Efficient harvesting of herbaceous mulberry is essential for reducing labor costs and ensuring high-quality stubble for rapid regrowth in sericulture production. However, existing mechanized harvesters rarely enable in situ measurement of cutting and conveying power under field conditions, and the influence of operational parameters on both energy consumption and stubble quality remains insufficiently quantified. In this study, a crawler-type prototype harvester equipped with three independently driven AC servo motors and real-time torque sensors was developed to monitor cutting, conveying, and baling processes. A Central Composite Design (CCD) combined with response surface methodology was employed to investigate the effects of forward speed, conveying speed, and average cutting speed on average cutting power per branch, average conveying power per branch, and stubble quality score. Field trials were conducted in Rizhao, Shandong Province, China, using the mulberry cultivar ‘Guishangyou 12’. The regression models exhibited high goodness of fit (R² = 0.9546∼0.9946) and non-significant lack of fit (p > 0.05). Results indicated that cutting power consumption was on average 3.7 times higher than conveying power, with cutting speed exerting the most significant influence on energy use (p < 0.01) and stubble quality (p < 0.01). The optimal parameter combination—forward speed of 0.55 m·s⁻¹, conveying speed of 0.96 m·s⁻¹, and cutting speed of 0.95 m·s⁻¹—reduced cutting power to 26.91 J·branch⁻¹, minimized conveying power to 6.64 J·branch⁻¹, and achieved a stubble quality score of 9.43. Validation experiments confirmed that deviations from predicted values were <5%. These findings provide a quantitative basis for operational optimization and energy efficiency improvement in herbaceous mulberry harvesting machinery.
The accurate tracking of individual calves is essential for health monitoring. However, existing multi tracking frameworks often encounter frequent ID abnormal switching issues during occlusion. To address these challenges, we propose a novel multi-object tracking framework named YSD-BPTrack for calves in occluded environments on cattle farms in this paper. This framework mainly consists of two stages: detection and tracking. Concerning the detection phase, the DCNv4 is integrated into the YOLOv8s model to capture spatial deformation features caused by occlusion, thereby enhancing detection performance under occlusion. Additionally, the Star operation of StarNet is also applied to the model to achieve excellent detection performance with lower computational costs. Concerning the tracking stage, we first propose an innovative rematching algorithm (Rematching module) and a new trajectory removal strategy (Trajectory removal module). The Rematching module performs rematching with detection boxes utilizing extended trajectory prediction boxes in cases of occlusion, resulting in a reduced probability of ID switch errors. Moreover, the Trajectory Removal module dynamically adjusts the removal time for lost matching trajectories, which decreases the likelihood of trajectories being mistakenly removed. Specifically, our proposed novel framework achieves a HOTA (Higher Order Tracking Accuracy) of 91.6%, surpassing other frameworks in both track accuracy and efficiency. Experimental results also validate the superiority of the YSD-BPTrack, with HOTA increasing by 17.6%, MOTA (Multiple Object Tracking Accuracy) by 13.9%, MOTP (Multiple Object Tracking Precision) by 1.8%, IDF1 (Identification F1 Score) by 15.4%, and reducing parameters by 49.1%, IDSw (Identification Switches) by 88.9%, and computational overhead by 39.2% compared to the other frameworks. Overall, the proposed multi-object tracking framework has great potential to revolutionize the tracking of calves.
João P. Manaia, Guilherme Pedreiro, João Paulo Dias
et al.
The transition to electric mobility is essential for sustainable development, driving a sharp rise in battery use for electric vehicles (EVs). Once these batteries have reached the end of their first vehicle life cycle (1st EOL), they are no longer suitable for vehicle traction, but can be repurposed for less demanding applications before being recycled. This approach prolongs battery lifespan while yielding measurable environmental and economic benefits. However, the widespread repurposing of these batteries is limited by a lack of standardised certification procedures. This study proposes a certification methodology for second-life lithium-ion batteries, based on Regulation (EU) 2023/1542 and the UL 1974 standard. The methodology comprises eight key steps to ensure safety, performance, and regulatory compliance for CE marking in the EU. A key step in the methodology is performance testing, which includes BMS functionality checks, open-circuit voltage, insulation resistance, capacity (via charge/discharge cycles), internal resistance, and self-discharge tests. These tests assess the battery’s state of health (SoH) and state of charge (SoC), enabling sorting and repurposing. A case study of a BMW plug-in hybrid battery module shows testing costs of 57.3–57.4 €/kWh, indicating economic feasibility. This work supports the safe and sustainable integration of repurposed EV batteries into new energy applications.
