In this study, we propose a novel method for detecting cyberattack behaviors by leveraging the combined strengths of large language models and a synchronized attention mechanism. Extensive experiments conducted on diverse datasets, including server logs, financial behaviors, and comment data, demonstrate the significant advantages of this method over existing models such as Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B in key performance metrics such as precision, recall, and accuracy. For instance, on the server log dataset, the method achieved a precision of 93%, a recall of 91%, and an accuracy of 92%; on the financial behavior dataset, it reached a precision of 90%, a recall of 87%, and an accuracy of 89%; and on the comment data dataset, it excelled with a precision of 95%, a recall of 93%, and an accuracy of 94%. The introduction of a synchronized attention mechanism and a newly designed synchronized loss function proved especially effective, enhancing the method’s ability to process multi-source data and providing superior performance in identifying complex cyberattack patterns. Ablation experiments further validated the crucial roles of these innovations in boosting model performance: the synchronous attention mechanism substantially improved the model’s precision, recall, and accuracy to 93%, 89%, and 91% respectively, far exceeding other attention mechanisms. Similarly, the synchronized loss showcased a significant advantage, achieving the best performance across all tested metrics compared to traditional cross-entropy loss, focal loss, and MSE. These results underscore the method’s ability to deeply mine and analyze semantic information and contextual relationships within text data as well as to effectively integrate and process multimodal data, thereby offering strong technical support for the accurate and efficient detection of cyberattack behaviors. Full article

Convolutional neural networks (CNNs) have demonstrated remarkable performance in many areas but require significant computation and storage resources. Quantization is an effective method to reduce CNN complexity and implementation. The main research objective is to develop a scalable quantization algorithm for CNN hardware design and model the performance metrics for the purpose of CNN implementation in resource-constrained devices (RCDs) and optimizing layers in deep neural networks (DNNs). The algorithm novelty is based on blending two quantization techniques to perform full model quantization with optimum accuracy, and without additional neurons. The algorithm is applied to a selected CNN model and implemented on an FPGA. Implementing CNN using broad data is not possible due to capacity issues. With the proposed quantization algorithm, we succeeded in implementing the model on the FPGA using 16-, 12-, and 8-bit quantization. Compared to the 16-bit design, the 8-bit design offers a 44% decrease in resource utilization, and achieves power and energy reductions of 41% and 42%, respectively. Models show that trading off one quantization bit yields savings of approximately 5.4K LUTs, 4% logic utilization, 46.9 mW power, and 147 $\mathsf{\mu }$J energy. The models were also used to estimate performance metrics for a sample DNN design. Full article

The concept of nature positive has recently emerged from the widespread recognition of the global scale at which nature is being lost and the threat this poses to the collective survival of the human race. Much of the interest in nature positive reflects the initial commitment to it by conservation organisations and by a number of international political initiatives. However, the pursuit of nature positive approaches by businesses has received little attention in the business and management literature. Building upon an analysis of secondary sources, this entry paper first examines how three international organisations suggest a nature positive strategy could be pursued. This paper then focuses on two multinational companies and how they plan to pursue a nature positive approach to their business activities. This article identifies a number of critical factors in developing a nature positive strategy: incorporating suppliers in this strategy, assessing corporate dependencies and impacts on nature, and reporting on nature positive initiatives and outcomes. At the same time, this article raises concerns that nature positive approaches could be driven more by business imperatives rather than fundamental corporate concerns about biodiversity and that many business commitments to nature positive could be seen as mainly aspirational. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mainly targets the upper respiratory tract. It gains entry by interacting with the host cell receptor angiotensin-converting enzyme 2 (ACE2) via its heavily glycosylated spike glycoprotein. SARS-CoV-2 can also affect the gastrointestinal tract. Given the significant role of glycosylation in the life cycle of proteins and the multisystem target of SARS-CoV-2, the role of glycosylation in the interaction of S1 with ACE2 in Caco-2 cells was investigated after modulation of their glycosylation patterns using N-butyldeoxynojirimycin (NB-DNJ) and 1-deoxymannojirimycin (dMM), in addition to mutant CHO cells harboring mutations at different stages of glycosylation. The data show a substantial reduction in the interactions between the altered glycosylation forms of S1 and ACE2 in the presence of NB-DNJ, while varied outcomes resulted from dMM treatment. These results highlight the promising effects of NB-DNJ and its potential use as an off-label drug to treat SARS-CoV-2 infections. Full article

Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m. Full article

The aim of this study was to explore and document the enablers and barriers of chiropractic care colocation in general practice at a large-scale private primary care centre in Australia. This study focused on the perceptions of healthcare professionals regarding this integration. The research setting was a large integrated primary care centre located in an outer metro, low-socioeconomic area in the City of Moreton Bay, Queensland, Australia. Participant inclusion criteria included general medical practitioners, practice nurses, and medical managers who self-reported interactions with the physically collocated and integrated chiropractic practice. Data was collected from 22 participants using face-to-face, qualitative, semi-structured interviews with an average duration of 32 min. The data collected included perceptions of chiropractic treatment, enablers to patient referral pathways, and views of the integrated chiropractic care model. A reflexive thematic analysis was conducted on the data set. All participants reported that this was their first exposure to the colocation of a chiropractor within a general medical practice. Four key enablers of chiropractic care integration were identified: (1) the practitioner [chiropractor], (2) the organisation [general practice], (3) consumer flow, and (4) the environment [shared spaces and tenant ecosystem]. The chiropractic integration enhanced knowledge sharing and interprofessional trust among healthcare providers. The formal reporting of patient outcomes and understanding of the chiropractor’s scope of practice further enabled referrals to the service. Shared administrative and business processes, including patient records, booking systems, and clinical meetings, facilitated relationship development between the chiropractor and referring health providers. Colocation as part of a larger primary care centre created proximity and convenience for health providers in terms of interprofessional communication, and for patients, in terms of access to chiropractic services. Existing governance structures supported communication, professional education, and shared values related to the delivery of patient-centred care. Identified barriers included limited public funding for chiropractic services resulting in reduced access for patients of low-socioeconomic status. Additionally, scepticism or negativity towards the discipline of chiropractic care was identified as an initial barrier to refer patients. In most cases, this view towards the chiropractor was overcome by regular patient reporting of positive treatment outcomes to their GP, the delivery of education sessions by the chiropractor for the health providers, and the development of interprofessional trust between the chiropractor and referring health providers. This study provides preliminary evidence and a conceptual framework of factors influencing the successful integration of chiropractic care within an Australian large primary care centre. The data collected indicated that integration of chiropractic care into a primary care centre serving a low-socioeconomic region can be achieved with a high degree of health provider satisfaction. Full article

