全文获取类型
收费全文 | 11107篇 |
免费 | 29篇 |
专业分类
管理学 | 1661篇 |
民族学 | 102篇 |
人口学 | 2446篇 |
丛书文集 | 2篇 |
理论方法论 | 547篇 |
综合类 | 298篇 |
社会学 | 4741篇 |
统计学 | 1339篇 |
出版年
2024年 | 1篇 |
2023年 | 4篇 |
2022年 | 4篇 |
2021年 | 10篇 |
2020年 | 25篇 |
2019年 | 36篇 |
2018年 | 1687篇 |
2017年 | 1679篇 |
2016年 | 1109篇 |
2015年 | 57篇 |
2014年 | 70篇 |
2013年 | 136篇 |
2012年 | 366篇 |
2011年 | 1172篇 |
2010年 | 1065篇 |
2009年 | 809篇 |
2008年 | 855篇 |
2007年 | 1017篇 |
2006年 | 24篇 |
2005年 | 253篇 |
2004年 | 274篇 |
2003年 | 229篇 |
2002年 | 101篇 |
2001年 | 20篇 |
2000年 | 22篇 |
1999年 | 17篇 |
1998年 | 3篇 |
1997年 | 7篇 |
1996年 | 33篇 |
1995年 | 5篇 |
1994年 | 2篇 |
1993年 | 5篇 |
1992年 | 1篇 |
1991年 | 1篇 |
1990年 | 3篇 |
1988年 | 11篇 |
1987年 | 2篇 |
1986年 | 2篇 |
1985年 | 3篇 |
1984年 | 5篇 |
1983年 | 3篇 |
1980年 | 1篇 |
1979年 | 1篇 |
1974年 | 3篇 |
1972年 | 2篇 |
1969年 | 1篇 |
排序方式: 共有10000条查询结果,搜索用时 15 毫秒
861.
Maria Törnroos Christian Hakulinen Mirka Hintsanen Sampsa Puttonen Taina Hintsa Laura Pulkki-Råback 《Work and stress》2017,31(1):63-81
Sleep problems are common and impair the health and productivity of employees. Work characteristics constitute one possible cause of sleep problems, and sleeping poorly might influence wellbeing and performance at work. This study examines the reciprocal associations between sleep problems and psychosocial work characteristics. The participants were 1744 full-time employed individuals (56% women; mean age 38 years in 2007) from the Young Finns study who responded to questionnaires on work characteristics (conceptualised by the demand–control model and effort–reward imbalance model) and sleep problems (Jenkins Sleep Scale) in 2007 and 2012. Cross-lagged structural equation models are used to examine the associations. The results show that low control and low rewards at baseline predicted sleep problems. Baseline sleep problems predicted higher effort, higher effort–reward imbalance, and lower reward. Sleep problems also predicted lower odds for belonging to the low (rather than high) job strain group and active jobs group. The association between work characteristics and sleep problems appears to be reciprocal, with a stressful work environment increasing sleep problems, and sleep problems influencing future work characteristics. The results emphasise the importance of interventions aimed at both enhancing sleep quality and reducing psychosocial risks at work. 相似文献
862.
863.
Glenn Hurlbert 《Journal of Combinatorial Optimization》2017,34(2):343-361
Graph pebbling is a network model for studying whether or not a given supply of discrete pebbles can satisfy a given demand via pebbling moves. A pebbling move across an edge of a graph takes two pebbles from one endpoint and places one pebble at the other endpoint; the other pebble is lost in transit as a toll. It has been shown that deciding whether a supply can meet a demand on a graph is NP-complete. The pebbling number of a graph is the smallest t such that every supply of t pebbles can satisfy every demand of one pebble byt a vertex. Deciding if the pebbling number is at most k is \(\Pi _2^\mathsf{P}\)-complete. In this paper we develop a tool, called the Weight Function Lemma, for computing upper bounds and sometimes exact values for pebbling numbers with the assistance of linear optimization. With this tool we are able to calculate the pebbling numbers of much larger graphs than in previous algorithms, and much more quickly as well. We also obtain results for many families of graphs, in many cases by hand, with much simpler and remarkably shorter proofs than given in previously existing arguments (certificates typically of size at most the number of vertices times the maximum degree), especially for highly symmetric graphs. Here we apply the Weight Function Lemma to several specific graphs, including the Petersen, Lemke, \(4\mathrm{th}\) weak Bruhat, and Lemke squared, as well as to a number of infinite families of graphs, such as trees, cycles, graph powers of cycles, cubes, and some generalized Petersen and Coxeter graphs. In doing so we partly answer a question of Pachter, et al., by computing the pebbling exponent of cycles to within an asymptotically small range. It is conceivable that this method yields an approximation algorithm for graph pebbling. 相似文献
864.
A universal labeling of a graph G is a labeling of the edge set in G such that in every orientation \(\ell \) of G for every two adjacent vertices v and u, the sum of incoming edges of v and u in the oriented graph are different from each other. The universal labeling number of a graph G is the minimum number k such that G has universal labeling from \(\{1,2,\ldots , k\}\) denoted it by \(\overrightarrow{\chi _{u}}(G) \). We have \(2\Delta (G)-2 \le \overrightarrow{\chi _{u}} (G)\le 2^{\Delta (G)}\), where \(\Delta (G)\) denotes the maximum degree of G. In this work, we offer a provocative question that is: “Is there any polynomial function f such that for every graph G, \(\overrightarrow{\chi _{u}} (G)\le f(\Delta (G))\)?”. Towards this question, we introduce some lower and upper bounds on their parameter of interest. Also, we prove that for every tree T, \(\overrightarrow{\chi _{u}}(T)={\mathcal {O}}(\Delta ^3) \). Next, we show that for a given 3-regular graph G, the universal labeling number of G is 4 if and only if G belongs to Class 1. Therefore, for a given 3-regular graph G, it is an \( {{\mathbf {N}}}{{\mathbf {P}}} \)-complete to determine whether the universal labeling number of G is 4. Finally, using probabilistic methods, we almost confirm a weaker version of the problem. 相似文献
865.
