Search Results (5,065)

This paper presents the design of a highly reconfigurable interdigital bandpass filter (BPF) developed through a three-stage design approach. In the first stage, the influence of four low-loss dielectric substrates on the filter response is systematically analyzed to identify the optimal configuration. The selected substrate demonstrates excellent performance, achieving an input return loss of −38 dB, an insertion loss of −0.9 dB at 4.30 GHz, and a wide passband corresponding to a bandwidth (BW) of 2.20 GHz. In the second stage, two variable capacitors were incorporated into the baseline geometry, enabling manual tuning of the center frequency ($f_0$) from 5.10 to 6.34 GHz, with $\left(S_{11}\right)$ better than −25 dB and ${(S}_{12})$ close to −0.60 dB. In the final stage, the capacitors were replaced by SMV1413 varactor diodes, transforming the design into a fully voltage-controlled tunable filter. This configuration provides continuous frequency agility from 4.70 to 5 GHz without modifying the physical structure, while achieving $\left(S_{11}\right)$ levels down to −40 dB and insertion loss as low as −0.7 dB. The proposed architecture offers a compact, low-loss, and electrically reconfigurable solution, making it a promising solution for next-generation RF front-ends, adaptive wireless systems, and cognitive radio applications. Two independent Electromagnetic solvers (EM) were employed to validate the filter’s performance: an EM based on the Finite Integration Technique and the Advanced Design System 2026 (ADS) solver using the Method of Moments (MoM). The close agreement between the results produced by both platforms confirms the accuracy and robustness of the proposed reconfigurable bandpass filter structure. Full article

This study examines how tourism companies in emerging markets respond to economic and geopolitical risks using a comprehensive panel data approach. Data from 23 tourism companies listed on the Istanbul Stock Exchange (BIST) between the first quarter of 2012 and the first quarter of 2025 were analyzed to assess their financial performance amid macroeconomic shocks and regional crises. The analysis addressed ‘multiple crisis’ scenarios such as the Russia–Turkey plane crisis, the COVID-19 pandemic, and a significant earthquake in 2023. Panel regression results reveal that financial leverage (LEV) and GDP growth have a significant and statistically meaningful impact on profitability and stock performance compared to sectoral volume indicators (number of tourists and spending). The findings emphasize that tourism companies are highly sensitive not only to sectoral demand but also to overall economic confidence and macroeconomic stability. This study highlights that adopting flexible capital structures is essential for corporate resilience in uncertain times. Full article

This study examines the performance implications of CEO duality from a configurational governance perspective, with particular attention given to its relevance within an ESG-oriented framework. While prior research on CEO duality has produced inconsistent findings, much of the literature relies on variable-centered approaches that overlook the systemic and context-dependent nature of governance mechanisms. Drawing on agency theory, stewardship theory, and resource dependence theory, we analyze 59 publicly listed South Korean firms between 2018 and 2022 using fuzzy-set qualitative comparative analysis (fsQCA). Five governance-related conditions—CEO duality, ownership concentration, CEO tenure, institutional ownership, and environmental dynamism—are calibrated into fuzzy sets to identify causal configurations associated with high firm performance, defined as membership in the top 30% of return on assets (ROA). The results reveal six equifinal pathways to high performance, two of which exhibit particularly strong consistency and coverage. These dominant configurations show that CEO duality contributes positively to performance when embedded in either strong internal governance alignment or robust external monitoring under dynamic conditions. By demonstrating that the effectiveness of CEO duality is contingent upon its governance configuration, this study challenges one-size-fits-all prescriptions and contributes to the ESG literature by highlighting the conditional role of leadership structure in sustainable value creation. Full article

