Search Results (16,707)

The framing of the Hebrew Bible in the Mesopotamian–Babylonian landscape is evident in two of its central themes. First, Abraham, the forefather of the Hebrews, is presented as a native of Ur in south Mesopotamia, whence he left for Harran and then reached the Promised Land. Second is the exile of the Judahite elites to Babylonia, and the later return of some of them to Jerusalem to build their Second Temple. As the Bible was written, rewritten, and compiled by Babylonian exiles, primarily authored after the Fall of Jerusalem, its compilation by Judean exiles reveals a certain legitimization for existence in Exile, namely, the first revelation of YHWH outside of the Promised Land. This article examines the impact of the Babylonian surroundings on the Exiles’ approach to the representation of YHWH. It surveys the role of the Levantine goddess ’Ašerah, while proposing that alongside ’Ašerah, there may have been a male god named ’Ašer who, in pre-exilic times, was probably part of the Yahwistic religion and who was subsequently eliminated or degraded by the Judean exilic compilers of the Bible as it has reached us. Full article

In crab pond environments, obstacles such as long aerobic pipelines, aerators, and ground cages are usually sparsely distributed. Automatic feeding boats can navigate while avoiding obstacles and execute feeding tasks along planned paths, thus improving feeding quality and operational efficiency. In large-scale crab pond farming, a single feeding operation often fails to achieve the complete coverage of the bait casting task due to the limited boat load. Therefore, this study proposes a multi-voyage path planning scheme for feeding boats. Firstly, a complete coverage path planning algorithm is proposed based on an improved genetic algorithm to achieve the complete coverage of the bait casting task. Secondly, to address the issue of an insufficient bait loading capacity in complete coverage operations, which requires the feeding boat to return to the loading wharf several times to replenish bait, a multi-voyage path planning algorithm is proposed. The return point of the feeding operation is predicted by the algorithm. Subsequently, the improved Q-Learning algorithm (I-QLA) is proposed to plan the optimal multi-voyage return paths by increasing the exploration of the diagonal direction, refining the reward mechanism and dynamically adjusting the exploration rate. The simulation results show that compared with the traditional genetic algorithm, the repetition rate, path length, and the number of 90° turns of the complete coverage path planned by the improved genetic algorithm are reduced by 59.62%, 1.27%, and 28%, respectively. Compared with the traditional Q-Learning algorithm, average path length, average number of turns, average training time, and average number of iterations planned by the I-QLA are reduced by 20.84%, 74.19%, 48.27%, and 45.08%, respectively. The crab pond experimental results show that compared with the Q-Learning algorithm, the path length, turning times, and energy consumption of the I-QLA algorithm are reduced by 29.7%, 77.8%, and 39.6%, respectively. This multi-voyage method enables efficient, low-energy, and precise feeding for crab farming. Full article

To address the efficiency bottlenecks of traditional full-wave simulation methods in the high-performance design and rapid optimization of microwave filters, this study proposes an efficient design method based on an improved surrogate model and a hybrid optimization algorithm. A one-dimensional dense convolutional autoencoder (1D-DenseCAE) model is constructed to enhance the model’s ability to extract key features and improve convergence speed. Additionally, the Ivy–Hiking optimization algorithm (IHOA) is introduced, combining the advantages of global search and local fine-tuning. Experiments demonstrate that this method achieves approximately a 25% improvement in convergence speed over the standard one-dimensional convolutional autoencoder (1D-CAE) in cavity filter design, and enables efficient optimization in complex structures such as interdigital filters and seventh-order cross-coupled cavity filters, meeting design requirements of return loss below −20 dB and in-band ripple under 0.5 dB. This method provides an effective technical pathway for the intelligent design of microwave filters. Full article

