AIM: To assess the efficacy of artificial natural light in preventing incident myopia in primary school-age children. METHODS: This is a prospective, randomized control, intervention study. A total of 1840 students from 39 classes in 4 primary schools in Foshan participated in this study. The whole randomization method was adopted to include classes as a group according to 1:1 randomized control. Classrooms in the control group were illuminated by usual light, and classrooms in the intervention group were illuminated by artificial natural light. All students received uncorrected visual acuity and best-corrected visual acuity measurement, non-cycloplegic autorefraction, ocular biometric examination, slit lamp and strabismus examination. Three-year follow-up, the students underwent same procedures. Myopia was defined as spherical equivalent refraction ≤ -0.50 D and uncorrected visual acuity <20/20. RESULTS: There were 894 students in the control group and 946 students in the intervention group with a mean±SD age of 7.50±0.53y. The three-year cumulative incidence rate of myopia was 26.4% (207 incident cases among 784 eligible participants at baseline) in the control group and 21.2% (164 incident cases among 774 eligible participants at baseline) in the intervention group [difference of 5.2% (95%CI, 3.7% to 10.1%); P=0.035]. There was also a significant difference in the three-year change in spherical equivalent refraction for the control group (-0.81 D) compared with the intervention group [-0.63 D; difference of 0.18 D (95%CI, 0.08 to 0.28 D); P<0.001]. Elongation of axial length was significantly different between in the control group (0.77 mm) and the intervention group [0.72 mm; difference of 0.05 mm (95%CI, 0.01 to 0.09 mm); P=0.003]. CONCLUSION: Artificial natural light in the classroom of primary schools can result in reducing incidence rate of myopia during a period of three years.

AIM: To gain insights into the global research hotspots and trends of myopia. METHODS: Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. RESULTS: A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. CONCLUSION: Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children’s health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

The ultra-high molecular weight polyethylene (UHMWPE) component of artificial joints is one of the most important factors affecting the clinical outcomes of joint arthroplasty. Although the possibility of in vivo UHMWPE degradation caused by absorbed lipids has been reported, a quantitative evaluation of this phenomenon has not yet been performed. This study aimed to establish the lipid index (LI) as a quantitative indicator of the amount of absorbed lipids and the first step to quantify their effects on UHMWPE. The LI was defined using the infrared spectrum obtained with a Fourier-transform infrared spectrophotometer and verified using the retrieved UHMWPE components. The LI was consistent with the amount of extract recovered in reflux extraction with hexane. In addition, the LI could replace lipid extraction for calculating the oxidation index (OI) because the value obtained by subtracting the LI from the OI showed good agreement with the OI obtained after lipid extraction. The LI represents the amount of lipids absorbed by UHMWPE and is useful for quantitatively evaluating the effects of lipids on UHMWPE. In addition, the LI enables OI measurements that are unaffected by absorbed lipids without requiring troublesome lipid-extraction procedures.

The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.

ABSTRACT Children involved in the juvenile justice system are more likely to struggle with substance abuse and intellectual disabilities. Substance abuse is a major public health concern among young individuals globally, including juvenile felons. This study examines the relationships and factors that influence juvenile felons’ substance abuse, intelligence, and delinquent behaviour. This study applies a descriptive cross-sectional survey design using a purposive sampling technique. The participants included 163 male juveniles from Tamil Nadu, India. The psychological measures used for the study were the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) scale, the Standard Progressive Matrices, and the Australian Self-reported Delinquency Scale. According to the study’s findings, intelligence does not have a significant impact on juvenile felons’ delinquent behaviour. Furthermore, the study identified repeated offences and runaway behaviour as factors that increase the risk of substance abuse. Juvenile felons’ substance abuse significantly influences and predicts their delinquent behaviour.

In this meta-analysis of 6 studies and 5,269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed-angle compared to gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively. This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle-closure in patients with glaucoma. We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity. The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5,269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI 83.8%-97.9%), whereas the pooled specificity was 93.6% (95% CI 85.7%-97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1% to 95.1%. Similarly, specificity results ranged from 90.3 to 94.5% with the removal of each individual study and recalculation of pooled specificity. DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.