For an integer \(k \ge 1\), a distance k-dominating set of a connected graph G is a set S of vertices of G such that every vertex of V(G) is at distance at most k from some vertex of S. The distance k-domination number \(\gamma _k(G)\) of G is the minimum cardinality of a distance k-dominating set of G. In this paper, we establish an upper bound on the distance k-domination number of a graph in terms of its order, minimum degree and maximum degree. We prove that for \(k \ge 2\), if G is a connected graph with minimum degree \(\delta \ge 2\) and maximum degree \(\Delta \) and of order \(n \ge \Delta + k - 1\), then \(\gamma _k(G) \le \frac{n + \delta - \Delta }{\delta + k - 1}\). This result improves existing known results. 相似文献
866.
Muhammad Kamran Siddiqui Deeba Afzal Muhammad Ramzan Faisal 《Journal of Combinatorial Optimization》2017,34(2):534-544
An edge irregular total k-labeling \(\varphi : V\cup E \rightarrow \{ 1,2, \dots , k \}\) of a graph \(G=(V,E)\) is a labeling of vertices and edges of G in such a way that for any different edges xy and \(x'y'\) their weights \(\varphi (x)+ \varphi (xy) + \varphi (y)\) and \(\varphi (x')+ \varphi (x'y') + \varphi (y')\) are distinct. The total edge irregularity strength, tes(G), is defined as the minimum k for which G has an edge irregular total k-labeling. We have determined the exact value of the total edge irregularity strength of accordion graphs. 相似文献
867.
Let \(G=G(V,E)\) be a graph. A proper coloring of G is a function \(f:V\rightarrow N\) such that \(f(x)\ne f(y)\) for every edge \(xy\in E\). A proper coloring of a graph G such that for every \(k\ge 1\), the union of any k color classes induces a \((k-1)\)-degenerate subgraph is called a degenerate coloring; a proper coloring of a graph with no two-colored \(P_{4}\) is called a star coloring. If a coloring is both degenerate and star, then we call it a degenerate star coloring of graph. The corresponding chromatic number is denoted as \(\chi _{sd}(G)\). In this paper, we employ entropy compression method to obtain a new upper bound \(\chi _{sd}(G)\le \lceil \frac{19}{6}\Delta ^{\frac{3}{2}}+5\Delta \rceil \) for general graph G. 相似文献
868.
Neighbourly set of a graph is a subset of edges which either share an end point or are joined by an edge of that graph. The maximum cardinality neighbourly set problem is known to be NP-complete for general graphs. Mahdian (Discret Appl Math 118:239–248, 2002) proved that it is in polynomial time for quadrilateral-free graphs and proposed an \(O(n^{11})\) algorithm for the same, here n is the number of vertices in the graph, (along with a note that by a straightforward but lengthy argument it can be proved to be solvable in \(O(n^5)\) running time). In this paper we propose an \(O(n^2)\) time algorithm for finding a maximum cardinality neighbourly set in a quadrilateral-free graph. 相似文献
869.
Peter Brown Yuedong Yang Yaoqi Zhou Wayne Pullan 《Journal of Combinatorial Optimization》2017,33(2):551-566
The linear sum assignment problem is a fundamental combinatorial optimisation problem and can be broadly defined as: given an \(n \times m, m \ge n\) benefit matrix \(B = (b_{ij})\), matching each row to a different column so that the sum of entries at the row-column intersections is maximised. This paper describes the application of a new fast heuristic algorithm, Asymmetric Greedy Search, to the asymmetric version (\(n \ne m\)) of the linear sum assignment problem. Extensive computational experiments, using a range of model graphs demonstrate the effectiveness of the algorithm. The heuristic was also incorporated within an algorithm for the non-sequential protein structure matching problem where non-sequential alignment between two proteins, normally of different numbers of amino acids, needs to be maximised. 相似文献
870.
Cong Chen Yinfeng Xu Yuqing Zhu Chengyu Sun 《Journal of Combinatorial Optimization》2017,33(2):590-608
MapReduce system is a popular big data processing framework, and the performance of it is closely related to the efficiency of the centralized scheduler. In practice, the centralized scheduler often has little information in advance, which means each job may be known only after being released. In this paper, hence, we consider the online MapReduce scheduling problem of minimizing the makespan, where jobs are released over time. Both preemptive and non-preemptive version of the problem are considered. In addition, we assume that reduce tasks cannot be parallelized because they are often complex and hard to be decomposed. For the non-preemptive version, we prove the lower bound is \(\frac{m+m(\Psi (m)-\Psi (k))}{k+m(\Psi (m)-\Psi (k))}\), higher than the basic online machine scheduling problem, where k is the root of the equation \(k=\big \lfloor {\frac{m-k}{1+\Psi (m)-\Psi (k)}+1 }\big \rfloor \) and m is the quantity of machines. Then we devise an \((2-\frac{1}{m})\)-competitive online algorithm called MF-LPT (Map First-Longest Processing Time) based on the LPT. For the preemptive version, we present a 1-competitive algorithm for two machines. 相似文献