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In recent years, the majority of additions to and deletions from the S&P 500 index have been stocks that were previously or subsequently included in the S&P 400 index. The announcement returns of these changes have been the opposite of what has been documented for all S&P 500 additions and deletions in an extensive literature. During 20162020, such 'upward additions' to the S&P 500 index resulted in an average announcement excess return of -2.48% over a 3-day period, while 'downward deletions' to the S&P 400 index resulted in an excess return of +1.37%. We explain these new results by the increasing total institutional ownership of S&P 400 stocks. Our results thus show the increasing benefits of being included in the mid-cap S&P 400 index relative to being included in the large-cap S&P 500 index. KEYWORDS index rebalancing, institutional ownership, institutional trading, Russell index, S&P 500, 1 | INTRODUCTION Starting with Shleifer (1986), one of the most established event study results in finance has been that stocks earn positive returns on addition to the S&P 500 [...]

The development of a scalable process for a complex intermediate featuring a chiral, quaternary cyclopropane moiety presented significant challenges. We report two generations of synthetic strategies appropriate for the respective stages of development. The initial approach utilized a stereoselective Simmons–Smith cyclopropanation of (R)-pyroglutamic acid ester, which predominantly yielded the undesired stereoisomer. To circumvent this issue, we implemented a strategy that combined isomerization, recycling of the undesired isomer, and selective crystallization to improve the yield of the desired product. An important insight was that the Simmons–Smith cyclopropanation exhibited opposite stereoselectivity with a benzoyl ester of a prolinol substrate, resulting in the desired stereoisomer as the major product. This understanding enabled the development of a second-generation process that facilitated the large-scale production of the targeted intermediate, thus supporting the advancement of clinical trials.

Surface plasmon resonance (SPR) is a prevalent sensing approach for detecting the additional mass concentrations as small as pg/ml and Refractive Index changes up to the orders of $0.1 \; \mu $ RIU. However, SPR possesses a penetration depth of approx. 200 nm limiting its application for microbes such as bacterial cells and viruses. Long-range SPR (LRSPR) possesses more equitable sensing mechanism and deeper penetration depth improving the performance than traditional SPR. In this work, we propose a theoretical design based on transfer matrix method (TMM) for developing LRSPR chip to achieve high penetration depth and figure of merit (FOM). Here, we have used low index prism (CaF2) for high penetration depth with optimized adhesive layer of 1900 and 600 nm for visible and IR wavelengths providing penetration depths of 817 and 1513 nm, respectively. Study revealed that for IR ranges, penetration depth increases comprising the FOM which can further be improved by additional 2-D material between metal-sensing layers. A variety of 2-D materials are used to obtain the best performance of the chip. The inclusion of a monolayer MoS2 improves FOM due to higher absorption of light as it possesses higher imaginary Refractive Index. The simulated findings reveal that the greatest penetration depth of the IR spectrum is 1508.93 nm near surrounding RI of 1.35 aliens with the index of aqueous biomolecular samples. The study demonstrates a potential use of 2-D material with enhanced LRSPR sensor performance with depths comparable to bacterial cells, viruses, and proteins.

We question previous evidence that managers mislead investors into selling their stock for artificially lowered prices by releasing overly negative earnings forecasts before stock repurchases. We rely on forecast errors (forecast minus actual earnings) and show that these are insignificantly different between repurchase and matching firms. Our tests are powerful at detecting small differences and yet, across multiple subsamples, we find no statistical difference in forecast errors of repurchase and matching firms. Prior evidence of managerial misleading relied on the negative stock market reaction to management forecasts. We show that this negative reaction is observed only in the subsample where board authorization of repurchase follows management forecast. When board authorization precedes forecast, market reaction to management forecasts for repurchase firms is somewhat higher than that for matching firms. Our evidence suggests that managers are not misleading but exploiting low market prices to the advantage of their long-term shareholders. KEYWORDS disclosure, earnings forecasts, earnings guidance, stock repurchases, 1 | INTRODUCTION Stock repurchases are currently the leading form of cash returned by U.S. firms to their outside shareholders. Aggregate repurchases by U.S. firms exceeded aggregate dividends for the [...]