The current standard for sea ice engineering in the Bohai Sea implements a 1/4° grid method, which cannot satisfy the safety of oil and gas activities in the southern Bohai Sea, and therefore more detailed information on ice conditions and a more refined ice zone division are necessary. In the present study, up to 1/12° resolution sea ice characteristic data (period, thickness, concentration, and strength) were obtained based on the NEMO-LIM2 ice–ocean coupling model. On this basis, the design sea ice strength parameters were derived with different return periods from 1 to 100 years. Among the total of 53 grids, the mean ice periods in the southern Bohai Sea from 1951 to 2022 were 2–35 days, the mean ice concentration values were 8.3–64.6%, and the mean ice thicknesses were 2–15 cm. The design uniaxial compressive strengths and shear strengths at almost all grids exceeded 2.00 MPa and 1.00 MPa for return periods over 20 years, respectively. The design flexural strengths for the 100-year return period ranged from 463 to 594 kPa. For the 100-year return period scenario, all grids exhibited design tensile strengths exceeding 200 kPa. Across the southern Bohai Sea, the most severe ice conditions occur in nearshore zones, and the ice conditions display a distinct spatial gradient with Bohai Bay > offshore deep-water areas > Laizhou Bay. The mean ice thickness, concentration, design flexural and tensile strengths derived in this study were lower compared to the ice parameters suggested in the current standard, and design uniaxial compressive and shear strengths derived here were comparable to those suggested in the current standard. The refined grid used here captures more detailed spatial variations in the design strength values of sea ice engineering parameters in the southern Bohai Sea, providing more accurate data support for the anti-ice design of marine structures. Full article

Optimization of stock selection strategies has been a topic of interest in finance. Although deep learning models have demonstrated superior performance over traditional methods, there are still shortcomings. For example, previous studies do not provide enough explanation for feature selection and usually use features such as closing price directly to make predictions; for example, most studies predict the trend of multiple stock indices or only individual stocks, which is difficult to be directly applied to actual stock selection. In this paper, a multivariate hybrid neural network model CNN-LSTM-GNN (CLGNN) for stock prediction is proposed, in which the CNN and the LSTM modules analyze the local and the whole, respectively, while the multivariate time series GNN module is added to explore the potential relationships between the data through the graph learning, graph convolutional, and temporal convolutional layers. CLGNN analyzes the potential relationships between the data based on the returns to classify stocks, and then develops a stock selection strategy, and directly outputs the returns and stock codes. In this paper, a hybrid filter approach based on entropy and Pearson correlation is proposed for feature selection, and experiments are conducted on all stocks in the CSI All Share Index (CSI); the results show that among multiple models, the returns obtained when the features of daily return, turnover rate, relative strength index, volume, and forward adjusted closing price are used as inputs are all the highest, and the return obtained by CLGNN is even higher than that of the other models (e.g., TCN, Transformer, etc.). Full article

Rapid urban growth poses distinct energy and environmental challenges in various regions of the world. This study evaluated the technical and economic feasibility of a grid-connected photovoltaic system in Santo Domingo Tehuantepec, Oaxaca, Mexico, using Homer Pro software, version 3.14.2, to simulate realistic scenarios. The analysis incorporated local climate data, residential load profiles, and updated economic parameters for 2024. System optimization resulted in an installed capacity of 173 kW of solar panels and 113 kW of inverters, yielding a levelized cost of energy (LCOE) of MXN 1.43/kWh, a return on investment (ROI) of 5.3%, an internal rate of return (IRR) of 8%, and a simple payback period of 10 years. The projected annual energy output was 281,175 kWh, covering 36% of the local energy demand. These results highlight the potential for integrating renewable energy into urban contexts, offering significant economic and environmental benefits. The integration of public policy with urban planning can enhance energy resilience and sustainability in intermediate cities. This study also supports the application of tools such as Homer Pro in designing energy solutions tailored to local conditions and contributes to a fair and decentralized energy transition. Full article

The effects of precision feeding regimes on broiler performance, organ weight, nutrient utilization, carcass yield, and calculated wholesale returns were investigated over 42 days. The treatments consisted of a standard four-phase commercial diet as the control, a precision nutrition blend diet based on a daily nutrient requirement, a precision nutrition adjusted diet based on weekly bird weight, and a standard commercial blend diet. Each dietary treatment was replicated 10 times with 11 birds per replicate. A total of 440 male Ross 308 (Aviagen, Goulburn, NSW, Australia) broiler chickens were offered experimental diets from days 11 to 42 post-hatch. Dietary treatments did not affect the feed intake and weight gain over the entire study. However, a reduced weight corrected FCR (higher feed efficiency) was observed in birds fed a precision nutrition adjusted blend diet compared to those fed the control diet from days 11 to 42 (p < 0.001). There were no significant differences in feed costs between treatments. Birds offered the precision nutrition adjusted diet improved AME (p = 0.002) measured from days 25 to 27 compared to the blended standard diet. Over the majority of time points, the precision nutrition adjusted diet significantly reduced the coefficient of variation in bird weight as compared to the control diet (p < 0.026). Full article

