Search Results (125)

The construction and enhancement of ecological niches are essential for small and medium-sized enterprises (SMEs), with digital platforms serving as key carriers for achieving niche improvement. However, SMEs encounter a “distinctiveness–conformity” paradox when leveraging digital platforms: they are expected to sustain differentiation to attract resource tilt while simultaneously integrating into the platform ecosystem to obtain a sense of belonging and complementary resources. Grounded in optimal distinctiveness theory, this study analyzes questionnaire data from 383 Chinese SMEs embedded in digital platforms. Results show that digital platform capabilities (integration and reconfiguration) enhance SMEs ecological niches through organizational agility and platform eco-embeddedness. Polynomial regression and response surface analyses reveal that balanced improvement in organizational agility and eco-embeddedness significantly strengthens niche enhancement, whereas imbalance between the two weakens it. This research clarifies how SMEs leverage digital platform capabilities to advance their ecological niches, offering theoretical and practical insights for achieving strategic balance between distinctiveness and conformity in digital platform ecosystems. Full article

Foam cement, as a building insulation material, encounters a major problem in practical application, which is the difficulty in achieving a balance between its strength and insulation performance. To achieve multi-objective optimization of foamed cement mix design, this study first determined the optimal ranges of nano-silica aerogel (NSA), foaming agent, and polypropylene (PP) fiber dosage through single-factor experiments. Then, response surface methodology (RSM) was employed to construct a quadratic polynomial regression model, systematically investigating the influence of different NSA contents, foaming agent contents, and PP fibers contents on the thermal conductivity and compressive strength of foamed cement. Finally, the optimal mix ratio was further predicted and experimentally validated. The results demonstrate that the regression model developed using RSM exhibits high accuracy and reliability. The correlation coefficients R² of the regression models established by the response surface method are 0.9756 and 0.9684, respectively, indicating good prediction accuracy. The optimized mix ratio was determined as follows: NSA content, 9.548%; foaming agent content, 0.533%; and PP fiber content, 0.1%. Under this mix, the model predicted a thermal conductivity of 0.123 W/(m·K) and a 28-day compressive strength of 1.081 MPa. Experimental verification confirmed that the errors between predicted and measured values for all performance indicators were within 5%, demonstrating the high reliability of the predictive model. This study provides support for the practical application of foam cement as a thermal insulation material in construction projects and offers guidance for optimizing its mixture composition. Full article

Global policy narratives on livestock development increasingly emphasize environmental concerns, often overlooking the social dimensions of the sector. In the case of dairy, the world’s most valuable agricultural commodity, its role in social and economic development remains poorly quantified. Our study contributes to a more balanced vision of the UN SDGs thanks to the inclusion of a socio-economic dimension. Here we present a novel empirical approach to assess the socio-economic impacts of dairy development using a new global dataset and non-parametric modelling techniques (local polynomial regressions), with yield as a proxy for sectoral performance. We find that as dairy systems intensify, the number of farm households engaged in production declines, yet household incomes rise. On-farm labour productivity also increases, accompanied by a reduction in employment but higher wages. In dairy processing, employment initially grows, peaks, and then contracts, again with rising wages. The most substantial impact is observed among consumers: an increased milk supply leads to lower prices and improved affordability, expanding the access to dairy products. Additionally, dairy development is associated with greater agricultural value added, an expanding tax base, and the increased formalization of the economy. These findings suggest that dairy development, beyond its environmental footprint, plays a significant and largely positive role in social transformation, yet is having to adapt sustainably while tackling labour force relocation, and that dairy development’s social impacts mimic the general agricultural sector. These results might be of interest for the assessment of policies regarding dairy development. Full article

To ensure confidentiality and integrity in the era of quantum computing, most post-quantum cryptographic schemes are designed to achieve either encryption or digital signature functionalities separately. Although a few signcryption schemes exist that combine these operations into a single, more efficient process, they typically rely on complex vector, matrix, or polynomial-based structures. In this work, a novel post-quantum public-key encryption and signature (PQES) scheme based entirely on scalar integer operations is presented. The proposed scheme employs a simplified structure where the ciphertext, keys, and core cryptographic operations are defined over scalar integers modulo n, significantly reducing computational and memory overhead. By avoiding high-dimensional lattices or ring-based constructions, the PQES approach enhances implementability on constrained devices while maintaining strong security properties. The design is inspired by modified learning-with-errors (LWE) assumptions, adapted to scalar settings, making it suitable for post-quantum applications. Security and performance evaluations, achieving a signcryption time of 0.0007 s and an unsigncryption time of 0.0011 s, demonstrate that the scheme achieves a practical balance between efficiency and resistance to quantum attacks. Full article

