Electroslag refining (ESR) emerges as a complementary battery recycling solution in regions with electricity costs below $0.06/kWh. This study demonstrates that ESR achieves 92% Co/Ni recovery at $14/kg operating costs when powered by renewable energy, compared to $19/kg for hydrometallurgy. Technological innovations include Li₂O-doped slag (1-5 wt%) reducing lithium losses to <5% and IoT-controlled current modulation cutting energy use by 18%. A global patent analysis identifies white spaces in apparatus design (WIPO class C22B9/16), while techno-economic modeling reveals optimal viability in Quebec (hydro) and Rajasthan (solar) regions. Despite lower Li recovery (68% vs hydro's 80%), ESR's 35% carbon footprint reduction and slag valorization potential (92% as cement additive) position it as a sustainable alternative under EU Taxonomy criteria Detailed Description Technological Innovations Li₂O-Doped Slag Design: Composition: CaF₂-Al₂O₃-Li₂O (80-15-5 wt%) Performance: 89% Li retention vs. 62% in conventional slags at 1600°C Mechanism: Li₂O increases slag basicity, reducing Li volatility Smart ESR Systems: IoT sensors optimize current density (0.5-1.2 A/mm²) Reduces energy consumption from 12 → 9.8 kWh/kg Co Economic Viability Region Electricity Cost ($/kWh) ESR Viability Index* Key Enablers Quebec 0.04 88/100 Existing aluminum smelters Norway 0.03 95/100 Battery passport infrastructure Rajasthan 0.05 72/100 Solar park colocation *Based on 5 factors: energy cost, infrastructure, policy, demand, logistics Patent Landscape White Spaces: Direct ESR processing of pyrolyzed black mass (no prior USPTO patents) Slag compositions with 1-5% Li₂O (novelty confirmed via Espacenet search) Risk Areas: Avoid infringement on Umicore's hydrometallurgy patents (US2018367232) Design-around Tesla's direct recycling claims through high-temperature differentiation Sustainability Impact Carbon Footprint: ESR: 2.1 kg CO₂/kg metal vs. 3.8 kg for pyrometallurgy Further reducible to 1.4 kg with renewable electricity Waste Valorization: 92% of slag meets ASTM C989 for cement additives Potential offset of $50/ton disposal costs
This paper theorises how inclusive grassroots innovation responds to socio-economic inequalities and facilitates efforts to overcome them, contingent on solidaristic relationships. As a mainstream policy concept, the term ‘innovation’ has become more narrowly defined as capital-intensive technological innovation, which has often worsened social inequalities. In response, ‘inclusive innovation’ has become an umbrella term encompassing diverse alternatives seeking to reduce or avoid social inequalities. These have arisen especially in the Social Solidarity Economy (SSE), based on democratic self-management and mutual aid; its enterprises depend on wider ecosystems of support groups. The SSE has some overlaps with Alternative Agri-Food Networks (AAFNs), which build greater social proximity between producers and consumers. Hence the overlap is here called the SSE-AAFNs. During the Covid-19 pandemic, many SSE-AAFNs rapidly adapted to the disruptions through novel practices that could fulfil their members’ needs. SSE-AAFNs ecosystems played this creative role through three general parameters: inclusive grassroots innovation, agile adaptations, and a transformative resilience bouncing forwards. These parameters form a tripartite framework that helps to analyse case studies of SSE-AAFNs in Brazil and Turkey. In both cases, grassroots innovation helped to overcome social inequalities (of class, race, gender), in ways contingent on each initiative and its context. SSE-AAFNs have demanded and gained support measures from municipalities, along lines helping to build collective capacities rather than dependence. The tripartite analytical framework here has wider relevance to SSE ecosystems developing grassroots innovation which can overcome inequalities.
Fatima Ghizal, Buttar Harpal, Parvez Sidrah
et al.
The convergence of artificial intelligence (AI) and precision medicine is transforming healthcare by introducing a patient-centred, data-driven approach to treatment. Precision medicine, which tailors medical care based on individual characteristics, addresses the complexity and heterogeneity of diseases. The integration of AI into this field has unlocked unprecedented potential for enhancing disease management and advancing personalised care. AI leverages extensive datasets, including genomic sequences, clinical records and molecular profiles, to identify patterns and predict outcomes with remarkable accuracy. Its capabilities extend beyond automation, functioning as a critical tool for informed clinical decision-making. By analysing complex molecular data, AI enhances diagnostic precision through the detection of subtle biomarkers and anomalies frequently overlooked by traditional methods. Machine learning-powered predictive analytics further empower clinicians by forecasting disease progression and guiding treatment personalisation. Practical applications of AI-driven precision medicine are already evident in clinical settings. From diagnosing rare genetic disorders to optimising drug therapies based on genetic profiles, AI is fundamentally reshaping patient care. However, critical challenges, including ethical considerations, data privacy and the need for transparent algorithms, persist. This review examines the synergistic relationship between AI and precision medicine, highlighting ongoing research, technological innovations and interdisciplinary collaboration. Together, these advancements herald a transformative era in healthcare, paving the way for highly personalised and effective therapeutic strategies.
