifying suitable metrics for performance evaluation. Additionally, factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.
The rapid growth in technologies for 3D sensors has made point cloud data increasingly available in different applications such as autonomous driving, robotics, and virtual and augmented reality. This raises a growing need for deep learning methods to process the data. Point clouds are difficult to be used directly as inputs in several deep learning techniques. The difficulty is raised by the unstructured and unordered nature of the point cloud data. So, machine learning models built for images or videos cannot be used directly on point cloud data. Although the research in the field of point clouds has gained high attention and different methods have been developed over the decade, very few research works directly with point cloud data, and most of them convert the point cloud data into 2D images or voxels by performing some pre-processing that causes information loss. Methods that directly work on point clouds are in the early stage and this affects the performance and accuracy of the models. Advanced techniques in classical convolutional neural networks, such as the attention mechanism, need to be transferred to the methods directly working with point clouds. In this research, an attention mechanism is proposed to be added to deep convolutional neural networks that process point clouds directly. The attention module was proposed based on specific pooling operations which are designed to be applied directly to point clouds to extract vital information from the point clouds. Segmentation of the ShapeNet dataset was performed to evaluate the method. The mean intersection over union (mIoU) score of the proposed framework was increased after applying the attention method compared to a base state-of-the-art framework that does not have the attention mechanism.
This study focuses on optimizing the high-speed dry end milling process for Duplex Stainless Steel (DSS 2205) and Super Duplex Stainless Steel (SDSS 2507), materials essential for chemical tankers due to their superior corrosion resistance but challenging machinability. The study employs Taguchi-Grey Relational Analysis (GRA) to investigate how different machining parameters affect surface roughness, cutting temperature, and force in these steels. Using an L27 Orthogonal Array design and analysed via MINITAB-19, the research identifies the optimal cutting conditions. The findings highlight that the depth of cut significantly influences machining outcomes, accounting for 47.67% in DSS 2205 and 46.82% in SDSS 2507, respectively. ANOVA results show spindle speed has a relatively smaller effect. SDSS 2507, due to its greater hardness exhibits superior performance under the tested conditions. The optimal combination of parameters, determined through GRA, includes a spindle speed of 4200 rpm, a feed rate of 50 mm/min, and a depth of cut of 0.35 mm. These optimized conditions closely align with the experimental predictions, confirming their practical application in the machining of DSS and SDSS steels. The study’s findings provide actionable insights into improving the efficiency and quality of machining for these materials, particularly in the context of chemical tanker manufacturing.
Penny Harrison explores the issue of genomics and discusses how they may impact the role and practice of the gastrointestinal nurse.
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