With the increasing demand for privacy preservation and strategy sharing in global financial markets, traditional centralized modeling approaches have become inadequate for multi-institutional collaborative tasks, particularly under the realistic challenges of multi-source heterogeneity and non-independent and identically distributed (non-IID) data. To address these limitations, a heterogeneity-aware Federated Quantitative Learning framework, Federated Quantitative Learning, is proposed to enable efficient cross-market financial strategy modeling while preserving data privacy. This framework integrates a Path Quality-Aware Aggregation Mechanism, a Gradient Clipping and Compression Module, and a Heterogeneity-Adaptive Optimizer, collectively enhancing model robustness and generalization. Empirical studies conducted on multiple real-world financial datasets, including those from the United States, European Union, and Asia-Pacific markets, demonstrate that Federated Quantitative Learning outperforms existing mainstream methods in key performance indicators such as annualized return, Sharpe ratio, maximum drawdown, and volatility. Under the full model configuration, Federated Quantitative Learning achieves an annualized return of 12.72%, a Sharpe ratio of 1.12, a maximum drawdown limited to 10.3%, and a reduced volatility of 9.7%, showing significant improvements over methods such as Federated Averaging, Federated Proximal Optimization, and Model-Contrastive Federated Learning. Moreover, module ablation studies and attention mechanism comparisons further validate the effectiveness of each core component in enhancing model performance. This study introduces a novel paradigm for secure strategy sharing and high-quality modeling in multi-institutional quantitative systems, offering practical feasibility and broad applicability. Full article

Portfolios incorporating environmental, social, and governance (ESG) criteria present distinct, unobserved risks, the empirical quantification of which has proven challenging. This difficulty stems from sustainable investment strategies being guided by both financial objectives and investors’ non-pecuniary preferences, which fundamentally alter a portfolio’s risk and return characteristics. To address this, we propose a novel methodology that identifies latent, ESG-specific risk factors by applying sparse principal component analysis (SPCA) to two-dimensional portfolio returns. Unlike approaches that rely on subjective judgment, our method extracts risk dimensions inherent to the return data itself. Our analysis reveals that the resulting firm-specific SPCA beta plays a dual role: it explains performance differentials across ESG-rated portfolios and exhibits a statistically significant, negative association with expected individual stock returns. The robust predictive performance of this SPCA-based risk factor confirms its practical utility for analyzing and managing diversification in ESG investing. Full article

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency. Full article

In this paper, a low-spurious and high-threshold limiter is proposed for C-band applications, where power dividers and phase shifters are used to improve the threshold and reduce the spurious response, respectively. Through the principles of multipath synthesis and phase cancellation, the enhancement of fundamental frequency signals and the suppression of harmonic spurs are achieved. The simulated and measured results demonstrate that the presented design can realize a harmonic suppression ratio (HSR) of more than 38.0 dB in the frequency band of 2.6–3.1 GHz. The threshold of the limiter is improved by 3.0 dB, the maximum insertion loss is less than 1.0 dB, and the return loss is more than 13.0 dB. Full article

Purpose: In 2020, a lockdown due to COVID-19 was ordered by the German government, resulting in population-wide restrictions. In this retrospective study, we question the extent to which health policy restrictions have influenced medical diagnoses. Methods: The incidence rates of relevant pregnancy complications during all trimesters of pregnancy were evaluated for a 6-month pre-pandemic period (April–September 2019), in comparison to the same period during the lockdown in 2020. Mothers and newborns who presented at the University Hospital St. Hedwig, Regensburg, Germany, were included in the study. The incidence rates of preeclampsia and suspected FGR (fetal growth retardation), as relevant obstetric diseases, were further compared with those in a post-pandemic period (April–September 2023). Results: A total of 5137 newborns were included, with 1709 born during the 6-month pre-pandemic period, 1806 during the 6-month lockdown period and 1687 during the 6-month post-pandemic period. During the pandemic period, significantly fewer patients were hospitalized due to hyperemesis gravidarum (1.8% vs. 0.9%, p = 0.04). No differences were observed concerning the incidence of miscarriages before and after 14 weeks of gestation (WG), preterm deliveries (<37 WG), gestational age at preterm birth and birth weight. Likewise, within the group of preterm-born babies, no difference was observed in preeclampsia among the two periods. However, in the pandemic period, the frequency of preterm-born babies with suspected FGR was significantly lower than in the pre-pandemic period (1.5% and 0.6%, p = 0.01). Regarding this point, we analyzed data of all newborns in a comparative post-pandemic period in 2023 (n = 1687). This group presented a significantly increased FGR incidence compared to that during the pandemic, therefore returning to the pre-pandemic level (1.5% and 1.4%, p = 0.145). Conclusions: During the COVID-19 pandemic, there were significantly fewer deliveries with suspected FGR. After all specific restrictions on elective and outpatient services were lifted, the incidence of suspected FGR returned to its initial level, suggesting that the diagnosis—as a solely machine-dependent and not symptom-based diagnosis—was partly exaggerated in both the pre-and post-pandemic periods. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article

