Previous Iteration Research Articles

Navigating the Waves of Change—Assessing the Evolving Landscape of China’s Revised Marine Environmental Protection Law

Since its inception in 1982, the Marine Environmental Protection Law (MEPL) has undergone several revisions in 1999, 2013, 2016, and 2017, capping in the inclusive overhaul of 2023. These successive revisions have gradually shaped a robust legal system encompassing the Constitution as the foundational basis, the MEPL as the core regulatory instrument, and several corresponding laws safeguarding specific marine elements as the driving force. The newly revised MEPL 2023, compared to its previous iterations, introduces significant improvements that are largely reflected in strengthening the protection of marine biodiversity conservation, advancing the prevention and control of ship-source marine pollution, optimising the supervision of the marine environment, enhancing the prevention and control of land-based marine pollution, and imposing stricter liabilities. This paper examines how the new MEPL 2023 improves China’s marine environmental protection, and incorporates international best practices within the legal framework. Additionally, the research explores potential areas for further development that provide some implications for policymakers and should have been considered.

Pyrazolo-Pyrimidinones with Improved Solubility and Selective Inhibition of Adenylyl Cyclase Type 1 Activity for Treatment of Inflammatory Pain.

Adenylyl cyclase isoform 1 (AC1) is considered a promising target for treating inflammatory pain. Our group identified the pyrazolyl-pyrimidinone scaffold as potent and selective inhibitors of Ca2+/CaM-mediated AC1 activity; however, the molecules suffered from poor aqueous solubility. The current study presents a strategy to improve aqueous solubility of the scaffold by reduction of crystal packing energy and increasing rotational degrees of freedom within the molecule. Structure-activity and property relationship studies identified the second generation lead 7-47A (AC10142A) that demonstrated and AC1 IC50 value of 0.26 μM and aqueous solubility of 74 ± 7 μM. After in vitro ADME characterization, the scaffold advanced to in vivo pharmacokinetic evaluation, demonstrating adequate levels of exposure. Finally, 7-47A exhibited antiallodynic efficacy in a rat complete Freund's adjuvant model for inflammatory pain showing improvement over previous iterations of this scaffold. These results further validate AC1 inhibition as a viable therapeutic strategy for treating chronic and inflammatory pain.

New 2024 Title IX regulations remain in limbo for many states

The long‐awaited Biden‐era Title IX regulations that were issued in April of this year represented a major development in higher education law, differing from previous iterations of Title IX rules in dramatic ways.

Embedded IoT Design for Bioreactor Sensor Integration

This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system’s effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes.

Fast-airflow tumble clothes dryer with small thermoelectric heat pump: Experimental evaluation

Residential clothes drying accounts for about 5 % of the total residential-sector energy consumption in the United States. Most dryers use electric resistance heaters to dry clothes and have low efficiencies. Higher-efficiency dryers that use vapor compression heat pumps are expensive and complex and have not gained a large market share in the United States. A novel tumble clothes dryer using a small thermoelectric heat pump with faster airflow than typical dryers is presented in this work. The benchtop performance of the thermoelectric heat pump and high-speed blower are presented, and the development of the prototype dryer is described. The dryer was tested for efficiency and dry time for a range of airflow rates and applied currents to the thermoelectric heat pump. The combined efficiency factor was 5.09–6.29 lbBDW/kWh (specific moisture extraction rate of 1.23–1.53 kgw/kWh) with 100–138 min dry times for these tests. The measured efficiency was 36 %–68 % greater than the minimum efficiency standard in the United States, and compared with vapor compression heat pump–based clothes dryers, the prototype dryer had less expensive, less complex components and did not use refrigerants. The performance of this small thermoelectric heat pump clothes dryer is also compared with previous iterations of the thermoelectric tumble clothes dryer described in the literature.

