Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi
et al.
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.
Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM), when common domain adaptation via LM fine-tuning is not possible due to a lack of labeled task data. Although popular, MLM requires a significant corpus of domain-related data, which is difficult to obtain for specific domains in languages other than English, such as the process industry in the German language. This paper introduces an efficient approach called ICL-augmented pretraining or ICL-APT that leverages in-context learning (ICL) and k-nearest neighbors (kNN) to augment target data with domain-related and in-domain texts, significantly reducing GPU time while maintaining strong model performance. Our results show that the best configuration of ICL-APT performed better than the state-of-the-art DAPT by 28.7% (7.87 points) and requires almost 4 times less GPU-computing time, providing a cost-effective solution for industries with limited computational capacity. The findings highlight the broader applicability of this framework to other low-resource industries, making NLP-based solutions more accessible and feasible in production environments.
High trade costs pose substantial barriers to the process of trade liberalization. This study aims to measure trade costs and explore the driving forces behind the growth of bilateral trade between Cambodia and its top 30 trading partners from 1993 to 2019. Using a micro-founded measure of trade costs derived from the gravity model, we find that Cambodia's average trade costs decreased by 35.43 percent between 1993 and 2019. Fluctuations in average trade costs persisted until 2014, despite Cambodia's accession to the World Trade Organization (WTO) in 2004. Since then, these costs have declined more rapidly. Cambodia's bilateral trade costs are lower with its major trading partners in Southeast Asia and East Asia than with those in South Asia, Oceania, Europe, and North America. Cambodia's average trade costs with developing and emerging economies are lower than those with developed economies. Between 2014 and 2019, Cambodia experienced a notable decline in average trade costs with trading partners along the Belt and Road Initiative (BRI) corridors by 34.78 percent, twice as fast as with non-BRI trading partners. Regarding the decomposition of trade growth, we find that the expansion of Cambodian trade over the period from 1993 to 2019 was driven by three factors: the rise in income (59.65 percent), the decline in trade costs (56.69 percent), and the decline in multilateral resistance (minus 16.34 percent). The findings of this study have significant implications for a better understanding of Cambodia's development process toward global trade integration over the past two decades. Our results suggest that Cambodia can optimize its trade expansion potential by focusing on its relations with trading partners exhibiting high economic growth potential and those showing substantial reductions in trade costs.
Visual livestock measurement techniques offer non-contact operation, high efficiency, and reduced animal stress. However, traditional 2D imaging lacks depth data, while 3D reconstruction faces computational and environmental constraints, limiting real-world applicability. This study presents a novel RGB-D fusion method for efficient sheep morphometric analysis. Using Kinect V2 sensors, top-down and lateral RGB-D data were captured from Dorper sheep. An optimized YOLOv8pose_slimneck framework was used to detect keypoints of body dimension in color images, with depth values derived from aligned RGB-D pairs. Six body dimensions—body length, height, rump height, chest depth, chest width, and rump width—were calculated. On-farm experiment demonstrated that the automated body dimension measurements achieved <5 % error, and subsequent liveweight prediction based on these visual measurements yielded a mean error of 5.3 %. This approach demonstrates strong practical feasibility for implementing precision sheep farming.
George Papadopoulos, Maria-Zoi Papantonatou, Havva Uyar
et al.
This review paper delved into the economic and environmental benefits of Digital Agricultural Technological Solutions (DATSs) in livestock farming systems. Synthesising data from 52 peer-reviewed papers it presents the outcomes of a systematic literature review on livestock farming DATSs, conducted with the use of the PRISMA methodology. The analysis highlighted the contribution of DATSs across three main livestock farming DATSs categories: Automated Milking Systems (AMS), Feed and Live Weight Measurement technologies, and Health Monitoring Systems. The results showed that AMS has the potential to boost cow productivity by up to 15 % while also reducing energy consumption by 35 %. Feed and Live Weight Measurement technologies contribute notably to sustainability and cost savings, with feed waste reductions of 75 % and feeding savings of 33 %. Health Monitoring Systems are especially effective in improving herd health and productivity through early detection of clinical issues, which directly enhances animal welfare and farm efficiency. Environmentally, AMS and health monitoring tools play a vital role in reducing greenhouse gas emissions, with AMS lowering global warming potential by up to 5.83 %. Overall, the findings of this review highlight the potentials of livestock DATSs towards economic viability and environmental sustainability, suggesting that the wider adoption could offer substantial benefits for the livestock farming sector. Up to now, DATSs have shown great potential in dairy cattle by improving milk yield, quality, and animal health, with advancements such as AMS increasing productivity and health monitoring systems enhancing early disease detection. In contrast, their application in sheep, goats, and pigs is still in its early stages, mainly limited to basic health monitoring and feeding technologies, despite the economic importance of these species, especially in the Mediterranean area, where most of the studies are conducted.
