This study investigates expedition service quality in Indonesia in response to rapid e-commerce growth and increasing logistics demand. Despite industry expansion, persistent service issues such as delivery delays, lost goods, and poor customer service remain prevalent. This research aims to identify priority attributes for service quality improvement by integrating Logistics Service Quality (LSQ) with a three-dimensional model consisting of the Kano model, Importance–Performance Analysis (IPA), and Goal Difficulty (GD). Data were collected from 372 customers and 11 logistics company representatives. The results indicate that customer service ethics, pick-up service, and compensation for damaged goods are the most critical attributes requiring improvement. This study contributes by proposing a practical prioritization framework that incorporates both customer perception and managerial feasibility. The findings provide actionable insights for logistics companies to enhance service performance and customer satisfaction.
Steam reforming of methane (SRM) supplies about half of the world’s hydrogen, but a high heat demand and carbon-forming side reactions that deactivate Ni catalysts limit its performance. HSC Chemistry 6 was used to compare the spontaneity of SRM with methane decomposition, water–gas shift, the Boudouard reaction, CO and CO2 methanation, dry reforming and partial oxidation between 100 and 1000 °C, and to determine by Gibbs free energy minimisation how the CH4/H2O feed ratio (1:1, 2:1, 1:2) and pressure (1–5 bar) shift the equilibrium composition. SRM is non-spontaneous at 25 °C and becomes spontaneous above about 620 °C. Methane decomposition becomes spontaneous earlier, at about 545 °C, and is the main thermodynamic carbon threat. A methane-rich feed deposits carbon equal to 47–49% of the methane fed above 640 °C, an equimolar feed forms carbon between about 440 and 840 °C, and a steam-rich feed is carbon-free and reaches 99% conversion at 820 °C. Raising the pressure from 1 to 5 bar lowers methane conversion (76% to 45% at 685 °C) and hydrogen yield, but suppresses carbon formation. The results support steam-rich, low-pressure, high-temperature operation.
The proposed review combines an analysis of event-driven encoding, spike-timing-dependent plasticity (STDP), and reward-modulated learning rules with a discussion of how these mechanisms translate into real-time, low-power decision-making pipelines for robotics, autonomous navigation, and financial signal processing. A simulated spiking decision-network is described and evaluated on benchmark event-based datasets, comparing its accuracy, latency, and energy consumption against conventional artificial neural network (ANN) baselines. The results indicate that neuromorphic decision-making systems can achieve competitive accuracy while consuming substantially less energy and responding with lower latency, making them attractive for edge and real-time autonomous applications.
A digital twin is a virtual replica of a physical entity that is continuously updated with real-world data and can be used to analyse, predict and test outcomes without risk to the original. Originally developed for aerospace and manufacturing, the concept is now being adapted to healthcare, where the twin may represent a single organ, an entire patient or a hospital. Conventional care relies on population averages, periodic check-ups and trial-and-error treatment, which leaves gaps in personalisation, early detection and safety. This paper reviews the concept of the healthcare digital twin, its enabling technologies (wearable sensors, artificial intelligence, cloud computing, medical imaging and health records, and 5G networks), a generic working architecture, and major application areas including personalised medicine, surgical planning, chronic disease management, drug development and hospital operations. It further discusses live patient monitoring, benefits, data privacy and regulatory considerations (HIPAA, GDPR and India’s DPDP Act 2023), and open challenges of cost, interoperability, workforce readiness and biological complexity. The review concludes that digital twins can make healthcare more predictive, personalised and safer, provided that privacy, standardisation and affordability are addressed.
The design of reinforced concrete structures in Brazil is governed by NBR 6118:2026. However, deterministic design provisions do not explicitly account for uncertainties in structural behavior, which may result in uneconomical designs or inadequate safety levels. Structural reliability analysis provides a probabilistic basis for assessing these uncertainties and quantifying structural safety. This study evaluates the reliability of a reinforced concrete beam with respect to flexural and shear limit states. Failure probabilities were estimated using Monte Carlo simulation, the First-Order Second-Moment method (FOSM), and the First-Order Reliability Method (FORM). Two analyses were conducted. First, the beam was assessed under its original design condition, with fck = 30 MPa. Second, the same beam was analyzed considering concrete strength classes C20, C30, C40, and C50. The three methods provided comparable reliability estimates. The results showed that the probability of failure generally decreased with increasing concrete compressive strength and that the relative importance of the random variables varied according to the failure mode.
