Search Results (371)

Central Environmental Protection Inspection (CEPI) is a major step in China’s environmental vertical supervision reform. With the multi-period Difference-in-Differences method, we assess the impact of CEPI on enterprise green transformation. In addition, we further explore the impact of enterprise environmental self-discipline. The results show that CEPI significantly promotes enterprise green transformation, and this effect on governance is further strengthened by environmental self-discipline. The synergistic governance effect of compound environmental regulation is pronounced, particularly in enterprises lacking government–enterprise relationships and in areas covered by CEPI “look back” initiatives and where local governments rigorously enforce environmental laws. The mechanism analysis reveals that CEPI mainly promotes enterprise green transformation by improving executive green cognition, boosting investment in environmental protection, and enhancing green innovation efficiency. This study provides a fresh perspective on analyzing the governance impact of CEPI and provides valuable insights for improving multi-collaborative environmental governance systems. Full article

Background: More than merely determining our sleep pattern, our body’s internal clock also improves the quality of our sleep, alleviates the symptoms of depression, and maintains the balance of our gut flora. Methods: We carried out a 12-week randomized controlled trial with 99 adults from Kolkata, New Delhi, and Pune who reported sleep problems and symptoms of depression or anxiety. Participants received either a probiotic formulated to improve sleep quality and reduce depressive symptoms or a placebo. We tracked sleep using overnight studies and wearable devices, assessed depressive symptoms with standardized questionnaires, and analyzed stool samples to profile gut bacteria and their metabolites using gene sequencing and metabolomics. Advanced statistics and machine learning helped us pinpoint the key microbial and metabolic factors tied to sleep and mental health. Results: At the start, participants with disrupted sleep and depressive symptoms had fewer beneficial gut bacteria like Bifidobacterium and Lactobacillus, more inflammation-related microbes, and lower levels of helpful short-chain fatty acids. These imbalances were linked to poorer sleep efficiency, less REM sleep, and higher depression and anxiety scores. After 12 weeks, those taking the circadian-supporting probiotic saw a statistically significant increase in beneficial gut bacteria, improved sleep efficiency (+7.4%, p = 0.02), and greater reductions in depression and anxiety compared to the placebo. Increases in SCFA-producing bacteria most strongly predicted improvements. Conclusions: Our results show that taking a probiotic supplement can help bring your gut back into balance, support better sleep, and lift symptoms of depression and anxiety. This offers a hopeful and practical option for people looking for real relief from these deeply connected challenges. Full article

The large-scale dynamics of the universe is generally described in terms of the time-dependent scale factor $a\left(t\right)$. To make contact with observational data, the $a\left(t\right)$ function needs to be related to the observable $z\left(r\right)$ function, redshift versus distance. Model fitting of data has shown that the equation that governs $z\left(r\right)$ needs to contain a constant term, which has been identified as Einstein’s cosmological constant. Here, it is shown that the required constant term is not a cosmological constant but is due to an overlooked geometric difference between proper time t and look-back time $t_{\mathrm{lb}}$ along lines of sight, which fan out isotropically in all directions of the 3D (3-dimensional) space that constitutes the observable universe. The constant term is needed to satisfy the requirement of spatial isotropy in the local limit. Its magnitude is independent of the epoch in which the observer lives and agrees with the value found by model fitting of observational data. Two of the observational consequences of this explanation are examined: an increase in the age of the universe from 13.8 Gyr to 15.4 Gyr, and a resolution of the $H_0$ tension, which restores consistency to cosmological theory. Full article

Social work with families has developed in response to the needs of people in the community but has moved away from them over the years of specialisation. The neoliberalisation of social work, with its emphasis on efficiency and procedure, has eclipsed the processes of collaboration with people, which are a prerequisite for hearing their voices and establishing a partnership in which we can co-create desired outcomes. In Slovenia, over the last 10 years, we have been looking for ways to bring social work with families in complex problem situations back into the community and to prioritise the processes of co-creating the desired outcomes in national and international projects. The most important milestones of the development, identified by the thematic analysis of the project documentation (58 documents) of seven projects, are presented here. Several themes were interwoven in the development and implementation of change: knowledge development; relevance of the institutional context; (micro-)innovation in social care; development of projects with practitioners and family representatives; broader social context centred on family support. Ten years of development in the field confirms that complex questions require complex answers, which must be (co-)created in collaboration between families, practitioners, policymakers and researchers. Full article