There is ongoing debate around rate versus rhythm control strategies for managing atrial fibrillation (AF), however, much of the data comes from Western cohorts. Kerala‐AF represents the largest prospective AF cohort study from the Indian subcontinent. To compare 12‐month outcomes between rate and rhythm control strategies. Patients aged ≥18 years with non‐transient AF were recruited from 53 hospitals across Kerala. Patients were stratified by rate or rhythm control. The primary outcome was a composite of all‐cause mortality, arterial thromboembolism, acute coronary syndrome or hospitalization due to heart failure or arrhythmia at 12 months. Secondary outcomes included bleeding events and individual components of the primary. Predictors of the composite outcome were analysed by logistic regression. A total of 2901 patients (mean age 64.6 years, 51% female) were included (2464 rate control, 437 rhythm control). Rates of the primary composite outcome did not differ between groups (29.7% vs 30.0%; p= .955), nor did any component of the primary. Bleeding outcomes were also similar (1.6% vs 1.9%; p= .848). Independent predictors of the primary composite outcome were older age (aOR 1.01; p= .013), BMI <18 (aOR 1.51; p= .025), permanent AF (aOR 0.78; p= .010), HFpEF (aOR 1.40; p= .023), HFrEF (aOR 1.39; p= .004), chronic kidney disease (aOR 1.36; p< .001), and prior thromboembolism (aOR 1.31; p= .014). In the Kerala‐AF registry, 12‐month outcomes did not differ between rate and rhythm control cohorts. No significant difference was found between rate and rhythm control approaches to atrial fibrillation management in the 1‐year follow‐up of the Kerala‐AF cohort. Independent predictors of adverse outcomes included age, underweight BMI, heart failure, and chronic renal failure.

Electric vehicles have seen substantial growth in recent years in the emerging economies of the world. The current market scenario of E-vehicles is dominated by PMSM or BLDC motors due to their higher torque-to-power density, and efficiency with added performance metrics of the motor. However, the higher capital cost of permanent magnets makes the motor designers to consider an alternative to magnetic motors, the so-called Switched Reluctance Motors (SRM). With proper FEM analysis and controller design, if SRMs can able to achieve the performance metrics of a PMSM/BLDC, it will be a breakthrough in the automotive sector with affordable prices. Based on the aforementioned facts, this research work focuses on SRM as a viable alternative to the existing PMSM/BLDC motor. The work concentrates on the design and development of an 8/6 SRM for a Tri-wheeler E-vehicle application. The designed motor is fabricated, tested and controlled by WAVECT, an FPGA-based real-time digital controller which is used to control the commutation of the 8/6 SRM. The yielded response satisfactorily proves that the designed 8/6 SRM can be effectively used for the Tri-wheeler E-vehicle application, thereby paving the way for sustainable energy conservation in the automotive sector.

This article proposes a novel approach for predicting concrete compressive strength using deep learning techniques. It overcomes limitations of traditional methods like memory footprint, training time, and computational requirements for predicting concrete compressive strength. Currently various filter pruning techniques are used to compress models by removing irrelevant information, but they cannot decrease memory consumption due to their large parameters. So, the entropy-based filter pruning is suggested to reduce the complexity of the model by decreasing the parameters. Then for training the CNN model, the modified resilient backpropagation technique (MRPROP) is suggested, because the previous backpropagation techniques take more time for training and also it loss the accuracy. This MRPROP improve the efficiency and convergence of CNN training and also it updates the models weight. The proposed approach demonstrated superior performance in mean squared error, root mean squared error, loss function, and regression analysis, as per the experimental results.

There is a growing demand for manufacturers from within and outside the company to embrace stakeholder-satisfying production techniques. As a result, companies undertake a wide range of assessments as part of their strategic planning and performance monitoring processes. The strengths, weaknesses, opportunities, and threats (SWOT) analysis is used in strategic planning that help businesses evaluate their current standing. In addition, through the analytic hierarchy process (AHP), a pairwise comparison may be made between elements or criteria to prioritize them, making the SWOT analysis and multicriteria decision-making method more effective. The purpose of the combined approach is to raise the effectiveness of strategic planning inside a business. This paper does a SWOT analysis of a process industry, outlining its strengths, weaknesses, opportunities, and threats. First, the company’s strengths, weaknesses, opportunities, and threats (SWOT) are determined via an internal poll. Then, the AHP compares alternatives by contrasting them head-to-head. The results of the company-wide poll of experts landed on seventeen distinct elements. With this information, the manufacturing firm can decide how best to boost its operations.