Robust optimization of train timetables with short-length and full-length services considering uncertain passenger volume and service choice behavior

Train timetables with a combination of short-length and full-length services can adapt to spatial and temporal variations in demand. However, with such timetables, some passengers may choose to catch an earlier short-length service and then transfer to a full-length service to reach their destinations rather than waiting for a crowded full-length service. This uncertain service choice behavior frequently occurs, which, however, was not well considered in most studies on demand-oriented timetabling. Therefore, this study presents a robust timetabling approach based on the scenario-based method considering uncertain passenger volumes and service choice behavior for choosing different types of train services. A customized decomposition-based method with an iterative solution procedure is designed to solve the proposed model. In each iteration, a multi-agent-based simulation algorithm is developed to update passengers’ travel utility and the service choice preference proportion based on the previous iteration’s timetabling results. To obtain high-quality solutions in an acceptable computing time, a hybrid “ALNS + GUROBI” algorithm is developed to handle the timetabling problems for large-scale cases. The proposed method optimizes the train timetables for Xi’an Metro Line 3 in China. Our results indicate that the proposed method can account for uncertain passenger volumes and service choice behavior by adjusting the short-length plans and train headways to match the transport capacity to passenger demand.

An iterative learning-based integrated motion planning and control method for autonomous patrolling of unmanned surface vehicles

Abstract The minimization of maritime patrol durations and the optimization of patrol trajectories are still a prevailing challenge for autonomous navigation of unmanned surface vehicles (USVs). In this paper, we propose an integrated trajectory planning and control method to achieve time-optimal autonomous patrol of USVs by a data-efficient iterative learning-based predictive control algorithm. We build the optimization problem of the algorithm by introducing a local cost and a local safe constraint set for closed-loop efficient data-driven learning. To guarantee recursive feasibility, stability and convergence properties of the optimization algorithm at each iteration, the local cost and the local constraint set are designed and update iteratively using the historical vehicle states at previous iterations as a dataset. Different from traditional optimization methods, the proposed method does not require a reference path. The cost function of the optimization method decreases monotonically and converge to obtain a time-optimal control law for trajectory planning and tracking of USVs only after several iterations. The proposed approach is validated on the robotic operation system in typical maritime patrol scenarios. The results show that the proposed method implement a 5% reduction of patrol time, fewer lateral track errors, and smoother trajectories compared to the traditional MPC algorithm under water flow conditions. In addition, Enhancements in patrol operation efficiency of USVs are achieved by the algorithm, even under the constraints of varied patrol paths, attesting to its versatility.

Burn Injury Severity in Adults: Proposed Definitions Based on the National Burn Research Dataset.

Previous iterations of burn severity (mild, moderate, and severe) were not data-driven and were outdated. Clustering analyses have gained popularity for identifying homogenous subgroups from heterogeneous medical conditions, such as asthma, sepsis, and lung disease. There is no consensus in burn literature regarding what constitutes massive burns. The current classification includes a 20% total body surface area (TBSA) burn and a 95% TBSA burn as severe. Latent class and hierarchical clustering analyses were applied to the American Burn Association National Burn Research Dataset. Cluster variables included length of stay, length of stay, intensive care unit length of, number and type of procedures, and number and type of complications. Non-clustering variables were evaluated after clustering, including burned TBSA, inhalation injury, mortality, discharge disposition, age, sex, and race. Latent class analysis suggested three clusters. Hierarchical clustering analysis was applied to the most severe latent class, creating four total burn severity groups. In total, 112,297 patients were included in the final analysis. The mean TBSA burned for each class is 4.26±4.91 for minor, 8.07±8.39 for moderate, 22.76±17.31 for severe and 36.72±21.61 for massive. The age and sex proportions were similar among all clusters. The clustering variables steadily increased for each severity cluster. Mortality was the highest in the massive cluster (18.2%). Data informed categories of burn severity were formed using clustering analyses, which will be helpful for triage, data-benchmarking, and class-specific research.

Alquist 5.0: Dialogue Trees Meet Generative Models, a Novel Approach for Enhancing SocialBot Conversations

This article introduces Alquist 5.0, our SocialBot that was designed for the Alexa Prize SocialBot Grand Challenge 5. Building upon previous iterations, we present the integration of our novel neural response generator (NRG) Barista within a hybrid architecture that combines traditional predefined dialogues with advanced neural response generation. We provide a comprehensive analysis of the current state-of-the-art NRGs and large language models (LLMs), leveraging these insights to enhance Barista’s capabilities. A key focus of our development was in ensuring the safety of our chatbot and implementing robust measures to prevent profanity and inappropriate content. Additionally, we incorporated a new search engine to improve information retrieval and response accuracy. Expanding the capabilities of our system, we designed Alquist 5.0 to accommodate multimodal devices, utilizing APL templates enriched with custom features to deliver an outstanding conversational experience complemented by an excellent user interface. This paper offers detailed insights into the development of Alquist 5.0, which effectively addresses evolving user demands while preserving its empathetic and knowledgeable conversational prowess across a wide range of topics.

