Experimental Investigation of Unconfined Compression Strength and Microstructure Characteristics of Slag and Fly Ash-Based Geopolymer Stabilized Riverside Soft Soil

To solve the issues of insufficient early strength of cement stabilized soil and high resource cost, high reduction cost, and high environmental cost induced by the application of cement, the slag and fly ash-based geopolymer was adopted as the stabilizer to treat riverside soft soil. This study mainly investigated the effects of stabilizer content, slag-to-fly ash ratio, and alkaline activator content on the strength of geopolymer stabilized soils with different curing ages. Unconfined compressive strength (UCS), scanning electron microscope (SEM), and X-ray energy spectrum analysis (EDS) tests were carried out. The results show that the stabilizer content, slag–fly ash ratio, and alkaline activator content have a decisive influence on the UCS of geopolymer-stabilized soil. The mix-proportions scheme of geopolymer stabilized riverside soft soil, with a geopolymer content of 15%, a slag–fly ash ratio of 80:20, and an alkaline activator content of 30%, is considered optimum. It is proven by SEM that the uniformly distributed gelatinous products formed in the geopolymer-stabilized soil bind the soil particles tightly. Moreover, the EDS analysis confirms that the gelatinous products are mainly composed of C-S-H gel and sodium-based aluminosilicate (N-A-S-H).

Economic Impacts of Violent Conflicts in Nasarawa State, Nigeria

The study analysed the impacts of violent conflicts on the economies of rural communities in Nasarawa State, Nigeria. Primary data were collected from the respondents using a structured interview schedule, while focus group discussions (FGD) sessions were employed to assess effects of conflicts on communities. Data were analysed using simple descriptive statistics, alternative resource cost estimation (ARCE) and content analysis while the Likert scale was used to measure the perception of respondents towards the causes of conflicts. There were six (6) major categories of conflicts identified in the area. These categories were communal conflicts, ethnic conflicts, resource conflicts (most often land conflicts), politically motivated conflicts, conflicts due to traditional chieftaincy, and the conflicts between state forces and militia groups. Expansion of agro-pastoralism (4.6) and Extensive sedentism (4.5) were very serious factors that were perceived to lead to conflicts. A total of ₦2,289,859,549 worth 30.28% of the State’s 2018 IGR (Internal Generated Revenue) were lost in these conflicts. It was recommended that laws regarding people with diverse backgrounds and socioeconomic needs and population growth in relation to limited resources should be developed with great care and attention paid to all the parties involved in the process. Participatory approaches to problem identification, conflict management and resolutions need to be established in the communities with regular interactions between and among locals periodically and frequently.

Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization

We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where the goal is to approximate the true Pareto set of solutions by minimizing the total resource cost of experiments. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive computational simulations. The key challenge is to select the sequence of experiments to uncover high-quality solutions using minimal resources. In this paper, we propose a general framework for solving MOO problems based on the principle of output space entropy (OSE) search: select the experiment that maximizes the information gained per unit resource cost about the true Pareto front. We appropriately instantiate the principle of OSE search to derive efficient algorithms for the following four MOO problem settings: 1) The most basic single-fidelity setting, where experiments are expensive and accurate; 2) Handling black-box constraints which cannot be evaluated without performing experiments; 3) The discrete multi-fidelity setting, where experiments can vary in the amount of resources consumed and their evaluation accuracy; and 4) The continuous-fidelity setting, where continuous function approximations result in a huge space of experiments. Experiments on diverse synthetic and real-world benchmarks show that our OSE search based algorithms improve over state-of-the-art methods in terms of both computational-efficiency and accuracy of MOO solutions.

Matching Document Pairs using Multi-Feature Semantic Fusion Based on Knowledge Graph

Discriminating the homology and heterogeneity of two documents in information retrieval is very important and difficult step. Existing methods mainly focus on word-based document duplicate checking or sentence pairs matching except manual verification which need a lot of human resource cost. The word-based document duplicate checking can not judge the similarity of two documents from the semantic level and the matching sentence pair methods can not effectively mine the semantic information from a long text which is frequent retrieval results. A concept-based Multi-Feature Semantic Fusion Model (MFSFM) is proposed. It employs multi-feature enhanced semantics to construct a concept map for represent the document, and employs a multi-convolution mixed residual CNN module to introduce local attention mechanism for improve the sensitivity of conceptual boundary information. To improve the feasibility of the proposed MFSFM based on concept maps, two multi-feature document data sets are set up. Each of them consists of about 500 actual scientific and technological project feasibility reports. Experimental results based on the actual datasets show that the proposed MFSFM converges quickly while expanding the latest methods of natural language matching at the accuracy rate.

Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.

Designing production-friendly machine learning

Building production ML applications is difficult because of their resource cost and complex failure modes. I will discuss these challenges from two perspectives: the Stanford DAWN Lab and experience with large-scale commercial ML users at Databricks. I will then present two emerging ideas to help address these challenges. The first is "ML platforms", an emerging class of software systems that standardize the interfaces used in ML applications to make them easier to build and maintain. I will give a few examples, including the open-source MLflow system from Databricks [3]. The second idea is models that are more "production-friendly" by design. As a concrete example, I will discuss retrieval-based NLP models such as Stanford's ColBERT [1, 2] that query documents from an updateable corpus to perform tasks such as question-answering, which gives multiple practical advantages, including low computational cost, high interpretability, and very fast updates to the model's "knowledge". These models are an exciting alternative to large language models such as GPT-3.

Analisis Daya Saing dan Dampak Kebijakan Pemerintah terhadap Komoditas Kelapa Sawit Rakyat

Various policies are undertaken to support the increase of production and export volume of palm oil products. This study aims to analyze the competitiveness and impact of government policies on palm oil commodities in West Pasaman Regency. The research was conducted by survey method on 30 samples taken intentionally through multistage purposive sampling. The data is analyzed using Policy Analysis Matrix and sensitivity analysis. The results showed that the commodity of palm oil in Pasaman Barat Regency is competitive based on competitive advantage and comparative advantage both in the form of Fresh Fruit Bunches (FFB) and Crude Palm Oil (CPO). This is evidenced by the value of Privat Cost Ratio on FFB of 0.72 and CPO of 0,86; Domestic Resource Cost Ratio on FFB of 0.66 and CPO of 0,96; the value of private profit on FFB of 87 million rupiah and CPO of 35 billion rupiah; and social profit on FFB of 122 million rupiah and CPO of 11 billion rupiah. The impact of government policy indicated that government policies are disincentive to output, protective to tradable input, and indicated a subsidy to domestic factors. This is showed by Nominal Protection Coefficient Output on FFB of 0.82 and CPO of 0.89; Nominal Protection Coefficient Input on FFB of 0.50 and CPO of 1.00; Effective Protection Coefficient on FFB of 0.93 and CPO of 0.80; Protection Coefficient on FFB of 0.71 and CPO of 3.21; and Subsidy Ratio to Produce on FFB of -0.09 and CPO of 0.09.