Neutral atomic hydrogen (H I) regulates galaxy growth and quenching, but direct 21 cm measurements remain observationally expensive and affected by selection biases. We develop Bayesian neural networks (BNNs)—a type of neural model that returns both a prediction and an associated uncertainty—to infer the H I mass, ${log}_{10}\left(M_{\mathrm{HI}}\right)$, from widely available optical properties (e.g., stellar mass, apparent magnitudes, and diagnostic colors) and simple structural parameters. For continuity with the photometric gas fraction (PGF) literature, we also report the gas-to-stellar-mass ratio, ${log}_{10}(G/S)$, where explicitly noted. Our dataset is a reproducible cross-match of SDSS DR12, the MPA–JHU value-added catalogs, and the 100% ALFALFA release, resulting in 31,501 galaxies after quality controls. To ensure fair evaluation, we adopt fixed train/validation/test partitions and an additional sky-holdout region to probe domain shift, i.e., how well the model extrapolates to sky regions that were not used for training. We also audit features to avoid information leakage and benchmark the BNNs against deterministic models, including a feed-forward neural network baseline and gradient-boosted trees (GBTs, a standard tree-based ensemble method in machine learning). Performance is assessed using mean absolute error (MAE), root-mean-square error (RMSE), and probabilistic diagnostics such as the negative log-likelihood (NLL, a loss that rewards models that assign high probability to the observed H I masses), reliability diagrams (plots comparing predicted probabilities to observed frequencies), and empirical 68%/95% coverage. The Bayesian models achieve point accuracy comparable to the deterministic baselines while additionally providing calibrated prediction intervals that adapt to stellar mass, surface density, and color. This enables galaxy-by-galaxy uncertainty estimation and prioritization for 21 cm follow-up that explicitly accounts for predicted uncertainties (“risk-aware” target selection). Overall, the results demonstrate that uncertainty-aware machine-learning methods offer a scalable and reproducible route to inferring galactic H I content from widely available optical data. Full article

Background: With the growing emphasis on full-process disease management, efficient post-discharge care has become increasingly critical. Although prior studies have examined follow-up services, resource allocation, and facility location in primary healthcare, model-based optimization of collaborative frameworks between comprehensive hospitals and primary care systems remains limited. Methods: We study a cooperative community hospital selection problem involving contractual cooperation, patient engagement, and follow-up resource allocation. A multi-objective mixed-integer programming model is developed to maximize patient accessibility and minimize total hospital costs, and an NSGA-II-based heuristic is proposed for solution generation. A real-world case study using data from a comprehensive hospital in Chengdu, China, is conducted. Results: The proposed approach produces a Pareto set that quantifies the accessibility–cost trade-off and reveals a knee region with diminishing returns: moderate expansion of cooperating providers substantially improves accessibility, whereas further expansion yields limited additional gains while increasing hospital cost. Sensitivity analyses indicate that cost-related parameters and follow-up frequencies are key drivers of the trade-off. Conclusions: The proposed optimization framework serves as an implementable decision aid for designing hospital–primary care collaboration for post-discharge follow-up: it supports partner selection and capacity planning and indicates levers to improve performance. Full article

Ceramic Matrix Composites (CMCs) have emerged as a structural material alternative to nickel superalloys for high-pressure turbines (HPT) components operating at high temperature, like shrouds. Despite the outstanding thermal stability of the CMCs, limited cooling is still necessary due to the extreme thermal operating conditions necessary to maximize engine performance and minimize fuel consumption. The design of CMC components, indeed, must consider a maximum service temperature that should not be exceeded to avoid damage and accelerated oxidation. The cooling, on the other hand, may induce the formation of thermal gradients and thermal stresses. In this work, different design options for the cooling system are investigated to minimize the thermal stresses of an HPT shroud-like geometry subjected to maximum temperature constraints on the material. Cooling is obtained via colder air jet streams (air taken from the compressor), whose impact position (the surface where the cold air impacts the component) has a different effect on the temperature field and on the induced stress field. Besides stress evaluation with different cooling systems, an ONERA damage model is investigated at a key location to potentially take into account stress components acting simultaneously and potential stiffness degradation of the CMC. Finally, the design evaluation of potential discrete crack propagation is discussed. A standard cohesive elements approach has been compared with a brittle element death approach. The results showed that the cohesive element approach resulted in shorter crack propagation, underestimating the actual crack behavior due to the embedded stiffness degradation method, while the element death returned encouraging results as a quicker, less complex, but still accurate design evaluation. Full article

Digital platforms offer cost-effective, accessible tools for small and medium-sized enterprises (SMEs). However, the mechanisms through which resource-constrained SMEs should participate in these platforms remain underexplored. By distinguishing participation breadth from participation depth and introducing value co-creation and external resource abundance as the mediating mechanism and boundary condition, respectively, this study offers an integrated account of how digital platform participation (DPP) relates to SME performance. Drawing on the theory of resource bricolage, this study conducted an online survey of 321 owners or managers, using small and medium-sized travel agencies in China as the empirical setting. This study develops an integrated mediation and moderation model and tests the hypotheses using confirmatory factor analysis, hierarchical regression, and bootstrapping analysis. The results show that both breadth and depth are positively associated with performance, with depth showing a significantly stronger association than breadth. Participation that combines transaction-oriented platforms with information-oriented platforms is associated with the largest performance gains. Value co-creation mediates the effect of depth on performance, whereas the mediation via breadth is not significant. External resource abundance weakens the performance returns to DPP. These findings inform resource-constrained SMEs’ platform strategies, particularly how to allocate scarce attention and resources between deepening and broadening participation and how to configure platform portfolios in relation to performance outcomes. Full article

In finance, accurately modelling the tail behaviour of extreme log returns is critical for understanding and mitigating risks across diverse asset classes. This research employs extreme value theory to identify the most suitable probability distributions (i.e., generalized extreme value (GEV), generalized logistic (GLO), Gumbel (GUM), generalized Pareto (GP), and reverse Gumbel (REV)) and estimation methods (least squares (LS), weighted least squares (WLS), maximum likelihood (ML), L-moments (LM), and relative least squares (RLS)) for modelling the tail behaviour of log returns from two financial datasets, each representing a distinct asset class with high (Ethereum, a digital asset class) and low (South African government bonds, a fixed-income asset class) volatility levels. The performance of each model and estimation method (25 different possibilities) is evaluated through goodness-of-fit and risk measures as the study aims to determine the optimal approach for each volatility level. Results from ranking different models and estimation methods show that across both asset classes, ML consistently emerges as the top-performing estimation method across all distributions. LM serves as a solid secondary option, while LS occasionally excels under GLO’s weekly minima for low volatility, whereas RLS occasionally surpasses ML in GLO’s monthly minima for high volatility. Finally, WLS uniquely outperforms under GEV and GLO’s monthly minima under low volatility. Full article

This paper investigates the relationship between sustainability performance, measured through environmental, social, and governance (ESG) indicators, and the stock performance of Saudi-listed firms from 2015 to 2022. The aim is to assess whether firms with higher ESG scores exhibit stronger stock returns within the Saudi market context. The analysis relies on panel data and employs pooled ordinary least squares, fixed-effects, and random-effects models to ensure a robust estimation. Across all specifications, the empirical results indicate a consistent positive association between ESG performance and stock returns, suggesting that sustainability-related practices are increasingly reflected in market valuations. The findings contribute to the expanding literature on sustainable finance in emerging markets by illustrating the developments within Saudi Arabia, a market undergoing rapid transformation under Vision 2030. This paper enhances understanding of the financial relevance of ESG performance in the region and offers timely insights for investors and market participants monitoring the integration of sustainability within Saudi Arabia’s capital market. Full article

This study presents the design, fabrication, and performance evaluation of a solar dryer capsule cabinet equipped with a parabola reflector, developed to enhance drying efficiency through the reflection of sunlight onto both the upper and lower surfaces of the product. Conventional solar drying exposes only the upper surface, resulting in uneven heating and the need for manual turning. The proposed system integrates a parabolic reflector and IoT-based monitoring sensors (BH1750 light sensor and DHT22 temperature-humidity sensor) to optimize heat distribution and record real-time environmental parameters. Dry experiments were conducted using Citrus hystrix DC. (Makrut lime) peels under natural sunlight from 9:00 a.m. to 5:00 p.m. The moisture loss achieved with the proposed dryer (P-DSD) was 45.66%, compared with 6.79% for direct solar drying (DSD). The drying rate increased from 3.05 g h⁻¹ (DSD) to 20.50 g h⁻¹ (P-DSD), while the specific energy consumption (SEC) decreased from 3519.75 kWh kg⁻¹ to 523.67 kWh kg⁻¹, representing an 85.13% energy reduction. Economic analysis showed a system cost of $1384 and a return on investment of 30.0%. These results demonstrate that the proposed solar dryer capsule cabinet with a parabola reflector offers a low-cost, eco-friendly, and high-efficiency solution for drying agricultural and herbal products, significantly shortening the drying time and improving product quality. Full article

