bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Effects of Inspiratory Muscle Training in Institutionalized Elderly Women: A Double-Blind Randomized Controlled Trial
Authors: Martín-Sánchez Carlos , Barbero-Iglesias Fausto José , Amor-Esteban Víctor , Martín-Sánchez Marta , Martín-Nogueras Ana María
Published: October 2022

Background: Inspiratory muscle training (IMT) has been applied to different populations, but evidence in elderly women and maintenance of improvements achieved during training is scarce. The objective of the study was to evaluate the effectiveness of IMT 6 months after finishing the training period in elderly women. Methods: A controlled, randomized, double-blind trial was conducted, with allocation concealment performed on 26 institutionalized elderly women distributed into 2 groups: high loads and low loads. Over an 8-week period, an IMT protocol was followed; later, elderly women continued for 6 months with their daily normal activities. Inspiratory strength, expiratory strength, and functional capacity were measured pre-IMT, post-IMT, and 6 months after intervention. Results: After an 8-week training, respiratory strength and functional capacity increased in both groups. Six months after finishing the intervention, these benefits were lost. Inspiratory strength decreased in the high-loads group but was 32% higher than initial values (p = 0.007); in the low-loads group, patients lost 41% (p = 0.015). Improvements were higher in the high-loads group after training (p = 0.000) and follow-up (p = 0.002). Functional capacity was similar to initial values in both groups. Conclusion: The improvements achieved with IMT in elderly women disappeared 6 months after the end of training. High loads were more effective to keep inspiratory strength than low loads.
Neurodevelopmental Antecedents and Sensory Phenomena in Obsessive Compulsive Disorder: A Systematic Review Supporting a Phenomenological-Developmental Model
Authors: Poletti Michele , Gebhardt Eva , Pelizza Lorenzo , Preti Antonio , Raballo Andrea
Published: October 2022

Background: The majority of models on obsessive compulsive disorder (OCD) endorse a top-down perspective on the cognitive mechanisms underlying OCD functioning and maintenance, whereas a bottom-up perspective is rarely pursued. Objectives: The aim of the study was to review the empirical literature on sensory phenomena (SP) and neurodevelopmental antecedents of OCD, which could support the conceptualization of an alternative, bottom-up perspective integrating neurodevelopmental and phenomenological levels of analysis on OCD. Methods: A systematic review according to PRISMA guidelines was performed in PubMed/MEDLINE, PsycInfo, the Cochrane Library, and Excerpta Medica Database (EMBASE) and focused on SP and “neurodevelopmental antecedents” (operationalized in early risk factors, neuroimaging signs, neurological soft signs, and sensory responsivity). The time interval was from inception up to March 31, 2022. Results: From the search in electronic databases, 48 studies were retained and reviewed. SP are highly prevalent in OCD patients and overrepresented in comparison with healthy controls. Similarly, OCD patients also present a higher prevalence of early environmental adversities and sensorimotor alterations in terms of neurological soft signs and sensory over-responsivity in the tactile and acoustic domains; additional findings included hypogyrification signs at neuroimaging. Both sensorimotor alterations and SP are associated with tic-related manifestations and poorer insight in OCD patients. Conclusions: On the ground of established common subjective experience of SP and premorbid neurodevelopmental features, we hypothesized an explanatory model for OCD, which considers the possible pathophysiological role for altered corollary discharge and enhanced error detection in the neurodevelopment of SP and obsessions. SP may represent the subjective experiential resonance of an individual history of persistently inaccurate sensory predictions, whereas accompanying manifestations, such as the obsessive need for order and symmetry, may represent a compensatory attempt to mitigate SP. This neurodevelopmental-phenomenological bottom-up model, describing a dimensional gradient of sensorimotor alterations and related subjective experiences, may contribute to explain the dimensional affinity between OCD and schizophrenia spectrum disorders. Furthermore, this model could be useful for the early detection of subjects at higher risk of OCD.
Enhanced convolutional neural network for non-small cell lung cancer classification
Authors: Tashtoush Yahya , Obeidat Rasha , Al-Shorman Abdallah , Darwish Omar , Al-Ramahi Mohammad , Darweesh Dirar
Published: October 2022

