{p=In this article, we apply the UN Guiding Principles on Business and Human Rights to the private equity (PE) business model. PE firms often adopt a controversial, ‘value extractive’, business model based on high debt and extreme cost-cutting to generate investor returns. PE firms own large numbers of companies, including in many rights-related sectors. The model is linked to increased human rights risks to workers, housing tenants, and in privatized health and social care. We map these risks and analyse the human rights responsibilities of PE firms. Our analysis has major implications for understandings of human rights responsibility. We argue that value extractive methods are the root cause of eventual harm to human rights, even though they may not harm rights directly. To respect human rights, PE firms must mitigate the risks of these value extractive methods. We define how human rights due diligence (HRDD) could achieve this and argue that given the extent of harm and the lack of a business case for adopting such a view of human rights responsibility, business strategy level HRDD should be a core component of forthcoming HRDD laws., h1=Abstract}

{p=The study intends to ascertain how enhancing the effectiveness of Islamic boarding school management in individual leadership contributes to the success of Islamic boarding schools. Darunnajah Ulujami, an Islamic boarding school, has implemented overall quality management and has successfully raised the education standard. This study aims to determine how seriously Islamic boarding schools should be managed to improve their TQM performance. The methodology in this study was qualitative. Data from observation, interviews, and relevant reading sources were used to collect the data. Triangulation was used to verify data. The method of data analysis performed was qualitative. The results showed: The individual leadership of the Darunnajah Islamic boarding school in South Jakarta has a good leadership performance based on the respondent's assessment in assessment that the leader pays attention to the rights and concerns of employees. The advantage in assessing individual leadership is inseparable from the quality and leadership style used or applied by the administration of the Islamic boarding school. These indicators can be seen in personal leadership in understanding their duties and responsibilities well. However, it has advantages and disadvantages and tends to be more authoritarian. The leadership style in Islamic boarding schools, the habit of a Kiai is highly respected because it has its advantages. A madrasa leader must have charisma and abilities that are above the power of the average community., h1=Abstract}

{p=[Lignin has enormous potential as a renewable feedstock for depolymerizing to numerous high-value chemicals. However, lignin depolymerization is challenging owing to its recalcitrant, heterogenous, and limited water-soluble nature. From the standpoint of environmental friendliness and sustainability, enzymatic depolymerization of lignin is of great significance. Notably, laccases play an essential role in the enzymatic depolymerization of lignin and are considered the ultimate green catalysts. Deep eutectic solvent (DES), an efficient media in biocatalysis, are increasingly recognized as the newest and utmost green solvent that highly dissolves lignin. This review centers on a lignin depolymerization strategy by harnessing the good lignin fractionating capability of DES and the high substrate and product selectivity of laccase. Recent progress and insights into the laccase–DES interactions, protein engineering strategies for improving DES compatibility with laccase, and controlling the product selectivity of lignin degradation by laccase or in DES systems are extensively provided. Lastly, the challenges and prospects of the alliance between DES and laccase for lignin depolymerization are discussed. The collaboration of laccase and DES provides a great opportunity to develop an enzymatic route for lignin depolymerization., Graphical Abstract, ], h1=Abstract}

This paper explores analytical connections between the perturbation methodology of the Australian Bureau of Statistics (ABS) and the differential privacy (DP) framework. We consider a single static counting query function and find the analytical form of the perturbation distribution with symmetric support for the ABS perturbation methodology. We then analytically measure the DP parameters, namely the $(\varepsilon, \delta)$ pair, for the ABS perturbation methodology under this setting. The results and insights obtained about the behaviour of $(\varepsilon, \delta)$ with respect to the perturbation support and variance are used to judiciously select the variance of the perturbation distribution to give a good $\delta$ in the DP framework for a given desired $\varepsilon$ and perturbation support. Finally, we propose a simple sampling scheme to implement the perturbation probability matrix in the ABS Cellkey method. The post sampling $(\varepsilon, \delta)$ pair is numerically analysed as a function of the Cellkey size. It is shown that the best results are obtained for a larger Cellkey size, because the $(\varepsilon, \delta)$ pair post-sampling measures remain almost identical when we compare sampling and theoretical results.

As one of the major branches of automatic speech recognition, attention-based models greatly improves the feature representation ability of the model. In particular, the multi-head mechanism is employed in the attention, hoping to learn speech features of more aspects in different attention subspaces. For speech recognition of complex languages, on the one hand, a small head size will lead to an obvious shortage of learnable aspects. On the other hand, we need to reduce the dimension of each subspace to keep the size of the overall feature space unchanged when we increase the number of heads, which will significantly weaken the ability to represent the feature of each subspace. Therefore, this paper explores how to use a small attention subspace to represent complete speech features while ensuring many heads. In this work we propose a novel neural network architecture, namely, pyramid multi-branch fusion DCNN with multi-head self-attention. The proposed architecture is inspired by Dilated Convolution Neural Networks (DCNN), it uses multiple branches with DCNN to extract the feature of the input speech under different receptive fields. To reduce the number of parameters, every two branches are merged until all the branches are merged into one. Thus, its shape is like a pyramid rotated 90 degrees. We demonstrate that on Aishell-1, a widely used Mandarin speech dataset, our model achieves a character error rate (CER) of 6.45% on the test sets.

{p=Irradiating solids with ultrashort laser pulses is known to initiate femtosecond timescale magnetization dynamics. However, sub-femtosecond spin dynamics have not yet been observed or predicted. Here, we explore ultrafast light-driven spin dynamics in a highly nonresonant strong-field regime. Through state-of-the-art ab initio calculations, we predict that a nonmagnetic material can transiently transform into a magnetic one via dynamical extremely nonlinear spin-flipping processes, which occur on attosecond timescales and are mediated by cascaded multi-photon and spin–orbit interactions. These are nonperturbative nonresonant analogs to the inverse Faraday effect, allowing the magnetization to evolve in very high harmonics of the laser frequency (e.g. here up to the 42nd, oscillating at ~100 attoseconds), and providing control over the speed of magnetization by tuning the laser power and wavelength. Remarkably, we show that even for linearly polarized driving, where one does not intuitively expect the onset of an induced magnetization, the magnetization transiently oscillates as the system interacts with light. This response is enabled by transverse light-driven currents in the solid, and typically occurs on timescales of ~500 attoseconds (with the slower femtosecond response suppressed). An experimental setup capable of measuring these dynamics through pump–probe transient absorption spectroscopy is simulated. Our results pave the way for attosecond regimes of manipulation of magnetism., h1=Abstract}

{p=The assessment of persistence (P), bioaccumulation (B), and toxicity (T) of a chemical is a crucial first step at ensuring chemical safety and is a cornerstone of the European Union’s chemicals regulation REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals). Existing methods for PBT assessment are overly complex and cumbersome, have produced incorrect conclusions, and rely heavily on animal-intensive testing. We explore how new-approach methodologies (NAMs) can overcome the limitations of current PBT assessment. We propose two innovative hazard indicators, termed cumulative toxicity equivalents (CTE) and persistent toxicity equivalents (PTE). Together they are intended to replace existing PBT indicators and can also accommodate the emerging concept of PMT (where M stands for mobility). The proposed “toxicity equivalents” can be measured with high throughput in vitro bioassays. CTE refers to the toxic effects measured directly in any given sample, including single chemicals, substitution products, or mixtures. PTE is the equivalent measure of cumulative toxicity equivalents measured after simulated environmental degradation of the sample. With an appropriate panel of animal-free or alternative in vitro bioassays, CTE and PTE comprise key environmental and human health hazard indicators. CTE and PTE do not require analytical identification of transformation products and mixture components but instead prompt two key questions: is the chemical or mixture toxic, and is this toxicity persistent or can it be attenuated by environmental degradation? Taken together, the proposed hazard indicators CTE and PTE have the potential to integrate P, B/M and T assessment into one high-throughput experimental workflow that sidesteps the need for analytical measurements and will support the Chemicals Strategy for Sustainability of the European Union., h1=Abstract}

