Insight into the electronic, elastic, structural, thermal, and optical characteristics of potassium magnesium fluoride KMgF3 with isotropic external stress.

The demand for increasingly fine detail in optical lithography for semiconductors necessitates the use of lower-wavelength lithographic light. This drives the need for lenses in optical lithography steppers made of vacuum ultraviolet-transparent (VUV-transparent) materials. In this work, the density functional theory (DFT) study of potassium magnesium fluoride KMgF3 is presented. Total energy was calculated with correlation functional generalized gradient approximation (GGA). The ground state quantities such as bulk modulus and lattice parameters have been evaluated. The material's cubic structure is scrutinized under various stress levels (0-100 GPa), revealing that KMgF3 starts to deform at 128 GPa. The C11, C12, and C44 independent elastic constants were used to analyze the structural stability of the KMgF3. The densities of states and electronic band structures have also been computed. According to electronic calculations, when stress is applied to KMgF3, the band gap increases for all values of stress (0-100 GPa). Mechanical parameters, including elastic constants and ratios, indicate the material's remarkable ductility and stability. Phonon density of states and thermal characteristics exhibit shifts and variations with increasing stress, providing insights into the material's behaviour below its melting point. The thermodynamic properties of KMgF3, such as enthalpy, free energy, entropy, heat capacity, and Debye temperatures at various temperatures ranging from 0 K to 1000 K, have also been examined to explore their basic properties. These findings contribute to a comprehensive understanding of KMgF3, opening avenues for its application in advanced technologies, particularly in the realms of semiconductors and optoelectronics.

ZnO/SrTiO3, ZnO/WO3, and ZnO/Zn2SnO4 Bilayer as Electron Transport Layers for Lead Sulfide Colloidal Quantum Dots Solar Cells.

In order to enhance the overall efficiency of colloidal quantum dots solar cells, it is crucial to suppress the recombination of charge carriers and minimize energy loss at the interfaces between the transparent electrode, electron transport layer (ETL), and colloidal quantum dots (CQDs) light-absorbing material. In the current study, ZnO/SrTiO3 (STO), ZnO/WO3 (TO), and ZnO/Zn2SnO4 (ZTO) bilayers are introduced as an ETL using a spin-coating technique. The ZTO interlayer exhibits a smoother surface with a root-mean-square (RMS) value of ≈ 3.28 nm compared to STO and TO interlayers, which enables it to cover the surface of the ITO/ZnO substrate entirely and helps to prevent direct contact between the CQDs absorber layer and the ITO/ZnO substrate, thereby effectively preventing efficient charge recombination at the interfaces of the ETL/CQDs. Furthermore, the ZTO interlayer possesses superior electron mobility, a higher visible light transmission, and a suitable energy band structure compared to STO and TO. These characteristics are advantageous for extracting charge carriers and facilitating electron transport. The PbS CQDs solar cell based on the ITO/ZnO/ZTO/PbS-FABr/PbS-EDT/NiO/Au device configuration exhibits the highest efficiency of 15.28%, which is significantly superior than the ITO/ZnO/PbS-FABr/PbS-EDT/NiO/Au solar cell device (PCE = 14.38%). This study is anticipated to offer a practical approach to develop ultrathin and compact ETL for highly efficient CQDSCs.

Effects of isotropic stress on the band structure elastic, optical, thermal, and x-ray diffraction properties of TiSnO3.