Due to a higher risk of maternal complications during pregnancy, as well as pregnancy complications such as stillbirth, SARS-CoV-2 contamination during pregnancy is a putative stress factor that could increase the risk of perinatal maternal mental health issues. We included women older than 18 years, who delivered a living baby at the Geneva University Hospitals’ maternity wards after 29 weeks of amenorrhea (w.a.) and excluded women who did not read or speak fluent French. We compared women who declared having had COVID-19, confirmed by a positive PCR test for SARS-CoV-2, during pregnancy with women who did not, both at delivery and at one month postpartum. We collected clinical data by auto-questionnaires between time of childbirth and the third day postpartum regarding the occurrence of perinatal depression, peritraumatic dissociation, and peritraumatic distress during childbirth, measured, respectively, by the EPDS (depression is score > 11), PDI (peritraumatic distress is score > 15), and PDEQ (scales). At one month postpartum, we compared the proportion of women with a diagnosis of postpartum depression (PPD) and birth-related posttraumatic stress disorder (CB-PTSD), using PCL-5 for CB-PTSD and using diagnosis criteria according DSM-5 for both PPD and CB-PTSD, in the context of a semi-structured interview, conducted by a clinician psychologist. Off the 257 women included, who delivered at the University Hospitals of Geneva between 25 January 2021 and 10 March 2022, 41 (16.1%) declared they had a positive PCR test for SARS-CoV-2 during their pregnancy. Regarding mental outcomes, except birth-related PTSD, all scores provided higher mean values in the group of women who declared having been infected by SARS-CoV-2, at delivery and at one month postpartum, without reaching any statistical significance: respectively, 7.8 (±5.2, 8:4–10.5) versus 6.5 (±4.7, 6:3–9), p = 0.139 ***, for continuous EPDS scores; 10 (25.0) versus 45 (21.1), p = 0.586 *, for dichotomous EPDS scores (≥11); 118 (55.7) versus 26 (63.4), p = 0.359 *, for continuous PDI scores; 18.3 (±6.8, 16:14–21) versus 21.1 (±10.7, 17:15–22), 0.231 ***, for dichotomous PDI scores (≥15); 14.7 (±5.9, 13:10–16) versus 15.7 (±7.1, 14:10–18), p = 0.636 ***, for continuous PDEQ scores; 64 (30.0) versus 17 (41.5), p = 0.151 *, for dichotomous PDEQ scores (≥15); and 2 (8.0) versus 5 (3.6), p = 0.289 *, for postpartum depression diagnosis, according DSM-5. We performed Chi-squared or Fisher’s exact tests, depending on applicability for the comparison of categorical variables and Mann–Whitney nonparametric tests for continuous variables; p < 0.05 was considered as statistically significant. Surprisingly, we did not find more birth-related PTSD as noted by the PCL-5 score at one month postpartum in women who declared a positive PCR test for SARS-CoV-2:15 (10.6) versus no case of birth related PTSD in women who were infected during pregnancy (p = 0.131 *). Our study showed that mental outcomes were differently distributed between women who declared having been infected by SARS-CoV-2 compared to women who were not infected. However, our study was underpowered to explore all the factors associated with psychiatric issues during pregnancy, postpartum, depending on the exposure to SARS-CoV-2 infection during pregnancy. Future longitudinal studies on bigger samples and more diverse populations over a longer period are needed to explore the long-term psychic impact on women who had COVID-19 during pregnancy. Full article

This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles. Full article

Previous research addressed the spatiotemporal variables of the drop jump (DJ) versus the horizontal drop jump (HDJ). This study compared the kinetic variables of the DJ versus the HDJ in elite jumpers and sprinters. In a single session, sixteen elite jumpers and sprinters performed two DJ attempts with three different fall heights (0.30 m, 0.40 m, and 0.50 m), and after 2 h, performed two HDJ attempts from the same fall heights (0.30 m, 0.40 m, and 0.50 m). Kinetic variables: eccentric ground reaction forces (GRFE) and concentric ground reaction forces; eccentric impulse (PE) and concentric impulse (PC); peak power in the concentric phase; and rate of force decrease (RFDe) were measured using a research-grade force plate. The Wilcoxon test was used to compare the vertical and anteroposterior axes. GRFE was significantly higher (p ≤ 0.05) in the DJ vs the HDJ with large effect sizes. The PE (p ≤ 0.006) and PC (p = 0.002) were significantly lower in the DJ than in the HDJ. The RFDe was also significantly lower in the DJ at 0.30 m vs. the HDJ at 0.30 m (p = 0.002). In summary, elite jumpers and sprinters may benefit from incorporating both the DJ and the HDJ into their training regimens, with the DJ being particularly advantageous for enhancing power metrics and RFDe. Full article

Adipose-derived mesenchymal stem cells (ASCs) are adult multipotent stem cells, able to differentiate toward neural elements other than cells of mesodermal lineage. The aim of this research was to test ASC neural differentiation using melatonin combined with conditioned media (CM) from glial cells. Isolated from the lipoaspirate of healthy donors, ASCs were expanded in a basal growth medium before undergoing neural differentiation procedures. For this purpose, CM obtained from olfactory ensheathing cells and from Schwann cells were used. In some samples, 1 µM of melatonin was added. After 1 and 7 days of culture, cells were studied using immunocytochemistry and flow cytometry to evaluate neural marker expression (Nestin, MAP2, Synapsin I, GFAP) under different conditions. The results confirmed that a successful neural differentiation was achieved by glial CM, whereas the addition of melatonin alone did not induce appreciable changes. When melatonin was combined with CM, ASC neural differentiation was enhanced, as demonstrated by a further improvement of neuronal marker expression, whereas glial differentiation was attenuated. A dynamic modulation was also observed, testing the expression of melatonin receptors. In conclusion, our data suggest that melatonin’s neurogenic differentiation ability can be usefully exploited to obtain neuronal-like differentiated ASCs for potential therapeutic strategies. Full article

