Daiseung Medics (South Korea)

The integration of artificial intelligence (AI) with big data analytics leads to substantial transformations in the retail sector. This research explores the impact of AI-powered image recognition checkout systems on the retail industry, focusing on operational efficiency, customer experience, and resource waste. Employing a mixed-methods approach, this study combines usability testing and data analytics to assess the viability of this technology in attaining automation and accuracy in retail operations. The study focuses on the creation of robust, resource-efficient systems that foster long-term industrial growth. The findings show that AI-powered solutions not only speed the checkout process but also contribute to sustainable infrastructure by reducing resource consumption and increasing energy efficiency. This report offers significant information, like the impact of AI-powered image recognition checkout systems on operational efficiency, customer experience, and the role of AI in promoting sustainable infrastructure for retailers and governments looking to advance the digitalization of the retail industry.

In the context of global economic integration, corporate bankruptcy risk poses a significant threat to economic stability, making accurate bankruptcy prediction critically important. As a binary classification problem, traditional statistical approaches struggle to handle nonlinear features, resulting in limited predictive accuracy. While machine learning-based intelligent models have improved prediction performance, the effectiveness of Kernel Extreme Learning Machine (KELM) heavily depends on the selection of the penalty parameter (C) and kernel parameter (γ). However, conventional optimization methods often suffer from low efficiency and a tendency to fall into local optima. The Zebra Optimization Algorithm (ZOA), a newly developed swarm intelligence algorithm, also faces limitations such as rapid loss of population diversity and weak local exploitation capabilities. To address these issues, this paper proposes an Enhanced Archive-based Zebra Optimization Algorithm (EAZOA). By incorporating a Levy mutation strategy guided by elite individuals, a dynamic elite archive mechanism, and a hybrid boundary handling technique, the proposed algorithm significantly improves optimization performance. EAZOA is then employed to optimize the parameters of KELM, resulting in the EAZOA-KELM bankruptcy prediction model. Experimental results on the CEC2020 and CEC2022 benchmark suites demonstrate that EAZOA outperforms several state-of-the-art algorithms in terms of convergence accuracy, stability, and scalability. Moreover, experiments on the Wieslaw financial dataset show that EAZOA-KELM achieves superior performance across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. These results validate the model’s effectiveness in corporate bankruptcy prediction and highlight its potential as a powerful tool for financial risk early warning.

The steps involved in applying the Analytic Hierarchy Process (AHP) to company sales data analysis include defining the analysis objectives, constructing a hierarchical model, conducting comparative analysis and constructing matrices, testing matrix consistency, and calculating weights and rankings. This process enables companies to better understand the drivers of sales data and, through multilevel analysis, identify effective strategies to enhance sales performance. In addition to AHP, this study further incorporates the Fuzzy Comprehensive Evaluation Method to address the qualitative and uncertain characteristics of digital sales capability indicators. Combined with expert scoring and survey data, the proposed AHP–fuzzy model converts subjective judgments into quantitative results, providing a more accurate and flexible evaluation of maturity. The empirical cases demonstrate that organizations with strong deployment of digital tools may still exhibit uneven maturity when data literacy or cultural readiness is insufficient. The model helps managers identify capability gaps, optimize digital enablement strategies, and guide continuous improvement in sales team digital transformation.

FFieldwork is an active learning approach that plays a vital role in numerous academic programmes. It provides practical, hands-on experiences that prepare students for real-world professional environments. This research aimed to investigate the benefits of fieldwork courses and their impact on the learning experiences and academic performance of undergraduate students enrolled in the Plantation Management Programme at Universiti Teknologi MARA (UiTM). This study employed quantitative research utilising a survey method. A series of questionnaires was randomly distributed via Google Forms, with responses received from 105 students. Statistical analyses, including descriptive, reliability, and regression analyses, were conducted using the Statistical Package for the Social Sciences (SPSS) version 28 to interpret the gathered data. The study findings reveal that respondents strongly agreed on the benefits of fieldwork, particularly in experiential learning (µ = 4.448), skill development (µ = 4.391), and teamwork and collaboration skills (µ = 4.438). Regression analysis indicated a significant impact on the development of teamwork and collaboration skills (p < 0.001). The study confirms that the benefits gained from fieldwork courses include experiential learning exposure, enhanced skill development, and improved teamwork and collaboration skills.

Summary A new semi-analytical reservoir simulator has been used to integrate pressure and rate transient analysis with the geo-seismic information. The simulator is based on the boundary element method for accurate simulation of fluid flow in reservoirs, without the need for numerical areal gridding. The user draws complex reservoir regions on a seismic structural map and the tool simulates well pressure analytically in a few seconds and compares it with the pressure measured by downhole pressure gauges in the same wells. This helps test different reservoir heterogeneities, fault transmissibility and connectivity in an instant, integrating pressure transient analysis with the geo-seismic information.

