Number Research Articles

Children under attack: trends and patterns of sexual offences against children in India

Introduction: Recently, the United Nations declared November 18 as the World Day for the Prevention of and Healing from Child Sexual Exploitation, Abuse, and Violence. Despite sexua l offences against children being frequently reported in Indian media, scientific studies analysing the annual trends and re gional patterns of these crimes are scarce in India. Objectives: To describe annual trends and regional patterns of sexual crimes against children (CAC) in India Methods: National Crimes Record Bureau (NCRB) data on sexual c rimes for years 2014-2021 were analysed to describe annual trends and regional patterns of sexual offences against childr en in India. Results: An incremental trend in sexual offences was reported dur ing the 2014-2021 period. Sexual offences had the highest crime rate in 2021 (12.1%). There was a continuous increase in sexual offences in each succeeding year during the 2014-2021 period. The highest year-over-year increase was reported in 2017 (142%). The proportion of sexual offences in all CAC increased from 10% in 2014 to 36% in 2021. A higher num ber of cases were reported among girls (mean=25112; SD=5371) than boys (mean=241; SD=47). In 2021, Delhi had the highest crime rate for all CAC, while Andaman and Nicobar Islands had the highest crime rate for sexual offences. Conclusions & Recommendations: Despite the legal framework av ailable to protect children, there is still an increase in sexual offences against children in India. Regional patterns suggest a diverse distribution of sexual offences across India. A higher concentration of sexual offences was reported on remote, g eographically smaller Indian islands as well as on the densely populated mainland of India. Interventions aimed at providi ng knowledge about reporting sexual offences, altering community attitudes, and encouraging a safe and healthy childhood a re recommended.

Multiple Fano resonator based on photonic crystal waveguide coupled with two micro-cavities for biomedical sensing application

In order to acquire a miniature refractive index (RI) biosensor with high sensitivity, fast and selective for ultra-low concentrations of molecules, a new Photonic Crystal (PhC) biosensor based on a waveguide coupled to a Fano resonator is proposed for cancer cells detection. An optimized structure of the biosensor can detect cells cancers (Besal, Hela, Jurkat and PC12) in a biological solution deposited on the surface of the resonator. The detection mechanism uses the refractive index as a detection element. The performance of the proposed biosensor is studied by analyzing the variations in the transmission spectrum of different normal and cancer cells. The proposed structure is multimode PhC, with silica as a dielectric material. The finite element method (FEM) have been implemented for studying and investigating the numerical values. The simulation results display that the proposed biosensor attains spectral sensitivities of ‘513.12 nm RIU−1’, ‘587.28 nm RIU−1 ’, ‘702.35 nm RIU−1 ’ and ‘690.57 nm RIU−1 ’ corresponding to Hela cells, PC12 cells, Basal cells and Jurkat cells, respectively. And he qualilty factor Q of the Fano resonance mode can reach 3040.26. Our optimized design ensures easy fabrication with ongoing techniques. This study may open a new way for the development of integrated optical circuits and biosensing.

Reanalysis of the top-quark pair production via the e+e− annihilation near the threshold region up to N3LO QCD corrections

In this paper, we present an improved analysis of the top-quark pair production via the process e+e−→γ⁎→tt¯ near the threshold region up to next-to-next-to-next-to-leading order (N3LO) QCD corrections. Near the threshold region, the top-quark velocity v tends to zero, leading to Coulomb singularity. To achieve a reasonable prediction in the threshold region, we reconstruct the analytical expression for the Coulomb-terms up to N3LO accuracy by using the PSLQ algorithm, whose numerical values agree well with the previous N3LO-level calculations. It is found that the N3LO series still has sizable renormalization scale dependence, and to improve the precision of the series, we apply the Principle of Maximum Conformality to eliminate such scale dependence. After that, the Coulomb part is resummed into a Sommerfeld-Gamow-Sakharov factor, which finally leads to a much more reasonable behavior near the threshold region.

Performance of direct oral anticoagulant (DOAC) testing by hemostasis laboratories: The Australasian/Asia-Pacific experience.

Direct oral anticoagulants (DOACs) reflect anticoagulation agents given to treat or prevent thrombosis, having largely replaced vitamin K antagonists (VKAs) such as warfarin. DOACs are given in fixed daily doses and generally do not need monitoring. However, there may be a variety of reasons that justify measurement of plasma DOAC levels in individual patients. We report updated findings for DOAC testing in our geographic region, using recent data from the RCPAQAP, an international external quality assessment (EQA) program, currently with some 40-60 participants in each of the different DOAC (rivaroxaban, apixaban, dabigatran) modules, to assess laboratory performance in this area. Data has been assessed for the past 5 years (2019-2023 inclusive), with 20 samples each per DOAC. Data shows a limited repertoire of assays in use, and mostly consistency in reported numerical values when assessing proficiency samples. Available assays mostly comprised reagents from four manufacturing suppliers. There was good consistency across what participants identified as 'DOAC detected', but some variability when participants attempted to grade DOAC levels as low vs moderate vs high. Inter-laboratory/method coefficient of variation (CVs) were generally <15% for each DOAC, when present at >100 ng/mL. We hope our findings, reflecting on mostly consistent reporting of DOAC levels and interpretation provides reassurance for clinicians requesting these measurements, and helps support their implementation in regions where there is a paucity of test availability.

Research on Dynamic Characteristic Coefficients of Integral Squeeze Film Damper

Integral squeeze film damper (ISFD) is a new type of structure that appeared on the basis of traditional squeeze film damper (SFD). Since the oil film in ISFD is a segmented structure without annular flow, the nonlinearity of the oil film force has been improved to a great extent. The dynamic characteristic coefficients of ISFD have a close relationship with its damping performance. This work investigates and studies the dynamic characteristic parameters of ISFD by means of numerical analysis and experimental validation techniques in order to examine the dynamic features and unveil the damping mechanism. The ISFD solid and fluid analysis models are created, and the computational fluid dynamics (CFD) and mechanical performance analyses are completed. The force acting on the ISFD’s S-type elastomer under excitation conditions is revealed in the mechanical property analysis, and the flow characteristics of the internal oil film are investigated in the CFD analysis. It is discovered that the ISFD has good linear damping and stiffness characteristics, and numerical analytical values for the ISFD’s damping and stiffness coefficients are obtained. By constructing a bi-directional excitation test rig, the experimental values of the ISFD stiffness coefficient and damping coefficient are determined. These values are in close agreement with the results of the numerical analysis, confirming the accuracy of the ISFD’s numerical analysis conclusions.

Numerical values modulate size perception.

The link between various codes of magnitude and their interactions has been studied extensively for many years. In the current study, we examined how the physical and numerical magnitudes of digits are mapped into a combined mental representation. In two psychophysical experiments, participants reported the physically larger digit among two digits. In the identical condition, participants compared digits of an identical value (e.g., "2" and "2"); in the different condition, participants compared digits of distinct numerical values (i.e., "2" and "5"). As anticipated, participants overestimated the physical size of a numerically larger digit and underestimated the physical size of a numerically smaller digit. Our results extend the shared-representation account of physical and numerical magnitudes.

Free convective heat flow from cold and heated conical shape bodies in Newtonian liquids

Investigation on the simulation effects of free convection flow from a cold and heated cone with a downward pointing tip in various stagnant fluids is presented in this study. Examining the steady-state isotherm patterns around the cone to determine heat transfer characteristics. Results are presented for friction factor and Nusselt number distributions Over the surface of the cone for distinct dimensionless quantities like Pr and Gr to deliver an extensive class of dimensionless numbers. The commercial program ANSYS CFX 16.0 was used to solve the regulating equations which governs the motion vis., momentum mass and energy were computed. The temperature as well as flow fields are coupled using the Boussinesq approximation in order to simplify the governing equations and capture natural convection. Heat transmission has a rather minimal influence on the local Nusselt number for low Grashof and Prandtl values, according to the findings. It gains strength as the Grashof and Prandtl numbers grow, because of the extreme curvature of the isotherms, the maximum value of the local Nusselt number may be found around the corner of the vertical cone in both cones. The current simulations closely match the numerical values provided in the printed matter.

The HUC progression score: A new method for determining KERATOCONUS progression.

To establish new criteria for the progression of keratoconus, taking into account a Pentacam HR (high resolution) tomographeŕs repeatability limit. This is a retrospective observational study, including 224 eyes in 154 patients diagnosed with keratoconus and patients treated with crosslinking, with a follow-up of at least one year, in which the new progression score of the Cruces University Hospital for keratoconus progression was analyzed. This score takes into account: maximum keratometry, thinnest corneal thickness, maximum posterior elevation, vertical coma and RMS of high order aberrations, all based on the tomographer repeatability limit. The effectiveness or not of crosslinking was determined. The Receiver Operating Characteristics (ROC) curves obtained in our validation met the criteria by being far from the reference diagonal. Moreover, young patients are more likely to have keratoconus that progresses, and the percentage of patients that showed progression was 14.3% of the eyes studied, with the most advanced keratoconus showing the least progression. Taking into account the new progression score of the Cruces Hospital, we would have indicated crosslinking in 2 eyes only and we observed that none progressed one year after treatment. The the new progression score of the Cruces University Hospital is a method based on the real repeatability limit for keratoconic eyes. Moreover, it is easy to interpret and can be implemented with Pentacam software. It provides a numerical value that evaluates both the anterior and posterior surfaces of the cornea and corneal aberrations in the evolution of keratoconus.

Quantifying the reduction in coal and increase in renewables in OECD (Organisation for Economic Co-operation and Development) countries: Proposal for a coal-renewable energy index and map

Reducing coal use and increasing renewable energy use are two major policies that have been recommended in many countries and regions to address climate change. However, the progress toward meeting these policy goals is unclear. Therefore, this study proposes the “C–R map”, which is a correlation map between the share of coal-fired plants and renewable energy in terms of annual consumption, and the “CR index”, which is an index composed of the above two indicators. The proposed methods objectively visualise and quantitatively analyse statistical data on global climate change associated with “reducing coal and increasing renewables”, whose trends or tendencies could only be obtained through fragmented statistical data and documents. Using the proposed C–R map and CR index, an international comparative analysis was conducted on the share of coal and renewables from 1990 to 2020 in 37 Organisation for Economic Cooperation Development member countries. The results quantitatively showed that 31 member countries clearly exhibited a trend of decreasing their share of coal and increasing their share of renewables, while only five countries, i.e. Chile, Turkey, Mexico, Japan, and Korea, showed unique and inconsistent trends relative to that of the other member countries. By using the highly transparent and simple map and index proposed here based on published statistical information and objective methods, the global trend of “reducing coal and increasing renewables” can be quantitatively visualised in graphs and numerical values, which are easily understood and interpretable, even for laymen.

A connectivity index based on adjacent vertices in cubic fuzzy graph with an application

A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generalized the neighborhood connectivity index in a cubic fuzzy graph with the aim of investigating the connection status of nodes with respect to adjacent vertices. In this survey, the neighborhood connectivity index was introduced in the form of two numerical and distance values. Some characteristics of the neighborhood connectivity index were investigated in cubic fuzzy cycles, saturated cubic fuzzy cycle, complete cubic fuzzy graph and complementary cubic fuzzy graph. The method of constructing a cubic fuzzy graph with arbitrary neighborhood connectivity index was the other point in this research. The results showed that the neighborhood connectivity index depends on the potential of nodes and the number of neighboring nodes. This research was conducted on the Central Bank’s data regarding inter-bank relations and its results were compared in terms of neighborhood connectivity index.

Learning topological defects formation with neural networks in a quantum phase transition

Neural networks possess formidable representational power, rendering them invaluable in solving complex quantum many-body systems. While they excel at analyzing static solutions, nonequilibrium processes, including critical dynamics during a quantum phase transition, pose a greater challenge for neural networks. To address this, we utilize neural networks and machine learning algorithms to investigate time evolutions, universal statistics, and correlations of topological defects in a one-dimensional transverse-field quantum Ising model. Specifically, our analysis involves computing the energy of the system during a quantum phase transition following a linear quench of the transverse magnetic field strength. The excitation energies satisfy a power-law relation to the quench rate, indicating a proportional relationship between the excitation energy and the kink numbers. Moreover, we establish a universal power-law relationship between the first three cumulants of the kink numbers and the quench rate, indicating a binomial distribution of the kinks. Finally, the normalized kink-kink correlations are also investigated and it is found that the numerical values are consistent with the analytic formula.

Tight Lieb–Robinson Bound for approximation ratio in quantum annealing

Quantum annealing (QA) holds promise for optimization problems in quantum computing, especially for combinatorial optimization. This analog framework attracts attention for its potential to address complex problems. Its gate-based homologous, QAOA with proven performance, has attracted a lot of attention to the NISQ era. Several numerical benchmarks try to compare these two metaheuristics, however, classical computational power highly limits the performance insights. In this work, we introduce a parametrized version of QA enabling a precise 1-local analysis of the algorithm. We develop a tight Lieb–Robinson bound for regular graphs, achieving the best-known numerical value to analyze QA locally. Studying MaxCut over cubic graph as a benchmark optimization problem, we show that a linear-schedule QA with a 1-local analysis achieves an approximation ratio over 0.7020, outperforming any known 1-local algorithms.

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Floating-Point Embedding: Enhancing the Mathematical Comprehension of Large Language Models

The processing and comprehension of numerical information in natural language represent pivotal focal points of scholarly inquiry. Across diverse applications spanning text analysis to information retrieval, the adept management and understanding of the numerical content within natural language are indispensable in achieving task success. Specialized encoding and embedding techniques tailored to numerical data offer an avenue toward improved performance in tasks involving masked prediction and numerical reasoning, inherently characterized by numerical values. Consequently, treating numbers in text merely as words is inadequate; their numerical semantics must be underscored. Recent years have witnessed the emergence of a range of specific encoding methodologies designed explicitly for numerical content, demonstrating promising outcomes. We observe similarities between the Transformer architecture and CPU architecture, with symmetry playing a crucial role. In light of this observation and drawing inspiration from computer system theory, we introduce a floating-point representation and devise a corresponding embedding module. The numerical representations correspond one-to-one with their semantic vector values, rendering both symmetric regarding intermediate transformation methods. Our proposed methodology facilitates the more comprehensive encoding and embedding of numerical information within a predefined precision range, thereby ensuring a distinctive encoding representation for each numerical entity. Rigorous testing on multiple encoder-only models and datasets yielded results that stand out in terms of competitiveness. In comparison to the default embedding methods employed by models, our approach achieved an improvement of approximately 3.8% in Top-1 accuracy and a reduction in perplexity of approximately 0.43. These outcomes affirm the efficacy of our proposed method. Furthermore, the enrichment of numerical semantics through a more comprehensive embedding contributes to the augmentation of the model’s capacity for semantic understanding.

Comparing Taguchi-based RSM and ANN for Shredder Blade Geometrical Parameter Optimization

In this study, a Taguchi-based RSM in conjunction with an ANN model was utilized to ascertain optimal geometric parameters for the shredding blade employed in a plastic bottle shredder. The shredding process is pivotal in plastic recycling, involving the reduction of waste plastic into smaller fragments to facilitate subsequent transportation and processing. Despite existing research on plastic shredders, further investigations are warranted to optimize shredding blade design. Consequently, a numerical analysis, providing an in-depth insight into understanding the shredder parameters to elucidate the influence of geometric factors was conducted. Subsequent validation was carried out using experimental designs prescribed by the Taguchi-based RSM and ANN models. Both models were then evaluated based on predictive effectiveness and error against simulation data. The predictive outcomes presented that the ANN model resulted in better prediction capacity and lower prediction error than the RSM model, 0.16197 μm and 0.15567 μm, while the numerical validation value was 0.162 μm. Both the original and optimal blades were fabricated and utilized for experiments, illustrating lower wear after measurement using a microscope from ICamScope®. As a result, it is evident from this inquiry that this methodology presents a viable avenue for enhancing the efficiency of plastic recycling machinery and broader industrial applications.

Mathematical modeling for predicting electrical energy harvested using piezoelectric composite materials for smart system applications

In the contemporary quest for sustainable energy, the potential of piezoelectric energy harvesters to convert mechanical vibrations into electrical energy has become increasingly important. This study focuses on piezoelectric composites, in particular a BaTiO3/PLA (Barium Titanate/ Polylactic Acid) system with different volume percentages of BaTiO3 ceramic particles (20%, 40% and 60%), with the aim of optimizing energy conversion efficiency. A mathematical model is introduced, encompassing material attributes, mechanical loading frequencies and electrical energy outputs. The central role of mathematical modeling in predicting harvested energy is highlighted, offering insights beyond experimental limitations. The model, which is functionally dependent on the properties of the ceramic and polymer, enables the systematic exploration of various compositions and the identification of optimal material ratios. Experimental validation of the model for different strains (0.4%, 0.8% and 1%) and compositions of BaTiO3/PLA reaffirms its reliability. Notably, the highest power harvest observed is around 4.5 μW under a strain of 1% with a BaTiO3 composition of 60%. With these specific numerical values, this approach merges materials science and energy technology, propelling the advancement of efficient piezoelectric materials for renewable energy applications.

Analysis of the crack problem under unified generalized thermoelasticity

The present article aims to manifest a two-dimensional dynamical problem based on a unified way of dual-phase lags generalized thermoelasticity. The problem deals with finite mode-I crack due to tensile force in an unbounded linear thermoelastic space under a specified stress distribution and temperature. Here the governing equations are constructed for a homogenous and isotropic medium. Boundary conditions convert the problem into four dual integral equations, whose solution corresponds to the solution of Fredholm’s integral equation of first order. The integral transform methods are applied to obtain the solution to a problem. An inversion of the Laplace transform using the Bellman method is applied to determine the numerical value of the components of temperature, stress, and displacement for copper material, which are interpreted graphically. Moreover, the stress intensity factor (SIF) near the crack center is calculated and shown graphically.

Benzoylation of Iota Carrageenan: Development of a Stable, Conductive, and Hydrophobic Drug Carrier with Reduced Toxicity and Improved Gel‐Forming Ability

AbstractIota carrageenan is characterized as a hydrocolloid, with the limitations of hygroscopicity and hydrophilicity. Here, each of these factors is addressed to enhance the stability, and hydrophobicity through the protection of highly reactive primary alcohol and the formation of stable, improved hydrophobic, and conductive gel for the loading of lipophilic drugs. The functionalized polymer is evaluated through the microscopic and scanning electron microscopy (SEM) image of the compounds, which confirm the confined nature of the functionalized polymer. Moreover, the values changed from compound 1 (iota carrageenan) to compound 2 (benzoyl liked iota carrageenan) included Log P (partition coefficient) from −0.97 to 0.90, calculated partition coefficient (cLog P) from −2.50 to 0.12, and total polar surface area (TPSA) from 214.1 to 208.57, which strongly suggest the enhanced hydrophobic character of functionalized polymers. The enhancement is further concluded by the loading of the lipophilic drug inside the core of functionalized polymer‐based gel. The enhanced loading of the drug clearly shows the improved hydrophobicity (56–86%) of the gel core of the functionalized compounds. In summary, a polymer with enhanced stability, hydrophobicity, conductivity, and capacity to make a more stable gel, with enhanced drug‐loading capability of water‐insoluble drugs, will be the best way to resolve the low bioavailability issue of the water‐insoluble drug.

Silent suffering: exploring the far-reaching impact of supervisor ostracism via sociometer theory

PurposeA limited number of empirical studies have indicated that individuals who experience workplace ostracism tend to engage in subtle and retaliatory behaviors as a means of seeking inner peace. However, research on organizational behavior and employee psychology in relation to ostracism is still in its nascent stages. Specifically, further investigation is warranted to explore how supervisor ostracism influences task procrastination (TP) through psychological processes such as self-efficacy, self-esteem and motivation. Notably, a significant gap exists in the ostracism literature, as it has yet to thoroughly examine employee behaviors related to knowledge hiding (KH) and TP in the context of individual or team-based work (Zhao et al., 2016; Brouwer and Jansen, 2019). Therefore, the present study aims to address this gap and expand the research stream within the education sector by introducing “threat to self-esteem” (TSE) as a mediating factor in the outcomes of ostracism.Design/methodology/approachThe study employed a quantitative approach, using questionnaires to collect data and mainly focused on statistics and standards. The authors used SPSS and Smart-PLS to employ numerical values developed from questionnaire surveys. Likewise, we employed primary data collection tools, including mixed survey analysis (self-reported and peer-reported). The data were collected from middle-level managers working in three public sector universities. By using a three-wave research design with a two-week interval in each phase, we were able to separate the measurement of the predictor and moderator factor [supervisor ostracism (SO) and individual resilience (IR)], mediator (TSE) and outcome variables (KH and TP).FindingsThe study has discovered a substantial relationship between variables, and all hypotheses are accepted according to the data results and findings. The study measures the effects of supervisor ostracism on knowledge hiding and task procrastination through mediating effect of threat to self-esteem, which individual resilience moderates. This study adds a few contributions to the current literature, following the goals stated above. First, this attempts to highlight employee KH behavior and TP behavior by identifying SO as the primary predictor.Research limitations/implicationsThe organization should closely monitor the level of workplace ostracism. One strategy to accomplish this goal is to routinely gauge the extent of ostracism at work using targeted techniques like surveys and observation. The organization can also create an employee assistance program for the workers to assist them in coping with the mistreatment and better adjusting to the workplace culture. Furthermore, employee empowerment and collaborative decision-making can boost workers' self-esteem, eventually leading to diminishing knowledge-hiding and procrastination habits inside the organization.Originality/valueThere is a research gap regarding the barriers to KH from the perspective of team dynamics and interpersonal mistreatment at work because prior research has focused on knowledge sharing, organizational culture and organizational obstruction. Research on organizational behavior and employee psychology in relation to ostracism is still in its nascent stages. Specifically, further investigation is warranted to explore how SO influences TP through psychological processes such as self-efficacy, self-esteem and motivation. Notably, a significant gap exists in the ostracism literature, as it has yet to thoroughly examine employee behaviors related to KH and TP in individual or team-based work.

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise.