bluuebunny/crossref_metadata_2025 huggingface dataset:
Toward enhancement of deep learning techniques using fuzzy logic: a survey
Authors: Hasan Dhafar Fakhry , Khidhir AdulSattar Mohammed
Published: January 2023

Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed.
Detecting network attacks model based on a convolutional neural network
Authors: Jasim Ali Teba Ali , Taher Jawhar Muna M.
Published: January 2023

Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
An intelligent system to detect slow denial of service attacks in software-defined networks
Authors: Mabel John Prathima , Babu Kasturi Nagappasetty Rama Mohan
Published: January 2023

Slow denial of service attack (DoS) is a tricky issue in software-defined network (SDN) as it uses less bandwidth to attack a server. In this paper, a slow-rate DoS attack called Slowloris is detected and mitigated on Apache2 and Nginx servers using a methodology called an intelligent system for slow DoS detection using machine learning (ISSDM) in SDN. Data generation module of ISSDM generates dataset with response time, the number of connections, timeout, and pattern match as features. Data are generated in a real environment using Apache2, Nginx server, Zodiac FX OpenFlow switch and Ryu controller. Monte Carlo simulation is used to estimate threshold values for attack classification. Further, ISSDM performs header inspection using regular expressions to mark flows as legitimate or attacked during data generation. The proposed feature selection module of ISSDM, called blended statistical and information gain (BSIG), selects those features that contribute best to classification. These features are used for classification by various machine learning and deep learning models. Results are compared with feature selection methods like Chi-square, T-test, and information gain.
Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit
Authors: Darmawan Refianto Damai , Kusuma Wisnu Ananta , Rahmawan Hendra
Published: January 2023

The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179.
A novel k-means powered algorithm for an efficient clustering in vehicular ad-hoc networks
Authors: Kandali Khalid , Bennis Lamyae , Halaq Hanan , Bennis Hamid
Published: January 2023

Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
Black spots identification on rural roads based on extreme learning machine
Authors: Mbarek Abdelilah , Jiber Mouna , Yahyaouy Ali , Sabri Abdelouahed
Published: January 2023

Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.
Medical image encryption techniques: a technical survey and potential challenges
Authors: Odeh Ammar , Abu Al-Haija Qasem
Published: January 2023

Among the most sensitive and important data in telemedicine systems are medical images. It is necessary to use a robust encryption method that is resistant to cryptographic assaults while transferring medical images over the internet. Confidentiality is the most crucial of the three security goals for protecting information systems, along with availability, integrity, and compliance. Encryption and watermarking of medical images address problems with confidentiality and integrity in telemedicine applications. The need to prioritize security issues in telemedicine applications makes the choice of a trustworthy and efficient strategy or framework all the more crucial. The paper examines various security issues and cutting-edge methods to secure medical images for use with telemedicine systems.
Design of programmable hardware security modules for enhancing blockchain based security framework
Authors: Kalathil Nandalal Devika , Bhakthavatchalu Ramesh
Published: January 2023

Globalization of the chip design and manufacturing industry has imposed significant threats to the hardware security of integrated circuits (ICs). It has made ICs more susceptible to various hardware attacks. Blockchain provides a trustworthy and distributed platform to store immutable records related to the evidence of intellectual property (IP) creation, authentication of provenance, and confidential data storage. However, blockchain encounters major security challenges due to its decentralized nature of ledgers that contain sensitive data. The research objective is to design a dedicated programmable hardware security modules scheme to safeguard and maintain sensitive information contained in the blockchain networks in the context of the IC supply chain. Thus, the blockchain framework could rely on the proposed hardware security modules and separate the entire cryptographic operations within the system as stand-alone hardware units. This work put forth a novel approach that could be considered and utilized to enhance blockchain security in real-time. The critical cryptographic components in blockchain secure hash algorithm-256 (SHA-256) and the elliptic curve digital signature algorithm are designed as separate entities to enhance the security of the blockchain framework. Physical unclonable functions are adopted to perform authentication of transactions in the blockchain. Relative comparison of designed modules with existing works clearly depicts the upper hand of the former in terms of performance parameters.
Secure authentication and data aggregation scheme for routing packets in wireless sensor network
Authors: Veeregowdanadoddi Chandraiah Rudramurthy , Ramalingappa Aparna
Published: January 2023

Wireless sensor networks (WSNs) comprise a huge number of sensors that sense real-time data; in general, WSNs are designed for monitoring in various application mainly internet of things based (IoT) application. Moreover, these sensors possess a certain amount of energy i.e., they are battery based; thus, the network model must be efficient. Furthermore, data aggregation is a mechanism that minimizes the energy; however, in addition, these aggregated data and networks can be subject to different types of attacks due to the vulnerable characteristics of the network. Hence it is important to provide end-to-end security in the data aggregation mechanism in this we design and develop dual layer integrated (DLI)-security architecture for secure data aggregation; DLI-security architecture is an integration of two distinctive layers. The first layer of architecture deals with developing an authentication for reputation-based communication; the second layer of architecture focuses on securing the aggregated data through a consensus-based approach. The experiment outcome shows that DLI identifies the correct data packets and discards the unsecured data packets in energy efficient way with minimal computation overhead and higher throughput in comparison with the existing model.
Intelligent Arabic letters speech recognition system based on mel frequency cepstral coefficients
Authors: Quteishat Anas , Younis Mahmoud , Qtaishat Ahmed , Abuhamdah Anmar
Published: January 2023

Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%.
A sigma-delta interface built-in self-test and calibration for microelectromechanical system accelerometer's utilizing interpolation method
Authors: Ahmed Anwer Sabah , Al-Gayem Qais
Published: January 2023

This work presents the capacitive micromechanical accelerometer with a completely differential high-order switched capacitor sigma-delta modulator interface. Such modulation interface circuit generates one-bit output data using a third sigma-delta modulator low-noise front-end, doing away with the requirement for a second enhanced converter of resolution to encode the feedback route analog signal. A capacitive micromechanical sensor unit with just a greater quality factor has been specifically employed to give greater resolution. The closed-loop and electrical correction control are used to dampen the high-Q values to get the system's stability with high-order. This microelectromechanical system (MEMS) capacitive accelerometer was calibrated using a lookup table and Akima interpolation to find manufacturing flaws by recalculating voltage levels for the test electrodes. To determine the proper electrode voltages for fault compensation, COMSOL software simulates a number of defects upon that spring as well as the fingers of the sensor system. When it comes time for the feedback phase of a proof mass displacement correction, these values are subsequently placed in the lookup table.
Facial emotion recognition using deep learning detector and classifier
Authors: Kit Ng Chin , Ooi Chee-Pun , Tan Wooi Haw , Tan Yi-Fei , Cheong Soon-Nyean
Published: January 2023

Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyper-parameters, achieving an overall accuracy of 86.42% on the testing video dataset.
Drivers’ drowsiness detection based on an optimized random forest classification and single-channel electroencephalogram
Authors: Elidrissi Mouad Elmouzoun , Essoukaki Elmaati , Ben Taleb Lhoucine , Mouhsen Azeddine , Harmouchi Mohammed
Published: January 2023

The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds).
Glottic lesion segmentation of computed tomography images using deep learning
Authors: Rao Divya , Koteshwara Prakashini , Singh Rohit , Jagannatha Vijayananda
Published: January 2023

The larynx, a common site for head and neck cancers, is often overlooked in automated contouring due to its small size and anatomically complex nature. More than 75% of laryngeal tumors originate in the glottis. This paper proposes a method to automatically delineate the glottic tumors present contrast computed tomography (CT) images of the head and neck. A novel dataset of 340 images with glottic tumors was acquired and pre-processed, and a senior radiologist created a detailed, manual slice-by-slice tumor annotation. An efficient deep-learning architecture, the U-Net, was modified and trained on our novel dataset to segment the glottic tumor automatically. The tumor was then visualized with the corresponding ground truth. Using a combined metric of dice score and binary cross-entropy, we obtained an overlap of 86.68% for the train set and 82.67% for the test set. The results are comparable to the limited work done in this area. This paper’s novelty lies in the compiled dataset and impressive results obtained with the size of the data. Limited research has been done on the automated detection and diagnosis of laryngeal cancers. Automating the segmentation process while ensuring malignancies are not overlooked is essential to saving the clinician’s time.
TFUZZY-OF: a new method for routing protocol for low-power and lossy networks load balancing using multi-criteria decision-making
Authors: Ahmed Ali Kamil , Farzaneh Behnam , Boochanpour Elahe , Alizadeh Emad , Farzaneh Shahin
Published: January 2023

