Production Distribution Research Articles

Anion-induced optimization of non-aqueous zinc-air battery performance: Insights into OTF− selection

Rechargeable non-alkaline zinc-air batteries (ZABs) address the CO₂ incompatibility and instability issues of conventional alkaline batteries. However, the impact of solid discharge products like zinc oxide on discharge capacity at the air cathode remains unexplored. In this study, we investigate how anions influence ion pair solvation structures using Zinc trifluoromethanesulfonate (Zn(OTF)₂)-N, N-dimethylformamide (DMF) and Zinc acetate (Zn(Ac)₂)-DMF electrolytes, and how these structures affect the distribution of discharge product. Our finding shows that anions with lower donor numbers (DNs), like the weaker ligand OTF- (20.5), enhance the zinc ion activity and promote rapid Zn2⁺ reactions with oxygen during discharge, leading to a more than tenfold increase in discharge capacity. In contrast, acetate anions with higher DNs (43.3) form stable complexes, inhibiting zinc ion activity and causing the formation of film-like discharge products, which reduces ZAB performance. The Zn(OTF)₂-DMF electrolyte also enables a battery lifespan of over 200 h, more than fivefold compared to the acetate-based system.

Boosting Acetylcholine Signaling by Cannabidiol in a Murine Model of Alzheimer's Disease.

Alzheimer's disease (AD) is a challenging medical issue that requires efficacious treatment options to improve long-term quality of life. Cannabidiol (CBD) is a cannabis-derived phytocannabinoid with potential health benefits, including reports from our laboratory and others showing a therapeutic role in the pre-clinical treatment of AD; however, the mechanisms whereby CBD affects AD progression remain undefined. Innate lymphoid cells (ILCs) are recently discovered immune cells that initiate and orchestrate inflammatory responses. ILC2, a sub-class of ILCs, is proposed to have a role in cognitive function via unknown mechanisms. In this present study, we explored whether CBD ameliorates AD symptoms via the enhancement of acetylcholine (ACh), a cholinergic neurotransmitter involved in cognition that may regulate ILC2. 5xFAD mice were chronically treated by inhalation of a formulation of broad-spectrum CBD for seven months. ACh production, ILC2s profile, brain histopathology, and long-term behavior were assessed. Together, our studies showed that long-term inhalation of CBD improved cognitive function and reduced senile plaques in a murine AD model, effects that were associated with enhanced ACh production and altered ILC2s distribution within the CNS. These findings indicate that inhaled CBD could offer a cost-effective, non-invasive, and effective treatment for managing AD. The beneficial effects of CBD inhalation may be linked to increased ACh production and an altered distribution of ILC2s, highlighting the need for further research in this area.

Enhancing energy release and controlled combustion of B-based MICs by the synergistic effect between polyvinyl pyrrolidone and nitrocellulose

To enhance energy release of boron-based metastable intermixed composites (MICs), a series of B-based MICs were constructed successfully by a facile one-step sol-freeze-drying (SFD) method, where copper oxide (CuO) was employed as oxidant. The structure, morphology, chemical state and specific surface area of B-based MICs were investigated by several characterization technologies, and their reactivity and combustion performance were further studied by various experimental tests. The results reveal that B and CuO nanoparticles were dispersed evenly on a nest network braided by the molecular interaction between polyvinyl pyrrolidone (PVP) and nitrocellulose (NC), and the constant volume combustion heat of B-based MICs even reaches 8956.6 ± 76.6 J·g−1 when PVP (1.25 wt%) and NC (5.0 wt%) were added into B/CuO. The heat value is about 1.7 times as big as pristine B/CuO (5345.5 ± 36.1 J·g−1) and approaches to that of raw B NPs (10335.5 ± 507.8 J·g−1). Moreover, the condensed phase residues of three composites were confirmed to be Cu and Cu2O, while the smallest particle size distribution of product proves that the combustion of B/CuO/NC(NC: 5.0 wt%, PVP: 1.25 wt%) is most complete. So the unique nest network can eliminate particle aggregation, shorten the transfer distance of mass and heat and improve the “solid − solid” thermite reaction. This work will provide a new strategy for promoting the application of boron

Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

Problem definition: We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory, and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and postlaunch distributional forecasts. Methodology/results: Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A prelaunch forecast is an ensemble with equal weights on the candidate models’ forecasts, whereas a postlaunch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel approach tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For postlaunch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. Managerial implications: In a data-driven operations environment, our model can produce accurate forecasts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0359 .

Collaboration of Artificial Intelligence and Journalists in Online Media from the Perspective of Human-Machine Communication

This study aims to analyze the collaboration between Artificial Intelligence (AI) and journalists in online media using the Human-Machine Communication (HMC) approach. The research method used is a literature study. Data is processed using Miles and Huberman data analysis techniques. The results of this study indicate that AI is used in three primary stages of journalism: news gathering, news production, and news distribution. At the news gathering stage, AI assists journalists in collecting news materials from various sources and analyzing audience interest in specific topics. AI is used in news script creation, editing, and proofreading at the news production stage. AI chatbots and NLP programs help with automatic news writing and factual verification. Meanwhile, at the news distribution stage, AI is used for content personalization, news recommendations, and SEO optimization in online media. From the HMC perspective, collaboration between AI and journalists can be conceptualized as a unidirectional and two-way process. Collaboration between AI and journalists in a social context also occurs at the micro, meso, and macro levels, where interactions between humans and machines affect the social situation, the immediate reality of individuals, and the structure of society as a whole.

Adapting a Novel for Television: A Systemic Critique

Using a multi-interdisciplinary theory (MIT) approach, this article argues for a process of altering a source literary text into a television production. This article illuminates that while the vision of adapting a source narrative intends to advance the vision of the adapter, the process to accomplish this is multifaceted and requires multi-collaborative efforts. This article further underlines some of the central features that are often altered in the adaptation of the novel and how they amplify and advance the storyline. What is worth noting, though, is that the television adaptation ought not to interlope with the principal storyline. In other words, the principal storyline of a novel or original narrative encapsulates its core themes, messages, and emotional resonance. Having said that, deviating too far from this core may result in a loss of the purposive impact and meaning. During the course of the article, it becomes clear that the examination of adapting a novel for television in Africa, with specific reference to isiXhosa, has not been afforded an adequate platform for theoretical and practical scholarly discourses. Specifically, the television industry in Africa has its dynamic forces, challenges, and opportunities. On account of this reality, exploring the practical dimensions of adapting novels for African television networks, considering factors such as production budgets, audience preferences, and distribution challenges, may be a valuable avenue for research. In view of this fact, this article should be regarded as one that presents contemporary insights into the arena of academic inquiry. This article presents concluding remarks with the anticipation that other scholars will continue the discourse and begin to address identified conundrums.

Analysis of Factors Affecting the Spatial Association Network of Food Security Level in China

Food security serves as the cornerstone of national security, intricately linked to social stability and economic progress. Currently, with the swift evolutions in social economy, logistics and transport, information dissemination, and technological advancements, there has been a marked increase in the cross-regional flow of food production, distribution, and consumption. Consequently, the spatial interdependence of food security across different regions has grown increasingly salient. This paper investigates the spatial interrelationship of food security levels in China through a network analysis framework, examining its determinants and network dynamics. The findings offer valuable insights for decision-makers aiming to optimize agricultural resource allocation and enhance national food security levels. This research establishes a comprehensive evaluation index system for assessing food security levels in China across four dimensions: production security, distribution security, supply security, and consumption security. Employing data from 30 provinces between 2008 and 2022, the entropy method quantifies food security levels, while a modified gravity model underpins the construction of a spatial association network. This framework subsequently examines the network’s structural characteristics and the factors influencing its formation. The results reveal that: (1) China’s food security levels demonstrate a consistent upward trajectory over the study period, though significant regional disparities persist. The central region surpasses the national average, while the eastern and western regions lag. Recently, the western region has shown accelerated improvements in food security, followed by the central area, with the eastern region maintaining steady growth. (2) A structurally robust spatial correlation network of food security has emerged, characterized by variations in the number of network relationships, fluctuations in network density, and a decline in network efficiency while still exhibiting pronounced small-world characteristics. (3) The network displays a clear core-periphery structure, with Shanghai, Beijing, and Jiangsu positioned centrally, playing pivotal intermediary roles, whereas remote provinces such as Gansu, Ningxia, and Liaoning occupy the periphery. (4) The four major regions demonstrate sparse internal connectivity yet robust inter-regional ties, resulting in pronounced spillover effects. (5) Various factors, including geographic distance, provincial proximity, disparities in economic development levels, variations in marketization, differences in agricultural human capital, and disparities in land productivity, significantly impact the establishment of spatial correlations in food security. The affirmative influences of geographic distance and neighboring relations, along with the beneficial shifts in economic development disparities, suggest that the flow of technology and resources plays a crucial role in reinforcing spatial connections.

Popularity and parity assessment for more inclusive and balanced aquaculture development

Aquaculture, a rapidly expanding food production system, holds promise for improving global food security and resilience. However, imbalanced growth has led to a highly uneven distribution of aquaculture production among countries, a concern that has not been comprehensively examined. This paper fills this knowledge gap by developing an innovative indicator system to assess this issue based on aquaculture development in ~ 200 countries over five decades. The system utilizes the “effective number of countries” (ENC) as a basic measure of production distribution and extends it into two novel indicators, popularity and parity, to gauge inclusiveness and balance. The assessment from 1970 to 2020 reveals that aquaculture has become a global enterprise, operating in nearly 90% of countries. Nevertheless, there is still substantial potential for growth in aquaculture popularity across most of 43 species groups examined here. Regarding concerns over persistently imbalanced aquaculture growth, our assessment reveals that aquaculture parity increased during 1970–2020 in the majority of 85 country groups examined here, including 18 of 27 regions and subregions. Global parity is also on the rise in the new millennium (2000–2020). However, the global aquaculture parity remains considerably lower than those of capture fisheries and terrestrial meat production. This suggests that imbalanced global aquaculture development cannot be solely attributed to countries’ comparative advantages. This extraordinary imbalance could compromise global food security and food system resilience, but it also signifies untapped growth potential. Mainstreaming aquaculture popularity and parity as policy indicators can foster more inclusive and balanced development and unlock this potential. The proposed indicator system can be applied across diverse sectors and scales, contributing to a broader and refined understanding of the dynamics within the global food system.

Social inequalities in adult mortality across Europe (18th-21st centuries): A critical analysis of theories and evidence

This study examines the historical evolution of socioeconomic disparities in adult mortality, with a focus on European societies. Despite the widespread improvements in population health, social inequalities in mortality are a pervasive phenomenon nowadays. This paper employs a critical analysis of both theoretical and empirical literature to investigate major international studies and their findings on longevity differences associated with socioeconomic status from the nineteenth century to the early twenty-first century. Findings reveal that adult mortality trajectories have differed notably across social classes and regions, with some areas exhibiting disparities before the demographic transition and others showing inequalities emerging later. Understanding these long-term health inequality trends sheds light on the changing influence of medical advances and their interplay with economic growth, educational disparities, environmental factors, state roles, and production distribution, which have shaped mortality disparities through different development stages. These factors elucidate the international heterogeneity of results until the mid-twentieth century and offer explanatory insights into observed north-south patterns in Europe. Our findings contribute to a deeper understanding of how advancements in public health, economic development, and social policies have shaped health outcomes over centuries. The implications of this research inform ongoing debates and health policy, emphasizing a nuanced interpretation of historical data to craft effective strategies that address health inequalities today.

Reconstructing a Fine Resolution Landscape of Annual Gross Primary Product (1895–2013) with Tree-Ring Indices

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century.

Beyond linear narratives: Complex copper ore exploitation strategies in Early Bronze Age China revealed by geochemical characterization of smelting remains

Despite decades of efforts to reconstruct the bronze production and metal distribution systems of the Shang period in Bronze Age China, there remains limited understanding of the ore choices and smelting practices of the Shang people. This study addresses this research gap by conducting a detailed investigation of Shang period copper ores and smelting remains uncovered at the Tongling site in the Middle Yangtze River valley. The results of lead isotope, copper isotope, trace element, and rare earth element (REE) pattern help to classify the slags from this site into two groups, associated with smelting sulphidic (Group A) and oxidic (Group B) copper ores, respectively. This finding not only serves as the first physical evidence of the use of sulphidic copper ores in Early Bronze Age China but also provides pivotal details of the copper resource exploitation strategies of the Shang people. It challenges the traditional narrative that the Shang people moved to this area solely for the high-grade supergene deposits. The parallel use of both supergene and hypogene ores at the same site complicates the notion of a linear, technological evolution from simpler to more advanced copper sources. Despite the presence of hypogene ores, the study reveals that the Shang people maintained labour-intensive smelting practices, including crushing slag to recover trapped metallic prills, to meet the demands of large-scale bronze casting. This nuanced approach to copper resource exploitation reflects a complex, context-dependent strategy rather than a technological revolution. By highlighting these intricate metallurgical choices, this research contributes to a broader rethinking of early technological development, underscoring the diversity and adaptability of ancient craft industries and their role in shaping Shang society.

Overexpression of CrSMT gene enhances salt stress tolerance by improving cotton peroxidation resistance

Salt stress is an important abiotic stress factor affecting crop production and plant geographical distribution. Salt stress negatively impacts molecular, biochemical, and physiological processes in cotton, resulting in inhibition of plant growth and development and, in severe cases, plant death. In this experiment, the CrSMT gene isolated from the unicellular eukaryote Chlamydomonas reinhardtii was overexpressed in cotton R15. Two transgenic lines, L17 and L25, were obtained. Treated with 200 mM NaCl experiments showed that the CrSMT-transgenic cotton had enhanced tolerance to salt stress. RNA-seq analysis revealed that CrSMT overexpression in cotton resulted in the synthesis of a large number of secondary metabolites responsive to salt stress. Correlation analysis between the wild type and the transgenic lines revealed that CrSMT overexpression did not affect the growth, agronomic traits, and fiber quality of cotton. The function of CrSMT holds potential to improve plant tolerance of abiotic stress factors.

EMPIRICAL ESTIMATION OF THE POTENTIAL ECONOMIC IMPACT OF TOURISM PROMOTION POLICY IN ALGERIA

Objective: The objective of this paper is to empirically assess the economic impact of tourism promotion policies in Algeria. By utilizing an economic model based on the 2021 Social Accounting Matrix (SAM) multipliers, the study aims to quantify the direct, indirect, and induced effects of increased tourism demand on various sectors of the national economy. Specifically, the research seeks to evaluate how tourism expenditure influences production, sectoral output, and income distribution, while also identifying which sectors and economic agents benefit the most from tourism growth. Theoretical Framework: Study is based on the Social Accounting Matrix (SAM) approach, which examines the interconnections between sectors of the economy and tracks the flow of income and expenditure. It incorporates input-output theory to measure the direct, indirect, and induced effects of increased tourism demand. The concept of economic multipliers is central, highlighting how tourism spending influences various sectors. Additionally, the framework assesses the distribution of benefits across different economic agents, focusing on how tourism impacts production, sectoral output, and income distribution in Algeria. Method: This study uses 2021 Social Accounting Matrix (SAM) data to analyze tourism demand in Algeria. It develops an economic model, simulates scenarios, and assesses the economic gains and distribution of benefits, providing a comprehensive evaluation. Results and Discussion: The SAM-based economic model shows that Algeria's tourism demand boosts production and output across sectors, particularly agriculture, hospitality, agri-food, construction, transportation, and energy. However, the benefits are not evenly distributed, potentially leading to income inequality. Policy interventions should focus on equitable income distribution and supporting key industries. Research Implications: Tourism can drive Algeria's economic diversification, reducing dependence on traditional sectors. Inclusive tourism policies are needed to ensure equitable benefits for wage earners and lower-income groups. Future research should focus on sector investment and employment effects. Originality/Value: This study analyzes the economic impacts of tourism promotion policies in Algeria, focusing on the 2021 Social Accounting Matrix. It provides insights into the distribution of benefits and emphasizes the need for inclusive policies. The findings provide actionable insights for policy development.

Analysis of Water Production Factors of Gas Wells in Sulige Gas Field

Abstract The Sulige gas field, characterized by its low porosity and poor permeability, faces challenges in managing the complex gas-water distribution, which has become a growing concern as the field develops. This complexity, coupled with the varied and intricate nature of water production, has impacted the field’s efficiency. To tackle this issue, a study was carried out on water-producing gas wells within the field, examining the production mechanisms, main factors, and distribution patterns. The study found that in the field’s central region, the gas-water ratio in water-producing wells is relatively stable, with some wells yielding over 0.8m3 of water per day. The water sources include formation water, pore water, and water from the wellbore edges and bottom, as well as industrial water usage. Understanding these factors helps mitigate the negative impact of water production on field development and provides guidance for managing similar gas fields effectively.

Assessing the economy-wide impacts of digital transformation in the Korean economy: A social accounting matrix multiplier approach

This study assesses the economic impact of digital transformation (DT) in Korea by utilizing a Social Accounting Matrix to explore interconnections across various datasets. Through a social accounting matrix multiplier analysis, this study empirically identifies the production inducement, income inducement, and income distribution effects driven by DT-related infrastructure investments. We analyze how DT influences production, income, and income distribution through infrastructure investments, focusing on its effects on the labor market and household income. The findings indicate that DT accelerates economic growth, favoring digital capital-intensive industries and high-skilled labor, potentially increasing employment polarization and exacerbating income inequality. This study highlights the need for dual approach as to enhance national competitiveness and inclusiveness in an economy driven by DT technologies. This approach involves two key components that work synergistically to maximize the benefits of DT while addressing its potential challenges, suggesting that industrial and innovation policies should be reconstructed to align with the demands of a DT-driven economy.

A multi-step mathematical model-based predictive strategy for software release timing during testing stage

Mathematically precise modeling is important to be established to accurately examine the quantitative relationship between software testing and software reliability. Software testing process is complex since it is concerned with various factors such as test case execution, defect debugging, tester expertise, test case selection, and so forth. For this reason, it is required to be meticulous in formulating the software testing process in a manner which is mathematically concise. The software release life cycle or sequential release timeline, referring to the process related to the development, testing and distribution of a software product comprises several critical stages, and the length of this particular life cycle reveals variations depending on different factors like the type of product, the intended use of it, industry security, general standards and compliance. One consideration software engineers have is related to the release date of the software so that future commitments about the software’s release time can be formulated beforehand. In view of these aspects, a multi-step strategy for predicting software release dates is proposed in the current study along with the following stages: firstly, the proposed technique selects the utmost reliability growth model that very well fits the observed test data halfway through the testing period, and then employs it to forecast the probable date of release. This technique entails approximating the unknown parameters of suitable Software Reliability Growth Models (SRGMs). Finally, the chosen SRGM is used to forecast the release date of the software under test by fitting it to available fault data. The proposed method is straightforward and applied to test on a total of ten actual datasets collected from the literature. The results of the proposed technique reveal that in the majority of the situations, nearly exact approximation of date of release can be made halfway through the testing period. Moreover, the proposed method’s performance is also compared to that of a number of previous strategies present in the literature. The outcomes obtained by our study demonstrate that the proposed strategy may be used to forecast the release date of software in practical situations.

Integrating machine learning with dynamic multi-objective optimization for real-time decision-making

Real-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper integrates machine learning with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve DMOPs and make real-time decisions. Learning-based methods have gained popularity for predicting solutions in new environments and capturing changing patterns in optimal solutions. However, existing approaches often struggle with training difficulty and reduced prediction accuracy due to irrelevant or redundant variables. Therefore, we introduce a new interdependent prediction (IDP) technique to identify correlations between variables and prediction targets and select significant variables for a predictive model. In this way, a better initial population is predicted. The IDP strategy is integrated within the dynamic NSGA-II, introducing a new algorithm called IDP-DNSGA-II. This integration facilitates rapid convergence, finding optimal or near-optimal solutions. The proposed method is evaluated against standard benchmarks, demonstrating superior performance in convergence speed and solution diversity with the changes in the problem environment. The IDP-DNSGA-II is validated through real-world optimization challenges in sustainable automobile production distribution in order-to-delivery systems to enhance environmental sustainability and operational efficiency. This study identifies the minimum frequency of change required in real-world problems to adequately track the optimal decision in real-time.

Strategic decision-making for sustainable production and distribution in automotive industry: a machine learning enabled dynamic multi-objective optimisation

ABSTRACT Over the last decade, numerous researchers have disclosed that major automotive companies do not conform to regulatory or societal expectations regarding their environmental and social performances. This paper explores the dynamic capabilities of production distribution within the sustainability practices of automotive industries. It offers insights to better grasp and articulate the environmental, economic, and social dimensions of sustainable supply chains. The research framework encloses all supply chain phases, from raw material sourcing to retailing finished products. Three conflicting objective functions are identified: social advantages maximisation, cost minimisation, and emission minimisation. Specifically, the study tackles a dynamic multi-objective optimisation model where each automobile type faces a series of dynamic demands. The dynamic nature of the problem poses significant challenges to conventional evolutionary algorithms for detecting the optimal solutions over time. Therefore, we introduce an interconnected prediction-based dynamic non-dominated sorting algorithm (ICP-DNSGA-II). Finally, extensive computational experiments are conducted to assess the effectiveness of this holistic approach. The findings offer valuable insights for automotive industry stakeholders and policymakers, illustrating its potential to enhance operational efficiency and sustainability performance across the supply chain. Most importantly, this paper proposes an automated decision-making approach to generate optimal solutions with dynamic changes in market demands.

Dual-Band Passive Beam Steering Antenna Technologies for Satellite Communication and Modern Wireless Systems: A Review.

Efficient beam steerable high-gain antennas enable high-speed data rates over long-distance networks, including wireless backhaul, satellite communications (SATCOM), and SATCOM On-the-Move. These characteristics are essential for advancing contemporary wireless communication networks, particularly within 5G and beyond. Various beam steering solutions have been proposed in the literature, with passive beam steering mechanisms employing planar metasurfaces emerging as cost-effective, power-efficient, and compact options. These attributes make them well-suited for use in confined spaces, large-scale production and widespread distribution to meet the demands of the mass market. Utilizing a dual-band antenna terminal setup is often advantageous for full duplex communication in wireless systems. Therefore, this article presents a comprehensive review of the dual-band beam steering techniques for enabling full-duplex communication in modern wireless systems, highlighting their design methodologies, scanning mechanisms, physical characteristics, and constraints. Despite the advantages of planar metasurface-based beam steering solutions, the literature on dual-band beam steering antennas supporting full duplex communication is limited. This review article identifies research gaps and outlines future directions for developing economically feasible passive dual-band beam steering solutions for mass deployment.

Probing gluon GTMDs of the proton in deep inelastic diffractive dijet production at HERA

We calculate several differential distributions for diffractive dijets production in ep→e′jet jetp in the perturbative QCD dipole approach using off diagonal unintegrated gluon distributions (generalized transverse momentum dependent distributions, GTMDs). Different models from the literature are used. We concentrate on the contribution from exclusive qq¯ dijets. Results of our calculations are compared to H1 and ZEUS data, including specific experimental cuts in our calculations. In general, except for one GTMD, our results are below the HERA data. The considered mechanism is expected to gives a sizeable contribution to the ZEUS data, while it is negligible in the kinematics of the H1 measurement. This is in contrast to recent results from the literature where the normalization was adjusted to some selected distributions of the H1 collaboration and no agreement with other observables was checked. The ZEUS data provide stricter limitations on the GTMDs than the H1 data. We conclude, based on comparison to different observables, that the calculated cross sections are only a small fraction of the measured ones which contain probably also processes with pomeron remnant. Alternatively one could try to explain the experimental data by inclusion of qq¯g component of the photon wave function. We present also azimuthal correlations between the sum and the difference of dijet transverse momenta. The cuts on transverse momenta of jets generate corresponding azimuthal correlations that can be misidentified as due to elliptic gluon distributions. Published by the American Physical Society 2024