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Abstract Introduction The use of the neodymium:yttrium–aluminum-garnet (Nd:YAG) laser to treat malignant glaucoma (MG) has been described in the literature since the 1980s. However, the technique has been reported to have a short-term effect, with a notable relapse rate. In the present study, we report the efficacy and durability of a modified Nd:YAG laser treatment methodology for treatment of pseudophakic or aphakic MG. Methods Patients with chronic angle-closure glaucoma and deemed at high risk of developing post-operative MG received prophylactic peripheral iridectomy during their conventional operation beginning in 2017. When the diagnosis of pseudophakic or aphakic MG was confirmed, a thorough Nd:YAG laser capsulo/zonulo-hyaloido-vitreolysis (CZHV) was performed through iridectomy, along with standardized pre- and post-laser medications. This retrospective case series includes 14 eyes of 11 patients with MG who had surgical preset iridectomy and modified Nd:YAG laser CZHV between 2017 and 2022. Outcome measures included resolution and recurrence of MG and incidence of treatment complications. Results The mean follow-up was 27.1 ± 15.0 months (range, 12–48). Long-term resolution of MG was obtained in all included eyes at the end of the follow-up. Six eyes (42.9%) achieved long-term resolution with a single Nd:YAG laser intervention. Eight eyes (57.1%) achieved long-term resolution following two to three laser interventions, with two eyes (14.3%) experiencing recurrence. There was no complication during the follow-up. At the final visit, a significant reduction (P = 0.0001) in the mean intraocular pressure (IOP) was observed (13.1 ± 2.8 mmHg) compared to presentation (21.4 ± 6.3 mmHg). Conclusion The modified Nd:YAG laser treatment methodology is a minimally invasive option to manage pseudophakic or aphakic MG with sustained effectiveness. Reduced inflammatory reactions due to prophylactic peripheral iridectomy, rapid diagnosis, and timely treatment initiation have all contributed to the favorable outcomes associated with this modified treatment methodology.

Abstract Deep Reinforcement Learning (DRL) has demonstrated its great potential for Adaptive Traffic Signal Control (ATSC) tasks at single‐intersection. In the transportation network multi‐agent environment, cooperative learning among multi‐agents has become a hot research topic. Based on the distributed control model, this paper presents a hybrid reward function model for the dynamic density method of intersections, which emphasizes the priority of emergency vehicles (EMV) while maximizing the traffic efficiency of social vehicles, and solves the problem of sparse reward due to the ambiguous guidance relationship between the multi‐agent Deep Reinforcement Learning (MDRL) state and reward function of the urban road network scenario. On the other hand, based on multi‐agent A2C (MA2C) algorithm, this paper presents Shared Experience MA2C (SEMA2C) between agents. In the transportation network, each intersection represented by an agent has similar task objectives. SMEA2C algorithm takes the current agent as the main body of self‐learning, and utilizes the principle of importance sampling to learn from the experience data of the agents located at adjacent intersections. The experimental results show that the proposed SEMA2C performs well in multi‐agent traffic signal control tasks, and has greater advantages than the similar algorithms.

We present a constant-round algorithm in the massively parallel computation (MPC) model for evaluating a natural join where every input relation has two attributes. Our algorithm achieves a load of $\tilde{O}(m/p^{1/\rho})$ where $m$ is the total size of the input relations, $p$ is the number of machines, $\rho$ is the join's fractional edge covering number, and $\tilde{O}(.)$ hides a polylogarithmic factor. The load matches a known lower bound up to a polylogarithmic factor. At the core of the proposed algorithm is a new theorem (which we name the "isolated cartesian product theorem") that provides fresh insight into the problem's mathematical structure. Our result implies that the subgraph enumeration problem, where the goal is to report all the occurrences of a constant-sized subgraph pattern, can be settled optimally (up to a polylogarithmic factor) in the MPC model.

Traditional Chinese fermented vegetables are a type of brine-salted fermented vegetable product. During the spontaneous fermentation, various compounds are produced, degraded, and converted, influencing the quality of the fermented pickle. To ascertain the effect of different containers on the fermentation process of the pickles, this study investigated the bacterial diversity and the chemical composition characteristics of the pickle (radish) fermented in commonly used containers including glass jars (GL), porcelain jars (PO), and plastic jars (PL). The correlation between chemical compounds and microbial community was further analyzed. The changes in pH values suggested that PL may facilitate the quickest fermentation of the pickles, while the process in PO progressed at the lowest rate. The PL brine samples contained higher levels of lactic acid and threonine, while more abundant volatile chemical compounds were evident in PO. The container materials had no significant influence on the microbial structure, wherein Lactobacillus was the absolute dominant genus in all containers. But container material did have an effect on the abundance of specific genus, such as Lactococcus and Pediococcus. The correlation between these major genera was also analyzed and gene function prediction indicated that the top three pathways were: carbohydrate metabolism, amino acid metabolism, and energy metabolism. Lactobacillus negatively correlated with methionine, tyrosine, lysine, and arginine, but positively correlated with ammonia, and lactic acid and acetic acid both just correlated with Pediococcus. This study provides new insights into the microbiota succession and chemical compounds involved in the vegetable fermentation.

The article explores variants of partial order multiway search (POMS) problem that involves an interaction between an algorithm and an oracle, the classical, taciturn and external memory models. Topics discussed include graph theory around a single-rooted directed acyclic graph G with n nodes, an arbitrary heavy-path depth first search-tree T of G and ordering on the vertices of G determined by T, and algorithms whose performance matches the lower bounds to achieve the probing complexity.