New 2024 Title IX regulations remain in limbo for many states

The long‐awaited Biden‐era Title IX regulations that were issued in April of this year represented a major development in higher education law, differing from previous iterations of Title IX rules in dramatic ways.

Federated Learning with Multi-Method Adaptive Aggregation for Enhanced Defect Detection in Power Systems

The detection and identification of defects in transmission lines using computer vision techniques is essential for maintaining the safety and reliability of power supply systems. However, existing training methods for transmission line defect detection models predominantly rely on single-node training, potentially limiting the enhancement of detection accuracy. To tackle this issue, this paper proposes a server-side adaptive parameter aggregation algorithm based on multi-method fusion (SAPAA-MMF) and formulates the corresponding objective function. Within the federated learning framework proposed in this paper, each client executes distributed synchronous training in alignment with the fundamental process of federated learning. The hierarchical difference between the global model, aggregated using the improved joint mean algorithm, and the global model from the previous iteration is computed and utilized as the pseudo-gradient for the adaptive aggregation algorithm. This enables the adaptive aggregation to produce a new global model with improved performance. To evaluate the potential of SAPAA-MMF, comprehensive experiments were conducted on five datasets, involving comparisons with several algorithms. The experimental results are analyzed independently for both the server and client sides. The findings indicate that SAPAA-MMF outperforms existing federated learning algorithms on both the server and client sides.

The Crowdless Future? Generative AI and Creative Problem-Solving

The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy business ideas generated by the human crowd (HC) and collaborative human-AI efforts using two alternative forms of solution search. The challenge attracted 125 global solvers from various industries, and we used strategic prompt engineering to generate the human-AI solutions. We recruited 300 external human evaluators to judge a randomized selection of 13 out of 234 solutions, totaling 3,900 evaluator-solution pairs. Our results indicate that while human crowd solutions exhibited higher novelty—both on average and for highly novel outcomes—human-AI solutions demonstrated superior strategic viability, financial and environmental value, and overall quality. Notably, human-AI solutions cocreated through differentiated search, where human-guided prompts instructed the large language model to sequentially generate outputs distinct from previous iterations, outperformed solutions generated through independent search. By incorporating “AI in the loop” into human-centered creative problem-solving, our study demonstrates a scalable, cost-effective approach to augment the early innovation phases and lays the groundwork for investigating how integrating human-AI solution search processes can drive more impactful innovations. Funding: This work was supported by Harvard Business School (Division of Research and Faculty Development) and the Laboratory for Innovation Science at Harvard (LISH) at the Digital Data and Design (D3) Institute at Harvard. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.18430 .

High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.

Fractional-based iterative learning-optimal model predictive control of speed induction motor regulation for electric vehicles application

Introduction. A new control strategy based on the combination of optimal model predictive control (OMPC) with fractional iterative learning control (F-ILC) for speed regulation of an induction motor (IM) for electric vehicles (EVs) application is presented. OMPC uses predictive models to optimize speed control actions by considering the dynamic behavior of the IM, when integrated with the F-ILC, the system learns and refines the speed control iteratively based on previous iterations, adapting to the specific characteristics of the IM and improving performance over time. The synergy between OMPC and F-ILC named F-ILC-OMPC enhances the precision and adaptability of speed control for IMs in EVs application, and optimizes the energy efficiency and responsiveness under varying driving conditions. The novelty lies in the conjunction of the OMPC with the ILC-based on the fractional calculus to regulate the speed of IMs, which is original. Purpose. The new control strategy provides increased performance, robustness and adaptability to changing operational conditions. Methods. The mathematical development of a control law that mitigates the disturbance and achieves accurate and efficient speed regulation. The effectiveness of the suggested control strategy was assessed via simulations in MATLAB conducted on an IM system. Results. The results clearly show the benefits of the F-ILC-OMPC methodology in attaining accurate speed control, minimizing steady-state error and enhanced disturbance rejection. Practical value. The main perspective lies in the development of a speed control strategy for IMs for EVs and the establishment of reliable and efficient electrical systems using ILC-OMPC control. This research has the prospect of a subsequent implementation of these results in experimental prototypes. References 24, tables 2, figures 9.

FPGA-assisted Design Space Exploration of Parameterized AI Accelerators: A Quickloop Approach

FPGAs facilitate prototyping and debug, and recently accelerate full-stack simulations due to their rapid turnaround time (TAT). However, this TAT is restrictive in exhaustive design space explorations of parameterized RTL generators, especially DNN accelerators that unleash an explosive full-stack search space. This paper presents Quickloop, an efficient and scalable framework to enable FPGA-accelerated exploration. Quickloop first abstracts away the cumbersome flow of RTL generation, software stack, FPGA toolflow, workload execution and metrics extraction by wrapping these stages into isolated Quicksteps, featuring cascadability, scalability, and replay. Then, we analytically minimize the FPGA toolflow TAT via a novel, data-driven strategy that intelligently utilizes build fragments from previous iterations, enhancing the loop efficiency and simultaneously lowering the toolflow’s compute utilization.Quickloop is built around the OpenAI Gym environment framework and thus supports drop-in regression and reinforcement learning explorations. With a Quickloop around a reference Berkeley’s Gemmini DNN accelerator, we exhaustively explore its parameter space and discover complex performance patterns, based on full-stack simulation of Imagenet benchmarks as a workload. Compared to conventional FPGA toolflow, we further show that Quickloop effectively reduces episodal time by above 30%, as the episode approaches realistic lengths.

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs.

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S-1. This factorization of S-1, written as ZZT = S-1, is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix-matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

Adapting the PEERS® for Young Adults Program for Autistic Adults across the Lifespan.

The Program for the Education and Enrichment of Relational Skills (PEERS®) is an evidence-based intervention developed for autistic individuals to support social communication, peer interactions, independence, and interpersonal relationships. Despite a demonstrated effectiveness for young autistic individuals in the US and several other countries, PEERS has yet to be modified to support the needs of autistic adults across the lifespan. The present study describes how our team sought autistic voices to adapt PEERS for adults of any age. Specifically, we aimed to address the needs of middle-aged and older adults and adapt the curriculum to be more neurodiversity-affirming. Between two cohorts that completed the program consecutively, we evaluated the acceptability of the adapted PEERS program and made refinements based on feedback from autistic participants and their study partners. Results indicated that Cohort 2 reported higher satisfaction with the PEERS components and overall program than Cohort 1, suggesting effective refinement. We present a framework of adaptations that more specifically address the needs of middle-aged and older adults in a neurodiverse-affirming way compared to previous iterations. Our approach to implementing an adapted PEERS curriculum across the adult lifespan may serve as a model for improved clinical care and cultivate the acceptance of neurodiversity in the interpersonal domains of autistic adults' lives.

Multidisciplinary design analysis and optimization of the aerodynamic shape of the SpaceLiner passenger stage

The SpaceLiner is a futuristic concept of a suborbital space plane intended to provide ultra-fast intercontinental passenger transport, currently in pre-development at DLR-SART. Vehicles traveling at hypersonic speeds inevitably produce shock waves that are perceived at ground level as sonic booms, with associated disturbance of the overflown populations. The reduction of this disturbance, together with the decrease of the noise associated to the launch and ascent phase, is critical for a viable future operation of the SpaceLiner. The objective of this work is the redesign of the passenger stage aerodynamic shape in order to improve its atmospheric re-entry performance, in terms of a reduction of both the heat flux and the disturbance of the overflown populations. For this purpose, a MDAO methodology was developed with the tasks of (1) computing vehicle performance from a wing shape parametrization using fast estimation methods, (2) exploring the design space and (3) finding a new promising aerodynamic shape. First, a Python tool-chain was developed to compute vehicles performances from a wing shape parametrization using fast estimation methods. The tool-chain was then systematically exploited to explore the design space by means of parametric studies, whose results informed the implementation of the forthcoming optimization. Afterwards, the wing shape was optimized using a three-objective evolutionary algorithm that minimized the vehicle mass and maximized its lift-to-drag ratio and lift coefficient. Simplified trajectory optimizations were then run on the set of non-dominated solutions in order to identify the most performing configurations. Finally, multi-objective evolutionary trajectory optimizations were run for the most promising candidate along intercontinental point-to-point routes of interest. Comparisons with the previous design iteration showed a significant improvement in terms of a reduction of both reentry heat flux and population disturbance.