In the era of big data, the security of information encryption systems has garnered extensive attention, particularly in critical domains such as financial transactions and medical data management. While traditional Shamir’s Secret Sharing (SSS) ensures secure integer sharing through threshold cryptography, it exhibits inherent limitations when applied to floating-point domains and high-precision numerical scenarios. To address these issues, this paper proposes an innovative algorithm to optimize SSS via type-specific coding for real numbers. By categorizing real numbers into four types—rational numbers, special irrationals, common irrationals, and general irrationals—our approach achieves lossless transmission for rational numbers, special irrationals, and common irrationals, while enabling low-loss recovery for general irrationals. The scheme leverages a type-coding system to embed data category identifiers in polynomial coefficients, combined with Bernoulli-distributed random bit injection to enhance security. The experimental results validate its effectiveness in balancing precision and security across various real-number types. Full article

Monitoring fetal growth throughout pregnancy is essential for early detection of developmental abnormalities. This study developed a Taiwan-specific fetal growth reference using a web-based data collection platform and polynomial regression modeling. We analyzed ultrasound data from 980 pregnant women, encompassing 8350 prenatal scans, to model six key fetal biometric parameters: abdominal circumference, crown–rump length, estimated fetal weight, head circumference, biparietal diameter, and femur length. Quadratic regression was selected based on a balance of performance and simplicity, with R² values exceeding 0.95 for most parameters. Confidence intervals and real-time anomaly detection were implemented through the platform. The results demonstrate the potential for efficient, population-specific fetal growth monitoring in clinical settings. Full article

In this study, a response surface methodology (RSM) using a central composite design (CCD) was implemented to investigate the influence of process variables on ethyl levulinate (EL) production from the ethanolysis of waste corn cob samples, using sulphuric acid as a catalyst. The effects of four independent variables, namely, the temperature (A), the corn cob content (B), corn cob/H₂SO₄ mass ratio (C) and the reaction time (D) on the yields of EL (Y₁), diethyl ether (DEE) (Y₂) and solid residue (Y₃) were explored. Using multiple regression analysis, the experimental results were fitted to quadratic polynomial models. The predicted yields based on the fitted models were well within the experimental uncertainties. Optimum conditions for maximising the EL yield were found to be 176 °C, 14.6 wt. %, 21:1 and 6.75 h for A to D, respectively. A moderate-to-high EL yield (29.2%) from corn cob was achieved in optimised conditions, a result comparable to those obtained from model C₆ carbohydrate compounds. Side products were also produced, including diethyl ether, furfural, levulinic acid, 5-hydroxymethyl furfural, ethyl acetate, ethyl formate and water. Total unknown losses of only 5.69% were reported after material balancing. The results suggest that lignocellulosic waste such as corn cob can be used as a potential feedstock for the production of ethyl levulinate by direct acid-catalysed ethanolysis, but that the treatment of side products will need to be considered. Full article

The effects of benzoyl peroxide (BPO) and dimethylaniline (DMA) composition on the induction time and the tensile strength of thermoplastic acrylic (PMMA) resins have been investigated in this study. Eighteen resin formulations were prepared with different BPO/DMA ratios (2.0–9.5) and DMA contents (0.28–0.65 mol%), and it was observed that tensile strengths reached up to 66 MPa, and induction times (ITs) ranged from 100 to 207 min. Higher BPO/DMA ratios improved tensile strength but shortened IT, while greater DMA content accelerated curing. Polynomial regression models were successfully established, i.e., a third-order equation for the strength and a second-order equation for the IT, based on the BPO/DMA ratio and DMA content to identify the optimal formulation to balance the strength and the IT time. Two selected formulations, P-4-0.5 and P-3-0.3, were applied in vacuum-assisted resin infusion of glass fiber composites. The best-performing unidirectional (UD) laminate achieved a tensile strength of 1244 MPa. As regards ±45° biaxial (BX45) laminates, they exhibited a tensile strength of 124 MPa and a failure strain of 9.02%, which, while lower than that of epoxy, indicates competitive performance. These results demonstrate that the resin was well infused, resulting in 64% higher fiber volume fraction than typical infused glass/epoxy composites, and compositionally optimized PMMA resins can deliver epoxy-comparable strength and enhance damage tolerance in structural composite applications. Full article

Low-plasticity silts (ML) found in Metro Manila, Philippines, characterized by low strength, stiffness, and bearing capacity, often require stabilization. Traditional methods using cement are associated with significant carbon emissions, causing environmental concerns. Sustainable materials such as agar biopolymers can be an alternative to cement to improve the strength of fine-grained soils. A comparative study was conducted on ML samples treated with agar and cement at different concentrations (1%, 3%, 5%, and 7%) and subjected to varying curing periods (7, 21, 28, and 35 days) under air-dried conditions using Unconfined Compressive Strength (UCS) tests. Agar-treated samples generally exhibited higher UCS values than cement-treated samples across the tested concentrations and curing periods. Samples with 3% and 5% agar were significantly stronger than their cement-treated counterparts. The strength of agar-treated soils peaked at a 5% concentration and subsequently decreased at 7% agar, possibly due to a masking effect. SEM-EDS analysis revealed that a 5% agar concentration achieved a balanced microstructure with effective particle bonding, while higher concentrations led to diminished strength due to reduced mechanical interlocking from excessive biopolymer coverage. Subsequent statistical analysis also indicated significant improvement using agar versus cement-treated and untreated soils, especially at 5% agar. A predictive polynomial regression model demonstrated the influence of curing days and agar concentration on UCS, attaining R² = 0.94 vs. experimental values. Using agar biopolymers presents a promising and potentially more sustainable approach to soil, highlighting the potential of utilizing a locally abundant resource for geotechnical engineering applications. Full article

Thresholding image segmentation aims to divide an image into a number of regions with different feature attributes in order to facilitate the extraction of image features in the context of image detection and pattern recognition. However, existing threshold image-segmentation methods suffer from the problem of easily falling into locally optimal thresholds, resulting in poor image segmentation. In order to improve the image-segmentation performance, this study proposes an enhanced Human Evolutionary Optimization Algorithm (HEOA), known as CLNBHEOA, which incorporates Otsu’s method as an objective function to significantly improve the image-segmentation performance. In the CLNBHEOA, firstly, population diversity is enhanced using the Chebyshev–Tent chaotic mapping refraction opposites-based learning strategy. Secondly, an adaptive learning strategy is proposed which combines differential learning and adaptive factors to improve the ability of the algorithm to jump out of the locally optimum threshold. In addition, a nonlinear control factor is proposed to better balance the global exploration phase and the local exploitation phase of the algorithm. Finally, a three-point guidance strategy based on Bernstein polynomials is proposed which enhances the local exploitation ability of the algorithm and effectively improves the efficiency of optimal threshold search. Subsequently, the optimization performance of the CLNBHEOA was evaluated on the CEC2017 benchmark functions. Experiments demonstrated that the CLNBHEOA outperformed the comparison algorithms by over 90%, exhibiting higher optimization performance and search efficiency. Finally, the CLNBHEOA was applied to solve six multi-threshold image-segmentation problems. The experimental results indicated that the CLNBHEOA achieved a winning rate of over 95% in terms of fitness function value, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM), suggesting that it can be considered a promising approach for multi-threshold image segmentation. Full article

The random fluctuations in inlet flow represent a common uncertainty in aero-engine compressors, necessitating the control of its effects through blade optimization design. To account for the impact of inlet flow fluctuations on performance in blade design optimization, an efficient multi-objective adaptive robust aerodynamic design optimization (ARADO) method is proposed. The optimization method employs a novel sparse polynomial chaos expansion (PCE) and the advanced noisy Gaussian process regression (NGPR) technique is used to establish an initial stochastic surrogate model (SSM) containing statistical moments of aerodynamic performance. By introducing advanced sparse signal processing concepts, the sparce PCE significantly enhances the efficiency of acquiring each training sample for SSM. During the optimization process, the initial SSM autonomously updates based on historical optimization data, without requiring high precision across the entire design space. Compared to traditional model-based aerodynamic robust optimizations, the proposed ARADO method exhibits a faster convergence speed and achieves a superior average level of the optimal solution set. It also better balances various optimization objectives, concentrating the space distribution of optimal solutions closer to the average level. Ultimately, the ARADO is applied to the aerodynamic robust design of a high-load compressor airfoil across all operating incidences. The optimization results enhance aerodynamic performance while reducing performance diversity, thus aligning more closely with practical engineering requirements. Through data analysis of the optimal solutions, robust design guidelines for blade aerodynamic shapes are obtained, along with insights into the flow mechanisms that enhance aerodynamic robustness. Full article

