Search Results (2,098)

Deposit return systems (DRS) can increase the capture of beverage packaging, but material circularity is not determined by return rate alone. A returned container contributes to high-value circularity only if it passes recognition, sorting, pre-processing, and material-specific quality gates. This article evaluates the material-quality performance of three returned beverage-packaging materials—polyethylene terephthalate (PET), aluminium and reusable glass—using a quality-gated high-value recovery framework. The model defines a high-quality recovery index, HQR = R × Q × Y, where R is the return rate, Q is the quality factor of the returned stream, and Y is the reprocessing or reuse yield. The HQR indicator describes the quality of the entire DRS process. The core purpose of the HQR model is to distinguish nominal packaging return from high-quality material recovery and to show whether returned PET, aluminium and reusable glass streams remain suitable for high-value circular pathways. A survey-supported early-stage return scenario (R = 0.50) is compared with the 77% and 90% separate-collection targets used in European policy. To strengthen the PET branch of the model, a pilot PET stream-quality and processing-yield dataset was incorporated, including PET purity, colour composition, non-PET impurities, residual moisture, organic residues, intrinsic viscosity, washed PET flake or pellet yield, and sorting/washing rejection. The pilot data indicate that Lithuania had higher PET quality (98.2% PET purity, 80% clear PET, 1.8% non-PET impurities, IV = 0.74 dL/g, and 84.5% washed PET yield) than the Polish regional average (94.3% PET purity, 72.7% clear PET, 5.7% non-PET impurities, IV = 0.721 dL/g, and 80.3% washed PET yield). At R = 0.50, the pilot-derived PET HQR is approximately 37.8% for Lithuania and 31.6% for the Polish regional average. The results indicate that the same nominal return rate can lead to substantially different high-quality recovery outcomes because PET is constrained by stream purity, colour, contamination, and processing yield; aluminium by alloy and remelting control; and reusable glass by inspection, breakage, and refill compatibility. The proposed framework can support structured DRS operator reporting by identifying the material-quality and yield variables that should be measured alongside mass collection; however, operator-level validation is required before the model can be used as a predictive performance tool. Full article

Hyperparameter optimization (HPO) aims to identify high-quality model configurations under a limited evaluation budget. To address mixed search spaces, sparse feedback, and low sample efficiency in reinforcement learning (RL)-based HPO, a Center-Guided Reinforcement Learning (CGRL) method is proposed. In CGRL, the policy output is reformulated from a configuration to be directly evaluated into a search center that defines a promising region, decoupling region-level guidance from exact configuration selection. A mixed candidate pool is generated around the center, and a promising candidate for real evaluation is selected by a Random Forest surrogate model. Meanwhile, a process-aware reward provides dense and informative feedback for policy learning. Experiments on 20 Yet Another Hyperparameter Optimization (YAHPO) Gym environments validate the effectiveness of CGRL. Compared with random search (RS), Tree-structured Parzen Estimator (TPE), Sequential Model-based Algorithm Configuration 3 (SMAC3), a Proximal Policy Optimization baseline (PPO-basic), Hyperparameter Optimization by Reinforcement Learning (Hyp-RL), and Q-Learning for Hyperparameter Optimization (HyperQ-Opt), CGRL achieves the best average rank of 1.800 in terms of the final best objective value, versus 6.000, 3.600, 2.200, 4.450, 6.350, and 3.600, respectively. For Low-Rank Adaptation (LoRA) HPO for relation extraction (RE) from ancient Chinese historical documents, CGRL improves Macro-F1 by 8.66%, 3.10%, 3.18%, and 5.13% on the validation set relative to RS, TPE, SMAC3, and PPO, respectively, and by 11.15%, 2.03%, 3.11%, and 9.59% on the test set. These results demonstrate the effectiveness of CGRL for limited-budget HPO and its applicability to practical RE tasks. Full article

