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The creation of sustainable artificial tropical forests is of utmost importance for addressing critical environmental issues like deforestation, habitat loss, and climate change. This research paper presents an innovative approach that considers ecological, socioeconomic, and technological factors. The proposed approach integrates multi-criteria decision-making (MCDM) methods that take into account the preferences and opinions of stakeholders. To analyze the various criteria involved, cubic picture fuzzy numbers (CPFNs) are utilized. To ensure fairness in evaluating CPFNs, the study focuses on developing operational laws and aggregation operators that promote neutrality and fairness. Specifically, the paper introduces the “cubic picture fuzzy fairly weighted average (CPFFWA) operator” and the “cubic picture fuzzy fairly ordered weighted averaging (CPFFOWA) operator.” By considering stakeholders and prioritizing their long-term sustainability and resilience, this approach proves to be highly valuable for policymakers, forest managers, and other relevant parties. This research avenue bridges the gap in current literature where limited exploration of fairly aggregation operators in tandem with picture fuzzy sets within tropical forest contexts exists. Ultimately, the research strives to contribute to the practical management of these vital ecosystems, fostering environmental conservation and sustainability through a cross-disciplinary approach amalgamating ecology, fuzzy logic, decision theory, and environmental science.

Wireless operation of $LC$ resonant sensors is based on magnetic coupling between two inductive coils, where the inductor of the sensor acts as a secondary coil for the magnetic coupling. An external reader coil is used as a primary coil to interrogate the sensor and detect the sensor’s response. Wireless $LC$ resonant sensors are used in many applications where cable connections for powering the sensor and acquiring its response are inconvenient. This review focuses on the fundamental operating principles of wireless $LC$ resonant sensors, their measurement techniques and challenges, as well as potential solutions. The main challenge in wireless measurement of the sensors is to accurately measure the resonance frequency and the quality factor of the sensor, which are solely dependent on the intrinsic parameters of the sensors. For practical wireless applications, it is crucial to interrogate $LC$ resonant sensors regardless of their wireless measurement distances. To interrogate the wireless resonant sensor, frequency and time domain measurements are commonly used. The coupling coefficient, which is greatly influenced by the geometrical dimensions and alignment of the two inductively coupled coils, has an adverse effect on distance independent measurement of the sensors in frequency domain phase dip technique. Furthermore, the presence of parasitic capacitance that appears in parallel to the readout coil of the sensor has also an adverse effect on distance independent measurement in both the frequency and time domains, resulting in an inaccurate measurement of the sensors’ resonance frequency. A parasitic capacitance compensation technique can be employed to reduce or even eliminate the presence of parasitic capacitance in the readout coil, which significantly improves the measurement accuracy of the $LC$ resonant sensors.

The prediction of energy consumption plays a significant role in energy conservation and reducing the cost of power generation, to improve energy sustainability and economic stability. Current studies show an increased interest in the application of Machine Learning algorithms to forecast energy utilisation in smart homes. The performance of these Machine Learning algorithms is evaluated using accuracy algorithms. The process of manually selecting best-performing Machine Learning algorithms is still very challenging for data analysts and decision makers because the algorithms might not work well in a different use case or data-set. To address this, we propose a decision algorithm model using machine learning based data mining and picture fuzzy operators. First, Machine Learning algorithms are trained and tested to predict energy consumption of smart home appliances with respect to the weather information. Second, score values of Lasso Regression are used to understand the patterns and features of weather information for smart house micro-climate. We then propose a decision matrix using fuzzy operators to aggregate Machine Learning algorithms, prior to ranking using a score function. Finally, the electricity consumption of appliances as well as total energy consumed in the smart home is provided in Kilowatts (KW).