An Update to the Stochastically Perturbed Parameterization Scheme of HarmonEPS

Abstract High-resolution, limited-area forecasting is strongly affected by errors in the initial atmospheric state, lateral boundary conditions (LBCs), and physical parameterizations used by numerical weather prediction (NWP) models. These errors need to be accounted for through the introduction of uncertainty in an ensemble prediction system (EPS). One approach to account for model error is to use a stochastically perturbed parameterizations (SPPs) scheme. A first version of the SPP scheme of HARMONIE EPS (HarmonEPS) has been tested, with promising improvements in ensemble spread. However, it introduced systematic biases and deteriorated skill scores for some variables. Here, we investigate the performance of an updated version of the HarmonEPS SPP scheme, which includes (i) the use of uniform distributions, (ii) the correlation of stochastic patterns between key SPP parameters, and (iii) the introduction of four additional parameters, in the microphysics and mass-flux schemes. Two five-parameter SPP-based setups are compared against initial and LBC perturbations setups for five forecast periods: (i) 22–28 March 2019, (ii) 6–12 July 2020, (iii) 20–26 February 2021, (iv) 13–26 January 2021, and (v) 20 May–2 June 2021. We find that SPP-based experiments show better probabilistic metrics for near-surface and cloud-related variables than the non-SPP experiments. The SPP-based ensembles show increased spatial spread (as indicated by dFSS), while maintaining similar spatial skill (as indicated by eFSS) with the non-SPP experiment. In addition, the systematic bias in the ensemble members of the previous SPP iteration has been alleviated with the use of uniform distributions. Finally, the use of microphysical and mass-flux perturbations improves the ensemble scores for cloud-related variables, precipitation, and visibility. Significance Statement Ensemble prediction systems are essential for weather forecasting. Their skill depends on the representation of uncertainties arising from errors in the initial atmospheric state and model physics. In this study, we present an update to the stochastically perturbed parameterizations scheme, which represents uncertainty arising from model errors, of the limited-area HARMONIE ensemble prediction system. The update to the scheme includes the introduction of uniform distributions and correlated perturbations for the perturbed parameters, as well as the addition of four new perturbed parameters. Overall, this revision removes systematic biases in ensemble members, which were present in the previous iteration of the scheme, and shows increased ensemble spread (up to 70%) and reduced model errors (up to 5%) for near-surface and cloud-related variables.

Prisons on the Edge: Perspectives on the Late Medieval and Early Modern History of Confinement in the Francosphere

Abstract It is high time we took stock of the field of carceral history—something particularly true for the French-speaking world. In the last twenty years, the Francosphere has seen a frenzy of research activity centred on the prison, and more broadly around multiple forms of confinement. The field is being radically transformed under the watchful eye of a whole new generation of researchers who have produced numerous monographs, articles and colloquia on the subject. The research and thinking about the prison model that the current generation is developing diverges so completely from the classical revisionist historiography as to be unrecognisable, transfigured from previous iterations. The purpose of this article is to explore those recent transformations of the field following three parameters. The first is linguistic (reviewing only the work emerging from the Francosphere); the second is one of academic discipline (History); and the third is temporal (the emphasis is on research touching on the long pre-Revolution period). These three parameters provide a focused view, while remaining sufficiently widely relevant to identify the dominant trends in French-language research. This is set in the context of past research, of work that has been done elsewhere, and of what sets Francophone research apart from corresponding English-language scholarship.