Interlimb neural coupling might underlie human bipedal locomotion, which is reflected in the fact that people swing their arms synchronously with leg movement in normal gait. Therefore, arm swing should be included in gait training to provide coordinated interlimb performance. The present study aimed to develop a Rotational Orthosis for Walking with Arm Swing (ROWAS), and evaluate its feasibility from the perspectives of implementation, acceptability and responsiveness. We developed the mechanical structures of the ROWAS system in SolidWorks, and implemented the concept in a prototype. Normal gait data were used as the reference performance of the shoulder, hip, knee and ankle joints of the prototype. The ROWAS prototype was tested for function assessment and further evaluated using five able-bodied subjects for user feedback. The ROWAS prototype produced coordinated performance in the upper and lower limbs, with joint profiles similar to those occurring in normal gait. The subjects reported a stronger feeling of walking with arm swing than without. The ROWAS system was deemed feasible according to the formal assessment criteria. People swing their arms synchronously with leg movement during walking due to interlimb neural linkage, in addition to mechanical factors. Although the arms have no direct function for propulsion . The formal criteria for feasibility assessment were  in Ruijin Hospital, Shanghai Institute of Orthopaedics and Traumatology, China. Ethical approval was obtained from the Ethics Committee at the Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China. Twenty-four able-bodied subjects were recruited and they provided written informed consent prior to participation.The experimental set-up consisted of a ten-camera Vicon MX13 motion capture system . Thirty-nine reflective markers of 14 mm diameter were affixed to specific anatomical landmarks  to record the segmental trajectories with a sampling frequency of 100 Hz  was used to develop the structure of the ROWAS system.In order to provide rehabilitation to users who are in an early post-injury phase, a rotational bed frame was developed, which allows practice in various positions. Depending on their physical situation, users can initiate walking training in a lying position Figure . Then asd; (ii) in an upright position when it is extended to its longest length p. The following equations describe the device geometry:We adopted a linear actuator MD60 from the Moteck Electric Corporation to rotate the bed up. As Figure b = 400. To secure an acceptable efficiency of the actuator, the angle of the actuator and the horizontal level θ should be larger than 45°. For the chosen linear actuator noted above, stroke = 300 mm; d = 530 mm; p = 830 mm; thus meeting these requirements. Using Equations (1) and (2), a = 560.2 mm and c = 212.5 mm. In this case, the angle between the linear actuator and the horizontal level θ was about 50°, which satisfies the above requirement.We assumed the original height of the bed was 400 mm, i.e., For ease of translation, a wheel-set was employed at the bottom of the bed frame. However, during gait training, the system should be supported by a stable base. Thus, a retractable wheel support was designed with the requirements: (i) to support the system when the system was to be moved; (ii) to allow the system to be supported by the bed leg when the system is in use for rehabilitation training.The wheel-set was attached by four parallel bars, which are lifted by a linear actuator. When the system is used for rehabilitation training, the wheel-set is retracted under the bed Figure  and the We designed a plate to support the leg during simulation of the stance phase. According to our systematic study on toe trajectories  was adopted to control the joint motors. The joint performance data from the overground experiment was the target movement for motors. To obtain satisfactory movement, the time course of each joint angle was fit using Fourier series functions. The fitted reference movements were sent to the corresponding motors via C++ software. Then the PID control algorithm imbedded in the Googol motion controller was employed to move the ROWAS prototype according to the target joint profiles.Experimental evaluation had two subtests: a test on the prototype to assess only the system function, and tests on able-bodied subjects to collect user feedback on the prototype. As a preliminary trial on this novel training modality, we recruited five able-bodied subjects to evaluate the performance Table . EthicalDid you have any sense of insecurity during the process of bed rotation, walking with arm swing or walking without arm movement?Did you feel any discomfort when using the system? If yes, please describe it.Which type of motion did you feel was more like walking: walking with arm swing or walking without arm movement?Do you have any suggestions for system improvement?During the experiment, the system produced walking at a cadence of 30 steps/min. In the preliminary test on the prototype only, the bed frame was rotated up at a speed of 1.5°/s to 75° relative to the ground. Then the system moved the frames for the upper and lower limbs. During the tests on the able-bodied subjects, we firstly collected their anthropometric data (Table error), phase shift (Terror) and root-mean-square error (RMSerror) for the angle performance  between overground walking and the ROWAS test were obtained from Eqs (4-6). A similar procedure was adopted to compare the ROM difference, phase shift and RMS difference of the shoulder , knee  and ankle  angle profiles from overground walking and the experiment. In order to obtain the phase shift of the shoulder and knee joints, the onsets of the maximal shoulder extension and knee flexion were analyzed. Thus,To compare the joint movements produced by the ROWAS prototype with those recorded during overground walking, three parameters were analyzed: difference of ROM , it was 2345 mm in height.The target performance of the ROWAS system was taken from the gait experiment as described above. The joint angles of a representative subject with a height of 1.72 m were selected for illustration , ki = 0  and kd = 0 (derivative parameter) provided acceptable results  the system included a rotational bed which allowed the potential user to try various positions from supine to upright walking; (ii) the main support area was flat, such as the trunk support plate and the support plate for the lower limbs, which facilitates easy transfer of the users from their wheelchair to the system; (iii) the shoe platform produced ground reaction forces on the foot sole; and (vi) synchronous movement between the upper and lower limbs. Inclusion of arm swing could be simply implemented by adding the upper limb frames with drives. The challenge was that the arm swing function restricted the mounting options for the hip joint actuators. For ease of control, it was desirable for the drives to directly actuate the joints. However, arm swing required free space on both sides, especially around the hip joint area. Therefore, we put the hip drives between the legs. Furthermore, the ROWAS system was easy to move due to a retractable wheel support. The ROWAS prototype met the design requirements and is an innovative system.The ROWAS system was tested regarding the acceptability criterion by the able-bodied subjects. Currently available gait orthoses normally have the arms fixed using a horizontal level bar for balance  had a sense of being lifted too high and had a sense of insecurity when tilted up. This insecurity resolved after the subjects got used to the height and it did not disturb their walking session. To solve this problem, the bed base could be improved to have an adjustable height. Another limitation was the fixed ROMs of the upper and lower limbs. Because of this, shorter users such as S1 thought the step length was too large. A variable walking style should be implemented.Future work should prepare a modified ROWAS system for clinical tests on patients from target populations with neurological impairments. The height of the bed base should be reduced so that the patients can be easily transferred to the system from their wheel chair. To provide clinical testing, we need to design a better body-weight support system which secures them, but without discomfort in the bottom area. Finally, the control algorithms need to be extended to allow compliant movement, and also to allow potential subjects, especially hemiplegic patients, to use their healthy side to control the impaired side. EMG responses should be investigated when patients use the system, as this will allow further investigation of interlimb neural coupling.In conclusion, we designed, manufactured and tested a ROWAS. During testing with five able-bodied subjects, the system could produce coordinated joint performance in the upper and lower limbs which was similar to normal overground gait. This system was deemed feasible, as far as the formal implementation, acceptability and responsiveness criteria were concerned.JF mainly developed this project, designed the system, manufactured and tested the system, analyzed the data and wrote the paper; QX helped develop the concept of this project and modified the manuscript; GY developed this project, supervised the experiments and analyzed the data and checked the paper; LX developed this project, helped to develop the design concepts, analyzed the data and checked the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The direct-current plasma (DCP) coupled to an echelle spectrometer has been used for over 6 years in our laboratory as one of several independent methods for the certification of Standard Reference Materials (SRMs). The demanding certification process requires maximum performance from an analytical method as well as a good understanding of the method capabilities and limitations. We have made a number of modifications to the conventional DCP spectrometer to improve accuracy, precision, and