An increased number of rogue waves, relative to standard distributions, can be induced by unidirectional waves passing over abrupt decreases in water depth. We investigate this phenomenon in a more general setting of multidirectional waves. We examine the influence of the directionality on the occurrence probability of rogue waves using laboratory experiments and fully nonlinear potential flow simulations. Based on the analysis of the statistics of random waves, we find that directional spreading reduces the formation probability of rogue waves relative to unidirectional seas. Nevertheless, for typical values of directional spreading in the ocean ($15^{\circ }\unicode{x2013}30^{\circ }$), our numerical results suggest that there is still a significant enhancement to the number of rogue waves just beyond the top of a depth discontinuity.

For decreasing sequences $\{t_{n}\}_{n=1}^{\infty }$ converging to zero and initial data $f\in H^s(\mathbb {R}^N)$, $N\geq 2$, we consider the almost everywhere convergence problem for sequences of Schrödinger means ${\rm e}^{it_{n}\Delta }f$, which was proposed by Sjölin, and was open until recently. In this paper, we prove that if $\{t_n\}_{n=1}^{\infty }$ belongs to Lorentz space ${\ell }^{r,\infty }(\mathbb {N})$, then the a.e. convergence results hold for $s>\min \{\frac {r}{\frac {N+1}{N}r+1},\,\frac {N}{2(N+1)}\}$. Inspired by the work of Lucà-Rogers, we construct a counterexample to show that our a.e. convergence results are sharp (up to endpoints). Our results imply that when $0< r<\frac {N}{N+1}$, there is a gain over the a.e. convergence result from Du-Guth-Li and Du-Zhang, but not when $r\geq \frac {N}{N+1}$, even though we are in the discrete case. Our approach can also be applied to get the a.e. convergence results for the fractional Schrödinger means and nonelliptic Schrödinger means.

The COVID-19 pandemic has had a profound impact on the mental health of healthcare workers (HCWs). We aimed to identify the factors associated with depression among HCWs during the pandemic. We conducted literature search using eight electronic databases up to July 27 2022. Observational studies with more than 200 participants investigating correlates of depression in HCWs after COVID-19 outbreak were included. We used fixed- and random-effects models to pool odds ratios (ORs) across studies, and Cochran's chi-squared test and I2 statistics to assess study heterogeneity. Publication bias was evaluated by funnel plots. Thirty-five studies involving 44,362 HCWs met the inclusion criteria. Female (OR=1.50, 95% CI [1.23,1.84]), single (OR=1.36, 95% CI [1.21,1.54]), nurse (OR=1.69, 95% CI [1.28,2.25]), history of mental diseases (OR=2.53, 95% CI [1.78,3.58]), frontline (OR=1.79, 95% CI [1.38,2.32]), health anxiety due to COVID-19 (OR=1.88, 95% CI [1.29,2.76]), working in isolation wards (OR=1.98, 95% CI [1.38,2.84]), and insufficient personal protective equipment (OR=1.49, 95% CI [1.33,1.67]) were associated with increased risk of depression. Instead, HCWs with a positive professional prospect (OR=0.34, 95% CI [0.24,0.49]) were less likely to be depressed. This meta-analysis provides up-to-date evidence on the factors linked to depression among HCWs during the COVID-19 pandemic. Given the persistent threats posed by COVID-19, early screening is crucial for the intervention and prevention of depression in HCWs.

Before the Omicron variant ran amok inside China in November 2022, the Chinese central government’s dynamic zero-COVID policy effectively contained the spread of the coronavirus and its variants during multiple waves of outbreaks. However, it was not without cost. This study examines the impacts of stringent lockdown interventions on urban residents’ mental health during the initial outbreak of the Omicron variant in the spring of 2022. Using survey data from 522 respondents within the same neighbourhood and a spatial quasi-experimental design, the results show that strict lockdown interventions are significantly related to higher levels of psychological distress after controlling for observed confounders and that lockdown interventions have further spillover effects on mental health for residents in adjacent residential compounds who are otherwise free. Moreover, the results show that the lack of material supplies and medical care plays a more salient role in explaining lockdown effects on psychological distress than residents’ social interaction and trust levels of COVID-19 policy. Policy and intervention implications are also discussed.

This article presents a system architecture and a set of interfaces that can build scalable information systems capable of large city modeling based on dynamic geospatial knowledge graphs to avoid pitfalls of Web 2.0 applications while blending artificial and human intelligence during the knowledge enhancement processes. We designed and developed a GeoSpatial Processor, an SQL2SPARQL Transformer, and a geospatial tiles ordering tasks and integrated them into a City Export Agent to visualize and interact with city models on an augmented 3D web client. We designed a Thematic Surface Discovery Agent to automatically upgrade the model’s level of detail to interact with thematic parts of city objects by other agents. We developed a City Information Agent to help retrieve contextual information, provide data concerning city regulations, and work with a City Energy Analyst Agent that automatically estimates the energy demands for city model members. We designed a Distance Agent to track the interactions with the model members on the web, calculate distances between objects of interest, and add new knowledge to the Cities Knowledge Graph. The logical foundations and CityGML-based conceptual schema used to describe cities in terms of the OntoCityGML ontology, together with the system of intelligent autonomous agents based on the J-Park Simulator Agent Framework, make such systems capable of assessing and maintaining ground truths with certainty. This new era of GeoWeb 2.5 systems lowers the risk of deliberate misinformation within geography web systems used for modeling critical infrastructures.