The increasing frequency and intensity of extreme weather events pose significant challenges to mountain communities, particularly in terms of flash flood risks. This study presents a framework for stress testing and evaluating flood resilience in mountain communities through the integration of high-resolution InfoWorks ICM two-dimensional hydrodynamic modeling and systematic resilience assessment. The framework makes three key innovations: (1) multi-scale temporal stress scenarios combining short-duration extreme events (1–2 h) with long-duration persistent events (24 h) and historical extremes; (2) integrated infrastructure–drainage stress analysis that explicitly models roads’ dual role as critical infrastructure and emergency drainage channels; and (3) dynamic resilience quantification under multiple stressors across 15 systematically designed stress conditions. Using Western Beijing as a case study, the model is validated, achieving Nash–Sutcliffe efficiency values exceeding 0.9, demonstrating its robust capability in simulating complex mountainous terrain flood processes. Through systematic analysis of fifteen rainfall scenarios designed based on Chicago rainfall patterns and historical events (including the July 2023 Haihe River basin flood), encompassing various intensities (30–200 mm/h), durations (1 h, 2 h, 24 h), and return periods (10, 50, 100 years), the key findings include the following: (1) A rainfall intensity of 60 mm/h represents a crucial threshold for system performance, beyond which significant impacts on community infrastructure emerge, with built-up areas experiencing inundation depths of 0.27–0.4 m that exceed safe passage limits. (2) Road networks become primary drainage channels during intense precipitation, with velocities exceeding 5 m/s in village roads and exceeding 5 m/s in country road sections, creating significant hazard potential. (3) Four major risk spots were identified with distinct waterlogging patterns, characterized by maximum depths ranging from 0.8 to 2.0 m and recovery periods varying from 2 to 12 hours depending on the topographic confluence effects and drainage efficiency. (4) The system demonstrates strong recovery capability, achieving >90% recovery within 3–6 hours for short-duration events, while showing vulnerability to extreme scenarios, with performance declining to 0.75–0.80, highlighting the coupling effects between water depth and flow velocity in steep terrain. This research provides quantitative insights for flood risk management and for enhancing community resilience in mountainous regions, offering valuable guidance for infrastructure improvement, emergency response optimization, and sustainable community development. This study primarily focuses on physical resilience aspects, with socioeconomic and institutional dimensions representing important directions for future research. Full article

Background/Objectives: Women with Polycystic Ovary Syndrome (PCOS) often report difficulties adhering to dietary interventions due to a combination of physiological and psychological barriers. Therefore, this study explores the feasibility of a Mediterranean diet (MedDiet) intervention as an effective and acceptable dietary approach for managing PCOS. Methods: Women with PCOS and a BMI ≥ 25 kg/m², aged 18–45 years were randomized to an ad libitum MedDiet or Healthy Eating (HE) diet (control). The 12-week intervention incorporated fortnightly, personalized dietary consultations and tailored resources. Primary outcomes were measures of feasibility, including recruitment metrics, data collection methods, and intervention adherence. Acceptability was examined using semi-structured interviews and surveys for those randomized to the MedDiet. Results: Study promotion resulted in n = 380 interested individuals; a total of n = 26 were randomized to either a MedDiet (n = 12) or HE (n = 14) group. Data collection was mostly appropriate as demonstrated by the collection of 100% of anthropometric and biochemical data; however, only 69% of the 4-day food records were returned. Participants reported the intervention was acceptable, and adherence was enhanced through individualized counselling and the provision of practical resources. At week 12, adherence was significantly greater in the MedDiet group compared to HE (8.1 ± 2.4 vs. 4.6 ± 1.0; p = 0.002). Adherence significantly improved from baseline to week 12 in both groups (MedDiet: 3.67 ± 1.32; 8.11 ± 2.37; p ≤ 0.001; HE: 3.57 ± 1.27; 4.57 ± 0.98; p = 0.02). Conclusions: We showed that a 12-week pilot MedDiet intervention is feasible and acceptable for women with PCOS and a BMI ≥ 25 kg/m². Future investigation warrants a larger, adequately powered study which addresses challenges to recruitment, attrition and collection of dietary intake data. Full article