Miracle-dz/UniFAR-FTUs huggingface dataset:
An Energy-Efficient Multipath Routing Protocol for Wireless Sensor Networks

The energy consumption is a key design criterion for the routing protocols in wireless sensor networks. Some of the conventional single path routing schemes may not be optimal to maximize the network lifetime and connectivity. In this paper, we propose a distributed, scalable and localized multipath search protocol to discover multiple node-disjoint paths between the sink and source nodes. We also propose a load balancing algorithm to distribute the traffic over the multiple paths discovered. We compare our proposed scheme with the directed diffusion, directed transmission, and the energy-aware routing protocols. Simulation results show that our proposed scheme has a higher node energy efficiency, lower average delay and control overhead than those protocols.
An Empirical Analysis of Local Search on Stochastic Optimization for Planner Strategy Selection

Optimization of expected values in a stochastic domain is common in real world applications. However, it is often difficult to solve such optimization problems without significant knowledge about the surface defined by the stochastic function. In this paper we examine local search techniques to solve stochastic optimization. In particular, we analyze assumptions of smoothness upon which these approaches often rely. We examine these assumptions in the context of optimizing search heuristics for a planner/scheduler on two problem domains. We compare three search algorithms to improve the heuristic sets and show that two local search algorithms perform well and also present empirical data that suggests this is due to smoothness properties of the search space.
Phases vs. Levels using Decision Trees for Intrusion Detection Systems

Security of computers and the networks that connect them is increasingly becoming of great significance. Intrusion detection system is one of the security defense tools for computer networks. This paper compares two different model Approaches for representing intrusion detection system by using decision tree techniques. These approaches are Phase-model approach and Level-model approach. Each model is implemented by using two techniques, New Attacks and Data partitioning techniques. The experimental results showed that Phase approach has higher classification rate in both New Attacks and Data Partitioning techniques than Level approach.
Games computers play: simulating characteristic function game playing agents with classifier systems

Many game theorists are turning to evolutionary simulations to model the behavior of boundedly rational agents. This new methodology allows researchers to observe purely adaptive behaviors in games, to observe differences of behavior due to changes in the games' parameters, to discover equilibria in games that are too complex to calculate analytically, and to discover new strategies for playing the games. I extend this methodology to a more complex class of games than had previously been attempted. I create a coevolutionary environment in which three agents, represented by classifier systems, play a characteristic function game. Although the agents have no computational capabilities, they learn to adapt reasonably intelligent behavior. <<ETX>>
INNOVATION CAPABILITY IN TALENT MANAGEMENT TOWARDS EMPLOYMENT CAPABILITY THROUGH LEADERSHIP

This research investigates the impact of Innovation Ability on Talent Management in shaping Work Ability through Leadership within organizational contexts. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) through Smart PLS software, the study reveals noteworthy insights. The results indicate a highly significant correlation between Work Ability and Leadership, as well as between Talent Management and both Work Ability and Leadership. Surprisingly, the statistical significance of the relationship between Innovation Ability and Work Ability or Leadership is lacking. This suggests that, within the scope of this study, Talent Management significantly contributes to both Employability and Leadership, while the effectiveness of Innovation Capability may be contingent on additional influencing factors. The findings underscore the intricate dynamics among innovation, talent management, employability, and leadership, offering valuable insights into the pivotal factors influencing organizational performance within this framework. This research contributes to a nuanced understanding of the interplay between these elements, shedding light on the complexity of their relationships.
An Optimization of Subarrayed Planar Array Pattern via Fractal Structure Thinning

: Dividing large planar arrays into several subarrays and then turning off some of them reduces the complexity (cost) of the system significantly. In this paper, two optimization stages for the formation of planar subarrays and the removal of some of them are proposed. The first optimization stage improves the pattern of the original planar array after dividing it into a set of rotational square and rectangular subarrays. In the second optimization stage, it works to remove some of the subarrays completely or partially, depending on new fractal structures derived from the conventional Sierpinski carpet structure. The proposed fractal-thinned planar array is based on amplitude-only excitation, i.e., the phases of the elements are set to zero. To execute the optimization steps above, a genetic algorithm (GA) is used. Some determinants are included in the optimization process to maintain the properties of the desired pattern. Simulation results showed the effectiveness of the proposed optimization method in achieving almost the same performance in both stages of optimization.
Traffic-related air pollution and brain development

