Brewery effluent may have dissolved heavy metals that remain in the receiving waters and affect both aquatic life and human health. This study explored the efficiency of the African pear seed activated carbon (APSAC) derived from Dacryodes edulis seed waste as a low-cost adsorbent for removing chromium ion (Cr²⁺) from brewery wastewater. APSAC was synthesized by the procedure involving drying and pulverization of the seed waste, carbonization at 600 °C for 2 h, 40% H₂SO₄ impregnation for 24 h, activation at 500 °C for 4 h, neutral washing, drying, and sieving into 250 \(\mu\)m fraction. Proximate analysis, Fourier-transform infrared spectroscopy, and scanning electron microscopy were employed for characterizing bulk properties, surface functional groups, and surface morphology. Batch experiments of the adsorption process were planned on the basis of central composite design to investigate the influence of contact time (15–75 min), initial Cr²⁺ concentration (0.005–0.025 mg L^\(-1\)), and adsorbent dosage (0.5–2.5 g). The maximum Cr²⁺ removal observed was 96% at 0.025 mg L^\(-1\), 1.5 g APSAC, and 45 min, while the maximum adsorption capacity of APSAC was 0.0048 mg g^\(-1\) at 0.015 mg L^\(-1\), 0.5 g APSAC, and 45 min. The developed model for the adsorption capacity response was statistically significant (\(F=13.94\), \(p=0.0049\)) and had high explanatory power (\(R^2=0.9617\)). The equilibrium data were better described by the Sips isotherm model (\(R^2=0.9849\)) indicating the heterogeneity of surface adsorption process. The kinetic and Boyd studies suggested that surface absorption and film diffusion controlled the process of chromium removal rate. The results confirm the effectiveness of APSAC as biomass-derived adsorbent for chromium removal from brewery effluent and the potential of valorization of the African pear seed waste in wastewater treatment.

Force regulation is one of the important requirements in robotics when objects being grasped are fragile, deformable, or vulnerable due to excessive contact forces. This paper describes the design, development, and experimental evaluation of an inexpensive force-controlled robotic gripper which allows to safely grasp fragile objects. The developed system includes screw drive mechanism, YZC-131 load cell, HX711 analog-to-digital conversion module, Arduino control unit, DRV8871 motor driver and GA25-370 DC geared motor. Measurement of gripping force is accomplished via the application of exponential moving average filter and the process of its regulation is realized by means of the Proportional-Derivative control law together with contact detection, deadzone logic and minimum pulse-width modulation compensation algorithm. The performance of the proposed prototype was analyzed in terms of force-sensor calibration, controller tuning, fingertips interface evaluation, repeatability tests and real grasping experiments. The calibration procedure revealed very high linearity of the dependence between applied force and digitized sensor readings, with \(R^2 = 0.99897\). Experimental results have confirmed the possibility of stable low-force regulation, minimal overshoot and satisfactory settling process, better contact stability using the silicon-coated fingertips interface, repeatable responses to applied force and successful grasp of a chicken egg without any shell damage.

In this paper, we develop an effective gauge-theoretic description of atmospheric electrical breakdown where the transition from weakly conducting air to a lightning channel is described via an order parameter instability. The formulation includes the interaction of the degree of ionization and the collective conducting amplitude with the electromagnetic potential. The key question is whether regularities found in lightning initiation, stepped leader propagation, channel confinement, and branching can be captured using topological invariants or just via threshold values of fields. Our model defines the critical field strength to be the point where the conducting order parameter becomes unstable, derives vortex channel solutions with a finite core and an effective flux increment, and connects branch geometry to symmetrically constrained weight vectors. We also describe the limited role played by instanton-like nucleation, anomalous transport terms, and dual confinement within the framework of an effective description rather than as a microscopically justified one. Predictions derived within our approach are formulated in terms of measurable quantities: vortex core length scales, branch angle clustering, field-temperature scaling, radio signals polarization dependence, and magnetic field increment near developing channels.