The height of the first fruiting branch node (HFFBN) is a core indicator for mechanical harvesting of cotton, and the development of molecular markers for this trait is important for accelerating the breeding process. In this study, using bulked segregant analysis coupled with whole-genome sequencing (BSA-seq), one quantitative trait locus (QTL) associated with the HFFBN was mapped; a molecular marker, qFBH7, associated with the HFFBN of cotton was developed; and its application value was systematically evaluated. A total of 20 lines with extreme phenotypes were selected from the recombinant inbred lines constructed using upland cotton Z3-146 and Z3-147 as parental lines. The screened lines with extreme phenotypes were used to construct the extreme high-HFFBN pool and the extreme low-HFFBN pool, which were subsequently used for BSA-seq. Using the upland cotton genome as a reference, relevant QTLs were mapped by BSA-seq. One relevant candidate region was identified, with a total length of 2.25 Mb. The validation experiments revealed that the genotyping results of the KASP_FBH7_03 molecular marker in the parental lines Z3-146 and Z3-147 were completely consistent with the BSA-seq data: Z3-146 had the TT genotype, and Z3-147 had the CC genotype. Among the 66 samples from the natural population, there was a significant difference (p < 0.05) in the HFFBN between the CC and TT genotypes, and the mean HFFBN of the TT genotype was greater than that of the CC genotype. In summary, the KASP_FBH7_03 molecular marker can be effectively used for selective breeding for the HFFBN of cotton, and the TT genotype has a positive regulatory effect on the HFFBN. This study not only provides resources for breeding cotton varieties suited to mechanical harvesting but also offers a robust tool for molecular marker-assisted selection. Full article

The four-way shuttle warehouse system (FWSWS), as a form of high-density automated warehousing equipment, is characterized by high storage density, high space utilization, and operational flexibility. The initial configuration of this warehousing system is constrained by spatial layout and equipment resources, exhibiting discrete, strongly coupled, and nonlinear characteristics. When solving such complex constrained problems, existing algorithms like MOEA/D and NSGA-II generally suffer from insufficient constraint-handling capabilities and low search efficiency for feasible solutions. To address these issues, this paper proposes the RL-IMOEA/D algorithm, integrating reinforcement learning and a knowledge-driven strategy (RL-IMOEA/D). This algorithm enhances the capability of searching for feasible solutions through a knowledge-driven feasible region repair mechanism and utilizes Q-learning to dynamically adjust the variable neighborhood search (VNS) strategy, thereby improving the convergence and diversity of the Pareto solution set. Experimental results show that, compared with other optimization algorithms, RL-IMOEA/D achieves an average hypervolume (HV) value of 1.2068—a 0.96% improvement over the standard MOEA/D baseline (1.1953)—while its inverted generational distance (IGD) value is reduced from 0.1487 to 0.0411. The proposed algorithm demonstrates superior performance in terms of convergence, solution diversity, and constraint-handling capability. The obtained configuration schemes satisfy the capacity and queueing stability constraints, verifying the effectiveness of the proposed algorithm in complex discrete optimization problems and providing an effective solution approach for the initial configuration optimization of four-way shuttle warehouse systems. Full article

Unlike conventional models that typically rely on a single memristive synapse, this study uniquely proposes a novel 5D fractional-order memristive Hopfield neural network (FOMHNN) modulated by dual memristors to simultaneously emulate internal synaptic plasticity and external electromagnetic radiation effects in brain-like computing. Analytically, the FOMHNN features multiple parallel lines of equilibria with double-zero eigenvalues, rigorously proving the generation of hidden attractors. The continuous dynamical behaviors are systematically evaluated using the Adomian Decomposition Method (ADM), revealing rich phenomena including transient chaos, grid multi-scroll hidden attractors, and frequency-controllable extreme multistability with fractal-like basin boundaries. The theoretical model is physically validated on a Field Programmable Gate Array (FPGA) platform, demonstrating high precision and ultra-low power consumption. To bridge theoretical dynamics with cryptographic applications, a novel pseudo-random number generator is designed. By incorporating a chaotic derivative extractor, the generated sequences significantly reduce topological periodicity, successfully passing all rigorous NIST SP 800-22 statistical tests. Furthermore, an adaptive color image encryption scheme is developed, utilizing bidirectional feedback diffusion and least significant bit (LSB) key embedding. Security analyses confirm that the cipher, under the fractional order $q=0.95$, achieves near-ideal information entropy, optimal resistance against differential attacks, with NPCR and UACI values reaching 99.6114% and 33.4910%, both extremely close to their theoretical ideals (99.6094% and 33.4635%), and robust resilience against noise. Ultimately, the FOMHNN provides a highly secure and physically realizable chaotic source for advanced secure communications. Full article