Despite the recent advances in Virtual Reality technology and its use in education, the review of the literature shows several gaps in research on how immersive virtual environments impact the learning process. In particular, the lack of curriculum-specific experiments along with investigations of the effects of different content, activity, and interaction types in the current VR studies has been identified as a significant shortcoming. This has been more significant in STEM fields, where VR has the potential to offer engaging experiential learning opportunities. The study reported here was designed to address this gap by assessing the effect of authentic visualization and interaction types on learning a particular scientific concept. A use case scenario of “orbital hybridization” in chemistry education was selected to create this experiment and to collect data for analysis. We collected data on learning outcomes, task-completion efficiency, accuracy, and subjective usability. A combination of learning content and tasks designed based on the relevant educational theories was presented to three groups: 2D, VR interaction type 1 (hand gestures), and VR interaction type 2 (ray casting). The results showed that VR could improve learning and that interaction type could influence efficiency and accuracy depending on the task. Full article

Mesenchymal stem cells (MSCs) are multipotent stromal cells with the ability to self-renew and multi-directional differentiation potential. Exogenously administered MSCs can migrate to damaged tissue sites and participate in the repair of damaged tissues. A large number of pre-clinical studies and clinical trials have demonstrated that MSCs have the potential to treat the abnormalities of congenital nervous system and neurodegenerative diseases. Therefore, MSCs hold great promise in the treatment of neurological diseases. Here, we summarize and highlight current progress in the understanding of the underlying mechanisms and strategies of MSC application in neurological diseases. Full article

Coastal dunes, formed and shaped by aeolian sediment transport, play a crucial role in ecosystem services and act as natural flood and coastal erosion defenses. This paper delves into theoretical equations and numerical models predicting sediment transport. Numerical models like cellular automata, XBeach-DUNA, the coastal dune model, and others are analyzed for their ability to simulate dune morphology, erosion processes, and vegetation impacts accurately. Evaluated are field observation and measurement techniques, such as sand traps, impact sensors, and optical sensors, for their precision in quantifying aeolian dynamics. Further examined is the effectiveness of vegetation and fencing in dune stabilization, noting species-specific responses and the influence of fence design on sediment accumulation. These tools offer insights into optimizing aeolian sediment management for coastal protection. By conducting a systematic review and connecting theoretical, empirical, and modeling findings, this study highlights the complex challenge of measuring and managing aeolian sediment transport and proposes integrated strategies for enhancing coastal dune resilience against the backdrop of climate change and erosion. This study’s objectives to bridge gaps in current understanding are met, highlighting the need for a multidisciplinary approach to coastal dune management and conservation, especially combining wind- and wave-driven processes. Full article

With the development of sustainable cities, densely populated higher education institutions increasingly emphasize the sustainability of campuses and their impact on the environment. However, there is a lack of means to quantify emission reduction measures. This study aims to propose an evaluation framework that can quantify energy conservation and emission reduction measures and incentive policies. To this end, this study adopts a mixed methods approach, using questionnaires to assess the effectiveness of management and communication interventions and the impact of incentives on residents’ willingness to participate in emission reduction efforts. The survey results show that although the support for the intervention measures is slightly higher than the average, specific measures such as adjusting dormitory lights-out time and providing sports equipment show superior emission reduction potential. Universities could reduce carbon emissions by about 560 tons per year without incentives and just using interventions. However, when incentives and interventions are combined, the university’s annual emissions reductions are expected to increase to 800 to 1045 tons. Research also highlights the importance of understanding the relationship between occupant behavior, energy consumption, and building carbon emissions. By quantifying the impact of carbon reduction measures and incentives on the daily behaviors of residents, universities can more effectively implement sustainable campus strategies. Full article