analysis speed. The advantages of wavelength-modulated detection over unmodulated detection schemes have been well-documented in the literature –3. SignaSevere nonlinear drift of emission intensity was a recurrent problem with one of our two DCP/echelle emission spectrometers. The drift was a result of the extreme sensitivity of the spectrometer to thermal variations and vibration. Two approaches were taken to reduce the spectrometer drift. The first involved thermal regulation of the spectrometer baseplate at a temperature higher than attained during normal operation, and vibrational isolation of the spectrometer. These actions significantly reduced both long- and short-term intensity fluctuations.Since the major heat source affecting the spectrometer was the plasma, another approach was removal of the plasma from contact with the spectrometer baseplate. The plasma was housed in a separate vented enclosure, and an optical rail with appropriate focusing optics was positioned between the spectrometer and the plasma. This configuration also effectively eliminated wavelength and order drift as major sources of intensity drift.An enhanced Apple II+ microcomputer (3.5 MHz 65C02 processor) coupled to an autosampler and controlled by a BASIC program was used to automate the DCP spectrometer operations. The most straightforward approach to eliminate matrix-induced interferences, and the one that requires the least number of compromises, is the complete separation of analyte from interfering species. When chromatographic methods are directly coupled to spectrometric methods for online separations, sensitivity and accuracy may be further enhanced by preconcentration of the analyte on the chromatographic column.We have applied an anion exchange method using an activated alumina (acid form) column for on-line preconcentration of phosphorus. Activated alumina has been found by several researchers ,5 to be


In this paper, we propose two schemes; position-aware mobility pattern (PAMP) and cooperative PAMP (Co PAMP). The first one is an optimization scheme that avoids void hole occurrence and minimizes the uncertainty in the position estimation of glider’s. The second one is a cooperative routing scheme that reduces the packet drop ratio by using the relay cooperation. Both techniques use gliders that stay at sojourn positions for a predefined time, at sojourn position self-confidence (s-confidence) and neighbor-confidence (n-confidence) regions that are estimated for balanced energy consumption. The transmission power of a glider is adjusted according to those confidence regions. Simulation results show that our proposed schemes outperform the compared existing one in terms of packet delivery ratio, void zones and energy consumption. Underwater wireless sensor networks (UWSNs) consist of sensor nodes and sinks or vehicles that cooperate to collect data from the aquatic environment. They have been deployed to perform monitoring tasks like oceanographic data collection, military surveillance, pollution monitoring, natural disaster prevention, navigation, etc. . HoweverTherefore, autonomous underwater vehicles (AUVs) are widely used for oceanography in the aquatic environment to collect data from sensor nodes, due to their capability to monitor an environment that is difficult to reach. For example, AUVs are used by the oil and gas industry to make maps for seafloor before building underwater infrastructure: pipelines can be installed with minimum cost in an effective manner. AUVs also allow survey companies to conduct precise surveys with low cost as compared to traditional survey methods. They are also used for surveillance, anti-submarine warfare, information operations, etc. .To achieve high network throughput, cooperative routing is used by utilizing the fact that sensor nodes of the network can overhear transmission from each other, in which relay nodes are selected in a priority order to transmit their data packets, if a top priority node fails to deliver a data packet within the predefined signal to noise ratio (SNR) ,5,6. TheTo increase the end to end reliability and avoid void zones, position information is very important because void zones (area or volume remains un-sensed or unvisited by the mobile sinks or AUVs during the underwater communication) increase data loss that results in a short network lifespan. Although AUVs visit the network periodically  by using global positioning system (GPS) to locate themselves, however, due to the dynamic nature of the underwater environment, it faces localization errors, drifting due to water currents, etc., resulting in position uncertainty of AUVs. Such uncertainties decrease the reliability, efficiency and quality of the collected data .AUVs (gliders) are used to minimize uncertainties by using predictable trajectories (sawtooth trajectories) that are used to predict positions. By using those predictable trajectories, energy consumption can be minimized; however, if the mobility pattern is predefined, then void zones increase with the increase in the sparsity of the network field , resultiThe rest of the paper is organized as follows. In Many routing protocols have been proposed to avoid void zones, achieve high network throughput and minimize energy consumption. Existing routing protocols about position uncertainty, mobility and cooperative routing are discussed in this section.In , the autWith the help of light beacons and a camera, the position of the AUV is estimated in . The setIn , an AUV The authors propose a novel scheme for estimation and navigation of the AUV state in the presence of water currents . They usA paradigm-changing geographic routing protocol is presented in , to modeIn ,9,10,11,The authors in  use AUVsA network of randomly-deployed identical sensor nodes is considered in . In thisIn , a routiThe channel-aware routing protocol (CARP)  uses linThe coverage hole problem is addressed in . Taking The discussed existing schemes ,17,18,19Cooperative routing takes the benefit of the broadcast nature of wireless communication in which the transmitted signal is overheard by unintended sensor nodes within the transmission range. It is an alternate to multi-hop communication for reducing link impairments and fading. In , the autThe authors propose in  a mathemTan et al.  propose In , the autThe discussed cooperative routing protocols ,24,26,27In this section, we present PAMP (position-aware mobility pattern) and Co PAMP (cooperative PAMP) for UWSNs. PAMP is proposed to minimize energy consumption and avoid void zone occurrence. Similarly, Co PAMP is presented to enhance the network throughput and to avoid void hole creation using cooperative routing. Details of both schemes are given in this section.n gliders in the network volume where N nodes are deployed. With the help of a pump and magnetic compass, gliders can change their vertical and horizontal motion and direction. The concept of sojourn positions [Let there be ositions  is used With the help of a magnetic compass, the direction of gliders is maintained, and gliders in the excess zone move outwards in order to avoid the void zone. After the movement, gliders stop at sojourn positions for a predefined ‘stay time’. During this time, control information and data packets are shared between neighbors. On the basis of control information, a transmitting glider (sender) predicts the position of the receiving glider (receiver) and estimates the n-confidence region. On the basis of these estimations, transmission power is adjusted. Uncertainty in the estimation of the position and n-confidence region of any glider may lead to packet drop and the waste of energy in terms of the transmission power. The protocol operation of PAMP is shown in The control information shown in X, Y and Z are the coordinates of the neighbors in the s-confidence region of the glider. X, Y and Z are used to check whether they lie in the depth range (R) of a glider; if yes, they are part of the s-confidence region that is the volume seen by the glider. X, Y and Z coordinates are computed as given in Equation TPtoSuppose that Glider 1 sends a control packet to Glider 2. After the successful reception of the control information, the neighboring glider does not rely only on the s-confidence region; rather, it estimates n-confidence region, which is the region around the glider ‘seen by its neighbors’, and the neighboring glider (Glider 2) is confident that the receiving glider (Glider 1) will successfully receive any packet that reaches this region. It is (n-confidence region) calculated as given in Equation .n denotes the number of bits transmitted in a packet. The BER is calculated according to Equation (Estimation of the confidence regions is performed in order to adjust the transmission power precisely and to minimize the packet error rate Equation .(4)BER=Equation .(5)SNR=f; and the total attenuation of the transmitted signal is computed on the basis of spreading factor [α is absorption loss in dB/km and f denotes operating frequency in Hz. Absorption loss g factor  and Thorg factor  given inl due to spreading loss and absorption loss, then:k is the spreading coefficient. If Equation .(9)N(f)β denotes the confidence factor of receiving the data successfully and The s-confidence region estimation solely depends on the present state of a glider irrespective of the channel state and the distance between neighbors; whereas, underwater, the channel state has a major impact on ongoing communication. Sojourn positions in the network decrease the probability of uncertainty in the estimation of the n-confidence region; however, the volume of a region will always be less than an s-confidence region. The neighboring glider estimates the confidence region by considering the link quality , the predicted direction of a selected glider is shared with the neighbors. The ST is calculated as given below:Equation .(16)TreGliders heading towards each other: Consider Gliders head towards each otherWhen gliders move to their next respective locations, the following possibilities may occur:The objective of Co PAMP is to achieve high network throughput. In Co PAMP, two best neighboring gliders of the source glider are selected as the relay, and they cooperate with the source glider to enhance the SNR of the received signal by re-transmitting the data packet at the destination and combine by using the MRC as the diversity technique.During the data transmission phase, the source glider transmits a data packet towards the destination glider, and neighboring gliders also receive that data packet. When the packet is received at the destination glider, the instantaneous SNR is calculated at the destination as follows :(17)γAF= follows :(18)γsg,If the SNR is greater than the packet acceptance threshold quations  and 20)YMaxEquation .(23)Th=Equation .(24)lq=Equation  shows thEquation (25a) ensures that the energy required for data communication should not exceed the initial energy of a sensor node. Moreover, Equation (25b) shows that the energy needed for gathering data packets must be above the minimum required energy. The Let us suppose that the total available energy is We have used the graphical method because of two decision variables in our schemes, which are at at at and at Graphical analysis: Let us suppose that we have a scenario, in which the numbers of gliders are five to 45; The intersection of lines L1, L2, L3, L4 and L5 is shown in In order to evaluate our schemes (PAMP and Co PAMP), we compare these with the QUO VADISscheme. QUO VADIS has considered a predefined trajectory of gliders throughout the network lifetime. However, it makes the estimation process easy in internal uncertainty regions, but the coverage hole problem in sparse regions and the distance between neighbors during the estimation of external uncertainty regions are not considered, which results in low network throughput and high end to end delay. We have taken these factors into account, and the results are shown via simulations and discussed in this section.β is Simulation parameters  of PAMP Packet delivery ratio: the number of correctly received data packets over the number of data packets sent.Energy consumption: the average amount of energy consumed to route one bit of information from the source to the destination. The unit of energy consumption is joule (J).Delay: the time taken by a data packet to reach the destination from the source. Its unit is second (s).Void zone: the network region that remains un-sensed or un-visited throughout the network lifetime. It is the ratio of the un-sensed region over the total volume of the network.We use the following performance metrics for evaluating our proposed schemes.The energy consumption comparison of the proposed schemes and QUO VADIS is shown in The delay comparison of QUO VADIS, PAMP and Co PAMP is shown in The void zone or coverage hole is the volume of the network that remains unvisited (un-sensed) by network nodes or gliders. Performance trade-offs of our proposed schemes and QUO VADIS are shown in We have proposed two routing protocols, PAMP and Co PAMP, to avoid void zones and achieve high network throughput. Due to the dynamic mobility pattern of gliders, void zones are avoided and energy is wasted due to the high packet drop ratio being minimized. For minimizing the uncertainty, predictable trajectories are used, and gliders stayed at static positions for a short interval to collect the data. The transmission power is adjusted according to the n-confidence region (neighboring nodes) for balanced energy consumption in estimating the s-confidence regions. In the estimation process of n-confidence and s-confidence regions, the distance between the source and the destination is considered, which helped in estimating the more precise position of the glider. The simulation results show that the recent proposed schemes (PAMP and Co PAMP) performed better than the baseline scheme (QUO VADIS) in terms of packet delivery ratio, energy consumption and void zone in the network.In the future, we have planned to analyze the performance of gliders at different depths of the aquatic environment and the effects of interference at different depths due to noises.


In the context of modern cyber-physical systems, the accuracy of underlying sensor data plays an increasingly important role in sensor data fusion and feature extraction. The raw events of multiple sensors have to be aligned in time to enable high quality sensor fusion results. However, the growing number of simultaneously connected sensor devices make the energy saving data acquisition and processing more and more difficult. Hence, most of the modern sensors offer a first-in-first-out (FIFO) interface to store multiple data samples and to relax timing constraints, when handling multiple sensor devices. However, using the FIFO interface increases the negative influence of individual clock drifts—introduced by fabrication inaccuracies, temperature changes and wear-out effects—onto the sampling data reconstruction. Furthermore, additional timing offset errors due to communication and software latencies increases with a growing number of sensor devices. In this article, we present an approach for an accurate sample time reconstruction independent of the actual clock drift with the help of an internal sensor timer. Such timers are already available in modern sensors, manufactured in micro-electromechanical systems (MEMS) technology. The presented approach focuses on calculating accurate time stamps using the sensor FIFO interface in a forward-only processing manner as a robust and energy saving solution. The proposed algorithm is able to lower the overall standard deviation of reconstructed sampling periods below 40  Applications for virtual and augmented reality place exacting requirements to sensor data, especially in case of movement tracking. In our previous work, we identified the negative influence of inaccurate time stamps onto subsequent processing algorithms . HoweverWith the increasing number of sensor devices, as well as sensor data types, the acquisition of the sensor data samples becomes time and energy consuming, which is undesirable on low power wearable devices. The sleep time of the hosts application processor gets interrupted by a variety of sensor devices to offer new sampled data. Shojafar et al. also proposes an adaption of the communication rate to lower the overall energy consumption . Most moHowever, some negative effects, e.g., clock drifts and communication delays, are amplified by the usage of the FIFO interface. The process of reading a greater amount of data takes more time and a reconstruction of the sampling times of the data samples in the FIFO stream is necessary. For example, considering the sensor data processing of a virtual or augmented reality (VR/AR) application, the integration of sampled data of a FIFO stream without consideration of the relative drift between the time bases of the host and the sensor device will introduce a severe error in the orientation vector. Our goal is to create an algorithm able to calculate accurate time stamps of sampling events on the host device targeting the compensation of drift and offset errors of multiple sensor streams. As a result, the reconstructed data of different sensors will be aligned in time. Furthermore, batch processing of sensor data fetched from FIFO interfaces must be possible in order to lower the overall energy consumption. In this study, we mainly focus on bus-driven communication between host and sensor devices.In this article, we present the accurate sample time reconstruction (ASTR) as an approach to calculate precise sampling time stamps using batched high frequency FIFO data of modern inertial MEMS sensors. Our proposed algorithm relies on the fact that the sensor doesn’t provide time stamps for each sample but instead the sensor time for the FIFO readout; the age of each sensor sample can be determined based on a fixed relation between the sampling time, a configurable output data rate and the current timer value of the sensor device. Hence, each sensor device must be able to offer the current sensor time accurately on a host read access. By fetching multiple of those sensor times, a comparison with the local host times allows the calculation of relative time based deviations between the sensor devices and the host. The sensor time stamp can also be added to the end of the fetched FIFO data, enabling the combination of data acquisition and time stamp synchronization.In the next Section, we will give an overview about existing works in the field of sensor data synchronization and do a classification of the ASTR and other algorithms. There have been many publications in the last years, dealing with the problem of accurate synchronization of sensor events. The existing works can mainly be classified into two major groups. Most of the available publications deal with the topic of clock synchronization. That means, the internal clock of the sensor devices can be adjusted in their value, frequency or both. That allows a direct synchronization of the wired or wireless reachable sensor devices in phase and frequency. The data acquisition itself happens without further modifications and fetched sensor time stamps equals the host time base.Cristian’s Algorithm  serves aAlthough clock synchronization is able to compensate for the error introduced by the relative clock drifts, there are many sensor devices that can’t adjust their internal clock. To synchronize the data of those sensors, the fetched sensor data time stamps have to be adjusted at the host in order to align different time bases. This mechanism is called time stamp synchronization.The idea of time synchronization without modifying the clocks of the distributed sensor devices was presented by Römer and Elson in ,15. AmonOur approach can also be classified as a time stamp synchronization approach, which is compatible with sensor devices that can’t adjust their local clock. While the main idea of a passive