Low quality home energy appliances and cables are responsible for huge electricity wastage in low-income households. Absence of energy efficiency appliances standards and labeling and a policy for low-income households are among the main causes of electricity bleeding in developing countries. Energy efficiency standards and labeling for appliances and cables initiative can save significant amount of energy in Pakistan. In this paper, we used four alternatives for energy efficiency improvement for low income households at national level in Pakistan. For this we developed an extended “multi-criteria decision-making” (MCDM) approach to choose best alternative as a solution that should be able to evaluate and select the best scenario of energy efficiency with respect to eight criteria. We also developed the q-ROF Einstein aggregation operators based MULTIMOORA approach. This approach is a new hybrid technique that is more efficient to capture uncertain and incomplete assessments of stakeholders in terms of q-ROFNs. MCDM approaches are effective tools for solving energy decision-making challenges. Our results show that adoption of standards and labeling criteria for home appliances can improve living standards of low income households. This adoption has great potential to conserve electricity, environment and cost with introducing low income energy policy at government level.

In this paper, we propose a peak-to-average-ratio (PAPR) based simultaneous wireless information and power transfer (SWIPT) system for Internet-of-Things (IoT) sensor applications. Conventional SWIPT system is based on power-hungry transmitter and receiver modules. The presence of such blocks directly impact the power consumption in IoT devices. This problem can be solved by proposing an ultra-low-power communication mechanism. Recently SWIPT with multi-tone is under consideration because of its increased power conversion efficiency (PCE). We proposed a PAPR based SWIPT receiver that utilizes the multi-tone waveform for information decoding in SWIPT system. An adaptive power splitter (APS) smartly regulates the distribution of received radio frequency (RF) signals between the energy harvesting (EH) path and the information decoding (ID) path. A digital controller was designed to control the demodulation of the PAPR based modulated signal and retrieves the digital information. Back-scattering modulation has been opted for up-link data transfer. For the EH path we design an RF-DC converter with an adaptive matching to increase the dynamic range of input power. The propose SWIPT system based on the PAPR demodulator is implemented on the 180 nm CMOS process. The digital clock frequency at the SWIPT receiver is 64 kHz, which can provide a data rate of 8 kbps with power consumption of $7.3~\mu \text{W}$ with area utilization of 0.4 mm2.

This paper proposes a high-performance Digital Feedback low-dropout voltage regulator (DF-LDO) for low power applications. In the DF-LDO regulator, digital feedback and applying spectrum spread clock generator (SSCG) technique are used to reduce output voltage ripples. In addition, it has triple operation modes i.e. coarse, fine, and retention for high efficiency and transient enhancement. The proposed hybrid DF-LDO uses arrays of PMOS transistors in coarse and fine mode whereas in retention mode, only one comparator and NMOS are active and digital controller goes into the sleep mode. This results in the reduction of the power consumption and improves the output voltage ripples. In the retention mode, minimum number of blocks operate that reduces the current consumption as compared to coarse and fine modes. To further reduce the current consumption, the comparator with hysteresis is used. The proposed circuit is designed using CMOS 55 nm process. The input voltage range is from 0.8 ~ 1.5 V and the measured output voltage range is 0.756 ~ 1.456 V. The measured line regulation is 6 mV / V, and the regulation starts when the input voltage is 0.8 V. The measured load regulation is 2.3 mV/mA for maximum load current of 5 mA. The peak current efficiency of the proposed DF-LDO is 99.996 % with a maximum output voltage ripples value of 1.9 mV. The proposed digital LDO regulator active chip area is 0.012 mm2.

Multivariate memory-type control charts that use information from both the current and previous process observations have been proposed. They are designed to detect shifts in both upper and downward directions with equal precision when monitoring the process mean vector. The absence of directional sensitivity can limit the charts’ application, particularly when users are interested in detecting variations in one direction than the other. This article proposes one-sided and two one-sided multivariate control charts for monitoring shifts in the process mean vector. The proposed charts are presented in the form of the multivariate homogeneously weighted moving average approach that yields efficient detection of shifts in the mean vector. We provide simulation studies under different shift sizes in the process mean vector and evaluate the performance of the proposed charts in terms of their run length properties. We compare the average run length (ARL) results of the charts with the conventional charts as well as the one-sided and two one-sided multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) charts. Our simulation results show that the proposed charts outperform the existing charts used for the same purpose, particularly when interest lies in detecting small shifts in the mean vector. We show how the charts can be designed to be robust to non-normal distributions and give a step-by-step implementation efficient application of the charts when their parameters are unknown and need to be estimated. Finally, an illustrative example is provided to show the application of the proposed charts.