Small and medium-sized medicinal and chemical foreign trade enterprises generally face strategic dilemmas such as weak risk resistance due to single-category operation and resource dispersion caused by blind diversification. Based on the Resource-based View (RBV) and Dynamic Capability Theory (DCT), this study takes Wuhan Kuda Hui Trading Co., Ltd. as the core case and selects three other enterprises of the same scale with different strategic types for comparative analysis. This study explores the internal logic and implementation paths of the “Core Category Deep Cultivation + Diversified Supplement” strategy. The findings reveal that this strategy accumulates heterogeneous resources through core categories and leverages dynamic capabilities to achieve synergistic expansion of supplementary categories, effectively balancing resource efficiency and risk resistance. Moreover, this study proposes a four-stage implementation path, including category selection, resource deep cultivation, diversified expansion, and synergistic optimization. The quantitative dimensions and implementation models of this strategy are industry-compatible. The conclusions provide a theoretical framework and practical guidance for small and medium-sized medicinal and chemical foreign trade enterprises to break through strategic dilemmas.

Abstract Introduction KH-001 besylate is a novel, botanically derived drug product developed for the on-demand treatment of premature ejaculation. KH-001 is an alkaloid derived from the South African Kanna plant and functions as a selective, high-potency serotonin reuptake transporter (SERT) inhibitor and phosphodiesterase inhibitor. In preclinical studies using the para-chloramphetamine-induced ejaculation model in male Wistar rats, KH-001 significantly and dose-dependently increased ejaculation latency and reduced the proportion of ejaculating animals. Objective To evaluate the first human exposure to KH-001 besylate oral solution in healthy male volunteers, focusing on pharmacokinetics (PK), safety, and tolerability. Methods A first-in-human Phase 1 clinical study was conducted in 74 healthy male volunteers to assess the single and multiple-dose PK profiles, safety, and tolerability of KH-001 besylate following oral administration. The study, approved by the local ethics committee and the UK regulatory authority (MHRA), was carried out at a specialized Phase 1 unit in the UK. In the single ascending dose (SAD) part of the study, cohorts of eight subjects (including two placebo recipients) received KH-001 besylate at doses ranging from 0.5 mg to 62.5 mg via oral administration. Pharmacokinetic sampling was performed over 24 hours post-dose. Safety assessments included monitoring for adverse events (AEs), vital signs, electrocardiograms (ECG) and routine blood and chemistry panels. Pharmacokinetic analysis was performed for KH-001 and its primary metabolite, KH-160, using a validated HPLC assay. Additional SAD cohorts were evaluated with holding in the mouth of the oral solution for 3 minutes from 5 mg to 15 mg. A multiple-dose evaluation was conducted in cohorts of nine subjects (including three placebo), who received repeat dosing of the oral solution over five days Results The starting dose of 0.5 mg was selected based on anticipated sub-therapeutic levels. Dose escalation proceeded up to 62.5 mg, with intermediate doses of 2.5 mg and 12.5 mg for oral administration. Oral ingestion resulted in KH-001 concentrations generally below the limit of quantification, except at the highest dose (62.5 mg), indicating extensive first-pass metabolism. In contrast, its metabolite KH-160 was detected at all dose levels and increased with dose, confirming rapid intestinal and/or hepatic metabolism. Holding in the mouth oral administration resulted in significantly higher systemic KH-001 exposure, with a time to maximum concentration (Tmax) of 15–20 minutes and no measurable levels after 4 hours, supporting a rapid onset and clearance suitable for on-demand use. Importantly, no accumulation of KH-001 was observed with repeat dosing. Oral doses held in the mouth of 5–10 mg were well tolerated with no adverse events reported. At 12.5 mg and 15 mg, some subjects reported mild and transient AEs, including nausea and light-headedness. No serious adverse events occurred. Vital signs, ECGs, and laboratory results showed no systemic changes attributable to KH-001. Conclusions KH-001 besylate, administered as a solution, was safe and well tolerated, with only mild, rapidly resolving and expected AEs consistent with known SERT inhibitor profiles. Pharmacokinetic data demonstrated effective systemic exposure via the holding in the mouth route, with reduced levels of the metabolite KH-160 and favourable onset and duration characteristics. These results support the PK, safety, and tolerability of KH-001 besylate and warrant further clinical investigation of its use as an on-demand treatment for premature ejaculation. Disclosure Yes, this is sponsored by industry/sponsor: Kanna Health Ltd trading as Kadence Bio Clarification: Industry initiated, executed and funded study Any of the authors act as a consultant, employee or shareholder of an industry for: Kanna Health Ltd trading as Kadence Bio

We report a sustainable and cost-effective synthesis of cobalt phthalocyanine (CoPc) using low-impact solvents and a streamlined purification procedure, achieving an 85% yield with limited waste generation, as indicated by an environmental factor (E-factor) of 18.6. This route provides an eco-friendly approach to producing a molecular electrocatalyst for reduction. The catalytic activity of the synthesized CoPc is validated by its integration into a gas diffusion electrode with carbon nanotube supports, exhibiting methanol production with a Faradaic efficiency up to 12%, matching the values reached with the conventionally synthesized counterparts. Notably, under high loading (micrometric crystallites of CoPc), -to-CO conversion reached 65%, in fair alignment with literature benchmarks. Our work shows that green synthesis can yield high-performance catalysts for electrochemical reduction ( ), enabling sustainable methanolproduction.