Electrical engineering. Electronics. Nuclear engineering, Energy industries. Energy policy. Fuel trade
The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics.
Peer-to-peer energy trading platforms enable direct electricity exchanges between peers who belong to the same energy community. In a semi-decentralized system, a community manager adheres to grid restrictions while optimizing social welfare. However, with no further supervision, some peers can be discriminated against from participating in the electricity trades. To solve this issue, this paper proposes an optimization-based mechanism to enable distributionally fair peer-to-peer electricity trading. For the implementation of our mechanism, peers are grouped by energy poverty level. The proposed model aims to redistribute the electricity trades to minimize the maximum Wasserstein distance among the transaction distributions linked to the groups while limiting the sacrifice level with a predefined parameter. We demonstrate the effectiveness of our proposal using the IEEE 33-bus distribution grid, simulating an energy community with 1600 peers. Results indicate that up to 70.1% of unfairness can be eliminated by using our proposed model, even achieving a full elimination when including a non-profit community photovoltaic plant.
The recent banking crisis has again emphasized the importance of understanding and mitigating systemic risk in financial networks. In this paper, we study a market-driven approach to rescue a bank in distress based on the idea of claims trading, a notion defined in Chapter 11 of the U.S. Bankruptcy Code. We formalize the idea in the context of financial networks by Eisenberg and Noe. For two given banks v and w, we consider the operation that w takes over some claims of v and in return gives liquidity to v to ultimately rescue v. We study the structural properties and computational complexity of decision and optimization problems for several variants of claims trading. When trading incoming edges of v, we show that there is no trade in which both banks v and w strictly improve their assets. We therefore consider creditor-positive trades, in which v profits strictly and w remains indifferent. For a given set C of incoming edges of v, we provide an efficient algorithm to compute payments by w that result in maximal assets of v. When the set C must also be chosen, the problem becomes weakly NP-hard. Our main result here is a bicriteria FPTAS to compute an approximate trade. The approximate trade results in nearly the optimal amount of assets of v in any exact trade. Our results extend to the case in which banks use general monotone payment functions and the emerging clearing state can be computed efficiently. In contrast, for trading outgoing edges of v, the goal is to maximize the increase in assets for the creditors of v. Notably, for these results the characteristics of the payment functions of the banks are essential. For payments ranking creditors one by one, we show NP-hardness of approximation within a factor polynomial in the network size, when the set of claims C is part of the input or not. Instead, for proportional payments, our results indicate more favorable conditions.
Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim
et al.
With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key statistical and machine learning techniques that have seen practical success in the process industries. To do so, we start with hybrid modeling to provide a methodological framework underlying core application areas: soft sensing, process optimization, and control. Soft sensing contains a wealth of industrial applications of statistical and machine learning methods. We quantitatively identify research trends, allowing insight into the most successful techniques in practice. We consider two distinct flavors for data-driven optimization and control: hybrid modeling in conjunction with mathematical programming techniques and reinforcement learning. Throughout these application areas, we discuss their respective industrial requirements and challenges. A common challenge is the interpretability and efficiency of purely data-driven methods. This suggests a need to carefully balance deep learning techniques with domain knowledge. As a result, we highlight ways prior knowledge may be integrated into industrial machine learning applications. The treatment of methods, problems, and applications presented here is poised to inform and inspire practitioners and researchers to develop impactful data-driven sensing, optimization, and control solutions in the process industries.
Recently, the Industry 5.0 is gaining attention as a novel paradigm, defining the next concrete steps toward more and more intelligent, green-aware and user-centric digital systems. In an era in which smart devices typically adopted in the industry domain are more and more sophisticated and autonomous, the Internet of Things and its evolution, known as the Internet of Everything (IoE, for short), involving also people, robots, processes and data in the network, represent the main driver to allow industries to put the experiences and needs of human beings at the center of their ecosystems. However, due to the extreme heterogeneity of the involved entities, their intrinsic need and capability to cooperate, and the aim to adapt to a dynamic user-centric context, special attention is required for the integration and processing of the data produced by such an IoE. This is the objective of the present paper, in which we propose a novel semantic model that formalizes the fundamental actors, elements and information of an IoE, along with their relationships. In our design, we focus on state-of-the-art design principles, in particular reuse, and abstraction, to build ``SemIoE'', a lightweight ontology inheriting and extending concepts from well-known and consolidated reference ontologies. The defined semantic layer represents a core data model that can be extended to embrace any modern industrial scenario. It represents the base of an IoE Knowledge Graph, on top of which, as an additional contribution, we analyze and define some essential services for an IoE-based industry.
Microgrid autonomous networks need an effective plan and control of power supply, energy storage, and retransmission. Prediction and monitoring of power quality (PQ) along with efficient utilization of Renewable Energy (RE) is unavoidable to optimize the system performance without abnormalities. Alterations and irregularities in PQ must remain within the prescribed norm ranges and characteristics to allow fault-tolerant operation of the detached system in various modes of attached equipment. The PQ data for all possible combinations of grid-attached household appliances and different inside/outside conditions cannot be measured completely or described exactly by physical equations. PQ predictions on a daily basis using Artificial Intelligence (AI) models are needed because atmospheric fluctuations and anomalies in local weather with uncertainties in system states primarily influence the induced power and operation of real off-grids. A novel soft-computing method using Differential Learning, which allows modelling of complex dynamics of weather-dependent systems, is presented and compared with the recent standard deep and probabilistic machine learning. The AI models were evolved using weather data and the binary status of attached equipment in the test predetermined daily training periods. Daily statistical models process 24-h forecast data and definition load series of trained input variables to calculate the target PQ parameters at the same times. Optimal utilization, efficiency, and failure-free operation of smart grids can be planned according to the suggested operable power consumption scenarios based on their PQ verification on a day-horizon. Executable load sequences can be automatically combined and scheduled in the system to be adapted to user needs, considering the RE production potential, charge state, and optimal PQ characteristics over the next 24 h. A parametric C++ application software with applied PQ and weather data is free available to allow reproducibility of the results.
When a high impedance fault (HIF) occurs in a distribution network, the detection efficiency of traditional protection devices is strongly limited by the weak fault information. In this study, a method based on S-transform (ST) and average singular entropy (ASE) is proposed to identify HIFs. First, a wavelet packet transform (WPT) was applied to extract the feature frequency band. Thereafter, the ST was investigated in each half cycle. Afterwards, the obtained time-frequency matrix was denoised by singular value decomposition (SVD), followed by the calculation of the ASE index. Finally, an appropriate threshold was selected to detect the HIFs. The advantages of this method are the ability of fine band division, adaptive time-frequency transformation, and quantitative expression of signal complexity. The performance of the proposed method was verified by simulated and field data, and further analysis revealed that it could still achieve good results under different conditions.
Energy conservation, Energy industries. Energy policy. Fuel trade
As cryptocurrency evolved, new financial instruments, such as lending and borrowing protocols, currency exchanges, fungible and non-fungible tokens (NFT), staking and mining protocols have emerged. A financial ecosystem built on top of a blockchain is supposed to be fair and transparent for each participating actor. Yet, there are sophisticated actors who turn their domain knowledge and market inefficiencies to their strategic advantage; thus extracting value from trades not accessible to others. This situation is further exacerbated by the fact that blockchain-based markets and decentralized finance (DeFi) instruments are mostly unregulated. Though a large body of work has already studied the unfairness of different aspects of DeFi and cryptocurrency trading, the economic intricacies of non-fungible token (NFT) trades necessitate further analysis and academic scrutiny. The trading volume of NFTs has skyrocketed in recent years. A single NFT trade worth over a million US dollars, or marketplaces making billions in revenue is not uncommon nowadays. While previous research indicated the presence of wrongdoings in the NFT market, to our knowledge, we are the first to study predatory trading practices, what we call opportunistic trading, in depth. Opportunistic traders are sophisticated actors who employ automated, high-frequency NFT trading strategies, which, oftentimes, are malicious, deceptive, or, at the very least, unfair. Such attackers weaponize their advanced technical knowledge and superior understanding of DeFi protocols to disrupt trades of unsuspecting users, and collect profits from economic situations that are inaccessible to ordinary users, in a "supposedly" fair market. In this paper, we explore three such broad classes of opportunistic strategies aiming to realize three distinct trading objectives, viz., acquire, instant profit generation, and loss minimization.
Faizal Anam Al Ubaidah Lubis, Saktiyono Sigit Tri Pamungkas, Fitria Nugraheni Sukmawati
Sugarcane (Saccharum officinarum L.) is a plantation crop that is used as raw material for the consumer sugar and industrial sugar. The need for sugar is increasing every year but is not matched by an increase in sugarcane production due to several factors including cultivation management that is not optimal. Sugarcane production begins with good nursery management, including using genetic of seeds and the right planting media. One alternative to improve the quality of growing media is to use humic acid (HA) as a soil enhancer. This study aims to determinate the effect of giving HA on the morphological characteristics of sugarcane seedlings of Bululawang variety (BL). This research was carried out in an integrated laboratory greenhouse at the Polytechnic LPP Yogyakarta from Maret to July 2021. This study used a non-factorial completely randomized design (CRD) with five treatments and three replications consisting of P0 (control), P1 (25 ml.polybag-1), P2 (50 ml.polybag-1), P3 (75 ml.polybag-1), and P4 (100 ml.polybag-1). The morphological characteristics observed is plants height (cm), number of leaves (strands), stem diameter (mm) and longest root length (cm). the results of the study were analyzed using ANOVA at the 5% level and continued using the Duncan Multiple Range Test (DMRT) at the 5% level. The result showed the effect on morphological characters on all observation variables where the P3 treatment had the best growth and morphological characters, so that in general the additional of HA affected the morphological characteristics of sugarcane seedlings of BL varieties.
Helder Cássio de Oliveira, Kelli Carneiro de Freitas Nakata, Luisa Daige Marques
Objetivo: Demonstrar o impacto econômico, sob a perspectiva da Secretaria Estadual da Saúde de Mato Grosso, das atividades desenvolvidas por uma Comissão Permanente de Farmácia e Terapêutica (CPFT-MT) com atuação transparente e fundamentada na avaliação de tecnologias em saúde. Métodos: Avaliou- -se o comportamento dos gastos com medicamentos não constantes nas listas de medicamentos essenciais do Sistema Único de Saúde durante e após a vigência de uma via administrativa planejada para dar acesso a população a esse tipo de medicamentos. Foram levantados os gastos com desembolsos diretos para aquisição de medicamentos destinados a atender a Portaria Estadual nº 172/2010 e ações judiciais com auxílio do Sistema Integrado de Planejamento, Contabilidade e Finanças (FIPLAN). Com a finalidade de trazer os valores gastos para os dias atuais, aplicou-se um ajuste inflacionário com base no Índice Nacional de Preços ao Consumidor Amplo (IPCA). Resultados: Os achados apontaram para gastos médios anuais expressivos e constantes com medicamentos não selecionados durante a vigência da via administrativa com consequente redução de gastos com ações judiciais a partir de sua revogação. Período este coincidente com a entrega de diversos trabalhos da CPFT-MT, especialmente a lista estadual de medicamentos e construção e atualização de protocolos clínicos estaduais, resultando em uma economia para o estado de R$ 6.222.196,90 (53,1%). Conclusão: Os resultados apontam para que uma comissão de farmácia e terapêutica capacitada e atuante, com apoio da gestão, pode contribuir para uma melhor utilização dos recursos financeiros e cooperar com uso racional de tecnologias em saúde.