This paper presents a 0.46 mW and 2.4 GHz; All-Digital Phase-Locked Loop (ADPLL) through an Injection-Locked Frequency Multiplier (ILFM) and Continuous Frequency Tracking Loop (CFTL) circuitry for low power Internet-of-Thing (IoT) applications. In the proposed ADPLL architecture to save power, the need for Time-to-Digital Converter (TDC) is eliminated through providing the CFTL circuitry. This feature makes the design compact, low power, and suitable for IoT applications. The proposed design is based on a synthesizable pulse injection and frequency-locked loop along with an ultra-low-power LC Digitally-Controlled Oscillator (LC-DCO). The presented CFTL circuit adjusts the frequency of the DCO continuously and prevents the frequency drift after the reference injection. Inside the designed LC-DCO core, the power consumption is minimized by optimizing the $\text{g}_{\mathrm {m}}~/\text{I}_{\mathrm {D}}$ and adjusting the power supply to 0.5 V. The proposed ILFM based ADPLL is fabricated in 55 nm CMOS technology and covers the operational frequency range of 2.402 GHz to 2.480 GHz with a reference frequency of 32 MHz. The measured phase noise performance of the ADPLL is −111.15 dBc/Hz at 1 MHz offset frequency from the carrier frequency of 2.4 GHz. It consumes only 0.46 mW power with an active area of 0.129 mm2.

Neutrosophic HyperSoft Set (NHSS) is a new approach towards computational intelligence and decision making under uncertainty. In this paper, we first consider distances for NHSS, and then propose similarity measures for NHSS. We also consider aggregated operation for aggregating NHSS decision matrix. TOPSIS (Technique for the order preference by similarity to ideal solution) is a strong approach for multi-criteria decision making (MCDM) which has been studied under various extensions of fuzzy sets. These approaches have drawbacks in depicting fuzzy decision-making information for handling MCDM situations under NHSS environment. To efficiently and accurately express fuzzy attribute values provided by decision-makers (DMs), we construct the TOPSIS based on the proposed distances and similarity measures of NHSS, called NHSS-TOPSIS. The proposed NHSS-TOPSIS provides the weights of DMs by utilizing similarity measures dependent on Hamming distance. We then aggregate the opinions of decision-makers using the proposed aggregated operation. Utilizing the relative closeness coefficient, we select the most ideal alternative in the proposed NHSS-TOPSIS procedures. To exhibit the relevance and adequacy of the proposed NHSS-TOPSIS, we apply it in a medical diagnosis and an optimal selection for the sustainable green security system. The proposed method reveals that the hypersoft set with the neutrosophic set theory can be very helpful to construct a connection between alternatives and attributes. It demonstrates that the proposed method is effective and useful in real applications.

The notion of a q-rung orthopair fuzzy soft rough set ( $^{q}ROFSRS$ ) appeared as an extension of q-rung orthopair fuzzy set ( $^{q}ROFS$ ) and q-rung orthopair fuzzy soft set ( $^{q}ROFSS$ ) with the aid of rough set (RS) definition. Thus, $^{q}ROFSRS$ and m-polar fuzzy set ( $_{m}PFS$ ) are convenient to deal with uncertain knowledge which helps us to solve many problems in decision making. In this paper, we define the soft rough q-rung orthopair m-polar fuzzy sets ( $^{\text {q}}RO_{\text {m}}PFS$ ) and q-rung orthopair m-polar fuzzy soft rough sets ( $^{q}RO_{\text {m}}PFSRS$ ) through crisp soft and q- rung orthopair (q-RO) m-polar fuzzy soft approximation space. The related characteristics of these models are also studied. Then, we construct two new algorithms for these models to solve MADM issues. The successful application and corresponding comparative analyses proves that our proposed models are rational and effective.