Automotive traffic-related air pollution (TRP) imposes an increasing health burden with global urbanization. Gestational and early child exposure to urban TRP is associated with higher risk of autism spectrum disorders and schizophrenia, as well as low birth weight. While cardio-respiratory effects from exposure are well documented, cognitive effects are only recently becoming widely recognized. This review discusses effects of TRP on brain and cognition in human and animal studies. The mechanisms underlying these epidemiological associations are studied with rodent models of pre- and neonatal exposure to TRP, which show persisting inflammatory changes and altered adult behaviors and cognition. Some behavioral and inflammatory changes show male bias. Rodent models may identify dietary and other interventions for neuroprotection to TRP.
ASSESSMENT OF SOCIAL VULNERABILITY TO THE IMPACT OF FLOOD HAZARD: A CASE STUDY OF KOPILI RIVER BASIN, ASSAM, INDIA

Abstract. In the present study, an attempt is made to understand the impact on Social Vulnerability of the Kopili basin due to various severities of flood hazard. The flood hazard is generated using multi-temporal historical satellite based analysis and integration of annual flood inundation layers. The census of India data of 2001 and 2011 is spatially joined with village database to study the impact at village level. Using 5 Census variables from both Census 2001 & 2011 as vulnerability indicators, the Social Vulnerability Index (SVI) is derived and classified into various vulnerable zones namely Low, Moderate and High Vulnerable zones. The findings of the study show that the number of villages falling in Low and High Vulnerable zones had decreased during Census 2011 when compared to 2001 and a rise of 6% in villages falling in moderate vulnerable zones during 2011 is observed. The spatial database generated is useful to understand the impact of floods on the Social Vulnerability status of the basin and can be a useful input to further study the Physical, Economic and Environmental Vulnerabilities of the basin.
Therapeutic Applications of Stem Cell-Derived Exosomes

Exosomes are extracellular vesicles of endosomal origin, ranging from 30 to 150 nm in diameter, that mediate intercellular transfer of various biomolecules, such as proteins, lipids, nucleic acids, and metabolites. They modulate the functions of recipient cells and participate in diverse physiological and pathological processes, such as immune responses, cell–cell communication, carcinogenesis, and viral infection. Stem cells (SCs) are pluripotent or multipotent cells that can differentiate into various cell types. SCs can also secrete exosomes, which exhibit remarkable therapeutic potential for various diseases, especially in the field of regenerative medicine. For example, exosomes derived from mesenchymal stem cells (MSCs) contain proteins, lipids, and miRNAs that can ameliorate endocrine disorders, such as diabetes and cancer. Exosomes from SCs (sc-exos) may offer similar advantages as SCs, but with reduced risks and challenges. Sc-exos have lower tumorigenicity, immunogenicity, and infectivity. They can also deliver drugs more efficiently and penetrate deeper into tissues. In this review, we provide an overview of the recent advances in sc-exos and their therapeutic applications in various diseases, such as diabetes and cancer. We also elucidate how the biological effects of sc-exos depend on their molecular composition. We also address the current challenges and future directions of using sc-exos.
Image Enhancement using Convolution Neural Networks

The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN). The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with images incrementally and extracting different attributes in each layer. The research also highlights CNN's ability to detect, learn and improve important details found in images through convolutions, filtering and data aggregation processes. The proposed CNN image enhancement model was developed and tested on both medical and normal images. The images were optimized using the proposed model and compared with other models. Various quality measures were used to evaluate the results. The results showed that the proposed model can significantly improve the quality of images.
Pesticide Residues in Natural Products with Pharmaceutical Use: Occurrence, Analytical Advances and Perspectives

A pharmaceutical raw material is every active or inactive substance used in the manufacturing process of a pharmaceutical dosage form. According to their function in the medicament, they could be classified into two groups: (i) active ingredients with pharmacological activity; and (ii) excipients which allow the active ingredients dosification and makes suitable the route of administration (de la Rosa et al., 1995). In spite of the high number of synthetically produced pharmaceuticals and the progress in biotechnology and gene engineering, there are a number of raw materials of natural origin, which are still used for manufacturing active pharmaceutical products (Balandrin et al., 1985). Quality is a mandatory requirement in the materials to accomplish the Pharmaceutical ‘Good Manufacturing Practices’. Nowadays, the presence of pesticides in animal and vegetal commodities is a topic of public concern for the potential health hazards derived from them (WHO, 1998). The presence of pesticide residues in animal or vegetal raw materials can be originated in agricultural practices, environmental contamination or cross contamination. For example, oils rendered from whole fish and from fish offal represent an important dietary component in many areas of the world. In addition, fish oils are extensively used as ingredients in food and in dietary supplements for their therapeutic benefits in the treatment of cardiovascular, arthritic and dermatological diseases. Fish oils, however, are susceptible to contamination with lipophilic organic chemicals, in particular many organochlorine chemicals are now ubiquitous contaminants of marine ecosystems, but there is no specification on the pesticide residue allowed (Jacobs et al., 1997). The number of trace pollutants to be monitored is continually growing and the levels at which these compounds need to be determined are becoming lower. Multi-residue methods (MRMs) are the preferred strategy over single analyte or class residue methods (SRMs). Analytical features of the MRMs allow to cover a large range of pesticides in a single analysis. Many MRMs employing different extraction and clean-up techniques and a variety
Mining Pro-ISIS Radicalisation Signals from Social Media Users