Over the past few years, aluminum 6061 alloys are considered one of the most versatile heat-treated alloys because of their maximum superior strength and corrosion resistance. It is necessary to select an appropriate alloy for enhancing various performances like machinability, surface quality, workability as well as extrudability. The few generally utilized industrial abrasives are aluminum oxide, boron nitride, diamond, silicon carbide, etc. In this paper, an aluminum matrix composite is employed for the stir casting process due to the high rate of flexibility, minimum cost, and easy management structure properties as well as mass production. Here, aluminum 6061 alloy incorporated with 3, 6, and 9% of titanium carbide (i.e., Al6061 with 3, 6, and 9% TiC) is investigated and computed for various factors namely actual density rate, impact energy rate as well as Rockwell hardness (i.e., Rockwell hardness B—HRB and Rockwell hardness C—HRC). Finally, the experimentation is carried out using Minitab software, and this type of software is mainly employed for multivariate investigations which involve designing the response surface. Here, three different tests are conducted, and the mean value is obtained for three different types of material composition namely Al6061 + 3%TiC, Al6061 + 6% TiC, and Al6061 + 9% TiC. Hence, from the evaluation results, it is revealed that Al6061 + 9% TiC composition provides better results in terms of density, impact energy as well as Rockwell hardness rate.

This research represents an innovative approach to combining solar energy storage with Battery Management System (BMS) technology for application in an electric vehicle. Solar photovoltaic panels to power an electric vehicle with an induction motor drive, existing BMS technology is inefficient. This proposed approach includes extensive control methods with experimental and simulation verification to regulate the BMS technology provides better results. For controlling the charging/discharging cycles of the Li-ion of battery system linked to an induction motor driven by solar panels, the suggested BMS method uses an FLC (Fuzzy Logic Controller). The BMS prevents the battery to becoming overcharged or drained. The BMS techniques are discussed in this work, including SOC (State-of-Charge), safe charging, and battery protection. The FLC implements reliable control shifting techniques during battery charging and discharging. FLC's methodology has been confirmed through simulation verifications in SIMULINK/MATLAB. The proposed BMS technique has a high State of Charge (SOC) of 80% and a charging efficiency of 95%.

Postoperative rehabilitation following rotator cuff repair is essential for a positive patient outcome. Each patient should have a program tailored to their individual tear and repair characteristics. Their plan should be advanced according to their progress, with therapist and surgeon communication. The rehabilitation progresses through 4 stages. This begins with a period of immobilization, passive range of motion, and protecting the repair and ends with functional and sport-specific exercises. Rehabilitation advances in progress-based steps during 24 weeks. The rehabilitation process aims to balance healing of the tendon repair and the risk of postoperative stiffness.

In recent times, vehicle type classification (VTC) is considered important for intelligent transport system. The VTC is a challenging research topic, due to the factors like minimum interclass variance and maximum intraclass variance. In this research manuscript, a novel algorithm is implemented for improving VTC. Firstly, the data are acquired from the MIOvision-Traffic Camera Database (MIO-TCD) and a toll booth database. Further, a camera response method is employed for improving the visual capability of the raw acquired data and then, the Kalman filtering technique and Gaussian mixture model are utilized for recognition and tracking of moving vehicles. Next, the feature-extraction techniques: local binary pattern, steerable pyramid transform, and dual tree complex wavelet transform are employed for extracting feature values, and further, the active feature values are selected using the enhanced reliefF algorithm. Finally, the long short-term memory is accomplished for classifying 11 vehicle categories on MIO-TCD, and six vehicle categories on toll booth database. The simulation results stated that the proposed algorithm attained 99.01% and 95.85% of accuracy on MIO-TCD and toll booth databases, respectively.

Many problems concerning appropriate calibration besides camera placement are focused by various researchers during measurement operations while dealing with thermal imaging camera. For easy processing of video stream, it is greatly necessitated to correct camera on a stand yaw/pitch/roll angles by utilizing various algorithms. The task is regarded as an easy one for hot object besides obviously visible in the infrared. Heat exchange process is greatly necessitated for registering initiation from a cold object. Boundary markers set positioning is accomplished on the supervised object in addition it requires an algorithm for recognition. A fuzzy assessed spatial relations-based approach is exploited previously for visual markers set detection on a rotating steel cylinder. However, that fuzzy assessed spatial relations-based approach not producing enough detection accuracy. To mitigate the above-mentioned issue this work introduces Intelligent Water Drop Optimization based Fuzzy Inference System (IWD-FIS) on the basis of fuzzy-intrinsic shape aspects such as objects, during a source image, and also their reciprocal reference frame. In this work Otsu algorithm is used for background as well as foreground segmentation. And then Features Extraction and Object Labelling are performed. Markers detection is done by using Proposed IWT-FIS based on the extracted features. The rule conclusions, parameter optimization and Membership Function (MF) parameters are concentrated mainly through this IWD-FIS. A state-of-the-art optimization sequence for the different FIS parameters is recommended rather than presenting a new algorithm.

Febuxostat is a novel, non-purine, selective inhibitor of xanthine oxidase for hyperuricemia in patients with gout. It is the first promising substitute for allopurinol in 40 years. Various synthetic routes [...]

The consistently expanding traffic, different postponement delicate administrations and energy utilisation compelled prerequisites have carried gigantic difficulties to the ongoing correspondence networks in the transportation framework. Because of the great speed and repeating topological variations of Vehicular Sensor Networks, determining an associated course with sufficient idleness is a difficult task with many requirements and barriers. As a result, in order to combat this, we developed a measurable method for dealing with presumably determining the heap clog and energy utilisation during the lifespan of the sensor network for transportation framework. The paper proposes a Secure and Effective Diffused Framework that spotlights on lower energy use and secure correspondence. The least bounce include in coordinated dissemination is utilised as the rule for developing the slope in this system, which further develops security and dependability by adding the possibility of the angle to flag the course and pace of information transmission.

NR-U enables 5G to access the unlicensed bands. However, a 5G NR-U transmitter has to follow the Listen Before Talk (LBT) procedure, where the transmitter has to sense the channel for a while prior to its transmission. Since NR-U is a scheduled technology, the transmission begins only at slot boundaries. But the LBT procedure may end before reaching the slot boundary, and then the transmitter has to wait for a gap period to align with the slot boundary. 5G has different numerologies, and the slot duration of each numerology is different. When nodes with different numerologies are contending for channel access, the node with a lower slot duration has a high probability of winning the channel since, after the LBT procedure, its next slot mostly comes sooner, which leads to fairness issues. In this letter, we propose an algorithm to improve the fairness in multinumerology contention by adaptively changing the Maximum Channel Occupation Time (MCOT) of each numerology according to the slot duration and the number of nodes in each numerology.

The MR damper parameters used in the vehicle model correspond to a real-world damper and are chosen so that the MR damper model characteristics match those of the experimental damper. The control and response statistics of a non-linear vehicle model utilising an MR damper are produced iteratively using the equivalent linearization method, and the findings are validated using rider optimisation (RO) simulation modelling. The suggested technique is easy to use, robust and well-suited for solving highly non-linear situations. The optimal parameters-arrived performance measure is examined, including ride comfort, tyre force, and protentional power, as well as the optimal design specifications for the front and rear dampers. The optimal control using preview reduces a performance index, which is a collection of vehicle performance parameters such as mass acceleration, displacement and both front and rear suspension stiffness. The results reveal that the developed RO algorithm is capable of determining the optimal parameters of the MR dampers.

Small changes in deltoid tension and moment arm due to glenosphere lateralization may be associated with an increase in acromion or scapular spine strain in reverse shoulder arthroplasty (RSA), which can lead to stress fracture. The coracoacromial ligament (CAL) may be protective and lower the strain seen on the acromion or scapular spine. This biomechanical study investigated the impact of glenosphere lateralization and CAL integrity on acromion and scapular spine strain after RSA.

Facial expression detection from images and videos has recently gained attention due to the wide variety of applications it has found in the field of computer vision such as advanced driving assistance systems (ADAS), augmented and virtual reality (AR/VR), video retrieval, and security systems. Facial terms, body language, hand gestures, and eye contact have all been researched as a means of deciphering and understanding human emotions. Automated facial expression recognition (FER) is a significant visual recognition procedure because human emotions are a worldwide signal used in non-verbal communication. The six primary universal manifestations of emotion are characterized as happiness, sadness, anger, contempt, fear, and surprise. While the accuracy of deep learning (DL)-based approaches has improved significantly across many domains, automated FER remains a difficult undertaking, especially when it comes to real-world applications. In this research work, two publicly available datasets such as FER2013 and EMOTIC are considered for validation process. Initially, pre-processing includes histogram equalization, image normalization and face detection using Multi-task Cascaded Convolutional Network (MT-CNN) is used. Then, DL-based EfficinetNetB0 is used to extract the features of pre-processed images for further process. Finally, the Weighted Kernel Extreme Learning Machine (WKELM) is used for classification of emotions, where the kernel parameters are optimized by Red Fox Optimizer (RFO). From the experimental analysis, the proposed model achieved 95.82% of accuracy, 95.81% of F1-score and 95% of recall for the testing data.

We model agents whose preferences exhibit envy. An envious agent's utility increases with what he has and decreases with what others have. With this setup, we are able to provide a new perspective on the nature of investment distortions with centralized and decentralized capital budgeting systems. Centralized capital budgeting leads to corporate socialism in investments in multidivisional firms, whereas decentralized capital budgeting leads to overinvestment. Numerous additional testable predictions are also generated.

The article presents an experimental investigation on the generation of hydrogen-rich syngas through oxy-steam gasification of corncobs, a key agro-residue available in many countries, to establish biomass gasification coupled with swing adsorption-based multi-component gas separation as a promising route for renewable methanol production. The gasification of corncobs is performed in a fixed-bed downdraft gasifier to generate syngas, which is further conditioned to adjust the gaseous mixture mole ratio to produce feed suitable for catalytic methanol synthesis. Conditioning involves a packed bed containing commercial type 4A sodium zeolites, where carbon dioxide adsorption takes place. The adaptation of a low-pressure operation to selectively adsorb carbon dioxide is a key feature of the separation process. The paper also reports on the characterization of contaminants toward establishing the gas quality for catalytic synthesis. Tars and trace gases are identified and quantified using two techniques: gas chromatography-mass spectroscopy and reagent-based gas detector tubes. The 10 kg/h biomass gasification system is operated at molar steam to biomass ratio of 2.3 and an equivalence ratio of 0.24. After gas separation, syngas with an average module value of 2.48 is obtained with total tar (phenol, benzofuran, naphthalene, 2-methyl naphthalene) and trace gases (HCl, HF, and H2S) being about 1.66 ppmV and less than 1.6 ppmV, respectively. This analysis establishes the agro-residue oxy-steam gasification followed by low-pressure swing adsorption as a potential route for producing module-adjusted clean syngas for catalytic methanol synthesis.

Using a sample of 336 carveouts that occurred in the period 1980-97, this article shows that the announcement-period returns increase with the ratio of subsidiary to nonsubsidiary assets. This finding contradicts the asymmetric information model proposed by Nanda. Additional tests relate the returns to the following divestiture-based explanations proposed by Schipper and Smith and others: refocusing of the parent and subsidiary operations, financing of new and existing projects, reducing the complexity of stock valuation, and enabling an eventual spinoff or third-party acquisition. The combined evidence rejects the asymmetric information hypothesis and supports the divestiture gains hypothesis of carveouts.

The current work reports on the experimental and numerical investigations of biomass char conversion in a CO2-dominated environment. Having established 800 °C as the transition temperature for char conversion in terms of a threshold conversion rate, the mass loss rate of biomass char particles of different sizes (8–20 mm diameter) and two densities (220 and 350 kg/m3) are studied under a wide range of temperatures (800–1000 °C), CO2concentrations (10%–100%), Reynolds numbers (0.01–100), and combinations thereof. The investigation broadly covers the operating conditions of practical systems. It is observed that increasing the temperature (12.5%) or reactant concentration (33%) or Reynolds number (10 times increase) reduces the conversion time scales by up to 50%. Contrary, a 22% increase in particle size and increase in particle density from 220 to 350 kg/m3increases the conversion time by 25% and 65%, respectively. It is of interest and significance to note that temperature and reactant concentration in combination explicitly control the conversion regime (kinetic limit to diffusion limit) and as such can be used as explicit control parameters. A numerical model is employed to explore the intraparticle dynamics under different conversion regimes. The numerical model enables analysis of intraparticle temperature and reactant distribution, typically challenging through the experimental approach. Extending the analysis, a novel, first of its kind method to identify the conversion regime based on temperature and reactant concentration is hypothesized and validated. The methodology estimates the conversion regime with sufficient accuracy, with the power-law coefficient being within the 10% band.

This study presents multi-variable analysis for prediction and optimization of the methanol yield from biomass-based syngas using a thermodynamic model. The biomass-based syngas has a lower H2content and higher CO2than the typical syngas obtained from natural gas reforming which is used for the commercial methanol synthesis process. Consequently, a thorough examination was done to determine the working range for the input parameters for a biomass-derived syngas, so that the output of the process becomes comparable to the existing commercial technologies. Two cases were examined, case 1 where the H2enriched bio-syngas was used for methanol synthesis and case 2 where the methanol was directly synthesized by the bio-syngas obtained by oxy-steam gasification process. A multi-variable analysis of the selected parameters is first performed; then, surrogate model for the methanol yield is constructed for both the cases. A selective set of response surfaces from the surrogate mathematical model for both the cases were used to illustrate the mutual effect of two parameters by keeping the other parameters at the base values. The analysis indicated that the methanol yield is most sensitive to temperature followed by pressure, CO2/CO, and H2/(CO + CO2) molar ratios in both the cases. The optimized values of methanol yield obtained for case 1 and case 2 were 92.72% and 45.35%, respectively, achieved at 473 K, 9 MPa. The CO2/CO and the H2/(CO+CO2) molar ratios were at 0.42 and 4.88 respectively for case 1 and for case 2; the values were 0.90 and 1.13 respectively. The high-pressure analysis involves the use of equations of states (EoS) to account for the deviations from ideality. Although many equations of state (EoS) are available, there is no one equation that would predict the properties of all the substances under all the conditions. An attempt has been made in this work to address the use of three such equations of state, namely Peng-Robinsons, Soave-Redlich-Kwong EoS and Newton’s universal curves, and to compare the results for the components involved in the methanol synthesis reaction. The results indicate that all three EoS provide similar correction factors in the studied temperature and pressure range for methanol synthesis.

Background: India has a dual burden of tuberculosis (TB) and diabetes mellitus (DM). Integrated care for TB/DM is still in the early phase in the country and can be considerably enhanced by understanding and addressing the challenges identified from stakeholders’ perspectives. This study explored the challenges and opportunities at individual, health system and policy level for integrated care of TB/DM comorbidities in India. Methods: We used an outlier case study approach and conducted stakeholder interviews and focus group discussions with relevant program personnel including field staff and program managers of TB and DM control programs as well as officials of partners in Indian states, Kerala and Bihar. Results: The integrated management requires strengthening the laboratory diagnosis and drug management components of the two individual programs for TB and DM. Focused training and sensitization of healthcare workers in public and private sector across all levels is essential. A district level management unit that coordinates the two vertical programs with a horizontal integration at the primary care level is the way forward. Substantial improvement in data infrastructure is essential to improve decision-making process. Conclusion: Bi-directional screening and management of TB/DM comorbidities in India requires substantial investment in human resources, infrastructure, drug availability, and data infrastructure.

Photovoltaic performance of a marine red seaweed Gracilaria corticatausing acetone and methanol as extracting solvents was examined in a dye-sensitized solar cell (DSSC) integrated with TiO2photoanode. The UV–vis absorption spectra and TLC analysis revealed the presence of natural photosynthetic pigments, such as chlorophyll a, chlorophyll b, pheophytin, and xanthophylls. DSSC performance parameters, such as short-circuit current density (Jsc) and open-circuit voltage (Voc), were directly measured from each current–voltage (J–V) characteristic curve, while the remaining photovoltaic parameters such as maximum current density (Jmax), maximum voltage (Vmax), maximum power (Pmax), fill factor (FF), and overall cell conversion efficiency (η) were calculated from the respective J–V characteristic curve. It is peculiar to note that DSSCs in both solvents disclosed a reasonably fill factor (FF). However, DSSCs sensitized by acetone extract of G. corticataexhibited relatively high photovoltaic performance (Jsc0.82 mA cm2, Vocof 702 mV, impressive FF (0.73) and an efficiency of 0.42%), which was twofold higher than the DSSCs sensitized by methanol extract (efficiency 0.18%). The results suggest the use of seaweed pigments as low cost, eco-friendly viable to alternate DSSC.

Complex elbow dislocations involve periarticular fractures in addition to capsuloligamentous disruption. These dislocations can result from a simple fall on an outstretched hand or from high-energy trauma, and the pattern of injury is affected by the direction of the force causing the injury. Fracture patterns in complex elbow dislocations vary based on whether the injury is posterolateral, anterior, posteromedial, or medial. The primary goals of care in treating complex elbow dislocations include restoring anatomical alignment of the ulnohumeral and radiocapitellar joints, providing stability, and facilitating early motion after definitive treatment. Depending on fracture pattern and joint stability, definitive treatment can be nonoperative with closed reduction and bracing or operative with capsuloligamentous repair, open reduction and internal fixation of fractures, arthroplasty, or application of an external fixator. Common complications include neurovascular injury, chronic instability, posttraumatic arthritis, compartment syndrome, stiffness, and heterotopic ossification. Understanding how best to treat each type of complex elbow dislocation is essential to optimizing long-term elbow mobility and function while avoiding potential complications.