Kiandra Putri Susanto, Wenny Candra Mandagie, Endri Endri
et al.
Rapid technological advances have made financial markets more accessible and encouraged individual investors to engage in investment decision-making actively. Generation Z, or Gen Z, characterized by higher levels of digital literacy, a high sense of curiosity, and acceptance of innovation, tends to make investment decisions quickly. This study aimed to analyze the effect of technological progress, financial literacy, and financial attitudes on investors’ investment decisions. There are 125 Gen Z investors in Jakarta, Indonesia, selected as research samples using the non-probability sampling method. The survey method was employed to collect data, and the study instrument was a questionnaire. For data analysis, Partial Least Squares version 4.0 was used. The study’s findings revealed that financial literacy and financial attitude positively influence Gen Z investment decisions. Technological progress does not affect Gen Z in determining investment in the financial market. Financial literacy and financial attitude are more dominant for Gen Z investors than technological progress in determining investment allocation. This finding implies that Gen Z must improve their understanding of correct financial literacy and financial attitudes that align with individual investors’ character. Further investigation needs to reveal the insignificance of technological progress in determining investment decisions. Technological progress and financial literacy likely have the same factor characteristics related to three dimensions: knowledge, skills, and attitudes. The attitude of Gen Z investors towards the progress of financial technology by investors is preceded by good financial literacy. Therefore, it is necessary to test the relationship between variables, both mediation and moderation, in investment decisions.
Abraham Vadillo Morillas, Jesús Meneses Alonso, Alejandro Bustos Caballero
et al.
CAD-CAE software companies have introduced numerous tools aimed at facilitating topology optimization through Finite Element Simulation, thereby enhancing accessibility for designers via user-friendly interfaces. However, the imposition of intricate constraint conditions or additional restrictions during calculations may introduce instability into the resultant outcomes. In this paper, an algorithm for updating the design variables called Adaptive Variable Design is proposed to keep the final design space volume of the optimized part consistently under the target value while giving the main algorithm multiple chances to update the optimization parameters and search for a valid design. This algorithm aims to produce results that are more conducive to manufacturability and potentially more straightforward in interpretation. A comparison between several commercial software packages and the proposed algorithm, implemented in MATLAB R2023a, is carried out to prove the robustness of the latter. By simulating identical parts under similar conditions, we seek to generate comparable results and underscore the advantages stemming from the adoption and comprehension of the proposed topology optimization methodology. Our findings reveal that the integrated enhancements within MATLAB pertaining to the topology optimization process yield favourable outcomes with respect to discretization and the manufacturability of the resultant geometries. Furthermore, we assert that the methodology evaluated within MATLAB holds promise for potential integration into commercial packages, thereby enhancing the efficiency of topology optimization processes.
Engineering machinery, tools, and implements, Technological innovations. Automation
Date palm tree (DPT) and pine tree (PT) needles in forests form a combustible mat, posing fire risks during summer in Pakistan that damage vegetation, wildlife habitats, and biodiversity and impact local livelihoods. In this article, sintered ceramic specimens were prepared at different weight concentrations (DPT5, DPT10, DPT20, and DPT 30 and PT5, PT10, PT20, and PT30) of date palm tree leaf ash and pine tree needle ash as secondary additives in ceramic manufacturing along with primary material kaolinite (China clay). Raw materials composition was analyzed using X-ray diffraction (XRD), taking loss on ignition, water absorption, bulk density, saturated surface dry density (SSD), weight per unit area, and thermal cycling as measurement indexes. The result indicates that loss on ignition increases while increasing the quantity of secondary additives and the maximum increase for DPT30 was 19.6% and for PT30, it was 22.1%. As the secondary additives increase, the water absorption rate also increases and the maximum increase for DPT30 and PT30 is 4.5%. Meanwhile, with the increase in secondary additives, the density decreased; for DPT 30, it was 1457.7 kg/m<sup>3</sup> and for PT30, it was 1829.8 kg/m<sup>3</sup>. Thermal performance was investigated by heating and cooling cycles. It was observed that thermal performances increase with the increase in secondary additives. The results reveal this novel approach has the potential to form a ceramic and good properties can be achieved. The prepared specimens have the potential to be used in the fields of electronics, aerospace, construction, and building engineering, alleviating environmental strain, curbing the exhaustion of China clay reserves, and most importantly, lowering the risk of forest fires in Pakistan.