Accredited laboratories participating in Proficiency Testing (PT) and Interlaboratory Comparison (ILC) typically submit measurement results (and associated uncertainties) to an organizer for performance evaluation using statistics such as the z-score and the En value. This requirement can undermine confidentiality when the disclosed plaintext values reveal commercially sensitive methods or client-related information. This paper proposes a secure-by-design PT/ILC workflow based on fully homomorphic encryption (FHE), enabling the required scoring computations to be executed directly on ciphertexts. Using the CKKS scheme (Microsoft SEAL), the organizer distributes encrypted assigned values and a public/evaluation key set; each participant locally encrypts pre-processed measurement data, evaluates encrypted z-score and En value, and returns only encrypted performance metrics. The organizer decrypts the metrics without receiving the ciphertexts of participants’ raw measurement values. We quantify feasibility via execution time, run-to-run variability across fresh key generations (coefficient of variation), and relative calculation error versus plaintext scoring. On commodity hardware, end-to-end score computation takes 1 to 8 s, the coefficient of variation can be reduced below 1e⁻¹⁰, and the relative error remains below 1e⁻⁶, indicating practical deployability and numerical stability for PT/ILC decision-making. Given that PT/ILC reporting cycles are typically on the order of days to weeks, a per-participant computation time of seconds is operationally negligible, while the observed coefficient of variation and relative error indicate that the CKKS approximation and key-dependent variability are far below typical decision thresholds used for pass/fail classification. Full article

Background: Age-related vestibular decline frequently accompanies presbycusis, and older adults undergoing cochlear implantation (CI) may be particularly vulnerable to postoperative dizziness due to a reduced compensatory capacity and a higher burden of comorbidities. Although CI is an effective treatment for severe-to-profound sensorineural hearing loss in the elderly, its impact on vestibular function remains a critical concern. This study aimed to compare pre and postoperative vestibular performance in older patients (≥65 years) versus younger adults undergoing CI in order to identify the risk factors for postoperative vestibular deterioration and critical issues that characterize this category and carry out personalized preoperative counseling. Methods: In this monocentric observational study, adults undergoing CI were divided into two groups: older patients (OPS, ≥65 years) and younger patients (YPS, <65 years). Vestibular function was assessed preoperatively and one month postoperatively through a Dizziness Handicap Inventory (DHI), history of recurrent falls, clinical examination, video head impulse test (VHIT), bithermal caloric testing, and computerized dynamic posturography (Sensory Organization Test, SOT). Risk factors for postoperative vestibular worsening were analyzed using ANOVA test and chi-square statistics, with significance set at p < 0.05. Results: A total of 63 patients were included, with 18 surgeries involving OPS and 45 involving YPS. Preoperatively, OPS showed significantly higher rates of vestibular abnormalities on caloric testing (55.5% vs. 17.7% bilateral hyporeflexia, p < 0.05) and a higher prevalence of recurrent falls (33.3% vs. 4.4%, p < 0.05). Early postoperative dizziness (DHI¹) increased significantly in both groups, but age ≥ 65 was a risk factor for ≥10% worsening (OR 2.2, p < 0.05). At one month, YPS returned to baseline DHI values, whereas OPS showed persistent dizziness with significantly higher DHI² scores (29.2 vs. 12.9, p < 0.05). Vestibular worsening was identified in 33.3% of VHIT assessments and 44.4% of caloric tests in OPS, with caloric testing proving more sensitive than VHIT. Implantation on the better-functioning vestibular side and the presence of ≥3 comorbidities increased the likelihood of persistent postoperative dizziness. Conclusions: Older age is a significant risk factor for persistent dizziness and vestibular impairment one month after CI. Given the reduced compensatory capacity typical of older adults, vestibular assessment should play a central role in preoperative decision-making, particularly for side selection. Bithermal caloric stimulation is recommended as the most sensitive tool for detecting clinically relevant vestibular changes. Preoperative counseling for older CI candidates should include a detailed discussion of vestibular risks and the possible need for postoperative rehabilitation. Full article