BACKGROUND: Cubic perovskite titanium stannous oxide (TiSnO3) is a promising material for various applications due to its functional properties. However, understanding how these properties change under external stress is crucial for its development and optimization. METHOD: This study employed density functional theory calculations to investigate the structural, electronic, optical, thermal, and mechanical properties of TiSnO3 under varying degrees of external static isotropic stress (0-120 GPa). RESULTS: The study reveals a significant decrease in the bandgap of TiSnO3 with increasing stress due to lattice modifications and the formation of delocalized electrons. Partial density of states analysis indicates that Sn and O states play a key role in shaping the electronic band structure. TiSnO3 exhibits increased light absorption with stress, accompanied by a blue shift in absorption peaks, whereas, both polarizability and refractive index decrease with increasing stress. Mechanically, all elastic moduli (bulk, shear, and Young's) show an increase under stress, signifying a stiffening response of the material under stress. Similarly, the Pugh ratio suggests a transition from ductile to brittle behaviour at elevated stress levels. Phonon dispersion calculations indicate the instability of the cubic phase at 0 K. However, a phonon gap emerges at 30 GPa and widens with increasing stress. X-ray diffraction further supports these findings by demonstrating a shift in diffraction peaks towards higher angles with increasing stress, consistent with the applied stress. CONCLUSION: In conclusion, this computational study offers a thorough understanding of how external stress influences the properties of TiSnO3, providing valuable insights for potential applications in various fields.

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.

Exploring the electrochemical properties of CuSe-decorated NiSe2 nanocubes for battery-supercapacitor hybrid devices.

Chalcogenides, a promising class of electrode materials, attracted massive popularity owing to their exciting features of high conductive nature, high capacity, rich redox activities, and structural functionalities, making them the first choice for the electrochemical energy domain. This paper reported a new NiSe2-CuSe nanocomposite prepared via a wet-chemical synthesis followed by a low-cost and simple hydrothermal reaction. The physical characterization showed cubes and nanoparticles type morphological features of NiSe2 and CuSe products, while their composite reveals a combined morphological characteristic. The electrochemical properties were tested in an aqueous solution, demonstrating that the NiSe2-CuSe nanocomposite exhibits a high capacity of 376 C g-1, low resistance, good reversibility and rate capability in a three-electrode mode than bulk counterparts. For practical aspects, a battery-hybrid supercapacitor (BHSC) is developed with NiSe2-CuSe nanocomposite, and activated carbon (AC) serves as cathode and anode in two-cell mode operation. The built NiSe2-CuSe||AC/KOH BHSC expanded the voltage to 1.8 V and delivered the highest capacitance of 148 F g-1 and 55 F g-1 from 1 to 10 A g-1, suppressing most of the previously existing literature reports. Also, our built NiSe2-CuSe||AC/KOH BHSC displayed a high-power delivery of 8928 W kg-1 at a maximum energy density of 66.6 W h kg-1 and retained 91.7% capacitance after a long way of 10,000 cycles. These outstanding results demonstrate that metal selenides can be effectively utilized as alternative electrodes with high energy, rate performance, and long-term durability for advanced energy conversion and storage devices.

Removal of Brilliant Green Dye from Water Using Ficus benghalensis Tree Leaves as an Efficient Biosorbent.

The presence of dyes in water stream is a major environmental problem that affects aquatic and human life negatively. Therefore, it is essential to remove dye from wastewater before its discharge into the water bodies. In this study, Banyan (Ficus benghalensis, F. benghalensis) tree leaves, a low-cost biosorbent, were used to remove brilliant green (BG), a cationic dye, from an aqueous solution. Batch model experiments were carried out by varying operational parameters, such as initial concentration of dye solution, contact time, adsorbent dose, and pH of the solution, to obtain optimum conditions for removing BG dye. Under optimum conditions, maximum percent removal of 97.3% and adsorption capacity (Qe) value of 19.5 mg/g were achieved (at pH 8, adsorbent dose 0.05 g, dye concentration 50 ppm, and 60 min contact time). The Langmuir and Freundlich adsorption isotherms were applied to the experimental data. The linear fit value, R2 of Freundlich adsorption isotherm, was 0.93, indicating its best fit to our experimental data. A kinetic study was also carried out by implementing the pseudo-first-order and pseudo-second-order kinetic models. The adsorption of BG on the selected biosorbent follows pseudo-second-order kinetics (R2 = 0.99), indicating that transfer of internal and external mass co-occurs. This study surfaces the excellent adsorption capacity of Banyan tree leaves to remove cationic BG dye from aqueous solutions, including tap water, river water, and filtered river water. Therefore, the selected biosorbent is a cost-effective and easily accessible approach for removing toxic dyes from industrial effluents and wastewater.

A robust prediction model for evaluation of plastic limit based on sieve # 200 passing material using gene expression programming.

This study aims to propose a novel and high-accuracy prediction model of plastic limit (PL) based on soil particles passing through sieve # 200 (0.075 mm) using gene expression programming (GEP). PL is used for the classification of fine-grained soils which are particles passing from sieve # 200. However, it is conventionally evaluated using sieve # 40 passing material. According to literature, PL should be determined using sieve # 200 passing material. Although, PL200 is considered the accurate representation of plasticity of soil, its' determination in laboratory is time consuming and difficult task. Additionally, it is influenced by clay and silt content along with sand particles. Thus, artificial intelligence-based techniques are considered viable solution to propose the prediction model which can incorporate multiple influencing parameters. In this regard, the laboratory experimental data was utilized to develop prediction model for PL200 using gene expression programming considering sand, clay, silt and PL using sieve 40 material (PL40) as input parameters. The prediction model was validated through multiple statistical checks such as correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and relatively squared error (RSE). The sensitivity and parametric studies were also performed to further justify the accuracy and reliability of the proposed model. The results show that the model meets all of the criteria and can be used in the field.

Histological, immunohistochemical, and morphometric analysis of negative pressure-assisted in-vivo nerve stretch-growth.

Axons respond well to mechanical stimuli and can be stretched mechanically to increase their growth rate. Although stretch growth of axons and their transient lengthening ex-vivo has been discussed in literature extensively, however, real applications of this phenomenon are scarcely found. This work presents a technique to translate ex-vivo axonal stretch growth to in-vivo nerve stretch growth. By establishing a rat model of completely transected sciatic nerve injury, the regrowth rate of the proximal nerve stump was examined under the effect of a stretching force developed by negative pressure. In this manuscript, results have been presented based on quantitative and qualitative analysis of the stained nerve tissues. Gross observations have explicitly confirmed that the proximal stump of a whole sectioned sciatic nerve of a Wistar rat stretched in a T-shaped nerve prosthesis using a controlled amount of negative pressure displayed a better outcome in terms of an increase in the total length of proximal nerve stump post-treatment and a higher number of blood vessels with respect to control. The histological and morphometric analyses confirmed that negative pressure-assisted nerve growth provides an alluring control over nerve's regrowth rate. Immunohistochemical staining also supported the existence of a positive correlation between nerve growth and in-vivo application of axial stress on it. This work presents the first holistic evidence on growing nerves in the continuum of in-vivo nerve stretch growth using negative pressure and concludes that systematic and controlled negative pressure applied directly to the resected ends of a sciatic nerve resulted in the enhanced growth rate of regenerating nerve fibres.

Negative Pressure Neurogenesis: A Novel Approach to Accelerate Nerve Regeneration after Complete Peripheral Nerve Transection.

Various modalities to facilitate nerve regeneration have been described in the literature with limited success. We hypothesized that negative pressure applied to a sectioned peripheral nerve would enhance nerve regeneration by promoting angiogenesis and axonal lengthening. METHODS: Wistar rats' sciatic nerves were cut (creating ~7 mm nerve gap) and placed into a silicone T-tube, to which negative pressure was applied. The rats were divided into 4 groups: control (no pressure), group A (low pressure: 10 mm Hg), group B (medium pressure: 20/30 mm Hg) and group C (high pressure: 50/70 mm Hg). The nerve segments were retrieved after 7 days for gross and histological analysis. RESULTS: In total, 22 rats completed the study. The control group showed insignificant nerve growth, whereas the 3 negative pressure groups showed nerve growth and nerve gap reduction. The true nerve growth was highest in group A (median: 3.54 mm) compared to group B, C, and control (medians: 1.19 mm, 1.3 mm, and 0.35 mm); however, only group A was found to be significantly different to the control group (**P < 0.01). Similarly, angiogenesis was observed to be significantly greater in group A (**P < 0.01) in comparison to the control. CONCLUSIONS: Negative pressure stimulated nerve lengthening and angiogenesis within an in vivo rat model. Low negative pressure (10 mm Hg) provided superior results over the higher negative pressure groups and the control, favoring axonal growth. Further studies are required with greater number of rats and longer recovery time to assess the functional outcome.

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT.

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish-subscribe-based protocol for the communication of sensor or event data. The publish-subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

A low-temperature solution-processed indium incorporated zinc oxide electron transport layer for high-efficiency lead sulfide colloidal quantum dot solar cells.

Colloidal quantum dot solar cells (CQDSCs) have achieved remarkable progress recently in terms of mainly surface passivation and composition-matching matrices on CQDs, while improving the overall photoelectric conversion efficiency (PCE) through electron transport layer (ETL) modifications is less explored. We report a low-temperature solution route to synthesize donor (Al3+/Ga3+/In3+) incorporated zinc oxide (AZO/GZO/IZO) ETL films for PbS CQDSCs. Spectroscopic characterization studies indicate that the IZO ETL fabricated with 150 °C annealing can increase the bandgap the most from 3.56 eV to 3.74 eV, possesses enhanced light transmission (∼94%) and finer particle sizes, and importantly shows the most suitable band alignment and charge transfer ability. Well-dispersed PbS CQDs of around 3 nm are synthesized by a N2-protected reflux method and are surface exchanged with 1-ethyl-3-methylimidazolium iodide (EMII) to allow I- grafting and ethanedithiol (EDT) for the active layer and hole transport layer, respectively. The IZO based PbS CQDSC, with a device architecture of ITO/IZO/PbS-EMII/PbS-EDT/Au, shows an enhanced PCE of 11.1% (comparatively 18% higher than that of the ZnO ETL), a VOC value of 0.64 V, and a JSC of 25.8 mA cm-2. The improved performances benefit from the higher recombination resistance and constrained photoluminescence emission with the utilization of the IZO ETL that provides a superior charge transfer property.

CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning.

Sensors' existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

Automatic identification of abnormal blood smear images using color and morphology variation of RBCS and central pallor.

The anatomy of red blood cells (RBCs) in blood smear images plays an important role in the detection of several diseases. The automated image-based technique is fast and accurate for the analysis of blood cells morphology that can save time of both pathologists as well as that of patients. In this paper, we propose a novel method which segment and identify varied RBCs in a given blood smear images. In the proposed method, the central pallor and whole cell information are used, after using color processing followed by double thresholding of blood smear images. The shape and size variances of cells are calculated for the identification of abnormalities in peripheral blood smear images. We used cross-validation accuracy weighted probabilistic ensemble (CAWPE). It is a heterogeneous ensembling technique of nearly equivalent classifiers produced on averagely significant better classifiers (regarding errors and probability estimates) as compared to a wide range of potential parent classifiers. The proposed method is tested on 3 sets of images. The sets of images were prepared in a local government hospital by expert pathologists. Each image set has varied photographic conditions. The method was found accurate in term of results, closer to the ground truth. The average accuracy of the proposed method is 97% for the segmentation of single cells and 96% for overlapped cells. The variance (σ2) of accuracy is 3.5 and the deviation (σ) is 1.87.

Chorionic Bump: Radiologic Features and Pregnancy Outcomes.

Background Chorionic bump is a rare condition defined as a bulge or protrusion from the choriodecidual surface into the gestational sac. The limited literature on this infrequent entity suggests that the pregnancies with multiple chorionic bumps mostly result in fetal demise. Aims To review the available literature and the patients from our institute having sonographic findings of chorionic bump and making the sonographers and radiologists aware of this known cause of first-trimester pregnancy loss. Study design A retrospective review of the cases diagnosed at our institute during the last four years. Methods and materials Single-center institutional data for four years (January 2016-December 2019) was accessed using ICD codes. IRB approval was waived owing to the anonymized use of patient data. Results Six female patients diagnosed with chorionic bump were included, with a mean age of 29.83±12 years. The average gestational age at the time of diagnosis was 8.16±3 weeks. The most common sonographic findings were a protrusion from the chorionic wall into the gestational sac cavity, having a central hypoechoic region with peripheral hyperechoic rim (isoechoic to the chorion) and having no vascularity (n=5), and the less common finding was a hyperechoic protrusion with no vascularity (n=1). n=5 had a single lesion, and n=1 had two lesions. The average diameter of the lesion in the largest dimension was 18±11 mm. n=3 pregnancies resulted in a first-trimester miscarriage, and n=3 pregnancies delivered healthy babies at term.  Conclusions A chorionic bump significantly increases the risk of a first-trimester miscarriage.

WITHDRAWN: Structural studies of vitrified biological proteins and macromolecules - A review on the microimaging aspects of cryo-electron microscopy.

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

COVID-19: Comparison between 8-days and extended 4-weeks outbreak periods through socioeconomic and natural factors

Since mid-March 2020, global COVID-19 pandemic has experienced an exponential growth in process from sporadic to sudden outbreaks. This paper selects the 8-day surge data of daily cases, death and recovery rates (March 19-26, 2020) from 18 countries with severe pandemic situation to discuss the impact of 9 factors of both socioeconomic and natural on the pathogen outbreak. Moreover, the paper also elaborates analysis and comparison of relatively slow 4-week (February 1-29, 2020) data of Chinas surge cases to determine the relationship between social and natural factors and on the spread of pandemic, which provides an effective reference for delaying and controlling the pandemic development.

Design and fabrication of a nerve-stretching device for in vivo mechanotransduction of peripheral nerve fibers.

The potential of peripheral nerves to regenerate under the effect of axial tensile forces was not previously extensively explored due to the lack of capabilities of translating ex vivo axonal stretch-growth to in vivo studies, until the development of a nerve stretcher. The nerve stretcher, which we have designed and manufactured recently, is a device that uses a controlled amount of axial tensile force (vacuum/negative gauge pressure) applied directly to a sectioned peripheral nerve in vivo to expedite nerve regrowth rate. Using this platform, a series of experiments was carried out to observe the effect of in vivo axial stretch on axonal lengthening. During these experiments, a few challenges necessitated redesigning the device like a sudden loss of stretching force due to vacuum leakage, erroneous feedback from vacuum sensor due to sensor drift, and inability to control and operate the device remotely. Here we present an improved design of the nerve stretcher along with its integration with a state-of-the-art online vacuum monitoring facility to control, collect, process, and visualize negative gauge pressure data in real-time.

A Case of Acute Life-Threatening Pulmonary Hemorrhage from Synthetic Cannabinoid Abuse.

We report a case of alveolar hemorrhage secondary to inhalation of synthetic cannabinoid. The patient developed hemoptysis and respiratory failure 48 hours after the episode. Alveolar hemorrhage from synthetic cannabinoid use is a rare entity that has been reported only thrice previously. The unique feature of this case was that the initial urine and blood toxicology screens were negative for cannabinoids and the diagnosis was confirmed via detection of serum metabolites of a synthetic cannabinoid.

Bridging larger gaps in peripheral nerves using neural prosthetics and physical therapeutic agents.

Peripheral nerve injuries are relatively common and can be caused by a variety of traumatic events such as motor vehicle accidents. They can lead to long-term disability, pain, and financial burden, and contribute to poor quality of life. In this review, we systematically analyze the contemporary literature on peripheral nerve gap management using nerve prostheses in conjunction with physical therapeutic agents. The use of nerve prostheses to assist nerve regeneration across large gaps (> 30 mm) has revolutionized neural surgery. The materials used for nerve prostheses have been greatly refined, making them suitable for repairing large nerve gaps. However, research on peripheral nerve gap management using nerve prostheses reports inconsistent functional outcomes, especially when prostheses are integrated with physical therapeutic agents, and thus warrants careful investigation. This review explores the effectiveness of nerve prostheses for bridging large nerve gaps and then addresses their use in combination with physical therapeutic agents.