<p style="text-align:justify">The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.</p>

As more pathology laboratories are transitioning to a digital workflow, the availability of commercial Artificial Intelligence assistance systems is also increasing. Today nearly 40 such products approved for diagnostic use are available. This article provides an overview of the most widely addressed use cases, including Immunohistochemistry scoring for breast cancer and non-small-cell lung cancer, Gleason grading for prostate cancer, or metastasis detection in lymph nodes. While automation alone already promises an increase in efficiency that may help to bridge the growing gap between supply (pathology work force) and demand (histological testing), this article introduces another category of Artificial Intelligence products that go beyond just mimicking today’s established score. Various Artificial Intelligence are being introduced that detect genetic alterations or stratify risk, directly from the standard hematoxylin & eosin staining. Finally, a brief outlook explains how basic AI models are currently finding their way into computational pathology and promise to further accelerate product developments by decreasing the time-to-model.

Seawater electrolysis holds great promise for sustainable green hydrogen generation, but its implementation is hindered by high energy consumption and electrode degradation. Two dimensional (2D) layered double hydroxide (LDH) exhibits remarkable stability, high catalytic activity, and excellent corrosion resistance in the harsh electrolytic environment. The synergistic effect between LDH and seawater ions enhances the oxygen evolution reaction, enabling efficient and sustainable green hydrogen generation. Here, we report a synthesis of low cost, novel 2D Vanadium Copper (VCu) LDH first time in the series of LDH's as a highly efficient bifunctional electrocatalyst. The electrochemical (EC) and photoelectrochemical (PEC) study of VCu LDH and VCu LDH/Graphite Carbon Nitride (g-C3N4) nanohybrid was performed in 0.5 M H2SO4 (acidic), 1 M KOH (basic), 0.5 M NaCl (artificial seawater), 0.5 M NaCl + 1 M KOH (artificial alkaline seawater), real seawater and 1 M KOH + real seawater (alkaline real seawater) electrolyte medium. It was found that VCu LDH shows a remarkable lower overpotential of 72 mV hydrogen evolution reaction (HER) and 254 mV oxygen evolution reaction (OER) at current density of 10 mA/cm2 under alkaline real seawater electrolysis exhibiting bifunctional activity and also showing better stability.

PurposeThis theme issue aims to explore the depth and breadth of automation and artificial intelligence’s (AI’s) impact on the hospitality and tourism industry.Design/methodology/approachThe thematic examination of the articles presented in this theme issue highlights the central results concerning the strategic inquiry, especially regarding their addition to knowledge, which is vital for fostering idea creation and theoretical research beneficial to various stakeholders in the tourism and hospitality sector. Given the swift evolution of these technologies, grasping their utilization, advantages and obstacles is essential for industry experts, decision-makers and scholars.FindingsThis theme issue encapsulates the transformative impact of automation and AI in the tourism and hospitality industry, highlighting the juxtaposition of enhanced operational efficiency against employment security concerns. It emphasizes the need for ethical governance to address AI’s ethical dilemmas and security vulnerabilities. The exploration of consumer attitudes towards AI, particularly in service robots and facial emotion recognition technologies, is deemed crucial for leveraging AI’s full potential in service delivery. Furthermore, AI’s entry into gastronomy prompts a reevaluation of traditional culinary practices, advocating for a balance between technological innovation and the preservation of cultural heritage.Originality/valueThis theme issue stands out for its focused exploration of the transformative effects of automation and AI within the hospitality and tourism industry. Unlike broader discussions on technology in business, it delves into specific AI-driven innovations, operational changes and the resultant shifts in consumer behavior and industry standards. By curating a collection of empirical research, theoretical analyses and content analyses, this issue is providing important insights into how automation and AI are reshaping service delivery, management practices and strategic decision-making in an industry characterized by its intense customer focus and reliance on human-to-human interaction.