Abstract: Organic eggplant production in the United States is challenged by flea beetles, which stunt eggplant growth and reduce yield. Across four experiments between 2019 and 2024, we compared the effects of various pest management strategies on flea beetle abundance, damage, and marketable yield in eggplant production, removing strategies that were ineffective in suppressing flea beetles from later years of the study. Low flea beetle pressure was observed in 2019 and 2020; consequently, experiments were moved to fields with a legacy of higher flea beetle pressure under organic management in 2021 and 2024. Prior to row cover removal, there were significantly more flea beetles in the control than fine-mesh row cover treatments in 2019, 2020, and 2021. Flea beetle feeding damage at flowering was significantly lower in all row cover treatments than the untreated control in 2019, 2021, and 2024 and the organic insecticide treatment in 2019 and 2021. There were no differences in marketable yield between treatments in 2019 and 2020, but marketable yields were significantly higher in fine-mesh row cover treatments than the control and the organic insecticide treatment in 2021 and 2024. These results indicate that fine-mesh row covers may be a viable pest management alternative to organic insecticides in organic eggplant production.

Abstract: Soil suction is a crucial factor affecting the hydraulic and mechanical property of unsaturated soils, playing an important role in geotechnical, geoenvironmental, and hydrological engineering applications such as slope stability, foundation design and irrigation planning. Conventionally, measuring and modeling soil suction and its associated curves like Soil Water Characteristic Curve (SWCC) and Soil Water Retention Curve (SWRC) require extensive, time-consuming tests in the laboratory. Recent progress in Machine Learning (ML) offers powerful as well as data-driven and reliable alternatives ways that can enhance the efficiency and accuracy of suction-related predictions across a wide range of soil conditions. This study aims to cover the current state of the art research on the integration of ML techniques into the prediction and analysis of soil suction behavior. Studies utilized various algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Multi-Expression Programming (MEP), K-Nearest Neighbors (KNN), and AdaBoost (AB) to predict soil suction. These models demonstrated high predictive performance (R² > 0.90 in majority cases) based on soil parameters which can be easily evaluated like soil texture, bulk density, climate parameters, and remotely sensed data. Overall, this study covers the understanding of the current research gap related to SWCC and SWRC using different data driven and ML techniques.

Abstract: Objectives: This narrative review aimed to synthesize available evidence on procedures used for bedside ultrasonography-guided verification of nasogastric tube (NGT) placement. Methods: A comprehensive search was conducted in five databases, supplemented by gray literature and clinical guidelines, without restrictions on language or publication year. Eligible studies focused on ultrasound-guided NGT insertion or verification in adults. Data were extracted and synthesized descriptively using the I-AIM framework (Indication, Acquisition, Interpretation, and decision-Making). Results: 29 studies were included, most of them observational and conducted in intensive care or emergency settings. Ultrasound was primarily indicated for enteral nutrition, with gastric decompression less frequently reported. Acquisition protocols varied, though supine positioning, convex abdominal probes, and linear cervical probes were most common. The gastric antrum and esophagus were the main landmarks, with interpretation based on direct tube visualization and dynamic fogging; color Doppler was occasionally employed. Radiography remained the reference standard in over 90% of studies, though a few initiated feeding based on ultrasound alone. Facilitators included bedside feasibility, absence of radiation, and timeliness, whereas barriers encompassed operator dependency, limited visualization in patients with obesity or gas interposition, and heterogeneity of protocols. Conclusions: Ultrasonography is a promising, safe, and innovative method for NGT verification that can reduce delays, avoid radiation exposure, and improve patient safety. With structured training, nurses can achieve accuracy comparable to physicians, supporting greater autonomy in clinical decision-making. Standardized protocols and integration into nursing education are essential to ensure reliable and widespread adoption.

Abstract: The inherent temperature-dependent sublattice preference of constituent atoms in FCC_CoCuNi multi-principal element alloys (MPEAs) is theoretically predicted by combining a two-sublattice model based on the L12_AuCu3 prototype with computational thermodynamics, which extends beyond the commonly-believed, yet unreasonable randomly mixing solid solution hypothesis. Based on the predicted sublattice occupied fractions (SOFs) and available computer resource, two MPEAs atom distributing models with different sizes are established for different applications, respectively, where the bigger size model is further employed to analyze statistically the atom distributing character quantitatively and graphically, while the smaller size model is employed to study the lattice distortion of MPEAs further using first-principles calculations density functional theory. The atom distributing configurations of some representative heat treatment temperatures, as well as the hypothetical randomly mixing structure are compared. It is revealed that FCC_CoCuNi MPEAs exhibit strong temperature-dependent ordering behavior. The SOFs-based configurations are (Ni1.0000)1a(Co0.4445Cu0.4444Ni0.1111)3c, (Co0.0653Cu0.0721Ni0.8626)1a(Co0.4227Cu0.4204Ni0.1569)3c, and (Co0.1574Cu0.1593Ni0.6833)1a(Co0.3920Cu0.3913Ni0.2167)3c at 100 K, 900 K, and 1400 K, respectively. Overall, Ni atoms always prefer to 1a sublattice and the preference tendency reduce a little bit at considerable high temperatures. The configurational entropies of FCC_CoCuNi MPEAs increase with the increase of heat treatment temperature, while they are considerably lower than that of the hypothetical ideal random solid solution. Based on the atom distributing model of MPEAs, the local atomic cluster characteristics are further investigated by statistically analyzing the coordination numbers of the constituent atom coordinated with the same type of atoms. The radial distribution function (RDF) further verified the atom aggregating behavior. For most family of crystal planes in FCC_MPEAs, except {1 1 1}, there are obviously different atom distributing characters between the even and odd layers. The atom distributing model of some representative bulk structure and surface structure are afforded valuably for reference and application both in experimental and theoretical investigation further. Thus, rich and indispensable structural genome data are afforded for the further research and development of the promise FCC_CoCuNi MPEAs intensively.

Abstract: Modelling of the food drying process is dependent on the understanding of the complex moisture transport mechanisms. This study analyzed the effect of drying temperatures ranging from 40 to 80 °C and diffusion path lengths (initial, average and final half-thicknesses) on the shrinkage, effective moisture diffusivity and activation energy of thin-layer red delicious apple samples under convective drying. Fick’s second law and Arrhenius model were utilized to determine the effective moisture diffusivity and activation energy. The mean shrinkage increased from 31.09% at 40 °C to a maximum of 42.65% at 70, then slightly decreased to 36.77% at 80 °C, indicating that shrinkage does not increase linearly with drying temperature. The initial, average and final half-thicknesses yielded effective moisture diffusivities ranging from 1.43×10–10 m2/s to 1.03×10–09 m2/s, with the average dimension providing the most realistic representation of the effective moisture diffusion path during drying. The linear regression models between the natural logarithm of the moisture ratio and drying time showed a strong fit with R2 values ranging from 0.9955 to 0.9971, confirming the reliability of Fick’s second law for describing the effective moisture diffusivity. The mean activation energy ranged from 21.56 to 26.03 kJ/mol across the different characteristic lengths, indicating the minimum energy requirement for moisture diffusion in red delicious apple samples during the convective drying.

Abstract: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease in which clinically apparent synovitis is preceded by a prolonged preclinical phase characterized by immune dysregulation and autoantibody formation. Growing evidence implicates gut dysbiosis, impaired intestinal barrier integrity, and gut-derived immune priming as upstream contributors to RA pathogenesis, occurring years before overt joint inflammation. In parallel, Ayurveda describes Amavata as a chronic systemic disorder arising from the formation of Ama, a pathogenic burden produced by impaired digestive and metabolic function (Agni), which accumulates silently, disseminates systemically, and later localizes to the joints. This conceptual review explores a functional correspondence between the Ayurvedic construct of Ama in Amavata and contemporary models of gut-derived immune activation in RA. Drawing on peer-reviewed biomedical and Ayurvedic literature, the paper examines shared temporal and systemic features of disease development, emphasizing that both frameworks locate disease initiation upstream of overt inflammation. Ama is interpreted not as inflammation or tissue injury, but as a preclinical, systemic pathogenic state—functionally analogous to chronic gut dysbiosis, barrier dysfunction, and immune priming described in RA. The proposed mapping is explicitly heuristic and non-reductive. It does not assert one-to-one equivalence between Ayurvedic and biomedical entities, nor does it seek to translate Ayurveda into molecular terms. Instead, it highlights a many-to-one contrast in explanatory logic: Ayurveda integrates multiple upstream processes into a single unifying construct, whereas biomedicine analytically separates them into discrete mechanisms. By situating both Amavata and RA within a shared preclinical, systemic disease logic, this framework reinforces the importance of early, preventive intervention targeting metabolic and gut-immune dysregulation prior to irreversible joint damage. The analysis demonstrates convergent reasoning across distinct medical traditions and supports integrative, systems-oriented perspectives on chronic inflammatory disease initiation.