Olivine, as (Mg, Fe)₂SiO₄ solid solution, governs the plastic flow of the Earth’s upper mantle. While extensive studies exist on natural olivine-rich rocks, the rheology of its Mg-end member, forsterite (Fo), remains less constrained, particularly for diffusion creep. Here we synthesize high-purity (≥98 vol.%), iron-free forsterite aggregates via pressureless sintering and perform axial compression experiments in a high-stress-precision Paterson gas-medium apparatus at 300 MPa, temperatures of 1423–1523 K, and differential stresses of 50–380 MPa. Our results reveal a stress exponent n = 1.0 ± 0.09, an activation energy Q = 365 ± 22.7 kJ/mol, and a grain size exponent p = 2.9 ± 0.23, demonstrating that forsterite deforms by diffusion creep under these conditions. The grain size exponent, close to the theoretical value of 3 for Coble creep, indicates that grain boundary diffusion is the rate-controlling mechanism. Compared to previous studies on forsterite and natural olivine, our flow law shows good agreement with the activation energy for olivine diffusion creep but provides a refined estimate grain size exponent. Critically, because our samples are chemically synthesized and iron-free, and lack the trace impurities that facilitate defect generation in natural olivine, they exhibit higher strength than natural Fe-bearing olivine. Our flow law therefore defines the Mg-end member for the olivine solid solution system and represents the viscosity upper bound for olivine-dominated mantle rocks deformed dominantly by diffusion creep. These findings not only fill a critical gap in the rheological data for the olivine solid solution end-members but also provide a robust basis for modeling viscosity variations in the upper mantle as functions of grain size, temperature, and iron content. Full article

Headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) combined with chemometrics was employed to track volatile aroma compounds during the nine-steaming and nine-drying process. A total of 84 volatile signals (including monomers, dimers, and polymers) were tentatively identified, of which 73 volatile compounds were semi-quantified. Principal component analysis (PCA) revealed a mixed clustering pattern across cycles, indicating that unsupervised methods alone could not adequately resolve the volatile changes. A partial least squares discriminant analysis (PLS-DA) model supported clear chemical discrimination among the three hypothesized stages—early (cycles 1–3), middle (4–6), and late (7–9). Based on VIP > 1 and ANOVA (q < 0.05), 29 key differential compounds were screened and categorized into three dynamic groups (decreasing, transient, and accumulating), revealing three proposed transformation windows: green-grassy odor dissipation (cycles 1–3), roasted/malty aroma generation (cycles 4–6), and aroma stabilization via end-product accumulation (cycles 7–9). Relative odor activity value (ROAV) analysis revealed a decoupling between abundance and sensory impact: high-abundance Maillard products like maltol showed negligible ROAV (<0.0003), whereas esters and aldehydes dominated the ROAV profiles, suggesting their potential as important contributors to the fruity, sweet, and malty notes. The maltol/α-cedrol ratio (M/C) increased by an order of magnitude after the seventh steaming, and color parameters E*ab correlated negatively with ester accumulation. This study provides chemical insights and quantitative indicators for aroma quality control and process determination in black ginseng production. Full article

As an integral component of environmental, social, and governance (ESG) reporting, climate risk disclosure (CRD) has received growing attention from firms, investors, and regulators. Using 4501 Chinese A-share listed firms over 2009–2024, this study examines the association between CRD and corporate financial performance using return on assets (ROA) and Tobin’s Q (TQ) as separate accounting- and market-based outcomes. We construct a firm-year CRD index from annual-report text and interpret it as a normalized measure of climate-related disclosure intensity. CRD is positively associated with both ROA and TQ, and the results remain robust across alternative disclosure construction, sample windows, future outcomes, high-dimensional fixed effects, and complementary endogeneity analyses. Pathway tests show that greater CRD is associated with lower financing costs and greater green innovation, both of which are associated with stronger financial outcomes. Institutional ownership and accounting information quality positively moderate the CRD–performance relationship. Heterogeneity analyses indicate stronger associations among non-state-owned and heavily polluting firms, while both physical and transition risk disclosure are positively associated with financial performance. Overall, the findings support a conditional value-relevance interpretation of climate risk disclosure. Full article