Grounded in the dynamic capabilities framework, this study examines how entrepreneurship and technological innovation jointly shape business performance and how sustainable management conditions these effects across its economic, social, and environmental dimensions. Using survey data from 300 firms across multiple industries, we find that entrepreneurship significantly enhances both non-financial and financial performance, and that technological innovation serves as a key mediating mechanism through which entrepreneurship translates into performance outcomes. The results reveal differentiated moderating effects of sustainable management. While the economic dimension of sustainable management shows a limited moderating influence, the social and environmental dimensions significantly amplify the returns to entrepreneurship and technological innovation. By disentangling sustainable management into distinct dimensions, this study moves beyond prior research and demonstrates that sustainability functions as a contextual capability that asymmetrically conditions the returns to entrepreneurship and innovation. The findings offer actionable insights for managers and policymakers seeking to align entrepreneurial initiatives and innovation strategies with social legitimacy and environmental stewardship to achieve sustained value creation. Full article

This paper investigates whether intelligent workplace technologies improve firm-level productivity and, if so, under what conditions, with particular attention to their implications for the economic and social sustainability of firms. This investigation occurs in a context where firms increasingly combine automation, artificial intelligence (AI), and work-from-home (WFH) practices to sustain performance under structural shocks such as the COVID-19 pandemic. Despite evidence that firms adopt these technologies jointly and reorganize work accordingly, existing research typically examines them in isolation. We develop a micro-founded, task-based production model in which firms allocate tasks between on-site and remote labor and automated capital in an optimal manner. This model allows both automation technologies and remote work collaboration tools to affect productivity and coordination costs that are central to long-term organizational sustainability. Using firm-level survey data from nearly 4000 large firms across industries and countries (2018–2021), we show that working from home (WFH) exhibits diminishing productivity returns when scaled in isolation, reflecting rising coordination frictions. In contrast, firms that combine WFH with automation and digital collaboration tools experience significantly higher labor productivity growth. These integrated technology systems support sustainable productivity by enabling capital deepening, resilient task reallocation, and more efficient use of labor resources over time. Overall, the findings suggest that productivity gains—and by extension sustainable firm performance—stem from integrated workplace technology systems rather than isolated investments, highlighting the importance of coherent technology strategies for organizing work in the post-pandemic economy. Full article

In Colombia, two main palm varieties, Elaeis guineensis and Elaeis oleifera, are cultivated for the production of crude palm oil (CPO). During the CPO extraction process, several residues are generated, including empty fruit bunches (EFB), nut fiber, palm kernel cake, and Palm Oil Mill Effluent (POME), among others. These residues are commonly used for biochar and compost production to improve soil quality, for biogas generation, and for energy production through biomass combustion. Because the rachis is rich in lignocellulosic material and exhibits physicochemical properties suitable for thermochemical processes, it is proposed as a feedstock for hydrogen synthesis through gasification. In this study, a techno-economic analysis and an FP²O resilience assessment were conducted for a hydrogen production process based on the utilization of palm rachis generated in María la Baja, northern Colombia. The economic evaluation results indicate that the capital investment required for plant installation is USD 10,111,255.23. The economic indicators show favorable performance with a Return on Investment (ROI) of 58.83%, a Net Present Value (NPV) of USD 25.01 million, a B/C ratio of 3.29, and a Discounted Payback Period (DPBP) of 4.54 years. Regarding techno-economic resilience, critical values for processing capacity, selling price, and feedstock cost were identified through parameter variation. The findings suggest that the process has opportunities for improvement, since small changes in these variables could significantly reduce its resilience. Finally, an On-Stream efficiency of 39.65% at the break-even point was obtained, indicating that the process can operate at less than 50% of its maximum capacity while still generating significant profits. Full article