Bincang Sains dan Teknologi

Decarbonization in Maritime Technology

2026-08-24T02:20:16+00:00

The decarbonization of the maritime sector represents a complex socio-technical transition rather than a purely technological shift, requiring the alignment of fuels, energy systems, and regulatory frameworks. Despite rapid innovation, the field remains fragmented, with competing technological pathways and limited integration across system levels. Guided by the targets of the International Maritime Organization, this study conducts a scoping review of literature published between 2020 and 2025 to critically examine how decarbonization strategies are conceptualized, developed, and operationalized. Using the PRISMA-ScR approach, this review synthesizes evidence across four interrelated domains: alternative fuels, energy efficiency technologies, electrification and hybrid systems, and digital optimization. Rather than identifying a dominant solution, the analysis reveals a persistent trade-off between technological maturity, scalability, and environmental impact. The findings highlight a systemic misalignment between technological innovation and supporting infrastructure, as well as a lack of integrated frameworks that connect engineering solutions with policy mechanisms. This review advances the literature by proposing an integrative perspective that reframes maritime decarbonization as a multi-layered transition problem, emphasizing cross-domain interoperability and policy-technology co-evolution. It identifies critical research gaps in hybrid energy systems, infrastructure readiness, and techno-economic scalability, and outlines a future research agenda centered on system integration and global implementation pathways toward net-zero shipping.

Production of an Adsorbent from Chicken Eggshell Waste for the Purification of Used Cooking Oil

2026-08-24T02:23:32+00:00

This study aimed to develop an adsorbent derived from chicken eggshell waste for the purification of used cooking oil. Repeated consumption of used cooking oil poses potential health risks due to the accumulation of degradation products; therefore, purification is necessary before further utilization. Chicken eggshells were selected as the raw material because of their high calcium carbonate content and porous structure, which contribute to their adsorption capacity. The preparation of the adsorbent involved carbonization followed by chemical activation using 3 M hydrochloric acid (HCl). The study evaluated the optimal adsorption time and adsorbent dosage for the treatment of used cooking oil. The results demonstrated that the optimum adsorption time was 90 minutes, reducing the free fatty acid (FFA) content from 5.12% to 1.02%, while odor and color intensity decreased by 87%. The optimum adsorbent dosage was 1.5 g, which exhibited the highest effectiveness in improving the quality of the used cooking oil. These findings suggest that eggshell-derived adsorbents offer a sustainable and environmentally friendly approach to managing used cooking oil waste while simultaneously valorizing organic eggshell waste as a low-cost adsorbent material.

Comparative Evaluation of Zero-Shot Vision-Language Models and YOLO for Image-Based Weld Defect Severity Assessment

2026-08-24T02:20:10+00:00

Automated visual inspection of welded joints increasingly uses supervised deep-learning models, particularly YOLO object detectors. Although effective for defect localization, these approaches require domain-specific annotated data and provide limited engineering-oriented textual explanations. This study compared supervised YOLO-based pipelines with zero-shot Vision-Language Models (VLMs) for image-based weld-defect severity assessment. A public Welding Defect–Object Detection dataset containing 1,983 images was used. A four-level ordinal severity rubric was developed as an AWS D1.1-informed engineering heuristic rather than a direct implementation of AWS acceptance criteria because the source images lacked consistent physical scale calibration. Two supervised detectors, YOLOv8n and YOLO26n, and two zero-shot VLMs, NVIDIA Nemotron and Meta Llama 3.2 Vision-90B, were evaluated. All pipelines processed 401 validation-and-test images, while primary comparison against independently rated human references used an adjudicated subset of 100 images. Human inter-rater agreement was high (Cohen’s κ = 0.839; weighted κ = 0.929). Nemotron achieved the highest accuracy (55.0%), followed by YOLOv8n (50.0%), YOLO26n (48.0%), and Llama Vision-90B (38.0%), and the highest Level-4 recall (0.871). Both YOLO pipelines showed zero recall for Level 2. Although unadjusted McNemar testing found p = 0.030 for Nemotron versus Llama Vision-90B, no pairwise comparison remained significant after Holm correction. VLM explanation quality was moderately associated with classification correctness (r = 0.533; p = 0.0001). None of the methods was sufficiently reliable for standalone engineering acceptance decisions, but their complementary failure patterns support VLMs as an auxiliary review layer within human-supervised weld inspection.

On the Construction of Multipolar Intuitionistic Fuzzy Positive Implicative Ideals in BCK-Algebras

2026-08-24T02:20:13+00:00

This research focuses on the generalization of the concept of multipolar fuzzy intuitionistic ideal in BCK algebra and bi-polar fuzzy intuitionistic positive implicative ideal in BCK algebra into multipolar fuzzy intuitionistic positive implicative ideal in BCK algebra. In this research, we define the concept of multipolar fuzzy intuitionistic positive implicative ideal in BCK algebra, and given the examples. In addition, we examine the conditions that must be met by a multipolar fuzzy intuitionistic ideal in BCK algebra to become a multipolar fuzzy intuitionistic positive implicative ideal in BCK algebra. One of the theorems produced in this research is that a multipolar intuitionistic fuzzy (P ̂,Q ̂) of X is a multipolar intuitionistic fuzzy implicative ideal (P ̂,Q ̂) of X if and only if (P ̂,Q ̂) is a multipolar intuitionistic fuzzy ideal of X and P ̂(x*y)≥P ̂((x*y)*y) and Q ̂(x*y)≤Q ̂((x*y)*y) apply, for all x,y,z∈X, where X is a BCK algebra.

Drivers and Barriers of Used Cooking Oil Management in Catering Businesses

2026-08-24T02:23:30+00:00

Used cooking oil (UCO) generated by food-service businesses presents environmental, food-safety, and resource-management challenges when it is repeatedly reused or improperly discarded. Effective management requires understanding not only individual factors but also the causal relationships among organizational, environmental, economic, and institutional determinants. This study examined the drivers and barriers influencing UCO management among catering businesses in Yogyakarta, Indonesia. Fifteen catering businesses participated from 25 businesses initially approached. Respondents were owners, managers, or employees directly familiar with operational activities and UCO handling. Eight driving factors and eight barrier factors were identified from the literature and field validation. Pairwise relationships among the factors were assessed using questionnaires and interviews and analyzed using the classical Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. Direct and indirect influence matrices, prominence (D+R), relation (D−R), threshold values, and influence relation maps were used to identify dominant factors. Internal management policy and commitment emerged as the dominant driver in five of the 15 businesses (33.3%), followed by public-health considerations in four (26.7%). Availability of facilities and social awareness were each dominant in two businesses (13.3%), whereas profit and product innovation were each dominant in one (6.7%). The most frequent dominant barrier was insufficient commitment to reverse logistics, identified in six businesses (40.0%), followed by insufficient environmental awareness in five (33.3%). External government policy was dominant in two businesses, while insufficient knowledge and stakeholder socialization were each dominant in one. UCO management in catering businesses is shaped primarily by organizational commitment and reverse-logistics capability, although substantial heterogeneity exists between firms. Interventions should integrate internal environmental policy, staff capability development, reverse-logistics systems, supporting infrastructure, and more explicit external governance.

From Field Data to Analytical Queueing Model

2026-08-24T02:20:07+00:00

Queueing systems at gas stations (SPBU) exhibit variable arrival and service characteristics; thus, selecting an appropriate queueing model requires aligning assumptions with empirical conditions. This study aims to identify the probability distribution characteristics of inter-arrival times and service times and to evaluate their fit with the assumptions of the M/M/2 queueing model at the Tugu 44.552.15 gas station. Data were collected through customer-level observations during two sessions—July 10, 2026, and August 12, 2026—between 12:00 and 15:00. Arrival and service time data were processed to derive inter-arrival and service times, which were then analyzed using distribution fitting in the Arena Input Analyzer. Several probability distributions were compared based on squared error and goodness-of-fit test results. The analysis revealed that the exponential distribution lacked statistical support to represent either inter-arrival times or service times. This pattern was consistently observed across both sessions. Thus, the system's empirical characteristics during the observation period do not support the Markovian assumptions underlying the M/M/2 model. These findings suggest that the M/M/2 model relies on relatively restrictive assumptions for representing the observed system, implying that empirical-based approaches—such as discrete-event simulation—may be suitable for further analysis.