Integrating Sphere (IS) is an instrument formed a cavity sphere with its inner surface act as a Lambertian reflector. IS is needed in many optical measurements which involves a high diffused reflection. In addition, IS very essential in photometry and radiometry measurement system. However, currently, IS is still an imported product which considered very expensive. The material for the sphere and inner surface coating affect the performance of the IS systems. Therefore, the main challenges in designing IS are the material engineering for the sphere and the procedure for the inner surface coating. The inner surface was coated using ${\mathrm{BaSO}}_4$ which has a low absorption and high diffuse reflection. Spectral responses of the IS system was characterized using USB2000+ and calibrated using a standard Spectralon from ocean optic. The obtained IS system used an inner surface coating from a mixed 80% Barium Sulfate (${\mathrm{BaSO}}_4$ ) and 20% Nippon Elastex paint which yield a reflection factor ρ = 0.955 and amplification factor M=10.69. The validation was conducted using an emitter with known specification from Luxeon of LXHL-DW01 which produced light flux 40.5 lumen with injection current 350 mA and junction temperature T_j 25°C.

While tolerance is recognised as important, especially to diverse societies, understanding tolerance poses complexities, both theoretically and in practical application. Tolerance is relevant in different social contexts, yet these pose distinct challenges to measurement. Thus, understanding the measurement of tolerance is important. This study provides a systematic review of tolerance scales, with the procedure based on the PRISMA statement. The initial collection of over 1600 papers through the systematic review process yielded a set of 11 papers. These papers trace the development of tolerance scales to encompass distinct social contexts over time, including political, cultural, ethnic and racial, racial and religious, gender and sexual, and social. Correspondingly, the approaches reflect particular challenges relevant to these domains for understanding and measuring tolerance. In contrast, some recent contributions aim to develop broader, less context-specific means to assess tolerance. Notwithstanding the breadth of articles included, this systematic review yielded articles that covered only selected Western-centric geographies: this indicates the opportunity for further research to access and integrate non-English-language articles to broaden the geographical and cultural perspectives on tolerance.

The integrating sphere (IS) is one of the most important device in characterization of illuminance of a light source, such as CFL, LED etc. to obtain their efficacy. IS is a hollowed sphere with its interior covered with a diffuse white reflective coating where its accuracy of the measurement is highly affected by reflectance of its interior coating. This paper report the preparation of inner surface coating of the IS with inner diameter of 25 cm attempt to create a durable and highly reflective interior coating by combining BaSO4 with a binding material (either Polyethylene Glycol (PEG) or white paint). The various inner surface coating mixture vary in weight % ratio of BaSO4:PEG or BaSO4:white paint were investigated. The results show that the inner surface coating mixture of BaSO4:PEG (99.8:0.2) has highest reflectance compared to others mixture. The IS with best mixture was calibrated with white LED and resulted an average sphere multiplier (M) was 8.7, and average reflectance (ρ) was 0.90. The result of the relative error of luminescence measurement using calibrated M and ρ is 6.7 %. It is concluded that the developed IS produced lower allowed error compared to the commercially available IS. However at lower wavelength shows lower intensity compare to the available datasheet of the LED under investigation.

Accurate prediction of shrimp body weight is critical for optimizing harvest timing, feed management, and stocking density decisions in intensive aquaculture. While prior studies emphasize environmental factors, operational management variables—particularly harvesting metrics—remain understudied. This study quantified the predictive importance of harvesting-related variables using 5 years of industrial-scale operational data from 12 ponds (5479 cleaned records, 34.94% retention rate). We trained seven machine learning models and applied three independent feature importance methods: consensus importance ranking, SHAP explainability analysis, and Pearson correlations. Main findings: Operational variables (days of culture: 2.833 SHAP, stocking density: 1.871, cumulative feed: 1.510) ranked substantially above environmental variables (temperature: 0.123, pH: 0.065, dissolved oxygen: 0.077). Partial harvest frequency showed bimodal clustering, indicating two distinct viable operational strategies. The Weighted Ensemble model achieved the highest performance (R² = 0.829, RMSE = 4.23 g, MAE = 3.12 g). Model stability analysis via 10-fold GroupKFold cross-validation showed that the Artificial Neural Network (ANN) exhibited the tightest confidence bounds (0.708 g width, 27.7% coefficient of variation), indicating exceptional consistency. This is the first study to systematically analyze the importance of harvesting variables using SHAP explainability, revealing that operational management decisions may yield greater returns than marginal environmental control investments. Our findings suggest that operational optimization may be more impactful than environmental fine-tuning in well-managed systems.