Cubic bipolar fuzzy set (CBFS) is a powerful model for dealing with bipolarity and vagueness altogether because it contains bipolar fuzzy information and interval-valued bipolar fuzzy information simultaneously. In this article, we define some new notions such as concentration, dilation, support and core of a CBFS. We introduce cubic bipolar fuzzy relations (CBFRs) and some of their types. As in statistics with real variables, we define variance and covariance between two CBFSs. Then, we propose correlation coefficients and their weighted extensions on the basis of variance and covariance of CBFSs. Later on, some properties of these correlation coefficients are discussed. We explore that their values lie in [−1,1]. Moreover, we discuss the applications of the proposed correlation coefficients in pattern recognition and clustering analysis. Numerical examples are provided for better understanding of the applicability and efficiency of proposed correlation coefficients.

In this paper, an ultra-low power, adaptive all-digital integer frequency-locked loop (FLL) with gain estimation and constant current digitally controlled oscillator (DCO) for Bluetooth low energy (BLE) transceiver in Internet-of-Things (IoT) is presented. For locking DCO frequency closest to the target channel, it adaptively controls capacitor banks with binary algorithm. With decrease in frequency resolution, DCO clock counts for each capacitor bank bit evaluation dynamically increases with the proposed technique for accurate frequency tracking. For compensating PVT variations and finding the BLE frequency deviation, the configurable digital DCO gain estimation is incorporated. The low power and constant current DCO operates in sub-threshold region and its power consumption is minimized by gm/ID methodology optimization, constant current source for limiting current in DCO core through adaptive low-dropout regulator (LDO) and lowering the supply voltage. The proposed design is integrated in an ADPLL for BLE transceiver and it is fabricated with 1P6M TSMC 55 nm CMOS technology. The all-digital adaptive FLL is fully synthesizable and its area is 1800 μm2 with 1.233 K gate count. The RMS current consumption is 103.32 μA from 1 V voltage supply with 103.32 μW power requirement. The experimental results reveal, DCO draws 480 μA current from 0.55 V supply voltage at center frequency. It has frequency resolution of 4.8 kHz. The oscillator PN, FOM and FOMT at 1-MHz offset frequency from 2.44 GHz carrier frequency are -122.85 dBc/Hz, 196.38 dBc/Hz and 208.19 dBc/Hz, respectively.

In most of the manufacturing processes, we encounter different quality characteristics of a product and process. These characteristics can be categorized into two kinds; study variables (variable of interest) and the supporting/explanatory variables. Sometime, a linear relationship might exist between the study and supporting variable, which is called simple linear profiles. This study focuses on the simple linear profiles under assorted control charting approach to detect the large, moderate and small disturbances in the process parameters. The evaluation of the proposed assorted method is assessed by using numerous performance measures, for instance, average run length, relative average run length, extra and sequential extra quadratic losses. A comparative analysis of the proposal is also carried out with some existing linear profile methods including Shewhart_3, Hotelling's T2, EWMA_3, EWMA/R and CUSUM_3 charts. Finally, a real-life application of the proposed assorted chart is presented to monitor thermal management of diamond-copper composite.

This paper presents a design of 6.8 mW all digital delay locked loop (ADDLL) with digitally controlled dither cancellation (DCDC) for time to digital converter (TDC) in ranging sensors. ADDLL uses the accumulator (ACC) to control the delay of digitally controlled delay line (DCDL) during phase locking which utilizes less power and area as compared to analog delay locked loop (DLL). In the lock state, the ACC value dithers due to the closed loop operation. A digital controller is proposed to detect the lock state, performs dither cancelation and selects the optimum ACC value for controlling the delay of the replica DCDL for TDC operation. It helps the jitter reduction in the ADDLL. Additionally, it provides robustness against glitches, false locking and unlocking in a noisy environment. The ADDLL peak to peak jitter and RMS jitter at 625 MHz are 6.5 ps and 1.2 ps respectively. The ADDLL including DCDC is implemented on $0.18~\mu \text{m}$ CMOS technology with an operational range of 350~900 MHz. It consumes only 6.8 mW at 625 MHz power with 1.8 V power supply. The area utilization is 0.06 mm2.