The emergence and actions of the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has received widespread news coverage across the World, largely due to their capture of large swathes of land across Syria and Iraq, and the publishing of execution and propaganda videos. Enticed by such material published on social media and attracted to the cause of ISIS, there have been numerous reports of individuals from European countries (the United Kingdom and France in particular) moving to Syria and joining ISIS. In this paper our aim to understand what happens to Europe-based Twitter users before, during, and after they exhibit pro-ISIS behaviour (i.e. using pro-ISIS terms, sharing content from pro-ISIS accounts), characterising such behaviour as radicalisation signals. We adopt a data-mining oriented approach to computationally determine time points of activation (i.e. when users begin to adopt pro-ISIS behaviour), characterise divergent behaviour (both lexically and socially), and quantify influence dynamics as pro-ISIS terms are adopted. Our findings show that: (i) of 154K users examined only 727 exhibited signs of pro-ISIS behaviour and the vast majority of those 727 users became \emph{activated} with such behaviour during the summer of 2014 when ISIS shared many beheading videos online; (ii) users exhibit significant behaviour divergence around the time of their activation, and; (iii) social homophily has a strong bearing on the diffusion process of pro-ISIS terms through Twitter.
Territorial intelligence of vulnerability systems -A transitional viewpoint of sustainable development

The use of vulnerability as a main concept, especially as an essential component of risk has been generalized from a decade or so. Together with resilience properties of human structures, it therefore constitutes the principal entry for crossing environmental and social sustainability factors, mostly apprehended from environmental harmfulness and risk perspectives, as shown in the related works in environmental psychology. The vulnerability of a system depends on the state of the system itself, on the capacity of a hazard to affect this state taking into account the exposure, the sensitivity and the adaptive capacity of the system to this event and on the undesired consequences the combination of the hazard and the vulnerability will eventually lead to.
Locality-Aware Many-Core Garbage Collection

The wide-scale deployment of multi-core and many-core processors will necessitate fundamental changes to garbage collectors. Highly parallel garbage collection is critical to the performance of these systems — today’s garbage collectors can quickly become the bottleneck for parallel programs. These processors will present additional new challenges — many contain non-uniform memory architectures in which some cores have faster access to certain regions of memory than other regions. This paper presents a new cache-aware approach to garbage collection. Our collector balances the competing concerns of data locality and heap utilization to improve performance. We have implemented our garbage collector and present results on a 64-core TILEPro64 processor. Our cache-aware parallel collector speeds up garbage collection by up to 46.7×.
Expert’s preference on escitalopram and clonazepam for treating depression and anxiety in Indian diabetic and hypertensive patients

Objective: The present study aims to identify the prevalence of anxiety and depression in patients with diabetes mellitus (DM) and hypertension (HTN) in India and assess the expert opinion on the treatment choices for these patients, specifically involving escitalopram and clonazepam. Methodology: The cross-sectional, questionnaire-based study collected data through a multiple-response questionnaire. The questionnaire comprised 38 questions, covering various aspects such as the prevalence, symptoms, and treatment options for anxiety and depression in patients with HTN and DM. Results: Of the 242 respondents, 52% and 41% reported that anxiety and depression are the most common psychiatric conditions reported in HTN and DM patients. The majority of respondents (66.53%) preferred using selective serotonin reuptake inhibitors (SSRIs) for treating these patients. In patients who were unresponsive to initial treatment with an anxiolytic medication, approximately 35% of specialists recommended switching to another anxiolytic medication within the same class. According to 36% and 32% of respondents, around 51-75% of these patients reported improvement in anxiety or depressive symptoms after receiving either escitalopram or clonazepam. To prevent dropout in these patients, nearly 42% of responders recommended initiating low-dose combination medications. Conclusion: In HTN and DM patients, SSRIs and benzodiazepines are recommended for the treatment of anxiety and depression. The experts highly recommend escitalopram and clonazepam medications in these patients for improvement in depression and anxiety levels. However, experts suggest initiating treatment with low-dose combination medications to prevent dropout and for the management of anxiety and depression.
A Compiler-Hardware Technique for Protecting Against Buffer Overflow Attacks