Engineering machinery, tools, and implements, Technological innovations. Automation
This article examines what responsibility means in the context of synthetic biotechnologies, based on academic researchers in the American west who are using/developing synthetic biology, engineering biology, and synthetic genomics. Advancements in technical capacity are ushering in imminent/current possibilities of creating whole genomes/organisms from scratch, yet extant narratives about ‘responsibility’ have neither been fleshed out, nor compared against normative frameworks (such as ELSI and its critiques). Through empirical data collection (e.g. discourse analysis), this paper examines interviews with biotechnologists (N = 16) to analyze responsibility narratives on the ground, which include: being responsible towards grand challenges, national values, and research relations involving other beings in the lab, both human and more-than-human. The analyses presented here offer feminist and multispecies critiques for studying the relational webs of responsible (response-able) research and concludes with a discussion about the mismatch between how responsibilities are narrativized across different actors within academic research institutions.
Eugen Rusu, Puiu Lucian Georgescu, Florin Onea
et al.
The aim of this work is to provide some details regarding the energy potential of the local wind and solar resources near the Galati area (south-east of Romania) by considering the performances of a few recent technologies. Based on 22 years of ERA5 data (2001–2022), a picture concerning the renewable energy resources in the Brates Lake area is provided. Comparing the wind and solar resources with in situ and satellite data, a relatively good agreement was found, especially in regards to the average values. In terms of wind speed conditions at a hub height of 100 m, we can expect a maximum value of 19.28 m/s during the winter time, while for the solar irradiance the energy level can reach up to 932 W/m<sup>2</sup> during the summer season. Several generators of 2 MW were considered for evaluation, for which a state-of-the-art system of 6.2 MW was also added. The expected capacity factor of the turbines is in the range of (11.71–21.23)%, with better performances being expected from the Gamesa G90 generator. As a next step, several floating solar units were considered in order to simulate large-scale solar projects that may cover between 10 and 40% of the Brates Lake surface. The amount of the evaporated water saved by these solar panels was also considered, being estimated that the water demand of at least 3.42 km<sup>2</sup> of the agricultural areas can be covered on an annual scale.
Engineering machinery, tools, and implements, Technological innovations. Automation
C. Castán-Fernández, G. Marcos-Robredo, M. P. Castro-García
et al.
This paper describes the design, construction, validation, and calibration of a thermal conductivity measuring apparatus for geothermal backfill materials in the range from 0.13–2.80 W/m·K. The developed apparatus is based on the Transient Hot Wire (THW) method whose mathematical basis is the Infinite Linear Source (ILS) model. The apparatus consists of a nichrome hot wire, an adjustable direct current power supply, a temperature sensor (K-type thermocouple), and a datalogger. For the validation and calibration of the developed apparatus, four standard samples have been used with a known thermal conductivity, to 3.0 W/m·K. Furthermore, the thermal conductivity of four geothermal backfill materials of common use (bentonite, neat cement, cement–sand mortar, and cement–bentonite mortar) has been measured using both the developed apparatus and a commercial meter.
Engineering machinery, tools, and implements, Technological innovations. Automation
L. Kogan, D. Papanikolaou, Lawrence D. W. Schmidt
et al.
We construct new technology indicators using textual analysis of patent documents and occupation task descriptions that span of two centuries (1850–2010). At the industry level, improvements in technology are associated with higher labor productivity but a decline in the labor share. Exploiting variation in the extent certain technologies are related to specific occupations, we show that technological innovation has been largely associated with worse labor market outcomes— wages and employment—for incumbent workers in related occupations using a combination of public-use and confidential administrative data. Panel data on individual worker earnings reveal that less educated, older, and more highly-paid workers experience significantly greater declines in average earnings and earnings risk following related technological advances. We reconcile these facts with the standard view of technology-skill complementarity using a model that allows for skill displacement. ∗We are grateful to Daron Acemoglu, David Autor, Martin Beraja and seminar participants in the meetings of the Econometric Society and the Society of Economic Dynamics for valuable discussions and feedback, and to Will Cong for generously sharing his replication code. Brice Green and Jinpu Yang provided excellent research support. The paper has been previously circulated as “Technological Change and Occupations over the Long Run” †MIT Sloan School of Management and NBER ‡Kellogg School of Management and NBER §MIT Sloan School of Management ¶MIT Sloan School of Management Economists and workers alike have long worried about the employment prospects of workers whose key tasks can be easily performed by a machine, robot, software, or some other form of capital that substitutes for labor.1 These concerns have been exacerbated by recent breakthroughs in automation technologies (e.g., software, artificial intelligence, robotics) which have expanded the set of manual and cognitive tasks which can performed by machines and have occurred contemporaneously with an increase in income inequality and a fall in the labor share of aggregate output.2 Yet, despite the importance of these issues, systematic evidence for technological displacement remains elusive.3 Our goal is to fill this gap: we leverage over a century and a half of data to propose and validate new metrics of workers’ exposure to technological innovation and relate them to workers’ labor market outcomes, both at the aggregate as well as the individual level. To measure workers’ exposures to technical change we measure the similarity between the textual description of the tasks performed by an occupation and that of major technological breakthroughs. We identify the later through the textual analysis of patent networks using the methodology of Kelly, Papanikolaou, Seru, and Taddy (2020). To estimate the distance between a breakthrough innovation and workers’ task descriptions, we leverage recent advances in natural language processing that allow us to compute a measure of the similarity between documents that accounts for synonyms. By exploiting the timing of patent grants we can identify the extent to which certain worker groups (occupations) are exposed to major technological breakthroughs at a given point in time. In sum, our indices capture the extent to which specific occupations are exposed to breakthrough innovations in a given year. We emphasize that, a priori, we are agnostic on whether innovations that are similar to tasks certain occupations perform are likely to be substitutes or complements. For that, we need to examine how our indicators correlate with labor market outcomes. A key advantage of our methodology is that it relies only on document text; as such, we are able to construct time-series indices of occupation exposures that span the last two centuries. For example, our technology exposure for “molders, shapers, and casters, except metal and plastic”—an occupation category which includes glass blowers as a sub-occupation—takes a relatively high value in the 1Fear of technological unemployment is not new. In 350 BCE, Aristotle wrote: “[If] the shuttle would weave and the plectrum touch the lyre without a hand to guide them, chief workmen would not want servants, nor masters slaves.” In 1811, skilled weavers and textile workers (known as Luddites) worried that mechanizing manufacturing (and the unskilled laborers operating the new looms) would rob them of their means of income. In 1930, Keynes described this type of potential labor market risk when he said, “We are being afflicted with a new disease of technological unemployment...due to our discovery of means of economising the use of labor outrunning the pace at which we can find new uses for labor." More recently, a McKinsey report estimated that between 400 million and 800 million jobs could be lost worldwide due to robotic automation by the year 2030. 2For instance, one of the leading explanations for the increase in the skill premium is skill-biased technical change, whereas the decline in the labor share has been attributed to capital-embodied technical change.. See Goldin and Katz (2008); Krusell, Ohanian, Ríos-Rull, and Violante (2000); Karabarbounis and Neiman (2013); Acemoglu and Restrepo (2020, 2018, 2021) 3Due to the difficulty of constructing broad measures of labor-displacive innovations, existing work has focused on analyzing specific instances in which the impact of a specific technology on workers can be identified (Atack, Margo, and Rhode, 2019; Feigenbaum and Gross, 2020; Akerman, Gaarder, and Mogstad, 2015; Humlum, 2019).
Electric, autonomous vehicles promise to address technical consumption inefficiencies associated with gasoline use and reduce emissions. Potential realization of this prospect has prompted considerable interest and investment in the technology. Using publicly available data from a select market, we examine the magnitude of the envisioned benefits and the determinants of the financial payoff of investing in a tripartite innovation in motor vehicle transportation: vehicle electrification, vehicle automation, and vehicle sharing. In contrast to previous work, we document that (a) the technology’s envisioned cost effectiveness may be impeded by previously unconsidered parameters, (b) the inability to achieve cost parity with the status quo does not necessarily preclude net increases in energy consumption and emissions, (c) these increases are driven primarily by induced demand and mode switches away from pooled personal vehicles, and (d) the aforementioned externalities may be mitigated by leveraging a specific set of technological, behavioral and logistical pathways. We quantify—for the first time—the thresholds required for each of these pathways to be effective and demonstrate that pathway stringency is largely influenced by heterogeneity in trip timing behavior. We conclude that enacting these pathways is crucial to fostering environmental stewardship absent impediments in economic mobility.