Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.
Abstract Constant power loads (CPLs) introduce negative impedance in direct current microgrids (DCMGs), which is a major challenge. This negative impedance can significantly reduce the overall damping of the system, making it less stable and harder to control. To address this issue, output virtual resistance (VR) shaping is commonly employed to enhance system damping and improve power‐sharing amongst distributed generators (DGs). The technique proposed in this work involves an adaptive variation of the DG virtual output resistance (RV) linearly with the output current. This shows improved power sharing between sources. The work compares the small signal stability criteria and the minor loop gain methods for linear, non‐linear, and inverse droop controllers to determine the controller parameters with constant power loads. The control scheme is extensively tested through simulations for four different droop control schemes. The work also validates the DCMG performance when the DERs work with different droop controllers (heterogenous of controllers) to assess constant power load penetration, performance in meshed configurations, and DG plug‐and‐play operations. Additionally, improved power sharing performance was validated through a controller hardware in the loop (CHIL) based implementation.
Energy industries. Energy policy. Fuel trade, Production of electric energy or power. Powerplants. Central stations
Nicolò Grasso, Benedetta Fasciolo, Giulia Bruno
et al.
Climate change and population increase are becoming a threat to human feeding. New technologies and practices are under development, and a significant effort is being put into developing indoor farming, which allows for all-year-round production of high-quality food, regardless of the climate. Moreover, indoor farming promises extreme water and chemical usage reduction, specifically when the system is autonomously regulated with an IoT architecture. Despite these attractive characteristics, indoor systems require considerable energy to provide adequate temperature and lighting for cultivated crops. This demand is often high enough to make the production system economically unsustainable. This work aims to develop a cultivation protocol for baby lettuce plants (up to three weeks old plants) that can increase overall productivity while mitigating the issue of high energy demand. To this aim, we performed a Design of Experiment to assess crop responses to different levels of nutrients, temperature, and light intensity with the productivity of the system and the quality of the harvested product. The collected data were used to design a dynamic cultivation protocol, which defines different growing conditions according to the plant development stage. Results demonstrate that the dynamic protocol can enhance system productivity by up to 25 % in biomass accumulation, compared with the productivity obtained with fixed growing conditions, while maintaining the same high quality. Furthermore, the improvement is achieved without increasing the resource use, confirming the potential of this approach to enhance the economic sustainability of indoor soilless farming.
Introduction. The use of the solid disperse systems method to increase the solubility of lipophilic active pharmaceutical ingredients is industrially applicable using different technologies, but the influence of particle size on the dissolution of these systems, depending on the method, is not sufficiently reflected in the literature.Aim. To study the influence of the particle size of amorphous solid disperse systems "darunavir-water-soluble polymer" obtained by solvent removal and hot melt extrusion on the dissolution of Darunavir in the biological pH range of 1.2; 4.5 and 6.8.Materials and methods. Amorphous solid disperse systems were obtained in two ways: solvent removal and hot melt extrusion. Amorphism was determined by X-ray powder diffraction and electron microscopy. The efficiency of disperse systems was compared based on the results of the "Dissolution" test of powders mechanically ground to the same particle size in the biological pH range. The concentration of Darunavir in solution was determined using high-performance liquid chromatography with diode array detection.Results and discussion. The best result was shown by a solid dispersion system based on the Eudragit® E PO polymer with a particle size D90 of less than 10 μm. The increase in the concentration of Darunavir relative to the crystalline form corresponding to Darunavir ethanolate was 324, 2485, and 740%, respectively, in dissolution media with pH 1.2; 4.5, and 6.8.Conclusions. Methods for obtaining solid dispersion systems, such as solvent removal and hot melt extrusion with the same particle size, do not affect the concentration of the Darunavir API in solution in the biological pH range during the Dissolution test.
[Objective] At present, wind power generation has been widely used, but wind power is volatile and unstable, and the grid voltage fluctuation and network loss increase will be caused after wind farms are connected to the grid. To solve these problems, a multi-objective reactive power optimization model of distribution network considering the reactive power output of doubly fed induction generator is proposed. [Method] Based on the segmentation idea, the uncertain dynamic problem of wind power was transformed into a definite static problem, and the improved whale optimization algorithm was used to solve the mathematical model. In order to solve the problems of low precision, slow convergence speed and easy to fall into local optimality of the traditional whale optimization algorithm, a hybrid strategy was adopted to improve whale optimization algorithm, and some improved methods such as tent mapping initialization, adaptive weight and adaptive probability threshold were introduced. [Result] Taking the improved IEEE33 node distribution network as an example, the improved whale optimization algorithm can improve the global search ability and convergence speed comparing the particle swarm optimization and gray wolf optimizer. [Conclusion] The optimal reactive power output of wind farm optimized by the proposed strategy can reduce more system losses and improve the voltage stability of distribution network.