The internet of things (IoT) based on a network layer perspective includes low-power and lossy networks (LLN) that are limited in terms of power consumption, memory, and energy usage. The routing protocol used in these networks is called routing over low-power and lossy networks (RPL). Therefore, the IoT networks include smart objects that need multiple routing for their interconnections which makes traffic load balancing techniques indispensable to RPL routing protocol. In this paper, we propose a method based on fuzzy logic and the technique for the order of prioritization by similarity to the ideal solution (TOPSIS) as a well-known multi-criteria decision-making method to solve the load balancing problem by routing metrics composition. For this purpose, a combination of both link and node routing metrics namely hop count, expected transmission count, and received signal strength indicator is used. The results of simulations show that this method can increase the quality of services in terms of packet delivery ratio and average end-to-end delay.
Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance
Authors: Manjunath Chinthakunta , Marimuthu Balamurugan , Ghosh Bikramaditya
Published: January 2023

Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifer (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1.
A novel triangular wave quadrature oscillator without passive components for sinusoidal pulse width modulation DC-AC power conversion
Authors: Garinto Dodi , Valerie Theodora , Yanto Harki Apri , Sutikno Tole , Simatupang Joni Welman
Published: January 2023

In this study, a low-cost quadrature triangle oscillator using a voltage-controlled closed-loop dual operational amplifier (Op-Amp) architecture is proposed. Unlike other typical designs, this oscillator does not require any passive components. The use of an Op-Amp-based circuit is attractive for a triangle oscillator because it is more cost-effective than a microcontroller-based solution. This is especially true for sinusoidal pulse width modulation (SPWM) DC-AC power conversion applications. The slew-rate restriction of an Op-Amp is a useful characteristic for producing a triangle waveform when seen from the perspective of wave shaping techniques. The MC4558 and the JRC4558D are two examples of dual Op-Amps that are evaluated, contrasted, and described in this article. At supply voltages of +7 V and -7 V, the suggested quadrature triangle oscillator that uses Op-Amps MC4558 and JRC4558D has the same oscillation frequency, which is 63 kHz, as demonstrated by simulation and experimental data. The frequency stability is estimated to be around 0.23%. In addition, the findings from the experiment demonstrate that the proposed oscillator is a practical solution for the SPWM DC-AC power conversion application.
The Psychology of Nuclear Brinkmanship
Authors: Pauly Reid B. C. , McDermott Rose
Published: January 2023

Conventional wisdom sees nuclear brinkmanship and Thomas Schelling's pathbreaking “threat that leaves something to chance” as a solution to the problem of agency in coercion. If leaders cannot credibly threaten to start a nuclear war, perhaps they can at least introduce uncertainty by signaling that the decision is out of their hands. It is not so easy to remove humans from crisis decision-making, however. Often in cases of nuclear brinkmanship, a human being retains a choice about whether to escalate. When two sides engage in rational decision-making, the chance of strategic nuclear exchange should be zero. Scholars have explained how risks associated with accidents, false warnings, and pre-delegation creep into nuclear crises. An investigation of how chance can still produce leverage while leaders retain a choice over whether and when to escalate adds to this scholarship. There remains an element of choice in chance. For a complete understanding of nuclear brinkmanship, psychology and emotion must be added to the analysis to explain how leaders make decisions under pressure. Human emotions can introduce chance into bargaining in ways that contradict the expectations of the rational cost-benefit assumptions that undergird deterrence theory. Three mechanisms of nuclear brinkmanship—accidents, self-control, and control of others—illustrate how a loss of control over the use of nuclear weapons is not a necessary element of the threat that leaves something to chance. Choice does not have to be eliminated for a risk of catastrophic destruction to remain.
Three new Spinirta species from China (Araneae, Corinnidae), with the first description of the male of S. leigongshanensis Jin & Zhang, 2020
Authors: ZHANG LU , JIN CHI , ZHANG FENG
Published: January 2023

Three new species of the genus Spinirta Jin & Zhang, 2020 are described from China: S. shenwushanensis sp. nov., S. lanceola sp. nov. and S. caudata sp. nov.. Additionally, the male of S. leigongshanensis Jin & Zhang, 2020 is described for the first time. Finally, we provide an updated distribution map of the genus.
Awkward Classes and India’s Rural Development
Authors: Harriss-White Barbara
Published: January 2023

Marx’s model of capital and labour, dynamised by contradictions and the compulsion to accumulate, leaves deviations from the polar classes of capital and labour ignored, regarded as precapitalist outliers, or as headed for extinction. But the two considered here, petty commodity production (and trade and services) and merchant’s or commercial capital, persist widely. Here in tribute to the major contributions to political economy of Utsa Patnaik and Prabhat Patnaik, their structure and dynamics are discussed in general and in the contemporary Indian case. They are argued to be awkward both analytically and politically. Petty production overlaps with both wage labour and small capitalist firms; it reproduces and expands by multiplication, not accumulation; it does not mobilise in a politically coherent way. Commercial capital is in turn suffused with productive activity; it encompasses petty trade and accumulating enterprises which pursue a reactive opportunistic politics which preserves their independence. Further awkwardness results from the disjuncture between analytically useful categories and the policy concepts used by the state.
Surplus Transfers from British Colonial Malaya
Authors: Peng Martin Khor Kok , K. S. Jomo
Published: January 2023

Martin Khor’s 1983 book on Malaysian economic dependence provided the first comprehensive estimates of the ‘colonial surplus’ from British Malaya. Khor described how Great Britain secured this from the colonial Malayan economy and estimated various types of surplus extracted via different means. Unsurprisingly, super-profits for privileged foreign capital owners are well known. The terms of trade have been important determinants of the colonial trade surplus. Financial surpluses are distinguished by those secured through the colonial currency board system, foreign commercial banks and colonial public finances, involving fiscal policy. The wealthy Malayan colony transferred funds to London at very low interest rates, but borrowed from British banks at exorbitant rates, besides paying high transactions charges. Colonial managerial privilege was also significant. Such surplus transfers were enabled by various colonial arrangements. Thus, colonial Malaya lost significant financial resources. Only some have changed in the post-colonial era, sometimes rather slowly.

JEL Codes: B27, N15
Revisiting the Kerala ‘Model’ of Development: A Sixty-year Assessment of Successes and Failures
Authors: Kannan K. P.
Published: January 2023

This article seeks to revisit the much-acclaimed Kerala ‘Model’ of Development since the formation of the state of Kerala. As such it subjects the development experience of Kerala for a period of six decades from 1960 to 2020. This exercise reveals that the development experience of Kerala is largely shaped by its historical past which privileged social development over purely economic development based on the growth of the goods producing sectors. State response to social and human development demands were more successful than the conventional efforts to enhance capital formation thereby transforming the material basis of the economy. The high human and social development led to large scale migration of persons, mostly men, to the Gulf countries that resulted in a kind of economic growth induced by outside money in the form of remittances. The national economic context was also one that led to a higher growth of the service sector than the material producing sectors. The limited opportunities afforded by the national state or the increased flow of outside money could not be seized effectively by the regional state to transform goods producing sectors such as agriculture and industry. This resulted in such major and spectacular failures as declining tax collection efficiency, increased net loss of state-owned public enterprises and massive waste of resources in implementing capital projects. I end the article with a call for a study of political economy of development by taking into account the specificities of the Kerala context within a larger national and international context.

JEL Codes: O15, R11, F24, J6, H21, H41, H75
Capitalism, Imperialism and the Food Question
Authors: Banerjee Arindam
Published: January 2023

The success and failure of capitalism have been commonly measured by the yardsticks of industrialisation, technical progress and innovations in the financial markets. The emergence of industrial agriculture under successful capitalist transitions is supposed to have taken care of all food and raw material constraints that could have arrested the development of industrial economies and societies in the North. Simultaneously, the problems of underdevelopment and food insecurity in the South is attributed to internal constraints and barriers. The work of Utsa Patnaik challenges these views as she explored multiple aspects of the contribution of colonies to the advance of capital accumulation in the North. She delves into the nineteenth century ‘international division of labour’, the role of tropical food and raw materials exports by commercialised southern agriculture and implication for food security in the colonies. The objective of this article is to engage with the anti-imperial scholarship of Utsa Patnaik with a specific focus on the food question in capitalism. The article traces the evolution of the food question as capitalism evolved and dominated the world economy.

JEL Codes: P1, F54, Q18
Patriarchy and Property: Goa’s Uniform Civil Code
Authors: Dewan Ritu
Published: January 2023

The fulcrum of gender equality in all its myriad manifestations is ownership and control over resources, and specifically in a developing economy, primarily and essentially land. However, these manifestations and also the intensity of interlinkages are to a deeply significant level determined by historical and regional specificities relating to economic as well as extra-economic factors and forces. Nowhere in India are these direct and indirect interconnects between gender equality and resources so intricate, nuanced and simultaneously complex as in the state of Goa which is the only state in India where women are guaranteed equal property rights. In this article the demystification of the link between patriarchy and women’s property rights is built on the prevailing intermixes of land, property and matrimonial rights according to the Portuguese Civil Code and the relevant laws and legislations including Family Laws, the Code of Comunidades, the Goa Mundkar (Protection from Eviction) Act of 1975, and the recent enactment in 2016 of the Goa Succession, Special Notaries and Inventory Proceeding Act, 2012.
Why Inter-caste Inequality in Educational Attainment Still Persists? Wealth and Caste Discrimination Matters
Authors: Thorat Sukhadeo , Khan Khalid
Published: January 2023

Using recent data for 2017/2018 the article provides empirical evidence on the persistence of the traditional inter-caste inequality in higher educational attainment—enrolment rate being much low for low castes than the high castes. The educational attainment of middle caste is lower than high castes but better than low caste untouchables, indicating a graded inequality in education attainment, unique feature of caste system. Between the income group the low income groups suffered the most from low education attainment than the high oncome groups. The inequalities in ownership of wealth and income and caste discrimination faced by low caste untouchables are the main reasons for low education attainment among the low caste and low income groups. The shift in the government policy towards privatisation of higher education also has taken the education beyond the reach of low income household. The caste discrimination result into high drop out among the scheduled castes. In the end based on the empirical results relevant (caste) group specific and income group specific policies are proposed to promote higher education among the low castes and low income groups.

JEL Codes: 123, J24, D63, J71
Strategic Dynamics of the Arms Race in South Asia
Authors: Ali Iftikhar , Sidhu Jatswan S
Published: January 2023

The relationship between the nuclear arms race and strategic stability has been important for scholars, politicians, and journalists who have shown keen interest in what causes a state to augment its military power and its effects on its rivalries with other states. As emerging technologies continue to change the security calculus of the states, the arms race is on for investing in the development and acquisition of offensive and defensive technologies in South Asia. The Indian strategic cooperation with the technologically advanced countries and the increase in the defense budget confirm its military modernization while upsetting the delicacies of the conventional military balance in South Asia. This study gives a substantive background to the developments and technological advancements in the post overt nuclearization era (1998) in the context of complications posed by India’s persistent animus dominandi to outpace Pakistan in an anarchic structure vis-à-vis its impact on strategic instability in South Asia by making a comprehensive analysis of an unending arms race for the maintenance of credible minimum deterrence.
An Ethnocultural Study of Posttraumatic Stress Disorder in African-American and White American Vietnam War Veterans
Authors: Trent Calvin R. , Rushlau Matthew G. , Munley Patrick H. , Bloem William , Driesenga Scott
Published: January 2023

This study examined the intensity of Posttraumatic Stress Disorder (PTSD) symptoms as measured by the Mississippi Scale, the Keane (PK), and the PTSD (PS) Scales of the MMPI-2 in a sample of 34 African-American and 34 White American Vietnam War Veterans who sought treatment in a Specialized Inpatient PTSD Unit. The scores of the two groups on the Beck Depression Inventory and the clinical scales of the MMPI-2 were also compared. The ethnoculturally different sample was matched on intensity of combat exposure, marital status, employment status, age, and education. No significant differences on the measures of PTSD symptoms were noted and no significant differences were found on the Beck scale or the MMPI-2 clinical scales.
Bacterial cellulose grown from kombucha: Assessment of textile performance properties using fashion apparel tests
Authors: Wood Jane , Verran Joanna , Redfern James
Published: January 2023

Bacterial cellulose (BC) has been suggested as a sustainable alternative textile for apparel. Previous studies have evaluated the production of BC sheets and the suitability of these to form garment shapes. The laboratory measured physical performance characteristics of BC from an apparel perspective remain relatively unexplored.

The aim of this study was to produce reproducible sheets of BC, enabling the evaluation of the performance of the BC in an apparel textile testing context, and comparison to other textile materials. Grown in sterile black tea with glucose, the BC presented as a mesh of non-woven nanofibers, and thus comparison was made with three non-woven fabrics. It has also been suggested that BC could be used as ‘vegetable’ leather; therefore, performance comparisons were conducted with animal skins.

Utilizing British, European and International standard test methods, the selected fabrics were evaluated for their performance in tensile, elongation, moisture vapor permeability and abrasion tests, relevant for an apparel end-use.

Tensile strength testing revealed that BC is weaker than its animal counterparts but does display similar physical characteristics at the point of failure; however, it displayed a higher tensile strength than the non-woven fabrics chosen for comparison.

BC was the least breathable and most moisture-retentive of all the fabrics tested, raising questions regarding its suitability and comfort for apparel applications in its untreated state.

However, BC displayed superior performance when tested for resistance to abrasion, suggesting it could be best utilized in the form of encapsulated patches in items subjected to this type of damage.
How the characteristics of words in child-directed speech differ from adult-directed speech to influence children’s productive vocabularies
Authors: Jones Gary , Cabiddu Francesco , Barrett Doug J. K. , Castro Antonio , Lee Bethany
Published: January 2023

Child-directed speech has long been known to influence children’s vocabulary learning. However, while we know that caregiver utterances differ from those directed at adults in various ways, little is known about any differences in the lexical properties of child-directed and adult-directed utterances. We compare over half a million word tokens from adult speech directed at children (from caregiver–child transcriptions) to the same quantity directed at adults. We show that child-directed speech contains greater numbers of words that are lower in phonemic length, higher in frequency, lower in phonotactic probability, and higher in neighborhood density than adult-directed speech; furthermore, child-directed speech explains over twice the variability of children’s productive noun vocabularies than adult-directed speech. These findings indicate that children’s word production is clearly influenced by the characteristics of the words spoken directly to them and that researchers need to be wary of using adult-directed language corpora when calculating lexical measures.
Can motivation and intentions of parental support predict musical achievement before the commencement of musical studies at the elementary school level?
Authors: Oliveira António , McPherson Gary E. , Ribeiro Luísa Mota , Oliveira-Silva Patrícia
Published: January 2023

Admission procedures to elementary school in Music Conservatoires in Portugal consist in the assessment of aural aptitude. This investigation aims at assessing the power of aural aptitude at predicting future musical achievement as well as the assessment of two other variables for the same purpose: motivation and intentions of parental support. For that matter, our sample includes the cohort of the admitted candidates and their parents to the 2019 to 2020 academic year. We used a longitudinal approach that followed the musical achievement of the sample during the 2019 to 2020 and 2020 to 2021 academic years. Questionnaires were administered to both children and parents to collect information on motivation to learn a musical instrument and anticipation of parental support provision. Our results point that, after 2 years of music education, neither of the tested variables were able to predict the students’ musical achievement. These results suggest that the model of admission procedures to Music Conservatoires based on aural aptitude must be rethought and that this line of investigation could be revisited later when our sample enters middle school to assess again the predicting power of motivation and parental support.
Translation and cross-cultural adaptation of the Integrated Palliative Care Outcome Scale in Hindi: Toward capturing palliative needs and concerns in Hindi speaking patients
Authors: Bhardwaj Tushti , Chambers Rachel L , Watson Harry , Srividya  , Higginson Irene J , Hocaoglu Mevhibe B
Published: January 2023

Background:

Culturally relevant patient-centered outcomes tools are needed to identify the needs of patients and to assess their palliative care concerns.

Aim:

To translate and culturally adapt the Integrated Palliative Care Outcome Scale (IPOS) into Hindi.

Design:

The study applied a standardized methodology entailing six phases for translation and content validation: equivalence setting through a three-step process; forward translation; blind backward translation; expert review by a panel of the POS team; cognitive de-briefing with patients; and proof-reading of the final tool. All interviews and focus groups were audio-recorded, transcribed and analyzed using content analysis.

Setting/participants:

(1) Healthcare professionals including doctors, nurses, psychologists, counselors, and volunteers working in Indian palliative care settings with expertise in both English and Hindi languages; (2) Hindi speaking patients diagnosed with cancer who were receiving palliative care in community settings. Caregivers, palliative care experts, and language translators contributed to the translation procedure.

