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Protein malnutrition is a major public health concern in the developing world. The livestock products are a good source of high-quality protein, but the livestock industry is a source of pollution and one of the leading causes of climate change because the slaughtering of animals results in the accumulation of waste, offals, and several inedible body portions. The rapid increase in the human population and inadequate supply of traditional protein sources have driven a search for novel and alternative protein sources such as edible insects. This review extensively explores the nutritional value, allergenicity, and safety considerations associated with consuming common house crickets and other related insect species. A wide range of cricket protein-based products are currently available and provide some attractive options to the consumers such as protein-enriched bakery products and gluten-free bread for celiac patients. The cricket protein hydrolysates are used as preservatives to improve the stability of cheddar cheese and goat meat emulsions during storage. The risks associated with edible crickets and their products are bacteria, mycotoxins, polychlorinated dibenzodioxins, pesticide residues, heavy metals, and the presence of allergenic proteins.

The focus on sustainable utilization of agricultural waste is currently a leading area of scientific research, driving significant advancements in technology and circular economy models. The fundamental capacity of bio-based products, bioprocessing techniques, and the crucial involvement of microbial treatments are opening opportunities for efficient solutions in various industries. One of the most popular green vegetables, peas are members of the Fabaceae family and have a pod-like structure. Every year, a significant amount of pea pods is discarded as waste products of peas that have negative impacts on our environment. In this comprehensive review, we explore innovative methods for utilizing pea pods to minimize their environmental footprint and optimize their viability across multiple industries. A large portion of the pea processing industry's output consists of pea pods. Variety of proteins, with major classes being globulin and albumin (13%), dietary fiber (43–58%), and minerals are abundant in these pods. Because of their diverse physiochemical properties, they find applications in many diverse fields. The porous pea pods comprised cellulose (61.35%) and lignin (22.12%), which could make them superior adsorbents. The components of these byproducts possess valuable attributes that make them applicable across treatment of wastewater, production of biofuels, synthesis of biocolors, development of nutraceuticals, functional foods, and enzymes for the textile industry, modification of oil, and inhibition of steel corrosion.

Approximately 5 % of all seeds at a seed company are discarded because of their underutilization. Bitter gourd contains a large number of seeds that are discarded, which are difficult to treat and cause severe environmental pollution. However, these seeds are a rich source of bioactive metabolites (such as glycosides, saponin, alkaloids, triterpenes, steroids, dietary fibers, essential oils, and saturated and unsaturated fatty acids) with antioxidant, antimicrobial properties, and health-promoting effects. So, it is important to find ways to treat this waste and use it in various food products and health-promoting effects to benefit humans and prevent environmental pollution as per Goal 12 (responsible consumption and production) of 17 sustainable development goals (SDGs) set by the United Nations. This review discussed the extraction of different functional compounds by using traditional and modern extraction techniques, nutraceutical and food applications, as well as the toxicological effects of the bitter gourd seeds, aiming to give a new research direction to the researchers of concerned fields to find more potential applications of this natural waste. Both in vivo and in vitro study results showed that bitter gourd seeds in powder, extract, and oils can be used as antioxidant and antitumor agents and showed antidiabetic, anti-obesity, antibacterial, anti-inflammatory, and anticancer activities. Bitter gourd seeds also have healthy and technological compounds that could be used in food industries to improve food products' quality, stability, and nutritional characteristics. Bitter gourd seeds can also be used to purify drinking water. Further studies are needed to find more applications of this natural by-product in developing functional and nutritious foods.

The trend for eating foods which confer health benefits is increasing day by day. Spices are widely used in the world for adding flavor or aroma to different food products. Besides adding unique flavor and aroma to products several spices have been documented to possess functional and therapeutic abilities. Regular eating of these spices reduces the incidence of many diseases. Bakery products are consumed throughout the world and constitute one of the biggest food industries. Some spices that are not a part of our daily diet, have been incorporated into bakery products to enhance the nutritive and functional potential of the products as consumer demand is higher for more nutritious and health beneficial foods. The population is rising and the incidence of diseases is also increasing, so it has become necessary to add components like these spices for health benefits and to prevent the occurrence of many diseases. This review summarizes the functional and therapeutic potential of some underrated spices including rosemary, thyme, fenugreek, clove, cinnamon, oregano, and their incorporation to formulate the valuable bakery products.

Abstracts: Edible oils play a vital role in human nutrition, serving as a crucial source of energy, essential fatty acids, and vitamins necessary for bodily functions. However, during handling, cooking, and storage these oils are susceptible to oxidation, resulting in the loss of nutritional value, the development of undesirable flavors, the production of harmful compounds, and a reduction in shelf life. To counteract oxidation and enhance oil stability, synthetic antioxidants have traditionally been employed. However, in recent times, consumer awareness has led to an increased preference for natural antioxidants because of the harmful effects of synthetic antioxidants on human health. This review provides a comprehensive overview of recent research focused on the oxidative stability of various edible vegetable oils through the incorporation of natural antioxidants derived from fruits and vegetable waste. These antioxidants obtained from waste through diverse techniques are blended with different vegetable oils in the form of free extracts, encapsulated extracts, nano-encapsulated extracts, and nano-emulsion oil extracts to assess their antioxidant efficacy. These antioxidant-rich waste extracts efficiently prevented oxidation by scavenging free radicals, chelating metal ions, and delaying the formation of peroxides, offering a safer and more effective alternative to synthetic counterparts. Beyond their antioxidative properties, these extracts contribute to the nutritional enhancement of oils, functioning as nutraceuticals. This approach not only offers a natural means to protect edible oils but also facilitates efficient waste valorization, mitigating potential environmental impacts and making it a sustainable and eco-friendly strategy.

The food sector produces immense amounts of waste daily, which is a great threat to the food system's sustainability. The great volume of waste generated by the food sector is a valuable source of different essential substances. The conversion of tonnes of waste into valuable substances by using various innovative ‘green’ technologies is a novel approach. Ohmic heating is a sustainable and green technology based on the utilization of electric energy, which is converted into thermal energy to heat the substance. It has wide-ranging applications in the food sector, particularly for processing i.e. as pasteurization, sterilization, blanching, extraction, and many more. It is a high energy-efficient technology, which requires less processing time and allows precise control of temperature, which makes it suitable for various purposes. Ohmic heating-assisted extraction (OHAE), an innovative approach, presents an opportunity to extract numerous valuable substances from waste materials by the application of electric current. This review provides a critical assessment of investigations centered on the recovery of various substances by the implementation of OHAE from waste materials of fruits, vegetables, poultry, fish, and cereals. The impact of various parameters used in OHAE on the effectiveness of the extraction process and yield of extracted products are discussed. The substances extracted from these waste materials encompass bioactive compounds, pectin, essential oils, proteins, and cellulose fibers, all of which find substantial applications in the food and related industries. Overall, the implementation of ohmic heating, either alone or in combination with other novel technologies, is very effective for the extraction of valuable substances from the waste of the food industries.

Nowadays, the effective utilization of food waste presents a significant challenge for the food industry. The transformation of food waste materials into useful products, and the production of healthy food products by the incorporation of food waste materials are a focus of many researchers these days. With the increasing world population and the rising challenge of low-cost but high-quality food, the utilization of food waste for the development of healthy dietary choices is a novel approach. Cashews become a demanding nut due to their remarkable health advantages and their byproducts have vast food and health applications. The constituents of cashew nutshell including proteins, dietary fibers, vitamins, and other phytonutrients are essential for human nutrition and they also possess strong antioxidant and anti-inflammatory potential. In this review, cashew nutshell, cashew testa or husk, cashew apple, and cashew apple pomace or bagasse that are considered waste materials derived from cashew nuts have been focused on exploring their nutritional composition, health benefits, and applications in the food industries. Cashew waste can be utilized in the confectionery, beverage, meat, and baking industry and to produce eco-friendly packaging materials. Cashew nutshells have shown evidence in improving certain biomarkers in health ailments like diabetes mellitus, obesity, hypercholesterolemia, and bone diseases.

Given the rising demand for low-power sensing, integrating additional devices into an existing wireless infrastructure calls for innovative energy- and spectrum-efficient wireless connectivity strategies. In this respect, wireless-powered or energy-harvesting symbiotic radio (EHSR) is gaining attention for establishing the secondary relationship with the primary wireless systems in terms of RF EH and opportunistically sharing the spectrum or schedule. In this paper, assuming the commensalistic relationship with the primary system, we consider the energy-efficient optimization of such an EHSR by intelligently making EH and transmission decisions under the inherent nonlinearity of the EH circuitry and dynamics of pre-scheduled primary devices. We present a state-of-the-art deep reinforcement learning (DRL)-engineered, energy-efficient transmission strategy, which intelligently orchestrates EHSR’s uplink transmissions, leveraging the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) scheme. We first formulate the energy efficiency (EE) optimization metric for EHSR considering the nonlinear EH model, and then we decompose the inherently complex, non-convex problem into two optimization layers. The strategy first derives the optimal transmit power and time-sharing coefficient parameters, using convex optimization. Subsequently, these inferred parameters are substituted in the subsequent layer, where the optimization problem with continuous action space is addressed via a DRL framework, named modified deep deterministic policy gradient (MDDPG). Simulation results reveal that, compared to the baseline DDPG algorithm, our proposed solution provides a 6% EE gain with the linear EH model and approximately a 7% EE gain with the non-linear EH model.

Tomatoes are cultivated and consumed in almost all countries of the world, highly valued for the abundance of nutritional compounds that contribute to their sweet-sour taste, widely appreciated globally. The unique taste of tomatoes is attributed to the volatile compounds and nutrition components, including acids, sugars, lipids, pigments, alcohols, aldehydes, ketones, esters, and terpenes. As climacteric fruit, tomatoes undergo various pos-harvest changes, and multiple factors contribute to their rapid spoilage, resulting in losse of this perishable commodity. Packaging plays a pivotal role in extending the shelf life of tomatoes and preserving their quality characteristics throughout the supply chain, from the farm to the end consumer. Conventional packaging methods have shown substantial losses, promoting a shift towards new and efficient packaging strategies as indicated by the previous investigations. Recent developments include edible coatings/films, modified atmosphere packaging, active packaging, and nanopackaging, which have proven to be more efficient than conventional methods. These advanced packaging techniques control physicochemical, microbiological, and environmental factors responsible for tomato spoilage, contributing to the reduction of postharvest losses of this valuable fruit.

Food waste presents a continuous challenge for the food industry, leading to environmental pollution and economic issues. A substantial amount of waste, including by-products from fruits and vegetables, non-edible food items, and other waste materials, is produced throughout the food supply chain, from production to consumption. Recycling and valorizing waste from perishable goods is emerging as a key multidisciplinary approach within the circular bio-economy framework. This waste, rich in raw by-products, can be repurposed as a natural source of ingredients. Researchers increasingly focus on biomass valorization to extract and use components that add significant value. Traditional methods for extracting these bio-compounds typically require the use of solvents and are time-consuming, underscoring the need for innovative techniques like ultrasound (US) extraction. Wastes from the processing of fruits and vegetables in the food industry can be used to develop functional foods and edible coatings, offering protection against various environmental factors. This comprehensive review paper discusses the valorization of waste from perishable items like fruits and vegetables using US technology, not only to extract valuable components from waste but also to treat wastewater in the beverage industry. It also covers the application of biomolecules recovered from this process in the development of functional foods and packaging.

Objective: To assess awareness of green dentistry among graduates, postgraduates and general dentists Study Design: Descriptive Cross-sectional. Place and Duration of Study: The study was conducted at Armed Forces Institute of Dentistry (AFID) Rawalpindi, Pakistan from August 2022 to October 2022. Methods: Based on a pre-validated survey questionnaire comprising of 20 close-ended questions assessing the awareness regarding green dentistry. Male and female respondents consisting of General dentists, graduates, postgraduate and house officers, and practitioners of AFID who gave consent were included in the study. The sample size was calculated using the WHO calculator and 332 respondents were recruited and the questionnaire was distributed, responses were then analyzed using SPSS version 22 and the p-value < 0.05 was considered significant. Results: Out of the 332 participants 148 were post-graduates, 109 were Graduates, 35 were consultants and 40 were general dentists with the age range of 25 to 50 years. 248 participants were females and 84 were males and 100% response rate was observed. Postgraduates (47%) were more aware of the eco-friendly dentistry concept the difference was found statistically significant (p-value

Communication networks are becoming increasingly important in military operations, with task fields like time-critical targeting, covert special operations, command and control, training, and logistics, all relying heavily on the communication network and its services. On the other hand, commercial communication has dramatically transformed our society and the way we communicate. The newest network mode at present, 5G and beyond (5GB), is characterized by high speed, low latency, high reliability, and high communication density. Although the use of 5GB commercial networks for defense agencies can offer greater flexibility and efficiency, they also face a new challenge that requires high standards of network protection and harsh working conditions and environments. In this paper, we discuss the significance of communication networks in several potential military applications, particularly for warfare, training/drilling, logistics, and special mission-specific stations. We present the communication trends adopted in military applications. Then, we open up various 5GB key performance indexes and their use cases for the military communication systems. We also elaborate on unique challenges of the military communication networks that are unlikely to be resolved via commercial 5GB research. The various 5GB enabling technologies for military communication systems are discussed. Lastly, we present and analyze 5GB new radio for the private military communication under C-band.

With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power wide-area network (LPWAN) technologies will inevitably face performance degradation due to congestion and interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. Meanwhile, new solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.

This study aimed to assess the food safety and quality in Pakistan’s supply chain and improvement improvement solutions to enhance consumer health and trust in the local food system by identifying critical points and proposing improvement solutions. To gather data, we conducted surveys, reviewed relevant literature, and analyzed industry reports from 500 food establishments. Our findings revealed that inadequate hygiene practices and improper handling were major contributors to foodborne illnesses in the supply chain. Based on our results, we recommend implementing standardized monitoring and testing protocols to ensure the safety and quality of food products. Additionally, the use of blockchain technology for traceability can help enhance transparency and accountability throughout the supply chain. The implications of this study are significant. Policymakers can use these findings to develop and enforce regulations that prioritize consumer safety. Industry professionals can adopt our recommendations to improve their practices and build consumer trust. Furthermore, researchers can further build upon this study to explore innovative solutions for food safety and quality in Pakistan. This study provides valuable insights into the challenges and opportunities for enhancing food safety and quality in Pakistan’s supply chain. By implementing the recommended measures, we can create a safer and more reliable food system for all consumers.

Fear is one of the psychological emotions that humans adapt to a potentially threatening situation, but when this fear is not calibrated well to the situation, it can become maladaptive. For instance, in the present wave of pandemic, the panic over the year is excessive, there may have deteriorating effects both on the individuals and the society. At individual level for example mental health problems and social anxiety may be seen, and at the level of society panic shopping, xenophobia, decline in the economy may well be noted. Pandemics have been seen in the history in many eras. They have brought about enormous changes in the existing world through ages. This rapid narrative review summarizes various aspects of the many pandemics in the history.

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Hybrid visible light communication and radio frequency (VLC/RF) networks provide combined benefits of both visible light and radio frequency technologies. This is an eminent technology for providing 5G services in an indoor environment. Because of small coverage range, multiple VLC cells co-exist in a close proximity. For efficiently utilizing resources of multiple VLC access points (VLC-APs), a different approach than conventional multi RF-APs based resource allocation schemes is required due to unique characteristics of light. In this paper, we provide an optimized solution for combined power and slot allocation in hybrid VLC/RF networks with the objective of maximizing sum rate for a downlink communication scenario. The resources are optimized on frame by frame basis, instead of per request basis. This frame-based optimization reduces the frequency of optimization and also improves the utilization of the resources. The non-convex problem of joint slot and power allocation is divided into less complex sub problems and solved iteratively. The performance of the joint slot and power allocation (JSPA) scheme has been analyzed for data rate and fairness factor for varying values of network size and minimum quality of service (QoS) requirements. The analysis exhibits on average more than 65% improvement in both fairness factor and average sum rate as compared to single VLC-AP based scheme (SV O RF), in which single VLC-AP is utilized for a user and RF-AP is utilized to offload traffic when VLC resources are not sufficient. Moreover, we propose a hybrid user-centeric and network-centeric (H-Uc/Nc) scheme for slot allocation which entails far less computational complexity. Although, H-Uc/Nc scheme performs inferior to optimized scheme, however, it exhibits much better performance than (SV O RF).

Electroencephalogram (EEG) is one of the fundamental tools for analyzing the behavior of brain and particularly helpful for treatment of epilepsy and detection of associated seizures. For long-term recording of EEG signals, current research is heading towards simple, unobtrusive and ambulatory devices with a small number of channels. The primary contribution of this paper is to assess the performance difference between the seizure detection results using features from all channels versus only the channels in/around the temporal region. For this purpose, we develop a supervised seizure detection algorithm that uses time domain features extracted sequentially for every 1-second epoch. By using this algorithm, we obtained sensitivity values of 0.95 and 0.92, specificity values of 0.99 and 0.99 and false positive per hour values as 0.16 and 0.21 for all 23 channels and 10 temporal region channels, respectively. These results show that restricting the EEG analysis to temporal region results only in a graceful and gradual degradation of classifier performance. We conclude that EEG ambulatory devices with a montage local to the temporal region could demonstrate satisfactory performance. This presents a promising way forward for the use of ambulatory devices with compact wearable design.

The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this paper, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band, and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this paper includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.

In a sensor, mesh, or ad hoc network, different transmissions take place between multiple source-destination pairs, which is referred to as data flows. In this paper, each data flow is associated with a certain duty cycle schedule to avoid collisions of multiple flows in an energy-efficient manner. Cooperative routes in the network are assumed to be linear and strip-shaped, where each hop or a level contains multiple nodes, and the distance between the nodes is kept constant. We assume all nodes in a cooperative route, which corresponds to one source-destination pair, follow the same duty cycle schedule, while schedules of different routes are orthogonal in time to avoid interference between routes. Therefore, when a new route is formed, it is important to select a duty cycle schedule that is not already in use by the nodes participating in the new route. As part of route set-up, an orthogonal frequency division multiplexing packet travels from the source to the destination, identifying the schedules in use by the nodes along the path. Our objective in this paper is to analyze the probabilities of detection for this method of detecting schedules, which is useful to gain insights into duty cycling-based medium access protocol design in the presence of multiple data flows. To this end, the successive transmissions in a linear network are modeled using an irreducible discrete time Markov chain. Also, three different approaches to detect a certain schedule are investigated, which are distinguished by the binary integration process with multiple sub-carriers corresponding to the schedule. We derive the probability transition matrix for the Markov chain based on different distributions of the received signal energy, the left eigenvector of which yields the probability of detection.

Quantum dots (QD) are playing a vital role due to their multifunctionality such as small size and tuning the range of band and have achieved a considerable attention in the recent decade. In recent research, the efficiency of the solar cell (SC) has greatly been increased by application of QD. If these dots are grown systematically, may lead to the formation of the intermediate band (IB) which leads to the absorption of photons with energy less than the actual band through intermediate absorption process and hence increasing the carrier generation rate which ultimately results in high current density keeping the output voltage unaffected. In this paper, we have proposed a method to control the band flatness of the IB in InAs/GaAs QDSC. The flatness is achieved by optimizing the doping concentration and Fermi level (FL) of the surrounding layers. Results show that the maximum 44.12% efficiency can be achieved under maximum optical intensity. Keywords: Solar cell, Quantum dot, Flat intermediate band, Quantum efficiency, Fermi energy level, Doping concentration

Continuous phase modulation (CPM) is a power and bandwidth efficient modulation scheme used in cellular, personal, and satellite communications among other applications. If the modulation index of the CPM waveform used by the transmitter is unavailable at the receiver, serious performance degradation may occur. This paper investigates the problem of modulation index estimation for partial response continuous phase modulation schemes. Specifically, we propose two novel estimators for the CPM signal observed in an additive white Gaussian noise channel. A non-data aided method of moments (MoM) estimator based upon the fourth-order cumulants and a data-aided best linear unbiased estimator (BLUE) based upon an approximate linear phase model of the received CPM signal have been proposed. Modified Cramer-Rao lower bound is derived for the partial response CPM schemes to assess the performance of the estimators. We also perform numerical simulations to compare the mean-squared error (MSE) performance of the proposed MoM and BLUE estimators with previously proposed estimators in the literature. It is shown that the MoM estimator exhibits good MSE performance at low signal-to-noise ratio (SNR) while the BLUE estimator performs very well at high SNRs. Moreover, the proposed estimators have lower complexity than existing methods and are based upon non-iterative algorithms.

Hybrid networks consisting of both millimeter wave (mmWave) and microwave $(\mu W)$ capabilities are strongly contested for next-generation cellular communications. A similar avenue of current research is device-to-device (D2D) communications, where users establish direct links with each other rather than using central base stations. However, a hybrid network, where D2D transmissions coexist, requires special attention in terms of efficient resource allocation. This paper investigates dynamic resource sharing between network entities in a downlink transmission scheme to maximize energy efficiency (EE) of the cellular users (CUs) served by either $(\mu W)$ macrocells or mmWave small cells while maintaining a minimum quality-of-service (QoS) for the D2D users. To address this problem, first, a self-adaptive power control mechanism for the D2D pairs is formulated, subject to an interference threshold for the CUs while satisfying their minimum QoS level. Subsequently, an EE optimization problem, which is aimed at maximizing the EE for both CUs and D2D pairs, has been solved. Simulation results demonstrate the effectiveness of our proposed algorithm, which studies the inherent tradeoffs between system EE, system sum rate, and outage probability for various QoS levels and varying densities of D2D pairs and CUs.

Reliable detection of the onset of epileptic seizures has seen renewed interest over the past few years, owing to several factors including, the global push toward digital health-care, the advancements in signal processing techniques, and the increased computational power of machines. A reliable automatic system could result in tremendous improvement in the quality of life of epilepsy patients. This paper presents dynamic mode decomposition (DMD), a data-driven dimensionality reduction technique, originally used in fluid mechanics, as an instrument for epileptic seizure detection from scalp electroencephalograph (EEG) data. DMD is employed in this paper to measure power of signals in different frequency bands. These subband-powers, along with signal curve lengths, are used as features for training random under-sampling boost decision-tree classifier. Post-processing measures ensure an acceptable balance between false positives and true positives. The proposed algorithm has been tested over a thousand hours of EEG data from two different data sets, the CHB-MIT data set and the KU Leuven data set, giving sensitivity values of 0.87 and 0.88, respectively, and specificity values of 0.99 for both the data sets.

Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies.

The deployment of heterogeneous networks (HetNets) inevitably demands the design of interference management techniques to elevate the overall network performance. This paper presents a novel interference mitigation technique known as reverse frequency allocation (RFA), which provides an efficient resource allocation compared with the other state-of-the-art techniques. RFA reverses the transmission direction of interferers, thereby minimizing the cross-tier interference. Eventually, better coverage as well as increased data rates are achieved by providing complementary spectrum to the macro and pico users. In this paper, we present a tractable approach for modeling HetNets under the proposed RFA scheme. Specifically, we employ well known tools from stochastic geometry to derive closed-form expressions for the coverage probability and rate coverage in two-tier cellular network employing RFA and its variants. The modeling is performed using two approaches; first, where the base stations and users are modeled as independent Poisson point processes (PPPs) and second, the interference is approximated using the fluid model. It is shown that the results obtained from the PPP model are accurate for higher values of path loss exponents, while the results from fluid model are useful for smaller values of path loss exponents. The plausibility of model is validated through the Monte-Carlo simulations and the network performance is evaluated in terms of coverage probability, coverage rate, and outage capacity. The results demonstrate that 2-RFA yields outage capacity gains of 13% as compared with the soft fractional frequency reuse scheme, whereas, the performance gains can be further improved by 14% by employing the proposed variants of RFA.

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users' quality-of-service requirements, namely users' minimum rate, and the base stations' (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network's performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model.

The use of quaternion orthogonal designs (QODs) to describe point-to-point communication among dual-polarized antennas has the potential to provide higher rate orthogonal and quasi-orthogonal complex designs exploiting polarization diversity among space and time diversities. Furthermore, it is essential to have a space time block code (STBC) which offers a linear and decoupled decoder which quasi-orthogonal designs fail to attain. In this paper, we show how the realm of quaternions unexpectedly offers us a possible solution and codes obtained from quaternion designs mostly achieve both linear and decoupled decoders. This motivated us to perform an indispensable search for QODs such that the code rate is bounded below by 1/2 and does not sharply decrease as the number of transmit antennas increases. It is shown that three famous recursive techniques do not satisfy this criteria and their code rates decrease rather rapidly. Therefore, we propose another method of constructing quaternion designs suitable for any number of transmit antennas and verify that these attain linear and decoupled decoders with the system model based on quaternionic channel. It is shown that such designs outperform others in terms of transmit diversity, code rates and the optimality of the proposed decoder is validated through simulation results.

Quaternion orthogonal designs (QODs) have been used to design STBCs that provide improved performance in terms of various design parameters. In this paper, we show that all QODs obtained from generic iterative construction techniques based on the Adams-Lax-Phillips approach have linear and decoupled decoders which significantly reduce the computational complexity at the receiver. Our result is based on the quaternionic description of communication channels among dual-polarized antennas. Another contribution of this work is the linear and decoupled decoder for quasi-orthogonal codes for non-square as well as square designs. The proposed solution promises diversity gains with the quaternionic channel model and the decoding solution is independent of the number of receive dual-polarized antennas. A brief comparison is presented at the end to demonstrate the effectiveness of quaternion designs in two dual-polarized antennas over available STBCs for four single-polarized antennas. Linear and decoupled decoding of two quasi-orthogonal designs is shown, which has failed to exit previously. In addition, a QOD for 2×1 dual-polarized antenna configuration using quaternionic channel model shows a 3 dB gain at 10−5 in comparison to the same code evaluated for 2×2 complex representation of the quaternionic channel. This gain is further enhanced when the received diversity for these the cases is matched i.e., 2×2. The code using the quaternionic channel model shows a further 13 dB improvement at 10−5 BER.

In this paper, a strip-shaped multi-hop ad hoc network is analyzed using a spatial Poisson point process (PPP) and stochastic geometry. The decode-and-forwardprotocol is considered for transmission over the multi-hop network where cooperative communications is employed at each hop. An analytical expression for the probability density function of the received power at an arbitrary node is derived, given a set of nodes transmits in the previous hop, which is further used to characterize the coverage performance of the network. The received power at a node becomes a doubly stochastic process owing to random path loss and a Rayleigh fading channel. The notions of one-hop success probability and coverage range are analyzed for various network parameters. An algorithm for conserving energy is also proposed by considering PPP thinning and its performance in terms of the fraction of energy saved is quantified. It is shown that the proposed algorithm is more energy efficient as compared with an independent thinning algorithm.

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures.

This paper presents cooperative transmission (CT), where multiple relays are used to achieve array and diversity gains, as an enabling technology for Internet of Things (IoT) networks with hardware-limited devices. We investigate a channel coding aided decode-and-forward (DF) relaying network, considering a two-hop multiple-relay network, where the data transmission between the source and the destination is realized with the help of DF relays. Low density parity check (LDPC) codes are adopted as forward error correction (FEC) codes to encode and decode the data both at the source and relays. We consider both fixed and variable code rates depending upon the quality-of-service (QoS) provisioning such as spectral efficiency and maximum energy efficiency. Furthermore, an optimal power allocation scheme is studied for the cooperative system under the energy efficiency constraint. We present the simulation results of our proposed scheme, compared with conventional methods, which show that if decoupled code rates are used on both hops then a trade-off has to be maintained between system complexity, transmission delay, and bit error rate (BER).

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

To study the influence of Perceived cost, risk, convenience and enjoyment on online consumer purchases. Being more convenient online shopping seems painless compared to in store shopping, why consumers are still reluctant to shop online?.A sample of 220 questionnaires was filled from different departments at University of Sargodha. Respondents were asked to fill the questionnaire based on four factors (Perceived cost, Perceived risk, Perceived convenience, Perceived enjoyment), there were 2 general questions. Out of 220, 207 questionnaires were returned. One sample test is applied in this study to check the reliability of independent variables. Analysis was also done on basis of gender and their ages. The most important factor out of four, which can persuade the customer's online buying decision, is delivery cost for purchased items and it has negative relationship with dependent variable, moreover perceived risk has also negative relationship with dependent one. Perceived convenience and Perceived enjoyment has positive relationship with online shopping preference. As online shopping is easier to do but due to extra delivery cost and risk factors consumers do not adopt online shopping and these factors should be minimized to promote online shopping. Online shopping should be promoted and to gain the consumer confidence, delivery cost and risk factor should be minimized. As online shopping is easier to do as compared to offline shopping but people still reluctant to use internet for online shopping, so to promote online shopping delivery cost and risk factor should be minimized to gain consumers attraction and confidence. People want to experience online shopping but it won’t be promoted until delivery cost and perceived risk factors be reduced. A little work has been done on exploring the factors that influence the online buying decision. These factors are called situational factors and include delivery charges, risk factors, convenience factors and enjoyment factors. In Pakistan consumers are not too much affiliated to online shopping as compared to European countries and not too much researches have been done related to online shopping in Pakistan , so this research been conducted to promote online shopping.

Objective: To consider the aetiological factors for causing myocardial infarction in younger age group and to include these perspectives for the diagnosis of coronary artery disease.​ Place and Duration of Study: Study was carried out at CCU in the Department of Medicine, Avicenna Medical College and Hospital Lahore, from Jun 2012 to May 2013. Material and Methods: All patients within the age group of 26-35 years were admitted in CCU on the basis of multiple clinical symptoms by using approach of regular clinical investigations under the aspect of Ischemic heart disease. Clinical investigations were comprised upon cardiac monitoring, lipid profile, ECG and rate for cardiac enzyme included. Results: A total of 18 patients completed the study with age group of 26-35 years. MI was diagnosed in all patients and maximum number of patients with acute MI varied from age 28-33 years. All patients were heavy smokers with high rate of physical stress. On gross clinical examination it was revealed that majority of the patients included in age of 28-33 years was professionally van drivers. Conclusion: The essence of acute MI was observed and critically analyzed in younger age group of people with certain aetiological factors. This should be essentially important in redefining the criterion on clinical grounds for CAD and this should be recognized at International level. As the studies under clinical grounds with the aspect of CAD being reported at International level so, the amount of parameters in clinical diagnosis has to be considered in regular manners for the cure and prevention of CAD in patients especially of younger age groups less than 40.

A strip-shaped sensor network is considered, where randomly placed nodes communicate cooperatively by forming an opportunistic large array (OLA). The transmission from a group of cooperating nodes to another group of nodes is modeled with a quasi-stationary Markov chain, where the transmission channel is assumed to experience lognormal shadowing and Rice fading. The distribution of received power at a node is calculated as a three-step process, which includes finding the distribution of random distance between nodes in addition to other channel impairments, that is, fading and shadowing. It is shown that, in the presence of all three channel impairments, the received power at a node follows a lognormal distribution. This approximation uses a series of steps that involves techniques such as moment matching and moment generating function (MGF). Using the underlying Markov chain properties, the one-hop success probability of the network is derived. The system performance and coverage range of the network are quantified as a function of various network parameters and node topologies. The theoretical results are validated by performing computer simulations.