Lung cancer is a common type of cancer that causes death if not detected early enough. Doctors use computed tomography (CT) images to diagnose lung cancer. The accuracy of the diagnosis relies highly on the doctor's expertise. Recently, clinical decision support systems based on deep learning valuable recommendations to doctors in their diagnoses. In this paper, we present several deep learning models to detect non-small cell lung cancer in CT images and differentiate its main subtypes namely adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. We adopted standard convolutional neural networks (CNN), visual geometry group-16 (VGG16), and VGG19. Besides, we introduce a variant of the CNN that is augmented with convolutional block attention modules (CBAM). CBAM aims to extract informative features by combining cross-channel and spatial information. We also propose variants of VGG16 and VGG19 that utilize a support vector machine (SVM) at the classification layer instead of SoftMax. We validated all models in this study through extensive experiments on a CT lung cancer dataset. Experimental results show that supplementing CNN with CBAM leads to consistent improvements over vanilla CNN. Results also show that the VGG variants that use the SVM classifier outperform the original VGGs by a significant margin.
Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure
Authors: Mahfuz Nurshazwani Muhamad , Yusoff Marina , Idrus Zainura
Published: October 2022

Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, k at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains.
Solving multiple sequence alignment problems by using a swarm intelligent optimization based approach
Authors: Paruchuri Tirumala , Kancharla Gangadhara Rao , Dara Suresh
Published: October 2022

In this article, the alignment of multiple sequences is examined through swarm intelligence based an improved particle swarm optimization (PSO). A random heuristic technique for solving discrete optimization problems and realistic estimation was recently discovered in PSO. The PSO approach is a nature-inspired technique based on intelligence and swarm movement. Thus, each solution is encoded as “chromosomes” in the genetic algorithm (GA). Based on the optimization of the objective function, the fitness function is designed to maximize the suitable components of the sequence and reduce the unsuitable components of the sequence. The availability of a public benchmark data set such as the Bali base is seen as an assessment of the proposed system performance, with the potential for PSO to reveal problems in adapting to better performance. This proposed system is compared with few existing approaches such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) alignment (DIALIGN), PILEUP8, hidden Markov model training (HMMT), rubber band technique-genetic algorithm (RBT-GA) and ML-PIMA. In many cases, the experimental results are well implemented in the proposed system compared to other existing approaches.
A pre-trained model vs dedicated convolution neural networks for emotion recognition
Authors: Nawaf Asmaa Yaseen , Jasim Wesam M.
Published: October 2022

Facial expression recognition (FER) is one of the most important methods influencing human-machine interaction (HMI). In this paper, a comparison was made between two models, a model that was built from scratch and trained on FER dataset only, and a model previously trained on a data set containing various images, which is the VGG16 model, then the model was reset and trained using FER dataset. The FER+ data set was augmented to be used in training phases using the two proposed models. The models will be evaluated (extra validation) by using images from the internet in order to find the best model for identifying human emotions, where Dlib detector and OpenCV libraries are used for face detection. The results showed that the proposed emotion recognition convolutional neural networks (ERCNN) model dedicated to identifying human emotions significantly outperformed the pre-trained model in terms of accuracy, speed, and performance, which was 87.133% in the public test and 82.648% in the private test. While it was 71.685% in the public test and 67.338% in the private test using the proposed VGG16 pre-trained model.
Critical clearing time estimation of multi-machine power system transient stability using fuzzy logic
Authors: Aziz Nagham Hikmat , Al-Flaiyeh Maha Abdulrhman
Published: October 2022

Studying network stability requires determining the best critical clearing time (CCT) for the network after the fault has occurred. CCT is an essential issue for transient stability assessment (TSA) in the operation, security, and maintenance of an electrical power system. This paper proposes an algorithm to obtain CCT based on fuzzy logic (FL) under fault conditions, for a multi-machine power system. CCT was estimated using a two-step fuzzy logic algorithm: the first step is to calculate Δt, which represents the output of the FL, while maximum angle deviation (δmax) represents the input. The second step is to classify the system if it is a stable or unstable system, based on two inputs for FL, the first mechanical input power (Pm), the second average accelerations (Aav). The results of the proposed method were compared with the time domain simulation (TDS) method. The results showed the accuracy and speed of the estimation using the FL method, with an error rate not exceeding 5%, and reduced the performance time by about half the time. The proposed approach is tested on both IEEE-9 bus and IEEE-39 bus systems using simulation in MATLAB.
Propose shot boundary detection methods by using visual hybrid features
Authors: Abdulsahib Muna Ghazi , Abdulmunim Matheel E.
Published: October 2022

Shot boundary detection is the fundamental technique that plays an important role in a variety of video processing tasks such as summarization, retrieval, object tracking, and so on. This technique involves segmenting a video sequence into shots, each of which is a sequence of interrelated temporal frames. This paper introduces two methods, where the first is for detecting the cut shot boundary via employing visual hybrid features, while the second method is to compare between them. This enhances the effectiveness of the performance of detecting the shot by selecting the strongest features. The first method was performed by utilizing hybrid features, which included statistics histogram of hue-saturation-value color space and grey level co-occurrence matrix. The second method was performed by utilizing hybrid features that include discrete wavelet transform and grey level co-occurrence matrix. The frame size decreased. This process had the advantage of reducing the computation time. Also used local adaptive thresholds, which enhanced the method’s performance. The tested videos were obtained from the BBC archive, which included BBC Learning English and BBC News. Experimental results have indicated that the second method has achieved (97.618%) accuracy performance, which was higher than the first and other methods using evaluation metrics.
Provably secure and efficient audio compression based on compressive sensing
Authors: Abood Enas Wahab , Alaa Hussien Zaid , Assy Kawi Haifaa , Abduljabbar Zaid Ameen , Omollo Nyangaresi Vincent , Ma Junchao , Al Sibahee Mustafa A. , Ahmad Ali Kalafy Saad
Published: October 2022

The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security.
Machine learning for Arabic phonemes recognition using electrolarynx speech
Authors: Mohammed Ameen Zinah Jaffar , Abdulrahman Kadhim Abdulkareem
Published: October 2022

Automatic speech recognition system is one of the essential ways of interaction with machines. Interests in speech based intelligent systems have grown in the past few decades. Therefore, there is a need to develop more efficient methods for human speech recognition to ensure the reliability of communication between individuals and machines. This paper is concerned with Arabic phoneme recognition of electrolarynx device. Electrolarynx is a device used by cancer patients having vocal laryngeal cords removed. Speech recognition here is considered to find the preferred machine learning model that can classify phonemes produced by electrolarynx device. The phonemes recognition employs different machine learning schemes, including convolutional neural network, recurrent neural network, artificial neural network (ANN), random forest, extreme gradient boosting (XGBoost), and long short-term memory. Modern standard Arabic is utilized for testing and training phases of the recognition system. The dataset covers both an ordinary speech and electrolarynx device speech recorded by the same person. Mel frequency cepstral coefficients are considered as speech features. The results show that the ANN machine learning method outperformed other methods with an accuracy rate of 75%, a precision value of 77%, and a phoneme error rate (PER) of 21.85%.
Design of an axial mode helical antenna with buffer layer for underwater applications
Authors: Jaafar Afiza Nur , Ja’afar Hajar , Yamada Yoshihide , Sadeghikia Fatemeh , Pasya Ibrahim Idnin , Adzhar Mahmood Mohd Khairil
Published: October 2022

Recently, there is an increasing demand for high-speed wireless communication network for short-range underwater communication. From previous research, most underwater antennas produced omnidirectional radiation pattern which has lower antenna gain. There are a few considerations that need to be taken if the antenna is designed to operate in water environment. This paper discusses the electromagnetic properties which affect the underwater antenna design. Physical properties such as electrical permittivity and conductivity of water contribute significant effect to the size of the antenna as it influences the behavior of electromagnetic signal that propagates in water. In this study, an axial mode helical antenna with waterproof container is presented which operates at 433 MHz. The axial mode helical antenna has circular polarization and is suitable to support wireless application which is surrounded by some obstruction. The proposed antenna produces a bidirectional radiation pattern by placing it into a waterproof casing. Good agreement between the simulation and measurement results validates the concept. However, a little discrepancy between the simulated and measured results may be attributed to the noise originated from the equipment and the environment.
Average symbol error rate analysis of reconfigurable intelligent surfaces-assisted free-space optical link over log-normal turbulence channels
Authors: Ai Duong Huu , Vuong Cong Dat , Dang Dai Tho
Published: October 2022

Optical wireless communication (OWC) has attracted significant interest recently in academia and industry. Free-space optical (FSO) communication systems are where free space acts as a communication channel between transceivers that are line of sight (LOS) for the successful transmission of optical signals. The FSO transmissions through the atmosphere, nevertheless, bring signiﬁcant challenges, besides the uncertainty of atmospheric channels, especially the signal fading due to the atmospheric turbulence, attenuation and pointing errors caused by the random beam misalignments between transceivers, signal obstruction due to buildings or trees can pre-vent the transmitted message to reach the destination. This study theoretically investigates the average symbol error rate (ASER) of reconfigurable intelligent surfaces (RIS) assisted FSO link over log-normal turbulence channels. The RIS effect is examined by considering the influence of link distance, transmitted optical power, and quadrature amplitude modulation (QAM) scheme on the ASER.
Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition
Authors: Mohammed Alshaimaa M. , Haytamy Samar Shaaban Abdelfattah , Omara Fatma A.
Published: October 2022

The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.
A fast and accurate global maximum power point tracking controller for photovoltaic systems under complex partial shadings
Authors: Atoui Adil , Kermadi Mostefa , Seghir Boucherit Mohamed , Benmansour Khelifa , Barkat Said , Akel Fethi , Mekhilef Saad
Published: October 2022

The operating conditions of partially shaded photovoltaic (PV) generators created a need to develop highly efficient global maximum power point tracking (GMPPT) methods to increase the PV system performance. This paper proposes a simple, efficient, and fast GMPPT based on fuzzy logic control to reach the point of global maximum power. The approach measures the PV generator current in the areas where it is almost constant to estimate the local maximums powers and extracts the highest among them. The performance of this method is evaluated firstly by simulation versus four well-known recent methods, namely the hybrid particle swarm optimization, modified cuckoo search, scrutinization fast algorithm, and shade-tolerant maximum power point tracking (MPPT) based on current-mode control. Then, experimental verification is conducted to verify the simulation findings. The results confirm that the proposed method exhibits high performance for complex partial irradiances and can be implemented in
 low-cost calculators.
Empirical evaluation of continuous auditing system use: a systematic review
Authors: Hassan Azizah , Salleh Norsaremah , Ismail Mohd Nasir , Nazir Ahmad Mohammad , Che Hussin Ab Razak
Published: October 2022

For more than two decades, the concept of continuous auditing (CA) has been introduced and many large firms had taken the initiatives to apply the CA system in supporting their audit functions. Despite the benefits that the CA system is offers, it is still not widely use and the number of people using the CA system is still considered low. This research focuses on the published papers on the use of CA system within the context of auditing system addressing the quality of system implementation. This paper analyzed primary studies collected using the pre-determined search strings on nine online databases. As a result, a total of 60 articles were carefully selected to undergo further analysis based on empirical evidence of CA system use. The articles were analyzed qualitatively using ATLAS.ti 7 and the elements for the CA system use are extracted from the selected papers. A total of four elements were identified contributing to the use of CA in practice. Those elements are the participant quality, system quality, information quality and products and services quality. This study answers five research objectives to understand the current studies on CA and to determine future research works on CA.
Automatic generation of business process models from user stories
Authors: Nasiri Samia , Adadi Amina , Lahmer Mohammed
Published: October 2022

In this paper, we propose an automated approach to extract business process models from requirements, which are presented as user stories. In agile software development, the user story is a simple description of the functionality of the software. It is presented from the user's point of view and is written in natural language. Acceptance criteria are a list of specifications on how a new software feature is expected to operate. Our approach analyzes a set of acceptance criteria accompanying the user story, in order, first, to automatically generate the components of the business model, and then to produce the business model as an activity diagram which is a unified modeling language (UML) behavioral diagram. We start with the use of natural language processing (NLP) techniques to extract the elements necessary to define the rules for retrieving artifacts from the business model. These rules are then developed in Prolog language and imported into Python code. The proposed approach was evaluated on a set of use cases using different performance measures. The results indicate that our method is capable of generating correct and accurate process models.
Implementation of a decision support process for evaluating the correlation between IT investment and of information systems success
Authors: Daghouri Ansar , Mansouri Khalifa
Published: October 2022

The main objective of this paper is to study the correlation between investment in information technologies and especially information systems and information system success based on data collection and a multi-criteria decision-making approach using technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) methods. The criteria of the hierarchical model for evaluating the information system success are chosen from Delone and McLean information systems (IS) success model. The proposed approach has been implemented in 3 sectors recognized by their variation in the use of information systems: the financial sector, the service companies sector, and the construction industry sector. Therefore, the results of this implementation show that massive investment in information systems does not always guarantee good information system success, and information system success is not always the result of massive investment in the information system.
Message querying telemetry transfer on IoT applications to enhance technology: a systematic review
Authors: Widianto Mochammad Haldi , Ranny Ranny
Published: October 2022

More things are connected to the Internet, making the internet of things (IoT) develop significantly. But IoT also has weaknesses in communication, one of which can be overcome by utilizing message querying telemetry transfer (MQTT) because there are too many benefits of MQTT. Because there have been many published studies regarding MQTT, this study aims to conduct a review utilizing Preferred reporting items for systematic reviews and meta-analyses (PRISMA) on the application of MQTT in IoT applications to enhance technology. The results of using PRISMA were 57 papers selected from this process, which starts from the identification stage, screening, eligibility, and included. The last author found components that can be discussed to enhance technology. In this discussion, several topics will be used to enhance technology, such as; smart technology, security, MQTT performance for IoT, monitoring systems, MQTT comparison, enhancement or optimization. This paper is expected to help academia and industry related to MQTT research that can help IoT to enhance technology quality.
Tintas paulistanas: as cicatrizes da desigualdade
Authors: Rahme Anna Maria Abrão Khoury
Published: October 2022

O artigo apresenta um olhar para as manifestações pictóricas nos espaços urbanos de São Paulo, o Grafite e a Pichação, cicatrizes da cultura paulistana, rasgos que marcam indelevelmente as superfícies dos edifícios, muros, equipamentos rebatendo os valores hegemônicos em todo e qualquer percurso na cidade. Movimentos, cujos valores e significados residuais e emergentes constituem experiências singulares dos grupos periféricos, que em ações anárquicas reverberam gritos de socorro e, embora recentes, já estavam inseridas nos estudos de Raymond Williams (1921-88) a partir de meados do século XX e, aqui selecionados, dão respaldo teórico e prático.
Visuomotor Mismatch Responses as a Hallmark of Explaining Away in Causal Inference
Authors: Mikulasch Fabian A. , Rudelt Lucas , Priesemann Viola
Published: October 2022

How are visuomotor mismatch responses in primary visual cortex embedded into cortical processing? We here show that mismatch responses can be understood as the result of a cooperation of motor and visual areas to jointly explain optic flow. This cooperation requires that optic flow is not explained redundantly by both areas, meaning that optic flow inputs to V1 that are predictable from motor neurons should be canceled (i.e., explained away). As a result, neurons in V1 represent only external causes of optic flow, which could allow the animal to easily detect movements that are independent of its own locomotion. We implement the proposed model in a spiking neural network, where coding errors are computed in dendrites and synaptic weights are learned with voltage-dependent plasticity rules. We find that both positive and negative mismatch responses arise, providing an alternative to the prevailing idea that visuomotor mismatch responses are linked to dedicated neurons for error computation. These results also provide a new perspective on several other recent observations of cross-modal neural interactions in cortex.
Inocybe kusadasiensis (Inocybaceae: Agaricomycetes), a new species from Turkey
Authors: KAYGUSUZ OĞUZHAN , BANDINI DITTE , ÇELİK ALİ
Published: October 2022

Inocybe kusadasiensis sp. nov., a novel species which was collected from Pinus brutia forest near Kuşadası (Turkey), in the coastal area of Aegean Sea, is introduced with full descriptions of macro and microscopic features, field photograph and line-drawings. A phylogenetic analysis of sequence data for the nuc rDNA internal transcribed spacer region ITS1-5.8S-ITS2 (ITS) indicated and supported the taxonomic uniqueness of the present new species which belongs to the genus Inocybe.
Hilliardiella elaeagnoides and H. oligocephala (Asteraceae, Vernonieae), two distinct species and the description of a new variety
Authors: HERMAN PAUL P.J.
Published: October 2022

The two species Hilliardiella elaeagnoides and H. oligocephala were confused with each other in the past. Examination of herbarium specimens proved that they are two distinct and separate species. H. oligocephala var. besteri is newly described.
Typification and taxonomic remarks on some names in Phlomoides (Phlomideae: Lamiaceae)
Authors: ALMASI MARYAM , SALMAKI YASAMAN , XIANG CHUN-LEI
Published: October 2022

The genus Phlomoides (Lamiaceae, tribe Phlomideae) has been recently re-circumscribed to include members of Eremostachys and a few other genera. As a consequence, several new combinations and new names have been suggested in this genus recently. Here, typification of 14 names in Eremostachys, viz. E. adpressa, E. alberti, E. boissieriana, E. czuiliensis, E. eriocalyx, E. gymnocalyx, E. hissarica, E. iliensis, E. laciniata var. labiosiformis, E. sarawschanica, E. speciosa var. schugnanica, E. subspicata, E. trautvetteriana and E. zenaidae is presented. Moreover, two new combinations (Phlomoides calophyta and P. transjordanica) and two new synonyms (P. kermanica and P. semnanensis) are proposed.
Impacts of Sea Surface Temperature Gradient on Monsoon Intraseasonal Oscillation
Authors: Li Baosheng , Zhou Lei , Lian Tao , Liu Ting , Qin Jianhuang , Du Yan , Chen Dake
Published: October 2022

The northward-propagating monsoon intraseasonal oscillation (MISO) is the most pronounced variability over the tropical Indian Ocean during the Indian summer monsoon (June–September). MISO is accompanied by significant air–sea interactions; however, the mechanism of the oceanic feedback to MISO is still a great scientific challenge. In this study, the role of the intraseasonal sea surface temperature (SST) gradient in MISO is diagnosed using reanalysis products and model sensitivity experiments. It is found that the positive meridional gradient of intraseasonal SST induces positive wind convergence in the planetary boundary layer (PBL) and leads convection by about 1–2 days. This accounts for approximately half of the total wind convergence in the PBL to the north of convection during MISO. The warm SST anomalies before convection accelerate the intraseasonal northerly wind in the PBL due to the enhanced downward transport of momentum from aloft. By contrast, the cold SST anomalies behind deep convection weaken the downward vertical momentum transport, thereby inducing a deceleration in the intraseasonal northerly. Consequently, changes in the speed of intraseasonal northerly along the meridional direction strengthen the PBL wind convergence ahead of deep convection. It finally results in the intensification of intraseasonal rainfall associated with MISO over the summer monsoon region. In addition, a strong (weak) SST meridional gradient at an intraseasonal time scale amplifies (lessens) the MISO intensity in the model simulation. Thus, this study highlights the role of SST meridional gradient in the feedback to MISO, which differs from the weak contribution of the warm SST itself to the wind convergence mentioned in previous studies. Collectively, these findings indicate that consideration of oceanic feedback is necessary to improve the understanding and simulation of MISO.
Disentangling the Driving Mechanisms of the Tripole Mode of Summer Rainfall over Eastern China
Authors: Duan Yawen , Yang Qing , Ma Zhuguo , Wu Peili , Chen Xiaolong , Duan Jianping
Published: October 2022

The spatial distribution of summer rainfall anomalies over eastern China often shows a tripole pattern with rainfall anomalies over the Yangtze River basin varies in opposite phase with North China and South China. It is not clear whether this tripole pattern is an intrinsic atmospheric mode or it is remotely forced. Using two sets of model outputs from 20 models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5), this paper investigates the driving mechanisms of this leading rainfall mode and its major influencing factors. One set (piControl) is fully coupled atmosphere–ocean simulations under constant preindustrial forcing and the other (sstClim) is atmosphere-alone models forced by prescribed climatological sea surface temperatures (SSTs). By comparing results from these two different sets of simulations, it is found that the tripole pattern is the leading mode of summer precipitation variability over eastern China with or without oceanic forcing. It can be regarded as an intrinsic atmospheric mode although air–sea interaction can modify its temporal variability. The cyclonic–anticyclonic atmospheric circulation anomaly over the northern North Pacific is identified as a key factor in both experiments. As atmospheric internal variability, it is related to a circumglobal zonal wave train propagating along the westerly jet stream. When air–sea interactions involved, modulation from SST anomalies is exerted through the meridional Pacific–Japan/East Asia–Pacific wave train propagating along the East Asian coast. Our results suggest that the North Pacific could be another key region providing potential predictability to the East Asian monsoon in addition to the Indo-Pacific.
Orographic Controls over Convection in an Inter-Andean Valley in Northern South America
Authors: Gomez-Rios Sebastian , Zuluaga Manuel D. , Hoyos Carlos D.
Published: October 2022

Convective processes are critical in the northernmost section of the Andes, where highly complex orography combines with tropical dynamics to produce intense precipitating systems. Data from satellite and ground radar have shown that Colombia’s central Magdalena valley has a marked precipitation diurnal cycle, with high rainfall rates produced by deep convective events that intensify on the foothills of the surrounding valley. This study aims to contribute to the understanding of the orographic controls imposed on the convective activity observed in this important tropical valley. The atmospheric environments associated with convective events are assessed using WRF high-resolution simulations for a two-week period that includes intense rainfall events and marked dry spells. In addition, a simulation with modified orography is evaluated to analyze further the effect of topography in controlling the observed convective systems. The results suggest that northward and southward wind regimes channeled by the valley’s topography and cross-valley flow result in a robust nocturnal convergence zone, which is associated with the strong diurnal cycle and the valley configuration. Furthermore, northerly low-level flow is an important source of moisture for convection in the valley. Additionally, results show midlevel tropospheric heating enhancement during days with more rainfall produced by convective systems in the western slope of the valley. The modified topography simulation suggests that circulation and precipitation in the valley are strongly dependent on the actual terrain features.
The Inotropic Actions of N-Acetylprocainamide: Blockade and Reversal by Propranolol
Authors: Lertora Juan J.L. , King Lucy W. , Donkor Kwabena A.
Published: October 2022

The inotropic actions of N-acetylprocainamide (NAPA) were studied in chloralose-urethane anesthetized dogs. Myocardial contractile force was measured with a Walton-Brodie strain gauge sutured to the right ventricle. A low-dose NAPA infusion (12 mg/kg i.v.) increased myocardial force by a maximum of 11.6±2.4% (mean±SEM), whereas a high dose of NAPA (60 mg/kg i.v.) increased myocardial force by 33.3±2.6% at the peak of the effect. The high-dose NAPA infusion also caused significant reductions in heart rate and blood pressure, while the low-dose NAPA infusion lacked significant chronotropic or hypotensive actions. Pretreatment with propranolol (0.5 mg/kg i.v. loading, followed by a 10 μg/kg/min infusion) did not block the positive inotropic actions of NAPA 12 mg/kg, but these actions were blocked in dogs pretreated with both propranolol and atropine (1 mg/kg). On the other hand, pretreatment with propranolol blocked and reversed the inotropic actions of NAPA 60 mg/kg, and potentiated its negative chronotropic effects. Thus, the positive inotropic actions of NAPA are indirect and more than one mechanism is involved; a component due to direct action related to the lengthening of cardiac repolarization is not discounted. At low doses, the increase in myocardial force seems related to NAPA's vagolytic properties, whereas at high doses the positive inotropic actions appear to be catecholamine-mediated. Furthermore, a negative inotropic action of high-dose NAPA is apparent after beta-adrenergic receptor blockade.
Pharmacokinetics of N-Acetylprocainamide
Authors: Atkinson Arthur J. , Ruo Tsuen Ih
Published: October 2022

Shortly after Dreyfus and his colleagues demonstrated that procainamide was metabolized by acetylation to N-acetylprocainamide (NAPA), Drayer, Reidenberg and Sevy reported that NAPA had antiarrhythmic activity in an animal model. We confirmed these findings and found that plasma levels of NAPA were high enough to warrant consideration in managing patients requiring procainamide therapy. However, the actual impetus for developing NAPA as an antiarrhythmic drug in its own right was provided by the initial studies of NAPA pharmacokinetics in normal subjects. In these studies, we showed that NAPA has an elimination-phase half-life that is more than twice as long as procainamide and suggested that patient compliance and arrhythmia suppression might be improved if NAPA were used to circumvent the inconvenience of the frequent dosing schedule that has been recommended for procainamide.

From the standpoint of managing individual patients with NAPA, the pharmacokinetics of this drug continue to provide the scientific basis for designing dose regimens that will have maximal antiarrhythmic efficacy and minimal toxicity. This review summarizes the salient features of NAPA pharmacokinetics and outlines an approach for individualizing therapy with this drug.
N-Acetylprocainamide's Antiarrhythmic Action in Patients with Ventricular Tachycardia
Authors: Somberg John , Wynn Jonathan , Miura Dennis , Torres Vilma , Williams Sharon , Keefe Deborah
Published: October 2022

Antiarrhythmic properties of N-acetylprocainamide, an active metabolite of procainamide, were studied in 15 patients who presented with a cardiac arrest or documented sustained ventricular tachycardia. Programmed electrical stimulation studies were performed. All patients tested had inducible ventricular tachycardia by programmed electrical stimulation techniques while off all antiarrhythmic therapy. Patients were then tested on procainamide 1000 mg administered intravenously, and ventricular tachycardia could be provoked in 8 of 10 patients. Twenty-four to 36 hours later, N-acetylprocainamide was administered, intravenously, and programmed stimulation was performed after 20 minutes. N-acetylprocainamide did not significantly change heart rate, mean arterial blood pressure, electrocardiographic intervals, A-H or H-V conduction times. N-acetylprocainamide prevented ventricular tachycardia induction in 6 of 15 patients. The mean serum N-acetylprocainamide levels in the group protected was 15.7±4 μg/ml and 16.2±4 μg/ml in the group not protected. These 6 patients were discharged on N-acetylprocainamide 1.5 grams orally every 8 hours. Three patients have been maintained on chronic N-acetylprocainamide every 8 hours. Three patients have been maintained on chronic N-acetylprocainamide therapy (6±2 months), two patients had breakthrough ventricular tachycardia on follow-up Holter monitoring and alternative therapy was given. N-acetylprocainamide has antiarrhythmic efficacy in preventing induction of ventricular tachycardia by programmed electrical stimulation in a high risk group of patients. On chronic oral therapy, N-acetylprocainamide appears to be well tolerated with antiarrhythmic efficacy that may be enhanced with further upward dose titration.
Investigation of the Spatial Generation of Stimulated Raman Scattering Using Computer Simulation and Experimentation
Authors: Eriksson Ronja , Gren Per , Sjödahl Mikael , Ramser Kerstin
Published: October 2022

Stimulated Raman scattering is a phenomenon with potential use in providing real-time molecular information in three-dimensions (3D) of a sample using imaging. For precise imaging, the knowledge about the spatial generation of stimulated Raman scattering is essential. To investigate the spatial behavior in an idealized case, computer simulations and experiments were performed. For the computer simulations, diffraction theory was used for the beam propagation complemented with nonlinear phase modulation describing the interaction between the light and matter. For the experiments, a volume of ethanol was illuminated by an expanded light beam and a plane inside the volume was imaged in transmission. For generating stimulated Raman scattering, a pump beam was focused into this volume and led to a beam dump after passing the volume. The pulse duration of the two beams were 6 ns and the pump beam energy ranged from 1 to 27 mJ. The effect of increasing pump power on the spatial distribution of the Raman gain and the spatial growth of the signal at different interaction lengths between the beam and the sample was investigated. The spatial width of the region where the stimulated Raman scattering signal was generated for experiments and simulation was 0.21 and 0.09 mm, respectively. The experimental and simulation results showed that most of the stimulated Raman scattering is generated close to the pump beam focus and the maximum peak of the Stokes intensity spatially comes shortly after the peak of the pump intensity.
Diet and Dental Health
Authors: Beal John F. , Dickson S.
Published: October 2022

A survey of mothers of 5-year-old children in the West Midlands showed that virtually all the mothers thought that apples and raw carrots were beneficial to the teeth and sweets harmful. Attitudes towards blackcurrant syrup were more varied, those from the higher social classes having a greater awareness of its possible danger to the teeth. However, this knowledge was not found to be reflected in consistent behaviour, especially with regard to sweet-eating. Nearly two-thirds of all the mothers admitted that their child ate sweets, with a frequency regarded as ‘often’. It is suggested that a greater effect may be produced by encouraging the spending of pocket money on lasting toys as an alternative to sweets which are soon finished, rather than the more negative approach of giving strict lectures to avoid sweets.
Self-starting control strategy of variable speed pumped storage unit based on adaptive full order state observer
Authors: Zhu Keke , Ruan Lin
Published: October 2022

When the variable speed pumped storage unit operates in the electrical state, the self-starting control strategy is generally used to start the process from zero speed to grid connection, which saves the starting device of the traditional constant speed unit and saves the construction cost of the unit. For the self-starting control process, the traditional open-loop flux calculation method will produce cumulative errors in the amplitude and angle of the flux when the motor parameters change. To solve this problem, an adaptive full order state observer is designed to observe the motor stator flux, which has stronger parameter robustness. Based on the flux observation, the speed adaptive law is designed to estimate the rotor speed and position angle, and the speed sensorless vector control of variable speed pumped storage unit is accomplished. The simulation results show that compared with the open-loop flux calculation method, the full order state observer can greatly reduce the flux estimation error and accurately estimate the rotor speed and position angle.
Recognising common developments and trends across Western child welfare systems: A comparison of Italy, Norway and Slovenia
Authors: Røkkum Nina Helen Aas , Parton Nigel , Heggem Kojan Bente
Published: October 2022

Based on observational data, individual interviews (N = 22) and focus groups (N = 8) with practitioners in Italy, Norway and Slovenia, this study applies a grounded theory approach in exploring frontline practitioners’ experiences of supporting families involved with child welfare services. It was anticipated that there would be significant differences between the countries regarding practitioners’ understandings of families’ needs and problems, and approaches to help. However, the analysis showed considerable transnational similarities. This study, therefore, offers a new perspective to the welfare regime literature in suggesting that there are important similarities to be recognised across child welfare typologies.
Non-governmental organizations’ enablers of asset accumulation and poverty reduction in Zimbabwe
Authors: Kabonga Itai , Zvokuomba Kwashirai , Musara Enock , Chikoko Witness , Mwapaura Kudzai , Nyabeze Kudzai
Published: October 2022

This Voices from Practice note delineates institutional and non-institutional enablers of asset accumulation in Zimbabwe. It argues that in promoting asset accumulation and poverty reduction, non-governmental organizations (NGOs) rely on the support of government ministries, partner NGOs, community volunteers, and community leaders. That perspective shifts and moves away from placing NGOs at the center of asset accumulation and poverty reduction efforts in communities. This article concludes that social work practice can be integral in contributing to poverty reduction when there are various stakeholders collaborating to enhance the welfare of communities at risk.
The utilisation of Ubuntu across cultures: A case study of a rural development programme in China
Authors: Xiang Yu , Leung Charles Tong-Lit
Published: October 2022

Ubuntu generally means humanity. It is an African philosophy and a principle that is being promoted by the international bodies of social work. Like other Western knowledge, its capacity for indigenisation should be a shared interest nowadays. This article assesses the utilisation of Ubuntu for a rural development programme in China. It illustrates Ubuntu’s unique perspectives about traditional values and human relationships for promoting a holistic understanding of professional practice. The implications encourage further discoveries of various potential uses of Ubuntu in respective localities for developing a polythetic community of international social work.