Buffer overflow attacks are widely acknowledged by computer security professionals to be one of the greatest threats to the security of computer systems. We present an integrated softwarehardware approach to protect against buffer overflow attacks while minimizing performance degradation, software development time, and deployment costs. Our technique does not change the processor core, but instead adds a hardware module in the form of a Field Programmable Gate Array (FPGA) that sits between cache and memory and that is able to defend return addresses from buffer overflow attacks. Our solution exhibits neither the performance overhead of software solutions nor the CPU redesign costs of hardware solutions.
On Estimating the Bandwidth Share of TCP Connections in Presence of Reverse Traffic

To improve the performance of TCP under different network scenarios, the idea of adjusting congestion window according to the bandwidth share estimation has been widely used. Various estimators are proposed in the literature. Some estimate the acquired bandwidth share, and some estimate the volume of traffic or packets queued in the buffers along the end-to-end path. We evaluate these estimation techniques with a common and effective performance metric, the number of DATA packets queued in the buffers and notice that because most estimators do not distinguish explicitly between the queuing delay on the forward path and that on the reverse path, in presence of large volumes of traffic on the reverse path these estimators become fragile. To address this problem, we propose a one-way queuing delay estimation scheme which only requires simple modifications to current TCP implementation (through a TCP option). With this estimation scheme, a one-way packet in queue estimator (OPQE) is proposed to provide a more robust and accurate estimation of the number of DATA packets queued along the forward path of a TCP connection. Simulation results show that OPQE has a good performance under different scenarios, even in the presence of reverse traffic.
A new search method for costas arrays by using difference triangle analysis

Costas arrays are used for constructing frequency sets for the frequency shift keying waveforms in low probability of intercept radars. Costas arrays are constructed mostly using the methods based on the finite field theory or they are found by exhaustive search. Whether a permutation matrix is a Costas array or not can be decided by using a difference triangle. In this work, the difference triangle approach is analyzed and a new method is proposed for the search of Costas arrays.
Global Behavior of Deconvolution Kernel Estimates

The desire to recover the unknown density when data are contaminated with errors leads to nonparametric deconvolution problems. The difficulty of deconvolution depends on both the smoothness of error distribution and the smoothness of the priori. Under a general class of smoothness constraints, we show that deconvolution kernel density k-l estimates achieve the best attainable global rates of convergence n - 2(kH)+1 under L p (1 ~ p < 00) norm, where I is the order of the derivative function of the unknown density to be estimated, k is the degrees of smoothness constraints, and {3 is the degree of the smoothness of the error distribution. Our results indicate that in present of errors, the bandwidth should be chosen larger than the ordinary density estimate. These results also constitute an extension of the ordinary kernel density estimates.
Implementing Modular Dialogue Systems: A Case of Study

This paper presents a study of the main modules of the UAH spoken dialogue system, designed to provide information in an academic environment. The novel feature in the implementation of this system is the use of a new module to automatically create speech recognition grammars with vocabulary extracted from a database. The development of new technologies in Computer Science and Telecommunications has fostered the user access to information anytime and anywhere. The emergence of Internet and its posterior expansion has enabled that many companies and public entities can be known all around the world through the web, which is accessible with mobile devices via GPRS (General Packet Radio Service) with high speed data transfers. Within a short time, this technology will be replaced by the third mobile network generation UMTS (Universal Mobile Telecommunications System), which will allow greater capability for applications such as electronic mail and instant
Compressed high dimensional features for speaker spoofing detection

The vulnerability in Automatic Speaker Verification (ASV) systems to spoofing attacks such as speech synthesis (SS) and voice conversion (VC) has been recently proved. High- dimensional magnitude and phase based features possess outstanding spoofing detection performance but are not compatible with the Gaussian Mixture Model (GMM) classifiers which are commonly deployed in speaker recognition systems. In this paper, a Compressed Sensing (CS) framework is initially combined with high-dimensional (HD) features and a derived CS-HD based feature is proposed. A standalone spoofing detector assembled with the GMM classifier is evaluated on the ASVspoof 2015 database. Two ASV systems integrated with the spoofing detector are also tested. For the separate detector, an equal error rate (EER) of 0.01% and 5.35% are reached on the evaluation set for known attack and unknown attack, respectively. While for the ASV systems, the best EERs of 0.02% and 5.26% are achieved. The proposed CS-HD feature can obtain similar results with lower dimension than other systems. This suggests that the verification system can be made more computationally efficient.