Pharmacy and materia medica, Pharmaceutical industry
We propose a non-linear observation-driven version of the Hasbrouck (1991) model for dynamically estimating trades' market impact and information content. We find that market impact displays an intraday pattern superimposed with large fluctuations. Some of them are exogenous, and, as an example, we investigate market impact dynamics around FOMC announcements. Contrary to Hasbrouck (1991), we find that the information content of trades depends on the local liquidity level and the recent history of prices and trades. Finally, we use the model to estimate the time-varying permanent impact parameter, which allows performing a dynamic transaction cost analysis.
З 19 по 20 травня у Київському національному університеті будівництва і архітектури проведено VII міжнародну науково-практичну конференцію «Transfer of Innovative Technologies 2021». На ній були представлені креативні ідеї, інноваційні проекти й практичні розробки в галузях будівництва, архітектури, розв’язання нагальних проблем інженерії й проектування об’єктів, захисту навколишнього середовища, сучасні тенденції в інформаційних технологіях та ін. На конференції, яка відбувалась в режимі відеоконференцзв’язку, прийняли участь вітчизняні науковці, викладачі та студенти навчальних закладів, представники виробництв, відомі фахівці країн світу. Усього подано 128 заявок від півтори сотні учасників, у тому числі 15 іноземних з Австралії, Польщі, Словаччини, США, Казахстану, Німеччини, Китаю.
Конкурсна комісія визначила кращі роботи в номінаціях: Презентація, Інноваційний проект, Публікація, відзначила Дипломами преможців 2021 року. Учасники отримали Сертифікати, а найактивніші − Подяки за проведену роботу, міжнародні наукові зв’язки та організаційну підтримку форуму. В Збірнику матеріалів конференції (онлайн) та в журналі «Transfer of Innovative Technologies», Vol.4, No.1 опубліковано препринт статті, а презентації учасників – на сайті конференції. Кращі роботи рекомендовано до публікації в міжнародних наукових журналах Transfer of Innovative Technologies, Підводні технології: промислова та цивільна інженерія. Прийнято рішення щодо підготовки й проведення наступного форуму в 2022 році, залучення до інноваційної діяльності креативних учасників та нових установ, подальшої інтеграції у світовий науковий простір. Оргкомітет дякує всім за представлені матеріали та впровадження інноваційних технологій у життя!
By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry~4.0 promotes integrating cyber-physical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects or physical processes. DT, in addition, uses built-in models on this precise image to simulate, analyze, predict, and optimize their real-time operation using feedback. DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research in next-generation (NextG) wireless technologies (e.g., 5G and beyond networks), various tools (e.g., age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing). Moreover, we discuss the DT deployment strategies at different industrial communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT's adoption.
Essa pesquisa analisa a utilização de sistemas de gerenciamento de armazéns no Agronegócio do Distrito Federal, e sua relação com os Condomínios de Armazéns Rurais. Para tanto foi realizada uma pesquisa aplicada, descritiva e qualitativa, através de estudo de caso e entrevista semiestruturada, analisados por meio da análise de conteúdo. Os resultados evidenciam que o WMS é importante para o gerenciamento e controle do armazenamento agrícola, automatização dos processos, redução de erros, melhoria na resolução de problemas fiscais e inventário, e controle de quebras e descontos percentuais. Constatou-se a necessidade de capacitação de colaboradores para operar o sistema, e alto custo de implementação e treinamento de pessoal para o WMS. Quanto aos Condomínios de Armazéns Rurais, o WMS pode auxiliar no processo de gerenciamento e controle da armazenagem agrícola, como os Condomínios podem se beneficiar das vantagens expostas, e diluir os custos com a implementação do WMS entre todos os condôminos.