{p=The COVID-19 pandemic arguably caused the most rapid and extensive educational reform ever witnessed. This article addresses the challenges, possibilities, and consequences that the pandemic has set for Finnish physics university teachers. The data were gathered with a literature-based online survey consisting of Likert scale claims and open-ended questions related to the following themes: the use of technology, interaction, learning and evaluation, experimental working, and the future. In 2021, the survey was distributed to all Finnish universities where physics is taught, and 52 responses were received. The results show the biggest challenges were related to the different aspects of interaction, experimental working, and adjusting evaluation. The opportunities were less recognized but the versatility and flexibility of instruction and the use of technology in the evaluation became apparent in the teachers’ responses. The consequences of pandemic time for physics teaching and learning were seen rather negatively, even if a majority of teachers intended to change their old teaching practices in the future. The results indicate that university teachers need more discipline-based pedagogical support for enhancing learning, interaction, and well-being as the physics instruction likely gets more versatile after the pandemic., h1=Abstract}

{p=[Background, Older inpatients, particularly those with frailty, have increased exposure to complex medication regimens. It is not known whether frailty and complexity of medication regimens influence attitudes toward deprescribing. This study aimed to investigate (1) older inpatients’ attitudes toward deprescribing; (2) if frailty and complexity of medication regimen influence attitudes and willingness to deprescribe - a relationship that has not been investigated in previous studies., Methods, In this cross-sectional study, older adults (≥ 65 years) recruited from general medicine and geriatric services in a New Zealand hospital completed the revised Patients’ Attitudes Towards Deprescribing (rPATD) questionnaire. Hospital frailty risk score (HFRS) was calculated using diagnostic codes and other relevant information present at the time of index hospital admission; higher scores indicate higher frailty risk. Medication regimen complexity was quantified using the medication regimen complexity index (MRCI); higher scores indicate greater complexity. Logistic regression analysis was used to identify predictors of attitudes and willingness to deprescribe., Results, A total of 222 patients were included in the study, the median age was 83 years and 63% were female. One in two patients reported feeling they were taking too many medications, and 1 in 5 considered their medications burdensome. Almost 3 in 4 (73%) wanted to be involved in decision-making about their medications, and 4 in 5 (84%) were willing to stop one or more of their medications if their prescriber said it was possible. Patients with higher MRCI had increased self-reported medication burden (adjusted odds ratio (AOR) 2.6, 95% CI 1.29, 5.29) and were more interested in being involved in decision-making about their medications (AOR 1.8, CI 0.99, 3.42) than those with lower MRCI. Patients with moderate HFRS had lower odds of willingness to deprescribe (AOR 0.45, CI 0.22,0.92) compared to the low-risk group. Female patients had a lower desire to be involved in decision-making. The oldest old age group( > 80 years) had lower self-reported medication burden and were less likely to want to try stopping their medications., Conclusion, Most older inpatients wanted to be involved in decision-making about their medications and were willing to stop one or more medications if proposed by their prescriber. Medication complexity and frailty status influence patients’ attitudes toward deprescribing and thus should be taken into consideration when making deprescribing decisions. Further research is needed to investigate the relationship between frailty and the complexity of medication regimens.], h1=Abstract}

With recent advancements in computer vision as well as machine learning (ML), video-based at-home exercise evaluation systems have become a popular topic of current research. However, performance depends heavily on the amount of available training data. Since labeled datasets specific to exercising are rare, we propose a method that makes use of the abundance of fitness videos available online. Specifically, we utilize the advantage that videos often not only show the exercises, but also provide language as an additional source of information. With push-ups as an example, we show that through the analysis of subtitle data using natural language processing (NLP), it is possible to create a labeled (irrelevant, relevant correct, relevant incorrect) dataset containing relevant information for pose analysis. In particular, we show that irrelevant clips ($n=332$) have significantly different joint visibility values compared to relevant clips ($n=298$). Inspecting cluster centroids also show different poses for the different classes.