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9$ \% $ to 62.8$ \% $$ (p<0.001) $; the average shortest path length decreased from 1.53 to 1.14 $ (p<0.001) $; the average betweenness reduced from 0.65 to 0.11$ (p<0.001) $; the average cluster size dropped from 4.05 to 2.72 $ (p=0.004) $; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 $ (p=0.099). $ Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

Arazyme, an alkaline metalloprotease, is produced by Serratia proteamaculans, a symbiotic bacterium isolated from the intestinal ecosystem of Nephila clavata. Arazyme is known to play a crucial role in facilitating the digestion process in N. clavata. Recently, there has been increasing interest in exploring invertebrate-associated gut symbionts as a valuable source of novel and biologically active enzymes. Animal husbandry has shown significant interest in this spider-derived bioactive enzyme. This paper aims to provide a comprehensive review of the current understanding and knowledge of arazyme in the context of animal husbandry, offering valuable references for potential applications of this enzyme.

Wave transformation is an intrinsic dynamic process in coastal areas. An essential part of this process is the variation of water depth, which plays a dominant role in the propagation features of water waves, including a change in wave amplitude during shoaling and de-shoaling, breaking, celerity variation, refraction and diffraction processes. Fundamental theoretical studies have revolved around the development of analytical frameworks to accurately describe such shoaling processes and wave group hydrodynamics in the transition between deep- and shallow-water conditions since the 1970s. Very recent pioneering experimental studies in state-of-the-art water wave facilities provided proof of concept validations and improved understanding of the formed extreme waves’ physical characteristics and statistics in variable water depth. This review recaps the related most significant theoretical developments and groundbreaking experimental advances, which have particularly thrived over the last decade.

Echinococcus shiquicus is peculiar to the Qinghai–Tibet plateau of China. Research on this parasite has mainly focused on epidemiological surveys and life cycle studies. So far, limited laboratory studies have been reported. Here, experimental infection of E. shiquicus metacestode in BALB/c mice and Mongolian jirds (Meriones unguiculatus) was carried out to establish alternative laboratory animal models. Intraperitoneal inoculation of metacestode material containing protoscoleces (PSCs) obtained from infected plateau pikas were conducted on BALB/c mice. Furthermore, metacestode material without PSCs deriving from infected BALB/c mice was intraperitoneally inoculated to Mongolian jirds. Experimental animals were dissected for macroscopic and histopathological examination. The growth of cysts in BALB/c mice was infiltrative, and they invaded the murine entire body. Most of the metacestode cysts were multicystic, but a few were unilocular. The cysts contained sterile vesicles, which had no PSCs. The metacestode materials were able to successfully infect new mice. In the jirds model, E. shiquicus cysts were typically formed freely in the peritoneal cavity; the majority of these cysts were free while a small portion adhered loosely to nearby organs. The proportion of fertile cysts was high, and contained many PSCs. The PSCs produced in Mongolian jirds also successfully infected new ones, which confirms that jirds can serve as an alternative experimental intermediate host. In conclusion, a laboratory animal infection was successfully established for E. shiquicus using BALB/c mice and Mongolian jirds. These results provide new models for the in-depth study of Echinococcus metacestode survival strategy, host interactions and immune escape mechanism.

The integrality of the numbers $A_{n,m}={(2n)!(2m)!}/{n!m!(n+m)!}$ was observed by Catalan as early as 1874 and Gessel named $A_{n,m}$ the super Catalan numbers. The positivity of the q-super Catalan numbers (q-analogue of the super Catalan numbers) was investigated by Warnaar and Zudilin [‘A q-rious positivity’, Aequationes Math. 81 (2011), 177–183]. We prove the divisibility of sums of q-super Catalan numbers, which establishes a q-analogue of Apagodu’s congruence involving super Catalan numbers.

This study aims to understand the epidemiological characteristics of SARS-CoV-2 infection in the paediatric population during the outbreak of the Omicron variant in Shanghai. We retrospectively analysed the population-based epidemiological characteristics and clinical outcome of SARS-CoV-2 Omicron variant infection in children in Minhang District, Shanghai, based on the citywide surveillance system during the outbreak period in 2022 (March to May). During this time, a total of 63,969 cases of SARS-CoV-2 infection were notified in Minhang District, out of which 4,652 (7.3%) were children and adolescents <18 years. The incidence rate of SARS-CoV-2 infections in children was 153 per 10,000. Of all paediatric cases, 50% reported to be clinically symptomatic within 1–3 days after PCR confirmation by parents or themselves, with 36.3% and 18.9% of paediatric cases reporting fever and cough. Also, 58.4% of paediatric cases had received at least one dose of the COVID-19 vaccine and 52.1% had received two doses of the COVID-19 vaccination. Our findings are informative for the implementation of appropriate measures to protect children from the threat of SARS-CoV-2 infection.