The growing population of hand dysfunction patients necessitates advanced rehabilitation technologies. Current robotic solutions face limitations in motion compatibility and systematic design frameworks. This study develops a rigid–soft coupling rehabilitation robot integrating linkage mechanisms with soft components. A machine vision system captures natural grasping trajectories, analyzed through polynomial regression. Hierarchical constraint modeling and an improved artificial bee colony algorithm optimize linkage dimensions and control strategies, achieving enhanced human–robot kinematic matching. Finite element simulations using a Yeoh hyperelastic model refine soft component geometry for balance compliance and coordination. Prototype validation demonstrates high-precision trajectory tracking, grasping across 20–70 mm objects, and steady fingertip forces during training. Experimental results confirm the system’s ability to replicate physiological motion patterns and adapt to multiple rehabilitation scenarios. Full article

To meet the active avoidance requirements of intelligent vehicles, this paper proposes an efficient hierarchical control system. The upper layer generates a safe avoidance trajectory through an optimized path planning algorithm, while the lower layer precisely controls the vehicle to follow the planned path. In the upper layer design, an improved quintic polynomial method is employed to generate the baseline trajectory. By dynamically adjusting lane change duration and utilizing an improved dual-quintic algorithm, collisions with preceding vehicles are effectively avoided. Additionally, a genetic algorithm is applied to automatically optimize parameters, ensuring both driving comfort and planning efficiency. The lower layer control is based on a three-degree-of-freedom monorail vehicle model and the Magic Formula tire model, employing a model predictive control (MPC) approach to continuously correct trajectory deviations in real time, thereby ensuring stable path tracking. To validate the proposed system, a co-simulation environment integrating CarSim, PreScan, and MATLAB was established. The system was tested under various vehicle speeds and road conditions, including wet and dry surfaces. Experimental results demonstrate that the proposed system achieves a path tracking error of less than 0.002 m, effectively reducing accident risks while enhancing the smoothness of the avoidance process. This hierarchical design decomposes the complex avoidance task into planning and control, simplifying system development while balancing safety and real-time performance. The proposed method provides a practical solution for active collision avoidance in intelligent vehicles. Full article

In this paper, we introduce and study new hybrid generalizations of bivariate-balancing and Lucas-balancing polynomials. We give some properties of bivariate-balancing and Lucas-balancing hybrinomials, among other Binet-type formulas, Catalan, Cassini, d’Ocagne, Vajda, Ruggles, and Honsberger identities. Moreover, we present matrix generators and generating functions of these hybrinomials. Full article

Twenty-four male piglets were randomly assigned to four dietary treatments, with increasing Tetraselmis sp. incorporation levels (0%, 5%, 10%, and 15%). Following a 4-day adaptation period to metabolic cages, the animals were used in a 2-week digestibility trial and slaughtered for digestive tract measurements and sampling. The apparent total tract digestibility (ATTD), N balance, small intestine digesta viscosity and histomorphology, and hindgut digesta volatile fatty acid (VFA) profile were determined. Polynomial contrasts were employed to examine linear and quadratic effects of Tetraselmis sp. dietary incorporation. The ATTD of most macronutrients and N retention efficiencies decreased linearly (p < 0.05) with Tetraselmis sp. dietary inclusion. The ileum villi height increased linearly (p < 0.001) and the hindgut VFA concentration increased linearly (p < 0.05) with dietary Tetraselmis sp. inclusion. The ATTD values estimated for Tetraselmis sp. biomass using the regression method were 68.3% ± 3.86 for dry matter (DM), 66.1% ± 5.11 for N, and 61.3% ± 4.28 for gross energy. The values calculated for digestible and metabolizable energy (MJ/kg DM) and digestible crude protein (% DM) for Tetraselmis sp. were 9.0, 8.8, and 18.3, respectively. Tetraselmis sp. biomass had lower ATTD values when compared to protein sources commonly used in swine nutrition. Full article