The steady magnetohydrodynamic boundary-layer flow of a conducting ferro-nanofluid over a permeable stretching sheet of variable thickness subject to Hall effect, thermal radiation, viscous dissipation, Brownian diffusion, thermophoresis and first order chemical reaction is analyzed. A uniform transverse magnetic field along with the consideration of the Hall effect produces an additional secondary fluid motion in association with the main stretching flow. Applying the similarity transformation technique, the governing equations of conservation of mass, momentum, energy and species concentrations are transformed into a nonlinear system of ordinary differential equations. The numerical solution of the similarity equation with proper boundary conditions is obtained using a fourth order Runge-Kutta shooting algorithm under appropriate convergence criteria. It is found that a higher magnetic parameter decreases the main flow velocity and increases the secondary velocity due to Hall effect. Thermal boundary layer thickness increases with increasing Brownian motion and thermophoresis but decreases with increasing chemical reaction rate and Lewis number. The wall gradient profiles reveal that an increase in the interaction between magnetic field and flow leads to enhance the magnitude of the primary shear as well as significantly changes the heat and mass transfer.

A privacy-based risk analysis demands a proper identification of whether a data element is private, non-private or potentially private. Though some of the personal characteristics may be inherently sensitive, others acquire sensitivity due to their statistical and semantic association with already known private variables. In this paper, we propose a feature-selection based technique to identify potentially private variables based on their relevance to known private variables. Our approach considers each feature as a target variable at a time, performs three different filter-based feature selection techniques: chi-square filter, correlation-based feature selection, fast correlation-based feature selection, generates feature-distance matrices based on the ranks obtained from these techniques and identifies relevant pairs based on the threshold distance. We have conducted experiments on the Adult dataset which shows that there are strong relations between workclass, occupation, marital-status, relationship, race, native-country, gender, income-class and age attributes. A relevant features subset for the income-class attribute that can predict as well as the complete feature set predicts with an 83% accuracy whereas the complete feature set predicts with an 85% accuracy. These findings show that feature selection can support privacy risk assessment by revealing implicit privacy dependencies that are not visible when variables are examined in isolation.

Interconnect scaling in nanometer CMOS technologies leads to capacitive coupling between adjacent signal lines as well as reduces noise margin. Furthermore, increase in operating temperature results in increase in interconnect resistance and decrease in drive strength of transistors, hence a small crosstalk pulse that is not harmful at nominal temperature may reach sufficient size for propagation through downstream logic due to temperature effects. This work examines the effect of temperature on crosstalk noise (TICN) in 22 nm CMOS interconnects based on a distributed \(6\)–\(\pi\) RC model and HSPICE simulation based on Predictive Technology Model devices. In particular, the proposed solution includes the temperature-controlled transmission-gate suppression circuit where the biases on PMOS and NMOS gates are provided by PTAT and CTAT voltages. Generated biases allow tuning the effective conductance of the transmission gate so that temperature-increased crosstalk pulses get attenuated before they reach the victim receiver threshold. Extensive simulations involving more than 500 cases with different values of interconnect length, coupling length, aggressor and victim strengths reveal average reduction of TICN of about 96%. In addition, complete cancellation of TICN occurs in 87% of examined cases without resizing of aggressor/victim drivers.

The present research studies the robust control of DC bus voltage in a proton-exchange membrane fuel cell (PEMFC)-fed power system using two-phase interleaved boost converter. The two-phase interleaved boost converter uses as a source two series-connected 6 kW PEMFC stacks that produce low-voltage DC source, which is converted to the stable 400V DC bus. The voltage control of the boost converter is realized by a simple proportional-integral voltage controller, which provides a duty-cycle signal for phase-shifted pulse-width modulation of the converter. The performance of the system is analyzed during variations of load demand, atmospheric pressure, and source/load disturbance. It is shown that the interleaved boost converter ensures the constant voltage level of the bus at 400V under step-changes of the load power and under decrease of the pressure from 1.0 bar to 0.3 bar. The decrease of the fuel cell voltage with increase of the load current and with decreasing of reactant pressure is compensated by adjusting the duty cycle of the converter and preventing the voltage collapse of the DC bus.

We investigate an inverse source problem for a general linear parabolic equation with space-time dependent coefficients. While the forward operator \(S: f \mapsto u\) is stable and characterized by a smoothing mechanism, its inversion is severely ill-posed. To stabilize the reconstruction of \(f\), we develop a framework based on orthogonal Hilbert-space expansions. By decomposing the evolution equation into an infinite system of ordinary differential equations (ODEs), we explicitly reveal how high-frequency noise is exponentially amplified during the recovery process. A spectral filtering regularization method is proposed, and we establish optimal convergence rates under suitable source conditions.