The water resources carrying capacity (WRCC) lays a foundational basis for long-term coordinated water resource governance. Based on the Driving–Pressure–State–Impact–Response (DPSIR) framework, this study constructed a WRCC evaluation system containing 21 indicators and adopted a combined weighting method integrating entropy weight and coefficient of variation. Weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and geographical detector tools were jointly applied to quantify spatial-temporal WRCC disparities within the Jialu River Basin, alongside extraction of core driving forces during 2010–2022. Marked spatial disparities existed across administrative units, with basin-average WRCC ranging from 0.18–0.35. Zhengzhou maintained relatively high carrying levels, Kaifeng stayed chronically low, Xuchang improved after 2019, while Zhoukou experienced an overall decline, forming a relatively stable spatial pattern: Zhengzhou > Zhoukou > Xuchang > Kaifeng. Socioeconomic factors stood among the major drivers of spatial divergence. R&D expenditure and urbanization rate exhibited the highest explanatory capacity, with respective q statistics of 0.58 and 0.57. In contrast, natural factors including precipitation and groundwater reserves showed limited impacts, with q values of only 0.11 and 0.09. Factor interaction analysis showed that bivariate enhancement was the primary interaction type (70.53%), followed by nonlinear enhancement (21.05%) and nonlinear weakening (8.42%). The mean q value of the interactive effects reached 0.58, which was 45.0% higher than that of individual factors, suggesting prominent multi-factor synergistic effects. These results deliver empirical evidence for differentiated watershed regulation and cross-jurisdictional water–ecological coordination, and offer actionable governance insights for densely urbanized plain tributary basins with intense human–water conflicts. Full article

Objective: Anti-Müllerian hormone (AMH) is the most widely used biochemical marker of ovarian reserve, but its associations with other reproductive hormones are usually examined one hormone at a time and without accounting for values reported at the assay floor. This study examined the associations between serum AMH and age, follicle-stimulating hormone (FSH), luteinising hormone (LH), estradiol, progesterone and prolactin in women attending a tertiary gynaecology clinic. Materials and Methods: In this retrospective, cross-sectional, single-centre study, the records of women who underwent serum AMH testing together with a reproductive hormone panel between 1 January 2020 and 31 December 2025 were reviewed. Women with polycystic ovary syndrome, primary ovarian insufficiency, pregnancy or previous ovarian surgery were excluded. Bivariate associations were assessed with Pearson and Spearman correlation coefficients on raw and Box–Cox-transformed variables. Because 12.82% (n = 15) of AMH results were left-censored at the analytical reporting floor of 0.02 ng/mL, a multivariable Tobit model with the censoring limit specified on the Box–Cox-transformed scale (λ = 0.309, threshold = −2.270) was fitted, with predictors selected a priori on clinical grounds. Sensitivity analyses examined the influence of high AMH values, an alternative transformation of AMH, and flexible modelling of age. Results: The analysis included 117 women aged 18–45 years. Mean AMH was 2.22 ± 2.24 ng/mL [median 1.61 ng/mL (Q1–Q3, 0.36–3.20)]. In bivariate correlation analysis, AMH showed statistically significant inverse associations with age (r = −0.376, p < 0.001) and FSH (r = −0.445, p < 0.001), whereas LH, estradiol, progesterone, and prolactin showed no statistically significant correlations with AMH (all p > 0.10). In the multivariable Tobit model, only age (β = −0.068; 95% CI [−0.117, −0.018]; p = 0.007) and transformed FSH (β = −3.582; 95% CI [−4.846, −2.317]; p < 0.001) remained independently associated with transformed AMH. LH, estradiol, progesterone, and prolactin were not independently associated with AMH in the multivariable model (all p > 0.05). The standardised association was greater for transformed FSH (β* = −0.559) than for age (β* = −0.234). The associations of age and FSH were consistent in direction and magnitude across all sensitivity analyses. Conclusions: In this selected clinical sample, age and FSH were the only variables independently associated with serum AMH after mutual adjustment. LH, estradiol, progesterone, and prolactin showed no statistically significant associations with AMH in either the unadjusted bivariate analyses or the multivariable model. The absence of independent associations for these hormones should be interpreted cautiously because hormone sampling was not standardised to menstrual cycle day, which may have introduced measurement variability. These findings describe associations within the study population and do not establish causal relationships, reference intervals, or a basis for modifying testing strategies. The analysis also highlights the importance of accounting for left-censoring when modelling AMH data containing a substantial proportion of results at the assay reporting floor. Full article