Abstract: Neonatal diabetes mellitus (NDM) is a rare monogenic disorder characterized by persistent hyperglycemia requiring insulin therapy, typically diagnosed within the first six months of life and less commonly between six and 12 months. NDM may be transient, with frequent relapses during puberty, or permanent and can be associated with extra-pancreatic manifestations or be part of a syndrome. This monogenic form of diabetes is caused by pathogenic variants in genes or chromosomal loci involved in the development and function of pancreatic beta-cells and in insulin synthesis and secretion. Mutations in more than 40 genes have been identified to be implicated in the pathogenesis of NDM. Abnormalities of the 6q24 locus have been recognized as the most common cause of transient NDM, whereas mutations in genes encoding ATP-sensitive potassium (KATP) channels, particularly KCNJ11, are more commonly identified in permanent NDΜ cases. In NDM cases, the clinical course, the presence of extra-pancreatic manifestations, and the optimal treatment depend on the causative gene. Therefore, genetic diagnosis is imperative, as it can facilitate the individualization of management strategies and long-term follow-up, as well as genetic counselling. However, hyperglycemia in the neonatal population, particularly in preterm and/or critically ill neonates, may be observed outside the NDM range due to immaturity and transient beta-cell dysregulation, insulin resistance, epigenetic modifications, or drug administration. The aim of this narrative review is to provide an overview of the genetic basis of NDM and the mechanisms underlying transient hyperglycemic states in neonates.

Abstract: The aim of this study was to analyze the effect of acute caffeine intake on maximal aerobic speed (MAS) assessed using the 30–15 Intermittent Fitness Test (IFT) in university soccer players. An experimental, randomized, double-blind, crossover design was employed, involving 26 male university team players (n=26). Each participant completed the test under two conditions: caffeine supplementation (220 mg) and placebo, separated by a 72-hour washout period. The final running speed achieved (VIFT) was used as an estimator of MAS. Statistical analysis included descriptive statistics, normality testing, and paired Student’s t-test, with a significance level set at p < 0.05. The results revealed a significant improvement in VIFT under the caffeine condition (19.94 ± 1.67 km/h) compared with placebo (18.72 ± 1.50 km/h), with a mean difference of 1.22 km/h (6.5%) and a large effect size (dz = 1.24; p < 0.001). It is concluded that acute caffeine intake produces a significant ergogenic effect on intermittent aerobic performance in university soccer players, representing a potentially useful strategy to optimize performance in competitive contexts.

Abstract: Global population growth underscores the increasing demands for food production, and therefore, for higher crop yields, especially in soybean, as the cheapest source of protein for animal and human nutrition. This scenario frequently leads to overuse of traditional chemical insecticides to maximize yields, thereby triggering adverse side effects. However, both consumers and governments around the world have been demanding reduction of chemical insecticides in agriculture. To address this challenge, pest control must be guided by proper adoption of economic thresholds (ETs), which indicate the most appropriate time to initiate insecticide applications. Despite the well-documented science behind ETs, not only its adoption but also its reliability has been questioned by farmers in a search for higher production, highlighting the importance of reviewing this topic. Thus, this review discusses, based on the available literature, the role of ETs to optimize insecticide application in soybean production, highlining the importance of their adoption to mitigate the overuse of chemicals. In Brazil, the major soybean producer in the world, not only did growers who adopted ETs to control pests in soybean reduce the amount of pesticides required, but also production costs associated with pest control, while achieving greater yields than conventional producers. The use of ETs improves soybean sustainability and farmers profit while benefitting the agroecosystem.

Abstract:

Interest in microalga-based technologies has emerged in recent years as a response to environmental challenges and the global food crisis for providing alternative and sustainable food products. This study used temperature variations between 18 and 32 °C, and nitrogen-to-phosphorus (N:P) ratios between 1.9 and 42.6, to model and optimize growth and composition of Chlorella vulgaris, a nutritionally interesting species. Lower temperatures appear ideal for this strain. An increase in average biomass productivity was observed with decreasing temperature, leading to a maximum of 122.27 mg_dw L^-1 d^-1 at 18 °C on the 4^th day of cultivation. The maximum productivities for total proteins, fatty acids, carbohydrates, and pigments were, respectively, 26.9 mg L^-1 d^-1, 26.4 mg L^-1 d^-1, 16.0 mg L^-1 d^-1, and 2.41 mg L^-1 d^-1, all referring to 18 °C. The fatty acid, carotenoid, and amino acid profiles were also ascertained; several indicators suggested that cultivation of this microalga under the aforementioned optimal conditions holds potential for the food industry. The high proportion of polyunsaturated fatty acids, including two essential fatty acids; the high production of lutein; and the presence of several essential amino acids are among the favorable indicators. Overall, the information generated by this study is helpful to support future pilot studies aimed at the commercial production of microalga-derived products.

Abstract: The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems.

Abstract: Background: Tendons adapt to mechanical loading by increasing cross-sectional area (CSA), stiffness, and matrix organization, with structural remodeling critical for both rehabilitation and performance. Collagen supplementation has been proposed to enhance this process by supplying key amino acids for collagen synthesis. However, inconsistent results across trials have limited its clinical and athletic application. Methods: A comprehensive search of PubMed, EMBASE, CINAHL, and Web of Science was conducted in May 2025. The risk of bias was assessed using the PEDro scale; studies scoring ≥6/10 were classified as good-to-excellent quality. Data extraction included collagen type, dose, training modality, intervention duration, and outcome measures. Results: Of 887 unique citations, 8 RCTs (n = 257; ages 18–52; 246 M:11 F) met inclusion criteria. All studies included resistance or plyometric training for 3–15 weeks. Three of four studies reported significantly greater increases in tendon CSA in collagen groups versus placebo. Four studies investigated tendon stiffness and Young’s modulus; the two using higher doses (15–30 g/day) demonstrated significant between-group improvements favoring collagen, while two lower-dose studies (~5 g) showed only within-group effects. Muscle strength improved with training in all trials, but no additive effects of collagen were observed. One study reported improvements in eccentric rate of force development and deceleration impulse with collagen, though gross explosive metrics (e.g., jump height) remained unaffected. Conclusion: Collagen supplementation (15–30g) with vitamin C (≥ 50mg) may enhance tendon remodeling when combined with high-intensity resistance training (≥70% 1RM). The current literature indicates a GRADE A recommendation (strong evidence) for increases in tendon cross-sectional area and tendon stiffness, GRADE A (strong evidence) against an effect on muscle strength, and GRADE C (conflicting evidence) for muscle cross-sectional area and physical performance. Limitations of the literature include small sample sizes, heterogeneous protocols and short intervention durations. Future trials should standardize protocols, include diverse populations, and examine long-term adaptations to optimize clinical and performance outcomes.

Abstract: Proton exchange membrane fuel cells (PEMFCs) are promising energy conversion devices owing to high efficiency and zero local emissions. Accurate PEMFC performance assessment and control require well-posed models, whose predictive accuracy is largely determined by the correct calibration of key parameters. Metaheuristic algorithms (MHAs) have therefore been widely applied to PEMFC stack parameter estimation, but their rapid proliferation calls for a more systematic and fine-grained synthesis. This review refines the taxonomy of PEMFC mathematical modeling approaches and summarizes Zero-Dimensional PEMFC modeling methods, key parameters, and representative improvement directions aimed at reducing identification difficulty while retaining physical meaning. Newly developed MHAs and enhanced variants of existing methods are then surveyed, and over 40 distinctive optimization approaches are selected for systematic comparison. Key fuel-cell parameters, evaluation criteria, and representative commercial PEMFC types are summarized. In addition, 26 representative algorithms and their variants are compiled and benchmarked across the five most widely used commercial PEMFC models to enable cross-model comparison.

