Note on Divergence of the Chapman-Enskog Expansion for Solving Boltzmann Equation

Within about a year (1916-1917) Chapman and Enskog independently proposed an important expansion for solving the Boltzmann equation. However, the expansion is divergent or indeterminant in the case of relaxation time $\tau \geq 1$. Even since this divergence problem has puzzled this subject for a century. By using a modified M\"obius series inversion formula, this paper proposes a modified Chapman-Enskog expansion with a variable upper limit of the summation. The new expansion can give not only a convergent summation but also provide the best-so-far explanation on some unbelievable scenarios occurred in previous practice.Comment: 4 pages, 0 figures, 2 table

ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases

Large Language Models (LLMs) have shown the potential to revolutionize natural language processing tasks in various domains, sparking great interest in vertical-specific large models. However, unlike proprietary models such as BloombergGPT and FinGPT, which have leveraged their unique data accumulations to make strides in the finance domain, there hasn't not many similar large language models in the Chinese legal domain to facilitate its digital transformation. In this paper, we propose an open-source legal large language model named ChatLaw. Due to the importance of data quality, we carefully designed a legal domain fine-tuning dataset. Additionally, to overcome the problem of model hallucinations in legal data screening during reference data retrieval, we introduce a method that combines vector database retrieval with keyword retrieval to effectively reduce the inaccuracy of relying solely on vector database retrieval. Furthermore, we propose a self-attention method to enhance the ability of large models to overcome errors present in reference data, further optimizing the issue of model hallucinations at the model level and improving the problem-solving capabilities of large models. We also open-sourced our model and part of the data at https://github.com/PKU-YuanGroup/ChatLaw

Optimization of \u3b2-carotene production by a newly isolated Serratia marcescens strain

Abstract \u3b2-carotene is a commonly used food colorant. In this work, a novel \u3b2-carotene producing strain, Serratia marcescens RB3, was isolated and identified by physiological and biochemical tests, as well as 16S rDNA sequence analysis. The production of \u3b2-carotene by S. marcescens RB3 was identified through HPLC analysis. The cultivation conditions for \u3b2-carotene production by S. marcescens RB3 were optimized as 2.0% lactose, 2.0% peptone, 0.3% beef extract, 1.0% NaCl supplemented with 0.05% Fe2+, pH 6.0 and 30\ubaC. Under the optimal conditions, the yield of \u3b2-carotene achieved 2.45 \u3bcg/mL

Binder-Free Nickel Oxide Lamellar Layer Anchored CoOx_{x} Nanoparticles on Nickel Foam for Supercapacitor Electrodes

To enhance the connection of electroactive materials/current collector and accelerate the transport efficiency of the electrons, a binder-free electrode composed of nickel oxide anchored CoO$_{x}$ nanoparticles on modified commercial nickel foam (NF) was developed. The nickel oxide layer with lamellar structure which supplied skeleton to load CoO_{x) electroactive materials directly grew on the NF surface, leading to a tight connection between the current collector and electroactive materials. The fabricated electrode exhibits a specific capacitance of 475 F/g at 1 mA/cm$^{2}$. A high capacitance retention of 96% after 3000 cycles is achieved, attributed to the binding improvement at the current collector/electroactive materials interface. Moreover, an asymmetric supercapacitor with an operating voltage window of 1.4 V was assembled using oxidized NF anchored with cobalt oxide as the cathode and activated stainless steel wire mesh as the anode. The device achieves a maximum energy density of 2.43 Wh/kg and power density of 0.18 kW/kg, respectively. The modified NF substrate conducted by a facile and effective electrolysis process, which also could be applied to deposit other electroactive materials for the energy storage device

Efficient capacity enhancement using OFDM with interleaved subcarrier number modulation in bandlimited UOWC systems

We propose and demonstrate an efficient capacity enhancement scheme for bandlimited underwater optical wireless communication (UOWC) systems by utilizing orthogonal frequency division multiplexing with interleaved subcarrier number modulation (OFDM-ISNM). In the proposed OFDM-ISNM, joint number and constellation mapping/de-mapping is utilized to avoid error propagation and subblock interleaving is further applied to address the low-pass effect of the bandlimited UOWC system. The feasibility and superiority of the proposed OFDM-ISNM scheme for practical bandlimited UOWC systems have been verified through both simulations and experiments. The obtained results demonstrate that the proposed OFDM-ISNM scheme is capable of efficiently improving the achievable data rate of the bandlimited UOWC system. Specifically, the experimental results show a significant 28.6% capacity enhancement by OFDM-ISNM over other benchmark schemes, achieving a data rate of 3.6 Gbps through a 2-m water channel

Original Article One stage laminoplasty and posterior herniotomy for the treatment of myelopathy caused by cervical stenosis with cervical disc herniation

Abstract: The aim of the study was to introduce a method of one stage laminoplasty and posterior herniotomy for myelopathy caused by cervical stenosis with cervical disc herniation and to evaluate the clinical efficacy of this surgery. From 1999 to 2008, 18 patients with myelopathy caused by cervical stenosis with cervical disc herniation who underwent this procedure were included. The average age was 63 years (range 48-74 years), and the average follow-up period was 46 months (range 3-108 months). Neurologic status was evaluated using the JOA scoring system. Neurological symptoms improvement was seen in all patients after surgery. The average JOA score was 14.22±1.86 by final follow-up, which was higher than preoperative values (P<0.01), and the average improvement in neurological function was 76.63%. Neurologic examination showed that excellent results had been obtained by 10 patients, good results by 8 patients, with no fair or poor results. 2 patients developed cerebrospinal fluid leakage after surgery and recovered during the follow-up period. One patient with cervical disc herniation developed postoperative C5 palsy on the axle side on the third day after surgery. She completely recovered by 1 month after surgery. No other patients experienced postoperative neurologic complications. Complete anterior and posterior decompression of the spinal cord was achieved after surgery. We concluded that one stage laminoplasty and posterior herniotomy is an effective, reliable, and safe procedure for the treatment of myelopathy caused by cervical stenosis with cervical disc herniation

Brake wear induced PM10 emissions during the world harmonised light-duty vehicle test procedure-brake cycle

In this work, the particulate matter less than 10 μm (PM10) emissions from a medium-sized passenger vehicle's front brake wear were studied using a finite element analysis (FEA) and experimental approaches. The world harmonised light-duty vehicle test procedure-brake (WLTP-B) cycle was chosen to simulate real-world driving. An electrical low-pressure impactor (ELPI+) was used to count the brake wear particles on a brake dynamometer sealed in a chamber. In addition, a machine learning method, namely, extreme gradient boosting (XGBoost), was employed to capture the feature importance rankings of braking conditions contributing to brake wear PM10 emissions. The simulated PM10 emissions were quite consistent with the measured ones, with an overall relative error of 9%, indicating that the proposed simulation approach is promising to predict brake wear PM10 during the WLTP-B cycle. The simulated and experimental PM10 emission factors during the WLTP-B cycle were 6.4 mg km−1 veh−1 and 7.0 mg km−1 veh−1, respectively. Among the 10 trips of the WLTP-B cycle, the measured PM10 of trip #10 was the largest contributor, accounting for 49% of total PM10 emissions. On the other hand, the XGBoost results revealed that the top five most important factors governing brake wear PM10 emissions were dissipation energy, initial braking speed, final rotor temperature, braking power, and deceleration rate. From the perspective of friendly driving behaviour and regulation, limiting severe braking and high-speed braking has the potential to reduce PM10 emissions from brake wear

Verification of Land-Atmosphere Coupling in Forecast Models, Reanalyses and Land Surface Models Using Flux Site Observations

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Our analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally-distributed observational monitoring

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce