Preference programming and inconsistent interval matrices

The problem of derivation of the weights of altematives from pairwise comparison matrices is long standing. In this paper,Lexicographic Goal Programming (LGP) has been used to find out weights from pairwise inconsistent interval judgment matrices. A number of properties and advantages of LGP as a weight determination technique have been explored. An algorithm for identification and modification of inconsistent bounds is also provided. The proposed technique has been illustrated by means of numerical examples.Analytic hierarchy process; Interval judgment; Preferente programming

Back-reaction of Non-supersymmetric Probes: Phase Transition and Stability

We consider back-reaction by non-supersymmetric D7/anti-D7 probe branes in the Kuperstein-Sonnenschein model at finite temperature. Using the smearing technique, we obtain an analytical solution for the back-reacted background to leading order in N_f/N_c. This back-reaction explicitly breaks the conformal invariance and introduces a dimension 6 operator in the dual field theory which is an irrelevant deformation of the original conformal field theory. We further probe this back-reacted background by introducing an additional set of probe brane/anti-brane. This additional probe sector undergoes a chiral phase transition at finite temperature, which is absent when the back-reaction vanishes. We investigate the corresponding phase diagram and the thermodynamics associated with this phase transition. We also argue that additional probes do not suffer from any instability caused by the back-reaction, which suggests that this system is stable beyond the probe limit.Comment: 56 pages, 8 figures. References updated, improved discussion on dimension eight operato

D3/D7 Quark-Gluon Plasma with Magnetically Induced Anisotropy

We study the effects of the temperature and of a magnetic field in the setup of an intersection of D3/D7 branes, where a large number of D7 branes is smeared in the transverse directions to allow for a perturbative solution in a backreaction parameter. The magnetic field sources an anisotropy in the plasma, and we investigate its physical consequences for the thermodynamics and energy loss of particles probing the system. In particular we comment on the stress-energy tensor of the plasma, the propagation of sound in the directions parallel and orthogonal to the magnetic field, the drag force of a quark moving through the medium and jet quenching.Comment: 29 pages + appendices, 5 figures. v2 Version to appear in JHEP, with minor revisions, references added and typos correcte

Histopathological changes observed in the heart and gizzard of quail chicks Coturnix coturnix japonica administrated by the different levels of chrome shaving

The present study showed the effects of different levels of tannery wastes or chrome shaving on quail hicks up to 9 months. The chrome shaving was given at 2.5 and 5% levels in replacement of animal proteins in chicks feed commercially available. Heart and gizzard were selected for thehistopathological studies and effects of different levels were studied in these tissues. Histopathological changes like splitting of muscle, vacuolation, necrosis, pyknosis, and loss of striation and degenerationin heart and gizzard of quail chicks were observed and more pronounced at 5% chrome shaving levels compared to 2.5% level in last 3 weeks. These changes were time and dose dependent

E-commerce adoption by SMEs in developing countries: evidence from Indonesia

This study aims to provide an overview of e-commerce adoption by SMEs in developing countries and, in particular, the extent of the adoption of e-commerce by Indonesian SMEs. It identifies the e-commerce benefits realized by these SMEs and investigates the relationship between the levels of e-commerce adoption and the benefits thus realized. The study was motivated by the limited studies related to e-commerce adoption by SMEs, especially in developing countries. In addition, it seems that most e-commerce studies are focused more on upstream issues: to see the factors that facilitate, or barriers faced regarding e-commerce adoption, rather than downstream issues: to see post-adoption benefits. This certainly limits our understanding about e-commerce adoption by SMEs in developing countries, as well as the post-adoption benefits of e-commerce. Indonesia was chosen as the place in which to conduct the study. A survey of 292 SMEs shows that the majority of them are still at an early stage in their adoption of e-commerce. Their use of e-commerce is dominated by marketing and purchasing and procurement activities. “Extending market reach”, “increased sales”, “improved external communication”, “improved company image”, “improved speed of processing”, and “increased employee productivity” are reported as the top six e-commerce benefits perceived by these SMEs. This study also shows that SMEs at the higher level of e-commerce adoption experience greater e-commerce benefits than those at other levels of adoption

Holographic zero sound at finite temperature in the Sakai-Sugimoto model

In this paper, we study the fate of the holographic zero sound mode at finite temperature and non-zero baryon density in the deconfined phase of the Sakai-Sugimoto model of holographic QCD. We establish the existence of such a mode for a wide range of temperatures and investigate the dispersion relation, quasi-normal modes, and spectral functions of the collective excitations in four different regimes, namely, the collisionless quantum, collisionless thermal, and two distinct hydrodynamic regimes. For sufficiently high temperatures, the zero sound completely disappears, and the low energy physics is dominated by an emergent diffusive mode. We compare our findings to Landau-Fermi liquid theory and to other holographic models.Comment: 1+24 pages, 19 figures, PDFTeX, v2: some comments and references added, v3: some clarifications relating to the different regimes added, matches version accepted for publication in JHEP, v4: corrected typo in eq. (3.18

Malaria mortality in Africa and Asia: evidence from INDEPTH health and demographic surveillance system sites.

BACKGROUND: Malaria continues to be a major cause of infectious disease mortality in tropical regions. However, deaths from malaria are most often not individually documented, and as a result overall understanding of malaria epidemiology is inadequate. INDEPTH Network members maintain population surveillance in Health and Demographic Surveillance System sites across Africa and Asia, in which individual deaths are followed up with verbal autopsies. OBJECTIVE: To present patterns of malaria mortality determined by verbal autopsy from INDEPTH sites across Africa and Asia, comparing these findings with other relevant information on malaria in the same regions. DESIGN: From a database covering 111,910 deaths over 12,204,043 person-years in 22 sites, in which verbal autopsy data were handled according to the WHO 2012 standard and processed using the InterVA-4 model, over 6,000 deaths were attributed to malaria. The overall period covered was 1992-2012, but two-thirds of the observations related to 2006-2012. These deaths were analysed by site, time period, age group and sex to investigate epidemiological differences in malaria mortality. RESULTS: Rates of malaria mortality varied by 1:10,000 across the sites, with generally low rates in Asia (one site recording no malaria deaths over 0.5 million person-years) and some of the highest rates in West Africa (Nouna, Burkina Faso: 2.47 per 1,000 person-years). Childhood malaria mortality rates were strongly correlated with Malaria Atlas Project estimates of Plasmodium falciparum parasite rates for the same locations. Adult malaria mortality rates, while lower than corresponding childhood rates, were strongly correlated with childhood rates at the site level. CONCLUSIONS: The wide variations observed in malaria mortality, which were nevertheless consistent with various other estimates, suggest that population-based registration of deaths using verbal autopsy is a useful approach to understanding the details of malaria epidemiology

Chiral Symmetry Breaking and External Fields in the Kuperstein-Sonnenschein Model

A novel holographic model of chiral symmetry breaking has been proposed by Kuperstein and Sonnenschein by embedding non-supersymmetric probe D7 and anti-D7 branes in the Klebanov-Witten background. We study the dynamics of the probe flavours in this model in the presence of finite temperature and a constant electromagnetic field. In keeping with the weakly coupled field theory intuition, we find the magnetic field promotes spontaneous breaking of chiral symmetry whereas the electric field restores it. The former effect is universally known as the "magnetic catalysis" in chiral symmetry breaking. In the presence of an electric field such a condensation is inhibited and a current flows. Thus we are faced with a steady-state situation rather than a system in equilibrium. We conjecture a definition of thermodynamic free energy for this steady-state phase and using this proposal we study the detailed phase structure when both electric and magnetic fields are present in two representative configurations: mutually perpendicular and parallel.Comment: 50 pages, multiple figures, minor typo fixed, references adde

An analysis of customer perception using lexicon-based sentiment analysis of Arabic Texts framework.

Sentiment Analysis (SA) employing Natural Language Processing (NLP) is pivotal in determining the positivity and negativity of customer feedback. Although significant research in SA is focused on English texts, there is a growing demand for SA in other widely spoken languages, such as Arabic. This is predominantly due to the global reach of social media which enables users to express opinions on products in any language and, in turn, necessitates a thorough understanding of customers' perceptions of new products based on social media conversations. However, the current research studies demonstrate inadequacies in furnishing text analysis for comprehending the perceptions of Arabic customers towards coffee and coffee products. Therefore, this study proposes a comprehensive Lexicon-based Sentiment Analysis on Arabic Texts (LSAnArTe) framework applied to social media data, to understand customer perceptions of coffee, a widely consumed product in the Arabic-speaking world. The LSAnArTe Framework incorporates the existing AraSenTi dictionary, an Arabic database of sentiment scores for Arabic words, and lemmatizes unknown words using the Qalasadi open platform. It classifies each word as positive, negative or neutral before conducting sentence-level sentiment classification. Data collected from X (formerly known as Twitter, resulted in a cleaned dataset of 10,769 tweets, is used to validate the proposed framework, which is then compared with Amazon Comprehend. The dataset was annotated manually to ensure maximum accuracy and reliability in validating the proposed LSAnArTe Framework. The results revealed that the proposed LSAnArTe Framework, with an accuracy score of 93.79 %, outperformed the Amazon Comprehend tool, which had an accuracy of 51.90 %

RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach.

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today's society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification