The development of Islamic economic thought has been closely intertwined with the trajectory of Islamic civilization since the era of Prophet Muhammad SAW and the Khulafaur Rasyidin. Following this formative period, the Umayyad, Abbasid, and Ottoman dynasties each contributed distinct approaches to managing economic affairs that reflected their political and social contexts. This study applies a historical-descriptive method to analyze the economic concepts and practices implemented by these three empires. The Umayyad dynasty, recognized as the first Islamic government to institutionalize hereditary leadership, emphasized administrative reforms such as the establishment of a structured bureaucracy, coinage system, and postal services, thereby creating stability conducive to economic growth. The Abbasid dynasty (750–1258 AD) marked the “Golden Age” of Islamic civilization, characterized by significant progress in trade, agriculture, and industry. State policies encouraged irrigation projects, mining, and agricultural expansion, while institutions like Baitul Mal played central roles in revenue management and redistribution. Under rulers such as Harun al-Rashid, economic prosperity supported advancements in science, education, and culture. Meanwhile, the Ottoman Empire (1300–1922 AD) reached its zenith under Sultan Sulaiman al-Qanuni, whose codification of Islamic law and territorial expansion enabled control of major trade routes and commercial centers. However, prolonged wars and intellectual stagnation ultimately weakened its economic base. This historical overview demonstrates that the integration of Islamic principles with practical governance significantly shaped economic systems across dynasties, leaving enduring legacies for contemporary Islamic economic thought.
This study develops and validates an instrument to integrate Banyumasan–Javanese local wisdom into project-based entrepreneurship learning (PJBL) in higher education. Using a mixed-methods, exploratory design, we first elicited value constructs through an in-depth interview and oral history with a culturally authoritative key informant (Ahmad Tohari), followed by a focus group discussion and expert triangulation to refine candidate indicators. Qualitative data were analyzed in MAXQDA via open–axial–selective coding, supported by a codebook, analytic memos, and member-checking, yielding 12 values (e.g., cablaka, guyub rukun, prewira, ajur ajer) mapped to PJBL outcomes (cognitive, affective, behavioral, artifact performance). To establish structural relevance and prioritize items, we applied Fuzzy-DEMATEL to expert judgments. All indicators exceeded the decision cut-off confirming demonstrated importance at the set level. Behavioral outcomes emerged as the most influential dimension, followed by affective and cognitive. The highest-prominence indicators were leadership, perseverance, harmonious but candid collaboration, self-reliance, openness, and adaptiveness as key levers for PJBL. Content validity was then examined with five experts using Aiken’s V, with all items meeting or exceeding accepted thresholds. The refined instrument was administered to entrepreneurship and business students. Reliability showed good internal consistency. Collectively, the qualitative grounding, Fuzzy-DEMATEL structural validation, content validity ratio (CVR), and student-based reliability evidence support a culturally grounded, psychometrically sound instrument. The results guide instructors to emphasize behavior-centric scaffolds and assessment while leveraging adaptive cognitive strategies and a supportive affective climate, thereby advancing contextualized, character-rich entrepreneurship education rooted in local wisdom.
Economic historians argue that before 1800, double-entry bookkeeping was used primarily to manage debt and oversee distant agents, factors, and partners. Its role in measuring overall profit or wealth was limited, usually arising only when accountability to others was required. These conclusions, drawn from primary records interpreted within their broader business context, contrast sharply with traditional antiquarian narratives. The latter emphasized technical detail, relied on presentist and teleological assumptions, and neglected contextual or critical reflection. As a result, they focused on the “what” and “how” of bookkeeping while leaving the more fundamental question of “why” unexplored, thereby obscuring motivations and producing questionable assumptions that became accepted as fact. This narrow, Eurocentric framing shaped by influential figures such as Pacioli, Besta, and Yamey placed double-entry bookkeeping at the center of accounting progress while marginalizing earlier systems and overlooking social, political, and ethical dimensions. A broader review of the literature, however, reveals more complexity. Classical scholarship remains highly technical, critical approaches reinterpret accounting as both disciplinary and ethical, global studies demonstrate diverse and non-linear development paths, and Indonesian research emphasizes ethics, prudence, and sustainability, with scandals such as Jiwasraya and Garuda Indonesia underscoring the importance of governance and transparency.
This article reinterprets the economic and cultural linkages between China, Southeast Asia, and the broader Asia–Pacific world during c.900–1650 through what may be termed a historical diamond economic pathway. At its core, the study situates Southeast Asia as the pivotal “axis” of the maritime silk roads that connected the Indian Ocean and the South China Sea. Drawing on textual, archaeological, and comparative historical sources, it reconstructs two principal phases of maritime development: the Song–Yuan commercial expansion (c.900–1350) and the Ming-era Age of Commerce (c.1400–1650). The findings highlight that, although overseas trade constituted a modest share of aggregate output, it played a transformative role in shaping coastal economies, technological diffusion, and cross-cultural exchange. Song liberalization of maritime policy, the Mongol maritime turn, and the Ming voyages under Zheng He each contributed to new circulation patterns of commodities such as ceramics, pepper, and cotton textiles. The paper also underscores the emergence of diasporic merchant networks as Arab, Indian, Malay, and Chinese that organized commerce through interlocking regional circuits. Methodologically, the study adopts a qualitative historical synthesis emphasizing multi-source triangulation and network interpretation. By re-envisioning the long-distance maritime economy as a “diamond” linking four vertices such as China, India, the Islamic world, and Southeast Asia the paper reframes the pre-modern global economy as a polycentric and interconnected system rather than a precursor to later European dominance.
A neighbor full sum distinguishing total coloring of a graph G is a proper total coloring ϕ such that no two adjacent vertices u and v of G satisfy ω (u)=ω (v) , where for any v ∈ V(G) , ω (v)=ϕ (v)+∑ _e∋ vϕ (e)+∑ _u∈ N(v)ϕ (u) and N(v) = {u ∈ V(G) | uv ∈ E(G)} . The minimum number of colors required for the coloring is called neighbor full sum distinguishing total chromatic number, denoted by ftndi_Σ (G) . In this paper, we show that ftndi_Σ (G) ≤Δ (G)+3 if G is a normal planar graph with girth g(G)≥ 6 and maximum degree Δ (G) ≥ 10 .
Aiming at the problem that existing methods could not fully capture the intrinsic structure of data without considering the higher-order neighborhood information of the data,we proposed an unsupervised feature selection algorithm based on graph filtering and self-representation.Firstly,a higher-order graph filter was applied to the data to obtain its smooth representation,and a regularizer was designed to combine the higher-order graph information for the self-representation matrix learning to capture the intrinsic structure of the data.Secondly,l2,1 norm was used to reconstruct the error term and feature selection matrix to enhance the robustness and row sparsity of the model to select the discriminant features.Finally,an iterative algorithm was applied to effectively solve the proposed objective function and simulation experiments were carried out to verify the effectiveness of the proposed algorithm.
Token-level data augmentation generates text samples by modifying the words of the sentences. However, data that are not easily classified can negatively affect the model. In particular, not considering the role of keywords when performing random augmentation operations on samples may lead to the generation of low-quality supplementary samples. Therefore, we propose a supervised contrast learning text classification model based on data quality augmentation. First, dynamic training is used to screen high-quality datasets containing beneficial information for model training. The selected data is then augmented with data based on important words with tag information. To obtain a better text representation to serve the downstream classification task, we employ a standard supervised contrast loss to train the model. Finally, we conduct experiments on five text classification datasets to validate the effectiveness of our model. In addition, ablation experiments are conducted to verify the impact of each module on classification.
In wireless sensor networks, the implementation of clustering and routing protocols has been crucial in prolonging the network’s operational duration by conserving energy. However, the challenge persists in efficiently optimizing energy usage to maximize the network’s longevity. This paper presents CHHFO, a new protocol that combines a fuzzy logic system with the collaborative Harris Hawks optimization algorithm to enhance the lifetime of networks. The fuzzy logic system utilizes descriptors like remaining energy, distance from the base station, and the number of neighboring nodes to designate each cluster head and establish optimal clusters, thereby alleviating potential hot spots. Moreover, the Collaborative Harris Hawks Optimization algorithm employs an inventive coding mechanism to choose the optimal relay cluster head for data transmission. According to the results, the network throughput, HHOCFR is 8.76%, 11.73%, 8.64% higher than HHO-UCRA, IHHO-F, and EFCR. In addition, he energy consumption of HHOCFR is lower than HHO-UCRA, IHHO-F, and EFCR by 0.88%, 39.79%, 34.25%, respectively.
Any organism in nature will inevitably be affected by uncertain factors. The deterministic model and stochastic model are no longer suitable for population dynamics analysis under uncertain noise environment. In order to simulate these problems more reasonably, we propose an uncertain logistic population model with Allee effect, which describes the population dynamic behavior through uncertain differential equation. In this paper, the solution and α -path of the uncertain Logistic population model with Allee effect are given, and the behavior analysis of the solution is also discussed. Besides, some numerical examples are put forward to illustrate the conclusions obtained in the paper.
用正算子扰动方法和锥上的不动点指数理论讨论具有非线性导数项的二阶常微分方程-u"(t)+a(t)u(t)=f(t,u(t),u'(t)),t∈ R正2π-周期解的存在性,其中:a:R→(0,+∞)连续,以2π为周期;f:R ×[0,+∞)× R→[0,+∞)连续,f(t,x,y)关于t以2π为周期.在非线性项f(t,x,y)满足适当的不等式条件下,得到了该方程正2π-周期解的存在性.
首先,针对实验数据有限、信息不完全背景下导致的发动机系统电磁脉冲易损性评估不确定性问题,通过引入灰色系统理论中区间灰数表征发动机系统部件敏感度阈值和故障逻辑关系的不确定性,提出一种灰色Bayes网络模型,以提升Bayes网络对不确定信息的处理能力.其次,以宽带高功率微波为例,计算发动机系统区间灰数失效概率和传感器区间灰数后验失效概率,其中前者表示发动机系统整体在强电磁脉冲作用下的生存能力,后者反应了发动机系统失效条件下各传感器的易损顺序,评估结论可为车辆电磁防护设计提供参考.
Firstly, based on the Black-Scholes stock price model, the neural stochastic differential equation (NSDE) model was established by parameterizing the asset return rate and volatility as a drift network and a diffusion network, respectively. Secondly, in the empirical analysis, the underlying asset as a single stock option was used as the research object, and real stock data was used for the network training and testing. The experimental results show that the NSDE model can overcome the defects of the constant assumption of the Black-Scholes model. Finally, for the case where the price of the underlying asset of the option was unobservable, we proposed that the price of any target option and the price of a known option could be constrained within the Wasserstein distance of their risk-neutral equivalent martingale measure, and theoretically proved the method.
以葡萄糖和SnCl4·5H2O溶液为原料,采用水热法制备超小SnO2纳米颗粒.在合成过程中,向溶液中加入不同量的磷酸(PA).利用X射线衍射仪(XRD)、扫描电子显微镜(SEM)和比表面积测试仪对SnO2进行表征,研究添加磷酸对气敏性能的影响,并分析其气敏机理.结果表明:最终产物具有超小的颗粒尺寸和较大的比表面积,其中掺杂0.6 mmol磷酸的SnO2所制备的气体传感器气敏性能最好,在最佳工作温度200 ℃下,灵敏度达7.5,且具有良好的稳定性;其气敏特性的提高归因于超小的颗粒尺寸和较大的比表面积,有利于乙醇气体吸附.
利用Hölder不等式和权函数的相关性质,给出RD(reverse doubling condition)空间上的分数次积分算子及BMO交换子在广义加权Morrey空间上的有界性,并给出相应的端点估计.
利用T-弱连续算子方法与经典的Galerkin技术,讨论二维有界光滑区域上一类不可压缩磁-微极流方程组的初边值问题,得到了该问题全局弱解的存在性与唯一性定理,并进一步提高了弱解的正则性.
Aiming at the problems of large number of data transmission node deaths and large transmission energy consumption output in energy-saving clustering routing communication of wireless sensor networks, an energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization is proposed. Firstly, a network communication energy consumption model is constructed, and the network node distribution is optimized in time with the goal of reducing energy consumption combined with differential bee colony algorithm; then, based on the network node distribution optimization results, an energy-saving clustering method for heterogeneous wireless sensor networks is formulated, and the cluster head is determined by using the energy iteration cluster selection method, and the cluster head radius is obtained to complete the energy-saving clustering of communication nodes in heterogeneous wireless sensor networks; finally, the distance between the communication cluster head node and the base station is set, the routing level of the node communication is determined, and the energy-saving routing communication of the heterogeneous wireless sensor network is realized by combining the multi-hop routing communication mode. The experimental results show that when the method is used for network energy-saving clustering routing communication, the number of data transmission nodes that die is at most 22, and the maximum energy consumption of node transmission is 21 nJ/bit , indicating that the node communication energy-saving effect of this method is good.
利用权系数方法和实分析技巧,讨论具有广义齐次核的半离散Hilbert型逆向不等式的构造问题,给出构造这类不等式的充分必要条件和最佳常数因子的计算公式以及不等式的算子表示.
针对物联网传输流多路复用性差、路由开销和平均时延均较高的问题,提出一种基于信息素算法的物联网传输流多路复用方法,该方法先通过分析物联网现状与需求,确定多路复用的必要性;然后以一次网络通信任务为基础,概述物联网传输流多路复用的基本思路;最后在解决多路复用存在的单路径问题基础上,利用信息素算法构建针对物联网传输流的多路复用模型,根据模型输出结果,实现物联网传输流多路复用.实验结果表明,该方法的复用结果包含了多路信号,信号未缺失,并且路由开销仅为3×104 Mb,平均时延仅为15 ms.
Aiming at the uncertain mechanism of node activation strategies and redundancy of feasible solution sets in the process of solving target coverage problem in wireless sensor networks, we proposed a deep learning based target coverage algorithm to learn the scheduling strategies of nodes in wireless sensor networks. Firstly , the algorithm abstracted the construction of feasible solution sets into Markov decision process, and intelligently selected activated sensor nodes as discrete actions according to the network environment. Secondly, a reward function evaluated the performance of the intelligent agent in selecting actions based on the coverage capacity and its residual energy of the active node. The simulation experiment result shows that the algorithm is effective in different network environments, and the network lifecycle is superior to the three greedy algorithms, the maximum lifetime coverage algorithm and the adaptive learning automaton algorithm.