Abstract: This study provides a rigorous examination of the early period of Diriyah's political history, spanning from Imam Mohammad ibn Saud taking power in 1727 CE through 1744 CE. The investigation starts with a key issue: even though Diriyah didn't expand much in size during this time, it still built a strong political and social system that helped it become a stable center in the chaotic environment of eighteenth-century Najd. Employing the analytical-historical method within a structuralist framework, the study explores the dialectical relationships among the regional political context, prevailing economic conditions, dominant patterns of political behavior, and the nature of political discourse during the period under examination. The analysis finds that from 1727 to 1744 CE, the emirate went through a calm but sensible founding period, focusing on careful strategies and building internal stability instead of rushing to expand or promote itself as exceptional. The findings affirm that the initial years under Imam Mohammad ibn Saud witnessed gradual institutional development and the progressive refinement of governance mechanisms, with emphasis placed on leveraging local resources and strengthening internal alliances while maintaining the tribal and social equilibria that prevailed across the Najd region. These policies contributed to achieving relative stability and laid the groundwork for the transformative developments that Diriyah would subsequently undergo. The significance of this phase resides in its establishment of a distinct political and administrative frame of reference for Diriyah, which shaped the administration of governance affairs and the formation of the emirate's identity. During this period, Imam Muhammad ibn Saud demonstrated an acute awareness of the necessity of avoiding internecine conflicts and steering clear of reckless expansionist ventures. As a result, looking closely at this time allows us to better understand how the First Saudi State began, highlighting the importance of wise leadership in creating the right political and social environment for the new political entity to form. In summation, the analysis of the first seventeen years of Imam Mohammad ibn Saud's rule constitutes an essential entry point for understanding the trajectory of political transformation in Najd. It shows how power was built and how the emirate's institutions were developed, confirming that choosing stability and taking things slowly were intentional strategies that helped Diriyah face challenges and maintain its history.

Abstract: Limb salvage surgery is the preferred treatment for pediatric bone tumors in the lower extremity when free margins can be achieved, particularly when adjacent joints can be preserved. Nevertheless, intercalary bone defects remain challenging. Common bio-logical reconstruction techniques include massive allograft, free vascularized fibular graft (FVFG), and the Capanna Technique (allograft combined with FVFG). This review summarizes current evidence on the proportion of complications associated with these techniques, with emphasis on non-union in relation to defect length. Finally, we propose directions for future research to refine patient selection for biological reconstruction. A systematic review was conducted using PubMed, Scopus, Embase, and Cochrane Library. Eligible studies reported outcomes of allograft, FVFG, or the Capanna technique for intercalary lower limb reconstruction in children or adolescents and reported on defect length. Fourteen articles met the inclusion criteria: two on allograft, two on FVFG, and ten on the Capanna technique. In total, 181 patients were included (allograft: 17, FVFG: 16, Capanna: 148 patients). Mean defect lengths were 15.5 cm (allograft), 11.4 cm (FVFG), and 15.0 cm (Capanna). Average union times were longest with allograft (15.5 months) and shortest with FVFG and Capanna (11.4 and 11.6 months). The Capanna technique demonstrated the lowest non-union proportion (16.5%), compared with FVFG (25%); non-union data of the allograft were inconsistently reported. Fractures and infections were least common in the Capanna group. The reconstruction strategy should be tailored to defect size, anatomical location, and patient-specific factors. The Capanna technique appears most favorable for larger defects in the lower extremities, but its potential role in smaller defects warrants further investigation.

Abstract: The lack of integrity at the implant-abutment junction (IAJ) contributes to problems such as micromovements and microbial colonization. This study aimed to (1) design a protocol for assessing microleakage at the IAJ using chromophore analysis that hasn’t been involved in other analysis, (2) compare gas and dye leakage using titanium (Ti) and cobalt chrome (CoCr) abutments, and (3) assess the effect of gold (Au) gilding on sealing. Forty abutments were divided into five groups: milled Ti (MTi); cast CoCr (CCoCr); milled CoCr (MCoCr); cast CoCr with Au gilding (CCoCrG); and milled CoCr with Au gilding (MCoCrG). Samples were connected to a pressurised gas and dye reservoir. Chromophore analysis using crystal violet was performed via UV-Vis spec-trometer to calculate leakage concentration. Scanning electron microscopy (SEM) analysis assessed surface morphology which revealed an intimate contact with the MTi and MCoCr but irregularities at the CCoCr abutments. Results showed gas leakage in CCoCr and MCoCr groups, while no true dye leakage occurred in MTi, MCoCr and MCoCrG assemblies. CCoCr exhibited the poorest seal; however, Au gilding improved the seal in these samples. Chromophore analysis using crystal violet provided an ac-curate quantitative assessment. Milled abutments demonstrated significantly less mi-croleakage than cast (non-gilded) versions, Au gilding effectively reduced leakage.

