This study aimed to determine the optimal mixture of sucrose, glucose syrup, and Suji leaf extract to produce Suji milk candy using Design Expert 11 with Mixture D-Optimal and evaluated the physical, chemical, and sensory properties of the candy. The optimization process resulted in an optimal formulation with the highest desirability value (0.811), identified in the sample formulated with 14 % sucrose, 5 % glucose syrup, and 3 % Suji leaf extract (A4). Verification of responses value showed that the optimum formulation of Suji leaf milk candy had levels of texture, lightness, redness, antioxidant activity, water content, and reducing sugar of 66.52 g/mm, 33.20, 3.80, 27.86 %, 6.82 %, 4.95 % respectively, and hedonic score (sensory) of color (3.97), taste (4.30), texture (4.23), overall acceptance (4.27).

Acidic soils constrain rice production in Malaysia by reducing yields and increasing input intensity, which affects energy usage and greenhouse gas (GHG) emissions. This study applied a manual life cycle assessment (LCA) framework to evaluate energy demand and GHG emissions under standard (pH > 5) and acidic (pH < 5) soil conditions in the Integrated Agricultural Development Area (IADA), Seberang Perak, Malaysia. Liming under acidic conditions increased total energy consumption by 2 % (23,500 MJ/ha) and GHG emissions by 139 % (2,326 kg CO₂-eq/ha). The regression analysis revealed high associations between soil pH, yield (R² = 0.81), energy efficiency (R² = 0.81), and GHG intensity (R² = 0.72), indicating soil conditions highly influenced sustainability performance.

Tractor rollovers remain a critical safety issue. We propose a risk assessment framework (Part 1) that estimates quantitative risk points (RP) from accident-factor variables available during or prior to operation. Using 1,164 de-identified tractor accident records from Japan and nine accessible variables, we trained a deep neural network regressor. The optimised model (five hidden layers) achieved r = 0.87 and R² = 0.71, with errors decreasing as RP increased. This model outperformed the baseline models in accuracy. The framework estimates potential risk points of the tractor operational environment and generates a numerical index to inform operators of the prevailing risk level, enabling proactive safety management. Part 2 incorporates a dynamic risk index derived from vehicle-behaviour signals to provide a comprehensive assessment.

We present a behaviour-based, real-time estimator of tractor rollover risk suitable for embedded use. A four-degree-of-freedom vehicle model generated labelled scenarios across speeds (0.5–4.0 m/s), slope angles (0–20 °), and ISO 8608 roughness classes. Two closed-form discriminants for pitch and roll require only three onboard signals—vehicle speed, vertical acceleration, and a 0.3 s double-integrated angular-acceleration angle. On simulated runs, the pitch discriminant achieved 96.4 % sensitivity and specificity; the roll discriminant reached 100 % and 99.5 %, with mean lead times of 0.88 s and 0.53 s before rollover onset. Discriminant scores are mapped to a unified Risk Point scale, enabling interpretable warnings for proactive operator assistance during field and road operation.

This study assessed the effects of dolomite powder addition on the flowability and gelatinization properties of potato and cassava starch (CS) powders. Dolomite, rich in calcium and magnesium, was added at 2 % (w/w), and alterations in bulk density, angle of repose, compressibility, and air permeability were assessed. Dolomite addition enhanced the flowability and reduced cohesion of potato starch (PS), whereas alterations in CS were minimal. Scanning electron microscopy revealed that dolomite particles adhered to the starch surfaces, affecting the powder morphology. Pasting property revealed that dolomite altered the gelatinization behavior, reducing the peak viscosity in PS while causing minor alterations in CS. These results indicate that the effect of dolomite addition on powder properties depends on the starch type.

Crystalline taro anomality, which arises up to 50 %, is characterized by high water content. In pouch products factory, these abnormal taros are removed manually with a lot of labor, thus a nondestructive method for its detection is highly needed. In this study, we used near-infrared spectroscopy (700–1,300 nm) to discriminate crystalline taro. A correlation analysis and multivariate analysis (PCA and SVM) were employed to select wavelength(s) and develop a discrimination model. As a result, normal and crystalline taros showed spectra with features at around 700 and 980 nm. The SVM model showed an accuracy rate of 72 %. This study is important since in factories at most 70 % of labor reduction could be expected in future.