The production of traditional crafts in Egyptian architecture, especially Islamic architecture, has been historically significant, reflecting cultural uniqueness. However, political, economic, and industrial factors have led to a decline in craft production, diminishing their cultural distinctiveness (Abdul Hamid, 2001). Traditional crafts currently face stagnation, lacking modernization in design, functional integration, and technological advancements. Artisans struggle to compete with imported products. This research aims to preserve and revitalize traditional crafts by integrating them with the creative industries. It explores modern Egyptian experiences, traces the craft’s development, and identifies key points for creating creative products. The study evaluates Egyptian models that introduce new products, assessing their suitability and overall development, including impacts on craftsmen, products, and the production process. The application of creative industry principles is also examined. Findings inform proposals and recommendations to preserve traditional crafts in Egypt and explore their local and global development potential.
In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.
In this article, we investigate a growing trend in the worldwide Quantum Technology (QT) education landscape, that of the development of masters programs, intended to provide graduates with the knowledge and skills to take a job in the quantum industry, while serving a much shorter timeline than a doctoral degree. Through a global survey, we identified 86 masters programs, with substantial growth since 2021. Over time masters have become increasingly interdisciplinary, organised by multiple faculties or through joint degree programs, and offer more hands-on experiences such as internships in companies. Information from program organisers and websites suggests that the intended career destinations of their graduates are a diverse range of industries, and therefore masters programs may be a boon to the industrialisation of quantum technologies. Finally, we identify a range of national efforts to grow the quantum workforce of many countries, quantum program enhancements, which augment the content of existing study programs with quantum content. This may further contribute to the growth and viability of masters programs as a route to the quantum industry.
In the last century the automotive industry has arguably transformed society, being one of the most complex, sophisticated and technologically advanced industries, with innovations ranging from hybrid, electric and self-driving smart cars to the development of IoT-connected cars. Due to its complexity, it requires the involvement of many Industry 4.0 technologies, like robotics, advanced manufacturing systems, cyber-physical systems or augmented reality. One of the latest technologies that can benefit the automotive industry is blockchain, which can enhance its data security, privacy, anonymity, traceability, accountability, integrity, robustness, transparency, trustworthiness and authentication, as well as provide long-term sustainability and a higher operational efficiency to the whole industry. This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features. Thus, the applicability of blockchain is evaluated after examining the state-of-the-art and devising the main stakeholders' current challenges. Furthermore, the article describes the most relevant use cases, since the broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications that can create new business models and even disrupt the car-sharing economy as we know it. Finally, after a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, some recommendations are enumerated with the aim of guiding researchers and companies in future cyber-resilient automotive industry developments.
Paula Fraga-Lamas, Tiago M Fernandez-Carames, Oscar Blanco-Novoa
et al.
Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud.