My 5 moments (M5M) was used less frequently among cleaning staff members, suggesting that a poor compliance score in this group may not indicate deficient handwashing. This quasi-experimental study compared hand hygiene compliance (HHC), hand hygiene (HH) moments, and HH time distribution in the control group (no HH intervention; n = 21), case group 1 (normal M5M intervention; n = 26), case group 2 (extensive novel six moments (NSM) training; n = 24), and case group 3 (refined NSM training; n = 18). The intervention’s effect was evaluated after 3 months. The HHC gap among the four groups gradually increased in the second intervention month (control group, 31.43%; case group 1, 38.74%; case group 2, 40.19%; case group 3, 52.21%; p < 0.05). After the intervention period, the HHC of case groups 2 and 3 improved significantly from the baseline (23.85% vs. 59.22%, 27.41% vs. 83.62%, respectively; p < 0.05). ‘After transferring medical waste from the site’ had the highest HHC in case group 3, 90.72% (95% confidence interval, 0.1926–0.3967). HH peak hours were from 6 AM to 9 AM and 2 PM to 3 PM. The study showed that the implementation of an NSM practice can serve as an HHC monitoring indicator and direct relevant training interventions to improve HH among hospital cleaning staff.

Sleep apnea is one of the most common sleep disorders. The consequences of undiagnosed sleep apnea can be very serious, increasing the risk of high blood pressure, heart disease, stroke, and Alzheimer’s disease over a long period of time. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is nighttime polysomnography monitoring in a specialized sleep laboratory. However, these diagnoses are expensive and the number of beds is limited, and there is insufficient monitoring in terms of time dimension. Existing methods for automated detection use no more than three physiological signals, but all other signals are also associated with the patient’s sleep. In addition, the limited amount of medical real annotation data, especially abnormal samples, lead to weak model generalization capability. The gap between model generalization capability and medical field needs still exists. In this paper, we propose a method for integrating medical interpretation rules into a long short-term memory neural network based on self-attention with multichannel respiratory signals as input. We obtain attention weights through a token-level attention mechanism and then extract key rules of medical interpretation to assist the weights, improving model generalization and reducing the dependence on data volume. Compared with the best prediction performance of existing methods, the average improvements of our method in accuracy, precision, and f1-score are 3.26%, 7.03%, and 1.78%, respectively. The algorithm tested the performance of our model on the Sleep Heart Health Study data set and found that the model outperformed existing methods and could help physicians make decisions in their practices.

OBJECTIVES/GOALS: Self-monitoring (SM) improves adherence to low-fat low-calorie (LFLC) diet for weight management. Ketogenic diet (KD) is a promising alternative to LFLC, however, it is unclear whether SM improves KD adherence. We examined the association between SM and KD adherence during the first 12 weeks of a 6-month technology-assisted lifestyle intervention. METHODS/STUDY POPULATION: We included 30 (50.8 ± 12.4 years, 70% female) overweight/obese (body mass index: 37.1 ± 7.2 kg/m2) participants in the analysis. They received personalized KD goals with very low-carbohydrate (22–62 g/d), moderate protein (52 -87 g/d), and high-fat (115 - 219g/d) and calorie intake goals (1338–2554 kcal/d). Additionally, participants performed daily diet, exercise, and weight SM. Adherence to KD was measured by (1) self-monitored dietary intake, and (2) percent of days in ketosis state (blood ketone≥0.5 mmol/L) captured by a fingerstick blood ketone meter. SM frequency was defined as percent of days participant logged food intake, wore fitness tracker, and weighed body weight. Pearson correlation coefficients were computed to examine the correlation between SM in diet, exercise, and weight with KD adherence. RESULTS/ANTICIPATED RESULTS: Percentage of days participants SM for diet, exercise, and weight was 58.4 ± 32.2%, 66.4 ± 30.9%, and 59.0 ± 32.6%, respectively. Correlational analysis more frequent diet SM was positively correlated with more days in ketosis (r = 0.58, p = 0.003), higher fat intake (r = 0.68, p = 0.0001), and higher calorie intake (r = 0.67, p = 0.002) within the fat and calorie goals set; more frequent weight SM was positively correlated with more days in ketosis (r = 0.48, p = 0.02), higher fat intake (r = 0.45, p = 0.023), and higher calorie intake (r = 0.44, p = 0.027). DISCUSSION/SIGNIFICANCE: We found that diet and weight SM were positively associated with fat and calorie intake, as well as days in ketosis. Given the reported promising effect of KD on weight loss and the challenges of adhering to KD, our findings suggested that promoting SM on diet and weight might be a promising avenue for improving KD adherence leading to successful weight loss.