Este estudo analisa a produção de biodiesel em Mato Grosso entre 2006 e 2024, com foco nos fatores econômicos e produtivos que sustentam o monopólio da soja como principal matéria-prima. A pesquisa se se justifica pela relevância do estado como maior produtor de soja do Brasil e pela necessidade de diversificar a matriz agroenergética, mitigando riscos econômicos e ambientais associados à dependência de uma única fonte. O Trabalho adota uma abordagem mista, com análise quantitativa fundamentada em regressão linear múltipla e métodos econométricos, como testes de multicolinearidade, heterocedasticidade e autocorrelação, além de análise qualitativa para contextualizar os achados. Os resultados indicaram que o custo de produção da soja e o aumento dos percentuais de biodiesel têm correlação positiva e significativa com a produção de biodiesel, refletindo maior eficiência produtiva e políticas públicas favoráveis. Por outro lado, o preço da saca de soja apresentou correlação negativa, sugerindo que o aumento nos custos da matéria-prima o que desestimula sua utilização. Concluiu-se que a dependência da soja como matéria-prima está profundamente enraizada em fatores econômicos e estruturais, mas estratégias de diversificação e inovação são essenciais para aumentar a resiliência e sustentabilidade do setor de biodiesel em Mato Grosso.
Groundwater recharge in mountain-front areas is a critical yet poorly constrained component of the water cycle in semiarid regions, particularly where traditional irrigation practices dominate. This study investigates the spatiotemporal dynamics of recharge induced by gravity-fed irrigation in the mountain-front of the Moroccan High Atlas, a key recharge zone for the Haouz aquifer. A simplified water balance approach, corrected for groundwater-based evapotranspiration, was applied to a 20-year dataset of irrigation diversions and remotely sensed evapotranspiration (MOD16A2), and validated against recharge estimates from the water table fluctuation (WTF) method. Results show strong spatial disparities, with upstream zones receiving disproportionately higher water allocations due to ancestral water rights, sustaining potential recharge in ∼90 % of months, while midstream and downstream zones consistently faced deficits. Despite local recharge events linked to flood years, statistically significant declining trends in recharge were observed across all zones, reflecting both reduced streamflow and intensified groundwater abstraction. Sensitivity tests revealed that neglecting rainfall and ΔS introduces only modest biases (≤12 % in upstream, ≤24 % in midstream zones), confirming the dominance of irrigation as the primary recharge driver. Potential recharge estimates aligned closely with WTF-derived values (differences of 5–14 %), further attesting to the reliability of the approach. These findings highlight the vulnerability of traditional irrigation systems under climate and human pressures and emphasize the urgent need for integrated water management strategies that safeguard ancestral irrigation practices while promoting adaptive measures such as managed aquifer recharge and climate-smart agriculture.
Shaoming Han, Cheng Qian, Nawal Abdalla Adam
et al.
This study examines the impact of financial inclusion on bank stability across 36 emerging economies, utilizing bank-level data from over 1,500 commercial banks spanning the period 2004 to 2023. Despite the recognized benefits of financial inclusion, its influence on banking stability remains complex and context dependent. The research employs advanced econometric methodologies, including fixed-effects models, Driscoll-Kraay standard errors to address heteroskedasticity and cross-sectional dependence, and system Generalized Method of Moments (GMM) estimation to control for endogeneity and dynamic effects. The findings reveal that financial inclusion generally enhances bank stability and positively influences operational efficiency and funding stability. However, during periods of lax financial regulations or excessive government intervention, banks may engage in riskier behaviors, potentially undermining stability. Key results indicate that (1) robust economic growth and stable policy environments amplify the positive effects of financial inclusion on bank stability, (2) excessive government control may foster risk-taking behaviors, (3) strong financial conditions mitigate adverse impacts, (4) financial inclusion improves risk management and operational efficiency, and (5) effective regulatory frameworks are pivotal in leveraging financial inclusion for sound banking operations. These insights suggest that policymakers in emerging markets should carefully balance the promotion of financial inclusion with safeguards that maintain financial stability.
Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.
Exploring measures to improve financial networks and mitigate systemic risks is an ongoing challenge. We study claims trading, a notion defined in Chapter 11 of the U.S. Bankruptcy Code. For a bank $v$ in distress and a trading partner $w$, the latter is taking over some claims of $v$ and in return giving liquidity to $v$. The idea is to rescue $v$ (or mitigate contagion effects from $v$'s insolvency). We focus on the impact of trading claims fractionally, when $v$ and $w$ can agree to trade only part of a claim. In addition, we study donations, in which $w$ only provides liquidity to $v$. They can be seen as special claims trades. When trading a single claim or making a single donation in networks without default cost, we show that it is impossible to strictly improve the assets of both banks $v$ and $w$. Since the goal is to rescue $v$ in distress, we study creditor-positive trades, in which $v$ improves and $w$ remains indifferent. We show that an optimal creditor-positive trade that maximizes the assets of $v$ can be computed in polynomial time. It also yields a (weak) Pareto-improvement for all banks in the entire network. In networks with default cost, we obtain a trade in polynomial time that weakly Pareto-improves all assets over the ones resulting from the optimal creditor-positive trade. We generalize these results to trading multiple claims for which $v$ is the creditor. Instead, when trading claims with a common debtor $u$, we obtain NP-hardness results for computing trades in networks with default cost that maximize the assets of the creditors and Pareto-improve the assets in the network. Similar results apply when $w$ donates to multiple banks in networks with default costs. For networks without default cost, we give an efficient algorithm to compute optimal donations to multiple banks.
Junaid Mushtaq Lone, Shinsuke Agehara, Amr Abd-Elrahman
Commercial strawberry (Fragaria ×ananassa Duch.) production in Florida relies heavily on bare-root transplants, which typically have 3–5 leaves with partially desiccated roots. Successful establishment requires sprinkler irrigation during daylight hours for the first 10–14 days, leading to substantial water consumption. To address this issue, we evaluated the efficacy of intermittent sprinkler irrigation as a water conservation strategy. We conducted field experiments over two growing seasons [Season 1 (2021–22) and Season 2 (2022–23)] in west-central Florida using three major strawberry cultivars, ‘Florida127’, ‘Florida Brilliance’, and ‘FL 16.30–128’. Plants were subjected to four different intermittent irrigation programs during establishment: 10/0 (continuous irrigation), 10/10, 10/15, and 10/20 min (on/off) from 0800 to 1800 HR for 12 days after transplanting. The impact of intermittent irrigation on marketable yield was cultivar- and season-dependent. 'Florida Brilliance' exhibited a 27 % yield increase in Season 1 but no significant difference in Season 2. By contrast, the other two cultivars exhibited no significant yield response in either season. In ‘Florida Brilliance’, marketable yield was strongly correlated with early canopy growth, suggesting that the yield increase was due partly to accelerated canopy establishment. This surprising result could be explained by the role of stress-induced leaf senescence in enhancing acclimation to adverse environmental conditions. It is speculated that increased heat stress from intermittent irrigation promotes senescence of initial leaves, facilitating nutrient translocation to the crown and subsequently accelerating the formation of new leaves and roots. Our results demonstrate that, without significant yield loss, intermittent sprinkler irrigation can reduce water use by 50–67 % during the establishment of strawberry bare-root transplants, accounting for 322–429 mm of water saving (3.2–4.3 million liters per hectare). Importantly, this water-conservation practice is easy to implement and does not negatively impact fruit quality.
Aiming at the problems of reduced winter wheat yield and aggravation of nitrogen leaching pollution caused by the waterlogging in the Middle-Lower Yangtze Plain, China, a two-year field experiment with three farmland water levels (W40, W60, W80) and three nitrogen application rates (N150, N225, N300) as well as a non-waterlogged treatment (CK) was carried out, to investigate the coupling effects of farmland water level and nitrogen application rate on the plant growth, grain yield, crop water productivity (WPC) and nitrogen load with waterlogging conditions. Three man-made waterlogging events were applied at winter wheat jointing-booting stage, heading-flowering stage and grain filling stage, respectively. The results indicated that with the farmland water level decreased from −40 cm to −60 cm and the nitrogen application rate increased from 150 kg∙ha−1 to 225 kg∙ha−1, the plant height, aboveground dry matter, leaf area index, spike length, grain yield, effective panicles, grain number per ear, 1000-grain weight and WPC in the waterlogging field increased significantly. However, since the nitrogen application rate exceeded 225 kg∙ha−1 and farmland water level lowered more than −60 cm, the favorable effects of nitrogen application rate and farmland water level for winter wheat growth and production reduced. Additionally, both the nitrogen load and partial factor productivity of nitrogen (PFPN) increased with the decline of farmland water level, while the nitrogen load increased and the PFPN decreased with the increasing nitrogen application rate. The raise of nitrogen rate from 150 kg∙ha−1 to 225 kg∙ha−1 was beneficial to plant growth, however, the increase of nitrogen application resulted in the decrease of PFPN and increase of drainage nitrogen loads. Compared with the water farmland water level of −40 cm and nitrogen application rate of 150 kg∙ha−1, the increase of nitrogen application rate and the decrease of farmland water level in the range of 50%-100% resulted in yield raise by 5.78%-32.29% approximately and the increase of nitrogen load by 36.20%-178.44% approximately. The comprehensive evaluation with TOPSIS-Entropy method for plant growth, grain yield, WPC, PFPN and nitrogen loads suggested that, the appropriate nitrogen application rate for winter wheat in the waterlogging areas of Middle-Lower Yangtze Plain in China was 225 kg∙ha−1, and the proper farmland water level was lowering to −80 cm in wet year and −60 cm in dry year within 3 days after waterlogging.
Eleonora Di Valentino, Leandros Perivolaropoulos, Jackson Levi Said
The Special Issue on "Modified Gravity Approaches to the Tensions of $Λ$CDM"} in the Universe journal tackles significant challenges faced by the $Λ$CDM model, including discrepancies in the Hubble constant, growth rate of structures, and cosmological anisotropies. These issues suggest foundational cracks in the model, raising questions about the validity of General Relativity, dark energy, and cosmological principles at large scales. This collection brings together leading researchers to delve into Modified Gravity theories as potential solutions. Covering approaches from Scalar-Tensor theories to $f(R,T)$ gravity and beyond, each contribution presents innovative research aimed at addressing the limitations of the $Λ$CDM model. This Special Issue not only highlights the theoretical and empirical strengths of Modified Gravity models but also opens avenues for future investigations, emphasizing the synergy between theoretical advancements and observational evidence to deepen our cosmological understanding.
Soil heavy metal pollution seriously endangers the soil ecological environment and food safety production. In this study, drip irrigation tests with four irrigation frequencies were conducted by controlling the lower limit of the soil matric potential (D1: −10 kPa; D2: −20 kPa; D3: −30 kPa; D4: −40 kPa). Through comparison with traditional surface irrigation, the effect of drip irrigation on the root zone soil environment under heavy metal pollution and the mechanism through which drip irrigation influences soybean heavy metal enrichment characteristics were explored. The conclusions are as follows. (i) Drip irrigation can improve the root zone soil environment of soybean under combined Cd, Pb and Cr(VI) pollution and is conducive to the recovery of bacterial community structure. (ii) Compared with surface irrigation, drip irrigation reduced the contents of Cd, Pb and Cr(VI) in the root zone soil, with maximum reductions of 34.88% (D1), 31.35% (D2) and 34.20% (D2), respectively. (iii) Drip irrigation increased the accumulation of Cd, Pb and Cr(VI) in soybean. However, compared with surface irrigation, drip irrigation changed the distribution of Cd, Pb and Cr(VI) in soybean organs so that more Cd, Pb and Cr(VI) were stored in roots and significantly less Cd and Cr(VI) were stored in seeds, with maximum reductions of 16.62% (D2) and 19.49% (D2), respectively. These results can be used to develop a new strategy for the prevention and control of soil heavy metal pollution.
Abstract This work presents the operation and control of a pico‐hydro‐solar photovoltaic (PV)‐battery energy storage (BES)‐based isolated renewable energy system (RES) feeding 3‐phase 4‐wire loads. For voltage regulation, to maintain frequency, and power quality improvement in this system, a 4‐leg VSC is used. The BES is connected to the DC‐link of the voltage source converter (VSC) through a bidirectional converter (BDC), which regulates the DC‐link voltage and controls the charging and discharging current of the battery. An advanced perturb and observe (AP&O)‐based MPPT control technique with drift free operation and capability to operate in the derated mode is adapted in this work. The VSC connected to PCC, injects or absorbs power from this system based on the difference of power between generation and the load. The modified complex co‐efficient filter (MCCF)‐based control technique monitors the power quality of this RES system and 4 leg VSC provides the source neutral current compensation. This control algorithm is used to extract the amplitude of the fundamental load current component with improved dynamic response, DC offset elimination and higher order harmonics removal capability. The ability of the presented control strategy for power quality improvement, power management, load balancing and neutral current compensation is reported in this work.
Production of electric energy or power. Powerplants. Central stations, Energy industries. Energy policy. Fuel trade
Trades, introduced by Hedayat, are two sets of blocks of elements which may be exchanged (traded) without altering the counts of certain subcollections of elements within their constituent blocks. They are of importance in applications where certain combinations of elements dynamically become prohibited from being placed in the same group of elements, since in this case one can trade the offending blocks with allowed ones. This is particularly the case in distributed storage systems, where due to privacy and other constraints, data of some groups of users cannot be stored together on the same server. We introduce a new class of balanced trades, important for access balancing of servers, and perturbation resilient balanced trades, important for studying the stability of server access frequencies with respect to changes in data popularity. The constructions and bounds on our new trade schemes rely on specialized selections of defining sets in minimal trades and number-theoretic analyses.
Palos Isidro, Moo-Puc Rosa, Vique-Sánchez José Luis
et al.
Trichomoniasis is a public health problem worldwide. Trichomoniasis treatment consists of the use of nitroimidazole derivatives; however, therapeutic ineffectiveness occurs in 5 to 20 % of the cases. Therefore, it is essential to propose new pharmacological agents against this disease. In this work, esters of quinoxaline-7-carboxylate-1,4-di-N-oxide (EQX-NO) were evaluated in in vitro assays as novel trichomonicidal agents. Additionally, an in vitro enzyme assay and molecular docking analysis against triosephosphate isomerase of Trichomonas vaginalis to confirm their mechanism of action were performed. Ethyl (compound 12) and n-propyl (compound 37) esters of quinoxaline-7-carboxy-late-1,4-di-N-oxide derivatives showed trichomonicidal activity comparable to nitazoxanide, whereas five methyl (compounds 5, 15, 19, 20 and 22), four isopropyl (compounds 28, 29, 30 and 34), three ethyl (compounds 4, 13 and 23) and one npropyl (compound 35) ester derivatives displayed activity comparable to albendazole. Compounds 6 and 20 decreased 100 % of the enzyme activity of recombinant protein triosephosphate isomerase.
Alena Worschech, Uli Schlachter, Henning Wigger
et al.
Flexible energy plants are one of the key requirements for future energy systems with high levels of fluctuating renewable energy. In the course of the transition to sustainable energy systems, regulatory frameworks and tax systems should promote carbon-reduced flexible power plants in a timely manner.This paper considers hybrid systems consisting of battery energy storage systems (BESS) and Power-to-Heat (PtH) modules which can contribute to a more flexible energy system by providing Frequency Containment Reserve (FCR). Contrary to many papers, this contribution explicitly focuses on taxes for FCR providing power plants, which are incurred annually or based on energy consumption. Additionally, regulatory frameworks are investigated, meaning requirements for FCR provision and conditions for energy trading. The effects of these factors on the economic efficiency of hybrid power plants providing FCR are analysed.The regulatory framework conditions and tax systems from three countries are analysed: Germany, France and Austria. For each of these countries four scenarios are simulated in which the net present values (NPV) are calculated considering the corresponding national tax systems and framework conditions. Additionally, operational strategies using the degrees of freedom (DoF) are examined regarding their influence on the economic performance.The comparison shows a huge influence of taxes on the profitability of the hybrid system. Framework conditions mostly play a minor role in this context. Compared to a benchmark scenario with uniform framework conditions and without taxes, on average the NPV decreases more rapidly considering taxes (−107 k€ in France to −710 k€ in Austria) than considering country specific framework conditions (−122 k€ in France to −308 k€ in Austria). Since framework conditions mostly determine the size of the battery capacity, they primarily affect the investment costs. Additionally, the longer the time slices and the earlier the gate closure is, the more often the hybrid systems violate requirements for FCR provision.
Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana
et al.
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications where AI techniques require centralized data collection and processing. However, this is not always feasible in realistic scenarios due to the high scalability of modern IIoT networks and growing industrial data confidentiality. Federated Learning (FL), as an emerging collaborative AI approach, is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge while helping protect user privacy. In this article, we provide a detailed overview and discussions of the emerging applications of FL in key IIoT services and applications. A case study is also provided to demonstrate the feasibility of FL in IIoT. Finally, we highlight a range of interesting open research topics that need to be addressed for the full realization of FL-IIoT in industries.
An important open question today is the understanding of the relevance that dark matter (DM) halo substructure may have for DM searches. In the standard cosmological framework, subhalos are predicted to be largely abundant inside larger halos, i.e., galaxies like ours, and are thought to form first and later merge to form larger structures. Dwarf satellite galaxies -- the most massive exponents of halo substructure in our own galaxy -- are already known to be excellent targets and, indeed, they are constantly scrutinized by current gamma-ray experiments in their search for DM annihilation signals. Lighter subhalos not massive enough to have a visible baryonic counterpart may be good targets as well given their typical number densities and distances. In addition, the clumpy distribution of subhalos residing in larger halos may boost the DM signals considerably. In an era in which gamma-ray experiments possess, for the first time, the exciting potential of reaching the most relevant regions of the DM parameter space, a profound knowledge of the DM targets and scenarios being tested at present is mandatory if we aim for accurate predictions of DM-induced fluxes, for investing significant telescope observing time to selected targets, and for deriving robust conclusions from our DM search efforts. In this regard, a precise characterization of the statistical and structural properties of subhalos becomes critical. With the Special Issue "The Role of Halo Substructure in Gamma-Ray Dark Matter Searches" [https://www.mdpi.com/journal/galaxies/special_issues/Gamma-RayDMS], we aimed to summarize where we stand today on our knowledge of the different aspects of the DM halo substructure; to identify what are the remaining big questions, how we could address these and, by doing so, to find new avenues for research.
We consider the problem of maximizing portfolio value when an agent has a subjective view on asset value which differs from the traded market price. The agent's trades will have a price impact which affect the price at which the asset is traded. In addition to the agent's trades affecting the market price, the agent may change his view on the asset's value if its difference from the market price persists. We also consider a situation of several agents interacting and trading simultaneously when they have a subjective view on the asset value. Two cases of the subjective views of agents are considered, one in which they all share the same information, and one in which they all have an individual signal correlated with price innovations. To study the large agent problem we take a mean-field game approach which remains tractable. After classifying the mean-field equilibrium we compute the cross-sectional distribution of agents' inventories and the dependence of price distribution on the amount of shared information among the agents.