In the context of global efforts to address climate change, research into regional carbon neutrality strategies has become especially critical. For developing countries and regions, it is essential to scientifically and rationally assessing the paths for small-scale regional transformations under carbon neutrality imperatives to effectively implement low-carbon transition measures. This study utilizes Chongming District in Shanghai of China as a case to establish a framework for forecasting carbon emission and sink from a multi-dimensional natural-social perspective. This facilitates the simulation and optimization of pathways for carbon neutrality transformation. The results indicate: (1) From 2000 to 2020, the total regional carbon emission exhibited a rising trend, while the total carbon sink initially declined then increased, indicating potential enhancement zone with significant potential and space for carbon neutrality development. (2) Enhanced management of ecological spaces and land use planning result in notable increases in carbon sink. Strategic measures such as emission and consumption reductions, alongside energy transitions, effectively controlled carbon emission growth and facilitated comprehensive decarbonization. (3) By combining ecological priority with enhanced control and balanced development with enhanced control, the region can achieve carbon neutrality. This showcases the effective role of policy regulation in facilitating high-quality carbon–neutral transformations. (4) Effective ecosystem management along with robust reduction and transition strategies enable county-level carbon–neutral transformations, offering a model and methodological support for other developing regions facing the twin challenges of economic growth and environmental sustainability.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
This study analyzes the consequences of first-order chemical reactions, radiation, and viscous dissipation on the unsteady magnetohydrodynamic natural convective flow of a viscous incompressible fluid over a vertically positioned semi-boundless oscillating plate with uniform mass diffusion and temperature. An implicit finite-difference technique is employed to solve a set of dimensionless, coupled, nonlinear partial differential equations. The numerical results for fluid velocity, concentration, and temperature at the plate under different dimensionless parameters are graphically displayed. Due to first-order homogeneous chemical reactions, it has been discovered that the velocity rises at the time of a generative reaction and drops during a destructive reaction. A decline in velocity is observed with an increase in the phase angle, radiation parameter, and chemical reaction parameter. Further, it has been revealed that plate oscillation, radiation, chemical reactions, and the magnetic field significantly influence the flow behavior.
Davi Carvalho Medeiros, Érika Maria Henriques Monteiro, Igor Rosa Meurer
et al.
Introdução
O avanço da ciência permitiu que, em alguns anos, o uso de antirretrovirais (ARVs) reduzisse o índice de mortalidade relacionado à doença e ampliasse a qualidade de vida dos indivíduos acometidos pelo HIV. Apesar dos inúmeros progressos neste sentido, existe atualmente uma baixa quantidade de pesquisas voltadas à uma análise mais aprofundada sobre a aprovação de ARVs no mundo. Embora o escopo do que se categorizava como “tecnologia de saúde” fosse amplo, a ATS aglutinou sua atenção inicial às tecnologias de produtos, como medicamentos, materiais e equipamentos. Traçando uma trajetória ao longo das últimas décadas, a ATS expandiu sua presença e visibilidade, transcendendo o velho continente para ecoar nos rincões globais fomentando ainda mais as nações em desenvolvimento. O Objetivo deste estudo foi avaliar o impacto orçamentário em um cenário global de aprovações dos ARVs para tratamento do HIV.
Métodos
Trata-se de um estudo de impacto orçamentário proveniente da base de dados de um estudo ecológico do cenário global de tratamento de HIV com ARVs. Foram considerados os custos diretos do medicamento para tratamento de HIV/AIDS seguindo o esquema terapêutico estabelecido pelos protocolos em cada região/país analisados: Brasil, Estados Unidos, Europa e Austrália. Os custos foram obtidos pela Câmara de Regulação do mercado de medicamentos (CMED) para o Brasil, a Base de données publique des médicaments para a Europa, simbolizando toda a União Europeia, para a Austrália foi utilizado instituições como a Pharmaceutical Benefits Scheme (PBS) e Australasian Socirty for HIV, Viral Hepatitis, and Sexual Health Medicine (ASHM). A análise de sensibilidade do IO foi realizada para 10.000 interações na simulação de Monte Carlo, pelo microssoft Excel® com Addin @Risk v8.05 da Palisade Corporation®.