Accurate predictions of the temperature of battery energy storage systems (BESSs) are crucial for ensuring their efficient and safe operation. Effectively addressing both the long-term historical periodic features embedded within long look-back windows and the nuanced short-term trends indicated by shorter windows are key factors in enhancing prediction accuracy. In this paper, we propose a BESS temperature prediction model based on a convolutional neural network (CNN), patch embedding, and the Kolmogorov–Arnold network (KAN). Firstly, a CNN block was established to extract multi-scale periodic temporal features from data embedded in long look-back windows and capture the multi-scale correlations among various monitored variables. Subsequently, a patch-embedding mechanism was introduced, endowing the model with the ability to extract local temporal features from segments within the long historical look-back windows. Next, a transformer encoder block was employed to encode the output from the patch-embedding stage. Finally, the KAN model was applied to extract key predictive information from the complex features generated by the aforementioned components, ultimately predicting BESS temperature. Experiments conducted on two real-world residential BESS datasets demonstrate that the proposed model achieved superior prediction accuracy compared to models such as Informer and iTransformer across temperature prediction tasks with various horizon lengths. When extending the prediction horizon from 24 h to 72 h, the root mean square error (RMSE) of the proposed model in relation to the two datasets degraded by only 11.93% and 19.71%, respectively, demonstrating high prediction stability. Furthermore, ablation studies validated the positive contribution of each component within the proposed architecture to performance enhancement. Full article

Transformer-based time series models are being increasingly employed for time series data analysis. However, their training remains memory intensive, especially with high-dimensional data and extended look-back windows, while model-level memory optimizations are well studied, the batch formation process remains an underexplored factor to performance inefficiency. This paper introduces a memory-efficient batching framework based on view-based sliding windows operating directly on GPU-resident tensors. This approach eliminates redundant data materialization caused by tensor stacking and reduces data transfer volumes without modifying model architectures. We present two variants of our solution: (1) per-batch optimization for datasets exceeding GPU memory, and (2) dataset-wise optimization for in-memory workloads. We evaluate our proposed batching framework systematically using peak GPU memory consumption and epoch runtime as efficiency metrics across varying batch sizes, sequence lengths, feature dimensions, and model architectures. Results show consistent memory savings, averaging 90% and runtime improvements of up to 33% across multiple transformer-based models (Informer, Autoformer, Transformer, and PatchTST) and a linear baseline (DLinear) without compromising model accuracy. We extensively validate our method using synthetic and standard real-world benchmarks, demonstrating accuracy preservation and practical scalability in distributed GPU environments. The proposed method highlights batch formation process as a critical component for improving training efficiency. Full article

Konparu Zenchiku (1405–c. 1470) was the son-in-law of Zeami Motokiyo. Zeami is the most famous nō actor–writer–composer–showman–impressario, but Zenchiku brought nō back from the shōgun’s court to the temples, effectively resacralising the art form for a troubled, violent age. This paper asks whether Zenchiku’s approach to theatre has anything to teach us as contemporary creators and audiences in our own unstable era and, simultaneously, whether contemporary modes of interpretation, such as queer musicology, can highlight new aspects of Zenchiku’s work. Focusing on the under-studied and under-performed play Mekari—which dramatises a ritual cutting of seaweed at the Kanmon Strait between the islands of Kyūshū and Honshū as the new lunar year dawns—this paper explores how Zenchiku’s work plays with—crosses back and forth over—multiple physical, temporal, and spiritual boundaries in both its text and performance, leaving the audience with a sense of ambiguity and questioning the received wisdom of conventional capitalist reality. This paper concludes with a look at Kyōto School philosopher Ueda Shizuteru’s concept of the hollow expanse, or a place of limitless possibility. This paper argues that the audience viewing these ambiguities cultivated by Zenchiku’s sacred dramas—via the music, words, and staging together—might themselves be given a glimpse into the radically open place of the ‘hollow expanse’. The first full English translation of Mekari is included in Appendix A. Full article

The present study looks at how gentilics, usually attested in traditional biblical topoi from the Pentateuch, are re-contextualized in Ezekiel to provide a mental map of the peoples of the known Earth during the Exilic period. The basic constituents of Ezekiel’s mental map of foreign peoples recall some of the configurations known from the Babylonian mental map tradition. One known iteration of the latter is the Babylonian World Map (BM 92687). The document presents several interesting features as to how mental maps are formed in the Babylonian context. Its composition may date back to the late eighth century BCE. It is an iteration of the Babylonian mental map with a unique unmarked epicentre. Furthermore, it was probably impressed on clay on the occasion of a military campaign or itinerant work concerning specific toponyms in southern Babylonia. Finally, it was copied for scribal purposes in the Neo-Babylonian period. The present study proposes that these dynamics of the Babylonian mental map help understand Ezekiel’s mental map of foreign peoples. Aspects of Ezekiel’s mental map owe to an older Hebrew tradition partly known from the Pentateuch, although it is a unique iteration for Ezekiel’s oracles against the nations with historical references to the Exilic period. Jerusalem is the epicentre. Two main rings of foreign peoples encircle Jerusalem. The first circle comprises Judah’s neighbours from the east, south, west, and northwest. The second circle picks up from the northwest going up the coast, then south to Egypt, and finally east and northeast with Gog of Magog. Ezekiel concludes with the Temple Vision confirming Jerusalem’s central position. This case study implies that Ezekiel encountered and independently adapted aspects of the Mesopotamian mental map. Comparisons such as the one attempted here can illustrate the potential of ancient Near Eastern intertextuality and cultural hybridity. Full article

This article argues that criminal law has overtaken immigration law to such an extent that the notion of “crimmigration” is no longer shocking. In Canada, where the population has long been supportive of immigration and where national politics have been remarkably consensual in matters of immigration, crimmigration now forms the basis of a new form of bipartisan consensus. By looking back on the Justin Trudeau Liberal government, we see that most of the Harper-era crimmigration measures were left in place, and the advance of crimmigration continued unabated. If we are to make any progress in recovering space for values other than crimmigration in our immigration law and politics, we need to both think more creatively about the future and recover our sense of outrage. Full article

The problem of the multidrug resistance of pathogenic bacteria is a serious concern, one which only becomes more pressing with every year that passes, motivating scientists to look for new therapeutic agents. In this situation, phage therapy, i.e., the use of phages to combat bacterial infections, is back in the spotlight of research interest. Bacterial viruses are highly strain-specific towards their hosts, which makes them particularly valuable for targeting pathogenic variants amidst non-pathogenic microflora, represented by such commensals of animals and humans as E. coli, S. aureus, etc. However, selecting phages for the treatment of bacterial infections is a complex task. The prospective candidates should meet a number of criteria; in particular, the selected phage must not contain potentially dangerous genes (e.g., antibiotic resistance genes, genes of toxins and virulence factors etc.)—or be capable of transferring them from their hosts. This work introduces a new approach to selecting T4-related coliphages; it allows one to identify strains which may be safer in terms of involvement in the horizontal gene transfer. The approach is based on the search for genes that reduce the frequency of genetic transduction. Full article

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors. Full article

The Internet Shopping Optimization Problem with multiple item Units looks for the best selection of stores where to buy various or individual units in a required list of items to minimize the final purchase cost. The problem belongs to the most challenging complexity class of optimization problems (NP-Hard). This paper adds to the already complex problem a more difficult situation with a second objective conflicting with the purchase cost minimization. As far as we know, this is the first state-of-the-art proposal with conflicting objectives for the Internet Shopping Optimization Problem or its variants. The objective in conflict with the minimization of the purchase final cost is the cash-back or reward points on personal or corporate credit cards, the most common payment method for online purchases. Due to the nature of the conflicting objectives, this paper proposes using evolutionary multi-objective optimization algorithms. We perform an experimental comparison using eight algorithms from the literature. The experimental results show that NSGA-II achieves the best overall performance for the studied instances from the state of the art. Full article

The non-stationary nature of energy data is a serious challenge for energy forecasting methods. Frequent model updates are necessary to adapt to distribution shifts and avoid performance degradation. However, retraining regression models with lookback windows large enough to capture energy patterns is computationally expensive, as increasing the number of features leads to longer training times. To address this problem, we propose an approach that guarantees fast convergence through dimensionality reduction. Using a synthetic neighborhood dataset, we first validate three deep learning models—an artificial neural network (ANN), a 1D convolutional neural network (1D-CNN), and a long short-term memory (LSTM) network. Then, in order to mitigate the long training time, we apply principal component analysis (PCA) and a variational autoencoder (VAE) for feature reduction. As a way to ensure the suitability of the proposed models for a residential context, we also explore the trade-off between low error and training speed by considering three test scenarios: a global model, a local model for each building, and a global model that is fine-tuned for each building. Our results demonstrate that by selecting the optimal dimensionality reduction method and model architecture, it is possible to decrease the mean squared error (MSE) by up to 63% and accelerate training by up to 80%. Full article

The matter of the quantification of the fraction of the dredged sediment that is released by a trailing suction hopper dredger into the surrounding waters, also known as the passive phase of the plume during dredging operations through the overflow, is a rather complex process. A number of processes, including sediment settling, propeller wash, and entrapment of air during sediment release, are only a few of the reasons why plumes are formed and sediments because of the overflow are released back into the environment. The present work attempts to examine the empirical considerations used for the estimation of the amount of sediments expected to be released through the overflow or via a reclamation by looking into the case of the Addu-City dredging and reclamation project. Moreover, the effectiveness of silt curtains as a turbidity containment measure is discussed. Based on the field data collected, it can be concluded that under normal hydrodynamic conditions, from the sediment source calculated based on the existing literature, only 20% of the fine sediments is available for dispersion. Moreover, the accurate and consistent follow-up of the work schedule execution and consistent monitoring as a part of environmental management can ensure compliance with environmental regulations further away from the project area. Full article

Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking comprehensive real-time validation and generalization capabilities. This study addresses these challenges by proposing a gated recurrent unit (GRU)-based deep learning framework optimized through Look_Back and Forward_Length labeling to capture complex temporal patterns. The model demonstrated state-of-the-art performance, surpassing existing unmanned aerial vehicles (UAV) and aircraft trajectory prediction approaches, including FlightBERT++, in terms of both accuracy and robustness. It achieved reliable long-range predictions up to 4 s, and its real-time feasibility was validated due to its efficient resource utilization. The model’s generalization capability was confirmed through evaluations on two independent UAV datasets, where it consistently predicted unseen trajectories with high accuracy. These findings highlight the model’s ability to handle rapid maneuvers, extend prediction horizons, and generalize across platforms. This work establishes a robust trajectory prediction framework with practical applications in collision avoidance, mission planning, and anti-drone systems, paving the way for safer and more scalable UAV operations. Full article