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Drawing on a review of 112 Advocacy Coalition Framework (ACF) applications to China published during 2006–2022, this article finds that, consistent with ACF assumptions, policy processes in China are complex processes of top‐down decision making, horizontal negotiation, networked influence, and bottom‐up initiation. The review finds policy processes in many subsystems of China inconsistent with one implicit assumption of the framework: the difficulty of reconciling conflicting beliefs between warring coalitions. Compared with the results from a review of the English ACF applications to countries around the globe, policy‐oriented learning and imposition by a hierarchically superior jurisdiction were identified more frequently as pathways to policy change in reviewed applications to China. The Xi Jinping administration encourages between‐coalition learning and negotiation to pursue the ideal of building a well‐off and equitable society. At the same time, his top‐level reforms have changed the long‐time status quo in some subsystems by national policy reforms. Related Articles: Nam, Aerang, and Christopher M. Weible. 2023. "Examining Experts' Discourse in South Korea's Nuclear Power Policy Making: An Advocacy Coalition Framework Approach to Policy Knowledge." Politics & Policy 51(2): 201–21. https://doi.org/10.1111/polp.12522. Nwalie, Martin Ike. 2019. "Advocacy Coalition Framework and Policy Changes in a Third‐World Country." Politics & Policy 47(3): 545–68. https://doi.org/10.1111/polp.12302. von Malmborg, Fredrik. 2023. "Combining the Advocacy Coalition Framework and Argumentative Discourse Analysis: The Case of the 'Energy Efficiency First' Principle in EU Energy and Climate Policy." Politics & Policy 51(2): 222–41. https://doi.org/10.1111/polp.12525. [ABSTRACT FROM AUTHOR]

Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage. Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries. Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings. [ABSTRACT FROM AUTHOR]

An essential research tool for studying colorectal cancer (CRC) is the use of 1, 2-dimethylhydrazine (DMH) as an animal model of colorectal carcinogenesis. Niclosamide (Nic), an oral anthelmintic drug, has been identified as a possible anticancer agent. The purpose of this research was to determine the potential antineoplastic effect of Nic on induced colorectal carcinogenesis. Five groups from thirty-five albino rats were created. Group I was given a vehicle for four weeks. Group II was administered Nic I/P at a dose of 20 mg/kg b.w. daily for four weeks. Group III was administered DMH S/C at a dose of 20 mg/kg b.w. twice weekly for four weeks. Group IV received DMH in the same manner as Group III, and following a week from the last DMH inj ection, they were given daily doses of 20 mg/kg b.w. of Nic I/P until the experiment concluded. Group V received for four weeks both DMH S/C at a dose of 20 mg/kg b.w. twice weekly and Nic I/P at a dose of 20 mg/kg b.w. daily. Upon completion of the experiment, which lasted 12 weeks, rats were sacrificed for sampling. Colons of rats in all groups were collected for aberrant crypt foci (ACF) counting using 0.2% methylene blue. Then tissue specimens were taken for histopathological examination. According to the topographical features of colon preneoplastic lesions, we found that group III had more ACF count and crypt multiplicity, whereas groups IV and V had a significantly lower number. Microscopically, rats receiving DMH exhibited moderate to severe dysplastic changes. These changes were significantly decreased in both Nic-treated groups, however, Group V showed the best improvement. These results indicated the obvious protective effect of Nic against ACF progression. [ABSTRACT FROM AUTHOR]

Boric acid (BA) is a naturally occurring weak Lewis acid containing boron, oxygen, and hydrogen elements that can be found in water, soil, and plants. Because of its numerous biological potentials including anti-proliferation actions, the present investigates the chemopreventive possessions of BA on azoxymethane (AOM)-induced colonic aberrant crypt foci (ACF) in rats. Thirty laboratory rats were divided into 5 groups: negative control (A) received two subcutaneous inoculations of normal saline and nourished on 10% Tween 20; groups B–E had two injections of 15 mg/kg azoxymethane followed by ingestion of 10% Tween 20 (B, cancer control), inoculation with intraperitoneal 35 mg/kg 5-fluorouracil injection (C, reference group), or ingested with boric acid 30 mg/kg (D) and 60 mg/kg (E). The gross morphology results showed significantly increased total colonic ACF in cancer controls, while BA treatment caused a significant reduction of ACF values. Histopathological evaluation of colons from cancer controls showed bizarrely elongated nuclei, stratified cells, and higher depletion of the submucosal glands than that of BA-treated groups. Boric acid treatment up-surged the pro-apoptotic (Bax) expression and reduced anti-apoptotic (Bcl-2) protein expressions. Moreover, BA ingestion caused upregulation of antioxidant enzymes (GPx, SOD, CAT), and lowered MDA contents in colon tissue homogenates. Boric acid–treated rats had significantly lower pro-inflammatory cytokines (TNF-α and IL-6) and higher anti-inflammatory cytokines (IL-10) based on serum analysis. The colorectal cancer attenuation by BA is shown by the reduced ACF numbers, anticipated by its regulatory potentials on the apoptotic proteins, antioxidants, and inflammatory cytokines originating from AOM-induced oxidative damage. [ABSTRACT FROM AUTHOR]

Introduction: cancer treatment is one of the great challenges facing today’s scientific society. In this health fight, drug-conjugated antibodies (ADCs) are being developed, drugs capable of causing cell death by transporting and releasing cytotoxic compounds into tumor cells. They are composed of a monoclonal antibody (of protein nature) linked to a cytotoxic drug (of lipophilic character) through a linker. Formulations must be designed to maintain this binding during storage and administration. Objective: identify the medicines marketed in Spain whose active ingredient is an antibody-drug conjugate, studying techno pharmaceutical aspects, especially the components of their formulations Method: since this type of drugs belongs to the ATC group L01F, they have been identified through the search engine of the Spanish Agency of Medicines and Health Products. The search for their technical sheets, along with articles of review and research related to the topic, as well as the Handbook of Pharmaceuticals Excipients, has enabled the execution of the techno pharmaceutical study. the formulation of the tested conjugates to drugs marketed in Spain belonging to the ATC L01F group corresponding to “monoclonal antibodies and tested conjugated to drugs” identified through the search engine of the Spanish Agency of Medicines and Health Products has been studied. Results: different aspects of this group of drugs have been analyzed, such as the pharmaceutical form, the route of administration, conservation and especially the techno pharmaceutical formulation. The nature of the active ingredient and the requirements of the formulations based on their characteristics have been studied in depth. Conclusions: the eight antibody-drug conjugates approved in Spain are presented in the form of lyophilized powder in a vial and should be stored between 2-8 ºC. For administration, they are reconstituted, initially obtaining a concentrate, which is then diluted and administered as an intravenous infusion or drip. Their typical formulation includes a lyoprotector, an antiaggregant, a pH regulator, and eventually antioxidants or viscosity reducers. [ABSTRACT FROM AUTHOR]

Heart failure (HF) has been declared as global pandemic and current therapies are still ineffective, especially in patients that develop concurrent cardio-renal syndrome. Considerable attention has been focused on the nitric oxide (NO)/soluble guanylyl cyclase (sGC)/cyclic guanosine monophosphate (cGMP) pathway. In the current study, we aimed to investigate the effectiveness of sGC stimulator (BAY41-8543) with the same mode of action as vericiguat, for the treatment of heart failure (HF) with cardio-renal syndrome. As a model, we chose heterozygous Ren-2 transgenic rats (TGR), with high-output heart failure, induced by aorto-caval fistula (ACF). The rats were subjected into three experimental protocols to evaluate short-term effects of the treatment, impact on blood pressure, and finally the long-term survival lasting 210 days. As control groups, we used hypertensive sham TGR and normotensive sham HanSD rats. We have shown that the sGC stimulator effectively increased the survival of rats with HF in comparison to untreated animals. After 60 days of sGC stimulator treatment, the survival was still 50% compared to 8% in the untreated rats. One-week treatment with sGC stimulator increased the excretion of cGMP in ACF TGR (109 ± 28 nnmol/12 h), but the ACE inhibitor decreased it (-63 ± 21 nnmol/12 h). Moreover, sGC stimulator caused a decrease in SBP, but this effect was only temporary (day 0: 117 ± 3; day 2: 108 ± 1; day 14: 124 ± 2 mmHg). These results support the concept that sGC stimulators might represent a valuable class of drugs to battle heart failure especially with cardio-renal syndrome, but further studies are necessary. [ABSTRACT FROM AUTHOR]

PCN/Bi0-BWO/Zn(CH3COO)2-ACF composite was compounded for the removal of n-hexane in this paper. Characterization results show that the composite was successfully prepared and the light response ability of the catalyst was improved. The photocatalytic properties of the composite were also tested; the removal rate of PCN/Bi0-BWO/Zn(CH3COO)2-ACF composite can reach more than 85% in 360 min. The main reason for the improvement of the performance of the composites was that the addition of phosphorus and the formation of Z-scheme heterojunction increased the efficiency of electron–hole separation on the surface of the catalyst. [ABSTRACT FROM AUTHOR]

The Narrative Policy Framework (NPF) has provided policy scholars with a valuable method to gain empirical insight into the power of narratives in the policy process. However, a significant limitation of the NPF has been its ability to deploy this framework on large N datasets due to the labor‐intensive nature of collecting narrative data. In recent years, NPF scholars have turned to computational social science tools to address this challenge. This study builds upon this emerging body of literature and our previous work, which uses sentiment analysis, a natural language processing technique, to evaluate the use of the angel/devil shift across coalitions before and after a major policy change. We examined Tweets that included the terms "fracking" and "New York" before and after the introduction of a moratorium. While sentiment analysis allowed us to gain insight into the narrative structure of the fracking policy discourse space, the labor involved in hand‐coding Twitter users into neutral‐, pro‐, or anti‐fracking groups was onerous. This project aims to supplement our natural language processing method by employing supervised machine learning techniques to increase the universe of respondents. We hand‐coded 500 Twitter users into neutral‐, pro‐, or anti‐fracking groups and trained a much larger dataset using an extreme gradient boost algorithm to classify a broader corpus of Tweets. This enabled us to expand the number of Tweets used in the analyses. We then applied sentiment analysis on this newly classified larger dataset to reveal differences in the pro‐fracking and anti‐fracking advocacy coalitions. By using machine learning to classify pro and con Tweets, we gained the ability to achieve significantly greater insight into how these two subgroups employed different narrative and linguistic devices in their Twitter discussions about fracking. Related Articles: Merry, Melissa K. 2022. "Trump's Tweets as Policy Narratives: Constructing the Immigration Issue via Social Media." Politics & Policy 50(4): 752–72. https://doi.org/10.1111/polp.12487. Robles, Pedro, and Daniel J. Mallinson. 2023. "Catching Up with AI: Pushing toward a Cohesive Governance Framework." Politics & Policy 51(3): 355–72. https://doi.org/10.1111/polp.12529. Shanahan, Elizabeth A., Mark K. McBeth, and Paul L. Hathaway. 2011. "Narrative Policy Framework: The Influence of Media Policy Narratives on Public Opinion." Politics & Policy 39(3): 373–400. https://doi.org/10.1111/j.1747‐1346.2011.00295.x. [ABSTRACT FROM AUTHOR]

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This paper presents a compact intelligent camera with traffic surveillance and enforcement as a primary application field. The camera's unique features are 1/ embedded object detection of user-defined objects and 2/ excellent image quality achieved by a custom HDR image acquisition and tone mapping. Intelligent cameras are essential parts of equipment in production, control, security and primarily traffic surveillance and enforcement. In many applications, they serve as a versatile source of images processed elsewhere, and reduction of required processing and communication achieved by embedded (pre)processing within the intelligent camera is always welcome. The key blocks of the camera are implemented in FPGA to reduce the CPU load and thus the power requirements. The control software uses an embedded CPU running Linux. The camera is designed to provide key images that are interesting from the application point of view and to provide metadata. On the camera prototype, we observed total power consumption of 8 W and object detection accuracy > 99%. Application designers can extensively modify the camera design and functionality to harness various applications, such as traffic surveillance and enforcement, quality control, etc. [ABSTRACT FROM AUTHOR]

We investigated preventive effects of soy culture fermented with a koji fungus and lactic acid bacteria (SKL) on early stages of carcinogenesis of colon cancer induced by 1,2-dimethylhydrazine dihydrochloride (DMH) in mice. First, we investigated whether SKL prevents the occurrence of aberrant crypt foci (ACF). SKL was mixed in feed at concentrations of 10%, 5%, and 2.5% (v/w) and administered freely until the end of the test for 70 days, whereas DMH was administered subcutaneously twice a week for 4 weeks at 20 mg/kg (bw). Because SKL significantly decreased the number of ACF in a dose-dependent manner, we assumed that SKL may have reduced DNA damage induced by DMH and this reduction led to the decrease of ACF. On the basis of this assumption, we further investigated whether SKL inhibits DNA damage of the cells of the liver and the colonic mucosal layer, by using single-cell gel electrophoresis assay (comet assay). Mice were fed SKL at the same concentration as in the ACF test freely for 2 weeks. DMH was administered intraperitoneally once at 20 mg/kg (bw) and the mice were killed 3 h later. Cells from the liver and the colonic mucosal layer were subjected to the comet assay, and DNA damage was evaluated by calculating the tail moment. Because SKL significantly decreased the DNA damage of the cells of both tissues in a dose-dependent manner, we subsequently investigated which component of SKL is essential for the decrease. We fractionated SKL into supernatant, fibers, and cells of lactic acid bacteria (LAB), and subjected them to the comet assay. Interestingly, the fraction of LAB cells effectively reduced DNA damage of the cells of both tissues more than the fractions of supernatant and fiber did. On the basis of these results, we concluded that the reduction of the DNA damage with the LAB cells in SKL is probably a primary cause of the decrease in the number of ACF, and this preventive effect of LAB cells at early stages of carcinogenesis of DMH may lead to the prevention of colon cancer. [ABSTRACT FROM AUTHOR]

We investigated preventive effects of soy culture fermented with a koji fungus and lactic acid bacteria (SKL) on early stages of carcinogenesis of colon cancer induced by 1,2-dimethylhydrazine dihydrochloride (DMH) in mice. First, we investigated whether SKL prevents the occurrence of aberrant crypt foci (ACF). SKL was mixed in feed at concentrations of 10%, 5%, and 2.5% (v/w) and administered freely until the end of the test for 70 days, whereas DMH was administered subcutaneously twice a week for 4 weeks at 20 mg/kg (bw). Because SKL significantly decreased the number of ACF in a dose-dependent manner, we assumed that SKL may have reduced DNA damage induced by DMH and this reduction led to the decrease of ACF. On the basis of this assumption, we further investigated whether SKL inhibits DNA damage of the cells of the liver and the colonic mucosal layer, by using single-cell gel electrophoresis assay (comet assay). Mice were fed SKL at the same concentration as in the ACF test freely for 2 weeks. DMH was administered intraperitoneally once at 20 mg/kg (bw) and the mice were killed 3 h later. Cells from the liver and the colonic mucosal layer were subjected to the comet assay, and DNA damage was evaluated by calculating the tail moment. Because SKL significantly decreased the DNA damage of the cells of both tissues in a dose-dependent manner, we subsequently investigated which component of SKL is essential for the decrease. We fractionated SKL into supernatant, fibers, and cells of lactic acid bacteria (LAB), and subjected them to the comet assay. Interestingly, the fraction of LAB cells effectively reduced DNA damage of the cells of both tissues more than the fractions of supernatant and fiber did. On the basis of these results, we concluded that the reduction of the DNA damage with the LAB cells in SKL is probably a primary cause of the decrease in the number of ACF, and this preventive effect of LAB cells at early stages of carcinogenesis of DMH may lead to the prevention of colon cancer. [ABSTRACT FROM AUTHOR]

Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interference of the background light. To reduce this kind of interference, we propose a novel method called annular convolution filtering (ACF). In our method, the region of interest (ROI) in the spot image is first searched by using the statistical properties of pixels. Then, the annular convolution strip is constructed based on the energy attenuation property of the laser and the convolution operation is performed in the ROI of the spot image. Finally, a feature similarity index is designed to estimate the parameters of the laser spot. Experiments on three datasets with different kinds of background light show the advantages of our ACF method, with comparison to the theoretical method based on international standard, the practical method used in the market products, and the recent benchmark methods AAMED and ALS. [ABSTRACT FROM AUTHOR]

This article combines, in a complementary way, the Advocacy Coalition Framework (ACF) with argumentative discourse analysis to analyze the politics of the "energy efficiency first" (EE1) principle as a new legal institute in European Union energy and climate policy. Two different discourses and storylines were identified. The first is where the meaning of energy efficiency policy is to fulfill multiple benefits. It is associated with a strong regulation of EE1. The second is where the purpose of energy efficiency policy is primarily to contribute to mitigating climate change. It is associated with weak regulation of EE1. Four advocacy coalitions with different secondary beliefs regarding the EE1 principle were identified. Analyzing the inter‐discursive communication between different coalitions provides insights to the policy‐oriented learning taking place. It is concluded that studies using the ACF could merit from focusing more on narratives and communication between belief systems. Related Articles: Kanol, Direnç. 2022. "Narrative Strategies for Emerging Disruptive Technologies: A Case Study of Blockchain for Europe." Politics & Policy 50(5): 952–66. https://doi.org/10.1111/polp.12492. Nowlin, Matthew C., Maren Trochmann, and Thomas M. Rabovsky. 2022. "Advocacy Coalitions and Political Control." Politics & Policy 50(2): 201–24. https://doi.org/10.1111/polp.12458. O'Donovan, Kristin T. 2018. "Does the Narrative Policy Framework Apply to Local Policy Issues?" Politics & Policy 46(4): 532–70. https://doi.org/10.1111/polp.12265. [ABSTRACT FROM AUTHOR]

This paper attempts to investigate the markup's evolution at the firm level for Iran's economy for the period 2003–2013, using Iran's manufacturing plant panel data of firms with ten or more workers. Following the ACF approach, a translog production function has been estimated for each industry. Then, after estimating each firm's labor elasticity trend, we used the DLW approach to estimate and analyze the distributional variation of a markup over time and among Iran's industries. We plot the kernel density of markups for the first and last year in the data and show that the last year's markup distribution has become fattened in its upper tail compared to the first year. Subsequently, it is demonstrated that markups' distribution considerably differs from one industry to another, and each industry has a different weighted average markup trend. Finally, it is illustrated that exporting firms have charged more markup compared to domestic ones, and sanctions have positively affected this markup premium.

Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA model was analyzed and compared with autoregressive (AR) and autoregressive integrated moving average (ARIMA) for the data obtained from the NASA Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis. The statistical analysis of the autocorrelation function (ACF), partial autocorrelation function (PACF), normal quantile–quantile (normal Q-Q) plot, density of the residuals, and variance of normal variable w(t) shows that ARMA (1,1) fits better than AR (1) and ARIMA (1, d, 1) for d = 1, 2.

In life, electricity is an important and primary energy sources to meet the needs of human life, in industry, economy and technology. Along with the increasing demand for electricity from time to time, the problem faced is the power distributed quantity so that the electricity distribution to customers must be optimized according to needs. The goal is to be able to take the appropriate action based on electricity needs and improve the service quality to customers. Therefore, an effective electric load forecasting method is needed in the future to optimized electricity demand. In this study, forecasting was carried out using Triple SARIMA method based on sample data from Denmark for 60 minutes in Mega Watt units obtained from open source secondary data. Forecasting is conducted by taking into account seasonal factors daily, weekly, and yearly. The purpose of this study is to determine the pattern of triple-seasonal electric load data on the effect of periodic time, evaluate the best triple-seasonal model to get the minimum error and subset, multiplicative and additive seasonal models. The result of this study was obtained from three temporary models then carried out the estimation and significance tests with Maximum likelihood and residual assumption test. Thus, (1,1,1)(1,1,1)24(1,1,1)168(0,1,1)8736 model is the best model with RMSE value 30,02746373 and MAPE 0,02298758004. In addition, it was also found that the multiplicative Triple SARIMA model is the best model compared with the subset and additive models where the AIC and SBC values are 489545 and 489597, respectively. [ABSTRACT FROM AUTHOR]

Identification of vehicles violating a certain speed by measuring the speed of vehicles in a nonintrusive manner with low cost and good results is very important, as it directly contributes to helping the traffic police instead of using radar, lidar or other expensive devices, the use of video analysis to estimate the speed of vehicles involved in traffic accidents is becoming increasingly common. In most cases, the estimate is based on in situ reference measurements or data derived using photogrammetry techniques. In this research, the suggested method estimates the average vehicle speed via computing the distance between consecutive tires using the "Pixel Image Scale Coefficient" and converts the results to accurate measurements. Using an installed camera or a mobile phone device at video frames per second (fps of 30 frames per second, a mathematical function to identify the speed of a passing vehicle predicated on its motion pattern vector and transform that speed from measurements of pixels in succeeding frames to realistic measurements is presented. Three steps were used in this work to implement the technique: In the first stage, a vehicle is distinguished from other objects utilizing artificial intelligence, particularly the Aggregated Channel Features(ACF) vehicle detectors algorithm. In the second step, automobiles are monitored by tracking successive video frames and then the third step, which is the working core of this The research, is to calculate the speed of the detected vehicles in successive video frames through the proposed mathematical equation that mainly depends on the product of the obtained Euclidean distance, and then convert this result to what is proportional to the realistic measurements of speed, which really is km/h. The project work was produced on a 64-bit system with an Intel Core i7 processor using Matlab R2021a. The software was used to analyze a series of locally captured movies that were used as the data source for a data set used to calculate the average vehicle speed. The proposed program's results were compared with the established driving velocity in order to verify the findings. A simulation of a vehicle moving at a known speed was employed. The mistake rate as a result of the vehicle speeds varied from 1 to 5 km/h. [ABSTRACT FROM AUTHOR]

In the present study, the ACF-TiO2 catalyst was synthesised and used as a catalyst for the destruction of toxic cyanide in both synthetic and real industrial wastewaters. The ACF-TiO2 catalyst was found to be micro-porous with the BET surface area of 163 m2/g. The effect of different operational parameters such as catalyst concentration, cyanide concentration, operation time, and ozone concentration were target parameters in the present study. The findings show that 500 mg/L of catalyst is the optimum value for the photocatalytical process to completely oxidise 25 mg/L of cyanide within 10 min. While it was found that 300 mg/L of catalyst in the presence of 200 mg/h ozone is enough to remove 25 mg/L cyanide within operation time of 24 min. Degradation of the cyanide in both investigated processes follow the pseudo-first-order kinetic and the reaction rate constant is almost 2 times higher in the photocatalytical process compared with catalytic ozonation process. Furthermore, the quenching tests using salicylic acid demonstrated the hydroxyl radical generation in the investigated process. Generation of highly oxidising hydroxyl radical makes the photocatalytical process a promising process for the transformation of noxious cyanide to much less toxic compounds (ammonia and nitrate). Accordingly, it can be concluded that ACF-TiO2 could be a catalyst for degradation and detoxification of cyanide. [ABSTRACT FROM AUTHOR]

Summary: Background: Active case finding (ACF) for tuberculosis (TB) is the cornerstone case-finding strategy in India's national TB policy. However, ACF strategies are highly diverse and pose implementation challenges in routine programming. We reviewed the literature to characterise ACF in India; assess the yield of ACF for different risk groups, screening locations, and screening criteria; and estimate losses to follow-up (LTFU) in screening and diagnosis. Methods: We searched PubMed, EMBASE, Scopus, and the Cochrane library to identify studies with ACF for TB in India from November 2010 to December 2020. We calculated 1) weighted mean number needed to screen (NNS) stratified by risk group, screening location, and screening strategy; and 2) the proportion of screening and pre-diagnostic LTFU. We assessed risk of bias using the AXIS tool for cross-sectional studies. Findings: Of 27,416 abstracts screened, we included 45 studies conducted in India. Most studies were from southern and western India and aimed to diagnose pulmonary TB at the primary health level in the public sector after screening. There was considerable heterogeneity in risk groups screened and ACF methodology across studies. Of the 17 risk groups identified, the lowest weighted mean NNS was seen in people with HIV (21, range 3–89, n=5), tribal populations (50, range 40–286, n=3), household contacts of people with TB (50, range 3-undefined, n=12), people with diabetes (65, range 21-undefined, n=3), and rural populations (131, range 23–737, n=5). ACF at facility-based screening (60, range 3-undefined, n=19) had lower weighted mean NNS than at other screening locations. Using the WHO symptom screen (135, 3-undefined, n=20) had lower weighted mean NNS than using criteria of abnormal chest x-ray or any symptom. Median screening and pre-diagnosis loss-to-follow-up was 6% (IQR 4.1%, 11.3%, range 0–32.5%, n=12) and 9.5% (IQR 2.4%, 34.4%, range 0–86.9%, n=27), respectively. Interpretation: For ACF to be impactful in India, its design must be based on contextual understanding. The narrow evidence base available currently is insufficient for effectively targeting ACF programming in a large and diverse country. Achieving case-finding targets in India requires evidence-based ACF implementation. Funding: WHO Global TB Programme.

The CO2 emitted by coal-fired power plants is the main factor leading to global warming, and the capture of CO2 in the flue gas of power plants is still the main task at this stage. Many adsorbents have been developed to capture CO2 in high-temperature flue gas, but some materials are complicated to synthesize or the cost is too high. Here, we used low-cost raw materials activated carbon fiber and PEI, and used green synthesis to synthesize new adsorbents in order to capture CO2 in high-temperature flue gas of a power plant. To improve the performance of highly porous activated carbon fiber (ACF) in CO2 capture and separation, an organic polymer polyethylenimine (PEI) was loaded successfully into the oxidized ACF. The modified adsorbent was tested by FT-IR, XRD and SEM, and the CO2 adsorption capacity and CO2/N2 selectivity were analyzed. The results showed that the as-synthesized PEI-modified adsorbent has a CO2 adsorption capacity of 2.5 mmol/g, which is 1.7 times better than that of the pristine ACF adsorbent (1.5 mmol/g), at 1 bar and 333 K, and it has excellent CO2/N2 selectivity, as calculated by ideal adsorption solution theory (IAST). These data indicate that PEI was loaded successfully into the oxidized ACF. In addition, the dual site Langmuir isotherm equation and Langmuir isotherm equation can be in good agreement with the adsorption curves of CO2 and N2. In comparison with other composite adsorbents, the preparation process of the present new adsorbent is highly environmentally friendly, the synthesis method is simple and the cost is low, which demonstrates potential applications in the separation of CO2 from the flue gas of power plants. [ABSTRACT FROM AUTHOR]

An acoustic matching layer is an essential component of an ultrasound transducer to achieve maximum ultrasound transmission efficiency. Here, we develop a flexible printed circuit board (FPCB) with a composite structure consisting of multiple polyimide and copper layers and demonstrate it as a novel acoustic matching layer. With a flexible substrate and robust ACF bonding, the FPCB not only serves as an acoustic matching layer between piezoelectric elements and the surrounding medium but also as a ground for the electrical connection between the transducer array elements and the folded substrate. A 1D linear ultrasound transducer array with the FPCB matching layer exhibits larger output pressure, wider -3dB bandwidth, and higher ultrasound beam intensity compared to that of an ultrasound transducer array with the alumina/epoxy matching layer, which is one of the most commonly applied composite matching layers. The enhanced transmission performance verifies that the proposed FPCB is an excellent matching layer for 1D linear ultrasound transducer arrays. [ABSTRACT FROM AUTHOR]

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p < 0:05) indicating that the previous day value of the stochastic term also had a significant effect on the present value of meteorological parameters in the environment. It was observed that in all the fitted models except for wind direction in Conakry and rainfall in Abidjan have all their Autoregressive term of order 1 significant (p < 0:05) which implies that previous day value of these meteorological parameter had a significant effect on the present day value of the parameters. Therefore, the forecast model indicates that maximum temperature are expected in February, March, April, and June while minimum temperatures in January, August, December. Although, the selected models cannot forecast the precise air temperature, this can also provide information that can be of help to create tactics for appropriate preparation of farming which can be used as tools for effective environmental preparation and policymaking. [ABSTRACT FROM AUTHOR]

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p