Resultados
Foi verificado os custos médios direto para Austrália R$ 20.012,15 (IC95%, 10.006,08 - 30.018,23) em torno (0,333 - 1,001) por paciente em uma população de 30.000 contaminados; Brasil R$ 47.415,98 (IC 95%, 23.707,99 - 71.123,97) cerca de (0,028 - 0,085) por paciente em uma população de 841.000 contaminados; Estados Unidos R$ 39.204,73 (IC95%,19.602,36 - 58.807,09) aproximadamente (0,019 - 0,057) por paciente em um total de 1.031.191 contaminados.; Europa R$ 21.678,00 (IC95%, 10.839,00 - 32.517,00) cerca de (0,004 - 0,012) por paciente em um total de 2.800.000 contaminados. A avaliação do impacto orçamentário está em construção, a ser considerado para isso prevalência da doença, as demandas reprimidas e taxa de letalidade.
Discussões e conclusões
Conclui-se que, dos diferentes protocolos que regem o tratamento, os EUA foi o país que mais teve novas tecnologia incorporadas, havendo maior custo médio para o Brasil e menor para a Austrália. Contudo, a análise dos dados farmacoeconômicos e epidemiológicos das diferentes regiões oferece informações valiosas para a tomada de decisões em prol da ATS no tratamento do HIV. Esses dados destacam a importância da alocação eficaz de recursos, a necessidade de equilibrar custos e efetividade, e a urgência de abordar desigualdades na cobertura de acesso. Ao melhorar o acesso, enfatizar a prevenção e adotar uma abordagem baseada em evidências, é possível otimizar o impacto orçamentário e alcançar resultados mais eficazes no controle do HIV.
Pharmacy and materia medica, Pharmaceutical industry
Studying the evolutionary patterns of rice agronomic traits in South China and analyzing the characteristics of rice improvement can provide insights into the developmental trajectory of rice breeding in South China and can guide further enhancement of variety yield. In this study, widely promoted varieties and core parents developed through dwarf breeding in the southern region, as well as landraces, were collected and planted in three different ecological regions. A total of 18 agronomic traits were investigated related to heading date, plant type, panicle type, grain type, and yield, and multiple comparisons, a correlation analysis, and a path analysis were conducted. The results indicate that dwarf breeding has significantly increased the yield of inbred indica rice varieties in South China. However, a reduction in plant height has also resulted in a reduction in flag leaf, shorter panicles, and decreased biomass, which have led to metabolic source and storage capacity deficiencies and limited yield potential. To address these limitations, breeders have employed strategies such as increasing flag leaf width, spikelet density, number of primary branches, and grain number per panicle. These measures have led to a gradual increase in yield. Additionally, starting from the 1980s, high-quality rice breeding has been pursued in South China, resulting in slender grain shape and reduced thousand grain weight. Given that total grain number per panicle has already increased significantly and the thousand grain weight cannot be reduced further, enhancing the effective tiller number, which decreases year by year, becomes an important approach to increasing the yield of inbred indica rice varieties in South China.
The inverse problem of special geometry (Seiberg-Witten geometry of 4d N=2 SCFT) asks for a recursive construction of all such geometries in rank $r$ by assembling together known lower-rank ``strata''. This leads to a program to understand/construct/classify all special geometries which looks surprising effective. After reviewing some advanced topics in special geometry, in this long note we define the inverse problem and introduce the basic tools of the trade. The program is essentially completed in rank 2, and we pave the way to proceed to higher ranks. A central role is played by various notions of geometric rigidity: in addition to the obvious one (triviality of the conformal manifold), Falting-Saito-Peters rigidity and Deligne-Simpson rigidity also enter in the story.
Achieving universal electricity access by 2030 is one of the energy-related development targets in Nigeria and the electricity access rate is estimated at 62%, with urban being 91% and rural 30%. In line with the set SDG7 target of achieving universal electricity access by 2030, this study examines the least-cost options for providing electricity access to thousands of unelectrified communities in Nigeria using the open source spatial electrification toolkit (OnSSET). The study focuses on the coverage area of one of the distribution companies, i.e. Kaduna Electricity Distribution Company (KEDCo) which covers Kaduna, Kebbi, Sokoto, and Zamfara States in the North-Western part of Nigeria. Spatial data covering different areas such as population, digital elevation, energy resource availability, coverage of the distribution lines, among otherswere obtained from different sources and used in the OnSSET model. The result shows that by 2030, mini-grid PV will be the least-cost technology option for 58.82% of the unelectrified communities, followed by grid-extension (20.87%), standalone PV (20.15%), and mini-grid hydro (0.17%). Further, by 2025, the total number of settlements to be electrified will be 18,182. The number of settlements electrified by 2030 will be 22,727 with an estimated population of 15.4 million. To the achieve universal electrification, a total investment of US$4.97 billion is required by 2025 with an additional US$2.88 billion by 2030. The study recommends that the state and local governments should play a key roles in providing enabling environment for community-led off-grid projects since most of the settlements require electrification projects that are less than 20 kW.