Human induced pluripotent stem cells (hiPSCs) are powerful tools for disease modeling and therapeutic development, though their utility remains sensitive to operator-dependent variability and media formulation. Commercial stem cell media formulations, such as mTeSR+ and StemFlex, are designed to support robust pluripotent stem cell maintenance and improve reproducibility. While both are widely used, direct comparisons are limited. We evaluated mTeSR+ and StemFlex using the CellXpress.ai automated tissue culture platform in combination with IN Carta machine-learning-based image analysis to monitor and standardize feeding, passaging, and maintenance. Wild-type iP11N hiPSCs were cultured under standardized automated conditions with growth kinetics, immunocytochemical marker expression, and RT-qPCR assessment. Automated analysis revealed no significant differences in proliferation rates between the formulations; however, longitudinal image-based quantification identified statistically significant differences (<1%) in differentiated cell area fraction across the imaging period. Immunocytochemistry demonstrated comparable expression and localization of pluripotency-associated markers, and RT-qPCR analysis confirmed similar expression of stemness factors. These findings demonstrate that both media support comparable growth and maintenance of pluripotency under standardized automated conditions on iP11N wild-type stem cells in the short-term culture conditions analyzed. Additionally, these data highlight the value of automated imaging and machine-learning-based analysis in reducing operator-dependent variability and improving consistency in hiPSC culture, supporting its application in high-throughput stem cell research. Full article

High levels of photovoltaic (PV) generation in distribution networks create substantial uncertainty and voltage variability, which limits the effectiveness of conventional deterministic distribution network reconfiguration (DNR) strategies. In PV-dominated feeders, rare but severe operating conditions may considerably influence active power losses and voltage stability. To address this challenge, this paper proposes a risk-informed optimization framework for DNR that combines reinforcement learning with probabilistic performance assessment. A Deep Q-Network (DQN) agent is designed to support the selection of feasible radial switching configurations by interacting with the distribution network environment. Throughout the learning process, candidate network topologies are evaluated through radial load flow calculations, while a composite objective function incorporating active power losses and voltage deviation steers the agent toward improved configurations. The training stage is based on deterministic performance indices; however, the final reconfiguration solution is assessed under uncertainty to examine its operational robustness. For this purpose, extensive Monte Carlo simulations are performed to capture the stochastic behavior of PV generation and load demand. Tail-based risk metrics, including Value at Risk (VaR) and Conditional Value at Risk (CVaR), are computed for both loss and voltage deviation indices, providing insight into the performance of the selected configuration under unfavorable operating scenarios. The proposed framework is first validated on the IEEE 33-bus distribution system and then further investigated on the IEEE 69-bus network. The obtained results demonstrate that the proposed DQN-based reconfiguration approach can enhance voltage profiles and reduce power losses under high PV penetration. In addition, the probabilistic analysis identifies meaningful trade-offs between efficiency and voltage robustness, highlighting the importance of considering uncertainty-driven risk assessment in computational decision-making for modern active distribution networks. Full article

