Search Results (13)

Compact, low-power absorption cooling supports decentralized refrigeration needs and is positioned here as a sustainable approach within environmental technologies. This paper presents the design, fabrication, and experimental validation of a compact LiBr–H₂O evaporator–absorber, in which a low-energy fan assists in transporting refrigerant vapor from the evaporator to the absorber within a single vertical falling-film vessel. Twelve heat-load phases were tested with the fan OFF/ON, while temperatures, pressures, and flow rates were continuously monitored. The analysis focuses on temperature and pressure separation metrics, as well as a dimensionless separation index. Results show that fan assistance stabilizes thermal and pressure differentials and attenuates oscillations across grouped loads. The most significant benefits are observed at low to intermediate heat inputs, whereas the effect becomes marginal at higher loads, indicating the dominance of natural transport mechanisms. The compact unit remains thermally stable under all tested conditions. These findings indicate that a simple, low-power mechanical enhancement can improve controllability in an integrated evaporator–absorber without complex internal geometries. Protected under a Mexican utility model (IMPI, MX 4573 B), this prototype provides a replicable experimental basis for supporting compact, low-power solutions for sustainable, decentralized cooling in the field of environmental technologies. Full article

Multiple cluster validity indices (CVIs) have been introduced for diverse applications. In practice, clusters exhibit varying shapes, sizes, densities, and closely spaced centers, which are typically unknown beforehand. It is desirable to develop a versatile CVI that performs well in general settings rather than being tailored to specific ones. Drawing inspiration from distance based on local density, where it is observed that cluster centers feature higher densities than their neighbors and are relatively distant from higher-density points, this paper introduces a novel CVI. This CVI employs a modified distance, adjusted for local density, to measure cluster compactness, replacing the traditional Euclidean distance with the minimum distance to a higher-density point. This adjustment accounts for cluster shapes and densities. The experimental results highlight the proposed index’s dual capability: it not only outperforms conventional methods by a remarkable margin of 32 percentage points in controlled synthetic environments but also maintains a 23+ percentage-point accuracy lead in real-world data regimes characterized by noise and heterogeneity. This consistency validates its generalizability across data modalities. Full article

The fuzzy C-means (FCM) clustering algorithm is a widely used unsupervised learning method known for its ability to identify natural groupings within datasets. While effective in many cases, FCM faces challenges such as sensitivity to initial cluster assignments, slow convergence, and difficulty in handling non-linear and overlapping clusters. Aimed at these limitations, this paper introduces a novel fractional fuzzy C-means (Frac-FCM) algorithm, which incorporates fractional derivatives into the FCM framework. By capturing non-local dependencies and long memory effects, fractional derivatives offer a more flexible and precise representation of data relationships, making the method more suitable for complex datasets. Additionally, a genetic algorithm (GA) is employed to optimize a new least-squares objective function that emphasizes the geometric properties of clusters, particularly focusing on the Fukuyama–Sugeno and Xie–Beni indices, thereby enhancing the balance between cluster compactness and separation. Furthermore, the Frac-FCM algorithm is evaluated on several benchmark datasets, including Iris, Seed, and Statlog, and compared against traditional methods like K-means, SOM, GMM, and FCM. The results indicate that Frac-FCM consistently outperforms these methods in terms of the Silhouette and Dunn indices. For instance, Frac-FCM achieves higher Silhouette scores of most cases, indicating more distinct and well-separated clusters. Dunn’s index further shows that Frac-FCM generates clusters that are better separated, surpassing the performance of traditional methods. These findings highlight the robustness and superior clustering performance of Frac-FCM. The Friedman test was employed to enhance and validate the effectiveness of Frac-FCM. Full article

Due to the need to continuously monitor and understand the thermal environment and its socioeconomic implications, this study used remotely sensed data to analyze thermal comfort variation in LCZs, including along the rural to urban gradient of the eThekwini Municipality in KwaZulu-Natal province of South Africa. LCZs were mapped using multi-temporal and multi-spectral Landsat 8 and Landsat 9 data using the approach by World Urban Database and Access Portal Tools (WUDAPT), while thermal data were used to retrieve land surface temperatures (LSTs). Data for training classification of LCZs and accuracy assessment were digitized from GoogleEarth guided by knowledge gained and data collected during a field survey in March 2022 as well as pre-existing maps. LCZs were mapped using the random forest classifier in SAGA GIS software while a single channel algorithm based on band 10 was used to compute LST for different scenes. The LSTs were adjusted and further used to derive thermal comfort based on the Universal Thermal Comfort Index (UTCI) categories as an indicator for outdoor thermal comfort on the extremely low- and extremely high-temperature periods in the cool and hot seasons, respectively. LCZs were mapped with high accuracy (overall accuracy of 90.1% and kappa of 0.88) while inter-class separability was high (>1.5) for all LCZ pairs. Built-up LCZs dominate the eastern parts of the municipality, signifying the influence of the sea on development within the area. Average LST was coolest in the dense forest, open low-rise and water LCZs in the cool and hot seasons, respectively. The compact high-rise LCZ was the warmest in both the hot (36 °C) and the cool (23 °C) seasons. The sea sands were among coolest regions in both seasons due to their high water content, attributed to their high water table and close proximity to the ocean. There was no thermal stress during the cool season, while most areas recorded moderate to strong heat stress in the hot season. Some areas in the densely built-up LCZs recorded very strong heat stress in the hot season. The findings suggest that policies and strategies should enhance heat mitigation capacities in strong-heat-stress areas during the hot season. Municipal authorities and citizens must work together to build strategies to minimize temperature extremes and associated socioeconomic pressures. Urban development policies, plans and strategies should consider implications on the thermal environment as well as the value of conservation of LCZs with high-heat mitigation value such as dense forests and expansion of built-up LCZs with low-heat absorption levels such as open low-rise. The study was based mainly on remotely sensed temperatures with some ground data used to validate results, which may limit the assessment. Overall, the study provides insights towards achievement of global sustainable and climate-smart development targets. Full article

Cluster validity indices (CVIs) for evaluating the result of the optimal number of clusters are critical measures in clustering problems. Most CVIs are designed for typical data-type objects called certain data objects. Certain data objects only have a singular value and include no uncertainty, so they are assumed to be information-abundant in the real world. In this study, new CVIs for uncertain data, based on kernel probabilistic distance measures to calculate the distance between two distributions in feature space, are proposed for uncertain clusters with arbitrary shapes, sub-clusters, and noise in objects. By transforming original uncertain data into kernel spaces, the proposed CVI accurately measures the compactness and separability of a cluster for arbitrary cluster shapes and is robust to noise and outliers in a cluster. The proposed CVI was evaluated for diverse types of simulated and real-life uncertain objects, confirming that the proposed validity indexes in feature space outperform the pre-existing ones in the original space. Full article

In this paper, novel green HPLC and HPTLC chromatographic methods were developed for the concurrent determination of moxifloxacin, levofloxacin, and gemifloxacin in bulk and pharmaceutical products. The green HPLC method was used on Thermo C18 (4.6 × 250 mm, 5 µm). By mixing ethanol and 20 mM sodium dihydrogen phosphate dihydrate (pH 5) in a ratio of 25:75, v/v, the mobile phase was created using isocratic elution. The flow rate was 1 mLmin⁻¹. The studied antibiotics were separated well within 9.5 min. The green HPTLC method was used on coated HPTLC aluminum sheets with Silica gel 60 F254 using a mobile phase mixture of water: acetone: ammonia (8:1:1, v/v/v). Compact and well-resolved peaks were obtained under chamber-saturation circumstances for the standard fluoroquinolone antibiotics. Both methods were optimized individually, validated by ICH, and assessed using the Green analytical procedure index (GAPI). The methods were applied to pharmaceutical products and compared with the published methods for the determination of each of these antibiotics individually, using Student’s t-test. They can be used by quality-control laboratories in pharmaceutical factories as sensitive eco-friendly methods for the analysis of these drugs and for the detection of cross-contamination during manufacturing processes. Full article

Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optimization algorithm utilizing ten UCI Machine Learning Repository datasets and eight Shape sets for performance evaluation. In addition to this, two clustering validity measures, Compact-separated and David–Bouldin, have been used for analyzing the efficiency of these algorithms. The experimental results show that the two proposed hybrid algorithms outperform the existing hybrid firefly particle swarm optimization algorithm. Full article

This study is a parametric investigation of the application of a compact type of pre-swirl duct for a slender aft-body 158k crude oil carrier. The International Maritime Organization (IMO) has created the Energy Efficiency Design Index (EEDI), which is an index related to carbon dioxide emissions, to enforce regulations on newly built ships; many solutions have been proposed, including an increase in propulsion efficiency. In the present study, a new type of energy-saving device (pre-swirl duct), the so-called ring stator, is proposed for 158k crude oil carriers, the hull form of which has been developed as a slender after-body full form to reduce the resistance by delaying separation. A conventional pre-swirl duct is no longer effective at the slender aft-body hull forms because of the small radial flow to the propeller. A parametric study was conducted through Computational Fluid Dynamics (CFD) analysis using the commercial program Star-CCM+, and an improvement of approximately 3% in the propulsion efficiency was achieved in the present study. The efficiency gain was validated through a comparison with the experimental results. The developed ring stator may become applicable to container ships through further parametric studies in the near future. Full article

The rapid and sensitive detection of human C-reactive protein (CRP) in a point-of-care (POC) may be conducive to the early diagnosis of various diseases. Biosensors have emerged as a new technology for rapid and accurate detection of CRP for POC applications. Here, we propose a rapid and highly stable guided-mode resonance (GMR) optofluidic biosensing system based on intensity detection with self-compensation, which substantially reduces the instability caused by environmental factors for a long detection time. In addition, a low-cost LED serving as the light source and a photodetector are used for intensity detection and real-time biosensing, and the system compactness facilitates POC applications. Self-compensation relies on a polarizing beam splitter to separate the transverse-magnetic-polarized light and transverse-electric-polarized light from the light source. The transverse-electric-polarized light is used as a background signal for compensating noise, while the transverse-magnetic-polarized light is used as the light source for the GMR biosensor. After compensation, noise is drastically reduced, and both the stability and performance of the system are enhanced over a long period. Refractive index experiments revealed a resolution improvement by 181% when using the proposed system with compensation. In addition, the system was successfully applied to CRP detection, and an outstanding limit of detection of 1.95 × 10⁻⁸ g/mL was achieved, validating the proposed measurement system for biochemical reaction detection. The proposed GMR biosensing sensing system can provide a low-cost, compact, rapid, sensitive, and highly stable solution for a variety of point-of-care applications. Full article

Weak evidence exists on the relationship between nutritional knowledge and diet quality. Many researchers claim that this could be in part because of inadequate validation of the questionnaires used. The aim of this study was to develop a compact reliable questionnaire on nutrition knowledge for young and adult athletes (NUKYA). Researchers and the sport clubs medical staff developed the questionnaire by taking into consideration the latest athlete dietary guidelines. The questionnaire content was validated by a panel of 12 nutrition experts, and finally tested by 445 participants including athletes (n = 264), nutrition students (n = 49) and non-athletes with no formal nutrition knowledge (n = 132). After consulting the experts, 59 of the 64 initial items remained in the questionnaire. To collect the evaluation of experts, we used the content validity index, obtaining high indices for relevance and ambiguity (0.99) as well as for clarity and simplicity (0.98). The final questionnaire included 24 questions with 59 items. We ensured construct validity and reliability through psychometric validation based on the Classical Test Theory and the Item–Response Theory (Rasch model). We found significant statistical differences comparing the groups of nutrition knowledgeable participants with the rest of the groups (ANOVA p < 0.001). We verified the questionnaire for test–retest reliability (R = 0.895, p < 0.001) and internal consistency (Cronbach’s $\alpha =0.849$). We successfully fit the questionnaire data to a rating scale model (global separation reliability of $0.861$) and examined discrimination and difficulty indices for items. Finally, we validated the NUKYA questionnaire as an effective tool to appraise nutrition knowledge in athletes. This questionnaire can be used for guiding in educational interventions, studying the influence of nutrition knowledge on nutrient intake and assessing/monitoring sport nutritional knowledge in large groups. Full article

Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then, it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI (University of California at Irvine) machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of fuzzy C-means by using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption. Full article

Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and can act as the objective function of clustering algorithms. In this paper, we first studied several well-known internal CVIs for categorical data clustering, and proved the ineffectiveness of evaluating the partitions of different numbers of clusters without any inter-cluster separation measures or assumptions; the accurateness of separation, along with its coordination with the intra-cluster compactness measures, can notably affect performance. Then, aiming to enhance the internal clustering validation measurement, we proposed a new internal CVI—clustering utility based on the averaged information gain of isolating each cluster (CUBAGE)—which measures both the compactness and the separation of the partition. The experimental results supported our findings with regard to the existing internal CVIs, and showed that the proposed CUBAGE outperforms other internal CVIs with or without a pre-known number of clusters. Full article

Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting into the objective function. We first formulate the membership and feature weighting, and analyze the membership of data points falling on the crisp boundary, then give the adjustment strategy. The proposed WFCS is validated both on simulated dataset and real dataset. The experimental results demonstrate that the proposed WFCS has the characteristics of hard clustering and fuzzy clustering, and outperforms many existing clustering algorithms with respect to three metrics: Rand Index, Xie-Beni Index and Within-Between(WB) Index. Full article