The objective of this work is to predict the impact of technology on employment demand by profession in Spain between 2023 and 2035. The evaluation of this effect involved the comparison of two scenarios: a trend scenario obtained by predicting the evolution of occupations in demand and a technological scenario anticipated in the case of technological progress. To accomplish this goal, a new approach was developed in the present study based on previous research. Thus, we estimated the proportion of jobs likely to be automated using a task-based approach. Each occupation was examined based on its components to determine the degree to which these tasks could be automated. The results suggest that technology may influence job demand but with low percentages (between 3% and 5% for both low- and high-qualified workers) in the long term. However, job losses are greater in absolute difference in low-skilled professions, where a great share of the labor force is engaged. Full article

In this work, we present and describe a spectral library (SL) with 15 vascular plant species from Montesinho Natural Park (MNP), a protected area in Northeast Portugal. We selected species from the vascular plants that are characteristic of the habitats in the MNP, based on their prevalence, and also included one invasive species: Alnus glutinosa (L.) Gaertn, Castanea sativa Mill., Cistus ladanifer L., Crataegus monogyna Jacq., Frangula alnus Mill., Fraxinus angustifolia Vahl, Quercus pyrenaica Willd., Quercus rotundifolia Lam., Trifolium repens L., Arbutus unedo L., Dactylis glomerata L., Genista falcata Brot., Cytisus multiflorus (L’Hér.) Sweet, Erica arborea L., and Acacia dealbata Link. We collected spectra (300–2500 nm) from five records per leaf and leaf side, which resulted in 538 spectra compiled in the SL. Additionally, we computed five vegetation indices from spectral data and analysed them to highlight specific characteristics and differences among the sampled species. We detail the data repository information and its organisation for a better understanding of the data and to facilitate its use. The SL structure can add valuable information about the selected plant species in MNP, contributing to conservation purposes. This plant species SL is publicly available in Zenodo platform. Full article

To further improve the ride comfort of commercial vehicles, a seat ISD (Inerter–Spring–Damper) suspension utilizing a mechatronic inerter is proposed in this paper. Firstly, a five-DOF (degree-of-freedom) commercial vehicle seat ISD model was built. Then, the positive real network constraint conditions of a biquadratic impedance transfer function were determined, and the meta-heuristic intelligent optimization algorithm was used to solve the parameters. According to the solution, the impedance transfer function was obtained and the specific network structure was realized by network synthesis. Lastly, this study compares the vibration isolation performance of the mechatronic ISD suspension of the vehicle seat with that of a passive suspension. In comparison to passive seat suspension, the seat mechatronic ISD suspension reduces seat vibration transmissibility by 16.33% and vertical acceleration by 16.78%. Results indicate that the new suspension system can be an effective improvement in ride comfort. Full article

The rapid growth of Natural Fiber Laminate (NFL) innovation is a direct response to environmental challenges, positioning these materials as superior alternatives to synthetic fiber composites. This paper delved into the outcomes of an extensive experimental study investigating the influence of sisal fiber (SLF), banana fiber (BF), and glass fiber (GF) on the mechanical and microstructural characteristics of concrete. The water absorption curves were established for sisal fiber concrete (SLFC), banana fiber concrete (BFC), and glass fiber concrete (GFC). Furthermore, Scanning Electron Microscope (SEM) observations were conducted to perform microanalysis and failure analysis of the tested specimens. The results revealed significant improvements in the concrete containing fibers compared to its counterpart in fiber-free concrete. For mixtures with a water-to-binder (W/B) ratio of 0.3, the most optimal mix (GF-30-135) showed improvements in compressive strength, flexural strength, and splitting tensile strengths by 4.13%, 8.93%, and 10.10%, respectively. On the other hand, for W/B of 0.4, mix GF-30-135 showed improvements of 5.05%, 8.55%, and 11.60%, respectively. Furthermore, as the fiber content increased, microscopic analyses revealed a weakening of the bond between the fibers and the rest of the matrix, contributing to the deterioration of the mechanical properties. Full article

Hypergraphs have received widespread attention in modeling complex data correlations due to their superior performance. In recent years, some researchers have used hypergraph structures to characterize complex non-pairwise joints in the human skeleton and model higher-order correlations of the human skeleton. However, traditional methods of constructing hypergraphs based on physical connections ignore the dependencies among non-physically connected joints or bones, and it is difficult to model the correlation among joints or bones that are highly correlated in human action but are physically connected at long distances. To address these issues, we propose a skeleton-based action recognition method for hypergraph learning based on skeleton correlation, which explores the effects of physically and non-physically connected skeleton information on accurate action recognition. Specifically, in this paper, spatio-temporal correlation modeling is performed on the natural connections inherent in humans (physical connections) and the joints or bones that are more dependent but not directly connected (non-physical connection) during human actions. In order to better learn the hypergraph structure, we construct a spatio-temporal hypergraph neural network to extract the higher-order correlations of the human skeleton. In addition, we use an attentional mechanism to compute the attentional weights among different hypergraph features, and adaptively fuse the rich feature information in different hypergraphs. Extensive experiments are conducted on two datasets, NTU-RGB+D 60 and Kinetics-Skeleton, and the results show that compared with the state-of-the-art skeleton-based methods, our proposed method can achieve an optimal level of performance with significant advantages, providing a more accurate environmental perception and action analysis for the development of embodied intelligence. Full article

Low P-containing wastewaters (LPWs) exhibit huge P recovery potential, considering their larger volume. P recovery via CaP crystallization using apatite as seed is documented as being potentially well suited for LPWs. However, its responsible mechanisms remain a subject for debate. Taking hydroxyapatite (HAP) as the seed of LPWs, this paper conducted HAP adsorption/dissolution experiments, titration experiments, and P recovery experiments to distinguish the primary responsible mechanism. Results showed that it was HAP dissolution, not P adsorption, that occurred when the initial P concentration was no higher than 5 mg/L, ruling out adsorption mechanism of P recovery from LPWs using HAP as the seed. Significant OH⁻ consumption and rapid P recovery occurred simultaneously within the first 60 s in titration experiments, suggesting CaP crystallization should be responsible for P recovery. Moreover, the continuous increase in P recovery efficiency with seed dosages observed in P recovery experiments seemed to follow well the mechanism of pre-nucleation ion clusters (PNCs) aggregation. During PNCs aggregation, P aggregates with Ca²⁺ quickly, generating CaP PNCs; then, CaP PNCs aggregate with seed particles, followed by CaP PNCs fusion, and ultimately transform into fines attached to the seed surface. PNCs’ aggregation mechanism was further supported by a comparison of seed SEM images before and after P recovery, since denser and smaller rod-shaped fines were observed on the seed surface after P recovery. This study suggests that PNCs’ aggregation is the dominant mechanism responsible for the recovery of P from LPWs via CaP crystallization using HAP as the seed. Full article

This work presents the research results on the development of an innovative, hydrometallurgical technology for the production of manganese(II) perrhenate dihydrate from recycled waste. These wastes are scraps of Ni-based superalloys containing Re and scraps of Li–ion batteries containing Mn—specifically, solutions from the leaching of black mass. This work presents the conditions for the production of Mn(ReO₄)₂·2H₂O. Thus, to obtain Mn(ReO₄)₂·2H₂O, manganese(II) oxide was used, precipitated from the solutions obtained after the leaching of black mass from Li–ion batteries scrap and purified from Cu, Fe and Al (pH = 5.2). MnO₂ precipitation was carried out at a temperature < 50 °C for 30 min using a stoichiometric amount of KMnO₄ in the presence of H₂O₂. MnO₂ precipitated in this way was purified using a 20% H₂SO₄ solution and then H₂O. Purified MnO₂ was then added alternately with a 30% H₂O₂ solution to an aqueous HReO₄ solution. The reaction was conducted at room temperature for 30 min to obtain a pH of 6–7. Mn(ReO₄)₂·2H₂O precipitated by evaporating the solution to dryness was purified by recrystallization from H₂O with the addition of H₂O₂ at least twice. Purified Mn(ReO₄)₂·2H₂O was dried at a temperature of 100–110 °C. Using the described procedure, Mn(ReO₄)₂·2H₂O was obtained with a purity of >99.0%. This technology is an example of the green transformation method, taking into account the 6R principles. Full article