Abstract: Camellia oleifera Abel, recognized as a woody oil-producing tree species, possesses considerable economic significance. To improve the breeding efficiency of C. oleifera, it is crucial to elucidate the genetic foundation underlying the mechanisms regulating fruit traits. In this study, a total of 6,252,197 high-quality single nucleotide polymorphisms (SNPs) were identified from 109 germplasm accessions. Through genetic structure analysis, these accessions were categorized into two distinct populations. The average fixation index (Fst) was found to be 0.0153, indicating weak population differentiation. The genome-wide association analysis (GWAS) identified 157 significant loci. From these loci, 110 candidate genes were selected, which were associated with disease resistance, reproduction, development, and RNA biosynthesis. Twenty-three genes were involved in metabolic pathways, including genetic information processing protein families, metabolic protein families, terpenoids and polyketides. The identification of gene loci closely related to fruit traits not only provides genetic data for studying the molecular mechanisms of fruit traits but also offers new research avenues for molecular breeding of C. oleifera.

Abstract: Accurate long-term electrical load forecasting is required for stable smart grid operation, yet remains difficult due to multi-scale periodic patterns and non-stationary temporal shifts across different prediction horizons. This work presents MoE-Transformer, a reinforcement learning-driven dual-domain framework that integrates frequency-domain processing with sparse expert networks for adaptive forecasting. An Extended Discrete Fourier Transform (Extended DFT) is introduced to address spectral misalignment by aligning the input spectrum with the frequency grid of the full prediction window. The model employs parallel Mixture-of-Experts (MoE) modules in the time and frequency domains (T-MoE and F-MoE), where domain-specific experts capture complementary temporal and spectral structures. Expert selection is formulated as a dual Markov Decision Process and optimized through a reinforcement learning routing mechanism that balances prediction accuracy, routing stability, and expert utilization diversity. Experiments on five benchmark datasets, including ETTh1, Electricity, and Traffic, across four forecasting horizons show that MoE-Transformer consistently outperforms state-of-the-art baselines, reducing Mean Squared Error (MSE) by 50.9--56.9%. Sparse expert activation lowers memory usage by 40% and reduces inference latency by 60%, supporting deployment in real-time forecasting settings. Ablation results further quantify the contributions of Extended DFT, dual-domain modeling, and reinforcement-driven routing, yielding performance gains of 5.8%, 4.6%, and up to 47.2%, respectively.

Abstract: High-performance difference patterns (DPs) are critical for compact and inte-grated microwave array systems, particularly in monopulse tracking and beam-scanning applications. However, the design of monopulse phased arrays with steep slopes, high directivity, low sidelobes, and symmetric main lobes remains challenging due to con-straints imposed by the array aperture and radome structure. In this paper, a novel design method is proposed to maximize the DP directivities for monopulse linear and planar phased arrays composed of microstrip patch antennas. The DP synthesis problem is first formulated as a nonconvex optimization model for directivity maximization. By fixing the reference phase of the DP slope and applying a first-order Taylor expansion of the quad-ratic function, the original problem is decomposed into a sequence of convex subproblems that can be solved efficiently. The proposed method fully exploits the flexibility of the phased array feed network, enabling directivity enhancement without altering the geo-metric configuration of the monopulse array. Finally, two numerical examples employing a radome-enclosed linear phased array and a uniform planar phased array are presented to demonstrate effectiveness of the proposed method in achieving the monopluse array DP synthesis with high directivity and symmetric main-lobes.

Abstract: Recent whole-brain cell atlases have uncovered a consistent cytoarchitectural feature: the greatest diversity of discrete neuronal cell types resides not in the regions with the most neurons (e.g. the cortex and cerebellum) but rather in deep subcortical structures like the hypothalamus and brain stem. We propose that this discrepancy reflects a fundamental algorithmic division in the vertebrate brain between a “learning subsystem” and a “steering subsystem.” The learning subsystem (cortex, striatum, cerebellum) scales via the replication of repetitive modules to maximize computational capacity, analogous to scaling up parameters in machine learning models. By contrast, the steering subsystem (hypothalamus, pallidum, brainstem) scales via diversification of bespoke cell types to encode innate drives and reflexes, functioning as a high-dimensional biological “reward function.” This framework explains the divergence in how evolution has influenced these subsystems, and offers a unified lens for understanding brain architecture, the etiology of brain disease, and may also inform model design for artificial intelligence.