In this paper, a high performance adaptive digital low-dropout voltage regulator (ADLDO) is proposed for Internet-of-Things (IoT) applications. In the proposed ADLDO, a fully synthesizable adaptive digital controller is designed. It automatically senses load variations and adaptively controls multi-loop architecture to reduce quiescent current, minimize output voltage ripples and achieve fast transient response. The multi-loop architecture with hill climbing reduces the total bi-directional shift registers length which results in the reduced leakage current in the transistor-switch-array (TSA), and improves the recovery time and output DC voltage accuracy. A dithering technique is introduced to eliminate the limit cycle oscillation (LCO) and improve the performance of the regulator. The dynamic frequency scaling (DFS) mechanism is proposed for reducing controller power consumption in steady state. In order to reduce the offset and output voltage error, a dynamic latch comparator is utilized. When the input supply voltage is varied from 0.5 V to 1 V, the measured output voltage ranges from 0.45 V to 0.95 V with 50 mV dropout voltage. The operating frequency is 10 MHz with fast transient response and quiescent current of 350 ns and 3.7μA, respectively. The maximum measured power and current efficiencies are 89.7 % and 99.97 %, respectively, with 1.9 mV output voltage ripples. Measured load and line regulations are 2.2 mV/mA and 9.5 mV/V respectively. The proposed circuit is implemented in 28 nm CMOS process and occupies 0.016 mm2 chip area.

A profile is a functional relationship between two or more variables used to monitor the process performance and its quality. Sometimes, the aforementioned relationship is linear or nonlinear depending upon the situation. A monitoring method based on the linear profiles is known as linear profiling which is commonly used due to its simplicity and efficacy. Linear profiling methods have been studied by many researchers with a fixed effect model. However random effect model provides a more suitable interpretation as compared to the fixed effect model under different real-time monitoring methods. Therefore in this article, we are intended to propose a linear profiling EWMA method (EWMAχ[R]-3 chart) and MEWMAx[R] chart based on the random effect model using different ranked set sampling techniques such as ranked set sampling (RSS), extreme RSS (ERSS), median RSS (MRSS), double RSS (DRSS), double ERSS (DERSS) and double MRSS (DMRSS). The ranked set sampling (RSS) schemes are not only cost-effective method but also an efficient mechanism as compared to simple random sampling. A designed simulation study used Average Run Length (ARL) as an evaluation measure to witness the detection ability of newly offered EWMAx[R]-3 chart, MEWMAx[R] chart and existing EWMAx[SRS]-3 chart. The extensive simulation showed that the proposed EWMAx[R]-3 chart and MEWMAx[R] chart have superiority to detect faults in the process compared to a competitive counterpart. The results are further justified with real data application related to a combined cycle power plant.

The dispersion control charts monitor the variability of a process that may increase or decrease. An increase in dispersion parameter implies deterioration in the process for an assignable cause, while a decrease in dispersion indicates an improvement in the process. Multivariate variability control charts are used to monitor the shifts in the process variance-covariance matrix. Although multivariate EWMA and CUSUM dispersion control charts are designed to detect the small amount of change in the covariance matrix but to gain more efficiency, we have developed a Mixed Multivariate EWMA-CUSUM (MMECD) chart. The proposed MMECD chart is compared with its existing counterparts by using some important performance run length-based properties such as ARL, SDRL, EQL, SEQL, and different quantile of run length distribution. A real application related to carbon fiber tubing process is presented for practical considerations.

Due to high fuel consumption, we face the problem of not only the increased cost, but it also affects greenhouse gas emission. This paper presents an assorted approach for monitoring fuel consumption in trucks with the objective to minimize fuel consumption. We propose a control charting structure for joint monitoring of mean and dispersion parameters based on the well-known max approach. The proposed joint assorted chart is evaluated through various performance measures such as average run length, extra quadratic loss, performance comparison index, and relative average run length. The comparison of the proposed chart is carried out with existing control charts, including a combination of X̅ and S, the maximum exponentially weighted moving average (Max-EWMA), combined mixed exponentially weighted moving average-cumulative sum (CMEC), maximum double exponentially weighted average (MDEWMA), and combined mixed double EWMA-CUSUM (CMDEC) charts. The implementation of the proposed chart is presented using real data regarding the monitoring of fuel consumption in trucks. The outcomes revealed that the joint assorted chart is very efficient to detect different kinds of shifts in process behaviors and has superior performance than its competitor charts.

In recent times, the trending modification of control chart is a result of its effective application in various aspects of life. In this paper, we propose a new charting scheme called mixed exponentially weighted moving average (EWMA) dual-cumulative sum (CUSUM) to monitor the location parameter. The driving goal, which inspired this scheme, was the enhancement in the sensitivity of the control scheme used by analyzers in the petro-chemical industry which identify different quantities of components present in different catalysts. The proposed scheme is the combination of a dual-CUSUM chart and a time-varying EWMA chart. We explore the scheme with robust estimators by evaluating their thresholds in the presence of disturbances and contaminations. We do this by introducing both location and variance contaminants into the process environment. The average and standard deviation of the run lengths were used as the benchmark for performance evaluation through the various estimators. We applied this scheme on real life datasets from refinery laboratory statistics which is the case study.

The application of wireless body area network (WBAN) in health sector is crucial. It prolongs the life expectancy and improves the quality of life by enabling remote health monitoring. Despite many advantages, it may result and exacerbates various extant health issues especially in the context of mobile multimedia. Due to high data rate of mobile multimedia applications, heat generated from sensor devices is considerably high and results in thermal dissipation. The thermal impact of sensors on human body has largely been a neglected topic. The paper rigorously analyzes the specific absorption rate (SAR) impact on human body. Moreover, the paper largely contributes by comprehensively evaluating SAR impact under various circumstances and dependency factors. The analysis and evaluation show promising results that can significantly help the successful design and deployment of WBAN strategies. The paper also provides great guidance to numerous practitioners of this domain.

The process structures of manufacturing industry are efficiently modeled using linear profiles. Classical and Bayesian set-ups are two well-appreciated schemes for designing control charts for the monitoring of process structures. Mostly in profiles monitoring the independent variables along with the process parameters are assumed fixed. There are manufacturing processes where these conditions may not hold. The advancement in technology and day-to-day changes in process structures caused the parametric uncertainty along with variability in explanatory variables. This paper considered the case of random X and assumes different conjugate and non-conjugate priors to handle parametric uncertainty using double exponentially weighted moving average (DEWMA) control charts. Three univariate DEWMA charts are designed for the monitoring of V-intercepts, slopes, and error variances. The average run length criterion has been used to evaluate the proposed and competing charts. The wide spread relative study identifies that the proposed Bayesian DEWMA control charts are better than the competing charts based on early detection of out-of-control profiles, particularly for smaller value shifts. The Bayesian DEWMA charts using conjugate priors are the quickest in all as they take less sample points to show out-of-control profile. A case study has been considered to further justify the superiority of Bayesian DEWMA charts over competing charts.

Virtually, detection of shifts in the dispersion parameter of the process is more valuable before monitoring the location parameter of the process. For the monitoring of the dispersion parameter, the S2 chart is a common choice in the literature. In this paper, we proposed a modified S2 chart based on modified successive sampling, which is cost effective relative to simple random sampling. The run length properties are used as comparative measure and the findings depict that all proposed schemes outperform the classical S2 chart. Finally, the application of the proposed scheme is demonstrated using a real-life engineering process.