Browse Articles

Multi-criteria inventory classification considering demand stability

Abstract This paper focuses on the demand stability criterion for inventory classification. Unstable demand may lead to inventory obsolescence or emergency purchasing. A case study of multi-criteria inventory classification (MCIC) for a tunnel boring machine manufacturer is conducted. An MCIC method based on a classical MCIC model is proposed. The proposed method first evaluates demand criticality using the mean, standard deviation and range of demand. And then, the proposed method calculates the final criticality scores for Stock Keeping Units (SKUs) based on demand criticality and other criteria, e.g., unit price or lead-time. Finally, SKUs are ranked in descending order and classified by the ABC principle. The classification results can draw the attention of inventory managers to the unstable SKUs. The inventory performance analysis shows that inventory classification considering demand stability can achieve higher fill rates at lower cost.

Author Correction: Pr and Pfr structures of plant phytochrome A

ClarityTrack for multi object tracking via hierarchical association and environment specific cost matching

Energy, power, and infrastructure demands from electrifying airport ground support equipment at United States airports

Latency-aware attitude control of underactuated quadrotor UAVs using barrier Lyapunov and fuzzy Padé approximation

Mutant ribosomal protein RPS15 drives B cell malignancy through oxidative stress and genomic instability

Early detection of opioid-induced constipation in patients initiating weak opioids for chronic non-cancer pain

Abstract This post hoc analysis of a Japanese cohort study aimed to identify factors that could assist in early prediction of opioid-induced constipation (OIC). The onset of OIC and the predictive accuracy (sensitivity, specificity, likelihood ratios, and positive predictive values) of early constipation symptoms for OIC onset were assessed within 6 days of weak opioid initiation. Of 63 patients (≥ 18 years) without prior constipation who initiated weak opioids for chronic musculoskeletal pain, 23.8% of them met the Rome IV diagnostic criteria for OIC onset after 3 days. Straining (34.9%), incomplete evacuation (25.4%), and lumpy/hard stools (15.9%) were the common symptoms, with the highest positive likelihood ratio of 2.4 for lumpy/hard stools. Decreased defecation frequency is commonly used for the diagnosis of OIC; however, it was reported in only 1.6% of patients at day 3. The positive predictive value of developing OIC by day 14, based on the early symptoms at day 3, was 70.0% for lumpy/hard stools, 59.1% for straining, 56.3% for sensation of incomplete evacuation, and 72.7% for self-awareness of constipation. Early observation of symptoms such as lumpy/hard stools and self-awareness of constipation could be useful for early prediction of the risk of developing OIC after initiation of weak opioids.

Interchain supramolecular interactions drive nearly 21% efficiency organic solar cells

Abstract A small-molecule acceptor, S-Cb, substituted with a cyclobutyl group that introduces high ring strain, was designed and synthesized. Thanks to the rigid and planar structure of cyclobutyl, S-Cb can form interchain supramolecular interactions through hydrogen bonding with L8-BO at the external side chains. This clamping effect not only effectively suppresses the electron-phonon coupling but also promotes the formation of high-quality acceptor alloy phases in the ternary active layer, thereby optimizing carrier behaviors and reducing non-radiative energy loss. The clamping effect reaches its maximum when S-Cb and L8-BO are in equal proportion, where organic solar cells (OSCs) based on D18:S-Cb:L8-BO achieved an impressive efficiency of 20.93%, with a certified efficiency of 20.74%. In summary, the cyclobutyl-mediated interchain supramolecular interactions suppress the electron-phonon coupling and optimize the acceptor alloy phase for efficient ternary OSCs.

EEG imagined speech neuro-signal preprocessing and deep learning classification

Abstract This study presents an advanced approach for classifying imagined speech from Electroencephalography (EEG) signals, leveraging deep learning architectures and tailored preprocessing techniques. Five Convolutional Neural Network (CNN)–Long Short-Term Memory (LSTM) hybrid architectures are proposed and investigated, to extract spatial and temporal features in EEG signals, in conjunction with a proposed six-phase preprocessing pipeline combining Independent Component Analysis (ICA) for artifact attenuation with zero-phase Frequency-Domain Filtering (FD-F) and adaptive normalization. The proposed approach is evaluated across single- and multi-category classification and across multiple cross-validation strategies including random splits, GroupKFold and Leave-One-Subject-Out (LOSO) using weighted metrics, per-class, and per-subject analysis. Experiment results demonstrate the superior performance achieved by FD-F, and that by integrating the most effective proposed bidirectional temporal modeling architecture CNN-2-Bi-LSTM, with the proposed preprocessing pipeline, the approach achieves higher accuracy (exceeding 99%) for 30-class classification maintaining cross-subject generalization against state-of-the-art.

Enhanced Red-billed Blue Magpie Optimizer for engineering optimization problems

Enhancement of CO2 capture in post combustion process using actived carbon modified by amino acids

Peptide gel, CK2-085, maintains operative field visibility during surgery

Association between metabolic dysfunction-associated steatotic liver disease and obstructive sleep apnea: a nationwide retrospective cohort study

Abstract Metabolic dysfunction-associated steatotic liver disease (MASLD) and obstructive sleep apnea (OSA) share overlapping metabolic and inflammatory pathways, yet population-level evidence linking MASLD and incident OSA remains limited. Using a nationwide cohort of 265,452 Korean adults aged ≥ 40 years, we evaluated OSA risk across five mutually exclusive phenotypes defined by steatosis (fatty liver index [FLI] ≥ 30), cardiometabolic risk factors (CMRFs), and alcohol intake: no steatotic liver disease (SLD) without CMRFs, no SLD with CMRFs, MASLD without alcohol, MASLD with alcohol intake below the metabolic-associated alcohol-related liver disease (MetALD) threshold (men < 210 g/week, women < 140 g/week), and MetALD. During a mean follow-up of 9.5 years, 1,025 participants developed OSA. Compared with the reference group, adjusted hazard ratios (aHRs) for OSA were 1.18 (95% confidence intervals [CI] 0.93–1.50) in individuals with CMRFs alone, 1.46 (95% CI 1.12–1.91) in MASLD without alcohol, 1.52 (95% CI 1.17–1.98) in MASLD with alcohol, and 1.40 (95% CI 1.01–1.94) in MetALD. Model-based absolute risk differences (ARDs) at 9.5 years showed consistent patterns (+ 0.05%, + 0.14%, + 0.16%, and + 0.12%, respectively). Sensitivity analyses using stricter steatosis criteria (FLI ≥ 60 or hepatic steatosis index ≥ 36) demonstrated a clearer dose-related gradient, with progressively higher OSA risk across MASLD without alcohol, MASLD with alcohol, and MetALD. These findings highlight MASLD—particularly alcohol-associated phenotypes—as important risk markers for OSA and underscore the need for targeted screening and early intervention strategies in this increasingly prevalent population.

Consumer credit evaluation model for free trade ports by a sparse attention transformer and graph neural network

Changes in body mass index and waist circumference as predictors of incident prediabetes: the Aichi Workers’ Cohort Study

An integrated VIKOR–AHP method for green energy systems based on q-fractional hesitant fuzzy multi-criteria decision-making

Optimizing precision in quantum metrology through engineered environments

Attribute development for a discrete choice experiment to examine preferences for long-acting HIV prevention products among pregnant and breastfeeding women

Plasma-modified biodegradable coatings for controlled nitrogen release from urea

Integrated 3D static modelling to assess hydrocarbon potential of the fluvial-alluvial sandstones of Nukhul Formation, October oil field, Gulf of Suez, Egypt

Abstract One of the biggest hydrocarbon accumulations in the Gulf of Suez, the huge October Oil Field, has a structurally complex syn-rift sequence with poorly limited reservoir distribution and quality. One of the most important target reservoirs is the Nukhul Formation. However, the field development is challenged by facies architecture, porosity, shale distribution, and fault geometries that complicate replication of the Nukhul reservoir’s reported heterogeneity. These difficulties are crucial for enhancing and improving field development, particularly from the Nukhul reservoir, which was depleted and had a very low oil rate. It was thought that this type of reservoir had disappeared, leaving little opportunity for development. This study aims to manage and increase oil production from the Nukhul reservoir by updating structural interpretation, which reveals revised fault geometry and compartment configurations, providing better restrictions on trap integrity and reservoir continuity. Historical datasets collected by multiple operators (1980–2020), combined with recently reprocessed seismic interpretations, were integrated to reconstruct stratigraphic geometries more accurately than previously achievable. It was crucial to update and modify the structural model to preserve favourable reservoir quality and extension across different locations, forming the basis for improved facies and static modelling. The Nukhul Formation was divided into four zones (K1–K4) based on detailed correlation integrated with petrological descriptions and dynamic data. This division highlights the dominance of low-porosity limestone–shale units in K1–K2, discontinuous fluvial sandstone bodies in K3, and thick, laterally linked channelized sandstones in K4. Model results identify undrilled reservoir extensions, attic accumulations, and high-quality sandstone corridors. These results provide actionable targets for near-field exploration and infill drilling, significantly reducing uncertainty in reservoir extent and quality, improving dynamic behaviour prediction, and supporting volumetric calculations, flow-unit delineation, and uncertainty quantification. The findings demonstrate that the Nukhul reservoir still retains production potential and can contribute to field redevelopment. The approach used here offers a stable and portable framework for characterizing heterogeneous syn-rift reservoirs in structurally dynamic basin-margin environments and promotes optimal field redevelopment plans.