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

Desert highways have long been subjected to aeolian sand hazards, and sand accumulation on the pavement significantly weakens the surface texture and deteriorates skid resistance, which has become one of the core contributing factors to traffic accidents on desert road sections. Current research predominantly focuses on the attenuation law of the macroscopic friction coefficient of sand-covered pavements; however, the quantitative correlation mechanism between three-dimensional micro-texture characteristics and skid resistance has not been sufficiently revealed, and there is a lack of high-precision skid resistance prediction methods under multi-condition coupling scenarios. To address the above research deficiencies, this paper takes the asphalt pavement in the Tengger Desert region as the research object. A handheld three-dimensional texture scanning system was employed to acquire the three-dimensional pavement morphology parameters under different sand coverages, and the sideway force coefficient (SFC) was synchronously measured under the corresponding conditions. Through Pearson correlation analysis and dual multiple comparison correction using the FDR-BH and Bonferroni methods, the core influencing indicators were identified. Subsequently, a skid resistance prediction model based on a BP neural network optimized by the particle swarm optimization (PSO) algorithm was constructed and horizontally compared and validated with LSTM and PSO-SVM models. The research results show the following: ① under dry conditions, the root mean square height ($S_q$), peak density ($S_{pd}$), arithmetic mean peak curvature ($S_{pc}$), valley void volume ($V_{vv}$), root mean square slope ($S_{dq}$), and developed interfacial area ratio ($S_{dr}$) are significantly linearly correlated with the SFC, among which $S_q$, $S_{pd}$, $S_{pc}$, and $V_{vv}$ are the core controlling indicators, with the absolute values of their correlation coefficients all exceeding 0.73, and ② the constructed PSO-BP prediction model achieved a coefficient of determination $R^2$ of 0.86093 on the test set, and its prediction accuracy and generalization ability are both superior to those of the LSTM and PSO-SVM models, enabling it to effectively characterize the nonlinear mapping relationship between multiple texture parameters and skid resistance. This study can provide theoretical support and a technical basis for skid resistance evaluation, sand accumulation disaster warning, and scientific maintenance decision-making for desert highways. Full article

Background/Objectives: Orlistat is a gastrointestinal lipase inhibitor used for weight management. Although its established safety profile is largely characterised by fat malabsorption-related gastrointestinal events, post-marketing reports suggest a broader spectrum of adverse events. Given the increasing intersection between obesity pharmacotherapy and the management of non-alcoholic fatty liver disease/metabolic dysfunction-associated steatotic liver disease (NAFLD/MASLD), this study evaluated gastrointestinal, hepatobiliary, pancreatic, and anorectal adverse-event reporting signals associated with orlistat in the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and assessed concordance with regulatory prescribing information. Methods: Quarterly FAERS files from 2004 Q1 through 2026 Q1 were analysed; some retained reports had Initial FDA Received Date (FDA_DT) values dating back to 1999. Descriptive analyses included all orlistat-associated reports, whereas disproportionality analyses were restricted to primary-suspect reports. Sixty-five prespecified Medical Dictionary for Regulatory Activities (MedDRA) preferred terms (PTs) were screened. Reporting odds ratios (RORs), 95% confidence intervals (CIs), and proportional reporting ratios (PRRs) were calculated. A signal was defined as ROR > 2.0, lower bound of the 95% CI > 1.0, and n ≥ 3. Two-sided p values calculated using Fisher’s exact test on the corresponding 2 × 2 contingency tables were adjusted using the Benjamini–Hochberg false discovery rate procedure to address multiple testing. Signal-positive PTs were cross-referenced with prescribing information from three jurisdictions. Results: Overall, 30,454 deduplicated orlistat-associated reports were identified, including 16,684 primary-suspect reports. Thirty-eight of the 65 prespecified MedDRA PTs met the signal criteria, and all remained statistically significant after Benjamini–Hochberg false discovery rate correction (q < 0.05). The strongest signals were rectal discharge (ROR 1175.53; 95% CI 1093.44–1263.78) and steatorrhoea (ROR 1147.99; 95% CI 1058.12–1245.50); change in bowel habit also showed a high ROR (47.32; 95% CI 38.27–58.51). Unlabelled signal-positive PTs included constipation, faeces hard, cholecystitis, and irritable bowel syndrome; change in bowel habit showed jurisdiction-specific labelling discordance. Conclusions: Orlistat-associated FAERS signals included both expected fat-malabsorption-related events and additional gastrointestinal, hepatobiliary, pancreatic, and anorectal PTs, several of which were not explicitly represented in the evaluated regulatory labels. These findings reflect disproportionate reporting rather than incidence, absolute risk, or causality and require confirmation in independent clinical or epidemiological data sources. They do not, by themselves, support regulatory labelling changes. Full article