This study proposes a wind farm active power dispatching (WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching (WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of high- speed turbines is effectively reduced.
Energy conservation, Energy industries. Energy policy. Fuel trade
Homa Hosseinzadeh-Bandbafha, Hamed Kazemi Shariat Panahi, Mona Dehhaghi
et al.
Sustainable socio-economic development largely depends on the sustainability of the energy supply from economic, environmental, and public health perspectives. Fossil fuel combustion only meets the first element of this equation and is hence rendered unsustainable. Biofuels are advantageous from a public health perspective, but their environmental and economic sustainability might be questioned considering the conflicts surrounding their feedstocks, including land use change and fuel vs. food conflict. Therefore, it is imperative to put more effort into addressing the downsides of biofuel production using advanced technologies, such as nanotechnology. In light of that, this review strives to scrutinize the latest developments in the application of nanotechnology in producing biodiesel, a promising alternative to fossil diesel with proven environmental and health benefits. The main focus is placed on nanotechnology applications in the feedstock production stage. First, the latest findings concerning the application of nanomaterials as nanofertilizers and nanopesticides to improve the performance of oil crops are presented and critically discussed. Then, the most promising results reported recently on applying nanotechnology to boost biomass and oil production by microalgae and facilitating microalgae harvesting are reviewed and mechanistically explained. Finally, the promises held by nanomaterials to enhance animal fat production in livestock, poultry, and aquaculture systems are elaborated. Despite the favorable features of using nanotechnology in biodiesel feedstock production, the presence of nanoparticles in living systems is also associated with important health and environmental challenges, which are critically covered and discussed in this work.
Fuel, Energy industries. Energy policy. Fuel trade
Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini
et al.
The Non-Fungible Token (NFT) market in the Ethereum blockchain experienced explosive growth in 2021, with a monthly trade volume reaching \$6 billion in January 2022. However, concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially. Our research examines the effects of wash trading on the NFT market in Ethereum from the beginning until January 2022, using multiple approaches. We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of \$3,406,110,774. We look at two ways to profit from wash trading: Artificially increasing the price of the NFT and taking advantage of the token reward systems provided by some marketplaces. Our findings show that exploiting the token reward systems of NFTMs is much more profitable (mean gain of successful operations is \$1.055M on LooksRare), more likely to succeed (more than 80% of operations), and less risky than reselling an NFT at a higher price using wash trading (50% of activities result in a loss). Our research highlights that wash trading is frequent in Ethereum and that NFTMs should implement protective mechanisms to stop such illicit behavior.
Roberto Figliè, Riccardo Amadio, Marios Tyrovolas
et al.
Today, one of the biggest challenges for digital transformation in the Industry 4.0 paradigm is the lack of mutual understanding between the academic and the industrial world. On the one hand, the industry fails to apply new technologies and innovations from scientific research. At the same time, academics struggle to find and focus on real-world applications for their developing technological solutions. Moreover, the increasing complexity of industrial challenges and technologies is widening this hiatus. To reduce this knowledge and communication gap, this article proposes a mixed approach of humanistic and engineering techniques applied to the technological and enterprise fields. The study's results are represented by a taxonomy in which industrial challenges and I4.0-focused technologies are categorized and connected through academic and grey literature analysis. This taxonomy also formed the basis for creating a public web platform where industrial practitioners can identify candidate solutions for an industrial challenge. At the same time, from the educational perspective, the learning procedure can be supported since, through this tool, academics can identify real-world scenarios to integrate digital technologies' teaching process.