synchronization, which is completely done on the host, operates nearly to the solution of Olson, in our approach, the host initiates the read access to the sensor devices. The process of synchronization is fully combined with the data acquisition and by using the correlation between the sensor timer and the sampling times, the exact age of the fetched sensor data can easily be determined. In contrast to existing approaches, we focus on stream processing the sensor data fetched from the FIFO interface. Hence, the approach includes the reconstruction of the single sensor samples from the FIFO stream as well as their drift and offset corrected sampling time stamps. In addition to our previous work in , the ASTWhile most modern mobile devices and wearables are equipped with a variety of sensors, more and more people are using applications that are based on the gathered sensor data. However, they are expecting highly accurate results, e.g., from activity recognition, step counting, heart rate measurements, indoor navigation and more. Sensor fusion plays a fundamental role in achieving those highly accurate results. Via sensor fusion, the information of multiple sensors gets combined to either improve the measured signal or to create a new logical sensor output .The synchronicity of the different sensor signals is of major importance, as, for example, shifted or stretched signals would blur sensed events and therefore distort subsequent feature extraction algorithms. Due to fabrication variances, wear-out effects and temperature fluctuations, the clocks of the sensor devices can drift and influence the sample timing. Also, due to varying communication latencies, the read out sensor data is influenced by individual jitters and offsets.To perform our time stamp reconstruction approach, the sensor devices should offer a free-running sensor timer value. Moreover, to lower the energy consumption of the whole system, using the FIFO interface of the sensor devices is recommended.The FIFO interface is a state-of-the-art storage feature in modern high data rate sensors, as e.g., inertial measurement units (IMUs). The measured sensor samples are stored in a ring buffer data structure and can be read out by the host in chronological order. As the FIFO interface allows the acquisition of multiple sensor samples at once, the host sensor communication happens less frequent and the host can stay longer in energy saving power modes. Additionally, data loss is avoided, as the host can read all available sensor data since the last FIFO access, as long as a FIFO storage overflow is avoided. It is obvious that compared to single frame readout, the total communication latency can be higher when reading many frames at once. This problem can be solved by reading the FIFO content twice. First, a bigger chunk of data is read a short time before the next upper layer event has to be computed and, just in time, a small number of sensor frames are read from FIFO, enabling low latency behavior. In m would be the eighth bit of the sensor timer.The sensor timer is an essential requirement of the ASTR. The host should be able to gather the latest timing information from the sensor devices to perform the time stamp reconstruction algorithm that is described in  toggles . Table 1Usually, the latest sensor timer value can be read by direct register access. Additionally, in , it is aWhen gathering the sensor data efficiently by using the FIFO interface, the host has to reconstruct the sensor signal and to calculate time stamps Equation , which iThe readout of the FIFO stream by the host can be triggered by a fill level interrupt of the sensor device. Once the FIFO storage exceeds a configurable amount of data, an interrupt is issued and the host starts reading the expected amount of data from the FIFO interface. At the same time, the host stores the time stamp Equation .(2)t0=tThe time stamp Equation .(3)tn=tJ increases steadily within one FIFO stream chunk.Since there is no information considered about the internal timing of the sensor system, The described basic signal reconstruction algorithm enables an energy efficient sensor data reading via the FIFO interface. Nevertheless, the reconstructed signal contains clock drift dependent jitter and communication latencies. Due to this fact, the calculated time stamps are not well synchronized, especially when using multiple different sensor devices with individual frequency drifts and communication systems.the ratio between the time bases of host and sensor has to be taken into account, andthe offset between sampling time and calculation of the time stamp has to be eliminated.To resolve this problemEach sensor system must be able to offer the current sensor time value instead of sample-related time stamps to enable the following time stamp reconstruction approach. Many modern sensors, e.g., ,19, alreD can be easily calculated by Algorithm 1. In this example, the sensor timer values are 24-bit wide and have a granularity of 39.0625 To calculate the ratio between the time bases of two devices, the host device can read the timing information of the sensor system at least two times, gathering the sensor time stamps Algorithm 1: Simple drift calculation. Sensor time stamps  D.This procedure can be done asynchronously, in parallel to the sensor data reading. However, it is advantageous to combine the reading of the sensor time information and the data reading, as the sleep time of the host application processor can be further increased. For example, the sensor system in  allows tAlgorithm 2: More stable drift calculation. The influence of communication jitter is reduced by increasing the time window, using  D to eliminate the temporal distortion between the different time bases. Equation , e.g., Equation  containsThe whole reconstruction of the FIFO stream including data samples and time stamps has a time complexity of To prove our ASTR approach, we conducted tests using two inertial sensor devices , as wellTwo inertial sensor devices of the type BMI160 [To gather information about the synchronization robustness at higher relative drifts between host and sensor devices, the algorithms were tested in a simulation based environment, too. Therefore a virtual prototype, as depicted in The virtual prototype was implemented as a high-level SystemC  model. IOn the real devices, we performed the basic and accurate sample time reconstruction for different configured FIFO fill levels in a range from 35 to 350 bytes. The sensor samples are generated with a rate of 200 Hz and each sample takes seven bytes. Hence, the FIFO stream is fetched every 25 to 250 ms, respectively. The readout of the FIFO stream, together with the sensor timer value, is triggered by the fill level interrupt in all tests. The SPI bus clock was driven at 10 MHz in all experiments. Each test case was executed 250 times for about 30 s on both sensors. While the first sensor, shown in In D were done. Therefore, the fill level of the FIFO interface was configured to 140 bytes. The data path settings of the simulated sensor were, equally to the real sensor experiments, configured to 200 Hz. Also the length of each test case was limited to 30 s of simulation time. The host-sensor communication was simulated using the SPI protocol running at 10 MHz. In total, 100 simulation runs with random time base ratios D in a range of −3.5% to +3.5% have been executed. For the time stamp reconstruction, the same algorithms were used as on the real device. The results are plotted in D. However, ignoring the timing information of the sensor device leads to a linear rising timing error concerning the absolute relative drift.To prove the independence from the relative drift, a couple of simulations with a randomly varying time base ratio Estimating the power consumption of the presented approaches is difficult, as it depends significantly on the underlying system architecture. It will vary to a large extent between an micro-sized embedded sensor hub controller and a more powerful processor of a state-of-the-art mobile phone. Nevertheless, to give an impression of the influence of the sample time reconstruction algorithms onto the power consumption, we measured the run-time of the full measurement and reconstruction setup running on the raspberry pi platform.We limited all test case executions to 30 s total length and used the time command of the linux bash to measure the active time of the process. For reading the sensor data sample by sample without doing any time stamp reconstruction, the absolute run-time accumulates to 0.93 s, which corresponds to 3.1% processor utilization. Blocking times, waiting for the read access to be finished, are not considered in this number. The results of the absolute run-time measurements are shown in With the increasing number of sensors that are built into mobile phones, fitness trackers, game controllers, clothes and many other mobile devices, the fusion of the locally available sensor data becomes more and more important. However, the acquisition of the data is increasingly time and power consuming on a growing number of connected devices. Data loss and timing inaccuracies due to communication delays make the sensor fusion and feature extraction more difficult. To relax this problem, many of the modern sensors allow the reading of multiple data samples in a FIFO ordering to lower the number of communication attempts and to avoid data loss. However, reading the data as a FIFO stream obligates the host device to reconstruct the temporal order of the single samples to allow an event-synchronized sensor fusion.In this paper, we presented an algorithm for an accurate reconstruction of the sampling times of each sensor sample in a FIFO stream. To prove the accuracy of the presented algorithm, a couple of experiments were done with two BMI160 IMUs [The study showed that the FIFO based sensor data acquisition using ASTR is an advantageous alternative to the currently, often implemented, single sample-based algorithms.


Inventory management has frequently been targeted by researchers as one of the most pivotal problems in supply chain management. With the expansion of research studies on inventory management in supply chains, perishable inventory has been introduced and its fundamental differences from non-perishable inventory have been emphasized. This article presents livestock as a type of inventory that has been less studied in the literature. Differences between different inventory types, affect various levels of strategic, tactical and operational decision-making. In most articles, different levels of decision-making are discussed independently and sequentially. In this paper, not only is the livestock inventory introduced, but also a model has been developed to integrate decisions across different levels of decision-making using bi-level programming. Computational results indicate that the proposed bi-level approach is more efficient than the sequential decision-making approach. With the introduction of the concept of supply chain management in the early 1980s, inventory management was also recognized as one of the most important drivers of supply chain. At first, most inventory studies in the field of inventory management focused exclusively on non-perishable inventories, but soon after, the concept of perishable inventory was proposed. Recognizing the importance of perishable inventory in human lives, many researchers have studied mathematical models to optimize perishable products policies in the supply chain .One of the most important types of perishable inventories are protein products. With careful examination of these type of products, it is clear that livestock inventory constitutes an important component in the supply chain of this products. For example, consider the red meat supply chain. This supply chain is composed of two parts: the livestock supply chain and the cold supply chain. The livestock supply chain includes the processes and steps associated with nurturing the animal from birth until slaughter; while the cold supply chain refers to the processes and steps that take place after slaughter. So far, researchers have focused almost exclusively on the cold part of supply chain –10. ThouIt should be noted that multi-level models are a special type of stakleberg game and are often used to model competition situations in which one of the players has more power and can make the first move. Because strategic decisions have more impact on cost and time than the other decision-making categories , strategic decisions can be considered as the leader while tactical and operational decisions constitute the followers. Different approaches have been used to solve multi-level models. In this article, fuzzy goal programming presented in  is used In section 2 of this article, the poultry supply chain, as one of the most important livestock supply chains, is investigated, and differences between livestock and non-livestock supply chains are studied. Next, the important but understudied variables that exist in such supply chains are examined. Section 3 presents a literature review and analyzes the gap in this research area to be filled by the present paper. Problem is described in section 4. A mathematical model to integrate the strategic and tactical decisions is proposed in section 5. Solution method and computational results are presented in sections 6 and 7 and finally, the conclusion is presented in section 8.The supply chain of protein products is amongst the most important and influential supply chains human beings must deal with. In some protein products’ supply chains including red meat, fish meat, and chicken meat, the inventory flow is divided into two parts: livestock inventory flow and cold inventory flow. All the steps from birth, and even before birth, involved in delivering protein goods such as packaged meat, eggs, and sausages, are part of this supply chain. To distinguish between the livestock parts and the cold parts of the supply chain, the meat chicken supply chain is considered here. As shown in The first step in the process of producing chickens is called pure line. Line chickens are the only chickens in the process that can produce their own counterpart. In the next level, grandparent chickens can be seen. The product of grandparent chickens is called a parent. After that, broiler flocks are produced. These four stages are the main stages of the meat chicken livestock supply chain. It should be noted that food supply units and hatchery units also have an important role in this supply chain. Food supply units, supply the food for diferent poultry farms and hatchery units can raise the different type of chickens like grandparent, parent of broiler chickens. After broiler, the chickens are sent for slaughter. The slaughter of living creatures, such as chickens, shifts the nature of the chain from a livestock chain to a cold chain. As illustrated in Special conditions and specific decision variables that exist in the livestock supply chain generate substantial differences between the livestock and the cold parts of the supply chain. One of the most important differences is growth and its side effects. The growth curve of the chicken indicates an obvious difference in growth rate over time. If the chickens are slaughtered they are younger, the number of hatching and breeding periods will be more. On the other hand, by maintaining each chicken for a longer period, benefits from scaling and prorating the fixed costs of breeding like less day-old chicken buying cost can be obtained. Thus, answering the question of the optimal age for slaughter considering different conditions is one of the central issues studied in this article. Another difference between livestock inventory and other types of inventory like non-perishabe inventory, is the mortality rate. Different breeding conditions lead to different rates of mortality in the animals. Planning must be conducted based on this rate. Moreover, the performance of these supply chains can change over time. For example, laying hens encounter reductions in their egg-laying rate as they age. On the other side, aging and the increased body weight cause a change in the costs imposed by time. Because more body weight cause in more feed consumption rate.Mentioned differences between livestock and non-livestock inventories necessitate independent attention to livestock supply chains. Thus, the literature available in the field of livestock inventory supply chains is reviewed and the use of multi-level models in decision-making process is studied in the following section.Little research has been conducted on livestock supply chain management for optimization, and most research studies in this area are limited to qualitative issues. To increase profit by identifying potential areas for improvement, Shamsuddoha  studied Darivandi et al.  has discSana  developeA goal programming model is developed by Sauian and Othman  to optimLapar et al.  developeAccording to this table, it is clear that there are very few number of articles that attempt to optimize the variables which are specially for livestock inventories like breeding period. Among these papers, decision variables such as price and production amount, are considered that not only they are not exclusive for the livestock inventory but also, they are considered in optimizing other types of inventories like perishable one. On one hand, it is clear that in none of the reviewed articles, the interaction of different decisions with which supply chain managers confront, is not addressed. On the other hand, it is even more important that even the articles which develop optimization models for livestock inventory, are not considered some of the major differences between livestock inventory and other types of inventory. For example, weight changes over time or mortality rates is important points that are not considered in these papers. Therefore, in this paper, a bi-level model is presented. In this model livestock supply chain issues and exclusive parameters are considered. In addition, the real situation’s decision-making variables that are less considered in the articles, such as the optimal breeding period, are came into account. Although, it seems that different levels of strategic, tactical and operational decisions have interaction and cannot be considered independent, multi-level planning is used to integrate strategic and tactical decisions. It should be noted that in order to ensure the effectiveness of the proposed approach, it is compared with the traditional sequential approach used in most articles.The next section describes the problem that our developed mathematical model seeks to resolve.Consider a bi-level supply chain including a poultryfarm and a slaughterhouse. Managing this supply chain includes different decision-making levels such as long-term, medium-term, and short-term. As shown in To facilitate making these decisions simultaneously and to consider the interactions, for example the effect of location on rearing period, this article develops and presents a model based on bi-level programming. According to this model, first at Level 1, long-term decisions are evaluated as leader variables. Then in Level 2, tactical and operational decisions are made.According to In the second phase of decision-making, tactical and operational decisions are considered. One of the most important decisions made in poultryfarms in this level is the breeding period. By increasing the length of the breeding period, economies of scaled and prorated fixed costs can be used. But by decreasing this age, the total number of breeding periods and, therefore, total annual production can be increased. Also, given that chicken weight growth rate does not increase linearly during the breeding period, the optimal breeding period should be calculated according to this growth curve. Another important consideration is the effect of various locations of poultryfarms on parameters such as growth rate and mortality. Depending on the geographical conditions of different parts of the country, effective parameters in the supply chain such as growth rate, mortality, and expenses may vary. Therefore, decisions should be optimized based on the special conditions of each supply chain.Other tactical decisions in the poultryfarms include hatching volume in each period, number of breeding periods per year, and the optimal product mix. Hatching volume is the number of day-old chicks that are raised by poultryfarms in each breeding period. Given how old chickens are when slaughtered, the number of breeding periods may vary during the year. However, because all poultry related products are different in terms of finished expense and sales price; in consideration of the market, the optimal combination of the products should be calculated as well. It should be noted that in this article, packaged chicken and cleaned and packaged chicken are considered the producible goods of the slaughterhouse.The different steps that need to be taken in this supply chain are as follows. First, day-old chickens are bought from the parent poultryfarm and delivered to the broiler poultryfarm location. Then, the breeding stage begins. In this stage, the poultryfarm raises chickens with a proper diet. In addition to feeding cost, this stage involves other costs including vaccines and drugs. After chickens reach the suitable age or weight, they are sent to the slaughterhouse. Between each two breeding periods, about 15 days should be spent on cleaning and preparing the poultryfarm. Then, adult poultries delivered to the slaughterhouse will be slaughtered and converted to the desired products. All stages face some form of deterioration. When the chickens are still alive, there is a mortality rate, and when they are slaughtered they experience weight loss, as blood and other materials exit from the chickens’ bodies. When delivering the product to the demand location, evaporation of water from the chickens’ bodies also causes weight loss. Therefore, special attention to this reduction is essential.In the next section, a mathematical model based on bi-level programming is proposed to integrate strategic and tactical decisions in poultry supply chain. All parameters and interactions between different types of decisions discussed above, are considered in proposed model.As mentioned, in this paper a mathematical model is presented to integrate the strategic and tactical decisions in poultry supply chain. Bi or multi-level optimization problems are problems that optimize conflicting goals in a hierarchical structure. In such problems, there are multiple decision-making levels, each of which control a part of available decision variables in decision-making , 30. EacSets, parameters, and variables of the model are as follows:Sets:i: The potential locations for poultryfarms j: The potential locations for slaughterhouses l: Demand points k: Products set (k = 1 for packaged and 2 for packaged and cleaned slaughtered poultry)c: Poultryfarm capacity set c’: Slaughterhouse capacity set st: Inventory condition set; st = chn (Day-old Chicken), i.plty (In-way Inventory), plty (Poultry), prdt Decision variables:Hch.Vlmi: Hatchery volume per period for poultryfarm placed in position iN.Hchi: Number of hatchery per year for poultryfarm placed in position iQjlk: Amount of product k sent to demand point l from slaughterhouse jT: Breeding periodPk: Production ratio of product kxic: 1 if poultryfarm with capacity c locates in i, else 0Fl: 1 if delivered product to demand point l is less than deman, else 0Parameters:Pg: Day-old chicken priceTr.Cst: Transportation cost per unit for inventory in condition stCic; Fixed-cost of poultryfarm location with capacity level c in location iDl: Demand level in point lCD: Cost per unit for feedV(T): Feed consumption functionC.dly: Other daily costs of breeding in poultryfarmT.Cok: Ratio of conversion poultry unit to product kW(T): Poultry growth functionPk: Unit price of product kDstcgi: Distance between parent poultryfarm and meat poultryfarm in point iDstcij: Distance between poultryfarm i and slaughterhouse jDstcjl: Distance between slaughterhouse j and demand point lAPk: Reduction percentage of surplus inventory selling priceST: Time needed for preparation and cleaning poultryfarm between two breeding periodsPCk: Direct cost of production each kilograms of product kCpc: Capacity of poultryfarm of type cWlk: Percentage of poultry weight loss in slaughteringTWlk: Percentage of weight loss in transporting productsModel assumptions:The number of chickens raised in different breeding periods is equal.A breeder poultryfarm exists in each province.Poultry and slaughterhouse that are established in one province will be located in a predetermined distance.Selling products in markets that have more demands is easier.The desired time span is limited and equals to one year.In case of producing more than the market need, extra products will be sold with APk percentage of the original price.Various weather conditions do not impact the growth and feed consumption rate.Given these parameters and variables, the mathematical model of the problem will be as follows:[1st level][2ndlevel]Subject to:The first level involves strategic decision variables while the second level includes tactical and operational variables. The upper level has two objective functions. The first objective function is to minimize the cost related to making strategic decisions. The first and the second terms of the first objective function represent the fixed cost of locating meat poultryfarms and slaughterhouses. The third term represents total annual costs of transportation for day-old chicks from parent poultryfarms to meat poultryfarms. Considering the possibility of mortality while transporting the day-old chicks, meat poultryfarms must purchase more chicks than needed; thus, coefficient (1+D.Ratechn) is multiplied in this term. plty percent of chickens would die during the breeding period, to calculate the number of raised mature chickens at the end of the period, mortality rate should be multiplied with the number of raised chickens. The fifth term calculates the cost of transporting final products from the slaughterhouses to the demand points. The second objective function of the first level deals with the cost of entering in new markets. The slaughterhouses can choose different markets to provide their products, but the costs of entering these markets and selling the products are different. According to model assumptions, the more potential demand in a market, the less the cost of entering that market. Considering demand volumes and the distance between slaughterhouses and demand points, the objective functions’ structure tries to send the products to nearer markets with more unmet demands.The fourth term of objective function provides the cost of transporting raised chickens from meat poultryfarms to the slaughterhouses. Because D.Ratek) in the amount of sent products. On the other hand, in conditions where the amount which is sent to the market is more than market demands, surplus products are sold with APk percent reduction of their real price. The third term demonstrates the income in conditions where the amount of the sent products is most equal to demands. The fourth term demonstrates total cost of preparation and cleaning the poultry between the two hatching periods. Obviously, the number of cleaning periods is one unit less than the number of hatching periods.The model’s second level concerns tactical and operational variables. In this level, the main objective is maximizing the annual profit. First and second terms demonstrate total income of the supply chain. The first term is a case when the volume of the sent product is more than the market demand, and the second term is a case where the sent product is less than the market demand. Because slaughtered chickens lose weight during transportation for different reasons such as evaporation of water in the body, there is a weight difference between the sent products from the slaughterhouse and the amount of delivered products in the market. The impact of this weight loss is applied by multiplying (1-WLD.ratechn) percent more than the desired number of chicks for raising. The sixth term illustrates the total direct cost of production for each kilogram of product. The seventh term is the total cost of feed consumption in the breeding period. To measure the feed consumption of each chicken, an integral of chicken feed consumption function overtime should be used. Finally, the eighth term demonstrates other daily production costs in the poultryfarms such as vaccine costs. Direct costs of poultry farming, which include buying day-old chickens and feeding are considered as well.The fifth term demonstrates the total cost of buying day-old chickens in a year. Because day-old chickens face mortality during transportation, the number of chickens bought must be , more than one chicken unit (kg.) is needed; therefore, the conversion ratio of any of these products should be considered. Constraint 7 guarantees this balance in the poultryfarm and illustrates that total sending from the poultryfarm should not be more than its total production. As shown in the mathematical model, total production of the poultryfarm equals the number of hatching periods multiplied by hatching volume. In this case, the mortality rate of the chickens should be considered as well because the volume considered for production will be different based on chickens’ final volume, and the number decreases due to mortality. In addition, since the amount of sent product from poultry to the slaughterhouse is calculated by weight, the right side of the equation must also be expressed in terms of weight; therefore, total weight of the poultry’s production each year equals the number of raised chicks (the number of hatching periods multiplied by hatching volume in each period) multiplied by the rate of chicks that stay alive and multiplied by an integral of weight function over time. The chicken weight over time can be expressed by a weight function. It should be noted that term Constraint 8 guarantees that products must only be sent from where the slaughterhouse is established. Considering there are different facilities with different capacities, constraint 9 ensures that the production amount of the poultryfarm should not be more than its capacity. Inequality 10 also demonstrates the capacity for slaughterhouse units. According to this constraint, total products received from poultryfarms for each slaughterhouse should be at most equal to the capacity of each slaughterhouse. Regarding the mortality rate while breeding the chickens and transporting the poultries to the slaughterhouses, the impact of mortality rate should be applied. Constraint 11 demonstrates total time spent for hatching during the year, which is equal to multiplying the number of hatching periods in the breeding period plus the time needed for preparation of the poultryfarm between hatching periods, which should be less than 365 days. Since Iran does not allow the movement of live poultry among different provinces, Constraint 12 guarantees that poultry and the slaughterhouse are established in the same province so there is no need for moving the live poultry among provinces. Constraint 13 demonstrates the total fund limit. Constraints 14 and 15 also show the variables’ value interval.Perhaps one of the most usable methods to solve multi-objective, multi-level problems, is the fuzzy mathematical planning technique , 5. One The steps of this algorithm are as follows:Step 1) Solving objective functions and their constraints independently to calculate maximum and minimum valuesStep 2) Use upper tolerance limits of goals for each levelStep 3) Calculate the weights:Step 4) Set l = 1Step 5) Evaluate membership functionsStep 6) Develop the below model for each for levels and solve itStep 7) Elicit variable vector uniting maximum and minimum tolerancesStep 8) Calculate the weights and membership functions:Step 9) if l< = p-1 go to step 5; otherwise go to step 10Step 10) Evaluate the weights and members’ functions of objective functions in p-th levelStep 11) Develop below model for problem and solve itTo solve the mathematical model, it is first linearized according to After linearizing model, to evaluate the proposed approach in this article, the problem is modelled and solved using multi-level programming and is then modelled and solved using a traditional sequential approach. In the sequential approach, decisions of different levels are made independently and results of upper decisions enter the next level as a parameter. In other words, in the sequential approach, the first decision about the supply chain’s structure is made, which includes the location and capacity of the poultryfarms and the slaughterhouses and the type of market allocation are determined. In the next stage, tactical and operational variables such as breeding period are evaluated. As shown in As shown in One of the main reasons for more annual profit using the bi-level decision-making approach is the consideration of tactical and operational variables while deciding strategic variables. For example, consider the breeding period. The average optimal breeding period using bi-level and sequential approaches respectively, would be 43.5 and 46 days. Differences in optimal breeding periods between the two approaches stem from the selected locations. For example, one of the selected locations in the bi-level approach is Gilan. Gilan has a humid climate, which is suitable for raising chickens. The mortality rate in this province is about 10 percent, while this rate is about 20 percent in Khuzistan, which is one of the selected locations in the sequential approach. Therefore, to avoid incurring the cost of upper mortality rate, the breeding period would be longer, and the number of chickens raised annually will reduce. In other words, the less poultry raised annually, the lower annual poultry losses.As shown in this table, selected locations for poultryfarms and slaughterhouses are different in bi-level and sequential approaches. One of the reasons for this difference is that the sequential approach solves the problem by minimizing location and allocation costs, while the bi-level approach considers the objective of the model’s second level, which are tactical and operational variables such as optimal breeding period. Another important point is the difference in breeding period between the two approaches.As mentioned, selected provinces for establishing poultryfarms and slaughterhouses in the bi-level and sequential decision-making processes have very different climates. A province like Gilan is mild and humid while Khuzestan is warm and dry. Therefore, in similar conditions, the mortality rate will be much higher in Khuzestan. According to computational results, the higher the mortality rate, the longer the optimal breeding period, and therefore, the lower the number of hatchery per year. Another important point is the optimal ratio of each product. Obviously, according to the outputs in both approaches, all slaughtered poultries should be converted to “packaged and cleaned chicken” production and then sent to demand points not in “just packaged chicken”. The selling prices of just packaged and packaged and cleaned chickens are more than cleaning process costs, so it is economical for the supply chain to sell a packaged and cleaned product. Moreover, the differences between breeding periods in different provinces should be noted. Due to the costs of poultryfarms and slaughterhouses, the day-cost of keeping a chicken is about 566 rials, so it is important to optimize the breeding period because keeping chickens for one extra day in the supply chain can incur a cost of up to 215,210,000 rials. As shown in One of the most important problems that poultry supply chains face is price fluctuation. This fluctuation occurs across components of the supply chain. Feed and day-old year prices are two important factors with the most price fluctuation. So, the effect of these factors’ fluctuations on different decision-making variables and objective functions should be considered. Historical data shows that day-old chicken price fluctuations in the past five years vary between 7000 to 18500 rials. As illustrated by this Unlike this analysis, in the real-world, the breeding period in supply chains is considered fixed. As is shown in Another important factor is feed price. As is shown, increasing the feed price leads to shorter breeding periods. Older poultries result in more feeding volume, and thus incur more feeding costs per day. Per the growth rate curves in In this paper, livestock inventory is presented as a type of inventory that has major differences with other types of inventory such as perishable products. Despite the importance of livestock inventory in human life, research studies often neglect it. When livestock inventory is researched, different levels of decisions are made sequentially. Thus, in this paper, a bi-level programming model is presented to optimize the variables in strategic, tactical, and operational levels in Iran’s meat poultry supply chain as one of the most important livestock supply chains in the country. The computational results indicate that the bi-level approach works more efficiently than the sequential approach. Other managerial insights from the computational results are listed below:Considering the interactions among different decision levels in the supply chain results in more profit and lower cost.Despite the dominant approach, livestock specific decision variables like breeding period are not predetermined and fixed. Rather, these decisions should be made according to environmental conditions like poultryfarm location.Livestock supply chains should be designed as agile to react to changes in parameters like feeding price, because these shifts can result in less costs.S1 Dataset(XLSX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


In recent years, a number of studies have been focused on the structural evaluation of protein complexes in order to get mechanistic insights into how proteins communicate at the molecular level within the cell. Specific sites of protein-aquaporin interaction have been evaluated and new forms of regulation of aquaporins described, based on these associations. Heterotetramerizations of aquaporin isoforms are considered as novel regulatory mechanisms for plasma membrane (PIPs) and tonoplast (TIPs) proteins, influencing their intrinsic permeability and trafficking dynamics in the adaptive response to changing environmental conditions. However, protein–protein interaction is an extensive theme that is difficult to tackle and new methodologies are being used to study the physical interactions involved. Bimolecular fluorescence complementation and the identification of cross-linked peptides based on tandem mass spectra, that are complementary to other methodologies such as heterologous expression, co-precipitation assays or confocal fluorescence microscopy, are discussed in this review. The chemical composition and the physical characteristics of the lipid bilayer also influence many aspects of membrane aquaporins, including their functionality. The molecular driving forces stabilizing the positions of the lipids around aquaporins could define their activity, thereby altering the conformational properties. Therefore, an integrative approach to the relevance of the membrane-aquaporin interaction to different processes related to plant cell physiology is provided. Finally, it is described how the interactions between aquaporins and copolymer matrixes or biological compounds offer an opportunity for the functional incorporation of aquaporins into new biotechnological advances. The current knowledge of membrane components is rather complete, but the physical and structural aspects of the lipid-protein interactions are still under investigation and underline the complexity of the biological membrane as a whole. Two different approaches have been established to determine the protein and lipid molecules interaction in a membrane: lipid- and protein-based approaches . Lipid-bPlants aquaporins (AQPs) are molecular proteinaceous membrane channels that finely control the passage of water through membranes. During the last 20 years, after their discovery, AQPs have been deeply investigated and this has highlighted the difficulty in characterizing an individual role for them. The great number of AQPs in plants, the different substrates that they transport  and the Biological membranes are constituted by lipids and proteins that establish physical and chemical communication between cells and their intracellular compartments. The lipid bilayer forms a fluid matrix to hold proteins. However, this lipid bilayer has been widely reported to be more than a passive fluid since it influences many aspects of membrane proteins, including their insertion into the membrane , assemblIn this review, the regulation of AQP isoforms by the physical and chemical characteristics of the lipid bilayer is considered, together with the interaction of the isoforms with other AQP isoforms and proteins. These characteristics are also described as novel regulators of membrane intrinsic proteins (MIPs), influencing the permeability and trafficking dynamics of the membrane in the adaptive response to changing environmental conditions. The molecular driving forces resulting from the positions of the lipids around AQPs, which modulate their activity, will be discussed in terms of future challenges.trans-membrane regions of AQPs, lipid-AQP stabilization is an important challenge to these methodologies. In fact, there is only limited information about pore conformation and this was determined for only two AQPs, AQP0  can be traced during the solubilization or purification steps, which improves the methodology. By contrast, the cellular expression of the fluorescence particles (GFP or YFP) does not always lead to an effective fluorophore; also, protein–protein interaction may impede the correct reconstitution of the fluorescent protein (Photobleaching fluorescence resonance energy transfer (FRET) has been applied to demonstrate the physical interaction between the maize AQP isoforms Zm-PIP1s and Zm-PIP2s  as well  protein .Thus, the study of protein–protein interaction partners is a young discipline, while there are a great number of reports concerning the functional and structural information of purified AQPs. The identification and analysis of these proteins which form complexes with other membrane proteins are still a major challenge, but the large number of interacting partners that affect AQP regulation makes this discipline a promising tool in cell biology that can provide a way to answer novel scientific questions.Protein interactions, ranging from the formation of stable complexes within the cell to transient complexes involved in cell signaling pathways, determine protein function. Membrane proteins are basic elements of the cell, allowing the transport of molecules across the membrane and communication with the external environment. Among them, the AQPs are transmembrane channels – organized in highly conserved tetrameric structures in the cell membranes – which facilitate the passage of water and/or small solutes . The funXenopus laevis oocytes. Thus, co-expression of PIP1 with PIP2 was necessary not only to increase PIP1 water permeability .The study of the structural basis for heteroligomers formation is still a challenge. In maize, a conserved cysteine (Cys) residue in the loop A of PIP1 and PIP2 proteins has been identified as responsible for the formation of a covalent disulfide bond between the monomers of these two subfamilies. Although this Cys did not directly regulate the activity or trafficking of the two PIPs, the residue increased the oligomer stability under unfavorable conditions, hence controlling PIP abundance in plant cells . Howeverf loop A . Also, rArabidopsis have been studied using heterologous expression in yeast  and the UDP-galactose transporter . The expression of this gene was enhanced under salinity , a multi-stress regulator, interacted with the AQP isoform at the level of the endoplasmic reticulum and Golgi membranes, resulting in a decreased abundance of PIP2;7 protein in the PM that prevented cell water loss (Xanthomonas oryzae pv. oryzae (Xoo), the pathogen that causes bacterial blight of rice. This may favor the virulence of plant pathogenic bacteria, with the AQPs acting as anchor elements  geometry . With thIn a study of the microviscosity of the hydrocarbon zone in the isolated tonoplast and PM, using a fluorescent diphenylhexatriene probe, it was demonstrated that both membranes do not differ in this parameter in the phase state of their lipid bilayer . The stuIt has always been assumed that the lipid composition of the membrane is the major factor determining its physical characteristics. The function of sterols in plants was thought to be related primarily to their ability to affect membrane structure and water permeability . HoweverArabidopsis as a response to environmental changes , which is rich in sterols and is considerably different from the total PM fraction, increased the osmotic water permeability of the PM at low and freezing temperatures and, hence, increased cell survival . Also, d changes . In fact changes . High sa changes . Howeverrigidity . This warigidity , and it rigidity .2 permeability by two orders of magnitude in phospholipid vesicles and intact cells. However, the permeability of CO2, and possibly other gasses, through AQP1 appeared to be increased by three orders of magnitude. Also, studies with kidney cells revealed that cholesterol depletion reduced the diffusion coefficient of AQP3 but not that of AQP5  lipids around lens-specific animal water channel aquaporin-0 (AQP0) showed that the positions of the constrained lipids in the 2D crystals are defined by the acyl chains rather than the head groups . FurtherSupported biomimetic membranes (SBMs) have attracted much interest as enabling components in promising applications such as water purification . Hence, In the same way, Therefore, the possibility of mimicking biological membranes and the challenges of fabricating separation devices based on such biomimetic membranes have both increased in recent years, which offer an opportunity for the functional incorporation of AQPs in new technologies.Figure 2). Also, it is becoming increasingly clear that AQP/AQP and AQP/protein interactions represent an important coordination system involved in cellular functions and need to be elucidated.There is a general understanding that membrane AQP proteins diffuse laterally, associated with several layers of lipids, with the positions of the lipids in the first solvation shell being modulated also by irregularities in the protein interface. Therefore, the physical properties and chemical composition of the membrane will influence the functionality of the AQPs and could regulate their open/closed state (Another aspect to consider is the mechanical or therapeutic/food uses of vesicles rich in AQPs. Filters with a tremendous potential use in engineering, for water filtration, are being developed. The therapeutic/food aspect has been initiated and will be a very exciting line of plant AQPs exploitation in the future . FurtherMM-B her contribution was reviewed the aspect of interactions of aquaporins with aquaporins in the membranes. MC her contribution was reviewed the aspects of interactions of aquaporins with lipids in the membranes. The completed manuscript was corrected as a whole.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.