Results:

Phrases like nausea, poor appetite, drowsiness, and depression were difficult to translate into Hindi. Response categories “occasional” and “sometimes” were overlapping. All items, instructions and response categories were simple to understand. A visual thermometer is a unique feature of Hindi IPOS to facilitate responses from less educated patients.

Conclusion:

Hindi IPOS has face and content validity for use in clinical practice and research. The Hindi IPOS has implications beyond Indian palliative care settings. Millions of Hindi speakers can now respond to IPOS, and have a tool for communicating their palliative care needs in their mother tongue to inform patient-centered care.
Assessing Psychometric Properties of the Self-Directed Learning Scale in Chinese University Students
Authors: Zhang Di , Yang Lan
Published: January 2023

As one of the 21st-century skills, self-directed learning (SDL) has received widespread attention and has become an essential research topic in education. In this study, we examined the psychometric properties of the SDL scale (SDLS) (Lounsbury et al., 2009) with a sample of 408 Chinese undergraduates. Previous research on Chinese students has validated SDLS by using confirmatory factor analysis directly assuming SDL as a unidimensional factor as it was tested in Western countries. However, considering the rich connotation of SDL, we applied exploratory factor analysis and further used Rasch analysis based on item response theory to enrich researchers’ and practitioners’ understanding of its psychometric properties. We found additional and good psychometric evidence of a two-factor structure of SDLS: students’ initiative and ability of SDL and their self-concept of SDL. The two factors of SDLS have good internal consistency and positive correlation with formative feedback orientation, showing evidence of its criterion validity.
Standardized Reporting on the Preoperative CT Assessment of Potential Living Renal Transplant Donors: Can We Create a Universal Report Standard to Meet the Needs of Transplant Urologists?
Authors: Benhabib Hadas , Crivellaro Priscila Sacilotto , Osman Heba , Gunaseelan Senthujan , Chung Andrew , Lee Jason Y. , Colak Errol , Leung Vincent , O’Sullivan Joseph , Walsh Cynthia , Kielar Ania
Published: January 2023

Purpose: Determine whether standardized template reporting for the preoperative assessment of potential living renal transplant donors improves the comprehensiveness of radiology reports to meet the needs of urologists performing renal transplants. Methods: Urologist and radiologist stakeholders from renal transplant centres in our province ratified a standardized reporting template for evaluation of potential renal donors. Three centres (A, B, and C) were designated “intervention” groups. Centre D was the control group, given employment of a site-specific standardized template prior to study commencement. Up to 100 consecutive CT scan reports per centre, pre- and post-implementation of standardized reporting, were evaluated for reporting specific outcome measures. Results: At baseline, all intervention groups demonstrated poor reporting of urologist-desired outcome measures. Centre A discussed 5/13 variables (38%), Centre B discussed 6/13 variables (46%), and Centre C only discussed 1/13 variables (8%) with ≥90% reliability. The control group exhibited consistent reporting, with 11/13 variables (85%) reported at ≥90% reliability. All institutions in the intervention group exhibited excellent compliance to structured reporting post-template implementation (Centres A = 95%, B = 100%, and C = 77%, respectively). Additionally, all intervention centres demonstrated a significant improvement in the comprehensiveness of reports post-template implementation, with statistically significant increases in the reporting of all variables under-reported at baseline ( P > .01). Conclusion: Standardized templates across our province for CT scans of potential renal donors promote completeness of reports. Radiologists can reliably provide our surgical colleagues with needed preoperative anatomy and incidental findings, helping to determine suitable transplant donors and reduce potential complications associated with organ retrieval.
Rethinking Efforts to Improve Dietary Patterns Among Long-Haul Truck Drivers: Transforming Truck Stop Retail Food Environments Through Upstream Change
Authors: Lemke Michael Kenneth , Houghtaling Bailey , Winkler Megan R. , Hege Adam
Published: January 2023

A multitude of upstream occupational exposures influence poor dietary patterns that contribute to cardiometabolic health disparities among long-haul truck drivers in the United States. Herein, we delineate the unique characteristics of the truck driving profession that shape dietary patterns. Next, we discuss current health promotion efforts and why they are unlikely to be sufficient for improving population-level dietary patterns. We then advocate for prioritizing health promotion efforts that target upstream factors that influence population dietary patterns and have the potential to holistically and sustainably support drivers’ nutrition. Finally, we propose novel research directions to catalyze upstream-oriented health promotion efforts.