A new heuristic algorithm for two-dimensional defective stock guillotine cutting stock problem with multiple stock sizes

U radu se uglavnom raspravlja o problemu rezanja giljotinom dvodimenzijske oštećene robe raspoložive u različitim veličinama. Za raspravu o problemu predlaže se novi heuristički algoritam u obliku stabla. Takav se algoritam sastoji od dva dijela: prvi dio je početno rješenje problema rezanja robe kad ne postoje oštećenja robe; drugi dio je konačno rješenje optimizacije utemeljeno na prvom dijelu uz razmatranje oštećenja. U radu se također ocjenjuju rezultati predloženog algoritma. Eksperimentalnim se rezultatima demonstrira učinkovitost algoritma za problem rezanja dvodimenzijske oštećene robe i pokazuje da se algoritmom može poboljšati ne samo stopa iskoristivosti robe već i stopa ponovne uporabe ostataka smanjenjem fragmentacije ostataka.This paper mainly addresses a two-dimensional defective stocks guillotine cutting stock problem where stock of different sizes is available. Herein a new heuristic algorithm which is based on tree is proposed to discuss this problem. In particular, such an algorithm consists of two parts: the first part is an initial solution of the cutting stock problem where there are no defects on the stocks; the second part is the final optimization solution which is set up on the basis of the first part and takes the defects into consideration. This paper also evaluates the performance of the proposed algorithm. The experimental results demonstrate the effectiveness of the algorithm for the two-dimensional defective stocks cutting stock problem and show that the algorithm can improve not only the utilization rate of stocks, but also the reuse rate of remainders by reducing the fragmentation of remainders

The Influence of Training Habits on The Lower Kinematics of Junior School Freshmen (Girls)

This study is aimed to find out the influence of training habits on the lower limb (hip, knee, ankle) kinematics of junior school girl students, and compare it with the parameter of adults to find out the characteristics of the lower limb kinematics in Juvenile stage, and more desired to explore the law about it to provide the basis for physical training in juvenile stage. Thirty junior school girl students age at 13 to 14 years old participated in this study, of which 15 participants have exercise habits and 15 participants without exercise habits. The Vicon kinematics analysis system (Oxford, Metrics, Ltd., Oxford, UK) with a shooting frequency of 200Hz was used to collect the three-dimensional kinematics of the hip, knee and ankle joint. The study found that the exercise group in step and pace were higher 7.1% and 6.4% respectively than non-exercise group, but in step frequency, 7.7% lower than non-exercise group. In terms of joint angle, compared with participants without exercise habits, participants with exercise habit showed decreased angle of ankle with dorsiflexion, increased angle of the ankle with plantarflexion, and significant peak angle of plantarflexion; meantime, exerted increased angle of eversion and decreased angle inversion which were more similar to the kinematics parameters of adult women. During the push-off period, there was an obvious increase in non-exercise group’s angle of ankle with eversion, which may be one of the reasons for the phenomenon of “outer eight feet” in the juvenile. The physical parameters of the participants with exercise were more approximated to the adults’, indicating that exercise habits have positive effects on the stability of joint, such as the joint force can be better controlled, improving the walking stability, and avoiding injury

Vehicle emission and atmospheric pollution in China: problems, progress, and prospects

China has been the largest vehicle market in the world since 2009. The stalemate between the rapid development of the vehicle industry and delayed vehicle emission control has become increasingly prominent. Vehicle emission has become a significant source of air pollution in China’s cities. Understanding the current barriers in the vehicle industry is necessary for the development of effective and sustainable measures and policy to manage vehicle-induced air pollution. This review provides insight into the circumstances and causes of vehicle-induced air pollution and outlines recent progress in policy-makers’ long-term strategies and regulations. The development of an integrated mechanism of social participation, technical revolution, and regulatory innovation in vehicles, fuel, and roads is suggested to break the stalemate between air pollution and the automobile boom in China; the implications of this review extend to other countries facing the similar atmospheric pollution problems

Volumes of hippocampal subfields suggest a continuum between schizophrenia, major depressive disorder and bipolar disorder