Bijin Joseph, Christoph Riedl, Alex Pentland
et al.
Social learning is a fundamental mechanism shaping decision-making across numerous social networks, including social trading platforms. In those platforms, investors combine traditional investing with copying the behavior of others. However, the underlying factors that drive mirroring decisions and their impact on performance remain poorly understood. Using high-resolution data on trades and social interactions from a large social trading platform, we uncover a fundamental tension between popularity and performance in shaping imitation behavior. Despite having access to performance data, people overwhelmingly choose whom to mirror based on social popularity, a signal poorly correlated with actual performance. This bias, reinforced by cognitive constraints and slow-changing popularity dynamics, results in widespread underperformance. However, traders who frequently revise their mirroring choices (trading explorers) consistently outperform those who maintain more static connections. Building an accurate model of social trading based on our findings, we show that prioritizing performance over popularity in social signals dramatically improves both individual and collective outcomes in trading platforms. These findings expose the hidden inefficiencies of social learning and suggest design principles for building more effective platforms.
Sagiraju Dileep Varma, Sarathbabu Duvvuri, Omkar Koduri
et al.
The widespread integration of wind energy poses numerous challenges, including ride-through capability issues, stability concerns, and power quality issues within the utility grid. Additionally, the inherent non-linear nature of wind energy systems, coupled with internal dynamics like model uncertainties, non-linearities, parametric variations, modeling errors, and external disturbances, significantly impacts system performance. Therefore, developing a robust controller becomes imperative to address the complexity, non-linearity, coupling, time variation, and uncertainties associated with wind energy systems, aiming to enhance transient performance in the presence of external and internal disturbances. The research presented in this manuscript focuses on devising a robust control scheme for a grid-tied Permanent Magnet Synchronous Generator (PMSG) wind turbine. The objective is to improve the wind turbine's performance under both normal and abnormal grid conditions. The innovation in Active Disturbance Rejection Control (ADRC) lies in its capacity to offer robust, adaptive, and disturbance-rejecting capabilities without relying on precise mathematical models. This quality makes ADRC a valuable and innovative tool for addressing challenges in complex and dynamic real-world applications where system parameters evolve over time. The wind energy system is inherently non-linear, time-varying, cross-coupled, and highly uncertain. It is also susceptible to parameter uncertainties, parametric variations, and external grid disturbances, all of which significantly influence its performance. The effectiveness of the proposed control scheme is validated to enhance ride-through capability and extract maximum power under internal disturbances, external grid disturbances, and parametric variations. To assess the proposed controller's efficacy, a comparative analysis is conducted using the Integral Time Absolute Error (ITAE) index for all abnormal grid disturbances. This analysis is performed in comparison to a Proportional Resonant Controller and a PI controller, providing evidence of the proposed controller's effectiveness. In summary, the incorporation of an Active Disturbance Rejection Controller emerges as a promising solution for enhancing the Low Voltage Ride-Through (LVRT) and High Voltage Ride-Through (HVRT) capabilities of grid-tied Permanent Magnet Synchronous Generator (PMSG)-based wind energy systems.
The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.
Benjamin Alt, Florian Stöckl, Silvan Müller
et al.
Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.
Industry is at the forefront of adopting new technologies, and the process followed by the adoption has a significant impact on the economy and society. In this work, we focus on analysing the current paradigm in which industry evolves, making it more sustainable and Trustworthy. In Industry 5.0, Artificial Intelligence (AI), among other technology enablers, is used to build services from a sustainable, human-centric and resilient perspective. It is crucial to understand those aspects that can bring AI to industry, respecting Trustworthy principles by collecting information to define how it is incorporated in the early stages, its impact, and the trends observed in the field. In addition, to understand the challenges and gaps in the transition from Industry 4.0 to Industry 5.0, a general perspective on the industry's readiness for new technologies is described. This provides practitioners with novel opportunities to be explored in pursuit of the adoption of Trustworthy AI in the sector.
The aim of this study was to evaluate changes on the Polish rapeseed market after accession to the European Union (EU). Special attention was paid to the demand, supply, and prices of rapeseed in Poland relative to other EU member states. The performance of the rapeseed sector was analyzed based on market data for 2005-2022. Changes on the Polish rapeseed market were assessed with the use of descriptive statistics, the Augmented Dickey-Fuller (ADF) test, and the ARiMA model. In Poland, rapeseed production is spatially diverse and dominates in the western and northern Polish provinces. The largest rapeseed area in 2022 was recorded in the following voivodeships: Lower Silesia (129.1 thousand ha), Wielkopolska (104 thousand ha) and West Pomerania (115.5 thousand ha). In the years 2005-2022 there were changes in the plans and harvest of rapeseed in Poland. The highest rapeseed yields were recorded in 2014 (34.4 dt/ha), 2022 (33.8 dt/ha) and 2021 (32.1 dt/ha), while the lowest in 2011 (22.4 dt/ha) and 2010 (23.6 dt/ha). The harvest were the largest in 2014 (3,275.8 thousand tons) and 2022 (3,643.4 thousand tons). Poland is an important producer of rapeseed in the EU and in 2022 it ranked third (3,300 thousand tons), after France (4,506 thousand tons) and Germany (4,295 thousand tons). Statistical analysis showed changes in the area, yields and harvest of rapeseed. The highest coefficient of variation was recorded for rapeseed harvest (0.236) and the lowest for yields (0.115). In the case of area, yields and harvests, the coefficient of variation was high, which proves the non-stationarity of the time series. The forecast developed for the years 2023-2027 showed downward trends for the area, yields and harvest of rapeseed.
Development of rainfed maize (Zea mays L.) is sensitive to fluctuations of environmental conditions, while whether the sensitivity varies across the growth stages is still unclear. Based on a 5-year dataset collected from consecutive observations, this study examined the sensitivities of biomass and yield production of rainfed maize to root zone soil water, air temperature and shortwave radiation at four growth stages in the Sanjiang Plain of Northeastern China under sub-humid cool-temperate climates. The multiple linear regression model was employed to establish functional relations between biomass and yield production and significant explanatory variables. A Monte-Carlo simulation was used to test sensitivities of biomass and yield production to perturbation of a single significant explanatory variable or co-perturbation of multiple significant explanatory variables. Results showed that root zone soil water prevailed over air temperature and shortwave radiation in affecting rainfed maize development for the most time of the growing period. Biomass production was most sensitive to root zone soil water which had positive variance contributions of 70 – 100% at the early and late vegetative stages and a negative variance contribution of −99.4% at the early reproductive stage. Yield production was also sensitive to root zone soil water at the early reproductive stage with a 100% positive variance contribution. Biomass and yield production were most sensitive to air temperature at the late reproductive stage and the positive variance contributions of air temperature were 97.7 – 100%. Shortwave radiation negatively contributed to biomass production by −28.6% at the late vegetative stage. The findings of this study suggest that more attention could be paid to the most sensitive factor at different growth stages of rainfed maize for great biomass accumulation and high grain yield.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
Ahmad Rostami, Dhruvin Patel, Madhusudan Giyyarpuram
et al.
5G non-public networks (NPNs) play a key role in enabling critical Industrial Internet of Things (IoT) applications in various vertical industries. Among other features, 5G NPNs enable novel operation models, where the roles and responsibilities for setting up and operating the network can be distributed among several stakeholders, i.e., among the public mobile network operators (MNOs), the industrial party who uses the 5G NPN services and 3rd parties. This results in many theoretically feasible operation models for 5G NPN, each with its own advantages and disadvantages. We investigate the resulting operation models and identify a set of nine prime models taking into account today's practical considerations. Additionally, we define a framework to qualitatively analyze the operation models and use it to evaluate and compare the identified operation models.
The traditions and planting techniques of oil palm cultivation by smallholders in the Muaro Jambi region, and the yields of fresh fruit bunches from smallholders vary. This result is because the plant’s number and the age of plant affects the amount of oil palm production and the farmer’s profit. The objective of research is to (1) analyze the smallholder replanting oil palm plantations’ feasibility evaluated from the financial point of views using investment criteria, (2) analyze his, her, their, etc. smallholder. Sensitivity of replanting oil palm plantations to changes in input and output prices. The study was conducted in Muaro Jambi district. The data analysis method usages investment criteria using NPV, IRR, BCR, PBP and BEP. Research data used data obtained from questionnaires. In the Muaro Jambi region, up to 60 pure independent smallholder farmers replanted, oil palm samples were obtained through snowball sampling. The results of the study show that smallholder oil palm planting is feasible through conventional replanting and understory replanting. The results of the sensitivity analysis showed that when the price of production factors increases by 15% and the selling price of FFB is considered constant, and the selling price of FFB decreased by 15% and the price of production factors remains, both types of replanting of oil palm plantations are still feasible. Changes in FFB prices are more sensitive to changes in the value of investment criteria than changes in production factor prices.
In this paper, we propose to consider the dependence structure of the trade/no trade categorical sequence of individual illiquid stocks returns. The framework considered here is wide as constant and time-varying zero returns probability are allowed. The ability of our approach in highlighting illiquid stock's features is underlined for a variety of situations. More specifically, we show that long-run effects for the trade/no trade categorical sequence may be spuriously detected in presence of a non-constant zero returns probability. Monte Carlo experiments, and the analysis of stocks taken from the Chilean financial market, illustrate the usefulness of the tools developed in the paper.
E. B. Shustov, A. V. Bunjat, A. G. Platonova
et al.
Introduction. Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease in the world. Non-alcoholic steatohepatitis (NASH), a clinically progressive morphological form of NAFLD, ranks second in the list of reasons for liver transplantation in the adult population. In the pathogenesis of this disease, metabolism and distribution of free fatty acids (FFA) play an important role. A large number of studies have established that the level of FFA in peripheral blood directly correlates with the severity of NASH, but it is still unclear what effect fluctuations in the profile of fatty acids (FA) in the liver have in steatohepatitis.Aim. Study of changes in the profile of fatty acids in the liver of laboratory animals with experimental non-alcoholic steatohepatitis.Materials and methods. The study was carried out on 17 white outbred male rats, which were randomized into two groups – intact (n = 6) and control (steatohepatitis) (n = 11). Steatohepatitis was modeled by 12-month use of a hypercaloric high-fat diet against the background of hypodynamia. The content of fatty acids in the liver was determined in the reaction of methanolysis and extraction with a hexane mixture of their methyl esters. The LC was separated by gas chromatography-mass spectrometry. Calibration for quantitative calculation was carried out with deuterated tridecanoic acid. The content of saturated and monounsaturated higher FAs, their aldehydes and hydroxy derivatives, as well as sterols were studied.Results and discussion. A total decrease in the content of FFA in the liver of animals with steatohepatitis was revealed. The most significant decrease occurred mainly in the class of monounsaturated fatty acids and cholesterol. Also, a significant decrease in the activity of Δ9-desaturase, a key enzyme in the synthesis of monounsaturated FAs from their precursor with the same carbon chain length, was revealed, which was manifested by a significant decrease in their amount in the liver. There were no statistically significant changes in the levels of aldehydes and hydroxy acids between the study groups, as well as in the level of sterols (except for cholesterol, the content of which decreased significantly).Conclusion. Thus, in the liver of rats with steatohepatitis caused by a combination of a hypercaloric diet and hypodynamia, statistically significant changes in the profile and concentration of fatty acids were found in comparison with healthy animals. The demonstrated shifts in FA composition may reflect both adaptive and pathological changes in the liver of animals with NAFLD and require further study.
Abstract
This study aims to obtain the best dose of clove extract anesthetic in the transportation of carp (Cyprinus carpio) from suppliers to consumers in the supply chain of carp. Alive, healthy, and not defective carp from the Fish Seed Center with 3-5 cm size were used as objects in this study. Carp fish bag and 50 x 35 x 30 cm Styrofoam boxes were used as containers which were placed randomly during the delivery of carp. The study was conducted with four treatments of different clove extract levels, treatment A as a control (0 ppm), B (3.3 ppm), C (6.7 ppm), and D (10 ppm), with three replications each. The examination of the anesthetic condition of carp was carried out four times during transportation, at 0, 6, 12, and 24 hours. Analysis of Variance (ANOVA) was conducted to see the impact of the treatments, and then the Tukey test was carried out to see the differences between treatments. The analysis was carried out using SPSS version 21. The results showed that increasing the anesthetic dose of clove extract impacted the health condition and survival rate of the carp seedlings during transportation. The highest survival rate (85%) was achieved at a concentration of 6.7 ppm.
Keywords: anesthesia dosage, clove extract, carp, the survival rate
Abstrak
Penelitian ini bertujuan untuk mendapatkan dosis anestesi ekstrak cengkeh terbaik dalam pengangkutan ikan karper (Cyprinus carpio) dari pemasok ke konsumen dalam rantai pasok ikan karper. Ikan karper yang digunakan sebagai objek dalam penelitian ini adalah ikan karper dari Balai Benih Ikan dengan ukuran 3-5 cm, dalam keadaan hidup, sehat, dan tidak cacat. Kantong ikan karper dan Styrofoam berukuran 50 x 35 x 30 cm digunakan sebagai wadah selama pengiriman ikan karper dan penempatannya dilakukan secara acak. Penelitian dilakukan dengan 4 perlakuan kadar ekstrak cengkeh yang berbeda, yaitu A sebagai kontrol (0 ppm), B (3,3 ppm), C (6,7 ppm), dan D (10 ppm) dengan masing-masing 3 ulangan. Pemeriksaan kondisi anestesi pada ikan karper dilakukan 4 kali selama pengangkutan, yaitu 0, 6, 12, dan 24 jam. Analysis of Variance (ANOVA) dilakukan untuk melihat dampak dari perlakuan kemudian uji Tukey dilakukan untuk melihat perbedaan antar perlakuan. Analisis dilakukan dengan SPSS versi 21. Hasil penelitian menunjukkan bahwa penambahan dosis anestesi ekstrak cengkeh berdampak pada keadaan kesehatan dan sintasan ikan karper saat transportasi. Sintasan tertinggi (85%) dicapai pada konsentrasi 6,7 ppm.
Kata kunci: dosis anestesi, ekstrak cengkeh, ikan karper, sintasan