Assessment of Noise Exposure of Sawmill Workers in Southwest,Nigeria

Economic wood processing employs the use of industrial machines for cutting, shaping, milling, and sawing timber, thereby leading to the generation of high levels of noise. Published data from empirical studies have categorized noise as an environmental hazard of global significance. Furthermore, noise exposure limits for different industries and all the industrial machines available has not been formally established as it presently exists in developed nations around the world. Therefore, this study assessed the daily exposure of sawmills workers to noise in Southwestern Nigeria. Reconnaissance surveys were first carried out in Osun, Oyo, Ondo, Ekiti, Lagos, and Ogun States to select sawmills that were fully operational and fit for the study. Two fully functional sawmills in two cities of each State were eventually selected for data collection, making a total of 24 sawmills, while the Circular Machines (CM), Planer Machines (PM), and Band-saw Machines (BM) were the machines in each sawmill considered. Two machines each of CM, PM, and BM were considered in each sawmill, making a total of forty-eight (48) machines each of CM, PM, and BM. Sound data were collected between 7 am and 7 pm each day for six days (between Monday and Saturday) using Extech 407732 sound level meter and all stabilized measurements were taken three times at different intervals. The data collected were in three different periods: Machine No-work Period (NP_m), Machine Idle Period (IP_m), and Machine Working Period (WP_m). A two–way Analysis of Variance (ANOVA) was carried out at P < 0.05 to determine whether there is a significant difference in the sound level average before and after the break, for both the idle and working periods of the three machines considered. This was also done to determine whether there is a significant difference between the sound level average of the results collected during idle and working periods of the three machines. Noise Pollution Levels (L_np) ranged from 83.20 dB (PM) to 107.65 (BM) and 93.42 (CM and PM) – 116.00 (BM) respectively, while IP_m also gave the least noise pollution level of 95.79 dB and WP_m gave the highest level of 102.88 dB. The results revealed that all the machines’ L_np values in the working period are more than the 90 dB acceptable limit the recommendation value of 90 dB while 89.6% of CMs, 75% of PMs, and 89.6% of BM had their L_np above 90 dB in the idle period respectively. The minimum and the maximum noise dose levels for IP_m, WP_m and overall are 0.09 (BM) and 2.37 (CM), 0.50 (CM), and 4.77 (PM) and 0.69 (BM) and 6.64 (PM) respectively. The study found out that the fundamental contributing factors to the high noise levels in sawmills are poor machine maintenance, use of old and obsolete machines, poor housekeeping strategy, limited space, workers’ negligence, lack of PPE, and lack of occupational safety training. The study recommends that proper workplace practices such as use of personal protective equipment, new and modern machines, training, and occupational safety programmes be implemented in the considered sawmills.     

供应链视角下的增值税改革影响研究

构建了一个制造商和一个零售商所组成的两级供应链模型,研究增值税税率下调对供应链决策和社会福利的影响.结果表明,下调制造商、零售商增值税税率都会使产品零售价降低,提高零售商和制造商的利润.下调增值税税率给制造商带来的利润增量大于给零售商带来的利润增量.制造商税率下调导致批发价降低,零售商税率下调反而提高批发价.社会福利的变化趋势与潜在需求规模有关.当潜在需求规模较高时,社会福利总是随降税幅度的增大而增大,且税率降低后的社会福利大于税率降低前;当潜在需求规模中等时,社会福利随降税幅度的增大而减小,但税率降低后的社会福利仍大于税率降低前;当潜在需求规模较小时,社会福利随降税幅度的增大而减小,增值税税率下调损害了社会福利.     

Prediction of the drag reduction effect of pulsating pipe flow based on machine learning

Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.     

单面柱局域共振声子晶体低频带隙特性分析及结构改进研究

基于有限元法对单面柱局域共振声子晶体进行带隙特性分析,研究了结构参数对该类型声子晶体的影响。结果表明:随着散射体高度的增加,单面柱声子晶体的第一完全带隙的起始频率逐渐降低,带宽逐渐增大;随着基板厚度的增大,单面柱声子晶体的起始频率逐渐升高,截止频率先增大后减小。并且在经典单面柱声子晶体的基础上,组合了两种新型的三组元单面柱声子晶体结构:嵌入式单面柱声子晶体(以下简称结构Ⅰ)和粘接式单面柱声子晶体(以下简称结构Ⅱ)。通过对其带隙特性的分析得出:这两种新结构与经典的单面柱声子晶体相比,都具有更低频的带隙,这对于低频减振降噪是非常有利的。本文的结果将对实际的工程应用提供一定的理论指导。     

Current trends in the description of lanthanum strontium manganite oxygen electrode reaction mechanism in a high-temperature solid oxide cell

This review summarizes recent progress in understanding the oxygen reduction and evolution reactions at the lanthanum strontium manganite electrode of a high-temperature solid oxide cell. Information provided here is put into the perspective of the defect chemistry of lanthanum strontium manganite and its impact on the electrode reaction mechanisms itself. After summarizing recent views on the oxygen reduction reaction mechanism, the focus turns to the oxygen evolution reaction, which is significantly less treated in the literature. A combination of the information in the literature on both reactions was the basis for modified reaction mechanism of the oxygen evolution reaction to be proposed under consideration of recent experimental observations and theoretical findings.     

Bicomponent Cocatalyst Decoration on Flux-assisted CaTaO2N Single Crystals for Photocatalytic CO2 Reduction under Visible Light

Cubic-like CaTaO₂N photocatalysts with high crystallinity and uniform particle size were successfully prepared by the flux-assisted nitridation method. The growth of CaTaO₂N single crystals under different synthesis conditions was systematically investigated to understand the effects of the crystallinity and optical property on photocatalytic performance of CO₂ reduction. Moreover, the modification of CaTaO₂N single crystals with core-shell Ni−Ag bicomponent cocatalyst by two-step decoration process gave a 2.4 times higher amount of CO evolution than the deposition of sole Ag cocatalyst, because of the synergistic effects of bicomponent cocatalyst on the interfacial electron transfer and surface catalytic process. This study provides a valuable way to construct high-crystalline photocatalysts with effective bicomponent cocatalyst for visible-light-driven CO₂ reduction with H₂O.     

Polymeric Carbon Nitride-based Single Atom Photocatalysts for CO2 Reduction to C1 Products

Photocatalytic CO₂ reduction to C₁ fuels is considered to be an important way for alleviating increasingly serious energy crisis and environmental pollution. Due to the environment-friendly, simple preparation, easy formation of highly-stable metal-nitrogen(M-N_x) coordination bonds, and suitable band structure, polymeric carbon nitride-based single-atom catalysts(C₃N₄-based SACs) are expected to become a potential for CO₂ reduction under visible-light irradiation. In this review, we summarize the recent advancement on C₃N₄-based SACs for photocatalytic CO₂ reduction to C₁ products, including the reaction mechanism for photocatalytic CO₂ reduction to C₁ products, the structure and synthesis methods of C₃N₄-based SACs and their applications toward photocatalytic CO₂ reduction reaction(CO₂RR) for C₁ production. The current challenges and future opportunities of C₃N₄-based SACs for photoreduction of CO₂ are also discussed.     

Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection parameters was analyzed. Scope of using non-intrusive technique has been analyzed by changing locations of microphone. Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up. Various frequency ranges of contributing noise and vibration sources were identified. Time-Frequency analysis showed the onset of various cyclic. Based on the identification of various frequency bands, it is possible to device suitable filters in order to extract more information.     

Enhancement of bioelectrochemical dioxygen reduction with oxygen-enriching materials

Bioelectrochemical dioxygen reduction reaction (ORR) catalyzed by multi-copper oxidases (MCOs) is a process of paramount importance occurring at the cathode of enzymatic biofuel cells (EBFCs), which is an energy harvester that holds promise of self-sustained implantable and wearable medical devices. The MCO biocathode is, however, frequently the limiting factor of a working EBFC. Besides the operational stability issue, enzymatic biocathodes are largely constrained by the relatively low solubility of dioxygen in aqueous solution. As an emerging topic, we here review the introduction of dioxygen-enriching materials to the cathodic bioelectrode for overcoming the dioxygen supply limitation, leading to improved ORR performance.     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.