ObjectiveThere is considerable debate as to whether the continuum of major psychiatric disorders exists and to what extent the boundaries extend. Converging evidence suggests that alterations in hippocampal volume are a common sign in psychiatric disorders; however, there is still no consensus on the nature and extent of hippocampal atrophy in schizophrenia (SZ), major depressive disorder (MDD) and bipolar disorder (BD). The aim of this study was to verify the continuum of SZ – BD – MDD at the level of hippocampal subfield volume and to compare the volume differences in hippocampal subfields in the continuum.MethodsA total of 412 participants (204 SZ, 98 MDD, and 110 BD) underwent 3 T MRI scans, structured clinical interviews, and clinical scales. We segmented the hippocampal subfields with FreeSurfer 7.1.1 and compared subfields volumes across the three diagnostic groups by controlling for age, gender, education, and intracranial volumes.ResultsThe results showed a gradual increase in hippocampal subfield volumes from SZ to MDD to BD. Significant volume differences in the total hippocampus and 13 of 26 hippocampal subfields, including CA1, CA3, CA4, GC-ML-DG, molecular layer and the whole hippocampus, bilaterally, and parasubiculum in the right hemisphere, were observed among diagnostic groups. Medication treatment had the most effect on subfields of MDD compared to SZ and BD. Subfield volumes were negatively correlated with illness duration of MDD. Positive correlations were found between subfield volumes and drug dose in SZ and MDD. There was no significant difference in laterality between diagnostic groups.ConclusionThe pattern of hippocampal volume reduction in SZ, MDD and BD suggests that there may be a continuum of the three disorders at the hippocampal level. The hippocampus represents a phenotype that is distinct from traditional diagnostic strategies. Combined with illness duration and drug intervention, it may better reflect shared pathophysiology and mechanisms across psychiatric disorders

A Survey of Large Language Models

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.Comment: ongoing work; 51 page

Clinical and radiological characteristics of pediatric COVID-19 before and after the Omicron outbreak: a multi-center study

IntroductionThe emergence of the Omicron variant has seen changes in the clinical and radiological presentations of COVID-19 in pediatric patients. We sought to compare these features between patients infected in the early phase of the pandemic and those during the Omicron outbreak.MethodsA retrospective study was conducted on 68 pediatric COVID-19 patients, of which 31 were infected with the original SARS-CoV-2 strain (original group) and 37 with the Omicron variant (Omicron group). Clinical symptoms and chest CT scans were examined to assess clinical characteristics, and the extent and severity of lung involvement.ResultsPediatric COVID-19 patients predominantly had normal or mild chest CT findings. The Omicron group demonstrated a significantly reduced CT severity score than the original group. Ground-glass opacities were the prevalent radiological findings in both sets. The Omicron group presented with fewer symptoms, had milder clinical manifestations, and recovered faster than the original group.DiscussionThe clinical and radiological characteristics of pediatric COVID-19 patients have evolved with the advent of the Omicron variant. For children displaying severe symptoms warranting CT examinations, it is crucial to weigh the implications of ionizing radiation and employ customized scanning protocols and protective measures. This research offers insights into the shifting disease spectrum, aiding in the effective diagnosis and treatment of pediatric COVID-19 patients

Real-Time Forecast of Tourists Distribution Based on the Improved k-Means Method

Tourist distribution, a vector to reflect the tourist number of every scenic spot in a certain period of time, serves as the foundation for a scenic spots manager to make a schedule scheme. In this paper, a forecast model is offered to forecast tourist distribution. First of all, based on the analysis of changing mechanism of tourist distribution, it is believed that the possibility for a scenic spot to have the same tourist distribution next time is high. To conduct this forecast, we just need to research on the similar tourist distributions of which time and tourist scale are close. Considering that it is time-consuming, an improved K-means cluster method is put forward to classify the historical data into several clusters so that little time will be needed to search for the most similar historical data. In the end, the case study of Jiuzhai Valley is adopted to illustrate the effectiveness of this forecast model